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Chapter 1 Framing and context


Report	Special Report on Climate Change and Land
Type	Special report
Year	2019

Understanding the impacts of 1.5°C global warming above pre-industrial levels and related global emission pathways in the context of strengthening the response to the threat of climate change, sustainable development and efforts to eradicate poverty.

ES Executive Summary

This chapter frames the context, knowledge-base and assessment approaches used to understand the impacts of 1.5°C global warming above pre-industrial levels and related global greenhouse gas emission pathways, building on the IPCC Fifth Assessment Report (AR5), in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty.

Human-induced warming reached approximately 1°C ( likely between 0.8°C and 1.2°C) above pre-industrial levels in 2017, increasing at 0.2°C ( likely between 0.1°C and 0.3°C) per decade ( high confidence ). Global warming is defined in this report as an increase in combined surface air and sea surface temperatures averaged over the globe and over a 30-year period. Unless otherwise specified, warming is expressed relative to the period 1850–1900, used as an approximation of pre-industrial temperatures in AR5. For periods shorter than 30 years, warming refers to the estimated average temperature over the 30 years centred on that shorter period, accounting for the impact of any temperature fluctuations or trend within those 30 years. Accordingly, warming from pre- industrial levels to the decade 2006–2015 is assessed to be 0.87°C ( likely between 0.75°C and 0.99°C). Since 2000, the estimated level of human-induced warming has been equal to the level of observed warming with a likely range of ±20% accounting for uncertainty due to contributions from solar and volcanic activity over the historical period ( high confidence ). {1.2.1}

Warming greater than the global average has already been experienced in many regions and seasons, with higher average warming over land than over the ocean ( high confidence ). Most land regions are experiencing greater warming than the global average, while most ocean regions are warming at a slower rate. Depending on the temperature dataset considered, 20–40% of the global human population live in regions that, by the decade 2006–2015, had already experienced warming of more than 1.5°C above pre-industrial in at least one season ( medium confidence ). {1.2.1, 1.2.2}

Past emissions alone are unlikely to raise global-mean temperature to 1.5°C above pre-industrial levels ( medium confidence ) , but past emissions do commit to other changes, such as further sea level rise ( high confidence ). If all anthropogenic emissions (including aerosol-related) were reduced to zero immediately, any further warming beyond the 1°C already experienced would likely be less than 0.5°C over the next two to three decades ( high confidence ), and likely less than 0.5°C on a century time scale ( medium confidence ), due to the opposing effects of different climate processes and drivers. A warming greater than 1.5°C is therefore not geophysically unavoidable: whether it will occur depends on future rates of emission reductions. {1.2.3, 1.2.4}

1.5°C emission pathways are defined as those that, given current knowledge of the climate response, provide a one- in-two to two-in-three chance of warming either remaining below 1.5°C or returning to 1.5°C by around 2100 following an overshoot. Overshoot pathways are characterized by the peak magnitude of the overshoot, which may have implications for impacts. All 1.5°C pathways involve limiting cumulative emissions of long-lived greenhouse gases, including carbon dioxide and nitrous oxide, and substantial reductions in other climate forcers ( high confidence ). Limiting cumulative emissions requires either reducing net global emissions of long-lived greenhouse gases to zero before the cumulative limit is reached, or net negative global emissions (anthropogenic removals) after the limit is exceeded. {1.2.3, 1.2.4, Cross-Chapter Boxes 1 and 2}

This report assesses projected impacts at a global average warming of 1.5°C and higher levels of warming. Global warming of 1.5°C is associated with global average surface temperatures fluctuating naturally on either side of 1.5°C, together with warming substantially greater than 1.5°C in many regions and seasons ( high confidence ), all of which must be considered in the assessment of impacts. Impacts at 1.5°C of warming also depend on the emission pathway to 1.5°C. Very different impacts result from pathways that remain below 1.5°C versus pathways that return to 1.5°C after a substantial overshoot, and when temperatures stabilize at 1.5°C versus a transient warming past 1.5°C ( medium confidence ). {1.2.3, 1.3}

Ethical considerations, and the principle of equity in particular, are central to this report, recognizing that many of the impacts of warming up to and beyond 1.5°C, and some potential impacts of mitigation actions required to limit warming to 1.5°C, fall disproportionately on the poor and vulnerable ( high confidence ). Equity has procedural and distributive dimensions and requires fairness in burden sharing both between generations and between and within nations. In framing the objective of holding the increase in the global average temperature rise to well below 2°C above pre-industrial levels, and to pursue efforts to limit warming to 1.5°C, the Paris Agreement associates the principle of equity with the broader goals of poverty eradication and sustainable development, recognising that effective responses to climate change require a global collective effort that may be guided by the 2015 United Nations Sustainable Development Goals. {1.1.1}

Climate adaptation refers to the actions taken to manage impacts of climate change by reducing vulnerability and exposure to its harmful effects and exploiting any potential benefits. Adaptation takes place at international, national and local levels. Subnational jurisdictions and entities, including urban and rural municipalities, are key to developing and reinforcing measures for reducing weather- and climate-related risks. Adaptation implementation faces several barriers including lack of up-to-date and locally relevant information, lack of finance and technology, social values and attitudes, and institutional constraints ( high confidence ). Adaptation is more likely to contribute to sustainable development when policies align with mitigation and poverty eradication goals ( medium confidence ). {1.1, 1.4}

Ambitious mitigation actions are indispensable to limit warming to 1.5°C while achieving sustainable development and poverty eradication ( high confidence ). Ill-designed responses, however, could pose challenges especially – but not exclusively – for countries and regions contending with poverty and those requiring significant transformation of their energy systems. This report focuses on ‘climate-resilient development pathways’, which aim to meet the goals of sustainable development, including climate adaptation and mitigation, poverty eradication and reducing inequalities. But any feasible pathway that remains within 1.5°C involves synergies and trade-offs ( high confidence ). Significant uncertainty remains as to which pathways are more consistent with the principle of equity.
{1.1.1, 1.4}

Multiple forms of knowledge, including scientific evidence, narrative scenarios and prospective pathways, inform the understanding of 1.5°C. This report is informed by traditional evidence of the physical climate system and associated impacts and vulnerabilities of climate change, together with knowledge drawn from the perceptions of risk and the experiences of climate impacts and governance systems. Scenarios and pathways are used to explore conditions enabling goal-oriented futures while recognizing the significance of ethical considerations, the principle of equity, and the societal transformation needed. {1.2.3, 1.5.2}

There is no single answer to the question of whether it is feasible to limit warming to 1.5°C and adapt to the consequences. Feasibility is considered in this report as the capacity of a system as a whole to achieve a specific outcome. The global transformation that would be needed to limit warming to 1.5°C requires enabling conditions that reflect the links, synergies and trade-offs between mitigation, adaptation and sustainable development. These enabling conditions are assessed across many dimensions of feasibility – geophysical, environmental-ecological, technological, economic, socio-cultural and institutional – that may be considered through the unifying lens of the Anthropocene, acknowledging profound, differential but increasingly geologically significant human influences on the Earth system as a whole. This framing also emphasises the global interconnectivity of past, present and future human–environment relations, highlighting the need and opportunities for integrated responses to achieve the goals of the Paris Agreement. {1.1, Cross-Chapter Box 1}

1.1 Assessing the Knowledge Base for a 1.5°C Warmer World

Human influence on climate has been the dominant cause of observed warming since the mid-20th century, while global average surface temperature warmed by 0.85°C between 1880 and 2012, as reported in the IPCC Fifth Assessment Report, or AR5 (IPCC, 2013b) ¹ . Many regions of the world have already greater regional-scale warming, with 20–40% of the global population (depending on the temperature dataset used) having experienced over 1.5°C of warming in at least one season (Figure 1.1; Chapter 3 Section 3.3.2.1). Temperature rise to date has already resulted in profound alterations to human and natural systems, including increases in droughts, floods, and some other types of extreme weather; sea level rise; and biodiversity loss – these changes are causing unprecedented risks to vulnerable persons and populations (IPCC, 2012a, 2014a; Mysiak et al., 2016; Chapter 3 Sections 3.4.5–3.4.13) ² , Chapter 3 Section 3.4). The most affected people live in low and middle income countries, some of which have experienced a decline in food security, which in turn is partly linked to rising migration and poverty (IPCC, 2012a) ³ . Small islands, megacities, coastal regions, and high mountain ranges are likewise among the most affected (Albert et al., 2017) ⁴ . Worldwide, numerous ecosystems are at risk of severe impacts, particularly warm-water tropical reefs and Arctic ecosystems (IPCC, 2014a) ⁵ .

This report assesses current knowledge of the environmental, technical, economic, financial, socio-cultural, and institutional dimensions of a 1.5°C warmer world (meaning, unless otherwise specified, a world in which warming has been limited to 1.5°C relative to pre-industrial levels). Differences in vulnerability and exposure arise from numerous non-climatic factors (IPCC, 2014a) ⁶ . Global economic growth has been accompanied by increased life expectancy and income in much of the world; however, in addition to environmental degradation and pollution, many regions remain characterised by significant poverty and severe inequalityin income distribution and access to resources, amplifying vulnerability to climate change (Dryzek, 2016; Pattberg and Zelli, 2016; Bäckstrand et al., 2017; Lövbrand et al., 2017) ⁷ . World population continues to rise, notably in hazard-prone small and medium-sized cities in low- and moderate-income countries (Birkmann et al., 2016) ⁸ . The spread of fossil-fuel-based material consumption and changing lifestyles is a major driver of global resource use, and the main contributor to rising greenhouse gas (GHG) emissions (Fleurbaey et al., 2014) ⁹ .

The overarching context of this report is this: human influence has become a principal agent of change on the planet, shifting the world out of the relatively stable Holocene period into a new geological era, often termed the Anthropocene (Box 1.1). Responding to climate change in the Anthropocene will require approaches that integrate multiple levels of interconnectivity across the global community.

This chapter is composed of seven sections linked to the remaining four chapters of the report. This introductory Section 1.1 situates the basic elements of the assessment within the context of sustainable development; considerations of ethics, equity and human rights; and the problem of poverty. Section 1.2 focuses on understanding 1.5°C, global versus regional warming, 1.5°C pathways, and associated emissions. Section 1.3 frames the impacts at 1.5°C and beyond on natural and human systems. The section on strengthening the global response (1.4) frames responses, governance and implementation, and trade-offs and synergies between mitigation, adaptation, and the Sustainable Development Goals (SDGs) under transformation, transformation pathways, and transition. Section 1.5 provides assessment frameworks and emerging methodologies that integrate climate change mitigation and adaptation with sustainable development. Section 1.6 defines approaches used to communicate confidence, uncertainty and risk, while 1.7 presents the storyline of the whole report.

Figure 1.1

Human experience of present-day warming. Different shades of pink to purple indicated by the inset histogram show estimated warming for the season that has warmed the most at a given location between the periods 1850–1900 and 2006–2015, during which global average temperatures rose by 0.91°C in this dataset (Cowtan and Way, 2014) and 0.87°C in […]

File:Https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/Chapter-1-figure-1-1024x568.png

Human experience of present-day warming. Different shades of pink to purple indicated by the inset histogram show estimated warming for the season that has warmed the most at a given location between the periods 1850–1900 and 2006–2015, during which global average temperatures rose by 0.91°C in this dataset (Cowtan and Way, 2014) ¹⁰ and 0.87°C in the multi-dataset average (Table 1.1 and Figure 1.3). The density of dots indicates the population (in 2010) in any 1° × 1° grid box. The underlay shows national Sustainable Development Goal (SDG) Global Index Scores indicating performance across the 17 SDGs. Hatching indicates missing SDG index data (e.g., Greenland). The histogram shows the population living in regions experiencing different levels of warming (at 0.25°C increments). See Supplementary Material 1.SM for further details.

Box 1.1 The Anthropocene: Strengthening the Global Response to 1.5°C Global Warming

Introduction

The concept of the Anthropocene can be linked to the aspiration of the Paris Agreement. The abundant empirical evidence of the unprecedented rate and global scale of impact of human influence on the Earth System (Steffen et al., 2016; Waters et al., 2016) ¹¹ has led many scientists to call for an acknowledgement that the Earth has entered a new geological epoch: the Anthropocene (Crutzen and Stoermer, 2000; Crutzen, 2002; Gradstein et al., 2012) ¹² . Although rates of change in the Anthropocene are necessarily assessed over much shorter periods than those used to calculate long-term baseline rates of change, and therefore present challenges for direct comparison, they are nevertheless striking. The rise in global CO ₂ concentration since 2000 is about 20 ppm per decade, which is up to 10 times faster than any sustained rise in CO ₂ during the past 800,000 years (Lüthi et al., 2008; Bereiter et al., 2015) ¹³ . AR5 found that the last geological epoch with similar atmospheric CO ₂ concentration was the Pliocene, 3.3 to 3.0 Ma (Masson-Delmotte et al., 2013) ¹⁴ . Since 1970 the global average temperature has been rising at a rate of 1.7°C per century, compared to a long-term decline over the past 7,000 years at a baseline rate of 0.01°C per century (NOAA, 2016; Marcott et al., 2013). These global-level rates of human-driven change far exceed the rates of change driven by geophysical or biosphere forces that have altered the Earth System trajectory in the past (e.g., Summerhayes 2015; Foster et al., 2017); even abrupt geophysical events do not approach current rates of human-driven change.

The Geological Dimension of the Anthropocene and 1.5°C Global Warming

The process of formalising the Anthropocene is on-going (Zalasiewicz et al., 2017) ¹⁵ , but a strong majority of the Anthropocene Working Group (AWG) established by the Subcommission on Quaternary Stratigraphy of the International Commission on Stratigraphy have agreed that: (i) the Anthropocene has a geological merit; (ii) it should follow the Holocene as a formal epoch in the Geological Time Scale; and, (iii) its onset should be defined as the mid-20th century. Potential markers in the stratigraphic record include an array of novel manufactured materials of human origin, and “these combined signals render the Anthropocene stratigraphically distinct from the Holocene and earlier epochs” (Waters et al., 2016) ¹⁶ . The Holocene period, which itself was formally adopted in 1885 by geological science community, began 11,700 years ago with a more stable warm climate providing for emergence of human civilisation and growing human-nature interactions that have expanded to give rise to the Anthropocene (Waters et al., 2016) ¹⁷ .

The Anthropocene and the Challenge of a 1.5° C Warmer World

The Anthropocene can be employed as a “boundary concept” (Brondizio et al., 2016) ¹⁸ that frames critical insights into understanding the drivers, dynamics and specific challenges in responding to the ambition of keeping global temperature well below 2°C while pursuing efforts towards and adapting to a 1.5°C warmer world. The United Nations Framework Convention on Climate Change (UNFCCC) and its Paris Agreement recognize the ability of humans to influence geophysical planetary processes (Chapter 2, Cross-Chapter Box 1 in this chapter). The Anthropocene offers a structured understanding of the culmination of past and present human–environmental relations and provides an opportunity to better visualize the future to minimize pitfalls (Pattberg and Zelli, 2016; Delanty and Mota, 2017) ¹⁹ , while acknowledging the differentiated responsibility and opportunity to limit global warming and invest in prospects for climate-resilient sustainable development (Harrington, 2016) ²⁰ (Chapter 5). The Anthropocene also provides an opportunity to raise questions regarding the regional differences, social inequities, and uneven capacities and drivers of global social–environmental changes, which in turn inform the search for solutions as explored in Chapter 4 of this report (Biermann et al., 2016) ²¹ . It links uneven influences of human actions on planetary functions to an uneven distribution of impacts (assessed in Chapter 3) as well as the responsibility and response capacity to, for example, limit global warming to no more than a 1.5°C rise above pre-industrial levels. Efforts to curtail greenhouse gas emissions without incorporating the intrinsic interconnectivity and disparities associated with the Anthropocene world may themselves negatively affect the development ambitions of some regions more than others and negate sustainable development efforts (see Chapter 2 and Chapter 5).

1.1.1 Equity and a 1.5°C Warmer World

The AR5 suggested that equity, sustainable development, and poverty eradication are best understood as mutually supportive and co-achievable within the context of climate action and are underpinned by various other international hard and soft law instruments (Denton et al., 2014; Fleurbaey et al., 2014; Klein et al., 2014; Olsson et al., 2014; Porter et al., 2014; Stavins et al., 2014) ²² . The aim of the Paris Agreement under the UNFCCC to ‘pursue efforts to limit’ the rise in global temperatures to 1.5°C above pre-industrial levels raises ethical concerns that have long been central to climate debates (Fleurbaey et al., 2014; Kolstad et al., 2014) ²³ . The Paris Agreement makes particular reference to the principle of equity, within the context of broader international goals of sustainable development and poverty eradication. Equity is a long-standing principle within international law and climate change law in particular (Shelton, 2008; Bodansky et al., 2017) ²⁴ .

The AR5 describes equity as having three dimensions: intergenerational (fairness between generations), international (fairness between states), and national (fairness between individuals) (Fleurbaey et al., 2014) ²⁵ . The principle is generally agreed to involve both procedural justice (i.e., participation in decision making) and distributive justice (i.e., how the costs and benefits of climate actions are distributed) (Kolstad et al., 2014; Savaresi, 2016; Reckien et al., 2017) ²⁶ . Concerns regarding equity have frequently been central to debates around mitigation, adaptation and climate governance (Caney, 2005; Schroeder et al., 2012; Ajibade, 2016; Reckien et al., 2017; Shue, 2018) ²⁷ . Hence, equity provides a framework for understanding the asymmetries between the distributions of benefits and costs relevant to climate action (Schleussner et al., 2016; Aaheim et al., 2017) ²⁸ .

Four key framing asymmetries associated with the conditions of a 1.5°C warmer world have been noted (Okereke, 2010; Harlan et al., 2015; Ajibade, 2016; Savaresi, 2016; Reckien et al., 2017) ²⁹ and are reflected in the report’s assessment. The first concerns differential contributions to the problem: the observation that the benefits from industrialization have been unevenly distributed and those who benefited most historically also have contributed most to the current climate problem and so bear greater responsibility (Shue, 2013; McKinnon, 2015; Otto et al., 2017; Skeie et al., 2017) ³⁰ . The second asymmetry concerns differential impact: the worst impacts tend to fall on those least responsible for the problem, within states, between states, and between generations (Fleurbaey et al., 2014; Shue, 2014; Ionesco et al., 2016) ³¹ . The third is the asymmetry in capacity to shape solutions and response strategies, such that the worst-affected states, groups, and individuals are not always well represented (Robinson and Shine, 2018) ³² . Fourth, there is an asymmetry in future response capacity: some states, groups, and places are at risk of being left behind as the world progresses to a low-carbon economy (Fleurbaey et al., 2014; Shue, 2014; Humphreys, 2017) ³³ .

A sizeable and growing literature exists on how best to operationalize climate equity considerations, drawing on other concepts mentioned in the Paris Agreement, notably its explicit reference to human rights (OHCHR, 2009; Caney, 2010; Adger et al., 2014; Fleurbaey et al., 2014; IBA, 2014; Knox, 2015; Duyck et al., 2018; Robinson and Shine, 2018) ³⁴ . Human rights comprise internationally agreed norms that align with the Paris ambitions of poverty eradication, sustainable development, and the reduction of vulnerability (Caney, 2010; Fleurbaey et al., 2014; OHCHR, 2015) ³⁵ . In addition to defining substantive rights (such as to life, health, and shelter) and procedural rights (such as to information and participation), human rights instruments prioritise the rights of marginalized groups, children, vulnerable and indigenous persons, and those discriminated against on grounds such as gender, race, age or disability (OHCHR, 2017) ³⁶ . Several international human rights obligations are relevant to the implementation of climate actions and consonant with UNFCCC undertakings in the areas of mitigation, adaptation, finance, and technology transfer (Knox, 2015; OHCHR, 2015; Humphreys, 2017) ³⁷ .

Much of this literature is still new and evolving (Holz et al., 2017; Dooley et al., 2018; Klinsky and Winkler, 2018) ³⁸ , permitting the present report to examine some broader equity concerns raised both by possible failure to limit warming to 1.5°C and by the range of ambitious mitigation efforts that may be undertaken to achieve that limit. Any comparison between 1.5°C and higher levels of warming implies risk assessments and value judgements and cannot straightforwardly be reduced to a cost-benefit analysis (Kolstad et al., 2014) ³⁹ . However, different levels of warming can nevertheless be understood in terms of their different implications for equity – that is, in the comparative distribution of benefits and burdens for specific states, persons, or generations, and in terms of their likely impacts on sustainable development and poverty (see especially Sections 2.3.4.2, 2.5, 3.4.5–3.4.13, 3.6, 5.4.1, 5.4.2, 5.6 and Cross-Chapter boxes 6 in Chapter 3 and 12 in Chapter 5).

1.1.2 Eradication of Poverty

This report assesses the role of poverty and its eradication in the context of strengthening the global response to the threat of climate change and sustainable development. A wide range of definitions for poverty exist. The AR5 discussed ‘poverty’ in terms of its multidimensionality, referring to ‘material circumstances’ (e.g., needs, patterns of deprivation, or limited resources), as well as to economic conditions (e.g., standard of living, inequality, or economic position), and/or social relationships (e.g., social class, dependency, lack of basic security, exclusion, or lack of entitlement; Olsson et al., 2014) ⁴⁰ . The UNDP now uses a Multidimensional Poverty Index and estimates that about 1.5 billion people globally live in multidimensional poverty, especially in rural areas of South Asia and Sub-Saharan Africa, with an additional billion at risk of falling into poverty (UNDP, 2016) ⁴¹ .

A large and rapidly growing body of knowledge explores the connections between climate change and poverty. Climatic variability and climate change are widely recognized as factors that may exacerbate poverty, particularly in countries and regions where poverty levels are high (Leichenko and Silva, 2014) ⁴² . The AR5 noted that climate change-driven impacts often act as a threat multiplier in that the impacts of climate change compound other drivers of poverty (Olsson et al., 2014) ⁴³ . Many vulnerable and poor people are dependent on activities such as agriculture that are highly susceptible to temperature increases and variability in precipitation patterns (Shiferaw et al., 2014; Miyan, 2015) ⁴⁴ . Even modest changes in rainfall and temperature patterns can push marginalized people into poverty as they lack the means to recover from associated impacts. Extreme events, such as floods, droughts, and heat waves, especially when they occur in series, can significantly erode poor people’s assets and further undermine their livelihoods in terms of labour productivity, housing, infrastructure and social networks (Olsson et al., 2014) ⁴⁵ .

1.1.3 Sustainable Development and a 1.5°C Warmer World

AR5 (IPCC, 2014c) ⁴⁶ noted with high confidence that ‘equity is an integral dimension of sustainable development’ and that ‘mitigation and adaptation measures can strongly affect broader sustainable development and equity objectives’ (Fleurbaey et al., 2014) ⁴⁷ . Limiting global warming to 1.5°C would require substantial societal and technological transformations, dependent in turn on global and regional sustainable development pathways. A range of pathways, both sustainable and not, are explored in this report, including implementation strategies to understand the enabling conditions and challenges required for such a transformation. These pathways and connected strategies are framed within the context of sustainable development, and in particular the United Nations 2030 Agenda for Sustainable Development (UN, 2015b) ⁴⁸ and Cross-Chapter Box 4 on SDGs (in this chapter). The feasibility of staying within 1.5°C depends upon a range of enabling conditions with geophysical, environmental–ecological, technological, economic, socio-cultural, and institutional dimensions. Limiting warming to 1.5°C also involves identifying technology and policy levers to accelerate the pace of transformation (see Chapter 4). Some pathways are more consistent than others with the requirements for sustainable development (see Chapter 5). Overall, the three-pronged emphasis on sustainable development, resilience, and transformation provides Chapter 5 an opportunity to assess the conditions of simultaneously reducing societal vulnerabilities, addressing entrenched inequalities, and breaking the circle of poverty.

The feasibility of any global commitment to a 1.5°C pathway depends, in part, on the cumulative influence of the nationally determined contributions (NDCs), committing nation states to specific GHG emission reductions. The current NDCs, extending only to 2030, do not limit warming to 1.5°C. Depending on mitigation decisions after 2030, they cumulatively track toward a warming of 3°-4°C above pre-industrial temperatures by 2100, with the potential for further warming thereafter (Rogelj et al., 2016a; UNFCCC, 2016) ⁴⁹ . The analysis of pathways in this report reveals opportunities for greater decoupling of economic growth from GHG emissions. Progress towards limiting warming to 1.5°C requires a significant acceleration of this trend. AR5 concluded that climate change constrains possible development paths, that synergies and trade-offs exist between climate responses and socio-economic contexts, and that opportunities for effective climate responses overlap with opportunities for sustainable development, noting that many existing societal patterns of consumption are intrinsically unsustainable (Fleurbaey et al., 2014) ⁵⁰ .

1.2 Understanding 1.5°C: Reference Levels, Probability, Transience, Overshoot, and Stabilization

1.2.1 Working Definitions of 1.5°C and 2°C Warming Relative to Pre-Industrial Levels

What is meant by ‘the increase in global average temperature… above pre-industrial levels’ referred to in the Paris Agreement depends on the choice of pre-industrial reference period, whether 1.5°C refers to total warming or the human-induced component of that warming, and which variables and geographical coverage are used to define global average temperature change. The cumulative impact of these definitional ambiguities (e.g., Hawkins et al., 2017; Pfleiderer et al., 2018) ⁵¹ is comparable to natural multi-decadal temperature variability on continental scales (Deser et al., 2012) ⁵² and primarily affects the historical period, particularly that prior to the early 20th century when data is sparse and of less certain quality. Most practical mitigation and adaptation decisions do not depend on quantifying historical warming to this level of precision, but a consistent working definition is necessary to ensure consistency across chapters and figures. We adopt definitions that are as consistent as possible with key findings of AR5 with respect to historical warming.

This report defines ‘warming’, unless otherwise qualified, as an increase in multi-decade global mean surface temperature (GMST) above pre-industrial levels. Specifically, warming at a given point in time is defined as the global average of combined land surface air and sea surface temperatures for a 30-year period centred on that time, expressed relative to the reference period 1850–1900 (adopted for consistency with Box SPM.1 Figure 1 of IPCC (2014a) ⁵³ ‘as an approximation of pre-industrial levels’, excluding the impact of natural climate fluctuations within that 30-year period and assuming any secular trend continues throughout that period, extrapolating into the future if necessary. There are multiple ways of accounting for natural fluctuations and trends (e.g., Foster and Rahmstorf, 2011; Haustein et al., 2017; Medhaug et al., 2017; Folland et al., 2018; Visser et al., 2018) ⁵⁴ , but all give similar results. A major volcanic eruption might temporarily reduce observed global temperatures, but would not reduce warming as defined here (Bethke et al., 2017) ⁵⁵ . Likewise, given that the level of warming is currently increasing at 0.3°C–0.7°C per 30 years ( likely range quoted in Kirtman et al., 2013 ⁵⁶ and supported by Folland et al., 2018) ⁵⁷ , the level of warming in 2017 was 0.15°C–0.35°C higher than average warming over the 30-year period 1988–2017.

In summary, this report adopts a working definition of ‘1.5°C relative to pre-industrial levels’ that corresponds to global average combined land surface air and sea surface temperatures either 1.5°C warmer than the average of the 51-year period 1850–1900, 0.87°C warmer than the 20-year period 1986–2005, or 0.63°C warmer than the decade 2006–2015. These offsets are based on all available published global datasets, combined and updated, which show that 1986–2005 was 0.63°C warmer than 1850–1900 (with a 5–95% range of 0.57°C–0.69°C based on observational uncertainties alone), and 2006–2015 was 0.87°C warmer than 1850–1900 (with a likely range of 0.75°C–0.99°C, also accounting for the possible impact of natural fluctuations). Where possible, estimates of impacts and mitigation pathways are evaluated relative to these more recent periods. Note that the 5–95% intervals often quoted in square brackets in AR5 correspond to very likely ranges, while likely ranges correspond to 17–83%, or the central two-thirds, of the distribution of uncertainty.

1.2.1.1 Definition of global average temperature

The IPCC has traditionally defined changes in observed GMST as a weighted average of near-surface air temperature (SAT) changes over land and sea surface temperature (SST) changes over the oceans (Morice et al., 2012; Hartmann et al., 2013) ⁵⁸ , while modelling studies have typically used a simple global average SAT. For ambitious mitigation goals, and under conditions of rapid warming or declining sea ice (Berger et al., 2017) ⁵⁹ , the difference can be significant. Cowtan et al. (2015) ⁶⁰ and Richardson et al. (2016) ⁶¹ show that the use of blended SAT/SST data and incomplete coverage together can give approximately 0.2°C less warming from the 19th century to the present relative to the use of complete global-average SAT (Stocker et al., 2013 ⁶² , Figure TFE8.1 and Figure 1.2). However, Richardson et al. (2018) ⁶³ show that this is primarily an issue for the interpretation of the historical record to date, with less absolute impact on projections of future changes, or estimated emissions budgets, under ambitious mitigation scenarios.

The three GMST reconstructions used in AR5 differ in their treatment of missing data. GISTEMP (Hansen et al., 2010) ⁶⁴ uses interpolation to infer trends in poorly observed regions like the Arctic (although even this product is spatially incomplete in the early record), while NOAAGlobalTemp (Vose et al., 2012) ⁶⁵ and HadCRUT (Morice et al., 2012) ⁶⁶ are progressively closer to a simple average of available observations. Since the AR5, considerable effort has been devoted to more sophisticated statistical modelling to account for the impact of incomplete observation coverage (Rohde et al., 2013; Cowtan and Way, 2014; Jones, 2016) ⁶⁷ . The main impact of statistical infilling is to increase estimated warming to date by about 0.1°C (Richardson et al., 2018 ⁶⁸ and Table 1.1).

We adopt a working definition of warming over the historical period based on an average of the four available global datasets that are supported by peer-reviewed publications: the three datasets used in the AR5, updated (Karl et al., 2015) ⁶⁹ , together with the Cowtan-Way infilled dataset (Cowtan and Way, 2014) ⁷⁰ . A further two datasets, Berkeley Earth (Rohde et al., 2013) ⁷¹ and that of the Japan Meteorological Agency (JMA), are provided in Table 1.1. This working definition provides an updated estimate of 0.86°C for the warming over the period 1880–2012 based on a linear trend. This quantity was quoted as 0.85°C in the AR5. Hence the inclusion of the Cowtan-Way dataset does not introduce any inconsistency with the AR5, whereas redefining GMST to represent global SAT could increase this figure by up to 20% (Table 1.1, blue lines in Figure 1.2 and Richardson et al., 2016) ⁷² .

Figure 1.2

Evolution of global mean surface temperature (GMST) over the period of instrumental observations.

File:Https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/figure-1.2-1024x626.png

Grey shaded line shows monthly mean GMST in the HadCRUT4, NOAAGlobalTemp, GISTEMP and Cowtan-Way datasets, expressed as departures from 1850–1900, with varying grey line thickness indicating inter-dataset range. All observational datasets shown represent GMST as a weighted average of near surface air temperature over land and sea surface temperature over oceans. Human-induced (yellow) and total (human- and naturally-forced, orange) contributions to these GMST changes are shown calculated following Otto et al. (2015) ⁷³ and Haustein et al. (2017) ⁷⁴ . Fractional uncertainty in the level of human-induced warming in 2017 is set equal to ±20% based on multiple lines of evidence. Thin blue lines show the modelled global mean surface air temperature (dashed) and blended surface air and sea surface temperature accounting for observational coverage (solid) from the CMIP5 historical ensemble average extended with RCP8.5 forcing (Cowtan et al., 2015; Richardson et al., 2018) ⁷⁵ . The pink shading indicates a range for temperature fluctuations over the Holocene (Marcott et al., 2013) ⁷⁶ . Light green plume shows the AR5 prediction for average GMST over 2016–2035 (Kirtman et al., 2013) ⁷⁷ . See Supplementary Material 1.SM for further details.

1.2.1.2 Choice of reference period

Any choice of reference period used to approximate ‘pre-industrial’ conditions is a compromise between data coverage and representativeness of typical pre-industrial solar and volcanic forcing conditions. This report adopts the 51-year reference period, 1850–1900 inclusive, assessed as an approximation of pre-industrial levels in AR5 (Box TS.5, Figure 1 of Field et al., 2014) ⁷⁸ . The years 1880–1900 are subject to strong but uncertain volcanic forcing, but in the HadCRUT4 dataset, average temperatures over 1850–1879, prior to the largest eruptions, are less than 0.01°C from the average for 1850–1900. Temperatures rose by 0.0°C–0.2°C from 1720–1800 to 1850–1900 (Hawkins et al., 2017) ⁷⁹ , but the anthropogenic contribution to this warming is uncertain (Abram et al., 2016; Schurer et al., 2017) ⁸⁰ . The 18th century represents a relatively cool period in the context of temperatures since the mid-Holocene (Marcott et al., 2013; Lüning and Vahrenholt, 2017; Marsicek et al., 2018) ⁸¹ , which is indicated by the pink shaded region in Figure 1.2.

Projections of responses to emission scenarios, and associated impacts, may use a more recent reference period, offset by historical observations, to avoid conflating uncertainty in past and future changes (e.g., Hawkins et al., 2017; Millar et al., 2017b; Simmons et al., 2017) ⁸² . Two recent reference periods are used in this report: 1986–2005 and 2006–2015. In the latter case, when using a single decade to represent a 30-year average centred on that decade, it is important to consider the potential impact of internal climate variability. The years 2008–2013 were characterised by persistent cool conditions in the Eastern Pacific (Kosaka and Xie, 2013; Medhaug et al., 2017) ⁸³ , related to both the El Niño-Southern Oscillation (ENSO) and, potentially, multi-decadal Pacific variability (e.g., England et al., 2014) ⁸⁴ , but these were partially compensated for by El Niño conditions in 2006 and 2015. Likewise, volcanic activity depressed temperatures in 1986–2005, partly offset by the very strong El Niño event in 1998. Figure 1.2 indicates that natural variability (internally generated and externally driven) had little net impact on average temperatures over 2006–2015, in that the average temperature of the decade is similar to the estimated externally driven warming. When solar, volcanic and ENSO-related variability is taken into account following the procedure of Foster and Rahmstorf (2011) ⁸⁵ , there is no indication of average temperatures in either 1986–2005 or 2006–2015 being substantially biased by short-term variability (see Supplementary Material 1.SM.2). The temperature difference between these two reference periods (0.21°C–0.27°C over 15 years across available datasets) is also consistent with the AR5 assessment of the current warming rate of 0.3°C–0.7°C over 30 years (Kirtman et al., 2013) ⁸⁶ .

On the definition of warming used here, warming to the decade 2006–2015 comprises an estimate of the 30-year average centred on this decade, or 1996–2025, assuming the current trend continues and that any volcanic eruptions that might occur over the final seven years are corrected for. Given this element of extrapolation, we use the AR5 near-term projection to provide a conservative uncertainty range. Combining the uncertainty in observed warming to 1986–2005 (±0.06°C) with the likely range in the current warming trend as assessed by AR5 (±0.2°C/30 years), assuming these are uncorrelated, and using observed warming relative to 1850–1900 to provide the central estimate (no evidence of bias from short-term variability), gives an assessed warming to the decade 2006–2015 of 0.87°C with a ±0.12°C likely range. This estimate has the advantage of traceability to the AR5, but more formal methods of quantifying externally driven warming (e.g., Bindoff et al., 2013; Jones et al., 2016; Haustein et al., 2017; Ribes et al., 2017) ⁸⁷ , which typically give smaller ranges of uncertainty, may be adopted in the future.

Table 1.1

Observed increase in global average surface temperature in various datasets. Numbers in square brackets correspond to 5–95% uncertainty ranges from individual datasets, encompassing known sources of observational uncertainty only.

Diagnostic / dataset	1850–1900 to (1) 2006–2015	1850–1900 to (2) 1986–2005	1986–2005 to (3) 2006–2015	1850–1900 to (4) 1981–2010	1850–1900 to (5) 1998–2017	Trend (6) 1880–2012	Trend (6) 1880–2015
HadCRUT4.6	0.84 [0.79–0.89]	0.60 [0.57–0.66]	0.22 [0.21–0.23]	0.62 [0.58–0.67]	0.83 [0.78–0.88]	0.83 [0.77–0.90]	0.88 [0.83–0.95]
NOAAGlobalTemp (7)	0.86	0.62	0.22	0.63	0.85	0.85	0.91
GISTEMP (7)	0.89	0.65	0.23	0.66	0.88	0.89	0.94
Cowtan-Way	0.91 [0.85–0.99]	0.65 [0.60–0.72]	0.26 [0.25–0.27]	0.65 [0.60–0.72]	0.88 [0.82–0.96]	0.88 [0.79–0.98]	0.93 [0.85–1.03]
Average (8)	0.87	0.63	0.23	0.64	0.86	0.86	0.92
Berkeley (9)	0.98	0.73	0.25	0.73	0.97	0.97	1.02
JMA (9)	0.82	0.59	0.17	0.60	0.81	0.82	0.87
ERA-Interim	N/A	N/A	0.26	N/A	N/A	N/A	N/A
JRA-55	N/A	N/A	0.23	N/A	N/A	N/A	N/A
CMIP5 global SAT (10)	0.99 [0.65–1.37]	0.62 [0.38–0.94]	0.38 [0.24–0.62]	0.62 [0.34–0.93]	0.89 [0.62–1.29]	0.81 [0.58–1.31]	0.86 [0.63–1.39]
CMIP5 SAT/SST blend—masked	0.86 [0.54–1.18]	0.50 [0.31–0.79]	0.34 [0.19–0.54]	0.48 [0.26–0.79]	0.75 [0.52–1.11]	0.68 [0.45–1.08]	0.74 [0.51–1.14]

Notes:

Most recent reference period used in this report.
Most recent reference period used in AR5.
Difference between recent reference periods.
Current WMO standard reference periods.
Most recent 20-year period.
Linear trends estimated by a straight-line fit, expressed in degrees yr ⁻¹ multiplied by 133 or 135 years respectively, with uncertainty ranges incorporating observational uncertainty only.
To estimate changes in the NOAAGlobalTemp and GISTEMP datasets relative to the 1850–1900 reference period, warming is computed relative to 1850–1900 using the HadCRUT4.6 dataset and scaled by the ratio of the linear trend 1880–2015 in the NOAAGlobalTemp or GISTEMP dataset with the corresponding linear trend computed from HadCRUT4.
Average of diagnostics derived – see (7) – from four peer-reviewed global datasets, HadCRUT4.6, NOAA, GISTEMP & Cowtan-Way. Note that differences between averages may not coincide with average differences because of rounding.
No peer-reviewed publication available for these global combined land–sea datasets.
CMIP5 changes estimated relative to 1861–80 plus 0.02°C for the offset in HadCRUT4.6 from 1850–1900. CMIP5 values are the mean of the RCP8.5 ensemble, with 5–95% ensemble range. They are included to illustrate the difference between a complete global surface air temperature record (SAT) and a blended surface air and sea surface temperature (SST) record accounting for incomplete coverage (masked), following Richardson et al. (2016) ⁸⁸ . Note that 1986–2005 temperatures in CMIP5 appear to have been depressed more than observed temperatures by the eruption of Mount Pinatubo.

1.2.1.3 Total versus human-induced warming and warming rates

Total warming refers to the actual temperature change, irrespective of cause, while human-induced warming refers to the component of that warming that is attributable to human activities. Mitigation studies focus on human-induced warming (that is not subject to internal climate variability), while studies of climate change impacts typically refer to total warming (often with the impact of internal variability minimised through the use of multi-decade averages).

In the absence of strong natural forcing due to changes in solar or volcanic activity, the difference between total and human-induced warming is small: assessing empirical studies quantifying solar and volcanic contributions to GMST from 1890 to 2010, AR5 (Figure 10.6 of Bindoff et al., 2013) ⁸⁹ found their net impact on warming over the full period to be less than plus or minus 0.1°C. Figure 1.2 shows that the level of human-induced warming has been indistinguishable from total observed warming since 2000, including over the decade 2006–2015. Bindoff et al. (2013) ⁹⁰ assessed the magnitude of human-induced warming over the period 1951–2010 to be 0.7°C ( likely between 0.6°C and 0.8°C), which is slightly greater than the 0.65°C observed warming over this period (Figures 10.4 and 10.5) with a likely range of ±14%. The key surface temperature attribution studies underlying this finding (Gillett et al., 2013; Jones et al., 2013; Ribes and Terray, 2013) ⁹¹ used temperatures since the 19th century to constrain human-induced warming, and so their results are equally applicable to the attribution of causes of warming over longer periods. Jones et al. (2016) ⁹² show (Figure 10) human-induced warming trends over the period 1905–2005 to be indistinguishable from the corresponding total observed warming trend accounting for natural variability using spatio-temporal detection patterns from 12 out of 15 CMIP5 models and from the multi-model average. Figures from Ribes and Terray (2013) ⁹³ , show the anthropogenic contribution to the observed linear warming trend 1880–2012 in the HadCRUT4 dataset (0.83°C in Table 1.1) to be 0.86°C using a multi-model average global diagnostic, with a 5–95% confidence interval of 0.72°C–1.00°C (see figure 1.SM.6). In all cases, since 2000 the estimated combined contribution of solar and volcanic activity to warming relative to 1850–1900 is found to be less than ±0.1°C (Gillett et al., 2013) ⁹⁴ , while anthropogenic warming is indistinguishable from, and if anything slightly greater than, the total observed warming, with 5–95% confidence intervals typically around ±20%.

Haustein et al. (2017) ⁹⁵ give a 5–95% confidence interval for human-induced warming in 2017 of 0.87°C–1.22°C, with a best estimate of 1.02°C, based on the HadCRUT4 dataset accounting for observational and forcing uncertainty and internal variability. Applying their method to the average of the four datasets shown in Figure 1.2 gives an average level of human-induced warming in 2017 of 1.04°C. They also estimate a human-induced warming trend over the past 20 years of 0.17°C (0.13°C–0.33°C) per decade, consistent with estimates of the total observed trend of Foster and Rahmstorf (2011) ⁹⁶ (0.17° ± 0.03°C per decade, uncertainty in linear trend only), Folland et al. (2018) ⁹⁷

and Kirtman et al. (2013) ⁹⁸ (0.3°C–0.7°C over 30 years, or 0.1°C–0.23°C per decade, likely range), and a best-estimate warming rate over the past five years of 0.215°C/decade (Leach et al., 2018) ⁹⁹ . Drawing on these multiple lines of evidence, human-induced warming is assessed to have reached 1.0°C in 2017, having increased by 0.13°C from the mid-point of 2006–2015, with a likely range of 0.8°C to 1.2°C (reduced from 5–95% to account for additional forcing and model uncertainty), increasing at 0.2°C per decade (with a likely range of 0.1°C to 0.3°C per decade: estimates of human-induced warming given to 0.1°C precision only).

Since warming is here defined in terms of a 30-year average, corrected for short-term natural fluctuations, when warming is considered to be at 1.5°C, global temperatures would fluctuate equally on either side of 1.5°C in the absence of a large cooling volcanic eruption (Bethke et al., 2017) ¹⁰⁰ . Figure 1.2 indicates there is a substantial chance of GMST in a single month fluctuating over 1.5°C between now and 2020 (or, by 2030, for a longer period: Henley and King, 2017) ¹⁰¹ , but this would not constitute temperatures ‘reaching 1.5°C’ on our working definition. Rogelj et al. (2017) ¹⁰² show limiting the probability of annual GMST exceeding 1.5°C to less than one-year-in-20 would require limiting warming, on the definition used here, to 1.31°C or lower.

1.2.2 Global versus Regional and Seasonal Warming

Warming is not observed or expected to be spatially or seasonally uniform (Collins et al., 2013) ¹⁰³ . A 1.5°C increase in GMST will be associated with warming substantially greater than 1.5°C in many land regions, and less than 1.5°C in most ocean regions. This is illustrated by Figure 1.3, which shows an estimate of the observed change in annual and seasonal average temperatures between the 1850–1900 pre-industrial reference period and the decade 2006–2015 in the Cowtan-Way dataset. These regional changes are associated with an observed GMST increase of 0.91°C in the dataset shown here, or 0.87°C in the four-dataset average (Table 1.1). This observed pattern reflects an on-going transient warming: features such as enhanced warming over land may be less pronounced, but still present, in equilibrium (Collins et al., 2013) ¹⁰⁴ . This figure illustrates the magnitude of spatial and seasonal differences, with many locations, particularly in Northern Hemisphere mid-latitude winter (December–February), already experiencing regional warming more than double the global average. Individual seasons may be substantially warmer, or cooler, than these expected changes in the long-term average.

Figure 1.3

Spatial and seasonal pattern of present-day warming.

File:Https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/Figure-1.3-1024x854.png

Regional warming for the 2006–2015 decade relative to 1850–1900 for the annual mean (top), the average of December, January, and February (bottom left) and for June, July, and August (bottom right). Warming is evaluated by regressing regional changes in the Cowtan and Way (2014) ¹⁰⁵ dataset onto the total (combined human and natural) externally forced warming (yellow line in Figure 1.2). See Supplementary Material 1.SM for further details and versions using alternative datasets. The definition of regions (green boxes and labels in top panel) is adopted from the AR5 (Christensen et al., 2013) ¹⁰⁶ .

1.2.3 Definition of 1.5°C Pathways: Probability, Transience, Stabilization and Overshoot

Pathways considered in this report, consistent with available literature on 1.5°C, primarily focus on the time scale up to 2100, recognising that the evolution of GMST after 2100 is also important. Two broad categories of 1.5°C pathways can be used to characterise mitigation options and impacts: pathways in which warming (defined as 30-year averaged GMST relative to pre-industrial levels, see Section 1.2.1) remains below 1.5°C throughout the 21st century, and pathways in which warming temporarily exceeds (‘overshoots’) 1.5°C and returns to 1.5°C either before or soon after 2100. Pathways in which warming exceeds 1.5°C before 2100, but might return to that level in some future century, are not considered 1.5°C pathways.

Because of uncertainty in the climate response, a ‘prospective’ mitigation pathway (see Cross-Chapter Box 1 in this chapter), in which emissions are prescribed, can only provide a level of probability of warming remaining below a temperature threshold. This probability cannot be quantified precisely since estimates depend on the method used (Rogelj et al., 2016b; Millar et al., 2017b; Goodwin et al., 2018; Tokarska and Gillett, 2018) ¹⁰⁷ . This report defines a ‘1.5°C pathway’ as a pathway of emissions and associated possible temperature responses in which the majority of approaches using presently available information assign a probability of approximately one-in-two to two-in-three to warming remaining below 1.5°C or, in the case of an overshoot pathway, to warming returning to 1.5°C by around 2100 or earlier. Recognizing the very different potential impacts and risks associated with high-overshoot pathways, this report singles out 1.5°C pathways with no or limited (<0.1°C) overshoot in many instances and pursues efforts to ensure that when the term ‘1.5°C pathway’ is used, the associated overshoot is made explicit where relevant. In Chapter 2, the classification of pathways is based on one modelling approach to avoid ambiguity, but probabilities of exceeding 1.5°C are checked against other approaches to verify that they lie within this approximate range. All these absolute probabilities are imprecise, depend on the information used to constrain them, and hence are expected to evolve in the future. Imprecise probabilities can nevertheless be useful for decision-making, provided the imprecision is acknowledged (Hall et al., 2007; Kriegler et al., 2009; Simpson et al., 2016) ¹⁰⁸ . Relative and rank probabilities can be assessed much more consistently: approaches may differ on the absolute probability assigned to individual outcomes, but typically agree on which outcomes are more probable.

Importantly, 1.5°C pathways allow a substantial (up to one-in-two) chance of warming still exceeding 1.5°C. An ‘adaptive’ mitigation pathway in which emissions are continuously adjusted to achieve a specific temperature outcome (e.g., Millar et al., 2017b) ¹⁰⁹ reduces uncertainty in the temperature outcome while increasing uncertainty in the emissions required to achieve it. It has been argued (Otto et al., 2015; Xu and Ramanathan, 2017) ¹¹⁰ that achieving very ambitious temperature goals will require such an adaptive approach to mitigation, but very few studies have been performed taking this approach (e.g., Jarvis et al., 2012) ¹¹¹ .

Figure 1.4 illustrates categories of (a) 1.5°C pathways and associated (b) annual and (c) cumulative emissions of CO ₂ . It also shows (d) an example of a ‘time-integrated impact’ that continues to increase even after GMST has stabilised, such as sea level rise. This schematic assumes for the purposes of illustration that the fractional contribution of non-CO ₂ climate forcers to total anthropogenic forcing (which is currently increasing, Myhre et al., 2017) ¹¹² is approximately constant from now on. Consequently, total human-induced warming is proportional to cumulative CO ₂ emissions (solid line in c), and GMST stabilises when emissions reach zero. This is only the case in the most ambitious scenarios for non-CO ₂ mitigation (Leach et al., 2018) ¹¹³ . A simple way of accounting for varying non-CO ₂ forcing in Figure 1.4 would be to note that every 1 W m ⁻² increase in non-CO ₂ forcing between now and the decade or two immediately prior to the time of peak warming reduces cumulative CO ₂ emissions consistent with the same peak warming by approximately 1100 GtCO ₂ , with a range of 900-1500 GtCO ₂ (using values from AR5: Myhre et al., 2013; Allen et al., 2018; Jenkins et al., 2018 ¹¹⁴ ; Cross-Chapter Box 2 in this chapter).

1.2.3.1 Pathways remaining below 1.5°C

In this category of 1.5°C pathways, human-induced warming either rises monotonically to stabilise at 1.5°C (Figure 1.4, brown lines) or peaks at or below 1.5°C and then declines (yellow lines). Figure 1.4b demonstrates that pathways remaining below 1.5°C require net annual CO ₂ emissions to peak and decline to near zero or below, depending on the long-term adjustment of the carbon cycle and non-CO ₂ emissions (Bowerman et al., 2013; Wigley, 2018) ¹¹⁵ . Reducing emissions to zero corresponds to stabilizing cumulative CO ₂ emissions (Figure 1.4c, solid lines) and falling concentrations of CO ₂ in the atmosphere (panel c dashed lines) (Matthews and Caldeira, 2008; Solomon et al., 2009) ¹¹⁶ , which is required to stabilize GMST if non-CO ₂ climate forcings are constant and positive. Stabilizing atmospheric greenhouse gas concentrations would result in continued warming (see Section 1.2.4).

If emission reductions do not begin until temperatures are close to the proposed limit, pathways remaining below 1.5°C necessarily involve much faster rates of net CO ₂ emission reductions (Figure 1.4, green lines), combined with rapid reductions in non-CO ₂ forcing and these pathways also reach 1.5°C earlier. Note that the emissions associated with these schematic temperature pathways may not correspond to feasible emission scenarios, but they do illustrate the fact that the timing of net zero emissions does not in itself determine peak warming: what matters is total cumulative emissions up to that time. Hence every year’s delay before initiating emission reductions decreases by approximately two years the remaining time available to reach zero emissions on a pathway still remaining below 1.5°C (Allen and Stocker, 2013; Leach et al., 2018) ¹¹⁷ .

1.2.3.2 Pathways temporarily exceeding 1.5°C

With the pathways in this category, also referred to as overshoot pathways, GMST rises above 1.5°C relative to pre-industrial before peaking and returning to 1.5°C around or before 2100 (Figure 1.4, blue lines), subsequently either stabilising or continuing to fall. This allows initially slower or delayed emission reductions, but lowering GMST requires net negative global CO ₂ emissions (net anthropogenic removal of CO ₂ ; Figure 1.4b). Cooling, or reduced warming, through sustained reductions of net non-CO ₂ climate forcing (Cross-Chapter Box 2 in this chapter) is also required, but their role is limited because emissions of most non-CO ₂ forcers cannot be reduced to below zero. Hence the feasibility and availability of large-scale CO ₂ removal limits the possible rate and magnitude of temperature decline. In this report, overshoot pathways are referred to as 1.5°C pathways, but qualified by the amount of the temperature overshoot, which can have a substantial impact on irreversible climate change impacts (Mathesius et al., 2015; Tokarska and Zickfeld, 2015) ¹¹⁸ .

1.2.3.3 Impacts at 1.5°C warming associated with different pathways: transience versus stabilisation

Figure 1.4 also illustrates time scales associated with different impacts. While many impacts scale with the change in GMST itself, some (such as those associated with ocean acidification) scale with the change in atmospheric CO ₂ concentration, indicated by the fraction of cumulative CO ₂ emissions remaining in the atmosphere (dotted lines in Figure 1.4c). Others may depend on the rate of change of GMST, while ‘time-integrated impacts’, such as sea level rise, shown in Figure 1.4d continue to increase even after GMST has stabilised.

Hence impacts that occur when GMST reaches 1.5°C could be very different depending on the pathway to 1.5°C. CO ₂ concentrations will be higher as GMST rises past 1.5°C (transient warming) than when GMST has stabilized at 1.5°C, while sea level and, potentially, global mean precipitation (Pendergrass et al., 2015) ¹¹⁹ would both be lower (see Figure 1.4). These differences could lead to very different impacts on agriculture, on some forms of extreme weather (e.g., Baker et al., 2018) ¹²⁰ , and on marine and terrestrial ecosystems (e.g., Mitchell et al., 2017 ¹²¹ and Boxes 3.1 and 3.2). Sea level would be higher still if GMST returns to 1.5°C after an overshoot (Figure 1.4 d), with potentially significantly different impacts in vulnerable regions. Temperature overshoot could also cause irreversible impacts (see Chapter 3).

Figure 1.4

Different 1.5°C pathways Schematic ¹ illustration of the relationship between (a) global mean surface temperature (GMST) change; (b) annual rates of CO ₂ emissions, assuming constant fractional contribution of non-CO ₂ forcing to total human-induced warming; (c) total cumulative CO ₂ emissions (solid lines) and the fraction thereof remaining in the atmosphere (dashed lines; these also indicates changes in atmospheric CO ₂ concentrations); and (d) a time-integrated impact, such as sea level rise, that continues to increase even after GMST has stabilized.

File:Https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/figure-1.4-1024x717.png

Different 1.5°C pathways Schematic ¹ illustration of the relationship between (a) global mean surface temperature (GMST) change; (b) annual rates of CO ₂ emissions, assuming constant fractional contribution of non-CO ₂ forcing to total human-induced warming; (c) total cumulative CO ₂ emissions (solid lines) and the fraction thereof remaining in the atmosphere (dashed lines; these also indicates changes in atmospheric CO ₂ concentrations); and (d) a time-integrated impact, such as sea level rise, that continues to increase even after GMST has stabilized. Colours indicate different 1.5°C pathways. Brown: GMST remaining below and stabilizing at 1.5°C in 2100; Green: a delayed start but faster emission reductions pathway with GMST remaining below and reaching 1.5°C earlier; Blue: a pathway temporarily exceeding 1.5°C, with temperatures reduced to 1.5°C by net negative CO ₂ emissions after temperatures peak; and Yellow: a pathway peaking at 1.5°C and subsequently declining. Temperatures are anchored to 1°C above pre-industrial in 2017; emissions–temperature relationships are computed using a simple climate model (Myhre et al., 2013; Millar et al., 2017a; Jenkins et al., 2018) ¹²² with a lower value of the Transient Climate Response (TCR) than used in the quantitative pathway assessments in Chapter 2 to illustrate qualitative differences between pathways: this figure is not intended to provide quantitative information. The time-integrated impact is illustrated by the semi-empirical sea level rise model of Kopp et al. (2016) ¹²³ .

Cross-Chapter Box 1: Scenarios and Pathways

Lead Authors

Mikiko Kainuma (Japan)
Kristie L. Ebi (United States)
Sabine Fuss (Germany)
Elmar Kriegler (Germany)
Keywan Riahi (Austria)
Joeri Rogelj (Austria, Belgium)
Petra Tschakert (Australia, Austria)
Rachel Warren (United Kingdom)

Climate change scenarios have been used in IPCC assessments since the First Assessment Report (Leggett et al., 1992) ¹²⁴ . The SRES scenarios (named after the IPCC Special Report on Emissions Scenarios published in 2000; IPCC, 2000) ¹²⁵ , consist of four scenarios that do not take into account any future measures to limit greenhouse gas (GHG) emissions. Subsequently, many policy scenarios have been developed based upon them (Morita et al., 2001) ¹²⁶ . The SRES scenarios are superseded by a set of scenarios based on the Representative Concentration Pathways (RCPs) and Shared Socio-Economic Pathways (SSPs) (Riahi et al., 2017) ¹²⁷ . The RCPs comprise a set of four GHG concentration trajectories that jointly span a large range of plausible human-caused climate forcing ranging from 2.6 W m ⁻² (RCP2.6) to 8.5 W m ⁻² (RCP8.5) by the end of the 21st century (van Vuuren et al., 2011) ¹²⁸ . They were used to develop climate projections in the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012) ¹²⁹ and were assessed in the IPCC Fifth Assessment Report (AR5). Based on the CMIP5 ensemble, RCP2.6, provides a better than two-in-three chance of staying below 2°C and a median warming of 1.6°C relative to 1850–1900 in 2100 (Collins et al., 2013) ¹³⁰ .

The SSPs were developed to complement the RCPs with varying socio-economic challenges to adaptation and mitigation. SSP-based scenarios were developed for a range of climate forcing levels, including the end-of-century forcing levels of the RCPs (Riahi et al., 2017) ¹³¹ and a level below RCP2.6 to explore pathways limiting warming to 1.5°C above pre-industrial levels (Rogelj et al., 2018) ¹³² . The SSP-based 1.5°C pathways are assessed in Chapter 2 of this report. These scenarios offer an integrated perspective on socio-economic, energy-system (Bauer et al., 2017) ¹³³ , land use (Popp et al., 2017) ¹³⁴ , air pollution (Rao et al., 2017) ¹³⁵ and, GHG emissions developments (Riahi et al., 2017) ¹³⁶ . Because of their harmonised assumptions, scenarios developed with the SSPs facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation and mitigation.

Scenarios and Pathways in this Report

This report focuses on pathways that could limit the increase of global mean surface temperature (GMST) to 1.5°C above pre-industrial levels and pathways that align with the goals of sustainable development and poverty eradication. The pace and scale of mitigation and adaptation are assessed in the context of historical evidence to determine where unprecedented change is required (see Chapter 4). Other scenarios are also assessed, primarily as benchmarks for comparison of mitigation, impacts, and/or adaptation requirements. These include baseline scenarios that assume no climate policy; scenarios that assume some kind of continuation of current climate policy trends and plans, many of which are used to assess the implications of the nationally determined contributions (NDCs); and scenarios holding warming below 2°C above pre-industrial levels. This report assesses the spectrum from global mitigation scenarios to local adaptation choices – complemented by a bottom-up assessment of individual mitigation and adaptation options, and their implementation (policies, finance, institutions, and governance, see Chapter 4). Regional, national, and local scenarios, as well as decision-making processes involving values and difficult trade-offs are important for understanding the challenges of limiting GMST increase to 1.5°C and are thus indispensable when assessing implementation.

Different climate policies result in different temperature pathways, which result in different levels of climate risks and actual climate impacts with associated long-term implications. Temperature pathways are classified into continued warming pathways (in the cases of baseline and reference scenarios), pathways that keep the temperature increase below a specific limit (like 1.5°C or 2°C), and pathways that temporarily exceed and later fall to a specific limit (overshoot pathways). In the case of a temperature overshoot, net negative CO ₂ emissions are required to remove excess CO ₂ from the atmosphere (Section 1.2.3).

In a ‘prospective’ mitigation pathway, emissions (or sometimes concentrations) are prescribed, giving a range of GMST outcomes because of uncertainty in the climate response. Prospective pathways are considered ‘1.5°C pathways’ in this report if, based on current knowledge, the majority of available approaches assign an approximate probability of one-in-two to two-in-three to temperatures either remaining below 1.5°C or returning to 1.5°C either before or around 2100. Most pathways assessed in Chapter 2 are prospective pathways, and therefore even ‘1.5°C pathways’ are also associated with risks of warming higher than 1.5°C, noting that many risks increase non-linearly with increasing GMST. In contrast, the ‘risks of warming of 1.5°C’ assessed in Chapter 3 refer to risks in a world in which GMST is either passing through (transient) or stabilized at 1.5°C, without considering probabilities of different GMST levels (unless otherwise qualified). To stay below any desired temperature limit, mitigation measures and strategies would need to be adjusted as knowledge of the climate response is updated (Millar et al., 2017b; Emori et al., 2018) ¹³⁷ . Such pathways can be called ‘adaptive’ mitigation pathways. Given there is always a possibility of a greater-than-expected climate response (Xu and Ramanathan, 2017) ¹³⁸ , adaptive mitigation pathways are important to minimise climate risks, but need also to consider the risks and feasibility (see Cross-Chapter Box 3 in this chapter) of faster-than-expected emission reductions. Chapter 5 includes assessments of two related topics: aligning mitigation and adaptation pathways with sustainable development pathways, and transformative visions for the future that would support avoiding negative impacts on the poorest and most disadvantaged populations and vulnerable sectors.

Definitions of Scenarios and Pathways

Climate scenarios and pathways are terms that are sometimes used interchangeably, with a wide range of overlapping definitions (Rosenbloom, 2017) ¹³⁹ .

A ‘ scenario’ is an internally consistent, plausible, and integrated description of a possible future of the human–environment system, including a narrative with qualitative trends and quantitative projections (IPCC, 2000) ¹⁴⁰ . Climate change scenarios provide a framework for developing and integrating projections of emissions, climate change, and climate impacts, including an assessment of their inherent uncertainties. The long-term and multi-faceted nature of climate change requires climate scenarios to describe how socio-economic trends in the 21st century could influence future energy and land use, resulting emissions and the evolution of human vulnerability and exposure. Such driving forces include population, GDP, technological innovation, governance and lifestyles. Climate change scenarios are used for analysing and contrasting climate policy choices.

The notion of a ‘pathway’ can have multiple meanings in the climate literature. It is often used to describe the temporal evolution of a set of scenario features, such as GHG emissions and socio-economic development. As such, it can describe individual scenario components or sometimes be used interchangeably with the word ‘scenario’. For example, the RCPs describe GHG concentration trajectories (van Vuuren et al., 2011) ¹⁴¹ and the SSPs are a set of narratives of societal futures augmented by quantitative projections of socio-economic determinants such as population, GDP and urbanization (Kriegler et al., 2012; O’Neill et al., 2014) ¹⁴² . Socio-economic driving forces consistent with any of the SSPs can be combined with a set of climate policy assumptions (Kriegler et al., 2014) ¹⁴³ that together would lead to emissions and concentration outcomes consistent with the RCPs (Riahi et al., 2017) ¹⁴⁴ . This is at the core of the scenario framework for climate change research that aims to facilitate creating scenarios integrating emissions and development pathways dimensions (Ebi et al., 2014; van Vuuren et al., 2014) ¹⁴⁵ .

In other parts of the literature, ‘pathway’ implies a solution-oriented trajectory describing a pathway from today’s world to achieving a set of future goals. Sustainable Development Pathways describe national and global pathways where climate policy becomes part of a larger sustainability transformation (Shukla and Chaturvedi, 2013; Fleurbaey et al., 2014; van Vuuren et al., 2015) ¹⁴⁶ . The AR5 presented c limate- r esilient pathways as sustainable development pathways that combine the goals of adaptation and mitigation (Denton et al., 2014) ¹⁴⁷ , more broadly defined as iterative processes for managing change within complex systems in order to reduce disruptions and enhance opportunities associated with climate change (IPCC, 2014a) ¹⁴⁸ . The AR5 also introduced the notion of climate-resilient development pathways, with a more explicit focus on dynamic livelihoods, multi-dimensional poverty, structural inequalities, and equity among poor and non-poor people (Olsson et al., 2014) ¹⁴⁹ . A daptation pathways are understood as a series of adaptation choices involving trade-offs between short-term and long-term goals and values (Reisinger et al., 2014) ¹⁵⁰ . They are decision-making processes sequenced over time with the purpose of deliberating and identifying socially salient solutions in specific places (Barnett et al., 2014; Wise et al., 2014; Fazey et al., 2016) ¹⁵¹ . There is a range of possible pathways for transformational change, often negotiated through iterative and inclusive processes (Harris et al., 2017; Fazey et al., 2018; Tàbara et al., 2018) ¹⁵² .

1.2.4 Geophysical Warming Commitment

It is frequently asked whether limiting warming to 1.5°C is ‘feasible’ (Cross-Chapter Box 3 in this chapter). There are many dimensions to this question, including the warming ‘commitment’ from past emissions of greenhouse gases and aerosol precursors. Quantifying commitment from past emissions is complicated by the very different behaviour of different climate forcers affected by human activity: emissions of long-lived greenhouse gases such as CO ₂ and nitrous oxide (N ₂ O) have a very persistent impact on radiative forcing (Myhre et al., 2013) ¹⁵³ , lasting from over a century (in the case of N ₂ O) to hundreds of thousands of years (for CO ₂ ). The radiative forcing impact of short-lived climate forcers (SLCFs) such as methane (CH ₄ ) and aerosols, in contrast, persists for at most about a decade (in the case of methane) down to only a few days. These different behaviours must be taken into account in assessing the implications of any approach to calculating aggregate emissions (Cross-Chapter Box 2 in this chapter).

Geophysical warming commitment is defined as the unavoidable future warming resulting from physical Earth system inertia. Different variants are discussed in the literature, including (i) the ‘constant composition commitment’ (CCC), defined by Meehl et al. (2007) ¹⁵⁴ as the further warming that would result if atmospheric concentrations of GHGs and other climate forcers were stabilised at the current level; and (ii) and the ‘zero emissions commitment’ (ZEC), defined as the further warming that would still occur if all future anthropogenic emissions of greenhouse gases and aerosol precursors were eliminated instantaneously (Meehl et al., 2007; Collins et al., 2013) ¹⁵⁵ .

The CCC is primarily associated with thermal inertia of the ocean (Hansen et al., 2005) ¹⁵⁶ , and has led to the misconception that substantial future warming is inevitable (Matthews and Solomon, 2013) ¹⁵⁷ . The CCC takes into account the warming from past emissions, but also includes warming from future emissions (declining but still non-zero) that are required to maintain a constant atmospheric composition. It is therefore not relevant to the warming commitment from past emissions alone.

The ZEC, although based on equally idealised assumptions, allows for a clear separation of the response to past emissions from the effects of future emissions. The magnitude and sign of the ZEC depend on the mix of GHGs and aerosols considered. For CO ₂ , which takes hundreds of thousands of years to be fully removed from the atmosphere by natural processes following its emission (Eby et al., 2009; Ciais et al., 2013) ¹⁵⁸ , the multi-century warming commitment from emissions to date in addition to warming already observed is estimated to range from slightly negative (i.e., a slight cooling relative to present-day) to slightly positive (Matthews and Caldeira, 2008; Lowe et al., 2009; Gillett et al., 2011; Collins et al., 2013) ¹⁵⁹ . Some studies estimate a larger ZEC from CO ₂ , but for cumulative emissions much higher than those up to present day (Frölicher et al., 2014; Ehlert and Zickfeld, 2017) ¹⁶⁰ . The ZEC from past CO ₂ emissions is small because the continued warming effect from ocean thermal inertia is approximately balanced by declining radiative forcing due to CO ₂ uptake by the ocean (Solomon et al., 2009; Goodwin et al., 2015; Williams et al., 2017) ¹⁶¹ . Thus, although present-day CO ₂ -induced warming is irreversible on millennial time scales (without human intervention such as active carbon dioxide removal or solar radiation modification; Section 1.4.1), past CO ₂ emissions do not commit to substantial further warming (Matthews and Solomon, 2013) ¹⁶² .

Sustained net zero anthropogenic emissions of CO ₂ and declining net anthropogenic non-CO ₂ radiative forcing over a multi-decade period would halt anthropogenic global warming over that period, although it would not halt sea level rise or many other aspects of climate system adjustment. The rate of decline of non-CO ₂ radiative forcing must be sufficient to compensate for the ongoing adjustment of the climate system to this forcing (assuming it remains positive) due to ocean thermal inertia. It therefore depends on deep ocean response time scales, which are uncertain but of order centuries, corresponding to decline rates of non-CO ₂ radiative forcing of less than 1% per year. In the longer term, Earth system feedbacks such as the release of carbon from melting permafrost may require net negative CO ₂ emissions to maintain stable temperatures (Lowe and Bernie, 2018) ¹⁶³ .

For warming SLCFs, meaning those associated with positive radiative forcing such as methane, the ZEC is negative. Eliminating emissions of these substances results in an immediate cooling relative to the present (Figure 1.5, magenta lines) (Frölicher and Joos, 2010; Matthews and Zickfeld, 2012; Mauritsen and Pincus, 2017) ¹⁶⁴ . Cooling SLCFs (those associated with negative radiative forcing) such as sulphate aerosols create a positive ZEC, as elimination of these forcers results in rapid increase in radiative forcing and warming (Figure 1.5, green lines) (Matthews and Zickfeld, 2012; Mauritsen and Pincus, 2017; Samset et al., 2018) ¹⁶⁵ . Estimates of the warming commitment from eliminating aerosol emissions are affected by large uncertainties in net aerosol radiative forcing (Myhre et al., 2013, 2017) ¹⁶⁶ and the impact of other measures affecting aerosol loading (e.g., Fernández et al., 2017) ¹⁶⁷ . If present-day emissions of all GHGs (short- and long-lived) and aerosols (including sulphate, nitrate and carbonaceous aerosols) are eliminated (Figure 1.5, yellow lines) GMST rises over the following decade, driven by the removal of negative aerosol radiative forcing. This initial warming is followed by a gradual cooling driven by the decline in radiative forcing of short-lived greenhouse gases (Matthews and Zickfeld, 2012; Collins et al., 2013) ¹⁶⁸ . Peak warming following elimination of all emissions was assessed at a few tenths of a degree in AR5, and century-scale warming was assessed to change only slightly relative to the time emissions are reduced to zero (Collins et al., 2013) ¹⁶⁹ . New evidence since AR5 suggests a larger methane forcing (Etminan et al., 2016) ¹⁷⁰ but no revision in the range of aerosol forcing (although this remains an active field of research, e.g., Myhre et al., 2017) ¹⁷¹ . This revised methane forcing estimate results in a smaller peak warming and a faster temperature decline than assessed in AR5 (Figure 1.5, yellow line).

Expert judgement based on the available evidence (including model simulations, radiative forcing and climate sensitivity) suggests that if all anthropogenic emissions were reduced to zero immediately, any further warming beyond the 1°C already experienced would likely be less than 0.5°C over the next two to three decades, and also likely less than 0.5°C on a century time scale.

Figure 1.5

Warming commitment from past emissions of greenhouse gases and aerosols.

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Radiative forcing (top) and global mean surface temperature change (bottom) for scenarios with different combinations of greenhouse gas and aerosol precursor emissions reduced to zero in 2020. Variables were calculated using a simple climate–carbon cycle model (Millar et al., 2017a) ¹⁷² with a simple representation of atmospheric chemistry (Smith et al., 2018) ¹⁷³ . The bars on the right-hand side indicate the median warming in 2100 and 5–95% uncertainty ranges (also indicated by the plume around the yellow line) taking into account one estimate of uncertainty in climate response, effective radiative forcing and carbon cycle sensitivity, and constraining simple model parameters with response ranges from AR5 combined with historical climate observations (Smith et al., 2018) ¹⁷⁴ . Temperatures continue to increase slightly after elimination of CO ₂ emissions (blue line) in response to constant non-CO ₂ forcing. The dashed blue line extrapolates one estimate of the current rate of warming, while dotted blue lines show a case where CO ₂ emissions are reduced linearly to zero assuming constant non-CO ₂ forcing after 2020. Under these highly idealized assumptions, the time to stabilize temperatures at 1.5°C is approximately double the time remaining to reach 1.5°C at the current warming rate.

Since most sources of emissions cannot, in reality, be brought to zero instantaneously due to techno-economic inertia, the current rate of emissions also constitutes a conditional commitment to future emissions and consequent warming depending on achievable rates of emission reductions. The current level and rate of human-induced warming determines both the time left before a temperature threshold is exceeded if warming continues (dashed blue line in Figure 1.5) and the time over which the warming rate must be reduced to avoid exceeding that threshold (approximately indicated by the dotted blue line in Figure 1.5). Leach et al. (2018) ¹⁷⁵ use a central estimate of human-induced warming of 1.02°C in 2017, increasing at 0.215°C per decade (Haustein et al., 2017) ¹⁷⁶ , to argue that it will take 13–32 years (one-standard-error range) to reach 1.5°C if the current warming rate continues, allowing 25–64 years to stabilise temperatures at 1.5°C if the warming rate is reduced at a constant rate of deceleration starting immediately. Applying a similar approach to the multi-dataset average GMST used in this report gives an assessed likely range for the date at which warming reaches 1.5°C of 2030 to 2052. The lower bound on this range, 2030, is supported by multiple lines of evidence, including the AR5 assessment for the likely range of warming (0.3°C–0.7°C) for the period 2016–2035 relative to 1986–2005. The upper bound, 2052, is supported by fewer lines of evidence, so we have used the upper bound of the 5–95% confidence interval given by the Leach et al. (2018) ¹⁷⁷ method applied to the multi-dataset average GMST, expressed as the upper limit of the likely range, to reflect the reliance on a single approach. Results are sensitive both to the confidence level chosen and the number of years used to estimate the current rate of anthropogenic warming (5 years used here, to capture the recent acceleration due to rising non-CO ₂ forcing). Since the rate of human-induced warming is proportional to the rate of CO ₂ emissions (Matthews et al., 2009; Zickfeld et al., 2009) ¹⁷⁸ plus a term approximately proportional to the rate of increase in non-CO ₂ radiative forcing (Gregory and Forster, 2008; Allen et al., 2018 ¹⁷⁹ ; Cross-Chapter Box 2 in this chapter), these time scales also provide an indication of minimum emission reduction rates required if a warming greater than 1.5°C is to be avoided (see Figure 1.5, Supplementary Material 1.SM.6 and FAQ 1.2).

Cross-Chapter Box 2: Measuring Progress to Net Zero Emissions Combining Long-Lived and Short-Lived Climate Forcers

Lead Authors

Piers Forster (United Kingdom)
Elmar Kriegler (Germany)
Joeri Rogelj (Austria, Belgium)
Seth Schultz (United States)
Drew Shindell (United States)
Kirsten Zickfeld (Canada, Germany)

Emissions of many different climate forcers will affect the rate and magnitude of climate change over the next few decades (Myhre et al., 2013) ¹⁸⁰ . Since these decades will determine when 1.5°C is reached or whether a warming greater than 1.5°C is avoided, understanding the aggregate impact of different forcing agents is particularly important in the context of 1.5°C pathways. Paragraph 17 of Decision 1 of the 21st Conference of the Parties on the adoption of the Paris Agreement specifically states that this report is to identify aggregate greenhouse gas emission levels compatible with holding the increase in global average temperatures to 1.5°C above pre-industrial levels (see Chapter 2). This request highlights the need to consider the implications of different methods of aggregating emissions of different gases, both for future temperatures and for other aspects of the climate system (Levasseur et al., 2016; Ocko et al., 2017) ¹⁸¹ .

To date, reporting of GHG emissions under the UNFCCC has used Global Warming Potentials (GWPs) evaluated over a 100-year time horizon (GWP ₁₀₀ ) to combine multiple climate forcers. IPCC Working Group 3 reports have also used GWP ₁₀₀ to represent multi-gas pathways (Clarke et al., 2014) ¹⁸² . For reasons of comparability and consistency with current practice, Chapter 2 in this Special Report continues to use this aggregation method. Numerous other methods of combining different climate forcers have been proposed, such as the Global Temperature-change Potential (GTP; Shine et al., 2005) ¹⁸³ and the Global Damage Potential (Tol et al., 2012; Deuber et al., 2013) ¹⁸⁴ .

Climate forcers fall into two broad categories in terms of their impact on global temperature (Smith et al., 2012) ¹⁸⁵ : long-lived GHGs, such as CO ₂ and nitrous oxide (N ₂ O), whose warming impact depends primarily on the total cumulative amount emitted over the past century or the entire industrial epoch; and short-lived climate forcers (SLCFs), such as methane and black carbon, whose warming impact depends primarily on current and recent annual emission rates (Reisinger et al., 2012; Myhre et al., 2013; Smith et al., 2013; Strefler et al., 2014) ¹⁸⁶ . These different dependencies affect the emissions reductions required of individual forcers to limit warming to 1.5°C or any other level.

Natural processes that remove CO ₂ permanently from the climate system are so slow that reducing the rate of CO ₂ -induced warming to zero requires net zero global anthropogenic CO ₂ emissions (Archer and Brovkin, 2008; Matthews and Caldeira, 2008; Solomon et al., 2009) ¹⁸⁷ , meaning almost all remaining anthropogenic CO ₂ emissions must be compensated for by an equal rate of anthropogenic carbon dioxide removal (CDR). Cumulative CO ₂ emissions are therefore an accurate indicator of CO ₂ -induced warming, except in periods of high negative CO ₂ emissions (Zickfeld et al., 2016) ¹⁸⁸ , and potentially in century-long periods of near-stable temperatures (Bowerman et al., 2011; Wigley, 2018) ¹⁸⁹ . In contrast, sustained constant emissions of a SLCF such as methane, would (after a few decades) be consistent with constant methane concentrations and hence very little additional methane-induced warming (Allen et al., 2018; Fuglestvedt et al., 2018) ¹⁹⁰ . Both GWP and GTP would equate sustained SLCF emissions with sustained constant CO ₂ emissions, which would continue to accumulate in the climate system, warming global temperatures indefinitely. Hence nominally ‘equivalent’ emissions of CO ₂ and SLCFs, if equated conventionally using GWP or GTP, have very different temperature impacts, and these differences are particularly evident under ambitious mitigation characterizing 1.5°C pathways.

Since the AR5, a revised usage of GWP has been proposed (Lauder et al., 2013; Allen et al., 2016) ¹⁹¹ , denoted GWP* (Allen et al., 2018) ¹⁹² , that addresses this issue by equating a permanently sustained change in the emission rate of an SLCF or SLCF-precursor (in tonnes-per-year), or other non-CO ₂ forcing (in watts per square metre), with a one-off pulse emission (in tonnes) of a fixed amount of CO ₂ . Specifically, GWP* equates a 1 tonne-per-year increase in emission rate of an SLCF with a pulse emission of GWP _H x H tonnes of CO ₂ , where is the conventional GWP _H of that SLCF evaluated over time GWP _H for SLCFs decreases with increasing time H, GWP _H x H for SLCFs is less dependent on the choice of time horizon. Similarly, a permanent 1 W m ⁻² increase in radiative forcing has a similar temperature impact as the cumulative emission of H /AGWP _H tonnes of CO ₂ , where AGWP _H is the Absolute Global Warming Potential of CO ₂ (Shine et al., 2005; Myhre et al., 2013; Allen et al., 2018) ¹⁹³ . This indicates approximately how future changes in non-CO ₂ radiative forcing affect cumulative CO ₂ emissions consistent with any given level of peak warming.

When combined using GWP*, cumulative aggregate GHG emissions are closely proportional to total GHG-induced warming, while the annual rate of GHG-induced warming is proportional to the annual rate of aggregate GHG emissions (see Cross-Chapter Box 2, Figure 1). This is not the case when emissions are aggregated using GWP or GTP, with discrepancies particularly pronounced when SLCF emissions are falling. Persistent net zero CO ₂ -equivalent emissions containing a residual positive forcing contribution from SLCFs and aggregated using GWP ₁₀₀ or GTP would result in a steady decline of GMST. Net zero global emissions aggregated using GWP* (which corresponds to zero net emissions of CO ₂ and other long-lived GHGs like nitrous oxide, combined with near-constant SLCF forcing – see Figure 1.5) results in approximately stable GMST (Allen et al., 2018; Fuglestvedt et al., 2018 ¹⁹⁴ and Cross-Chapter Box 2, Figure 1, below).

Whatever method is used to relate emissions of different greenhouse gases, scenarios achieving stable GMST well below 2°C require both near-zero net emissions of long-lived greenhouse gases and deep reductions in warming SLCFs (Chapter 2), in part to compensate for the reductions in cooling SLCFs that are expected to accompany reductions in CO ₂ emissions (Rogelj et al., 2016b; Hienola et al., 2018) ¹⁹⁵ . Understanding the implications of different methods of combining emissions of different climate forcers is, however, helpful in tracking progress towards temperature stabilisation and ‘balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases’ as stated in Article 4 of the Paris Agreement. Fuglestvedt et al. (2018) ¹⁹⁶ and Tanaka and O’Neill (2018) ¹⁹⁷ show that when, and even whether, aggregate GHG emissions need to reach net zero before 2100 to limit warming to 1.5°C depends on the scenario, aggregation method and mix of long-lived and short-lived climate forcers.

The comparison of the impacts of different climate forcers can also consider more than their effects on GMST (Johansson, 2012; Tol et al., 2012; Deuber et al., 2013; Myhre et al., 2013; Cherubini and Tanaka, 2016) ¹⁹⁸ . Climate impacts arise from both magnitude and rate of climate change, and from other variables such as precipitation (Shine et al., 2015) ¹⁹⁹ . Even if GMST is stabilised, sea level rise and associated impacts will continue to increase (Sterner et al., 2014) ²⁰⁰ , while impacts that depend on CO ₂ concentrations such as ocean acidification may begin to reverse. From an economic perspective, comparison of different climate forcers ideally reflects the ratio of marginal economic damages if used to determine the exchange ratio of different GHGs under multi-gas regulation (Tol et al., 2012; Deuber et al., 2013; Kolstad et al., 2014) ²⁰¹ .

Emission reductions can interact with other dimensions of sustainable development (see Chapter 5). In particular, early action on some SLCFs (including actions that may warm the climate, such as reducing sulphur dioxide emissions) may have considerable societal co-benefits, such as reduced air pollution and improved public health with associated economic benefits (OECD, 2016; Shindell et al., 2016) ²⁰² . Valuation of broadly defined social costs attempts to account for many of these additional non-climate factors along with climate-related impacts (Shindell, 2015; Sarofim et al., 2017; Shindell et al., 2017) ²⁰³ . See Chapter 4, Section 4.3.6, for a discussions of mitigation options, noting that mitigation priorities for different climate forcers depend on multiple economic and social criteria that vary between sectors, regions and countries.

Cross Chapter Box 2: Figure 1

Implications of different approaches to calculating aggregate greenhouse gas emissions on a pathway to net zero.

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(a) Aggregate emissions of well-mixed greenhouse gases (WMGHGs) under the RCP2.6 mitigation scenario expressed as CO2-equivalent using GWP100 (blue); GTP100 (green) and GWP* (yellow). Aggregate WMGHG emissions appear to fall more rapidly if calculated using GWP* than using either GWP or GTP, primarily because GWP* equates a falling methane emission rate with negative CO ₂ emissions, as only active CO ₂ removal would have the same impact on radiative forcing and GMST as a reduction in methane emission rate. (b) Cumulative emissions of WMGHGs combined as in panel (a) (blue, green and yellow lines & left hand axis) and warming response to combined emissions (black dotted line and right hand axis, Millar et al. (2017a) ²⁰⁴ . The temperature response under ambitious mitigation is closely correlated with cumulative WMGHG emissions aggregated using GWP*, but with neither emission rate nor cumulative emissions if aggregated using GWP or GTP.

1.3 Impacts at 1.5°C and Beyond

1.3.1 Definitions

Consistent with the AR5 (IPCC, 2014a) ²⁰⁵ , ‘impact’ in this report refers to the effects of climate change on human and natural systems. Impacts may include the effects of changing hazards, such as the frequency and intensity of heat waves. ‘Risk’ refers to potential negative impacts of climate change where something of value is at stake, recognizing the diversity of values. Risks depend on hazards, exposure, vulnerability (including sensitivity and capacity to respond) and likelihood. Climate change risks can be managed through efforts to mitigate climate change forcers, adaptation of impacted systems, and remedial measures (Section 1.4.1).

In the context of this report, regional impacts of global warming at 1.5°C and 2°C are assessed in Chapter 3. The ‘ warming experience at 1.5°C ’ is that of regional climate change (temperature, rainfall, and other changes) at the time when global average temperatures, as defined in Section 1.2.1, reach 1.5°C above pre-industrial (the same principle applies to impacts at any other global mean temperature). Over the decade 2006–2015, many regions have experienced higher than average levels of warming and some are already now 1.5°C or more warmer with respect to the pre-industrial period (Figure 1.3). At a global warming of 1.5°C, some seasons will be substantially warmer than 1.5°C above pre-industrial (Seneviratne et al., 2016) ²⁰⁶ . Therefore, most regional impacts of a global mean warming of 1.5°C will be different from those of a regional warming by 1.5°C.

The impacts of 1.5°C global warming will vary in both space and time (Ebi et al., 2016) ²⁰⁷ . For many regions, an increase in global mean temperature by 1.5°C or 2°C implies substantial increases in the occurrence and/or intensity of some extreme events (Fischer and Knutti, 2015; Karmalkar and Bradley, 2017; King et al., 2017; Chevuturi et al., 2018) ²⁰⁸ , resulting in different impacts (see Chapter 3). By comparing impacts at 1.5°C versus those at 2°C, this report discusses the ‘avoided impacts’ by maintaining global temperature increase at or below 1.5°C as compared to 2°C, noting that these also depend on the pathway taken to 1.5°C (see Section 1.2.3 and Cross-Chapter Box 8 in Chapter 3 on 1.5°C warmer worlds). Many impacts take time to observe, and because of the warming trend, impacts over the past 20 years were associated with a level of human-induced warming that was, on average, 0.1°C–0.23°C colder than its present level, based on the AR5 estimate of the warming trend over this period (Section 1.2.1 and Kirtman et al., 2013) ²⁰⁹ . Attribution studies (e.g., van Oldenborgh et al., 2017) ²¹⁰ can address this bias, but informal estimates of ‘recent impact experience’ in a rapidly warming world necessarily understate the temperature-related impacts of the current level of warming.

1.3.2 Drivers of Impacts

Impacts of climate change are due to multiple environmental drivers besides rising temperatures, such as rising atmospheric CO ₂ , shifting rainfall patterns (Lee et al., 2018) ²¹¹ , rising sea levels, increasing ocean acidification, and extreme events, such as floods, droughts, and heat waves (IPCC, 2014a) ²¹² . Changes in rainfall affect the hydrological cycle and water availability (Schewe et al., 2014; Döll et al., 2018; Saeed et al., 2018) ²¹³ . Several impacts depend on atmospheric composition, increasing atmospheric carbon dioxide levels leading to changes in plant productivity (Forkel et al., 2016) ²¹⁴ , but also to ocean acidification (Hoegh-Guldberg et al., 2007) ²¹⁵ . Other impacts are driven by changes in ocean heat content such as the destabilization of coastal ice sheets and sea level rise (Bindoff et al., 2007; Chen et al., 2017) ²¹⁶ , whereas impacts due to heat waves depend directly on ambient air or ocean temperature (Matthews et al., 2017) ²¹⁷ . Impacts can be direct, such as coral bleaching due to ocean warming, and indirect, such as reduced tourism due to coral bleaching. Indirect impacts can also arise from mitigation efforts such as changed agricultural management (Section 3.6.2) or remedial measures such as solar radiation modification (Section 4.3.8, Cross-Chapter Box 10 in Chapter 4).

Impacts may also be triggered by combinations of factors, including ‘impact cascades’ (Cramer et al., 2014) ²¹⁸ through secondary consequences of changed systems. Changes in agricultural water availability caused by upstream changes in glacier volume are a typical example. Recent studies also identify compound events (e.g., droughts and heat waves), that is, when impacts are induced by the combination of several climate events (AghaKouchak et al., 2014; Leonard et al., 2014; Martius et al., 2016; Zscheischler and Seneviratne, 2017) ²¹⁹ .

There are now techniques to attribute impacts formally to anthropogenic global warming and associated rainfall changes (Rosenzweig et al., 2008; Cramer et al., 2014; Hansen et al., 2016) ²²⁰ , taking into account other drivers such as land-use change (Oliver and Morecroft, 2014) ²²¹ and pollution (e.g., tropospheric ozone; Sitch et al., 2007) ²²² . There are multiple lines of evidence that climate change has observable and often severely negative effects on people, especially where climate-sensitive biophysical conditions and socio-economic and political constraints on adaptive capacities combine to create high vulnerabilities (IPCC, 2012a; 2014a; World Bank, 2013) ²²³ . The character and severity of impacts depend not only on the hazards (e.g., changed climate averages and extremes) but also on the vulnerability (including sensitivities and adaptive capacities) of different communities and their exposure to climate threats. These impacts also affect a range of natural and human systems, such as terrestrial, coastal and marine ecosystems and their services; agricultural production; infrastructure; the built environment; human health; and other socio-economic systems (Rosenzweig et al., 2017) ²²⁴ .

Sensitivity to changing drivers varies markedly across systems and regions. Impacts of climate change on natural and managed ecosystems can imply loss or increase in growth, biomass or diversity at the level of species populations, interspecific relationships such as pollination, landscapes or entire biomes. Impacts occur in addition to the natural variation in growth, ecosystem dynamics, disturbance, succession and other processes, rendering attribution of impacts at lower levels of warming difficult in certain situations. The same magnitude of warming can be lethal during one phase of the life of an organism and irrelevant during another. Many ecosystems (notably forests, coral reefs and others) undergo long-term successional processes characterised by varying levels of resilience to environmental change over time. Organisms and ecosystems may adapt to environmental change to a certain degree, through changes in physiology, ecosystem structure, species composition or evolution. Large-scale shifts in ecosystems may cause important feedbacks, in terms of changing water and carbon fluxes through impacted ecosystems – these can amplify or dampen atmospheric change at regional to continental scale. Of particular concern is the response of most of the world’s forests and seagrass ecosystems, which play key roles as carbon sinks (Settele et al., 2014; Marbà et al., 2015) ²²⁵ .

Some ambitious efforts to constrain atmospheric greenhouse gas concentrations may themselves impact ecosystems. In particular, changes in land use, potentially required for massively enhanced production of biofuels (either as simple replacement of fossil fuels, or as part of bioenergy with carbon capture and storage, BECCS) impact all other land ecosystems through competition for land (e.g., Creutzig, 2016) ²²⁶ (see Cross-Chapter Box 7 in Chapter 3, Section 3.6.2.1).

Human adaptive capacity to a 1.5°C warmer world varies markedly for individual sectors and across sectors such as water supply, public health, infrastructure, ecosystems and food supply. For example, density and risk exposure, infrastructure vulnerability and resilience, governance, and institutional capacity all drive different impacts across a range of human settlement types (Dasgupta et al., 2014; Revi et al., 2014; Rosenzweig et al., 2018) ²²⁷ . Additionally, the adaptive capacity of communities and human settlements in both rural and urban areas, especially in highly populated regions, raises equity, social justice and sustainable development issues. Vulnerabilities due to gender, age, level of education and culture act as compounding factors (Arora-Jonsson, 2011; Cardona et al., 2012; Resurrección, 2013; Olsson et al., 2014; Vincent et al., 2014) ²²⁸ .

1.3.3 Uncertainty and Non-Linearity of Impacts

Uncertainties in projections of future climate change and impacts come from a variety of different sources, including the assumptions made regarding future emission pathways (Moss et al., 2010) ²²⁹ , the inherent limitations and assumptions of the climate models used for the projections, including limitations in simulating regional climate variability (James et al., 2017) ²³⁰ , downscaling and bias-correction methods (Ekström et al., 2015) ²³¹ , the assumption of a linear scaling of impacts with GMST used in many studies (Lewis et al., 2017; King et al., 2018b) ²³² , and in impact models (e.g., Asseng et al., 2013) ²³³ . The evolution of climate change also affects uncertainty with respect to impacts. For example, the impacts of overshooting 1.5°C and stabilization at a later stage compared to stabilization at 1.5°C without overshoot may differ in magnitude (Schleussner et al., 2016) ²³⁴ .

AR5 (IPCC, 2013b) ²³⁵ and World Bank (2013) ²³⁶ underscored the non-linearity of risks and impacts as temperature rises from 2°C to 4°C of warming, particularly in relation to water availability, heat extremes, bleaching of coral reefs, and more. Recent studies (Schleussner et al., 2016; James et al., 2017; Barcikowska et al., 2018; King et al., 2018a) ²³⁷ assess the impacts of 1.5°C versus 2°C warming, with the same message of non-linearity. The resilience of ecosystems, meaning their ability either to resist change or to recover after a disturbance, may change, and often decline, in a non-linear way. An example are reef ecosystems, with some studies suggesting that reefs will change, rather than disappear entirely, and with particular species showing greater tolerance to coral bleaching than others (Pörtner et al., 2014) ²³⁸ . A key issue is therefore whether ecosystems such as coral reefs survive an overshoot scenario, and to what extent they would be able to recover after stabilization at 1.5°C or higher levels of warming (see Box 3.4).

1.4 Strengthening the Global Response

This section frames the implementation options, enabling conditions (discussed further in Cross-Chapter Box 3 on feasibility in this chapter), capacities and types of knowledge and their availability (Blicharska et al., 2017) ²³⁹ that can allow institutions, communities and societies to respond to the 1.5°C challenge in the context of sustainable development and the Sustainable Development Goals (SDGs). It also addresses other relevant international agreements such as the Sendai Framework for Disaster Risk Reduction. Equity and ethics are recognised as issues of importance in reducing vulnerability and eradicating poverty.

The connection between the enabling conditions for limiting global warming to 1.5°C and the ambitions of the SDGs are complex across scale and multi-faceted (Chapter 5). Climate mitigation–adaptation linkages, including synergies and trade-offs, are important when considering opportunities and threats for sustainable development. The IPCC AR5 acknowledged that ‘adaptation and mitigation have the potential to both contribute to and impede sustainable development, and sustainable development strategies and choices have the potential to both contribute to and impede climate change responses’ (Denton et al., 2014) ²⁴⁰ . Climate mitigation and adaptation measures and actions can reflect and enforce specific patterns of development and governance that differ amongst the world’s regions (Gouldson et al., 2015; Termeer et al., 2017) ²⁴¹ . The role of limited adaptation and mitigation capacity, limits to adaptation and mitigation, and conditions of mal-adaptation and mal-mitigation are assessed in this report (Chapters 4 and 5).

1.4.1 Classifying Response Options

Key broad categories of responses to the climate change problem are framed here. Mitigation refers to efforts to reduce or prevent the emission of greenhouse gases, or to enhance the absorption of gases already emitted, thus limiting the magnitude of future warming (IPCC, 2014b) ²⁴² . Mitigation requires the use of new technologies, clean energy sources, reduced deforestation, improved sustainable agricultural methods, and changes in individual and collective behaviour. Many of these may provide substantial co-benefits for air quality, biodiversity and sustainable development. Mal-mitigation includes changes that could reduce emissions in the short-term but could lock in technology choices or practices that include significant trade-offs for effectiveness of future adaptation and other forms of mitigation (Chapters 2 and 4).

Carbon dioxide removal (CDR) or ‘negative emissions’ activities are considered in this report as distinct from the above mitigation activities. While most mitigation activities focus on reducing the amount of carbon dioxide or other greenhouse gases emitted, CDR aims to reduce concentrations already in the atmosphere. Technologies for CDR are mostly in their infancy despite their importance to ambitious climate change mitigation pathways (Minx et al., 2017) ²⁴³ . Although some CDR activities such as reforestation and ecosystem restoration are well understood, the feasibility of massive-scale deployment of many CDR technologies remains an open question (IPCC, 2014d; Leung et al., 2014) ²⁴⁴ (Chapters 2 and 4). Technologies for the active removal of other greenhouse gases, such as methane, are even less developed, and are briefly discussed in Chapter 4.

Climate change adaptation refers to the actions taken to manage the impacts of climate change (IPCC, 2014a) ²⁴⁵ . The aim is to reduce vulnerability and exposure to the harmful effects of climate change (e.g., sea level rise, more intense extreme weather events or food insecurity). It also includes exploring the potential beneficial opportunities associated with climate change (for example, longer growing seasons or increased yields in some regions). Different adaptation pathways can be undertaken. Adaptation can be incremental, or transformational, meaning fundamental attributes of the system are changed (Chapter 3 and 4). There can be limits to ecosystem-based adaptation or the ability of humans to adapt (Chapter 4). If there is no possibility for adaptive actions that can be applied to avoid an intolerable risk, these are referred to as hard adaptation limits, while soft adaptation limits are identified when there are currently no options to avoid intolerable risks, but they are theoretically possible (Chapter 3 and 4). While climate change is a global issue, impacts are experienced locally. Cities and municipalities are at the frontline of adaptation (Rosenzweig et al., 2018) ²⁴⁶ , focusing on reducing and managing disaster risks due to extreme and slow-onset weather and climate events, installing flood and drought early warning systems, and improving water storage and use (Chapters 3 and 4 and Cross-Chapter Box 12 in Chapter 5). Agricultural and rural areas, including often highly vulnerable remote and indigenous communities, also need to address climate-related risks by strengthening and making more resilient agricultural and other natural resource extraction systems.

Remedial measures are distinct from mitigation or adaptation, as the aim is to temporarily reduce or offset warming (IPCC, 2012b) ²⁴⁷ . One such measure is solar radiation modification (SRM), also referred to as solar radiation management in the literature, which involves deliberate changes to the albedo of the Earth system, with the net effect of increasing the amount of solar radiation reflected from the Earth to reduce the peak temperature from climate change (The Royal Society, 2009; Smith and Rasch, 2013; Schäfer et al., 2015) ²⁴⁸ . It should be noted that while some radiation modification measures, such as cirrus cloud thinning (Kristjánsson et al., 2016) ²⁴⁹ , aim at enhancing outgoing long-wave radiation, SRM is used in this report to refer to all direct interventions on the planetary radiation budget. This report does not use the term ‘geo-engineering’ because of inconsistencies in the literature, which uses this term to cover SRM, CDR or both, whereas this report explicitly differentiates between CDR and SRM. Large-scale SRM could potentially be used to supplement mitigation in overshoot scenarios to keep the global mean temperature below 1.5°C and temporarily reduce the severity of near-term impacts (e.g., MacMartin et al., 2018) ²⁵⁰ . The impacts of SRM (both biophysical and societal), costs, technical feasibility, governance and ethical issues associated need to be carefully considered (Schäfer et al., 2015 ²⁵¹ ; Section 4.3.8 and Cross-Chapter Box 10 in Chapter 4).

1.4.2 Governance, Implementation and Policies

A challenge in creating the enabling conditions of a 1.5°C warmer world is the governance capacity of institutions to develop, implement and evaluate the changes needed within diverse and highly interlinked global social-ecological systems (Busby, 2016) ²⁵² (Chapter 4). Policy arenas, governance structures and robust institutions are key enabling conditions for transformative climate action (Chapter 4). It is through governance that justice, ethics and equity within the adaptation–mitigation–sustainable development nexus can be addressed (Von Stechow et al., 2016) ²⁵³ (Chapter 5).

Governance capacity includes a wide range of activities and efforts needed by different actors to develop coordinated climate mitigation and adaptation strategies in the context of sustainable development, taking into account equity, justice and poverty eradication. Significant governance challenges include the ability to incorporate multiple stakeholder perspectives in the decision-making process to reach meaningful and equitable decisions, interactions and coordination between different levels of government, and the capacity to raise financing and support for both technological and human resource development. For example, Lövbrand et al. (2017) ²⁵⁴ , argue that the voluntary pledges submitted by states and non-state actors to meet the conditions of the Paris Agreement will need to be more firmly coordinated, evaluated and upscaled.

Barriers for transitioning from climate change mitigation and adaptation planning to practical policy implementation include finance, information, technology, public attitudes, social values and practices (Whitmarsh et al., 2011; Corner and Clarke, 2017) ²⁵⁵ , and human resource constraints. Institutional capacity to deploy available knowledge and resources is also needed (Mimura et al., 2014) ²⁵⁶ . Incorporating strong linkages across sectors, devolution of power and resources to sub-national and local governments with the support of national government, and facilitating partnerships among public, civic, private sectors and higher education institutions (Leal Filho et al., 2018) ²⁵⁷ can help in the implementation of identified response options (Chapter 4). Implementation challenges of 1.5°C pathways are larger than for those that are consistent with limiting warming to well below 2°C, particularly concerning scale and speed of the transition and the distributional impacts on ecosystems and socio-economic actors. Uncertainties in climate change at different scales and capacities to respond combined with the complexities of coupled social and ecological systems point to a need for diverse and adaptive implementation options within and among different regions involving different actors. The large regional diversity between highly carbon-invested economies and emerging economies are important considerations for sustainable development and equity in pursuing efforts to limit warming to 1.5°C. Key sectors, including energy, food systems, health, and water supply, also are critical to understanding these connections.

Cross-Chapter Box 3: Framing Feasibility: Key Concepts and Conditions for Limiting Global Temperature Increases to 1.5°C

Lead Authors

William Solecki (United States)
Anton Cartwright (South Africa)
Wolfgang Cramer (France, Germany)
James Ford (United Kingdom, Canada)
Kejun Jiang (China)
Joana Portugal Pereira (United Kingdom, Portugal)
Joeri Rogelj (Austria, Belgium)
Linda Steg (Netherlands)
Henri Waisman (France)

This Cross-Chapter Box describes the concept of feasibility in relation to efforts to limit global warming to 1.5°C in the context of sustainable development and efforts to eradicate poverty and draws from the understanding of feasibility emerging within the IPCC (IPCC, 2017) ²⁵⁸ . Feasibility can be assessed in different ways, and no single answer exists as to the question of whether it is feasible to limit warming to 1.5°C. This implies that an assessment of feasibility would go beyond a ‘yes’ or a ‘no’. Rather, feasibility provides a frame to understand the different conditions and potential responses for implementing adaptation and mitigation pathways, and options compatible with a 1.5°C warmer world. This report assesses the overall feasibility of limiting warming to 1.5°C, and the feasibility of adaptation and mitigation options compatible with a 1.5°C warmer world, in six dimensions:

Geophysical : What global emission pathways could be consistent with conditions of a 1.5°C warmer world? What are the physical potentials for adaptation?

Environmental-ecological : What are the ecosystem services and resources, including geological storage capacity and related rate of needed land-use change, available to promote transformations, and to what extent are they compatible with enhanced resilience?

Technological : What technologies are available to support transformation?

Economic : What economic conditions could support transformation?

Socio-cultural : What conditions could support transformations in behaviour and lifestyles? To what extent are the transformations socially acceptable and consistent with equity?

Institutional : What institutional conditions are in place to support transformations, including multi-level governance, institutional capacity, and political support?

Assessment of feasibility in this report starts by evaluating the unavoidable warming from past emissions (Section 1.2.4) and identifying mitigation pathways that would lead to a 1.5°C world, which indicates that rapid and deep deviations from current emission pathways are necessary (Chapter 2). In the case of adaptation, an assessment of feasibility starts from an evaluation of the risks and impacts of climate change (Chapter 3). To mitigate and adapt to climate risks, system-wide technical, institutional and socio-economic transitions would be required, as well as the implementation of a range of specific mitigation and adaptation options. Chapter 4 applies various indicators categorised in these six dimensions to assess the feasibility of illustrative examples of relevant mitigation and adaptation options (Section 4.5.1). Such options and pathways have different effects on sustainable development, poverty eradication and adaptation capacity (Chapter 5).

The six feasibility dimensions interact in complex and place-specific ways. Synergies and trade-offs may occur between the feasibility dimensions, and between specific mitigation and adaptation options (Section 4.5.4). The presence or absence of enabling conditions would affect the options that comprise feasibility pathways (Section 4.4), and can reduce trade-offs and amplify synergies between options.

Sustainable development, eradicating poverty and reducing inequalities are not only preconditions for feasible transformations, but the interplay between climate action (both mitigation and adaptation options) and the development patterns to which they apply may actually enhance the feasibility of particular options (see Chapter 5).

The connections between the feasibility dimensions can be specified across three types of effects (discussed below). Each of these dimensions presents challenges and opportunities in realizing conditions consistent with a 1.5°C warmer world.

Systemic effects: Conditions that have embedded within them system-level functions that could include linear and non-linear connections and feedbacks. For example, the deployment of technology and large installations (e.g., renewable or low carbon energy mega-projects) depends upon economic conditions (costs, capacity to mobilize investments for R&D), social or cultural conditions (acceptability), and institutional conditions (political support; e.g., Sovacool et al., 2015) ²⁵⁹ . Case studies can demonstrate system-level interactions and positive or negative feedback effects between the different conditions (Jacobson et al., 2015; Loftus et al., 2015) ²⁶⁰ . This suggests that each set of conditions and their interactions need to be considered to understand synergies, inequities and unintended consequences.

Dynamic effects: Conditions that are highly dynamic and vary over time, especially under potential conditions of overshoot or no overshoot. Some dimensions might be more time sensitive or sequential than others (i.e., if conditions are such that it is no longer geophysically feasible to avoid overshooting 1.5°C, the social and institutional feasibility of avoiding overshoot will be no longer relevant). Path dependencies, risks of legacy lock-ins related to existing infrastructures, and possibilities of acceleration permitted by cumulative effects (e.g., dramatic cost decreases driven by learning-by-doing) are all key features to be captured. The effects can play out over various time scales and thus require understanding the connections between near-term (meaning within the next several years to two decades) and long-term implications (meaning over the next several decades) when assessing feasibility conditions.

Spatial effects : Conditions that are spatially variable and scale dependent, according to context-specific factors such as regional-scale environmental resource limits and endowment; economic wealth of local populations; social organisation, cultural beliefs, values and worldviews; spatial organisation, including conditions of urbanisation; and financial and institutional and governance capacity. This means that the conditions for achieving the global transformation required for a 1.5°C world will be heterogeneous and vary according to the specific context. On the other hand, the satisfaction of these conditions may depend upon global-scale drivers, such as international flows of finance, technologies or capacities. This points to the need for understanding feasibility to capture the interplay between the conditions at different scales.

With each effect, the interplay between different conditions influences the feasibility of both pathways (Chapter 2) and options (Chapter 4), which in turn affect the likelihood of limiting warming to 1.5°C. The complexity of these interplays triggers unavoidable uncertainties, requiring transformations that remain robust under a range of possible futures that limit warming to 1.5°C.

1.4.3 Transformation, Transformation Pathways, and Transition: Evaluating Trade-Offs and Synergies Between Mitigation, Adaptation and Sustainable Development Goals

Embedded in the goal of limiting warming to 1.5°C is the opportunity for intentional societal transformation (see Box 1.1 on the Anthropocene). The form and process of transformation are varied and multifaceted (Pelling, 2011; O’Brien et al., 2012; O’Brien and Selboe, 2015; Pelling et al., 2015) ²⁶¹ . Fundamental elements of 1.5°C-related transformation include a decoupling of economic growth from energy demand and CO ₂ emissions; leap-frogging development to new and emerging low-carbon, zero-carbon and carbon-negative technologies; and synergistically linking climate mitigation and adaptation to global scale trends (e.g., global trade and urbanization) that will enhance the prospects for effective climate action, as well as enhanced poverty reduction and greater equity (Tschakert et al., 2013; Rogelj et al., 2015; Patterson et al., 2017) ²⁶² (Chapters 4 and 5). The connection between transformative climate action and sustainable development illustrates a complex coupling of systems that have important spatial and time scale lag effects and implications for process and procedural equity, including intergenerational equity and for non-human species (Cross-Chapter Box 4 in this chapter, Chapter 5). Adaptation and mitigation transition pathways highlight the importance of cultural norms and values, sector-specific context, and proximate (i.e., occurrence of an extreme event) drivers that when acting together enhance the conditions for societal transformation (Solecki et al., 2017; Rosenzweig et al., 2018) ²⁶³ (Chapters 4 and 5).

Diversity and flexibility in implementation choices exist for adaptation, mitigation (including carbon dioxide removal, CDR) and remedial measures (such as solar radiation modification, SRM), and a potential for trade-offs and synergies between these choices and sustainable development (IPCC, 2014d; Olsson et al., 2014) ²⁶⁴ . The responses chosen could act to synergistically enhance mitigation, adaptation and sustainable development, or they may result in trade-offs which positively impact some aspects and negatively impact others. Climate change is expected to decrease the likelihood of achieving the Sustainable Development Goals (SDGs). While some strategies limiting warming towards 1.5°C are expected to significantly increase the likelihood of meeting those goals while also providing synergies for climate adaptation and mitigation (Chapter 5).

Dramatic transformations required to achieve the enabling conditions for a 1.5°C warmer world could impose trade-offs on dimensions of development (IPCC, 2014c; Olsson et al., 2014) ²⁶⁵ . Some choices of adaptation methods also could adversely impact development (Olsson et al., 2014) ²⁶⁶ . This report recognizes the potential for adverse impacts and focuses on finding the synergies between limiting warming, sustainable development, and eradicating poverty, thus highlighting pathways that do not constrain other goals, such as sustainable development and eradicating poverty.

The report is framed to address these multiple goals simultaneously and assesses the conditions to achieve a cost-effective and socially acceptable solution, rather than addressing these goals piecemeal (von Stechow et al., 2016) ²⁶⁷ (Section 4.5.4 and Chapter 5), although there may be different synergies and trade-offs between a 2°C (von Stechow et al., 2016) ²⁶⁸ and 1.5°C warmer world (Kainuma et al., 2017) ²⁶⁹ . Climate-resilient development pathways (see Cross-Chapter Box 12 in Chapter 5 and Glossary) are trajectories that strengthen sustainable development, including mitigating and adapting to climate change and efforts to eradicate poverty while promoting fair and cross-scalar resilience in a changing climate. They take into account dynamic livelihoods; the multiple dimensions of poverty, structural inequalities; and equity between and among poor and non-poor people (Olsson et al., 2014) ²⁷⁰ . Climate-resilient development pathways can be considered at different scales, including cities, rural areas, regions or at global level (Denton et al., 2014 ²⁷¹ ; Chapter 5).

Cross-Chapter Box 4: Sustainable Development and the Sustainable Development Goals

Lead Authors

Diana Liverman (United States)
Mustafa Babiker (Sudan)
Purnamita Dasgupta (India)
Riyanti Djalante (Japan, Indonesia)
Stephen Humphreys (United Kingdom, Ireland)
Natalie Mahowald (United States)
Yacob Mulugetta (United Kingdom, Ethiopia)
Maria Virginia Vilariño (Argentina)
Henri Waisman (France)

Sustainable development is most often defined as ‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (WCED, 1987) ²⁷² and includes balancing social well-being, economic prosperity and environmental protection. The AR5 used this definition and linked it to climate change (Denton et al., 2014) ²⁷³ . The most significant step since AR5 is the adoption of the UN Sustainable Development Goals, and the emergence of literature that links them to climate (von Stechow et al., 2015; Wright et al., 2015; Epstein and Theuer, 2017; Hammill and Price-Kelly, 2017; Kelman, 2017; Lofts et al., 2017; Maupin, 2017; Gomez-Echeverri, 2018) ²⁷⁴ .

In September 2015, the UN endorsed a universal agenda – ‘Transforming our World: the 2030 Agenda for Sustainable Development’ – which aims ‘to take the bold and transformative steps which are urgently needed to shift the world onto a sustainable and resilient path’. Based on a participatory process, the resolution in support of the 2030 agenda adopted 17 non-legally-binding Sustainable Development Goals (SDGs) and 169 targets to support people, prosperity, peace, partnerships and the planet (Kanie and Biermann, 2017) ²⁷⁵ .

The SDGs expanded efforts to reduce poverty and other deprivations under the UN Millennium Development Goals (MDGs). There were improvements under the MDGs between 1990 and 2015, including reducing overall poverty and hunger, reducing infant mortality, and improving access to drinking water (United Nations, 2015a) ²⁷⁶ . However, greenhouse gas emissions increased by more than 50% from 1990 to 2015, and 1.6 billion people were still living in multidimensional poverty with persistent inequalities in 2015 (Alkire et al., 2015) ²⁷⁷ .

The SDGs raise the ambition for eliminating poverty, hunger, inequality and other societal problems while protecting the environment. They have been criticised: as too many and too complex, needing more realistic targets, overly focused on 2030 at the expense of longer-term objectives, not embracing all aspects of sustainable development, and even contradicting each other (Horton, 2014; Death and Gabay, 2015; Biermann et al., 2017; Weber, 2017; Winkler and Satterthwaite, 2017) ²⁷⁸ .

Climate change is an integral influence on sustainable development, closely related to the economic, social and environmental dimensions of the SDGs. The IPCC has woven the concept of sustainable development into recent assessments, showing how climate change might undermine sustainable development, and the synergies between sustainable development and responses to climate change (Denton et al., 2014) ²⁷⁹ . Climate change is also explicit in the SDGs. SDG13 specifically requires ‘urgent action to address climate change and its impacts’. The targets include strengthening resilience and adaptive capacity to climate-related hazards and natural disasters; integrating climate change measures into national policies, strategies and planning; and improving education, awareness-raising and human and institutional capacity.

Targets also include implementing the commitment undertaken by developed-country parties to the UNFCCC to the goal of mobilizing jointly 100 billion USD annually by 2020 and operationalizing the Green Climate Fund, as well as promoting mechanisms for raising capacity for effective climate change-related planning and management in least developed countries and Small Island Developing States, including focusing on women, youth and local and marginalised communities. SDG13 also acknowledges that the UNFCCC is the primary international, intergovernmental forum for negotiating the global response to climate change.

Climate change is also mentioned in SDGs beyond SDG13, for example in goal targets 1.5, 2.4, 11.B, 12.8.1 related to poverty, hunger, cities and education respectively. The UNFCCC addresses other SDGs in commitments to ‘control, reduce or prevent anthropogenic emissions of greenhouse gases […] in all relevant sectors, including the energy, transport, industry, agriculture, forestry and waste management sectors’ (Art4, 1(c)) and to work towards ‘the conservation and enhancement, as appropriate, of […] biomass, forests and oceans as well as other terrestrial, coastal and marine ecosystems’ (Art4, 1(d)). This corresponds to SDGs that seek clean energy for all (Goal 7), sustainable industry (Goal 9) and cities (Goal 11) and the protection of life on land and below water (14 and 15).

The SDGs and UNFCCC also differ in their time horizons. The SDGs focus primarily on 2030 whereas the Paris Agreement sets out that ‘Parties aim […] to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century’.

The IPCC decision to prepare this report on the impacts of 1.5°C and associated emission pathways explicitly asked for the assessment to be in the context of sustainable development and efforts to eradicate poverty. Chapter 1 frames the interaction between sustainable development, poverty eradication and ethics and equity. Chapter 2 assesses how risks and synergies of individual mitigation measures interact with 1.5°C pathways within the context of the SDGs and how these vary according to the mix of measures in alternative mitigation portfolios (Section 2.5). Chapter 3 examines the impacts of 1.5°C global warming on natural and human systems with comparison to 2°C and provides the basis for considering the interactions of climate change with sustainable development in Chapter 5. Chapter 4 analyses strategies for strengthening the response to climate change, many of which interact with sustainable development. Chapter 5 takes sustainable development, eradicating poverty and reducing inequalities as its focal point for the analysis of pathways to 1.5°C and discusses explicitly the linkages between achieving SDGs while eradicating poverty and reducing inequality.

Cross-Chapter Box 4: Figure 1 Climate action is number 13 of the UN Sustainable Development Goals

File:Https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/box-4-fig-1-1024x584.jpg

1.5 Assessment Frameworks and Emerging Methodologies that Integrate Climate Change Mitigation and Adaptation with Sustainable Development

This report employs information and data that are global in scope and include region-scale analysis. It also includes syntheses of municipal, sub-national, and national case studies. Global level statistics including physical and social science data are used, as well as detailed and illustrative case study material of particular conditions and contexts. The assessment provides the state of knowledge, including an assessment of confidence and uncertainty. The main time scale of the assessment is the 21st century and the time is separated into the near-, medium-, and long-term. Near-term refers to the coming decade, medium-term to the period 2030–2050, while long-term refers to 2050–2100. Spatial and temporal contexts are illustrated throughout, including: assessment tools that include dynamic projections of emission trajectories and the underlying energy and land transformation (Chapter 2); methods for assessing observed impacts and projected risks in natural and managed ecosystems and at 1.5°C and higher levels of warming in natural and managed ecosystems and human systems (Chapter 3); assessments of the feasibility of mitigation and adaptation options (Chapter 4); and linkages of the Shared Socioeconomic Pathways (SSPs) and Sustainable Development Goals (SDGs) (Cross-Chapter Boxes 1 and 4 in this chapter, Chapter 2 and Chapter 5).

1.5.1 Knowledge Sources and Evidence Used in the Report

This report is based on a comprehensive assessment of documented evidence of the enabling conditions to pursuing efforts to limit the global average temperature rise to 1.5°C and adapting to this level of warming in the overarching context of the Anthropocene (Delanty and Mota, 2017) ²⁸⁰ . Two sources of evidence are used: peer-reviewed scientific literature and ‘grey’ literature in accordance with procedure on the use of literature in IPCC reports (IPCC, 2013a ²⁸¹ , Annex 2 to Appendix A), with the former being the dominant source. Grey literature is largely used on key issues not covered in peer-reviewed literature.

The peer-reviewed literature includes the following sources: 1) knowledge regarding the physical climate system and human-induced changes, associated impacts, vulnerabilities, and adaptation options, established from work based on empirical evidence, simulations, modelling, and scenarios, with emphasis on new information since the publication of the IPCC AR5 to the cut-off date for this report (15th of May 2018); 2) humanities and social science theory and knowledge from actual human experiences of climate change risks and vulnerability in the context of social-ecological systems, development, equity, justice, and governance, and from indigenous knowledge systems; and 3) mitigation pathways based on climate projections into the future.

The grey literature category extends to empirical observations, interviews, and reports from government, industry, research institutes, conference proceedings and international or other organisations. Incorporating knowledge from different sources, settings and information channels while building awareness at various levels will advance decision-making and motivate implementation of context-specific responses to 1.5°C warming (Somanathan et al., 2014) ²⁸² . The assessment does not assess non-written evidence and does not use oral evidence, media reports or newspaper publications. With important exceptions, such as China, published knowledge from the most vulnerable parts of the world to climate change is limited (Czerniewicz et al., 2017) ²⁸³ .

1.5.2 Assessment Frameworks and Methodologies

Climate models and associated simulations

The multiple sources of climate model information used in this assessment are provided in Chapter 2 (Section 2.2) and Chapter 3 (Section 3.2). Results from global simulations, which have also been assessed in previous IPCC reports and that are conducted as part of the World Climate Research Programme (WCRP) Coupled Models Intercomparison Project (CMIP) are used. The IPCC AR4 and Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) reports were mostly based on simulations from the CMIP3 experiment, while the AR5 was mostly based on simulations from the CMIP5 experiment. The simulations of the CMIP3 and CMIP5 experiments were found to be very similar (e.g., Knutti and Sedláček, 2012; Mueller and Seneviratne, 2014) ²⁸⁴ . In addition to the CMIP3 and CMIP5 experiments, results from coordinated regional climate model experiments (e.g., the Coordinated Regional Climate Downscaling Experiment, CORDEX) have been assessed and are available for different regions (Giorgi and Gutowski, 2015) ²⁸⁵ . For instance, assessments based on publications from an extension of the IMPACT2C project (Vautard et al., 2014; Jacob and Solman, 2017) ²⁸⁶ are newly available for 1.5°C projections. Recently, simulations from the ‘Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) multimodel experiment have been performed to specifically assess climate changes at 1.5°C vs 2°C global warming (Mitchell et al., 2016) ²⁸⁷ . The HAPPI protocol consists of coupled land–atmosphere initial condition ensemble simulations with prescribed sea surface temperatures (SSTs); sea ice, GHG and aerosol concentrations; and solar and volcanic activity that coincide with three forced climate states: present-day (2006–2015) (see Section 1.2.1) and future (2091–2100) either with 1.5°C or 2°C global warming (prescribed by modified SSTs).

Detection and attribution of change in climate and impacted systems

Formalized scientific methods are available to detect and attribute impacts of greenhouse gas forcing on observed changes in climate (e.g., Hegerl et al., 2007; Seneviratne et al., 2012; Bindoff et al., 2013) ²⁸⁸ and impacts of climate change on natural and human systems (e.g., Stone et al., 2013; Hansen and Cramer, 2015; Hansen et al., 2016) ²⁸⁹ . The reader is referred to these sources, as well as to the AR5 for more background on these methods.

Global climate warming has already reached approximately 1°C (see Section 1.2.1) relative to pre-industrial conditions, and thus ‘climate at 1.5°C global warming’ corresponds to approximately the addition of only half a degree of warming compared to the present day, comparable to the warming that has occurred since the 1970s (Bindoff et al., 2013) ²⁹⁰ . Methods used in the attribution of observed changes associate with this recent warming are therefore also applicable to assessments of future changes in climate at 1.5°C warming, especially in cases where no climate model simulations or analyses are available.

Impacts of 1.5°C global warming can be assessed in part from regional and global climate changes that have already been detected and attributed to human influence (e.g., Schleussner et al., 2017) ²⁹¹ and are components of the climate system that are most responsive to current and projected future forcing. For this reason, when specific projections are missing for 1.5°C global warming, some of the assessments of climate change provided in Chapter 3 (Section 3.3) build upon joint assessments of (i) changes that were observed and attributed to human influence up to the present, that is, for 1°C global warming and (ii) projections for higher levels of warming (e.g., 2°C, 3°C or 4°C) to assess the changes at 1.5°C. Such assessments are for transient changes only (see Chapter 3, Section 3.3).

Besides quantitative detection and attribution methods, assessments can also be based on indigenous and local knowledge (see Chapter 4, Box 4.3). While climate observations may not be available to assess impacts from a scientific perspective, local community knowledge can also indicate actual impacts (Brinkman et al., 2016; Kabir et al., 2016) ²⁹² . The challenge is that a community’s perception of loss due to the impacts of climate change is an area that requires further research (Tschakert et al., 2017) ²⁹³ .

Costs and benefits analysis

Cost–benefit analyses are common tools used for decision-making, whereby the costs of impacts are compared to the benefits from different response actions (IPCC, 2014a, b) ²⁹⁴ . However, for the case of climate change, recognising the complex inter-linkages of the Anthropocene, cost–benefit analysis tools can be difficult to use because of disparate impacts versus costs and complex interconnectivity within the global social-ecological system (see Box 1.1 and Cross-Chapter Box 5 in Chapter 2). Some costs are relatively easily quantifiable in monetary terms but not all. Climate change impacts human lives and livelihoods, culture and values, and whole ecosystems. It has unpredictable feedback loops and impacts on other regions (IPCC, 2014a) ²⁹⁵ , giving rise to indirect, secondary, tertiary and opportunity costs that are typically extremely difficult to quantify. Monetary quantification is further complicated by the fact that costs and benefits can occur in different regions at very different times, possibly spanning centuries, while it is extremely difficult if not impossible to meaningfully estimate discount rates for future costs and benefits. Thus standard cost–benefit analyses become difficult to justify (IPCC, 2014a; Dietz et al., 2016) ²⁹⁶ and are not used as an assessment tool in this report.

1.6 Confidence, Uncertainty and Risk

This report relies on the IPCC’s uncertainty guidance provided in Mastrandrea et al. (2011) ²⁹⁷ and sources given therein. Two metrics for qualifying key findings are used:

Confidence: Five qualifiers are used to express levels of confidence in key findings, ranging from very low , through low , medium , high , to very high . The assessment of confidence involves at least two dimensions, one being the type, quality, amount or internal consistency of individual lines of evidence, and the second being the level of agreement between different lines of evidence. Very high confidence findings must either be supported by a high level of agreement across multiple lines of mutually independent and individually robust lines of evidence or, if only a single line of evidence is available, by a very high level of understanding underlying that evidence. Findings of low or very low confidence are presented only if they address a topic of major concern.

Likelihood: A calibrated language scale is used to communicate assessed probabilities of outcomes, ranging from exceptionally unlikely (<1%), extremely unlikely (<5%), very unlikely (<10%), unlikely (<33%), about as likely as not (33–66%), likely (>66%), very likely (>90%), extremely likely (>95%) to virtually certain (>99%). These terms are normally only applied to findings associated with high or very high confidence. Frequency of occurrence within a model ensemble does not correspond to actual assessed probability of outcome unless the ensemble is judged to capture and represent the full range of relevant uncertainties.

Three specific challenges arise in the treatment of uncertainty and risk in this report. First, the current state of the scientific literature on 1.5°C means that findings based on multiple lines of robust evidence for which quantitative probabilistic results can be expressed may be few in number, and those that do exist may not be the most policy-relevant. Hence many key findings are expressed using confidence qualifiers alone.

Second, many of the most important findings of this report are conditional because they refer to ambitious mitigation scenarios, potentially involving large-scale technological or societal transformation. Conditional probabilities often depend strongly on how conditions are specified, such as whether temperature goals are met through early emission reductions, reliance on negative emissions, or through a low climate response. Whether a certain risk is considered high at 1.5°C may therefore depend strongly on how 1.5°C is specified, whereas a statement that a certain risk may be substantially higher at 2°C relative to 1.5°C may be much more robust.

Third, achieving ambitious mitigation goals will require active, goal-directed efforts aiming explicitly for specific outcomes and incorporating new information as it becomes available (Otto et al., 2015) ²⁹⁸ . This shifts the focus of uncertainty from the climate outcome itself to the level of mitigation effort that may be required to achieve it. Probabilistic statements about human decisions are always problematic, but in the context of robust decision-making, many near-term policies that are needed to keep open the option of limiting warming to 1.5°C may be the same, regardless of the actual probability that the goal will be met (Knutti et al., 2015) ²⁹⁹ .

1.7 Storyline of the Report

The storyline of this report (Figure 1.6) includes a set of interconnected components. The report consists of five chapters (plus Supplementary Material for Chapters 1 through 4), a Technical Summary and a Summary for Policymakers. It also includes a set of boxes to elucidate specific or cross-cutting themes, as well as Frequently Asked Questions for each chapter, a Glossary, and several other Annexes.

At a time of unequivocal and rapid global warming, this report emerges from the long-term temperature goal of the Paris Agreement – strengthening the global response to the threat of climate change by pursuing efforts to limit warming to 1.5°C through reducing emissions to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases. The assessment focuses first, in Chapter 1, on how 1.5°C is defined and understood, what is the current level of warming to date, and the present trajectory of change. The framing presented in Chapter 1 provides the basis through which to understand the enabling conditions of a 1.5°C warmer world and connections to the SDGs, poverty eradication, and equity and ethics.

In Chapter 2, scenarios of a 1.5°C warmer world and the associated pathways are assessed. The pathways assessment builds upon the AR5 with a greater emphasis on sustainable development in mitigation pathways. All pathways begin now and involve rapid and unprecedented societal transformation. An important framing device for this report is the recognition that choices that determine emissions pathways, whether ambitious mitigation or ‘no policy’ scenarios, do not occur independently of these other changes and are, in fact, highly interdependent.

Projected impacts that emerge in a 1.5°C warmer world and beyond are dominant narrative threads of the report and are assessed in Chapter 3. The chapter focuses on observed and attributable global and regional climate changes and impacts and vulnerabilities. The projected impacts have diverse and uneven spatial, temporal, human, economic, and ecological system-level manifestations. Central to the assessment is the reporting of impacts at 1.5°C and 2°C, potential impacts avoided through limiting warming to 1.5°C, and, where possible, adaptation potential and limits to adaptive capacity.

Response options and associated enabling conditions emerge next, in Chapter 4. Attention is directed to exploring questions of adaptation and mitigation implementation, integration, and transformation in a highly interdependent world, with consideration of synergies and trade-offs. Emission pathways, in particular, are broken down into policy options and instruments. The role of technological choices, institutional capacity and global-scale trends like urbanization and changes in ecosystems are assessed.

Chapter 5 covers linkages between achieving the SDGs and a 1.5°C warmer world and turns toward identifying opportunities and challenges of transformation. This is assessed within a transition to climate-resilient development pathways and connection between the evolution towards 1.5°C, associated impacts, and emission pathways. Positive and negative effects of adaptation and mitigation response measures and pathways for a 1.5°C warmer world are examined. Progress along these pathways involves inclusive processes, institutional integration, adequate finance and technology, and attention to issues of power, values, and inequalities to maximize the benefits of pursuing climate stabilisation at 1.5°C and the goals of sustainable development at multiple scales of human and natural systems from global, regional, national to local and community levels.

Figure 1.6. Schematic of report storyline

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Original Creation for this Report

FAQs Frequently Asked Questions

FAQ 1.1 Why are we talking about 1.5°C?

Summary: Climate change represents an urgent and potentially irreversible threat to human societies and the planet. In recognition of this, the overwhelming majority of countries around the world adopted the Paris Agreement in December 2015, the central aim of which includes pursuing efforts to limit global temperature rise to 1.5°C. In doing so, these countries, through the United Nations Framework Convention on Climate Change (UNFCCC), also invited the IPCC to provide a Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emissions pathways.

At the 21st Conference of the Parties (COP21) in December 2015, 195 nations adopted the Paris Agreement ² . The first instrument of its kind, the landmark agreement includes the aim to strengthen the global response to the threat of climate change by ‘holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels’.

The first UNFCCC document to mention a limit to global warming of 1.5°C was the Cancun Agreement, adopted at the sixteenth COP (COP16) in 2010. The Cancun Agreement established a process to periodically review the ‘adequacy of the long-term global goal (LTGG) in the light of the ultimate objective of the Convention and the overall progress made towards achieving the LTGG, including a consideration of the implementation of the commitments under the Convention’. The definition of LTGG in the Cancun Agreement was ‘to hold the increase in global average temperature below 2°C above pre-industrial levels’. The agreement also recognised the need to consider ‘strengthening the long-term global goal on the basis of the best available scientific knowledge…to a global average temperature rise of 1.5°C’.

Beginning in 2013 and ending at the COP21 in Paris in 2015, the first review period of the long-term global goal largely consisted of the Structured Expert Dialogue (SED). This was a fact-finding, face-to-face exchange of views between invited experts and UNFCCC delegates. The final report of the SED ³ concluded that ‘in some regions and vulnerable ecosystems, high risks are projected even for warming above 1.5°C’. The SED report also suggested that Parties would profit from restating the temperature limit of the long-term global goal as a ‘defence line’ or ‘buffer zone’, instead of a ‘guardrail’ up to which all would be safe, adding that this new understanding would ‘probably also favour emission pathways that will limit warming to a range of temperatures below 2°C’. Specifically on strengthening the temperature limit of 2°C, the SED’s key message was: ‘While science on the 1.5°C warming limit is less robust, efforts should be made to push the defence line as low as possible’. The findings of the SED, in turn, fed into the draft decision adopted at COP21.

With the adoption of the Paris Agreement, the UNFCCC invited the IPCC to provide a Special Report in 2018 on ‘the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emissions pathways’. The request was that the report, known as SR1.5, should not only assess what a 1.5°C warmer world would look like but also the different pathways by which global temperature rise could be limited to 1.5°C. In 2016, the IPCC accepted the invitation, adding that the Special Report would also look at these issues in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty.

The combination of rising exposure to climate change and the fact that there is a limited capacity to adapt to its impacts amplifies the risks posed by warming of 1.5°C and 2°C. This is particularly true for developing and island countries in the tropics and other vulnerable countries and areas. The risks posed by global warming of 1.5°C are greater than for present-day conditions but lower than at 2°C.

FAQ1.1, Figure 1

A timeline of notable dates in preparing the IPCC Special Report on Global Warming of 1.5°C (blue) embedded within processes and milestones of the United Nations Framework Convention on Climate Change (UNFCCC; grey), including events that may be relevant for discussion of temperature limits.

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FAQ 1.2 How close are we to 1.5°C?

Summary: Human-induced warming has already reached about 1°C above pre-industrial levels at the time of writing of this Special Report. By the decade 2006–2015, human activity had warmed the world by 0.87°C (±0.12°C) compared to pre-industrial times (1850–1900). If the current warming rate continues, the world would reach human-induced global warming of 1.5°C around 2040.

Under the 2015 Paris Agreement, countries agreed to cut greenhouse gas emissions with a view to ‘holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels’. While the overall intention of strengthening the global response to climate change is clear, the Paris Agreement does not specify precisely what is meant by ‘global average temperature’, or what period in history should be considered ‘pre-industrial’. To answer the question of how close are we to 1.5°C of warming, we need to first be clear about how both terms are defined in this Special Report.

The choice of pre-industrial reference period, along with the method used to calculate global average temperature, can alter scientists’ estimates of historical warming by a couple of tenths of a degree Celsius. Such differences become important in the context of a global temperature limit just half a degree above where we are now. But provided consistent definitions are used, they do not affect our understanding of how human activity is influencing the climate.

In principle, ‘pre-industrial levels’ could refer to any period of time before the start of the industrial revolution. But the number of direct temperature measurements decreases as we go back in time. Defining a ‘pre-industrial’ reference period is, therefore, a compromise between the reliability of the temperature information and how representative it is of truly pre-industrial conditions. Some pre-industrial periods are cooler than others for purely natural reasons. This could be because of spontaneous climate variability or the response of the climate to natural perturbations, such as volcanic eruptions and variations in the sun’s activity. This IPCC Special Report on Global Warming of 1.5°C uses the reference period 1850–1900 to represent pre-industrial temperature. This is the earliest period with near-global observations and is the reference period used as an approximation of pre-industrial temperatures in the IPCC Fifth Assessment Report.

Once scientists have defined ‘pre-industrial’, the next step is to calculate the amount of warming at any given time relative to that reference period. In this report, warming is defined as the increase in the 30-year global average of combined air temperature over land and water temperature at the ocean surface. The 30-year timespan accounts for the effect of natural variability, which can cause global temperatures to fluctuate from one year to the next. For example, 2015 and 2016 were both affected by a strong El Niño event, which amplified the underlying human-caused warming.

In the decade 2006–2015, warming reached 0.87°C (±0.12°C) relative to 1850–1900, predominantly due to human activity increasing the amount of greenhouse gases in the atmosphere. Given that global temperature is currently rising by 0.2°C (±0.1°C) per decade, human-induced warming reached 1°C above pre-industrial levels around 2017 and, if this pace of warming continues, would reach 1.5°C around 2040.

While the change in global average temperature tells researchers about how the planet as a whole is changing, looking more closely at specific regions, countries and seasons reveals important details. Since the 1970s, most land regions have been warming faster than the global average, for example. This means that warming in many regions has already exceeded 1.5°C above pre-industrial levels. Over a fifth of the global population live in regions that have already experienced warming in at least one season that is greater than 1.5°C above pre-industrial levels.

FAQ1.2, Figure 1

Human-induced warming reached approximately 1°C above pre-industrial levels in 2017.

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At the present rate, global temperatures would reach 1.5°C around 2040. Stylized 1.5°C pathway shown here involves emission reductions beginning immediately, and CO ₂ emissions reaching zero by 2055.

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Footnotes

An animated version of Figure 1.4 will be embedded in the web-based version of this Special Report
Paris Agreement FCCC/CP/2015/10/Add.1 https://unfccc.int/documents/9097
Structured Expert Dialogue (SED) final report FCCC/SB/2015/INF.1 https://unfccc.int/documents/8707

References

IPCC, 2013b: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–29.
IPCC, 2012a: Summary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–21.

IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.

Mysiak, J., S. Surminski, A. Thieken, R. Mechler, and J. Aerts, 2016: Brief communication: Sendai framework for disaster risk reduction – Success or warning sign for Paris? Natural Hazards and Earth System Sciences , 16(10) , 2189–2193, doi: 10.5194/nhess-16-2189-2016 .
IPCC, 2012a: Summary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–21.
Albert, S. et al., 2017: Heading for the hills: climate-driven community relocations in the Solomon Islands and Alaska provide insight for a 1.5°C future. Regional Environmental Change , 1–12, doi: 10.1007/s10113-017-1256-8 .
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Dryzek, J.S., 2016: Institutions for the Anthropocene: Governance in a Changing Earth System. British Journal of Political Science , 46(04) , 937–956, doi: 10.1017/s0007123414000453 .

Pattberg, P. and F. Zelli (eds.), 2016: Environmental politics and governance in the anthropocene: Institutions and legitimacy in a complex world . Routledge, London, UK, 268 pp., doi: 10.4324/9781315697468 .

Bäckstrand, K., J.W. Kuyper, B.-O. Linnér, and E. Lövbrand, 2017: Non-state actors in global climate governance: from Copenhagen to Paris and beyond. Environmental Politics , 26(4) , 561–579, doi: 10.1080/09644016.2017.1327485 .

Lövbrand, E., M. Hjerpe, and B.-O. Linnér, 2017: Making climate governance global: how UN climate summitry comes to matter in a complex climate regime. Environmental Politics , 26(4) , 580–599, doi: 10.1080/09644016.2017.1319019 .
Birkmann, J., T. Welle, W. Solecki, S. Lwasa, and M. Garschagen, 2016: Boost resilience of small and mid-sized cities. Nature , 537(7622) , 605–608, doi: 10.1038/537605a .
Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society , 140(683) , 1935–1944, doi: 10.1002/qj.2297 .
Steffen, W. et al., 2016: Stratigraphic and Earth System approaches to defining the Anthropocene. Earth’s Future , 4(8) , 324–345, doi: 10.1002/2016ef000379 .
Waters, C.N. et al., 2016: The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science , 351(6269) , aad2622–aad2622, doi: 10.1126/science.aad2622 .
Crutzen, P.J. and E.F. Stoermer, 2000: The Anthropocene. Global Change Newsletter , 41 , 17–18, http://www.igbp.net/download/18.316f18321323470177580001401/1376383088452/nl41.pdf .

Crutzen, P.J., 2002: Geology of mankind. Nature , 415(6867) , 23, doi: 10.1038/415023a .

Gradstein, F.M., J.G. Ogg, M.D. Schmitz, and G.M. Ogg (eds.), 2012: The Geologic Time Scale . Elsevier BV, Boston, MA, USA, 1144 pp., doi: 10.1016/b978-0-444-59425-9.01001-5 .
Lüthi, D. et al., 2008: High-resolution carbon dioxide concentration record 650,000–800,000 years before present. Nature , 453(7193) , 379–382, doi: 10.1038/nature06949 .
Bereiter, B. et al., 2015: Revision of the EPICA Dome C CO ₂ record from 800 to 600-kyr before present. Geophysical Research Letters , 42(2) , 542–549, doi: 10.1002/2014gl061957 .
Masson-Delmotte, V. et al., 2013: Information from Paleoclimate Archives. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 383–464.
Zalasiewicz, J. et al., 2017: Making the case for a formal Anthropocene Epoch: an analysis of ongoing critiques. Newsletters on Stratigraphy , 50(2) , 205–226, doi: 10.1127/nos/2017/0385 .
Waters, C.N. et al., 2016: The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science , 351(6269) , aad2622–aad2622, doi: 10.1126/science.aad2622 .
Waters, C.N. et al., 2016: The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science , 351(6269) , aad2622–aad2622, doi: 10.1126/science.aad2622 .
Brondizio, E.S. et al., 2016: Re-conceptualizing the Anthropocene: A call for collaboration. Global Environmental Change , 39 , 318–327, doi: 10.1016/j.gloenvcha.2016.02.006 .
Pattberg, P. and F. Zelli (eds.), 2016: Environmental politics and governance in the anthropocene: Institutions and legitimacy in a complex world . Routledge, London, UK, 268 pp., doi: 10.4324/9781315697468 .
Delanty, G. and A. Mota, 2017: Governing the Anthropocene. European Journal of Social Theory , 20(1) , 9–38, doi: 10.1177/1368431016668535 .
Harrington, C., 2016: The Ends of the World: International Relations and the Anthropocene. Millennium: Journal of International Studies , 44(3) , 478–498, doi: 10.1177/0305829816638745 .
Biermann, F. et al., 2016: Down to Earth: Contextualizing the Anthropocene. Global Environmental Change , 39 , 341–350, doi: 10.1016/j.gloenvcha.2015.11.004 .
Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.

Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.

Klein, R.J.T. et al., 2014: Adaptation opportunities, constraints, and limits. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 899–943.

Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.

Porter, J.R. et al., 2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485–533.

Stavins, R. et al., 2014: International Cooperation: Agreements and Instruments. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1001–1082.
Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.
Shelton, D., 2008: Equity. In: The Oxford Handbook of International Environmental Law [Bodansky, D., J. Brunnée, and E. Hey (eds.)]. Oxford University Press, Oxford, UK, pp. 639–662, doi: 10.1093/oxfordhb/9780199552153.013.0027 .
Bodansky, D., J. Brunnée, and L. Rajamani, 2017: International Climate Change Law . Oxford University Press, Oxford, UK, 416 pp.
Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.

Savaresi, A., 2016: The Paris Agreement: a new beginning? Journal of Energy & Natural Resources Law , 34(1) , 16–26, doi: 10.1080/02646811.2016.1133983 .

Reckien, D. et al., 2017: Climate change, equity and the Sustainable Development Goals: an urban perspective. Environment & Urbanization , 29(1) , 159–182, doi: 10.1177/0956247816677778 .
Caney, S., 2005: Cosmopolitan Justice, Responsibility, and Global Climate Change. Leiden Journal of International Law , 18(04) , 747–75, doi: 10.1017/s0922156505002992 .

Schroeder, H., M.T. Boykoff, and L. Spiers, 2012: Equity and state representations in climate negotiations. Nature Climate Change , 2 , 834–836, doi: 10.1038/nclimate1742 .

Ajibade, I., 2016: Distributive justice and human rights in climate policy: the long road to Paris. Journal of Sustainable Development Law and Policy (The) , 7(2) , 65–80, doi: 10.4314/jsdlp.v7i2.4 .

Reckien, D. et al., 2017: Climate change, equity and the Sustainable Development Goals: an urban perspective. Environment & Urbanization , 29(1) , 159–182, doi: 10.1177/0956247816677778 .

Shue, H., 2018: Mitigation gambles: uncertainty, urgency and the last gamble possible. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2017.0105 .
Schleussner, C.-F. et al., 2016: Differential climate impacts for policy relevant limits to global warming: the case of 1.5°C and 2°C. Earth System Dynamics , 7(2) , 327–351, doi: 10.5194/esd-7-327-2016 .
Aaheim, A., T. Wei, and B. Romstad, 2017: Conflicts of economic interests by limiting global warming to +3°C. Mitigation and Adaptation Strategies for Global Change , 22(8) , 1131–1148, doi: 10.1007/s11027-016-9718-8 .
Okereke, C., 2010: Climate justice and the international regime. Wiley Interdisciplinary Reviews: Climate Change , 1(3) , 462–474, doi: 10.1002/wcc.52 .

Harlan, S.L. et al., 2015: Climate Justice and Inequality: Insights from Sociology. In: Climate Change and Society: Sociological Perspectives [Dunlap, R.E. and R.J. Brulle (eds.)]. Oxford University Press, New York, NY, USA, pp. 127–163, doi: 10.1093/acprof:oso/9780199356102.003.0005 .

Ajibade, I., 2016: Distributive justice and human rights in climate policy: the long road to Paris. Journal of Sustainable Development Law and Policy (The) , 7(2) , 65–80, doi: 10.4314/jsdlp.v7i2.4 .

Savaresi, A., 2016: The Paris Agreement: a new beginning? Journal of Energy & Natural Resources Law , 34(1) , 16–26, doi: 10.1080/02646811.2016.1133983 .

Reckien, D. et al., 2017: Climate change, equity and the Sustainable Development Goals: an urban perspective. Environment & Urbanization , 29(1) , 159–182, doi: 10.1177/0956247816677778 .
Shue, H., 2013: Climate Hope: Implementing the Exit Strategy. Chicago Journal of International Law , 13(2) , 381–402, https://chicagounbound.uchicago.edu/cjil/vol13/iss2/6/ .

McKinnon, C., 2015: Climate justice in a carbon budget. Climatic Change , 133(3) , 375–384, doi: 10.1007/s10584-015-1382-6 .

Otto, F.E.L., R.B. Skeie, J.S. Fuglestvedt, T. Berntsen, and M.R. Allen, 2017: Assigning historic responsibility for extreme weather events. Nature Climate Change , 7(11) , 757–759, doi: 10.1038/nclimate3419 .

Skeie, R.B. et al., 2017: Perspective has a strong effect on the calculation of historical contributions to global warming. Environmental Research Letters , 12(2) , 024022, doi: 10.1088/1748-9326/aa5b0a .
Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.

Shue, H., 2014: Climate Justice: Vulnerability and Protection . Oxford University Press, Oxford, UK, 368 pp.

Ionesco, D., D. Mokhnacheva, and F. Gemenne, 2016: Atlas de Migrations Environnmentales (in French) . Presses de Sciences Po, Paris, France, 152 pp.
Robinson, M. and T. Shine, 2018: Achieving a climate justice pathway to 1.5°C. Nature Climate Change , 8(7) , 564–569, doi: 10.1038/s41558-018-0189-7 .
Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.

Shue, H., 2014: Climate Justice: Vulnerability and Protection . Oxford University Press, Oxford, UK, 368 pp.

Humphreys, S., 2017: Climate, Technology, ‘Justice’. In: Protecting the Environment for Future Generations – Principles and Actors in International Environmental Law [Proelß, A. (ed.)]. Erich Schmidt Verlag, Berlin, Germany, pp. 171–190.
OHCHR, 2009: Report of the Office of the United Nations High Commissioner for Human Rights on the relationship between climate change and human rights . A/HRC/10/61, Office of the United Nations High Commissioner for Human Rights (OHCHR), 32 pp.

Caney, S., 2010: Climate change and the duties of the advantaged. Critical Review of International Social and Political Philosophy , 13(1) , 203–228, doi: 10.1080/13698230903326331 .

Adger, W.N. et al., 2014: Human Security. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 755–791.

Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.

IBA, 2014: Achieving Justice and Human Rights in an Era of Climate Disruption . International Bar Association (IBA), London, UK, 240 pp.

Knox, J.H., 2015: Human Rights Principles and Climate Change. In: Oxford Handbook of International Climate Change Law [Carlarne, C., K.R. Gray, and R. Tarasofsky (eds.)]. Oxford University Press, Oxford, UK, pp. 213–238, doi: 10.1093/law/9780199684601.003.0011 .

Duyck, S., S. Jodoin, and A. Johl (eds.), 2018: Routledge Handbook of Human Rights and Climate Governance . Routledge, Abingdon, UK, 430 pp.

Robinson, M. and T. Shine, 2018: Achieving a climate justice pathway to 1.5°C. Nature Climate Change , 8(7) , 564–569, doi: 10.1038/s41558-018-0189-7 .
Caney, S., 2010: Climate change and the duties of the advantaged. Critical Review of International Social and Political Philosophy , 13(1) , 203–228, doi: 10.1080/13698230903326331 .

Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.

OHCHR, 2015: Understanding Human Rights and Climate Change . Submission of the Office of the High Commissioner for Human Rights to the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change, Office of the United Nations High Commissioner for Human Rights (OHCHR), 28 pp.
OHCHR, 2017: Analytical study on the relationship between climate change and the full and effective enjoyment of the rights of the child . A/HRC/35/13, Office of the United Nations High Commissioner for Human Rights (OHCHR), 18 pp.
Knox, J.H., 2015: Human Rights Principles and Climate Change. In: Oxford Handbook of International Climate Change Law [Carlarne, C., K.R. Gray, and R. Tarasofsky (eds.)]. Oxford University Press, Oxford, UK, pp. 213–238, doi: 10.1093/law/9780199684601.003.0011 .

OHCHR, 2015: Understanding Human Rights and Climate Change . Submission of the Office of the High Commissioner for Human Rights to the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change, Office of the United Nations High Commissioner for Human Rights (OHCHR), 28 pp.

Humphreys, S., 2017: Climate, Technology, ‘Justice’. In: Protecting the Environment for Future Generations – Principles and Actors in International Environmental Law [Proelß, A. (ed.)]. Erich Schmidt Verlag, Berlin, Germany, pp. 171–190.
Holz, C., S. Kartha, and T. Athanasiou, 2017: Fairly sharing 1.5: national fair shares of a 1.5°C-compliant global mitigation effort. International Environmental Agreements: Politics, Law and Economics , 18(1) , 1–18, doi: 10.1007/s10784-017-9371-z .

Dooley, K., J. Gupta, and A. Patwardhan, 2018: INEA editorial: Achieving 1.5°C and climate justice. International Environmental Agreements: Politics, Law and Economics , 18(1) , 1–9, doi: 10.1007/s10784-018-9389-x .

Klinsky, S. and H. Winkler, 2018: Building equity in: strategies for integrating equity into modelling for a 1.5°C world. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2016.0461 .
Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
UNDP, 2016: Human Development Report 2016: Human Development for Everyone . United Nations Development Programme (UNDP), New York, NY, USA, 286 pp.
Leichenko, R. and J.A. Silva, 2014: Climate change and poverty: Vulnerability, impacts, and alleviation strategies. Wiley Interdisciplinary Reviews: Climate Change , 5(4) , 539–556, doi: 10.1002/wcc.287 .
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
Shiferaw, B. et al., 2014: Managing vulnerability to drought and enhancing livelihood resilience in sub-Saharan Africa: Technological, institutional and policy options. Weather and Climate Extremes , 3 , 67–79, doi: 10.1016/j.wace.2014.04.004 .
Miyan, M.A., 2015: Droughts in Asian Least Developed Countries: Vulnerability and sustainability. Weather and Climate Extremes , 7 , 8–23, doi: 10.1016/j.wace.2014.06.003 .
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
IPCC, 2014c: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.
Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
UN, 2015b: Transforming our world: The 2030 agenda for sustainable development . A/RES/70/1, United Nations General Assembly (UNGA), 35 pp.
Rogelj, J. et al., 2016a: Paris Agreement climate proposals need boost to keep warming well below 2°C. Nature Climate Change , 534 , 631–639, doi: 10.1038/nature18307 .
UNFCCC, 2016: Aggregate effect of the intended nationally determined contributions: an update . FCCC/CP/2016/2, United Nations Framework Convention on Climate Change (UNFCCC), 75 pp.
Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
Hawkins, E. et al., 2017: Estimating changes in global temperature since the pre-industrial period. Bulletin of the American Meteorological Society , BAMS–D–16–0007.1, doi: 10.1175/bams-d-16-0007.1 .
Pfleiderer, P., C.-F. Schleussner, M. Mengel, and J. Rogelj, 2018: Global mean temperature indicators linked to warming levels avoiding climate risks. Environmental Research Letters , 13(6) , 064015, doi: 10.1088/1748-9326/aac319 .
Deser, C., R. Knutti, S. Solomon, and A.S. Phillips, 2012: Communication of the Role of Natural Variability in Future North American Climate. Nature Climate Change , 2(11) , 775–779, doi: 10.1038/nclimate1562 .
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Foster, G. and S. Rahmstorf, 2011: Global temperature evolution 1979–2010. Environmental Research Letters , 6(4) , 044022, doi: 10.1088/1748-9326/6/4/044022 .

Haustein, K. et al., 2017: A real-time Global Warming Index. Scientific Reports , 7(1) , 15417, doi: 10.1038/s41598-017-14828-5 .

Medhaug, I., M.B. Stolpe, E.M. Fischer, and R. Knutti, 2017: Reconciling controversies about the ‘global warming hiatus’. Nature , 545(7652) , 41–47, doi: 10.1038/nature22315 .

Folland, C.K., O. Boucher, A. Colman, and D.E. Parker, 2018: Causes of irregularities in trends of global mean surface temperature since the late 19th century. Science Advances , 4(6) , eaao5297, doi: 10.1126/sciadv.aao5297 .

Visser, H., S. Dangendorf, D.P. van Vuuren, B. Bregman, and A.C. Petersen, 2018: Signal detection in global mean temperatures after “Paris”: an uncertainty and sensitivity analysis. Climate of the Past , 14(2) , 139–155, doi: 10.5194/cp-14-139-2018 .
Bethke, I. et al., 2017: Potential volcanic impacts on future climate variability. Nature Climate Change , 7(11) , 799–805, doi: 10.1038/nclimate3394 .
Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.
Folland, C.K., O. Boucher, A. Colman, and D.E. Parker, 2018: Causes of irregularities in trends of global mean surface temperature since the late 19th century. Science Advances , 4(6) , eaao5297, doi: 10.1126/sciadv.aao5297 .
Morice, C.P., J.J. Kennedy, N.A. Rayner, and P.D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. Journal of Geophysical Research: Atmospheres , 117(D8) , D08101, doi: 10.1029/2011jd017187 .
Hartmann, D.J. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.
Berger, A., Q. Yin, H. Nifenecker, and J. Poitou, 2017: Slowdown of global surface air temperature increase and acceleration of ice melting. Earth’s Future , 5(7) , 811–822, doi: 10.1002/2017ef000554 .
Cowtan, K. et al., 2015: Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures. Geophysical Research Letters , 42(15) , 6526–6534, doi: 10.1002/2015gl064888 .
Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. Nature Climate Change , 6(10) , 931–935, doi: 10.1038/nclimate3066 .
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115.
Richardson, M., K. Cowtan, and R.J. Millar, 2018: Global temperature definition affects achievement of long-term climate goals. Environmental Research Letters , 13(5) , 054004, doi: 10.1088/1748-9326/aab305 .
Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Reviews of Geophysics , 48(4) , RG4004, doi: 10.1029/2010rg000345 .
Vose, R.S. et al., 2012: NOAA’s merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society , 93(11) , 1677–1685, doi: 10.1175/bams-d-11-00241.1 .
Morice, C.P., J.J. Kennedy, N.A. Rayner, and P.D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. Journal of Geophysical Research: Atmospheres , 117(D8) , D08101, doi: 10.1029/2011jd017187 .
Rohde, R. et al., 2013: Berkeley Earth Temperature Averaging Process. Geoinformatics & Geostatistics: An Overview , 1(2) , 1–13, doi: 10.4172/2327-4581.1000103 .

Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society , 140(683) , 1935–1944, doi: 10.1002/qj.2297 .

Jones, P., 2016: The reliability of global and hemispheric surface temperature records. Advances in Atmospheric Sciences , 33(3) , 269–282, doi: 10.1007/s00376-015-5194-4 .
Richardson, M., K. Cowtan, and R.J. Millar, 2018: Global temperature definition affects achievement of long-term climate goals. Environmental Research Letters , 13(5) , 054004, doi: 10.1088/1748-9326/aab305 .
Karl, T.R. et al., 2015: Possible artifacts of data biases in the recent global surface warming hiatus. Science , 348(6242) , 1469–1472, doi: 10.1126/science.aaa5632 .
Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society , 140(683) , 1935–1944, doi: 10.1002/qj.2297 .
Rohde, R. et al., 2013: Berkeley Earth Temperature Averaging Process. Geoinformatics & Geostatistics: An Overview , 1(2) , 1–13, doi: 10.4172/2327-4581.1000103 .
Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. Nature Climate Change , 6(10) , 931–935, doi: 10.1038/nclimate3066 .
Otto, F.E.L., D.J. Frame, A. Otto, and M.R. Allen, 2015: Embracing uncertainty in climate change policy. Nature Climate Change , 5 , 1–5, doi: 10.1038/nclimate2716 .
Haustein, K. et al., 2017: A real-time Global Warming Index. Scientific Reports , 7(1) , 15417, doi: 10.1038/s41598-017-14828-5 .
Cowtan, K. et al., 2015: Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures. Geophysical Research Letters , 42(15) , 6526–6534, doi: 10.1002/2015gl064888 .
Richardson, M., K. Cowtan, and R.J. Millar, 2018: Global temperature definition affects achievement of long-term climate goals. Environmental Research Letters , 13(5) , 054004, doi: 10.1088/1748-9326/aab305 .
Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A reconstruction of regional and global temperature for the past 11,300 years. Science , 339(6124) , 1198–201, doi: 10.1126/science.1228026 .
Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.
Field, C.B. et al., 2014: Technical Summary. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 35–94.
Hawkins, E. et al., 2017: Estimating changes in global temperature since the pre-industrial period. Bulletin of the American Meteorological Society , BAMS–D–16–0007.1, doi: 10.1175/bams-d-16-0007.1 .
Abram, N.J. et al., 2016: Early onset of industrial-era warming across the oceans and continents. Nature , 536 , 411–418, doi: 10.1038/nature19082 .
Schurer, A.P., M.E. Mann, E. Hawkins, S.F.B. Tett, and G.C. Hegerl, 2017: Importance of the pre-industrial baseline for likelihood of exceeding Paris goals. Nature Climate Change , 7(8) , 563–567, doi: 10.1038/nclimate3345 .
Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A reconstruction of regional and global temperature for the past 11,300 years. Science , 339(6124) , 1198–201, doi: 10.1126/science.1228026 .

Lüning, S. and F. Vahrenholt, 2017: Paleoclimatological Context and Reference Level of the 2°C and 1.5°C Paris Agreement Long-Term Temperature Limits. Frontiers in Earth Science , 5 , 104, doi: 10.3389/feart.2017.00104 .

Marsicek, J., B.N. Shuman, P.J. Bartlein, S.L. Shafer, and S. Brewer, 2018: Reconciling divergent trends and millennial variations in Holocene temperatures. Nature , 554(7690) , 92–96, doi: 10.1038/nature25464 .
Hawkins, E. et al., 2017: Estimating changes in global temperature since the pre-industrial period. Bulletin of the American Meteorological Society , BAMS–D–16–0007.1, doi: 10.1175/bams-d-16-0007.1 .

Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. Nature Geoscience , 10(10) , 741–747, doi: 10.1038/ngeo3031 .

Simmons, A.J. et al., 2017: A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. Quarterly Journal of the Royal Meteorological Society , 143(702) , 101–119, doi: 10.1002/qj.2949 .
Kosaka, Y. and S.P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature , 501(7467) , 403–407, doi: 10.1038/nature12534 .
Medhaug, I., M.B. Stolpe, E.M. Fischer, and R. Knutti, 2017: Reconciling controversies about the ‘global warming hiatus’. Nature , 545(7652) , 41–47, doi: 10.1038/nature22315 .
England, M.H. et al., 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Climate Change , 4(3) , 222–227, doi: 10.1038/nclimate2106 .
Foster, G. and S. Rahmstorf, 2011: Global temperature evolution 1979–2010. Environmental Research Letters , 6(4) , 044022, doi: 10.1088/1748-9326/6/4/044022 .
Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.
Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.

Jones, G.S., P.A. Stott, and J.F.B. Mitchell, 2016: Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction. Journal of Geophysical Research: Atmospheres , 121(12) , 6969–6992, doi: 10.1002/2015jd024337 .

Haustein, K. et al., 2017: A real-time Global Warming Index. Scientific Reports , 7(1) , 15417, doi: 10.1038/s41598-017-14828-5 .

Ribes, A., F.W. Zwiers, J.-M. Azaïs, and P. Naveau, 2017: A new statistical approach to climate change detection and attribution. Climate Dynamics , 48(1) , 367–386, doi: 10.1007/s00382-016-3079-6 .
Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. Nature Climate Change , 6(10) , 931–935, doi: 10.1038/nclimate3066 .
Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.
Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.
Gillett, N.P., V.K. Arora, D. Matthews, and M.R. Allen, 2013: Constraining the ratio of global warming to cumulative CO ₂ emissions using CMIP5 simulations. Journal of Climate , 26(18) , 6844–6858, doi: 10.1175/jcli-d-12-00476.1 .

Jones, G.S., P.A. Stott, and N. Christidis, 2013: Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. Journal of Geophysical Research: Atmospheres , 118(10) , 4001–4024, doi: 10.1002/jgrd.50239 .

Ribes, A. and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature. Climate Dynamics , 41(11) , 2837–2853, doi: 10.1007/s00382-013-1736-6 .
Jones, G.S., P.A. Stott, and J.F.B. Mitchell, 2016: Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction. Journal of Geophysical Research: Atmospheres , 121(12) , 6969–6992, doi: 10.1002/2015jd024337 .
Ribes, A. and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature. Climate Dynamics , 41(11) , 2837–2853, doi: 10.1007/s00382-013-1736-6 .
Gillett, N.P., V.K. Arora, D. Matthews, and M.R. Allen, 2013: Constraining the ratio of global warming to cumulative CO ₂ emissions using CMIP5 simulations. Journal of Climate , 26(18) , 6844–6858, doi: 10.1175/jcli-d-12-00476.1 .
Haustein, K. et al., 2017: A real-time Global Warming Index. Scientific Reports , 7(1) , 15417, doi: 10.1038/s41598-017-14828-5 .
Foster, G. and S. Rahmstorf, 2011: Global temperature evolution 1979–2010. Environmental Research Letters , 6(4) , 044022, doi: 10.1088/1748-9326/6/4/044022 .
Folland, C.K., O. Boucher, A. Colman, and D.E. Parker, 2018: Causes of irregularities in trends of global mean surface temperature since the late 19th century. Science Advances , 4(6) , eaao5297, doi: 10.1126/sciadv.aao5297 .
Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.
Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. Nature Geoscience , 11(8) , 574–579, doi: 10.1038/s41561-018-0156-y .
Bethke, I. et al., 2017: Potential volcanic impacts on future climate variability. Nature Climate Change , 7(11) , 799–805, doi: 10.1038/nclimate3394 .
Henley, B.J. and A.D. King, 2017: Trajectories toward the 1.5°C Paris target: Modulation by the Interdecadal Pacific Oscillation. Geophysical Research Letters , 44(9) , 4256–4262, doi: 10.1002/2017gl073480 .
Rogelj, J., C.-F. Schleussner, and W. Hare, 2017: Getting It Right Matters: Temperature Goal Interpretations in Geoscience Research. Geophysical Research Letters , 44(20) , 10,662–10,665, doi: 10.1002/2017gl075612 .
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society , 140(683) , 1935–1944, doi: 10.1002/qj.2297 .
Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
Rogelj, J. et al., 2016b: Differences between carbon budget estimates unravelled. Nature Climate Change , 6(3) , 245–252, doi: 10.1038/nclimate2868 .

Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. Nature Geoscience , 10(10) , 741–747, doi: 10.1038/ngeo3031 .

Goodwin, P. et al., 2018: Pathways to 1.5°C and 2°C warming based on observational and geological constraints. Nature Geoscience , 11(2) , 102–107, doi: 10.1038/s41561-017-0054-8 .

Tokarska, K.B. and N.P. Gillett, 2018: Cumulative carbon emissions budgets consistent with 1.5°C global warming. Nature Climate Change , 8(4) , 296–299, doi: 10.1038/s41558-018-0118-9 .
Hall, J., G. Fu, and J. Lawry, 2007: Imprecise probabilities of climate change: Aggregation of fuzzy scenarios and model uncertainties. Climatic Change , 81(3–4) , 265–281, doi: 10.1007/s10584-006-9175-6 .

Kriegler, E., J.W. Hall, H. Held, R. Dawson, and H.J. Schellnhuber, 2009: Imprecise probability assessment of tipping points in the climate system. Proceedings of the National Academy of Sciences , 106(13) , 5041–5046, doi: 10.1073/pnas.0809117106 .

Simpson, M. et al., 2016: Decision Analysis for Management of Natural Hazards. Annual Review of Environment and Resources , 41(1) , 489–516, doi: 10.1146/annurev-environ-110615-090011 .
Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. Nature Geoscience , 10(10) , 741–747, doi: 10.1038/ngeo3031 .
Otto, F.E.L., D.J. Frame, A. Otto, and M.R. Allen, 2015: Embracing uncertainty in climate change policy. Nature Climate Change , 5 , 1–5, doi: 10.1038/nclimate2716 .
Xu, Y. and V. Ramanathan, 2017: Well below 2°C: Mitigation strategies for avoiding dangerous to catastrophic climate changes. Proceedings of the National Academy of Sciences , 1–9, doi: 10.1073/pnas.1618481114 .
Jarvis, A.J., D.T. Leedal, and C.N. Hewitt, 2012: Climate-society feedbacks and the avoidance of dangerous climate change. Nature Climate Change , 2(9) , 668–671, doi: 10.1038/nclimate1586 .
Myhre, G. et al., 2017: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015. Atmospheric Chemistry and Physics , 17(4) , 2709–2720, doi: 10.5194/acp-17-2709-2017 .
Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. Nature Geoscience , 11(8) , 574–579, doi: 10.1038/s41561-018-0156-y .
Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.

Allen, M.R. et al., 2018: A solution to the misrepresentations of CO ₂ -equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Climate and Atmospheric Science , 1(1) , 16, doi: 10.1038/s41612-018-0026-8 .

Jenkins, S., R.J. Millar, N. Leach, and M.R. Allen, 2018: Framing Climate Goals in Terms of Cumulative CO ₂ -Forcing-Equivalent Emissions. Geophysical Research Letters , 45(6) , 2795–2804, doi: 10.1002/2017gl076173 .
Bowerman, N.H.A. et al., 2013: The role of short-lived climate pollutants in meeting temperature goals. Nature Climate Change , 3(12) , 1021–1024, doi: 10.1038/nclimate2034 .
Wigley, T.M.L., 2018: The Paris warming targets: emissions requirements and sea level consequences. Climatic Change , 147(1–2) , 31–45, doi: 10.1007/s10584-017-2119-5 .
Matthews, H.D. and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. Geophysical Research Letters , 35(4) , L04705, doi: 10.1029/2007gl032388 .
Solomon, S., G.-K.G. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences , 106(6) , 1704–9, doi: 10.1073/pnas.0812721106 .
Allen, M.R. and T.F. Stocker, 2013: Impact of delay in reducing carbon dioxide emissions. Nature Climate Change , 4(1) , 23–26, doi: 10.1038/nclimate2077 .
Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. Nature Geoscience , 11(8) , 574–579, doi: 10.1038/s41561-018-0156-y .
Mathesius, S., M. Hofmann, K. Caldeira, and H.J. Schellnhuber, 2015: Long-term response of oceans to CO ₂ removal from the atmosphere. Nature Climate Change , 5(12) , 1107–1113, doi: 10.1038/nclimate2729 .
Tokarska, K.B. and K. Zickfeld, 2015: The effectiveness of net negative carbon dioxide emissions in reversing anthropogenic climate change. Environmental Research Letters , 10(9) , 094013, doi: 10.1088/1748-9326/10/9/094013 .
Pendergrass, A.G., F. Lehner, B.M. Sanderson, and Y. Xu, 2015: Does extreme precipitation intensity depend on the emissions scenario? Geophysical Research Letters , 42(20) , 8767–8774, doi: 10.1002/2015gl065854 .
Baker, H.S. et al., 2018: Higher CO ₂ concentrations increase extreme event risk in a 1.5°C world. Nature Climate Change , 8(7) , 604–608, doi: 10.1038/s41558-018-0190-1 .
Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development , 10(2) , 571–583, doi: 10.5194/gmd-10-571-2017 .
Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.

Millar, R.J., Z.R. Nicholls, P. Friedlingstein, and M.R. Allen, 2017a: A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmospheric Chemistry and Physics , 17(11) , 7213–7228, doi: 10.5194/acp-17-7213-2017 .

Jenkins, S., R.J. Millar, N. Leach, and M.R. Allen, 2018: Framing Climate Goals in Terms of Cumulative CO ₂ -Forcing-Equivalent Emissions. Geophysical Research Letters , 45(6) , 2795–2804, doi: 10.1002/2017gl076173 .
Kopp, R.E. et al., 2016: Temperature-driven global sea-level variability in the Common Era. Proceedings of the National Academy of Sciences , 113(11) , 1–8, doi: 10.1073/pnas.1517056113 .
Leggett, J. et al., 1992: Emissions scenarios for the IPCC: an update. In: Climate change 1992: The Supplementary Report to the IPCC Scientific Assessment [Houghton, J.T., B.A. Callander, and S.K. Varney (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 69–95.
IPCC, 2000: Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. [Nakićenović, N. and R. Swart (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 570 pp.
Morita, T. et al., 2001: Greenhouse Gas Emission Mitigation Scenarios and Implications. In: Climate Change 2001: Mitigation. Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O. Davidson, R. Swart, and J. Pan (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 115–164.
Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change , 42 , 153–168, doi: 10.1016/j.gloenvcha.2016.05.009 .
van Vuuren, D.P. et al., 2011: The representative concentration pathways: An overview. Climatic Change , 109(1) , 5–31, doi: 10.1007/s10584-011-0148-z .
Taylor, K.E., R.J. Stouffer, and G.A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society , 93(4) , 485–498, doi: 10.1175/bams-d-11-00094.1 .
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change , 42 , 153–168, doi: 10.1016/j.gloenvcha.2016.05.009 .
Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°C. Nature Climate Change , 8(4) , 325–332, doi: 10.1038/s41558-018-0091-3 .
Bauer, N. et al., 2017: Shared Socio-Economic Pathways of the Energy Sector – Quantifying the Narratives. Global Environmental Change , 42 , 316–330, doi: 10.1016/j.gloenvcha.2016.07.006 .
Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Global Environmental Change , 42 , 331–345, doi: 10.1016/j.gloenvcha.2016.10.002 .
Rao, S. et al., 2017: Future Air Pollution in the Shared Socio-Economic Pathways. Global Environmental Change , 42 , 346–358, doi: 10.1016/j.gloenvcha.2016.05.012 .
Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change , 42 , 153–168, doi: 10.1016/j.gloenvcha.2016.05.009 .
Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. Nature Geoscience , 10(10) , 741–747, doi: 10.1038/ngeo3031 .
Emori, S. et al., 2018: Risk implications of long-term global climate goals: overall conclusions of the ICA-RUS project. Sustainability Science , 13(2) , 279–289, doi: 10.1007/s11625-018-0530-0 .
Xu, Y. and V. Ramanathan, 2017: Well below 2°C: Mitigation strategies for avoiding dangerous to catastrophic climate changes. Proceedings of the National Academy of Sciences , 1–9, doi: 10.1073/pnas.1618481114 .
Rosenbloom, D., 2017: Pathways: An emerging concept for the theory and governance of low-carbon transitions. Global Environmental Change , 43 , 37–50, doi: 10.1016/j.gloenvcha.2016.12.011 .
IPCC, 2000: Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. [Nakićenović, N. and R. Swart (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 570 pp.
van Vuuren, D.P. et al., 2011: The representative concentration pathways: An overview. Climatic Change , 109(1) , 5–31, doi: 10.1007/s10584-011-0148-z .
Kriegler, E. et al., 2012: The need for and use of socio-economic scenarios for climate change analysis: A new approach based on shared socio-economic pathways. Global Environmental Change , 22(4) , 807–822, doi: 10.1016/j.gloenvcha.2012.05.005 .
O’Neill, B.C. et al., 2014: A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Climatic Change , 122(3) , 387–400, doi: 10.1007/s10584-013-0905-2 .
Kriegler, E. et al., 2014: A new scenario framework for climate change research: The concept of shared climate policy assumptions. Climatic Change , 122(3) , 401–414, doi: 10.1007/s10584-013-0971-5 .
Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change , 42 , 153–168, doi: 10.1016/j.gloenvcha.2016.05.009 .
Ebi, K.L. et al., 2014: A new scenario framework for climate change research: Background, process, and future directions. Climatic Change , 122(3) , 363–372, doi: 10.1007/s10584-013-0912-3 .
van Vuuren, D.P. et al., 2014: A new scenario framework for Climate Change Research: Scenario matrix architecture. Climatic Change , 122(3) , 373–386, doi: 10.1007/s10584-013-0906-1 .
Shukla, P.R. and V. Chaturvedi, 2013: Sustainable energy transformations in India under climate policy. Sustainable Development , 21(1) , 48–59, doi: 10.1002/sd.516 .

Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.

van Vuuren, D.P. et al., 2015: Pathways to achieve a set of ambitious global sustainability objectives by 2050: Explorations using the IMAGE integrated assessment model. Technological Forecasting and Social Change , 98 , 303–323, doi: 10.1016/j.techfore.2015.03.005 .
Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
Reisinger, A. et al., 2014: Australasia. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1371–1438.
Barnett, J. et al., 2014: A local coastal adaptation pathway. Nature Climate Change , 4(12) , 1103–1108, doi: 10.1038/nclimate2383 .

Wise, R.M. et al., 2014: Reconceptualising adaptation to climate change as part of pathways of change and response. Global Environmental Change , 28 , 325–336, doi: 10.1016/j.gloenvcha.2013.12.002 .

Fazey, I. et al., 2016: Past and future adaptation pathways. Climate and Development , 8(1) , 26–44, doi: 10.1080/17565529.2014.989192 .
Harris, L.M., E.K. Chu, and G. Ziervogel, 2017: Negotiated resilience. Resilience , 3293 , 1–19, doi: 10.1080/21693293.2017.1353196 .

Fazey, I. et al., 2018: Community resilience for a 1.5°C world. Current Opinion in Environmental Sustainability , 31 , 30–40, doi: 10.1016/j.cosust.2017.12.006 .

Tàbara, J.D. et al., 2018: Positive tipping points in a rapidly warming world. Current Opinion in Environmental Sustainability , 31 , 120–129, doi: 10.1016/j.cosust.2018.01.012 .
Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
Meehl, G.A. et al., 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 747–845.
Meehl, G.A. et al., 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 747–845.
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Hansen, J. et al., 2005: Earth’s energy imbalance: confirmation and implications. Science , 308 , 1431–1435, doi: 10.1126/science.1110252 .
Matthews, H.D. and S. Solomon, 2013: Irreversible Does Not Mean Unavoidable. Science , 340(6131) , 438–439, doi: 10.1126/science.1236372 .
Eby, M. et al., 2009: Lifetime of anthropogenic climate change: Millennial time scales of potential CO ₂ and surface temperature perturbations. Journal of Climate , 22(10) , 2501–2511, doi: 10.1175/2008jcli2554.1 .
Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
Matthews, H.D. and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. Geophysical Research Letters , 35(4) , L04705, doi: 10.1029/2007gl032388 .

Lowe, J.A. et al., 2009: How difficult is it to recover from dangerous levels of global warming? Environmental Research Letters , 4(1) , 014012, doi: 10.1088/1748-9326/4/1/014012 .

Gillett, N.P., V.K. Arora, K. Zickfeld, S.J. Marshall, and W.J. Merryfield, 2011: Ongoing climate change following a complete cessation of carbon dioxide emissions. Nature Geoscience , 4 , 83–87, doi: 10.1038/ngeo1047 .

Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Frölicher, T.L., M. Winton, and J.L. Sarmiento, 2014: Continued global warming after CO ₂ emissions stoppage. Nature Climate Change , 4(1) , 40–44, doi: 10.1038/nclimate2060 .
Ehlert, D. and K. Zickfeld, 2017: What determines the warming commitment after cessation of CO ₂ emissions? Environmental Research Letters , 12(1) , 015002, doi: 10.1088/1748-9326/aa564a .
Solomon, S., G.-K.G. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences , 106(6) , 1704–9, doi: 10.1073/pnas.0812721106 .

Goodwin, P., R.G. Williams, and A. Ridgwell, 2015: Sensitivity of climate to cumulative carbon emissions due to compensation of ocean heat and carbon uptake. Nature Geoscience , 8(1) , 29–34, doi: 10.1038/ngeo2304 .

Williams, R.G., V. Roussenov, T.L. Frölicher, and P. Goodwin, 2017: Drivers of Continued Surface Warming After Cessation of Carbon Emissions. Geophysical Research Letters , 44(20) , 10,633–10,642, doi: 10.1002/2017gl075080 .
Matthews, H.D. and S. Solomon, 2013: Irreversible Does Not Mean Unavoidable. Science , 340(6131) , 438–439, doi: 10.1126/science.1236372 .
Lowe, J.A. and D. Bernie, 2018: The impact of Earth system feedbacks on carbon budgets and climate response. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2017.0263 .
Frölicher, T.L. and F. Joos, 2010: Reversible and irreversible impacts of greenhouse gas emissions in multi-century projections with the NCAR global coupled carbon cycle-climate model. Climate Dynamics , 35(7) , 1439–1459, doi: 10.1007/s00382-009-0727-0 .

Matthews, H.D. and K. Zickfeld, 2012: Climate response to zeroed emissions of greenhouse gases and aerosols. Nature Climate Change , 2(5) , 338–341, doi: 10.1038/nclimate1424 .

Mauritsen, T. and R. Pincus, 2017: Committed warming inferred from observations. Nature Climate Change , 2 , 1–5, doi: 10.1038/nclimate3357 .
Matthews, H.D. and K. Zickfeld, 2012: Climate response to zeroed emissions of greenhouse gases and aerosols. Nature Climate Change , 2(5) , 338–341, doi: 10.1038/nclimate1424 .

Mauritsen, T. and R. Pincus, 2017: Committed warming inferred from observations. Nature Climate Change , 2 , 1–5, doi: 10.1038/nclimate3357 .

Samset, B.H. et al., 2018: Climate Impacts From a Removal of Anthropogenic Aerosol Emissions. Geophysical Research Letters , 45(2) , 1020–1029, doi: 10.1002/2017gl076079 .
Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
Myhre, G. et al., 2017: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015. Atmospheric Chemistry and Physics , 17(4) , 2709–2720, doi: 10.5194/acp-17-2709-2017 .
Fernández, A.J. et al., 2017: Aerosol optical, microphysical and radiative forcing properties during variable intensity African dust events in the Iberian Peninsula. Atmospheric Research , 196 , 129–141, doi: 10.1016/j.atmosres.2017.06.019 .
Matthews, H.D. and K. Zickfeld, 2012: Climate response to zeroed emissions of greenhouse gases and aerosols. Nature Climate Change , 2(5) , 338–341, doi: 10.1038/nclimate1424 .
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Etminan, M., G. Myhre, E.J. Highwood, and K.P. Shine, 2016: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing. Geophysical Research Letters , 43(24) , 12,614–12,623, doi: 10.1002/2016gl071930 .
Myhre, G. et al., 2017: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015. Atmospheric Chemistry and Physics , 17(4) , 2709–2720, doi: 10.5194/acp-17-2709-2017 .
Millar, R.J., Z.R. Nicholls, P. Friedlingstein, and M.R. Allen, 2017a: A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmospheric Chemistry and Physics , 17(11) , 7213–7228, doi: 10.5194/acp-17-7213-2017 .
Smith, C.J. et al., 2018: FAIR v1.3: a simple emissions-based impulse response and carbon cycle model. Geoscientific Model Development , 11(6) , 2273–2297, doi: 10.5194/gmd-11-2273-2018 .
Smith, C.J. et al., 2018: FAIR v1.3: a simple emissions-based impulse response and carbon cycle model. Geoscientific Model Development , 11(6) , 2273–2297, doi: 10.5194/gmd-11-2273-2018 .
Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. Nature Geoscience , 11(8) , 574–579, doi: 10.1038/s41561-018-0156-y .
Haustein, K. et al., 2017: A real-time Global Warming Index. Scientific Reports , 7(1) , 15417, doi: 10.1038/s41598-017-14828-5 .
Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. Nature Geoscience , 11(8) , 574–579, doi: 10.1038/s41561-018-0156-y .
Matthews, H.D., N.P. Gillett, P. Stott, and K. Zickfeld, 2009: The proportionality of global warming to cumulative carbon emissions. Nature , 459(7248) , 829–32, doi: 10.1038/nature08047 .
Zickfeld, K., M. Eby, H.D. Matthews, and A.J. Weaver, 2009: Setting cumulative emissions targets to reduce the risk of dangerous climate change. Proceedings of the National Academy of Sciences , 106(38) , 16129–16134, doi: 10.1073/pnas.0805800106 .
Gregory, J.M. and P.M. Forster, 2008: Transient climate response estimated from radiative forcing and observed temperature change. Journal of Geophysical Research: Atmospheres , 113(D23) , D23105, doi: 10.1029/2008jd010405 .
Allen, M.R. et al., 2018: A solution to the misrepresentations of CO ₂ -equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Climate and Atmospheric Science , 1(1) , 16, doi: 10.1038/s41612-018-0026-8 .
Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
Levasseur, A. et al., 2016: Enhancing life cycle impact assessment from climate science: Review of recent findings and recommendations for application to LCA. Ecological Indicators , 71 , 163–174, doi: 10.1016/j.ecolind.2016.06.049 .
Ocko, I.B. et al., 2017: Unmask temporal trade-offs in climate policy debates. Science , 356(6337) , 492–493, doi: 10.1126/science.aaj2350 .
Clarke, L.E. et al., 2014: Assessing transformation pathways. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 413–510.
Shine, K.P., J.S. Fuglestvedt, K. Hailemariam, and N. Stuber, 2005: Alternatives to the Global Warming Potential for comparing climate impacts of emissions of greenhouse gases. Climatic Change , 68(3) , 281–302, doi: 10.1007/s10584-005-1146-9 .
Tol, R.S.J., T.K. Berntsen, B.C. O’Neill, Fuglestvedt, and P.S. Keith, 2012: A unifying framework for metrics for aggregating the climate effect of different emissions. Environmental Research Letters , 7(4) , 044006.
Deuber, O., G. Luderer, and O. Edenhofer, 2013: Physico-economic evaluation of climate metrics: A conceptual framework. Environmental Science & Policy , 29(0) , 37–45, doi: 10.1016/j.envsci.2013.01.018 .
Smith, S.M. et al., 2012: Equivalence of greenhouse-gas emissions for peak temperature limits. Nature Climate Change , 2(7) , 535–538, doi: 10.1038/nclimate1496 .
Reisinger, A. et al., 2012: Implications of alternative metrics for global mitigation costs and greenhouse gas emissions from agriculture. Climatic Change , 1–14, doi: 10.1007/s10584-012-0593-3 .

Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.

Smith, S.J., J. Karas, J. Edmonds, J. Eom, and A. Mizrahi, 2013: Sensitivity of multi-gas climate policy to emission metrics. Climatic Change , 117(4) , 663–675, doi: 10.1007/s10584-012-0565-7 .

Strefler, J., G. Luderer, T. Aboumahboub, and E. Kriegler, 2014: Economic impacts of alternative greenhouse gas emission metrics: a model-based assessment. Climatic Change , 125(3–4) , 319–331, doi: 10.1007/s10584-014-1188-y .
Archer, D. and V. Brovkin, 2008: The millennial atmospheric lifetime of anthropogenic CO ₂ . Climatic Change , 90(3) , 283–297, doi: 10.1007/s10584-008-9413-1 .

Matthews, H.D. and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. Geophysical Research Letters , 35(4) , L04705, doi: 10.1029/2007gl032388 .

Solomon, S., G.-K.G. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences , 106(6) , 1704–9, doi: 10.1073/pnas.0812721106 .
Zickfeld, K., A.H. MacDougall, and H.D. Matthews, 2016: On the proportionality between global temperature change and cumulative CO ₂ emissions during periods of net negative CO ₂ emissions. Environmental Research Letters , 11(5) , 055006, doi: 10.1088/1748-9326/11/5/055006 .
Bowerman, N.H.A., D.J. Frame, C. Huntingford, J.A. Lowe, and M.R. Allen, 2011: Cumulative carbon emissions, emissions floors and short-term rates of warming: implications for policy. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 369(1934) , 45–66, doi: 10.1098/rsta.2010.0288 .
Wigley, T.M.L., 2018: The Paris warming targets: emissions requirements and sea level consequences. Climatic Change , 147(1–2) , 31–45, doi: 10.1007/s10584-017-2119-5 .
Allen, M.R. et al., 2018: A solution to the misrepresentations of CO ₂ -equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Climate and Atmospheric Science , 1(1) , 16, doi: 10.1038/s41612-018-0026-8 .
Fuglestvedt, J. et al., 2018: Implications of possible interpretations of ‘greenhouse gas balance’ in the Paris Agreement. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2016.0445 .
Lauder, A.R. et al., 2013: Offsetting methane emissions – An alternative to emission equivalence metrics. International Journal of Greenhouse Gas Control , 12 , 419–429, doi: 10.1016/j.ijggc.2012.11.028 .
Allen, M.R. et al., 2016: New use of global warming potentials to compare cumulative and short-lived climate pollutants. Nature Climate Change , 6 , 1–5, doi: 10.1038/nclimate2998 .
Allen, M.R. et al., 2018: A solution to the misrepresentations of CO ₂ -equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Climate and Atmospheric Science , 1(1) , 16, doi: 10.1038/s41612-018-0026-8 .
Shine, K.P., J.S. Fuglestvedt, K. Hailemariam, and N. Stuber, 2005: Alternatives to the Global Warming Potential for comparing climate impacts of emissions of greenhouse gases. Climatic Change , 68(3) , 281–302, doi: 10.1007/s10584-005-1146-9 .

Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.

Allen, M.R. et al., 2018: A solution to the misrepresentations of CO ₂ -equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Climate and Atmospheric Science , 1(1) , 16, doi: 10.1038/s41612-018-0026-8 .
Allen, M.R. et al., 2018: A solution to the misrepresentations of CO ₂ -equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Climate and Atmospheric Science , 1(1) , 16, doi: 10.1038/s41612-018-0026-8 .
Fuglestvedt, J. et al., 2018: Implications of possible interpretations of ‘greenhouse gas balance’ in the Paris Agreement. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2016.0445 .
Rogelj, J. et al., 2016b: Differences between carbon budget estimates unravelled. Nature Climate Change , 6(3) , 245–252, doi: 10.1038/nclimate2868 .
Hienola, A. et al., 2018: The impact of aerosol emissions on the 1.5°C pathways. Environmental Research Letters , 13(4) , 044011.
Fuglestvedt, J. et al., 2018: Implications of possible interpretations of ‘greenhouse gas balance’ in the Paris Agreement. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2016.0445 .
Tanaka, K. and B.C. O’Neill, 2018: The Paris Agreement zero-emissions goal is not always consistent with the 1.5°C and 2°C temperature targets. Nature Climate Change , 8(4) , 319–324, doi: 10.1038/s41558-018-0097-x .
Johansson, D.J.A., 2012: Economics- and physical-based metrics for comparing greenhouse gases. Climatic Change , 110(1–2) , 123–141, doi: 10.1007/s10584-011-0072-2 .

Tol, R.S.J., T.K. Berntsen, B.C. O’Neill, Fuglestvedt, and P.S. Keith, 2012: A unifying framework for metrics for aggregating the climate effect of different emissions. Environmental Research Letters , 7(4) , 044006.

Deuber, O., G. Luderer, and O. Edenhofer, 2013: Physico-economic evaluation of climate metrics: A conceptual framework. Environmental Science & Policy , 29(0) , 37–45, doi: 10.1016/j.envsci.2013.01.018 .

Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.

Cherubini, F. and K. Tanaka, 2016: Amending the Inadequacy of a Single Indicator for Climate Impact Analyses. Environmental Science & Technology , 50(23) , 12530–12531, doi: 10.1021/acs.est.6b05343 .
Shine, K.P., R.P. Allan, W.J. Collins, and J.S. Fuglestvedt, 2015: Metrics for linking emissions of gases and aerosols to global precipitation changes. Earth System Dynamics , 6(2) , 525–540, doi: 10.5194/esd-6-525-2015 .
Sterner, E., D.J.A. Johansson, and C. Azar, 2014: Emission metrics and sea level rise. Climatic Change , 127(2) , 335–351, doi: 10.1007/s10584-014-1258-1 .
Tol, R.S.J., T.K. Berntsen, B.C. O’Neill, Fuglestvedt, and P.S. Keith, 2012: A unifying framework for metrics for aggregating the climate effect of different emissions. Environmental Research Letters , 7(4) , 044006.

Deuber, O., G. Luderer, and O. Edenhofer, 2013: Physico-economic evaluation of climate metrics: A conceptual framework. Environmental Science & Policy , 29(0) , 37–45, doi: 10.1016/j.envsci.2013.01.018 .

Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.
OECD, 2016: The OECD supporting action on climate change . Organisation for Economic Co-operation and Development (OECD), Paris, France, 18 pp.
Shindell, D.T., Y. Lee, and G. Faluvegi, 2016: Climate and health impacts of US emissions reductions consistent with 2°C. Nature Climate Change , 6 , 503–507, doi: 10.1038/nclimate2935 .
Shindell, D.T., 2015: The social cost of atmospheric release. Climatic Change , 130(2) , 313–326, doi: 10.1007/s10584-015-1343-0 .

Sarofim, M.C., S.T. Waldhoff, and S.C. Anenberg, 2017: Valuing the Ozone-Related Health Benefits of Methane Emission Controls. Environmental and Resource Economics , 66(1) , 45–63, doi: 10.1007/s10640-015-9937-6 .

Shindell, D.T., J.S. Fuglestvedt, and W.J. Collins, 2017: The social cost of methane: theory and applications. Faraday Discussions , 200 , 429–451, doi: 10.1039/c7fd00009j .
Millar, R.J., Z.R. Nicholls, P. Friedlingstein, and M.R. Allen, 2017a: A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmospheric Chemistry and Physics , 17(11) , 7213–7228, doi: 10.5194/acp-17-7213-2017 .
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO ₂ emissions based on regional and impact-related climate targets. Nature , 529(7587) , 477–483, doi: 10.1038/nature16542 .
Ebi, K.L., L.H. Ziska, and G.W. Yohe, 2016: The shape of impacts to come: lessons and opportunities for adaptation from uneven increases in global and regional temperatures. Climatic Change , 139(3) , 341–349, doi: 10.1007/s10584-016-1816-9 .
Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change , 5(6) , 560–564, doi: 10.1038/nclimate2617 .

Karmalkar, A. and R.S. Bradley, 2017: Consequences of Global Warming of 1.5°C and 2°C for Regional Temperature and Precipitation Changes in the Contiguous United States. PLOS ONE , 12(1) , e0168697, doi: 10.1371/journal.pone.0168697 .

King, A.D., D.J. Karoly, and B.J. Henley, 2017: Australian climate extremes at 1.5°C and 2°C of global warming. Nature Climate Change , 7(6) , 412–416, doi: 10.1038/nclimate3296 .

Chevuturi, A., N.P. Klingaman, A.G. Turner, and S. Hannah, 2018: Projected Changes in the Asian-Australian Monsoon Region in 1.5°C and 2.0°C Global-Warming Scenarios. Earth’s Future , 6(3) , 339–358, doi: 10.1002/2017ef000734 .
Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.
van Oldenborgh, G.J. et al., 2017: Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environmental Research Letters , 12(12) , 124009, doi: 10.1088/1748-9326/aa9ef2 .
Lee, D. et al., 2018: Impacts of half a degree additional warming on the Asian summer monsoon rainfall characteristics. Environmental Research Letters , 13(4) , 044033, doi: 10.1088/1748-9326/aab55d .
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Schewe, J. et al., 2014: Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences , 111(9) , 3245–3250, doi: 10.1073/pnas.1222460110 .

Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters , 13(4) , 044038, doi: 10.1088/1748-9326/aab792 .

Saeed, F. et al., 2018: Robust changes in tropical rainy season length at 1.5°C and 2°C. Environmental Research Letters , 13(6) , 064024, doi: 10.1088/1748-9326/aab797 .
Forkel, M. et al., 2016: Enhanced seasonal CO ₂ exchange caused by amplified plant productivity in northern ecosystems. Science , 351(6274) , 696–699, doi: 10.1126/science.aac4971 .
Hoegh-Guldberg, O. et al., 2007: Coral Reefs Under Rapid Climate Change and Ocean Acidification. Science , 318(5857) , 1737–1742, doi: 10.1126/science.1152509 .
Bindoff, N.L. et al., 2007: Observations: Oceanic Climate Change and Sea Level. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 385–432.
Chen, X. et al., 2017: The increasing rate of global mean sea-level rise during 1993-2014. Nature Climate Change , 7(7) , 492–495, doi: 10.1038/nclimate3325 .
Matthews, T.K.R., R.L. Wilby, and C. Murphy, 2017: Communicating the deadly consequences of global warming for human heat stress. Proceedings of the National Academy of Sciences , 114(15) , 3861–3866, doi: 10.1073/pnas.1617526114 .
Cramer, W. et al., 2014: Detection and attribution of observed impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037.
AghaKouchak, A., L. Cheng, O. Mazdiyasni, and A. Farahmand, 2014: Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophysical Research Letters , 41(24) , 8847–8852, doi: 10.1002/2014gl062308 .

Leonard, M. et al., 2014: A compound event framework for understanding extreme impacts. Wiley Interdisciplinary Reviews: Climate Change , 5(1) , 113–128, doi: 10.1002/wcc.252 .

Martius, O., S. Pfahl, and C. Chevalier, 2016: A global quantification of compound precipitation and wind extremes. Geophysical Research Letters , 43(14) , 7709–7717, doi: 10.1002/2016gl070017 .

Zscheischler, J. and S.I. Seneviratne, 2017: Dependence of drivers affects risks associated with compound events. Science Advances , 3(6) , e1700263, doi: 10.1126/sciadv.1700263 .
Rosenzweig, C. et al., 2008: Attributing physical and biological impacts to anthropogenic climate change. Nature , 453(7193) , 353–357, doi: 10.1038/nature06937 .

Cramer, W. et al., 2014: Detection and attribution of observed impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037.

Hansen, G., D. Stone, M. Auffhammer, C. Huggel, and W. Cramer, 2016: Linking local impacts to changes in climate: a guide to attribution. Regional Environmental Change , 16(2) , 527–541, doi: 10.1007/s10113-015-0760-y .
Oliver, T.H. and M.D. Morecroft, 2014: Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdisciplinary Reviews: Climate Change , 5(3) , 317–335, doi: 10.1002/wcc.271 .
Sitch, S., P.M. Cox, W.J. Collins, and C. Huntingford, 2007: Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature , 448(7155) , 791–794, doi: 10.1038/nature06059 .
IPCC, 2012a: Summary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–21.

World Bank, 2013: Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience . The World Bank, Washington DC, USA, 254 pp.

IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Rosenzweig, C. et al., 2017: Assessing inter-sectoral climate change risks: the role of ISIMIP. Environmental Research Letters , 12(1) , 010301.
Settele, J. et al., 2014: Terrestrial and inland water systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359.
Marbà, N. et al., 2015: Impact of seagrass loss and subsequent revegetation on carbon sequestration and stocks. Journal of Ecology , 103(2) , 296–302, doi: 10.1111/1365-2745.12370 .
Creutzig, F., 2016: Economic and ecological views on climate change mitigation with bioenergy and negative emissions. GCB Bioenergy , 8(1) , 4–10, doi: 10.1111/gcbb.12235 .
Dasgupta, P. et al., 2014: Rural areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 613–657.

Revi, A. et al., 2014: Urban areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 535–612.

Rosenzweig, C., W. Solecki, P. Romeo-Lankao, M. Shagun, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network . Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp.
Arora-Jonsson, S., 2011: Virtue and vulnerability: Discourses on women, gender and climate change. Global Environmental Change , 21(2) , 744–751, doi: 10.1016/j.gloenvcha.2011.01.005 .

Cardona, O.D. et al., 2012: Determinants of Risk: Exposure and Vulnerablity. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 65–108.

Resurrección, B.P., 2013: Persistent women and environment linkages in climate change and sustainable development agendas. Women’s Studies International Forum , 40 , 33–43, doi: 10.1016/j.wsif.2013.03.011 .

Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.

Vincent, K.E., P. Tschakert, J. Barnett, M.G. Rivera-Ferre, and A. Woodward, 2014: Cross-chapter box on gender and climate change. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 105–107.
Moss, R.H. et al., 2010: The next generation of scenarios for climate change research and assessment. Nature , 463(7282) , 747–756, doi: 10.1038/nature08823 .
James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. Wiley Interdisciplinary Reviews: Climate Change , 8(2) , e457, doi: 10.1002/wcc.457 .
Ekström, M., M.R. Grose, and P.H. Whetton, 2015: An appraisal of downscaling methods used in climate change research. Wiley Interdisciplinary Reviews: Climate Change , 6(3) , 301–319, doi: 10.1002/wcc.339 .
Lewis, S.C., A.D. King, and D.M. Mitchell, 2017: Australia’s Unprecedented Future Temperature Extremes Under Paris Limits to Warming. Geophysical Research Letters , 44(19) , 9947–9956, doi: 10.1002/2017gl074612 .
King, A.D. et al., 2018b: On the Linearity of Local and Regional Temperature Changes from 1.5°C to 2°C of Global Warming. Journal of Climate , 31(18) , 7495–7514, doi: 10.1175/jcli-d-17-0649.1 .
Asseng, S. et al., 2013: Uncertainty in simulating wheat yields under climate change. Nature Climate Change , 3(9) , 827–832, doi: 10.1038/nclimate1916 .
Schleussner, C.-F. et al., 2016: Differential climate impacts for policy relevant limits to global warming: the case of 1.5°C and 2°C. Earth System Dynamics , 7(2) , 327–351, doi: 10.5194/esd-7-327-2016 .
IPCC, 2013b: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–29.
World Bank, 2013: Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience . The World Bank, Washington DC, USA, 254 pp.
Schleussner, C.-F. et al., 2016: Differential climate impacts for policy relevant limits to global warming: the case of 1.5°C and 2°C. Earth System Dynamics , 7(2) , 327–351, doi: 10.5194/esd-7-327-2016 .

James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. Wiley Interdisciplinary Reviews: Climate Change , 8(2) , e457, doi: 10.1002/wcc.457 .

Barcikowska, M.J. et al., 2018: Euro-Atlantic winter storminess and precipitation extremes under 1.5°C vs. 2°C warming scenarios. Earth System Dynamics , 9(2) , 679–699, doi: 10.5194/esd-9-679-2018 .

King, A.D. et al., 2018a: Reduced heat exposure by limiting global warming to 1.5°C. Nature Climate Change , 8(7) , 549–551, doi: 10.1038/s41558-018-0191-0 .
Pörtner, H.-O. et al., 2014: Ocean systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Blicharska, M. et al., 2017: Steps to overcome the North–South divide in research relevant to climate change policy and practice. Nature Climate Change , 7(1) , 21–27, doi: 10.1038/nclimate3163 .
Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.
Gouldson, A. et al., 2015: Exploring the economic case for climate action in cities. Global Environmental Change , 35 , 93–105, doi: 10.1016/j.gloenvcha.2015.07.009 .
Termeer, C.J.A.M., A. Dewulf, and G.R. Biesbroek, 2017: Transformational change: governance interventions for climate change adaptation from a continuous change perspective. Journal of Environmental Planning and Management , 60(4) , 558–576, doi: 10.1080/09640568.2016.1168288 .
IPCC, 2014b: Summary for Policymakers. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–30.
Minx, J.C., W.F. Lamb, M.W. Callaghan, L. Bornmann, and S. Fuss, 2017: Fast growing research on negative emissions. Environmental Research Letters , 12(3) , 035007, doi: 10.1088/1748-9326/aa5ee5 .
IPCC, 2014b: Summary for Policymakers. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–30.
Leung, D.Y.C., G. Caramanna, and M.M. Maroto-Valer, 2014: An overview of current status of carbon dioxide capture and storage technologies. Renewable and Sustainable Energy Reviews , 39 , 426–443, doi: 10.1016/j.rser.2014.07.093 .
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Rosenzweig, C., W. Solecki, P. Romeo-Lankao, M. Shagun, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network . Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp.
IPCC, 2012b: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Geoengineering. [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, C. Field, V. Barros, T.F. Stocker, Q. Dahe, J. Minx, K. Mach, G.-K. Plattner, S. Schlömer, G. Hansen, and M. Mastrandrea (eds.)]. IPCC Working Group III Technical Support Unit, Potsdam Institute for Climate Impact Research, Potsdam, Germany, 99 pp.
The Royal Society, 2009: Geoengineering the climate: science, governance and uncertainty . RS Policy document 10/09, The Royal Society, London, UK, 82 pp.

Smith, S.J. and P.J. Rasch, 2013: The long-term policy context for solar radiation management. Climatic Change , 121(3) , 487–497, doi: 10.1007/s10584-012-0577-3 .

Schäfer, S., M. Lawrence, H. Stelzer, W. Born, and S. Low (eds.), 2015: The European Transdisciplinary Assessment of Climate Engineering (EuTRACE): Removing Greenhouse Gases from the Atmosphere and Reflecting Sunlight away from Earth . The European Transdisciplinary Assessment of Climate Engineering (EuTRACE), 170 pp.
Kristjánsson, J.E., M. Helene, and S. Hauke, 2016: The hydrological cycle response to cirrus cloud thinning. Geophysical Research Letters , 42(24) , 10,807–810,815, doi: 10.1002/2015gl066795 .
MacMartin, D.G., K.L. Ricke, and D.W. Keith, 2018: Solar geoengineering as part of an overall strategy for meeting the 1.5°C Paris target. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2016.0454 .
Schäfer, S., M. Lawrence, H. Stelzer, W. Born, and S. Low (eds.), 2015: The European Transdisciplinary Assessment of Climate Engineering (EuTRACE): Removing Greenhouse Gases from the Atmosphere and Reflecting Sunlight away from Earth . The European Transdisciplinary Assessment of Climate Engineering (EuTRACE), 170 pp.
Busby, J., 2016: After Paris: Good enough climate governance. Current History , 15(777) , 3–9, http://www.currenthistory.com/busby_currenthistory.pdf .
von Stechow, C. et al., 2016: 2°C and the SDGs: United they stand, divided they fall? Environmental Research Letters , 11(3) , 034022, doi: 10.1088/1748-9326/11/3/034022 .
Lövbrand, E., M. Hjerpe, and B.-O. Linnér, 2017: Making climate governance global: how UN climate summitry comes to matter in a complex climate regime. Environmental Politics , 26(4) , 580–599, doi: 10.1080/09644016.2017.1319019 .
Whitmarsh, L., S. O’Neill, and I. Lorenzoni (eds.), 2011: Engaging the Public with Climate Change: Behaviour Change and Communication . Earthscan, London, UK and Washington, DC, USA, 289 pp.
Corner, A. and J. Clarke, 2017: Talking Climate – From Research to Practice in Public Engagement . Palgrave Macmillan, Oxford, UK, 146 pp., doi: 10.1007/978-3-319-46744-3 .
Mimura, N. et al., 2014: Adaptation planning and implementation. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 869–898.
Leal Filho, W. et al., 2018: Implementing climate change research at universities: Barriers, potential and actions. Journal of Cleaner Production , 170 , 269–277, doi: 10.1016/j.jclepro.2017.09.105 .
IPCC, 2017: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Mitigation, Sustainability and Climate Stabilization Scenarios. [Shukla, P.R., J. Skea, R. Diemen, E. Huntley, M. Pathak, J. Portugal-Pereira, J. Scull, and R. Slade (eds.)]. IPCC Working Group III Technical Support Unit, Imperial College London, London, UK, 44 pp.
Sovacool, B.K., B.-O. Linnér, and M.E. Goodsite, 2015: The political economy of climate adaptation. Nature Climate Change , 5(7) , 616–618, doi: 10.1038/nclimate2665 .
Jacobson, M.Z. et al., 2015: 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States. Energy & Environmental Science , 8(7) , 2093–2117, doi: 10.1039/c5ee01283j .
Loftus, P.J., A.M. Cohen, J.C.S. Long, and J.D. Jenkins, 2015: A critical review of global decarbonization scenarios: What do they tell us about feasibility? Wiley Interdisciplinary Reviews: Climate Change , 6(1) , 93–112, doi: 10.1002/wcc.324 .
Pelling, M., 2011: Adaptation to Climate Change: From Resilience to Transformation . Routledge, Abingdon, Oxon, UK and New York, NY, USA, 224 pp.

O’Brien, K. et al., 2012: Toward a sustainable and resilient future. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 437–486.

O’Brien, K. and E. Selboe, 2015: Social transformation. In: The Adaptive Challenge of Climate Change [O’Brien, K. and E. Selboe (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY , USA, pp. 311–324, doi: 10.1017/cbo9781139149389.018 .

Pelling, M., K. O’Brien, and D. Matyas, 2015: Adaptation and transformation. Climatic Change , 133(1) , 113–127, doi: 10.1007/s10584-014-1303-0 .
Tschakert, P., B. van Oort, A.L. St. Clair, and A. LaMadrid, 2013: Inequality and transformation analyses: a complementary lens for addressing vulnerability to climate change. Climate and Development , 5(4) , 340–350, doi: 10.1080/17565529.2013.828583 .

Rogelj, J. et al., 2015: Energy system transformations for limiting end-of-century warming to below 1.5°C. Nature Climate Change , 5(6) , 519–527, doi: 10.1038/nclimate2572 .

Patterson, J. et al., 2017: Exploring the governance and politics of transformations towards sustainability. Environmental Innovation and Societal Transitions , 24 , 1–16, doi: 10.1016/j.eist.2016.09.001 .
Solecki, W., M. Pelling, and M. Garschagen, 2017: Transitions between risk management regimes in cities. Ecology and Society , 22(2) , 38, doi: 10.5751/es-09102-220238 .
Rosenzweig, C., W. Solecki, P. Romeo-Lankao, M. Shagun, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network . Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp.
IPCC, 2014d: Summary for Policymakers. In: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, pp. 2–34.
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
IPCC, 2014d: Summary for Policymakers. In: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, pp. 2–34.
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
von Stechow, C. et al., 2016: 2°C and the SDGs: United they stand, divided they fall? Environmental Research Letters , 11(3) , 034022, doi: 10.1088/1748-9326/11/3/034022 .
von Stechow, C. et al., 2016: 2°C and the SDGs: United they stand, divided they fall? Environmental Research Letters , 11(3) , 034022, doi: 10.1088/1748-9326/11/3/034022 .
Kainuma, M., R. Pandey, T. Masui, and S. Nishioka, 2017: Methodologies for leapfrogging to low carbon and sustainable development in Asia. Journal of Renewable and Sustainable Energy , 9(2) , 021406, doi: 10.1063/1.4978469 .
Olsson, L. et al., 2014: Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.
WCED, 1987: Our Common Future . World Commission on Environment and Development (WCED), Geneva, Switzerland, 383 pp., doi: 10.2307/2621529 .
Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.
von Stechow, C. et al., 2015: Integrating Global Climate Change Mitigation Goals with Other Sustainability Objectives: A Synthesis. Annual Review of Environment and Resources , 40(1) , 363–394, doi: 10.1146/annurev-environ-021113-095626 .

Wright, H., S. Huq, and J. Reeves, 2015: Impact of climate change on least developed countries: are the SDGs possible? IIED Briefing May 2015, International Institute for Environment and Development (IIED), London, UK, 4 pp.

Epstein, A.H. and S.L.H. Theuer, 2017: Sustainable development and climate action: thoughts on an integrated approach to SDG and climate policy implementation. In: Papers from Interconnections 2017 . Interconnections 2017, pp. 50.

Hammill, A. and H. Price-Kelly, 2017: Using NDCs , NAPs and the SDGs to Advance Climate-Resilient Development . NDC Expert perspectives for the NDC Partnership, NDC Partnership, Washington, DC, USA and Bonn, Germany, 10 pp.

Kelman, I., 2017: Linking disaster risk reduction, climate change, and the sustainable development goals. Disaster Prevention and Management: An International Journal , 26(3) , 254–258, doi: 10.1108/dpm-02-2017-0043 .

Lofts, K., S. Shamin, T.S. Zaman, and R. Kibugi, 2017: Brief on Sustainable Development Goal 13 on Taking Action on Climate Change and Its Impacts: Contributions of International Law, Policy and Governance,. McGill Journal of Sustainable Development Law , 11(1) , 183–192, doi: 10.3868/s050-004-015-0003-8 .

Maupin, A., 2017: The SDG13 to combat climate change: an opportunity for Africa to become a trailblazer? African Geographical Review , 36(2) , 131–145, doi: 10.1080/19376812.2016.1171156 .

Gomez-Echeverri, L., 2018: Climate and development: enhancing impact through stronger linkages in the implementation of the Paris Agreement and the Sustainable Development Goals (SDGs). Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 376(2119) , doi: 10.1098/rsta.2016.0444 .
Kanie, N. and F. Biermann (eds.), 2017: Governing through Goals: Sustainable Development Goals as Governance Innovation . MIT Press, Cambridge, MA, USA, 352 pp.
UN, 2015a: The Millennium Development Goals Report 2015 . United Nations (UN), New York, NY, USA, 75 pp.
Alkire, S., C. Jindra, G. Robles Aguilar, S. Seth, and A. Vaz, 2015: Global Multidimensional Poverty Index 2015 . Briefing 31, Oxford Poverty & Human Development Initiative, University of Oxford, Oxford, UK, 8 pp.
Horton, R., 2014: Why the sustainable development goals will fail. The Lancet , 383(9936) , 2196, doi: 10.1016/s0140-6736(14)61046-1 .

Death, C. and C. Gabay, 2015: Doing biopolitics differently? Radical potential in the post-2015 MDG and SDG debates. Globalizations , 12(4) , 597–612, doi: 10.1080/14747731.2015.1033172 .

Biermann, F., N. Kanie, and R.E. Kim, 2017: Global governance by goal-setting: the novel approach of the UN Sustainable Development Goals. Current Opinion in Environmental Sustainability , 26–27 , 26–31, doi: 10.1016/j.cosust.2017.01.010 .

Weber, H., 2017: Politics of ‘Leaving No One Behind’: Contesting the 2030 Sustainable Development Goals Agenda. Globalizations , 14(3) , 399–414, doi: 10.1080/14747731.2016.1275404 .

Winkler, I.T. and M.L. Satterthwaite, 2017: Leaving no one behind? Persistent inequalities in the SDGs. The International Journal of Human Rights , 21(8) , 1073–1097, doi: 10.1080/13642987.2017.1348702 .
Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.
Delanty, G. and A. Mota, 2017: Governing the Anthropocene. European Journal of Social Theory , 20(1) , 9–38, doi: 10.1177/1368431016668535 .
IPCC, 2013a: Principles Governing IPCC Work . Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, 2 pp.
Somanathan, E. et al., 2014: National and Sub-national Policies and Institutions. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1141–1205.
Czerniewicz, L., S. Goodier, and R. Morrell, 2017: Southern knowledge online? Climate change research discoverability and communication practices. Information, Communication & Society , 20(3) , 386–405, doi: 10.1080/1369118x.2016.1168473 .
Knutti, R. and J. Sedláček, 2012: Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change , 3(4) , 369–373, doi: 10.1038/nclimate1716 .
Mueller, B. and S.I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophysical Research Letters , 41(1) , 128–134, doi: 10.1002/2013gl058055 .
Giorgi, F. and W.J. Gutowski, 2015: Regional Dynamical Downscaling and the CORDEX Initiative. Annual Review of Environment and Resources , 40(1) , 467–490, doi: 10.1146/annurev-environ-102014-021217 .
Vautard, R. et al., 2014: The European climate under a 2°C global warming. Environmental Research Letters , 9(3) , 034006, doi: 10.1088/1748-9326/9/3/034006 .
Jacob, D. and S. Solman, 2017: IMPACT2C – An introduction. Climate Services , 7 , 1–2, doi: 10.1016/j.cliser.2017.07.006 .
Mitchell, D. et al., 2016: Realizing the impacts of a 1.5°C warmer world. Nature Climate Change , 6(8) , 735–737, doi: 10.1038/nclimate3055 .
Hegerl, G.C. et al., 2007: Understanding and Attributing Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 663–745.

Seneviratne, S.I. et al., 2012: Changes in climate extremes and their impacts on the natural physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230.

Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.
Stone, D. et al., 2013: The challenge to detect and attribute effects of climate change on human and natural systems. Climatic Change , 121(2) , 381–395, doi: 10.1007/s10584-013-0873-6 .

Hansen, G. and W. Cramer, 2015: Global distribution of observed climate change impacts. Nature Climate Change , 5(3) , 182–185, doi: 10.1038/nclimate2529 .

Hansen, G., D. Stone, M. Auffhammer, C. Huggel, and W. Cramer, 2016: Linking local impacts to changes in climate: a guide to attribution. Regional Environmental Change , 16(2) , 527–541, doi: 10.1007/s10113-015-0760-y .
Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.
Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change , 7(7) , 460–462, doi: 10.1038/nclimate3320 .
Brinkman, T.J. et al., 2016: Arctic communities perceive climate impacts on access as a critical challenge to availability of subsistence resources. Climatic Change , 139(3–4) , 413–427, doi: 10.1007/s10584-016-1819-6 .
Kabir, M.I. et al., 2016: Knowledge and perception about climate change and human health: findings from a baseline survey among vulnerable communities in Bangladesh. BMC Public Health , 16(1) , 266, doi: 10.1186/s12889-016-2930-3 .
Tschakert, P. et al., 2017: Climate change and loss, as if people mattered: values, places, and experiences. Wiley Interdisciplinary Reviews: Climate Change , 8(5) , e476, doi: 10.1002/wcc.476 .
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
IPCC, 2014b: Summary for Policymakers. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–30.
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
IPCC, 2014a: Summary for Policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
Dietz, S., B. Groom, and W.A. Pizer, 2016: Weighing the Costs and Benefits of Climate Change to Our Children. The Future of Children , 26(1) , 133–155, http://www.jstor.org/stable/43755234 .
Mastrandrea, M.D. et al., 2011: The IPCC AR5 guidance note on consistent treatment of uncertainties: a common approach across the working groups. Climatic Change , 108(4) , 675–691, doi: 10.1007/s10584-011-0178-6 .
Otto, F.E.L., D.J. Frame, A. Otto, and M.R. Allen, 2015: Embracing uncertainty in climate change policy. Nature Climate Change , 5 , 1–5, doi: 10.1038/nclimate2716 .
Knutti, R., J. Rogelj, J. Sedláček, and E.M. Fischer, 2015: A scientific critique of the two-degree climate change target. Nature Geoscience , 9(1) , 13–18, doi: 10.1038/ngeo2595 .
Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A reconstruction of regional and global temperature for the past 11,300 years. Science , 339(6124) , 1198–201, doi: 10.1126/science.1228026 .
NOAA, 2016: State of the Climate: Global Climate Report for Annual 2015. National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI). Retrieved from: http://www.ncdc.noaa.gov/sotc/global/201513 .
Summerhayes, C.P., 2015: Earth’s Climate Evolution . John Wiley & Sons Ltd, Chichester, UK, 394 pp., doi: 10.1002/9781118897362 .
Foster, G.L., D.L. Royer, and D.J. Lunt, 2017: Future climate forcing potentially without precedent in the last 420 million years. Nature Communications , 8 , 14845, doi: 10.1038/ncomms14845 .

Mitigation pathways compatible with 1.5°C in the context of sustainable development

Next Chapter

Contributors

Coordinating Lead Authors:
Almut Arneth (Germany), Fatima Denton (The Gambia)

Lead Authors:
Fahmuddin Agus (Indonesia), Aziz Elbehri (Morocco), Karlheinz Erb (Italy), Balgis Osman Elasha (Côte d’Ivoire), Mohammad Rahimi (Iran), Mark Rounsevell (United Kingdom), Adrian Spence (Jamaica), Riccardo Valentini (Italy)

Contributing Authors:
Peter Alexander (United Kingdom), Yuping Bai (China), Ana Bastos (Portugal/Germany), Niels Debonne (The Netherlands), Jan Fuglestvedt (Norway), Rafaela Hillerbrand (Germany), Baldur Janz (Germany), Thomas Kastner (Austria), Ylva Longva (United Kingdom), Patrick Meyfroidt (Belgium), Michael O’Sullivan (United Kingdom)

Review Editors:
Edvin Aldrian (Indonesia), Bruce McCarl (The United States of America), María José Sanz Sánchez (Spain)

Chapter Scientists:

Yuping Bai (China), Baldur Janz (Germany)

This chapter should be cited as:

Arneth, A., F. Denton, F. Agus, A. Elbehri, K. Erb, B. Osman Elasha, M. Rahimi, M. Rounsevell, A. Spence, R. Valentini, 2019: Framing and Context. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D.C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley,

K. Kissick, M. Belkacemi, J. Malley, (eds.)]. https://doi.org/10.1017/9781009157988.003

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+Understanding the impacts of 1.5°C global warming above pre-industrial levels and related global emission pathways in the context of strengthening the response to the threat of climate change, sustainable development and efforts to eradicate poverty.
-<span id="es-executive-summary"></span>
 == ES Executive Summary ==
+This chapter frames the context, knowledge-base and assessment approaches used to understand the impacts of 1.5°C global warming above pre-industrial levels and related global greenhouse gas emission pathways, building on the IPCC Fifth Assessment Report (AR5), in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty.
-</div>
+'''Human-induced warming reached approximately 1°C ( ''likely'' between 0.8°C and 1.2°C) above pre-industrial levels in 2017, increasing at 0.2°C ( ''likely'' between 0.1°C and 0.3°C) per decade ( ''high confidence'' ).''' Global warming is defined in this report as an increase in combined surface air and sea surface temperatures averaged over the globe and over a 30-year period. Unless otherwise specified, warming is expressed relative to the period 1850–1900, used as an approximation of pre-industrial temperatures in AR5. For periods shorter than 30 years, warming refers to the estimated average temperature over the 30 years centred on that shorter period, accounting for the impact of any temperature fluctuations or trend within those 30 years. Accordingly, warming from pre- industrial levels to the decade 2006–2015 is assessed to be 0.87°C ( ''likely'' between 0.75°C and 0.99°C). Since 2000, the estimated level of human-induced warming has been equal to the level of observed warming with a ''likely'' range of ±20% accounting for uncertainty due to contributions from solar and volcanic activity over the historical period ( ''high confidence'' ). {1.2.1}
-<div>
-<div id="article-executive-summary-block-1">
+'''Warming greater than the global average has already been experienced in many regions and seasons, with higher average warming over land than over the ocean ( ''high confidence'' ).''' Most land regions are experiencing greater warming than the global average, while most ocean regions are warming at a slower rate. Depending on the temperature dataset considered, 20–40% of the global human population live in regions that, by the decade 2006–2015, had already experienced warming of more than 1.5°C above pre-industrial in at least one season ( ''medium confidence'' ). {1.2.1, 1.2.2}
-'''Land, including its water bodies, provides the basis for human livelihoods and well-being through primary productivity, the supply of food, freshwater, and multiple other ecosystem services ( ''high confidence'' )''' . Neither our individual or societal identities, nor the world’s economy would exist without the multiple resources, services and livelihood systems provided by land ecosystems and biodiversity. The annual value of the world’s total terrestrial ecosystem services has been estimated at 75 trillion USD in 2011, approximately equivalent to the annual global Gross Domestic Product (based on USD2007 values) ( ''medium confidence'' ). Land and its biodiversity also represent essential, intangible benefits to humans, such as cognitive and spiritual enrichment, sense of belonging and aesthetic and recreational values. Valuing ecosystem services with monetary methods often overlooks these intangible services that shape societies, cultures and quality of life and the intrinsic value of biodiversity. The Earth’s land area is finite. Using land resources sustainably is fundamental for human well-being ( ''high confidence'' ). {1.1.1}
+'''Past emissions alone are ''unlikely'' to raise global-mean temperature to 1.5°C above pre-industrial levels ( ''medium confidence'' )''' , but past emissions do commit to other changes, such as further sea level rise ( ''high confidence'' ). If all anthropogenic emissions (including aerosol-related) were reduced to zero immediately, any further warming beyond the 1°C already experienced would ''likely'' be less than 0.5°C over the next two to three decades ( ''high confidence'' ), and ''likely'' less than 0.5°C on a century time scale ( ''medium confidence'' ), due to the opposing effects of different climate processes and drivers. A warming greater than 1.5°C is therefore not geophysically unavoidable: whether it will occur depends on future rates of emission reductions. {1.2.3, 1.2.4}
-'''The current geographic spread of the use of land, the large appropriation of multiple ecosystem services and the loss of biodiversity are unprecedented in human history ( ''high confidence'' ).''' By 2015, about three-quarters of the global ice-free land surface was affected by human use. Humans appropriate one-quarter to one-third of global terrestrial potential net primary production ( ''high confidence'' ). Croplands cover 12–14% of the global ice-free surface. Since 1961, the supply of global per capita food calories increased by about one-third, with the consumption of vegetable oils and meat more than doubling. At the same time, the use of inorganic nitrogen fertiliser increased by nearly ninefold, and the use of irrigation water roughly doubled ( ''high confidence'' ). Human use, at varying intensities, affects about 60–85% of forests and 70–90% of other natural ecosystems (e.g., savannahs, natural grasslands) ( ''high confidence'' ). Land use caused global biodiversity to decrease by around 11–14% ( ''medium confidence'' ). {1.1.2}
+'''1.5°C emission pathways are defined as those that, given current knowledge of the climate response, provide a one- in-two to two-in-three chance of warming either remaining below 1.5°C or returning to 1.5°C by around 2100 following an overshoot.''' Overshoot pathways are characterized by the peak magnitude of the overshoot, which may have implications for impacts. All 1.5°C pathways involve limiting cumulative emissions of long-lived greenhouse gases, including carbon dioxide and nitrous oxide, and substantial reductions in other climate forcers ( ''high confidence'' ). Limiting cumulative emissions requires either reducing net global emissions of long-lived greenhouse gases to zero before the cumulative limit is reached, or net negative global emissions (anthropogenic removals) after the limit is exceeded. {1.2.3, 1.2.4, Cross-Chapter Boxes 1 and 2}
-'''Warming over land has occurred at a faster rate than the global mean and this has had observable impacts on the land system ( ''high confidence'' ).''' The average temperature over land for the period 2006–2015 was 1.53°C higher than for the period 1850–1900, and 0.66°C larger than the equivalent global mean temperature change. These warmer temperatures (with changing precipitation patterns) have altered the start and end of growing seasons, contributed to regional crop yield reductions, reduced freshwater availability, and put biodiversity under further stress and increased tree mortality ( ''high confidence'' ). Increasing levels of atmospheric CO <sub>2</sub> , have contributed to observed increases in plant growth as well as to increases in woody plant cover in grasslands and savannahs ( ''medium confidence'' ). {1.1.2}
+'''This report assesses projected impacts at a global average warming of 1.5°C and higher levels of warming.''' Global warming of 1.5°C is associated with global average surface temperatures fluctuating naturally on either side of 1.5°C, together with warming substantially greater than 1.5°C in many regions and seasons ( ''high confidence'' ), all of which must be considered in the assessment of impacts. Impacts at 1.5°C of warming also depend on the emission pathway to 1.5°C. Very different impacts result from pathways that remain below 1.5°C versus pathways that return to 1.5°C after a substantial overshoot, and when temperatures stabilize at 1.5°C versus a transient warming past 1.5°C ( ''medium confidence'' ). {1.2.3, 1.3}
-'''Urgent action to stop and reverse the over-exploitation of land resources would buffer the negative impacts of multiple pressures, including climate change, on ecosystems and society ( ''high confidence'' ).''' Socio-economic drivers of land-use change such as technological development, population growth and increasing per capita demand for multiple ecosystem services are projected to continue into the future ( ''high confidence'' ). These and other drivers can amplify existing environmental and societal challenges, such as the conversion of natural ecosystems into managed land, rapid urbanisation, pollution from the intensification of land management and equitable access to land resources ( ''high confidence'' ). Climate change will add to these challenges through direct, negative impacts on ecosystems and the services they provide ( ''high confidence'' ). Acting immediately and simultaneously on these multiple drivers would enhance food, fibre and water security, alleviate desertification, and reverse land degradation, without compromising the non-material or regulating benefits from land ( ''high confidence'' ). {1.1.2, 1.2.1, 1.3.2–1.3.6, Cross-Chapter Box 1 in Chapter 1}
+'''Ethical considerations, and the principle of equity in particular, are central to this report, recognizing that many of the impacts of warming up to and beyond 1.5°C, and some potential impacts of mitigation actions required to limit warming to 1.5°C, fall disproportionately on the poor and vulnerable ( ''high confidence'' ).''' Equity has procedural and distributive dimensions and requires fairness in burden sharing both between generations and between and within nations. In framing the objective of holding the increase in the global average temperature rise to well below 2°C above pre-industrial levels, and to pursue efforts to limit warming to 1.5°C, the Paris Agreement associates the principle of equity with the broader goals of poverty eradication and sustainable development, recognising that effective responses to climate change require a global collective effort that may be guided by the 2015 United Nations Sustainable Development Goals. {1.1.1}
-'''Rapid reductions in anthropogenic greenhouse gas (GHG) emissions that restrict warming to ''“well-below”'' 2°C would greatly reduce the negative impacts of climate change on land ecosystems ( ''high confidence'' ). In the absence of rapid emissions reductions, reliance on large-scale, land-based, climate change mitigation is projected to increase, which would aggravate existing pressures on land ( ''high confidence'' ).''' Climate change mitigation efforts that require large land areas (e.g., bioenergy and afforestation/reforestation) are projected to compete with existing uses of land ( ''high confidence'' ). The competition for land could increase food prices and lead to further intensification (e.g., fertiliser and water use) with implications for water and air pollution, and the further loss of biodiversity ( ''medium confidence'' ). Such consequences would jeopardise societies’ capacity to achieve many Sustainable Development Goals (SDGs) that depend on land ( ''high confidence'' ). {1.3.1, Cross-Chapter Box 2 in Chapter 1}
+'''Climate adaptation refers to the actions taken to manage impacts of climate change by reducing vulnerability and exposure to its harmful effects and exploiting any potential benefits.''' Adaptation takes place at international, national and local levels. Subnational jurisdictions and entities, including urban and rural municipalities, are key to developing and reinforcing measures for reducing weather- and climate-related risks. Adaptation implementation faces several barriers including lack of up-to-date and locally relevant information, lack of finance and technology, social values and attitudes, and institutional constraints ( ''high confidence'' ). Adaptation is more ''likely'' to contribute to sustainable development when policies align with mitigation and poverty eradication goals ( ''medium confidence'' ). {1.1, 1.4}
-'''Nonetheless, there are many land-related climate change mitigation options that do not increase the competition for land ( ''high confidence'' ). Many of these options have co-benefits for climate change adaptation ( ''medium confidence'' ).''' Land use contributes about one-quarter of global greenhouse gas emissions, notably CO <sub>2</sub> emissions from deforestation, CH <sub>4</sub> emissions from rice and ruminant livestock and N <sub>2</sub> O emissions from fertiliser use ( ''high confidence'' ). Land ecosystems also take up large amounts of carbon ( ''high confidence'' ). Many land management options exist to both reduce the magnitude of emissions and enhance carbon uptake. These options enhance crop productivity, soil nutrient status, microclimate or biodiversity, and thus, support adaptation to climate change ( ''high confidence'' ). In addition, changes in consumer behaviour, such as reducing the over-consumption of food and energy would benefit the reduction of GHG emissions from land ( ''high confidence'' ). The barriers to the implementation of mitigation and adaptation options include skills deficit, financial and institutional barriers, absence of incentives, access to relevant technologies, consumer awareness and the limited spatial scale at which the success of these practices and methods have been demonstrated. {1.2.1, 1.3.2, 1.3.3, 1.3.4, 1.3.5, 1.3.6}
+'''Ambitious mitigation actions are indispensable to limit warming to 1.5°C while achieving sustainable development and poverty eradication ( ''high confidence'' ).''' Ill-designed responses, however, could pose challenges especially – but not exclusively – for countries and regions contending with poverty and those requiring significant transformation of their energy systems. This report focuses on ‘climate-resilient development pathways’, which aim to meet the goals of sustainable development, including climate adaptation and mitigation, poverty eradication and reducing inequalities. But any feasible pathway that remains within 1.5°C involves synergies and trade-offs ( ''high confidence'' ). Significant uncertainty remains as to which pathways are more consistent with the principle of equity.<br />
+{1.1.1, 1.4}
-'''Sustainable food supply and food consumption, based on nutritionally balanced and diverse diets, would enhance food security under climate and socio-economic changes ( ''high confidence'' ).''' Improving food access, utilisation, quality and safety to enhance nutrition, and promoting globally equitable diets compatible with lower emissions have demonstrable positive impacts on land use and food security ( ''high confidence'' ). Food security is also negatively affected by food loss and waste (estimated as 25–30% of total food produced) ( ''medium confidence'' ). Barriers to improved food security include economic drivers (prices, availability and stability of supply) and traditional, social and cultural norms around food eating practices . Climate change is expected to increase variability in food production and prices globally ( ''high confidence'' ), but the trade in food commodities can buffer these effects. Trade can provide embodied flows of water, land and nutrients ( ''medium confidence'' ). Food trade can also have negative environmental impacts by displacing the effects of overconsumption ( ''medium confidence'' ). Future food systems and trade patterns will be shaped as much by policies as by economics ( ''medium confidence'' ). {1.2.1, 1.3.3}
+'''Multiple forms of knowledge, including scientific evidence, narrative scenarios and prospective pathways, inform the understanding of 1.5°C.''' This report is informed by traditional evidence of the physical climate system and associated impacts and vulnerabilities of climate change, together with knowledge drawn from the perceptions of risk and the experiences of climate impacts and governance systems. Scenarios and pathways are used to explore conditions enabling goal-oriented futures while recognizing the significance of ethical considerations, the principle of equity, and the societal transformation needed. {1.2.3, 1.5.2}
-'''A g''' '''ender-inclusive approach offers opportunities to enhance the sustainable management of land ( ''medium confidence'' ).''' Women play a significant role in agriculture and rural economies globally. In many world regions, laws, c ultural restrictions, patriarchy and social structures such as discriminatory customary laws and norms reduce women’s capacity in supporting the sustainable use of land resources ( ''medium confidence'' ). Therefore, acknowledging women’s land rights and bringing women’s land management knowledge into land-related decision-making would support the alleviation of land degradation, and facilitate the take-up of integrated adaptation and mitigation measures ( ''medium confidence'' ). {1.4.1, 1.4.2}
+'''There is no single answer to the question of whether it is feasible to limit warming to 1.5°C and adapt to the consequences.''' Feasibility is considered in this report as the capacity of a system as a whole to achieve a specific outcome. The global transformation that would be needed to limit warming to 1.5°C requires enabling conditions that reflect the links, synergies and trade-offs between mitigation, adaptation and sustainable development. These enabling conditions are assessed across many dimensions of feasibility – geophysical, environmental-ecological, technological, economic, socio-cultural and institutional – that may be considered through the unifying lens of the Anthropocene, acknowledging profound, differential but increasingly geologically significant human influences on the Earth system as a whole. This framing also emphasises the global interconnectivity of past, present and future human–environment relations, highlighting the need and opportunities for integrated responses to achieve the goals of the Paris Agreement. {1.1, Cross-Chapter Box 1}
-'''Regional and country specific contexts affect the capacity to respond to climate change and its impacts, through adaptation and mitigation ( ''high confidence'' ).''' There is large variability in the availability and use of land resources between regions, countries and land management systems. In addition, differences in socio-economic conditions, such as wealth, degree of industrialisation, institutions and governance, affect the capacity to respond to climate change, food insecurity, land degradation and desertification. The capacity to respond is also strongly affected by local land ownership. Hence, climate change will affect regions and communities differently ( ''high confidence'' ). {1.3, 1.4}
+== 1.1 Assessing the Knowledge Base for a 1.5°C Warmer World ==
-'''Cross-scale, cross-sectoral and inclusive governance can enable coordinated policy that supports effective adaptation and mitigation ( ''high confidence'' ).''' There is a lack of coordination across governance levels, for example, local, national, transboundary and international, in addressing climate change and sustainable land management challenges. Policy design and formulation is often strongly sectoral, which poses further barriers when integrating international decisions into relevant (sub)national policies. A portfolio of policy instruments that are inclusive of the diversity of governance actors would enable responses to complex land and climate challenges ( ''high confidence'' ). Inclusive governance that considers women’s and indigenous people’s rights to access and use land enhances the equitable sharing of land resources, fosters food security and increases the existing knowledge about land use, which can increase opportunities for adaptation and mitigation ( ''medium confidence'' ). {1.3.5, 1.4.1, 1.4.2, 1.4.3}
+Human influence on climate has been the dominant cause of observed warming since the mid-20th century, while global average surface temperature warmed by 0.85°C between 1880 and 2012, as reported in the IPCC Fifth Assessment Report, or AR5 (IPCC, 2013b) <sup>[[#fn:r1|1]]</sup> . Many regions of the world have already greater regional-scale warming, with 20–40% of the global population (depending on the temperature dataset used) having experienced over 1.5°C of warming in at least one season (Figure 1.1; Chapter 3 Section 3.3.2.1). Temperature rise to date has already resulted in profound alterations to human and natural systems, including increases in droughts, floods, and some other types of extreme weather; sea level rise; and biodiversity loss – these changes are causing unprecedented risks to vulnerable persons and populations (IPCC, 2012a, 2014a; Mysiak et al., 2016; Chapter 3 Sections 3.4.5–3.4.13) <sup>[[#fn:r2|2]]</sup> , Chapter 3 Section 3.4). The most affected people live in low and middle income countries, some of which have experienced a decline in food security, which in turn is partly linked to rising migration and poverty (IPCC, 2012a) <sup>[[#fn:r3|3]]</sup> . Small islands, megacities, coastal regions, and high mountain ranges are likewise among the most affected (Albert et al., 2017) <sup>[[#fn:r4|4]]</sup> . Worldwide, numerous ecosystems are at risk of severe impacts, particularly warm-water tropical reefs and Arctic ecosystems (IPCC, 2014a) <sup>[[#fn:r5|5]]</sup> .
-'''Scenarios and models are important tools to explore the trade-offs and co-benefits of land management decisions under uncertain futures ( ''high confidence'' ).''' Participatory, co-creation processes with stakeholders can facilitate the use of scenarios in designing future sustainable development strategies ( ''medium confidence'' ). In addition to qualitative approaches, models are critical in quantifying scenarios, but uncertainties in models arise from, for example, differences in baseline datasets, land cover classes and modelling paradigms ( ''medium confidence'' ). Current scenario approaches are limited in quantifying time-dependent policy and management decisions that can lead from today to desirable futures or visions. Advances in scenario analysis and modelling are needed to better account for full environmental costs and non-monetary values as part of human decision-making processes. {1.2.2, Cross-Chapter Box 1 in Chapter 1}
+This report assesses current knowledge of the environmental, technical, economic, financial, socio-cultural, and institutional dimensions of a 1.5°C warmer world (meaning, unless otherwise specified, a world in which warming has been limited to 1.5°C relative to pre-industrial levels). Differences in vulnerability and exposure arise from numerous non-climatic factors (IPCC, 2014a) <sup>[[#fn:r6|6]]</sup> . Global economic growth has been accompanied by increased life expectancy and income in much of the world; however, in addition to environmental degradation and pollution, many regions remain characterised by significant poverty and severe inequalityin income distribution and access to resources, amplifying vulnerability to climate change (Dryzek, 2016; Pattberg and Zelli, 2016; Bäckstrand et al., 2017; Lövbrand et al., 2017) <sup>[[#fn:r7|7]]</sup> . World population continues to rise, notably in hazard-prone small and medium-sized cities in low- and moderate-income countries (Birkmann et al., 2016) <sup>[[#fn:r8|8]]</sup> . The spread of fossil-fuel-based material consumption and changing lifestyles is a major driver of global resource use, and the main contributor to rising greenhouse gas (GHG) emissions (Fleurbaey et al., 2014) <sup>[[#fn:r9|9]]</sup> .
+The overarching context of this report is this: human influence has become a principal agent of change on the planet, shifting the world out of the relatively stable Holocene period into a new geological era, often termed the Anthropocene (Box 1.1). Responding to climate change in the Anthropocene will require approaches that integrate multiple levels of interconnectivity across the global community.
-</div>
+This chapter is composed of seven sections linked to the remaining four chapters of the report. This introductory Section 1.1 situates the basic elements of the assessment within the context of sustainable development; considerations of ethics, equity and human rights; and the problem of poverty. Section 1.2 focuses on understanding 1.5°C, global versus regional warming, 1.5°C pathways, and associated emissions. Section 1.3 frames the impacts at 1.5°C and beyond on natural and human systems. The section on strengthening the global response (1.4) frames responses, governance and implementation, and trade-offs and synergies between mitigation, adaptation, and the Sustainable Development Goals (SDGs) under transformation, transformation pathways, and transition. Section 1.5 provides assessment frameworks and emerging methodologies that integrate climate change mitigation and adaptation with sustainable development. Section 1.6 defines approaches used to communicate confidence, uncertainty and risk, while 1.7 presents the storyline of the whole report.
-</div>
+====== Figure 1.1 ======
-</div>
-<div>
-<div>
-<span id="introduction-and-scope-of-the-report"></span>
-== 1.1 Introduction and scope of the report ==
-</div>
-<div class="section">
-<div>
-<div>
-<span id="objectives-and-scope-of-the-assessment"></span>
-== 1.1.1 Objectives and scope of the assessment ==
-</div>
-<div>
-<div id="section-1-1-1-objectives-and-scope-of-the-assessment-block-1">
-Land, including its water bodies, provides the basis for our livelihoods through basic processes such as net primary production that fundamentally sustain the supply of food, bioenergy and freshwater, and the delivery of multiple other ecosystem services and biodiversity (Hoekstra and Wiedmann 2014 <sup>[[#fn:r1|1]]</sup> ; Mace et al. 2012 <sup>[[#fn:r2|2]]</sup> ; Newbold et al. 2015 <sup>[[#fn:r3|3]]</sup> ; Runting et al. 2017 <sup>[[#fn:r4|4]]</sup> ; Isbell et al. 2017 <sup>[[#fn:r5|5]]</sup> ) (Cross-Chapter Box 8 in Chapter 6). The annual value of the world’s total terrestrial ecosystem services has been estimated to be about 75 trillion USD in 2011, approximately equivalent to the annual global Gross Domestic Product (based on USD2007 values) (Costanza et al. 2014 <sup>[[#fn:r6|6]]</sup> ; IMF 2018 <sup>[[#fn:r7|7]]</sup> ). Land also supports non-material ecosystem services such as cognitive and spiritual enrichment and aesthetic values (Hernández-Morcillo et al. 2013 <sup>[[#fn:r8|8]]</sup> ; Fish et al. 2016 <sup>[[#fn:r9|9]]</sup> ), intangible services that shape societies, cultures and human well-being. Exposure of people living in cities to (semi-)natural environments has been found to decrease mortality, cardiovascular disease and depression (Rook 2013 <sup>[[#fn:r10|10]]</sup> ; Terraube et al. 2017 <sup>[[#fn:r11|11]]</sup> ). Non-material and regulating ecosystem services have been found to decline globally and rapidly, often at the expense of increasing material services (Fischer et al. 2018 <sup>[[#fn:r12|12]]</sup> ; IPBES 2018a <sup>[[#fn:r13|13]]</sup> ). Climate change will exacerbate diminishing land and freshwater resources, increase biodiversity loss, and will intensify societal vulnerabilities, especially in regions where economies are highly dependent on natural resources. Enhancing food security and reducing malnutrition, whilst also halting and reversing desertification and land degradation, are fundamental societal challenges that are increasingly aggravated by the need to both adapt to and mitigate climate change impacts without compromising the non-material benefits of land (Kongsager et al. 2016 <sup>[[#fn:r14|14]]</sup> ; FAO et al. 2018 <sup>[[#fn:r15|15]]</sup> ).
-Annual emissions of GHGs and other climate forcers continue to increase unabatedly. ''Confidence'' is ''very high'' that the window of opportunity, the period when significant change can be made, for limiting climate change within tolerable boundaries is rapidly narrowing (Schaeffer et al. 2015 <sup>[[#fn:r16|16]]</sup> ; Bertram et al. 2015 <sup>[[#fn:r17|17]]</sup> ; Riahi et al. 2015 <sup>[[#fn:r18|18]]</sup> ; Millar et al. 2017 <sup>[[#fn:r19|19]]</sup> ; Rogelj et al.   2018a <sup>[[#fn:r20|20]]</sup> ). The Paris Agreement formulates the goal of limiting global warming this century to well below 2°C above pre-industrial levels, for which rapid actions are required across the energy, transport, infrastructure and agricultural sectors, while factoring in the need for these sectors to accommodate a growing human population (Wynes and Nicholas 2017 <sup>[[#fn:r21|21]]</sup> ; Le Quere et al. 2018 <sup>[[#fn:r22|22]]</sup> ). Conversion of natural land, and land management, are significant net contributors to GHG emissions and climate change, but land ecosystems are also a GHG sink (Smith et al. 2014 <sup>[[#fn:r23|23]]</sup> ; Tubiello et al. 2015 <sup>[[#fn:r24|24]]</sup> ; Le Quere et al. 2018 <sup>[[#fn:r25|25]]</sup> ; Ciais et al. 2013a <sup>[[#fn:r26|26]]</sup> ). It is not surprising, therefore, that land plays a prominent role in many of the Nationally Determined Contributions (NDCs) of the parties to the Paris Agreement (Rogelj et al. 2018a <sup>[[#fn:r27|27]]</sup> ,b <sup>[[#fn:r28|28]]</sup> ; Grassi et al. 2017 <sup>[[#fn:r29|29]]</sup> ; Forsell et al. 2016 <sup>[[#fn:r30|30]]</sup> ), and land-measures will be part of the NDC review by 2023.
-A range of different climate change mitigation and adaptation options on land exist, which differ in terms of their environmental and societal implications (Meyfroidt 2018 <sup>[[#fn:r31|31]]</sup> ; Bonsch et al. 2016 <sup>[[#fn:r32|32]]</sup> ; Crist et al. 2017 <sup>[[#fn:r|]]</sup> 33 ; Humpenoder et al. 2014 <sup>[[#fn:r34|34]]</sup> ; Harvey and Pilgrim 2011 <sup>[[#fn:r35|35]]</sup> ; Mouratiadou et al. 2016 <sup>[[#fn:r36|36]]</sup> ; Zhang et al. 2015 <sup>[[#fn:r37|37]]</sup> ; Sanz-Sanchez et al. 2017 <sup>[[#fn:r38|38]]</sup> ; Pereira et al. 2010 <sup>[[#fn:r39|39]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r40|40]]</sup> ; Rogelj et al. 2018a <sup>[[#fn:r41|41]]</sup> ) (Chapters 4–6). The Special Report on climate change, desertification, land degradation, sustainable land management, food security, and GHG fluxes in terrestrial ecosystems (SRCCL) synthesises the current state of scientific knowledge on the issues specified in the report’s title (Figure 1.1 and Figure 1.2). This knowledge is assessed in the context of the Paris Agreement, but many of the SRCCL issues concern other international conventions such as the United Nations Convention on Biodiversity (UNCBD), the UN Convention to Combat Desertification (UNCCD), the UN Sendai Framework for Disaster Risk Reduction (UNISDR) and the UN Agenda 2030 and its Sustainable Development Goals (SDGs). The SRCCL is the first report in which land is the central focus since the IPCC Special Report on land use, land-use change and forestry (Watson et al. 2000 <sup>[[#fn:r42|42]]</sup> ) (Box 1.1). The main objectives of the SRCCL are to:
+==== Human experience of present-day warming. Different shades of pink to purple indicated by the inset histogram show estimated warming for the season that has warmed the most at a given location between the periods 1850–1900 and 2006–2015, during which global average temperatures rose by 0.91°C in this dataset (Cowtan and Way, 2014) and 0.87°C in […] ====
-# Assess the current state of the scientific knowledge on the impacts of socio-economic drivers and their interactions with climate change on land, including degradation, desertification and food security;
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/Chapter-1-figure-1-1024x568.png]]
-# Evaluate the feasibility of different land-based response options to GHG mitigation, and assess the potential synergies and trade-offs with ecosystem services and sustainable development;
-# Examine adaptation options under a changing climate to tackle land degradation and desertification and to build resilient food systems, as well as evaluating the synergies and trade-offs between mitigation and adaptation;
-# Delineate the policy, governance and other enabling conditions to support climate mitigation, land ecosystem resilience and food security in the context of risks, uncertainties and remaining knowledge gaps.
+Human experience of present-day warming. Different shades of pink to purple indicated by the inset histogram show estimated warming for the season that has warmed the most at a given location between the periods 1850–1900 and 2006–2015, during which global average temperatures rose by 0.91°C in this dataset (Cowtan and Way, 2014) <sup>[[#fn:r10|10]]</sup> and 0.87°C in the multi-dataset average (Table 1.1 and Figure 1.3). The density of dots indicates the population (in 2010) in any 1° × 1° grid box. The underlay shows national Sustainable Development Goal (SDG) Global Index Scores indicating performance across the 17 SDGs. Hatching indicates missing SDG index data (e.g., Greenland). The histogram shows the population living in regions experiencing different levels of warming (at 0.25°C increments). See Supplementary Material 1.SM for further details.
-</div>
+== Box 1.1 The Anthropocene: Strengthening the Global Response to 1.5°C Global Warming ==
-<div id="section-1-1-1-objectives-and-scope-of-the-assessment-block-2">
-<div class="figure">
+'''Introduction '''
-<span id="figure-1.1"></span>
+The concept of the Anthropocene can be linked to the aspiration of the Paris Agreement. The abundant empirical evidence of the unprecedented rate and global scale of impact of human influence on the Earth System (Steffen et al., 2016; Waters et al., 2016) <sup>[[#fn:r11|11]]</sup> has led many scientists to call for an acknowledgement that the Earth has entered a new geological epoch: the Anthropocene (Crutzen and Stoermer, 2000; Crutzen, 2002; Gradstein et al., 2012) <sup>[[#fn:r12|12]]</sup> . Although rates of change in the Anthropocene are necessarily assessed over much shorter periods than those used to calculate long-term baseline rates of change, and therefore present challenges for direct comparison, they are nevertheless striking. The rise in global CO <sub>2</sub> concentration since 2000 is about 20 ppm per decade, which is up to 10 times faster than any sustained rise in CO <sub>2</sub> during the past 800,000 years (Lüthi et al., 2008; Bereiter et al., 2015) <sup>[[#fn:r13|13]]</sup> . AR5 found that the last geological epoch with similar atmospheric CO <sub>2</sub> concentration was the Pliocene, 3.3 to 3.0 Ma (Masson-Delmotte et al., 2013) <sup>[[#fn:r14|14]]</sup> . Since 1970 the global average temperature has been rising at a rate of 1.7°C per century, compared to a long-term decline over the past 7,000 years at a baseline rate of 0.01°C per century (NOAA, 2016; Marcott et al., 2013). These global-level rates of human-driven change far exceed the rates of change driven by geophysical or biosphere forces that have altered the Earth System trajectory in the past (e.g., Summerhayes 2015; Foster et al., 2017); even abrupt geophysical events do not approach current rates of human-driven change.
-====== Figure 1.1 ======
-<span id="a-representation-of-the-principal-land-challenges-and-land-climate-system-processes-covered-in-this-assessment-report.-a.-the-warming-curves-are-averages-of-four-datasets-section-2.1-figure-2.2-and-table-2.1.-b.-n2o-and-ch4-from-agriculture-are-from-faostat-net-land-use-change-emissions-of-co2-from-forestry-and-other-land-use-including-emissions"></span>
+'''The Geological Dimension of the Anthropocene and 1.5°C Global Warming'''
-==== A representation of the principal land challenges and land-climate system processes covered in this assessment report. A. The warming curves are averages of four datasets (Section 2.1, Figure 2.2 and Table 2.1). B. N2O and CH4 from agriculture are from FAOSTAT; Net land-use change emissions of CO2 from forestry and other land use (including emissions […] ====
-[[File:https://www.ipcc.ch/site/assets/uploads/sites/4/2019/12/SPM1-approval-v7-USletter-791x1024.png]]
+The process of formalising the Anthropocene is on-going (Zalasiewicz et al., 2017) <sup>[[#fn:r15|15]]</sup> , but a strong majority of the Anthropocene Working Group (AWG) established by the Subcommission on Quaternary Stratigraphy of the International Commission on Stratigraphy have agreed that: (i) the Anthropocene has a geological merit; (ii) it should follow the Holocene as a formal epoch in the Geological Time Scale; and, (iii) its onset should be defined as the mid-20th century. Potential markers in the stratigraphic record include an array of novel manufactured materials of human origin, and “these combined signals render the Anthropocene stratigraphically distinct from the Holocene and earlier epochs” (Waters et al., 2016) <sup>[[#fn:r16|16]]</sup> . The Holocene period, which itself was formally adopted in 1885 by geological science community, began 11,700 years ago with a more stable warm climate providing for emergence of human civilisation and growing human-nature interactions that have expanded to give rise to the Anthropocene (Waters et al., 2016) <sup>[[#fn:r17|17]]</sup> .
-<div>
-A representation of the principal land challenges and land-climate system processes covered in this assessment report.<br />
+'''The Anthropocene and the Challenge of a 1.5° C Warmer World'''
-'''A''' . The warming curves are averages of four datasets (Section 2.1, Figure 2.2 and Table 2.1). '''B''' . N <sub>2</sub> O and CH <sub>4</sub> from agriculture are from FAOSTAT; Net land-use change emissions of CO <sub>2</sub> from forestry and other land use (including emissions from peatland fires since 1997) are from the annual Global Carbon Budget, using the mean of two bookkeeping models. All values expressed in units of CO <sub>2</sub> -eq are based on AR5 100-year Global Warming Potential values without climate-carbon feedbacks (N <sub>2</sub> O = 265; CH <sub>4</sub> = 28) (Table SPM.1 and Section 2.3). '''C''' . Depicts shares of different uses of the global, ice-free land area for approximately the year 2015, ordered along a gradient of decreasing land-use intensity from left to right. Each bar represents a broad land cover category; the numbers on top are the total percentage of the ice-free area covered, with uncertainty ranges in brackets. Intensive pasture is defined as having a livestock density greater than 100 animals/km². The area of ‘forest managed for timber and other uses’ was calculated as total forest area minus ‘primary/intact’ forest area. (Section 1.2, Table 1.1, Figure 1.3). '''D''' . Note that fertiliser use is shown on a split axis (source: International Fertiliser Industry Association, www.ifastat.org/databases). The large percentage change in fertiliser use reflects the low level of use in 1961 and relates to both increasing fertiliser input per area as well as the expansion of fertilised cropland and grassland to increase food production (1.1, Figure 1.3). '''E''' . Overweight population is defined as having a body mass index (BMI) &gt;25 kg m <sup>–2</sup> (source: Abarca-Gómez et al. 2017 <sup>[[#fn:r43|43]]</sup> ); underweight is defined as BMI &lt;18.5 kg m <sup>–2</sup> . (Population density, source: United Nations, Department of Economic and Social Affairs 2017 <sup>[[#fn:r44|44]]</sup> ) (Sections 5.1 and 5.2). '''F''' . Dryland areas were estimated using TerraClimate precipitation and potential evapotranspiration (1980–2015) (Abatzoglou et al. 2018 <sup>[[#fn:r45|45]]</sup> ) to identify areas where the Aridity Index is below 0.65. Areas experiencing human caused desertification, after accounting for precipitation variability and CO <sub>2</sub> fertilisation, are identified in Le et al. 2016. Population data for these areas were extracted from the gridded historical population database HYDE3.2 (Goldewijk et al. 2017 <sup>[[#fn:r46|46]]</sup> ). Areas in drought are based on the 12-month accumulation Global Precipitation Climatology Centre Drought Index (Ziese et al. 2014 <sup>[[#fn:r47|47]]</sup> ). The area in drought was calculated for each month (Drought Index below –1), and the mean over the year was used to calculate the percentage of drylands in drought that year. The inland wetland extent (including peatlands) is based on aggregated data from more than 2000 time series that report changes in local wetland area over time (Dixon et al. 2016 <sup>[[#fn:r48|48]]</sup> ; Darrah et al. 2019 <sup>[[#fn:r49|49]]</sup> ) (Sections 3.1, 4.2 and 4.6).
+The Anthropocene can be employed as a “boundary concept” (Brondizio et al., 2016) <sup>[[#fn:r18|18]]</sup> that frames critical insights into understanding the drivers, dynamics and specific challenges in responding to the ambition of keeping global temperature well below 2°C while pursuing efforts towards and adapting to a 1.5°C warmer world. The United Nations Framework Convention on Climate Change (UNFCCC) and its Paris Agreement recognize the ability of humans to influence geophysical planetary processes (Chapter 2, Cross-Chapter Box 1 in this chapter). The Anthropocene offers a structured understanding of the culmination of past and present human–environmental relations and provides an opportunity to better visualize the future to minimize pitfalls (Pattberg and Zelli, 2016; Delanty and Mota, 2017) <sup>[[#fn:r19|19]]</sup> ,  while acknowledging the differentiated responsibility and opportunity to limit global warming and invest in prospects for climate-resilient sustainable development (Harrington, 2016) <sup>[[#fn:r20|20]]</sup> (Chapter 5). The Anthropocene also provides an opportunity to raise questions regarding the regional differences, social inequities, and uneven capacities and drivers of global social–environmental changes, which in turn inform the search for solutions as explored in Chapter 4 of this report (Biermann et al., 2016) <sup>[[#fn:r21|21]]</sup> . It links uneven influences of human actions on planetary functions to an uneven distribution of impacts (assessed in Chapter 3) as well as the responsibility and response capacity to, for example, limit global warming to no more than a 1.5°C rise above pre-industrial levels. Efforts to curtail greenhouse gas emissions without incorporating the intrinsic interconnectivity and disparities associated with the Anthropocene world may themselves negatively affect the development ambitions of some regions more than others and negate sustainable development efforts (see Chapter 2 and Chapter 5).
-</div>
+== 1.1.1 Equity and a 1.5°C Warmer World ==
-</div>
+The AR5 suggested that equity, sustainable development, and poverty eradication are best understood as mutually supportive and co-achievable within the context of climate action and are underpinned by various other international hard and soft law instruments (Denton et al., 2014; Fleurbaey et al., 2014; Klein et al., 2014; Olsson et al., 2014; Porter et al., 2014; Stavins et al., 2014) <sup>[[#fn:r22|22]]</sup> . The aim of the Paris Agreement under the UNFCCC to ‘pursue efforts to limit’ the rise in global temperatures to 1.5°C above pre-industrial levels raises ethical concerns that have long been central to climate debates (Fleurbaey et al., 2014; Kolstad et al., 2014) <sup>[[#fn:r23|23]]</sup> . The Paris Agreement makes particular reference to the principle of equity, within the context of broader international goals of sustainable development and poverty eradication. Equity is a long-standing principle within international law and climate change law in particular (Shelton, 2008; Bodansky et al., 2017) <sup>[[#fn:r24|24]]</sup> .
-</div>
+The AR5 describes equity as having three dimensions: intergenerational (fairness between generations), international (fairness between states), and national (fairness between individuals) (Fleurbaey et al., 2014) <sup>[[#fn:r25|25]]</sup> . The principle is generally agreed to involve both procedural justice (i.e., participation in decision making) and distributive justice (i.e., how the costs and benefits of climate actions are distributed) (Kolstad et al., 2014; Savaresi, 2016; Reckien et al., 2017) <sup>[[#fn:r26|26]]</sup> . Concerns regarding equity have frequently been central to debates around mitigation, adaptation and climate governance (Caney, 2005; Schroeder et al., 2012; Ajibade, 2016; Reckien et al., 2017; Shue, 2018) <sup>[[#fn:r27|27]]</sup> . Hence, equity provides a framework for understanding the asymmetries between the distributions of benefits and costs relevant to climate action (Schleussner et al., 2016; Aaheim et al., 2017) <sup>[[#fn:r28|28]]</sup> .
-<div id="section-1-1-1-objectives-and-scope-of-the-assessment-block-3" class="box">
-<div>
+Four key framing asymmetries associated with the conditions of a 1.5°C warmer world have been noted (Okereke, 2010; Harlan et al., 2015; Ajibade, 2016; Savaresi, 2016; Reckien et al., 2017) <sup>[[#fn:r29|29]]</sup> and are reflected in the report’s assessment. The first concerns differential contributions to the problem: the observation that the benefits from industrialization have been unevenly distributed and those who benefited most historically also have contributed most to the current climate problem and so bear greater responsibility (Shue, 2013; McKinnon, 2015; Otto et al., 2017; Skeie et al., 2017) <sup>[[#fn:r30|30]]</sup> . The second asymmetry concerns differential impact: the worst impacts tend to fall on those least responsible for the problem, within states, between states, and between generations (Fleurbaey et al., 2014; Shue, 2014; Ionesco et al., 2016) <sup>[[#fn:r31|31]]</sup> . The third is the asymmetry in capacity to shape solutions and response strategies, such that the worst-affected states, groups, and individuals are not always well represented (Robinson and Shine, 2018) <sup>[[#fn:r32|32]]</sup> . Fourth, there is an asymmetry in future response capacity: some states, groups, and places are at risk of being left behind as the world progresses to a low-carbon economy (Fleurbaey et al., 2014; Shue, 2014; Humphreys, 2017) <sup>[[#fn:r33|33]]</sup> .
-<div>
+A sizeable and growing literature exists on how best to operationalize climate equity considerations, drawing on other concepts mentioned in the Paris Agreement, notably its explicit reference to human rights (OHCHR, 2009; Caney, 2010; Adger et al., 2014; Fleurbaey et al., 2014; IBA, 2014; Knox, 2015; Duyck et al., 2018; Robinson and Shine, 2018) <sup>[[#fn:r34|34]]</sup> . Human rights comprise internationally agreed norms that align with the Paris ambitions of poverty eradication, sustainable development, and the reduction of vulnerability (Caney, 2010; Fleurbaey et al., 2014; OHCHR, 2015) <sup>[[#fn:r35|35]]</sup> . In addition to defining substantive rights (such as to life, health, and shelter) and procedural rights (such as to information and participation), human rights instruments prioritise the rights of marginalized groups, children, vulnerable and indigenous persons, and those discriminated against on grounds such as gender, race, age or disability (OHCHR, 2017) <sup>[[#fn:r36|36]]</sup> . Several international human rights obligations are relevant to the implementation of climate actions and consonant with UNFCCC undertakings in the areas of mitigation, adaptation, finance, and technology transfer (Knox, 2015; OHCHR, 2015; Humphreys, 2017) <sup>[[#fn:r37|37]]</sup> .
-<span id="box-1.1-land-in-previous-ipcc-and-other-relevant-reports"></span>
+Much of this literature is still new and evolving (Holz et al., 2017; Dooley et al., 2018; Klinsky and Winkler, 2018) <sup>[[#fn:r38|38]]</sup> , permitting the present report to examine some broader equity concerns raised both by possible failure to limit warming to 1.5°C and by the range of ambitious mitigation efforts that may be undertaken to achieve that limit. Any comparison between 1.5°C and higher levels of warming implies risk assessments and value judgements and cannot straightforwardly be reduced to a cost-benefit analysis (Kolstad et al., 2014) <sup>[[#fn:r39|39]]</sup> . However, different levels of warming can nevertheless be understood in terms of their different implications for equity – that is, in the comparative distribution of benefits and burdens for specific states, persons, or generations, and in terms of their likely impacts on sustainable development and poverty (see especially Sections 2.3.4.2, 2.5, 3.4.5–3.4.13, 3.6, 5.4.1, 5.4.2, 5.6 and Cross-Chapter boxes 6 in Chapter 3 and 12 in Chapter 5).
-== Box 1.1 Land in previous IPCC and other relevant reports ==
+== 1.1.2 Eradication of Poverty ==
-</div>
+This report assesses the role of poverty and its eradication in the context of strengthening the global response to the threat of climate change and sustainable development. A wide range of definitions for ''poverty'' exist. The AR5 discussed ‘poverty’ in terms of its multidimensionality, referring to ‘material circumstances’ (e.g., needs, patterns of deprivation, or limited resources), as well as to economic conditions (e.g., standard of living, inequality, or economic position), and/or social relationships (e.g., social class, dependency, lack of basic security, exclusion, or lack of entitlement; Olsson et al., 2014) <sup>[[#fn:r40|40]]</sup> . The UNDP now uses a Multidimensional Poverty Index and estimates that about 1.5 billion people globally live in multidimensional poverty, especially in rural areas of South Asia and Sub-Saharan Africa, with an additional billion at risk of falling into poverty (UNDP, 2016) <sup>[[#fn:r41|41]]</sup> .
-<div>
-<div id="section-1-1-1-objectives-and-scope-of-the-assessment-block-1">
+A large and rapidly growing body of knowledge explores the connections between climate change and poverty. Climatic variability and climate change are widely recognized as factors that may exacerbate poverty, particularly in countries and regions where poverty levels are high (Leichenko and Silva, 2014) <sup>[[#fn:r42|42]]</sup> . The AR5 noted that climate change-driven impacts often act as a threat multiplier in that the impacts of climate change compound other drivers of poverty (Olsson et al., 2014) <sup>[[#fn:r43|43]]</sup> . Many vulnerable and poor people are dependent on activities such as agriculture that are highly susceptible to temperature increases and variability in precipitation patterns (Shiferaw et al., 2014; Miyan, 2015) <sup>[[#fn:r44|44]]</sup> . Even modest changes in rainfall and temperature patterns can push marginalized people into poverty as they lack the means to recover from associated impacts. Extreme events, such as floods, droughts, and heat waves, especially when they occur in series, can significantly erode poor people’s assets and further undermine their livelihoods in terms of labour productivity, housing, infrastructure and social networks (Olsson et al., 2014) <sup>[[#fn:r45|45]]</sup> .
-Previous IPCC reports have made reference to land and its role in the climate system. Threats to agriculture, forestry and other ecosystems, but also the role of land and forest management in climate change, have been documented since the IPCC Second Assessment Report, especially so in the Special Report on land use, land-use change and forestry (Watson et al. 2000 <sup>[[#fn:r50|50]]</sup> ). The IPCC Special Report on extreme events (SREX) discussed sustainable land management, including land-use planning, and ecosystem management and restoration among the potential low-regret measures that provide benefits under current climate and a range of future, climate change scenarios. Low-regret measures are defined in the report as those with the potential to offer benefits now  and lay the foundation for tackling future, projected change. Compared to previous IPCC reports, the SRCCL offers a more integrated analysis of the land system as it embraces multiple direct and indirect drivers of natural resource management (related to food, water and energy securities), which have not previously been addressed to a similar depth (Field et al. 2014a <sup>[[#fn:r51|51]]</sup> ; Edenhofer et al. 2014 <sup>[[#fn:r52|52]]</sup> ).
+== 1.1.3 Sustainable Development and a 1.5°C Warmer World ==
-The recent IPCC Special Report on Global Warming of 1.5°C (SR15) targeted specifically the Paris Agreement, without exploring the possibility of future global warming trajectories above 2°C (IPCC 2018 <sup>[[#fn:r53|53]]</sup> ). Limiting global warming to 1.5°C compared to 2°C is projected to lower the impacts on terrestrial, freshwater and coastal ecosystems and to retain more of their services for people. In many scenarios proposed in this report, large-scale land use features as a mitigation measure. In the reports of the Food and Agriculture Organization (FAO), land degradation is discussed in relation to ecosystem goods and services, principally from a food security perspective (FAO and ITPS 2015 <sup>[[#fn:r54|54]]</sup> ). The UNCCD report (2014) discusses land degradation through the prism of desertification. It devotes due attention to how land management can contribute to reversing the negative impacts of desertification and land degradation. The IPBES assessments (2018a <sup>[[#fn:r55|55]]</sup> , b <sup>[[#fn:r56|56]]</sup> , c <sup>[[#fn:r57|57]]</sup> , d <sup>[[#fn:r58|58]]</sup> , e <sup>[[#fn:r59|59]]</sup> ) focus on biodiversity drivers, including a focus on land degradation and desertification, with poverty as a limiting factor. The reports draw attention to a world in peril in which resource scarcity conspires with drivers of biophysical and social vulnerability to derail the attainment of sustainable development goals. As discussed in Chapter 4 of the SRCCL, different definitions of degradation have been applied in the IPBES degradation assessment (IPBES 2018b <sup>[[#fn:r929|929]]</sup> ), which potentially can lead to different conclusions for restoration and ecosystem management.
+AR5 (IPCC, 2014c) <sup>[[#fn:r46|46]]</sup> noted with ''high confidence'' that ‘equity is an integral dimension of sustainable development’ and that ‘mitigation and adaptation measures can strongly affect broader sustainable development and equity objectives’ (Fleurbaey et al., 2014) <sup>[[#fn:r47|47]]</sup> . Limiting global warming to 1.5°C would require substantial societal and technological transformations, dependent in turn on global and regional sustainable development pathways. A range of pathways, both sustainable and not, are explored in this report, including implementation strategies to understand the enabling conditions and challenges required for such a transformation. These pathways and connected strategies are framed within the context of sustainable development, and in particular the United Nations 2030 Agenda for Sustainable Development (UN, 2015b) <sup>[[#fn:r48|48]]</sup> and Cross-Chapter Box 4 on SDGs (in this chapter). The feasibility of staying within 1.5°C depends upon a range of enabling conditions with geophysical, environmental–ecological, technological, economic, socio-cultural, and institutional dimensions. Limiting warming to 1.5°C also involves identifying technology and policy levers to accelerate the pace of transformation (see Chapter 4). Some pathways are more consistent than others with the requirements for sustainable development (see Chapter 5). Overall, the three-pronged emphasis on sustainable development, resilience, and transformation provides Chapter 5 an opportunity to assess the conditions of simultaneously reducing societal vulnerabilities, addressing entrenched inequalities, and breaking the circle of poverty.
-The SRCCL complements and adds to previous assessments, whilst keeping the IPCC-specific ‘climate perspective’. It includes a focussed assessment of risks arising from maladaptation and land-based mitigation (i.e. not only restricted to direct risks from climate change impacts) and the co-benefits and trade-offs with sustainable development objectives. As the SRCCL cuts across different policy sectors it provides the opportunity to address a number of challenges in an integrative way at the same time, and it progresses beyond other IPCC reports in having a much more comprehensive perspective on land.
+The feasibility of any global commitment to a 1.5°C pathway depends, in part, on the cumulative influence of the nationally determined contributions (NDCs), committing nation states to specific GHG emission reductions. The current NDCs, extending only to 2030, do not limit warming to 1.5°C. Depending on mitigation decisions after 2030, they cumulatively track toward a warming of 3°-4°C above pre-industrial temperatures by 2100, with the potential for further warming thereafter (Rogelj et al., 2016a; UNFCCC, 2016) <sup>[[#fn:r49|49]]</sup> . The analysis of pathways in this report reveals opportunities for greater decoupling of economic growth from GHG emissions. Progress towards limiting warming to 1.5°C requires a significant acceleration of this trend. AR5 concluded that climate change constrains possible development paths, that synergies and trade-offs exist between climate responses and socio-economic contexts, and that opportunities for effective climate responses overlap with opportunities for sustainable development, noting that many existing societal patterns of consumption are intrinsically unsustainable (Fleurbaey et al., 2014) <sup>[[#fn:r50|50]]</sup> .
+== 1.2 Understanding 1.5°C: Reference Levels, Probability, Transience, Overshoot, and Stabilization ==
-</div>
+== 1.2.1 Working Definitions of 1.5°C and 2°C Warming Relative to Pre-Industrial Levels ==
-</div>
+What is meant by ‘the increase in global average temperature… above pre-industrial levels’ referred to in the Paris Agreement depends on the choice of pre-industrial reference period, whether 1.5°C refers to total warming or the human-induced component of that warming, and which variables and geographical coverage are used to define global average temperature change. The cumulative impact of these definitional ambiguities (e.g., Hawkins et al., 2017; Pfleiderer et al., 2018) <sup>[[#fn:r51|51]]</sup> is comparable to natural multi-decadal temperature variability on continental scales (Deser et al., 2012) <sup>[[#fn:r52|52]]</sup> and primarily affects the historical period, particularly that prior to the early 20th century when data is sparse and of less certain quality. Most practical mitigation and adaptation decisions do not depend on quantifying historical warming to this level of precision, but a consistent working definition is necessary to ensure consistency across chapters and figures. We adopt definitions that are as consistent as possible with key findings of AR5 with respect to historical warming.
-</div>
+This report defines ‘warming’, unless otherwise qualified, as an increase in multi-decade global mean surface temperature (GMST) above pre-industrial levels. Specifically, warming at a given point in time is defined as the global average of combined land surface air and sea surface temperatures for a 30-year period centred on that time, expressed relative to the reference period 1850–1900 (adopted for consistency with Box SPM.1 Figure 1 of IPCC (2014a) <sup>[[#fn:r53|53]]</sup> ‘as an approximation of pre-industrial levels’, excluding the impact of natural climate fluctuations within that 30-year period and assuming any secular trend continues throughout that period, extrapolating into the future if necessary. There are multiple ways of accounting for natural fluctuations and trends (e.g., Foster and Rahmstorf, 2011; Haustein et al., 2017; Medhaug et al., 2017; Folland et al., 2018; Visser et al., 2018) <sup>[[#fn:r54|54]]</sup> , but all give similar results. A major volcanic eruption might temporarily reduce observed global temperatures, but would not reduce warming as defined here (Bethke et al., 2017) <sup>[[#fn:r55|55]]</sup> . Likewise, given that the level of warming is currently increasing at 0.3°C–0.7°C per 30 years ( ''likely'' range quoted in Kirtman et al., 2013 <sup>[[#fn:r56|56]]</sup> and supported by Folland et al., 2018) <sup>[[#fn:r57|57]]</sup> , the level of warming in 2017 was 0.15°C–0.35°C higher than average warming over the 30-year period 1988–2017.
-</div>
+In summary, this report adopts a working definition of ‘1.5°C relative to pre-industrial levels’ that corresponds to global average combined land surface air and sea surface temperatures either 1.5°C warmer than the average of the 51-year period 1850–1900, 0.87°C warmer than the 20-year period 1986–2005, or 0.63°C warmer than the decade 2006–2015. These offsets are based on all available published global datasets, combined and updated, which show that 1986–2005 was 0.63°C warmer than 1850–1900 (with a 5–95% range of 0.57°C–0.69°C based on observational uncertainties alone), and 2006–2015 was 0.87°C warmer than 1850–1900 (with a ''likely'' range of 0.75°C–0.99°C, also accounting for the possible impact of natural fluctuations). Where possible, estimates of impacts and mitigation pathways are evaluated relative to these more recent periods. Note that the 5–95% intervals often quoted in square brackets in AR5 correspond to ''very likely'' ranges, while ''likely'' ranges correspond to 17–83%, or the central two-thirds, of the distribution of uncertainty.
-<div id="section-1-1-1-objectives-and-scope-of-the-assessment-block-3">
-The SRCCL identifies and assesses land-related challenges and response options in an integrative way, aiming to be policy relevant across sectors. Chapter 1 provides a synopsis of the main issues addressed in this report, which are explored in more detail in Chapters 2–7. Chapter 1 also introduces important concepts and definitions and highlights discrepancies with previous reports that arise from different objectives (a full set of definitions is provided in the Glossary). Chapter 2 focuses on the natural system dynamics, assessing recent progress towards understanding the impacts of climate change on land, and the feedbacks arising from altered biogeochemical and biophysical exchange fluxes (Figure 1.2).
+== 1.2.1.1 Definition of global average temperature ==
+The IPCC has traditionally defined changes in observed GMST as a weighted average of near-surface air temperature (SAT) changes over land and sea surface temperature (SST) changes over the oceans (Morice et al., 2012; Hartmann et al., 2013) <sup>[[#fn:r58|58]]</sup> , while modelling studies have typically used a simple global average SAT. For ambitious mitigation goals, and under conditions of rapid warming or declining sea ice (Berger et al., 2017) <sup>[[#fn:r59|59]]</sup> , the difference can be significant. Cowtan et al. (2015) <sup>[[#fn:r60|60]]</sup> and Richardson et al. (2016) <sup>[[#fn:r61|61]]</sup> show that the use of blended SAT/SST data and incomplete coverage together can give approximately 0.2°C less warming from the 19th century to the present relative to the use of complete global-average SAT (Stocker et al., 2013 <sup>[[#fn:r62|62]]</sup> , Figure TFE8.1 and Figure 1.2). However, Richardson et al. (2018) <sup>[[#fn:r63|63]]</sup>  show that this is primarily an issue for the interpretation of the historical record to date, with less absolute impact on projections of future changes, or estimated emissions budgets, under ambitious mitigation scenarios.
-</div>
+The three GMST reconstructions used in AR5 differ in their treatment of missing data. GISTEMP (Hansen et al., 2010) <sup>[[#fn:r64|64]]</sup> uses interpolation to infer trends in poorly observed regions like the Arctic (although even this product is spatially incomplete in the early record), while NOAAGlobalTemp (Vose et al., 2012) <sup>[[#fn:r65|65]]</sup> and HadCRUT (Morice et al., 2012) <sup>[[#fn:r66|66]]</sup> are progressively closer to a simple average of available observations. Since the AR5, considerable effort has been devoted to more sophisticated statistical modelling to account for the impact of incomplete observation coverage (Rohde et al., 2013; Cowtan and Way, 2014; Jones, 2016) <sup>[[#fn:r67|67]]</sup> . The main impact of statistical infilling is to increase estimated warming to date by about 0.1°C (Richardson et al., 2018 <sup>[[#fn:r68|68]]</sup> and Table 1.1).
-<div id="section-1-1-1-objectives-and-scope-of-the-assessment-block-4">
-<div class="figure">
+We adopt a working definition of warming over the historical period based on an average of the four available global datasets that are supported by peer-reviewed publications: the three datasets used in the AR5, updated (Karl et al., 2015) <sup>[[#fn:r69|69]]</sup> , together with the Cowtan-Way infilled dataset (Cowtan and Way, 2014) <sup>[[#fn:r70|70]]</sup> . A further two datasets, Berkeley Earth (Rohde et al., 2013) <sup>[[#fn:r71|71]]</sup> and that of the Japan Meteorological Agency (JMA), are provided in Table 1.1. This working definition provides an updated estimate of 0.86°C for the warming over the period 1880–2012 based on a linear trend. This quantity was quoted as 0.85°C in the AR5. Hence the inclusion of the Cowtan-Way dataset does not introduce any inconsistency with the AR5, whereas redefining GMST to represent global SAT could increase this figure by up to 20% (Table 1.1, blue lines in Figure 1.2 and Richardson et al., 2016) <sup>[[#fn:r72|72]]</sup> .
-<span id="figure-1.2"></span>
 ====== Figure 1.2 ======
-<span id="overview-over-the-srccl."></span>
+==== Evolution of global mean surface temperature (GMST) over the period of instrumental observations. ====
-==== Overview over the SRCCL. ====
-[[File:https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/Figure-1.2-1024x301.jpg]]
-<div>
-Overview over the SRCCL.
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="section">
-<div>
-<div>
-<span id="status-and-dynamics-of-the-global-land-system"></span>
-== 1.1.2 Status and dynamics of the (global) land system ==
-</div>
-<div id="section-1-1-2-1-land-ecosystems-and-climate-change">
-<div>
-<div>
-<span id="land-ecosystems-and-climate-change"></span>
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/figure-1.2-1024x626.png]]
-== 1.1.2.1 1.1.2.1 Land ecosystems and climate change ==
+Grey shaded line shows monthly mean GMST in the HadCRUT4, NOAAGlobalTemp, GISTEMP and Cowtan-Way datasets, expressed as departures from 1850–1900, with varying grey line thickness indicating inter-dataset range. All observational datasets shown represent GMST as a weighted average of near surface air temperature over land and sea surface temperature over oceans. Human-induced (yellow) and total (human- and naturally-forced, orange) contributions to these GMST changes are shown calculated following Otto et al. (2015) <sup>[[#fn:r73|73]]</sup> and Haustein et al. (2017) <sup>[[#fn:r74|74]]</sup> . Fractional uncertainty in the level of human-induced warming in 2017 is set equal to ±20% based on multiple lines of evidence. Thin blue lines show the modelled global mean surface air temperature (dashed) and blended surface air and sea surface temperature accounting for observational coverage (solid) from the CMIP5 historical ensemble average extended with RCP8.5 forcing (Cowtan et al., 2015; Richardson et al., 2018) <sup>[[#fn:r75|75]]</sup> . The pink shading indicates a range for temperature fluctuations over the Holocene (Marcott et al., 2013) <sup>[[#fn:r76|76]]</sup> . Light green plume shows the AR5 prediction for average GMST over 2016–2035 (Kirtman et al., 2013) <sup>[[#fn:r77|77]]</sup> . See Supplementary Material 1.SM for further details.
-</div>
+== 1.2.1.2 Choice of reference period ==
-<div>
-<div id="section-1-1-2-1-land-ecosystems-and-climate-change-block-1">
+Any choice of reference period used to approximate ‘pre-industrial’ conditions is a compromise between data coverage and representativeness of typical pre-industrial solar and volcanic forcing conditions. This report adopts the 51-year reference period, 1850–1900 inclusive, assessed as an approximation of pre-industrial levels in AR5 (Box TS.5, Figure 1 of Field et al., 2014) <sup>[[#fn:r78|78]]</sup> . The years 1880–1900 are subject to strong but uncertain volcanic forcing, but in the HadCRUT4 dataset, average temperatures over 1850–1879, prior to the largest eruptions, are less than 0.01°C from the average for 1850–1900. Temperatures rose by 0.0°C–0.2°C from 1720–1800 to 1850–1900 (Hawkins et al., 2017) <sup>[[#fn:r79|79]]</sup> , but the anthropogenic contribution to this warming is uncertain (Abram et al., 2016; Schurer et al., 2017) <sup>[[#fn:r80|80]]</sup> . The 18th century represents a relatively cool period in the context of temperatures since the mid-Holocene (Marcott et al., 2013; Lüning and Vahrenholt, 2017; Marsicek et al., 2018) <sup>[[#fn:r81|81]]</sup> , which is indicated by the pink shaded region in Figure 1.2.
-Land ecosystems play a key role in the climate system, due to their large carbon pools and carbon exchange fluxes with the atmosphere (Ciais et al. 2013b <sup>[[#fn:r60|60]]</sup> ). Land use, the total of arrangements, activities and inputs applied to a parcel of land (such as agriculture, grazing, timber extraction, conservation or city dwelling; see Glossary), and land management (sum of land-use practices that take place within broader land-use categories; see Glossary) considerably alter terrestrial ecosystems and play a key role in the global climate system. An estimated one-quarter of total anthropogenic GHG emissions arise mainly from deforestation, ruminant livestock and fertiliser application (Smith et al. 2014 <sup>[[#fn:r61|61]]</sup> ; Tubiello et al. 2015 <sup>[[#fn:r62|62]]</sup> ; Le Quere et al. 2018 <sup>[[#fn:r63|63]]</sup> ; Ciais et al. 2013a <sup>[[#fn:r64|64]]</sup> ), and especially methane (CH <sub>4</sub> ) and nitrous oxide (N <sub>2</sub> O) emissions from agriculture have been rapidly increasing over the last decades (Hoesly et al. 2018 <sup>[[#fn:r65|65]]</sup> ; Tian et al. 2019 <sup>[[#fn:r66|66]]</sup> ) (Figure 1.1 and Sections 2.3.2–2.3.3).
+Projections of responses to emission scenarios, and associated impacts, may use a more recent reference period, offset by historical observations, to avoid conflating uncertainty in past and future changes (e.g., Hawkins et al., 2017; Millar et al., 2017b; Simmons et al., 2017) <sup>[[#fn:r82|82]]</sup> . Two recent reference periods are used in this report: 1986–2005 and 2006–2015. In the latter case, when using a single decade to represent a 30-year average centred on that decade, it is important to consider the potential impact of internal climate variability. The years 2008–2013 were characterised by persistent cool conditions in the Eastern Pacific (Kosaka and Xie, 2013; Medhaug et al., 2017) <sup>[[#fn:r83|83]]</sup> , related to both the El Niño-Southern Oscillation (ENSO) and, potentially, multi-decadal Pacific variability (e.g., England et al., 2014) <sup>[[#fn:r84|84]]</sup> , but these were partially compensated for by El Niño conditions in 2006 and 2015. Likewise, volcanic activity depressed temperatures in 1986–2005, partly offset by the very strong El Niño event in 1998. Figure 1.2 indicates that natural variability (internally generated and externally driven) had little net impact on average temperatures over 2006–2015, in that the average temperature of the decade is similar to the estimated externally driven warming. When solar, volcanic and ENSO-related variability is taken into account following the procedure of Foster and Rahmstorf (2011) <sup>[[#fn:r85|85]]</sup> , there is no indication of average temperatures in either 1986–2005 or 2006–2015 being substantially biased by short-term variability (see Supplementary Material 1.SM.2). The temperature difference between these two reference periods (0.21°C–0.27°C over 15 years across available datasets) is also consistent with the AR5 assessment of the current warming rate of 0.3°C–0.7°C over 30 years (Kirtman et al., 2013) <sup>[[#fn:r86|86]]</sup> .
-Globally, land also serves as a large CO <sub>2</sub> sink, which was estimated for the period 2008–2017 to be nearly 30% of total anthropogenic emissions (Le Quere et al. 2015 <sup>[[#fn:r67|67]]</sup> ; Canadell and Schulze 2014 <sup>[[#fn:r68|68]]</sup> ; Ciais et al. 2013a <sup>[[#fn:r69|69]]</sup> ; Zhu et al. 2016 <sup>[[#fn:r70|70]]</sup> ) (Section 2.3.1). This sink has been attributed to increasing atmospheric CO <sub>2</sub> concentration, a prolonged growing season in cool environments, or forest regrowth (Le Quéré et al. 2013 <sup>[[#fn:r71|71]]</sup> ; Pugh et al. 2019 <sup>[[#fn:r72|72]]</sup> ; Le Quéré et al. 2018 <sup>[[#fn:r73|73]]</sup> ; Ciais et al. 2013a <sup>[[#fn:r74|74]]</sup> ; Zhu et al. 2016 <sup>[[#fn:r75|75]]</sup> ). Whether or not this sink will persist into the future is one of the largest uncertainties in carbon cycle and climate modelling (Ciais et al. 2013a <sup>[[#fn:r76|76]]</sup> ; Bloom et al. 2016 <sup>[[#fn:r77|77]]</sup> ; Friend et al. 2014 <sup>[[#fn:r78|78]]</sup> ; Le Quere et al. 2018 <sup>[[#fn:r79|79]]</sup> ). In addition, changes in vegetation cover caused by land use (such as conversion of forest to cropland or grassland, and vice versa) can result in regional cooling or warming through altered energy and momentum transfer between ecosystems and the atmosphere. Regional impacts can be substantial, but whether the effect leads to warming or cooling depends on the local context (Lee et al. 2011 <sup>[[#fn:r80|80]]</sup> ; Zhang et al. 2014 <sup>[[#fn:r81|81]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r82|82]]</sup> ) (Section 2.6). Due to the current magnitude of GHG emissions and CO <sub>2</sub> carbon dioxide removal in land ecosystems, there is ''high confidence'' that GHG reduction measures in agriculture, livestock management and forestry would have substantial climate change mitigation potential, with co-benefits for biodiversity and ecosystem services (Smith and Gregory 2013 <sup>[[#fn:r84|84]]</sup> ; Smith et al. 2014 <sup>[[#fn:r85|85]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r86|86]]</sup> ) (Sections 2.6 and 6.3).
+On the definition of warming used here, warming to the decade 2006–2015 comprises an estimate of the 30-year average centred on this decade, or 1996–2025, assuming the current trend continues and that any volcanic eruptions that might occur over the final seven years are corrected for. Given this element of extrapolation, we use the AR5 near-term projection to provide a conservative uncertainty range. Combining the uncertainty in observed warming to 1986–2005 (±0.06°C) with the ''likely'' range in the current warming trend as assessed by AR5 (±0.2°C/30 years), assuming these are uncorrelated, and using observed warming relative to 1850–1900 to provide the central estimate (no evidence of bias from short-term variability), gives an assessed warming to the decade 2006–2015 of 0.87°C with a ±0.12°C ''likely''  range. This estimate has the advantage of traceability to the AR5, but more formal methods of quantifying externally driven warming (e.g., Bindoff et al., 2013; Jones et al., 2016; Haustein et al., 2017; Ribes et al., 2017) <sup>[[#fn:r87|87]]</sup> , which typically give smaller ranges of uncertainty, may be adopted in the future.
-The mean temperature over land for the period 2006–2015 was 1.53°C higher than for the period 1850–1900, and 0.66°C larger than the equivalent global mean temperature change (Section 2.2). Climate change affects land ecosystems in various ways (Section 7.2). Growing seasons and natural biome boundaries shift in response to warming or changes in precipitation (Gonzalez et al. 2010 <sup>[[#fn:r87|87]]</sup> ; Wärlind et al. 2014 <sup>[[#fn:r88|88]]</sup> ; Davies-Barnard et al. 2015 <sup>[[#fn:r89|89]]</sup> ; Nakamura et al. 2017 <sup>[[#fn:r90|90]]</sup> ). Atmospheric CO <sub>2</sub> increases have been attributed to underlie, at least partially, observed woody plant cover increase in grasslands and savannahs (Donohue et al. 2013 <sup>[[#fn:r91|91]]</sup> ). Climate change-induced shifts in habitats, together with warmer temperatures, cause pressure on plants and animals (Pimm et al. 2014 <sup>[[#fn:r92|92]]</sup> ; Urban et al. 2016 <sup>[[#fn:r93|93]]</sup> ). National cereal crop losses of nearly 10% have been estimated for the period 1964–2007 as a consequence of heat and drought weather extremes (Deryng et al. 2014 <sup>[[#fn:r94|94]]</sup> ; Lesk et al. 2016 <sup>[[#fn:r95|95]]</sup> ). Climate change is expected to reduce yields in areas that are already under heat and water stress (Schlenker and Lobell 2010 <sup>[[#fn:r96|96]]</sup> ; Lobell et al. 2011 <sup>[[#fn:r97|97]]</sup> , 2012 <sup>[[#fn:r98|98]]</sup> ; Challinor et al. 2014 <sup>[[#fn:r99|99]]</sup> ) (Section 5.2.2). At the same time, warmer temperatures can increase productivity in cooler regions (Moore and Lobell 2015 <sup>[[#fn:r100|100]]</sup> ) and might open opportunities for crop area expansion, but any overall benefits might be counterbalanced by reduced suitability in warmer regions (Pugh et al. 2016 <sup>[[#fn:r101|101]]</sup> ; Di Paola et al. 2018 <sup>[[#fn:r102|102]]</sup> ). Increasing atmospheric CO <sub>2</sub> is expected to increase productivity and water use efficiency in crops and in forests (Muller et al. 2015 <sup>[[#fn:r103|103]]</sup> ; Nakamura et al. 2017 <sup>[[#fn:r104|104]]</sup> ; Kimball 2016 <sup>[[#fn:r105|105]]</sup> ). The increasing number of extreme weather events linked to climate change is also expected to result in forest losses; heat waves and droughts foster wildfires (Seidl et al. 2017 <sup>[[#fn:r106|106]]</sup> ; Fasullo et al. 2018 <sup>[[#fn:r107|107]]</sup> ) (Cross-Chapter Box 3 in Chapter 2). Episodes of observed enhanced tree mortality across many world regions have been attributed to heat and drought stress (Allen et al. 2010 <sup>[[#fn:r108|108]]</sup> ; Anderegg et al. 2012 <sup>[[#fn:r109|109]]</sup> ), whilst weather extremes also impact local infrastructure and hence transportation and trade in land-related goods (Schweikert et al. 2014 <sup>[[#fn:r110|110]]</sup> ; Chappin and van der Lei 2014 <sup>[[#fn:r111|111]]</sup> ). Thus, adaptation is a key challenge to reduce adverse impacts on land systems (Section 1.3.6).
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-1-2-2-current-patterns-of-land-use-and-land-cover">
-<div>
-<div>
-<span id="current-patterns-of-land-use-and-land-cover"></span>
-== 1.1.2.2 Current patterns of land use and land cover ==
-</div>
-<div>
-<div id="section-1-1-2-2-current-patterns-of-land-use-and-land-cover-block-1">
-Around three-quarters of the global ice-free land, and most of the highly productive land area, are by now under some form of land use (Erb et al. 2016a <sup>[[#fn:r112|112]]</sup> ; Luyssaert et al. 2014 <sup>[[#fn:r113|113]]</sup> ; Venter et al. 2016 <sup>[[#fn:r114|114]]</sup> ) (Table 1.1). One-third of used land is associated with changed land cover. Grazing land is the single largest land-use category, followed by used forestland and cropland. The total land area used to raise livestock is notable: it includes all grazing land and an estimated additional one-fifth of cropland for feed production (Foley et al. 2011 <sup>[[#fn:r115|115]]</sup> ). Globally, 60–85% of the total forested area is used, at different levels of intensity, but information on management practices globally is scarce (Erb et al. 2016a). Large areas of unused (primary) forests remain only in the tropics and northern boreal zones (Luyssaert et al. 2014 <sup>[[#fn:r116|116]]</sup> ; Birdsey and Pan 2015 <sup>[[#fn:r117|117]]</sup> ; Morales-Hidalgo et al. 2015 <sup>[[#fn:r118|118]]</sup> ; Potapov et al. 2017 <sup>[[#fn:r119|119]]</sup> ; Erb et al. 2017 <sup>[[#fn:r120|120]]</sup> ), while 73–89% of other, non-forested natural ecosystems (natural grasslands, savannahs, etc.) are used. Large uncertainties relate to the extent of forest (32.0–42.5 million km <sup>2</sup> ) and grazing land (39–62 million km <sup>2</sup> ), due to discrepancies in definitions and observation methods (Luyssaert et al. 2014 <sup>[[#fn:r121|121]]</sup> ; Erb et al. 2017; Putz and Redford 2010 <sup>[[#fn:r122|122]]</sup> ; Schepaschenko et al. 2015 <sup>[[#fn:r123|123]]</sup> ; Birdsey and Pan 2015 <sup>[[#fn:r124|124]]</sup> ; FAO 2015a <sup>[[#fn:r125|125]]</sup> ; Chazdon et al. 2016a <sup>[[#fn:r126|126]]</sup> ; FAO 2018a <sup>[[#fn:r127|127]]</sup> ). Infrastructure areas (including settlements, transportation and mining), while being almost negligible in terms of extent, represent particularly pervasive land-use activities, with far-reaching ecological, social and economic implications (Cherlet et al. 2018 <sup>[[#fn:r128|128]]</sup> ; Laurance et al. 2014 <sup>[[#fn:r129|129]]</sup> ).
-The large imprint of humans on the land surface has led to the definition of anthromes, i.e. large-scale ecological patterns created by the sustained interactions between social and ecological drivers. The dynamics of these ‘anthropogenic biomes’ are key for land-use impacts as well as for the design of integrated response options (Ellis and Ramankutty 2008 <sup>[[#fn:r130|130]]</sup> ; Ellis et al. 2010 <sup>[[#fn:r131|131]]</sup> ; Cherlet et al. 2018 <sup>[[#fn:r132|132]]</sup> ; Ellis et al. 2010 <sup>[[#fn:r133|133]]</sup> ) (Chapter 6).
-The intensity of land use varies hugely within and among different land-use types and regions. Averaged globally, around 10% of the ice-free land surface was estimated to be intensively managed (such as tree plantations, high livestock density grazing, large agricultural inputs), two-thirds moderately and the remainder at low intensities (Erb et al. 2016a <sup>[[#fn:r134|134]]</sup> ). Practically all cropland is fertilised, with large regional variations. Irrigation is responsible for 70% of ground- or surface-water withdrawals by humans (Wisser et al. 2008 <sup>[[#fn:r135|135]]</sup> ; Chaturvedi et al. 2015 <sup>[[#fn:r136|136]]</sup> ; Siebert et al. 2015 <sup>[[#fn:r137|137]]</sup> ; FAOSTAT 2018 <sup>[[#fn:r138|138]]</sup> ). Humans appropriate one-quarter to one-third of the total potential net primary production (NPP), i.e. the NPP that would prevail in the absence of land use (estimated at about 60 GtC yr <sup>–1</sup> ; Bajželj et al. 2014 <sup>[[#fn:r139|139]]</sup> ; Haberl et al. 2014 <sup>[[#fn:r140|140]]</sup> ), about equally through biomass harvest and changes in NPP due to land management. The current total of agricultural (cropland and grazing) biomass harvest is estimated at about 6 GtC yr <sup>–1</sup> , around 50–60% of this is consumed by livestock. Forestry harvest for timber and wood fuel amounts to about 1 GtC yr <sup>–1</sup> (Alexander et al. 2017 <sup>[[#fn:r141|141]]</sup> ; Bodirsky and Müller 2014 <sup>[[#fn:r142|142]]</sup> ; Lassaletta et al. 2014 <sup>[[#fn:r143|143]]</sup> , 2016; Mottet et al. 2017 <sup>[[#fn:r144|144]]</sup> ; Haberl et al. 2014 <sup>[[#fn:r145|145]]</sup> ; Smith et al. 2014 <sup>[[#fn:r146|146]]</sup> ; Bais et al. 2015 <sup>[[#fn:r147|147]]</sup> ; Bajželj et al. 2014 <sup>[[#fn:r148|148]]</sup> ) (Cross-Chapter Box 7 in Chapter 6).
-</div>
-<div id="section-1-1-2-2-current-patterns-of-land-use-and-land-cover-block-2">
-<div class="figure">
-<span id="table-1.1"></span>
 ====== Table 1.1 ======
-<span id="extent-of-global-land-use-and-management-around-the-year-2015."></span>
-==== Extent of global land use and management around the year 2015. ====
-[[File:../../../site/assets/uploads/sites/4/2019/12/table-1.1a.png]] [[File:../../../site/assets/uploads/sites/4/2019/12/table-1.1b.png]]
+==== Observed increase in global average surface temperature in various datasets. Numbers in square brackets correspond to 5–95% uncertainty ranges from individual datasets, encompassing known sources of observational uncertainty only. ====
-</div>
-</div>
+{| class="wikitable"
+|-
+| '''Diagnostic / dataset'''
+| '''1850–1900 to (1)<br />
+''' '''2006–2015'''
+| '''1850–1900 to (2)<br />
+''' '''1986–2005'''
+| '''1986–2005 to (3)<br />
+''' '''2006–2015'''
+| '''1850–1900 to (4)<br />
+''' '''1981–2010'''
+| '''1850–1900 to (5)<br />
+''' '''1998–2017'''
+| '''Trend (6)<br />
+''' '''1880–2012'''
+| '''Trend (6)<br />
+''' '''1880–2015'''
+|-
+| '''HadCRUT4.6'''
+| 0.84
+[0.79–0.89]
-</div>
+| 0.60
+[0.57–0.66]
-</div>
+| 0.22
+[0.21–0.23]
-</div>
+| 0.62
-<div id="section-1-1-2-3-past-and-ongoing-trends">
+[0.58–0.67]
-<div>
+| 0.83
+[0.78–0.88]
-<div>
+| 0.83
+[0.77–0.90]
-<span id="past-and-ongoing-trends"></span>
+| 0.88
-== 1.1.2.3 Past and ongoing trends ==
+[0.83–0.95]
+|-
+| '''NOAAGlobalTemp (7)'''
+| 0.86
+| 0.62
+| 0.22
+| 0.63
+| 0.85
+| 0.85
+| 0.91
+|-
+| '''GISTEMP (7)'''
+| 0.89
+| 0.65
+| 0.23
+| 0.66
+| 0.88
+| 0.89
+| 0.94
+|-
+| '''Cowtan-Way'''
+| 0.91
+[0.85–0.99]
-</div>
+| 0.65
-<div>
+[0.60–0.72]
-<div id="section-1-1-2-3-past-and-ongoing-trends-block-1">
+| 0.26
+[0.25–0.27]
-Globally, cropland area changed by +15% and the area of permanent pastures by +8% since the early 1960s (FAOSTAT 2018 <sup>[[#fn:r149|149]]</sup> ), with strong regional differences (Figure 1.3). In contrast, cropland production since 1961 increased by about 3.5 times, the production of animal products by 2.5 times, and forestry by 1.5 times; in parallel with strong yield (production per unit area) increases (FAOSTAT 2018 <sup>[[#fn:r150|150]]</sup> ) (Figure 1.3). Per capita calorie supply increased by 17% since 1970 (Kastner et al. 2012 <sup>[[#fn:r151|151]]</sup> ), and diet composition changed markedly, tightly associated with economic development and lifestyle: since the early 1960s, per capita dairy product consumption increased by a factor of 1.2, and meat and vegetable oil consumption more than doubled (FAO 2017 <sup>[[#fn:r152|152]]</sup> , 2018b <sup>[[#fn:r153|153]]</sup> ; Tilman and Clark 2014 <sup>[[#fn:r154|154]]</sup> ; Marques et al. 2019 <sup>[[#fn:r155|155]]</sup> ). Population and livestock production represent key drivers of the global expansion of cropland for food production, only partly compensated by yield increases at the global level (Alexander et al. 2015 <sup>[[#fn:r156|156]]</sup> ). A number of studies have reported reduced growth rates or stagnation in yields in some regions in the last decades ( ''medium evidence, high agreement'' ; Lin and Huybers 2012 <sup>[[#fn:r157|157]]</sup> ; Ray et al. 2012 <sup>[[#fn:r158|158]]</sup> ; Elbehri, Aziz, Joshua Elliott 2015 <sup>[[#fn:r159|159]]</sup> ) (Section 5.2.2).
+| 0.65
+[0.60–0.72]
-The past increases in agricultural production have been associated with strong increases in agricultural inputs (Foley et al. 2011 <sup>[[#fn:r160|160]]</sup> ; Siebert et al. 2015 <sup>[[#fn:r161|161]]</sup> ; Lassaletta et al. 2016 <sup>[[#fn:r162|162]]</sup> ) (Figures 1.1 and 1.3). Irrigation area doubled, total nitrogen fertiliser use increased by 800% (FAOSTAT 2018 <sup>[[#fn:r163|163]]</sup> ; IFASTAT 2018 <sup>[[#fn:r164|164]]</sup> ) since the early 1960s. Biomass trade volumes grew by a factor of nine (in tonnes dry matter yr <sup>–1</sup> ) in this period, which is much stronger than production (FAOSTAT 2018 <sup>[[#fn:r165|165]]</sup> ), resulting in a growing spatial disconnect between regions of production and consumption (Friis et al. 2016 <sup>[[#fn:r166|166]]</sup> ; Friis and Nielsen 2017 <sup>[[#fn:r167|167]]</sup> ; Schröter et al. 2018 <sup>[[#fn:r168|168]]</sup> ; Liu et al. 2013 <sup>[[#fn:r169|169]]</sup> ; Krausmann and Langthaler 2019 <sup>[[#fn:r170|170]]</sup> ). Urban and other infrastructure areas expanded by a factor of two since 1960 (Krausmann et al. 2013 <sup>[[#fn:r171|171]]</sup> ), resulting in disproportionally large losses of highly fertile cropland (Seto and Reenberg 2014 <sup>[[#fn:r172|172]]</sup> ; Martellozzo et al. 2015 <sup>[[#fn:r173|173]]</sup> ; Bren d’Amour et al. 2016 <sup>[[#fn:r174|174]]</sup> ; Seto and Ramankutty 2016 <sup>[[#fn:r175|175]]</sup> ; van Vliet et al. 2017 <sup>[[#fn:r176|176]]</sup> ). World regions show distinct patterns of change (Figure 1.3).
+| 0.88
+[0.82–0.96]
+| 0.88
+[0.79–0.98]
-</div>
+| 0.93
-<div id="section-1-1-2-3-past-and-ongoing-trends-block-2">
+[0.85–1.03]
-<div class="figure">
+|-
+| '''Average (8)'''
+| '''0.87'''
+| 0.63
+| 0.23
+| 0.64
+| 0.86
+| 0.86
+| 0.92
+|-
+| '''Berkeley (9)'''
+| 0.98
+| 0.73
+| 0.25
+| 0.73
+| 0.97
+| 0.97
+| 1.02
+|-
+| '''JMA (9)'''
+| 0.82
+| 0.59
+| 0.17
+| 0.60
+| 0.81
+| 0.82
+| 0.87
+|-
+| '''ERA-Interim'''
+| N/A
+| N/A
+| 0.26
+| N/A
+| N/A
+| N/A
+| N/A
+|-
+| '''JRA-55'''
+| N/A
+| N/A
+| 0.23
+| N/A
+| N/A
+| N/A
+| N/A
+|-
+| '''CMIP5 global SAT (10)'''
+| 0.99
+[0.65–1.37]
-<span id="figure-1.3"></span>
+| 0.62
-====== Figure 1.3 ======
+[0.38–0.94]
-<span id="status-and-trends-in-the-global-land-system-a.-trends-in-area-production-and-trade-and-drivers-of-change.-the-map-shows-the-global-pattern-of-land-systems-combination-of-maps-nachtergaele-2008-ellis-et-al.-2010-potapov-et-al.-2017-faos-animal-production-and-health-division-2018-livestock-lowhigh-relates-to-low-or-high"></span>
+| 0.38
-==== Status and trends in the global land system: A. Trends in area, production and trade, and drivers of change. The map shows the global pattern of land systems (combination of maps Nachtergaele (2008); Ellis et al. (2010); Potapov et al. (2017); FAO’s Animal Production and Health Division (2018); livestock low/high relates to low or high […] ====
+[0.24–0.62]
-[[File:https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/Figure-1.3-724x1024.png]]
+| 0.62
-<div>
+[0.34–0.93]
-Status and trends in the global land system: '''A''' . Trends in area, production and trade, and drivers of change. The map shows the global pattern of land systems (combination of maps Nachtergaele (2008) <sup>[[#fn:r177|177]]</sup> ; Ellis et al. (2010) <sup>[[#fn:r178|178]]</sup> ; Potapov et al. (2017) <sup>[[#fn:r179|179]]</sup> ; FAO’s Animal Production and Health Division (2018); livestock low/high relates to low or high livestock density, respectively). The inlay figures show, for the globe and seven world regions, from left to right: (a) Cropland, permanent pastures and forest (used and unused) areas, standardised to total land area, (b) production in dry matter per year per total land area, (c) trade in dry matter in percent of total domestic production, all for 1961 to 2014 (data from FAOSTAT (2018) <sup>[[#fn:r180|180]]</sup> and FAO (1963) <sup>[[#fn:r181|181]]</sup> for forest area 1961). (d) drivers of cropland for food production between 1994 and 2011 (Alexander et al. 2015 <sup>[[#fn:r182|182]]</sup> ). See panel “global” for legend. “Plant Produc., Animal P.”: changes in consumption of plant-based products and animal-products, respectively. '''B''' .Selected land-use pressures and impacts. The map shows the ratio between impacts on biomass stocks of land-cover conversions and of land management (changes that occur with land-cover types; only changes larger than 30 gC m <sup>–2</sup> displayed; Erb et al. 2017 <sup>[[#fn:r183|183]]</sup> ), compared to the biomass stocks of the potential vegetation (vegetation that would prevail in the absence of land use, but with current climate). The inlay figures show, from left to right (e) the global Human Appropriation of Net Primary production (HANPP) in the year 2005, in gC m <sup>–2</sup> yr <sup>–1</sup> (Krausmann et al. 2013 <sup>[[#fn:r184|184]]</sup> ). The sum of the three components represents the NPP of the potential vegetation and consist of: (i) NPP <sub>eco</sub> , i.e. the amount of NPP remaining in ecosystem after harvest, (ii) HANPP <sub>harv</sub> , i.e. NPP harvested or killed during harvest, and (iii) HANPP <sub>luc</sub> , i.e. NPP foregone due to land-use change. The sum of NPP <sub>eco</sub> and HANPP <sub>harv</sub> is the NPP of the actual vegetation (Haberl et al. 2014 <sup>[[#fn:r185|185]]</sup> ; Krausmann et al. 2013 <sup>[[#fn:r186|186]]</sup> ). The two central inlay figures show changes in land-use intensity, standardised to 2014, related to (f) cropland (yields, fertilisation, irrigated area) and (g) forestry harvest per forest area, and grazers and monogastric livestock density per agricultural area (FAOSTAT 2018). (h) Cumulative CO <sub>2</sub> fluxes between land and the atmosphere between 2000 and 2014. LUC: annual CO <sub>2</sub> land use flux due to changes in land cover and forest management; Sink <sub>land</sub> : the annual CO <sub>2</sub> land sink caused mainly by the indirect anthropogenic effects of environmental change (e.g, climate change and the fertilising effects of rising CO <sub>2</sub> and N concentrations), excluding impacts of land-use change (Le Quéré et al. 2018 <sup>[[#fn:r187|187]]</sup> ) (Section 2.3)
+| 0.89
+[0.62–1.29]
+| 0.81
+[0.58–1.31]
-</div>
+| 0.86
+[0.63–1.39]
-</div>
+|-
+| '''CMIP5 SAT/SST blend—masked'''
+| 0.86
+[0.54–1.18]
-</div>
+| 0.50
-<div id="section-1-1-2-3-past-and-ongoing-trends-block-3">
+[0.31–0.79]
-While most pastureland expansion replaced natural grasslands, cropland expansion replaced mainly forests (Ramankutty et al. 2018 <sup>[[#fn:r188|188]]</sup> ; Ordway et al. 2017 <sup>[[#fn:r189|189]]</sup> ; Richards and Friess 2016 <sup>[[#fn:r190|190]]</sup> ). Noteworthy large conversions occurred in tropical dry woodlands and savannahs, for example, in the Brazilian Cerrado (Lehmann and Parr 2016 <sup>[[#fn:r191|191]]</sup> ; Strassburg et al. 2017 <sup>[[#fn:r192|192]]</sup> ), the South American Caatinga and Chaco regions (Parr et al. 2014 <sup>[[#fn:r193|193]]</sup> ; Lehmann and Parr 2016 <sup>[[#fn:r194|194]]</sup> ) or African savannahs (Ryan et al. 2016 <sup>[[#fn:r195|195]]</sup> ). More than half of the original 4.3–12.6 million km <sup>2</sup> global wetlands (Erb et al. 2016a <sup>[[#fn:r196|196]]</sup> ; Davidson 2014 <sup>[[#fn:r197|197]]</sup> ; Dixon et al. 2016 <sup>[[#fn:r198|198]]</sup> ) have been drained; since 1970 the wetland extent index, developed by aggregating data field-site time series that report changes in local inland wetland area, indicates a decline of more than 30% (Darrah et al. 2019 <sup>[[#fn:r199|199]]</sup> ) (Figure 1.1 and Section 4.2.1). Likewise, one-third of the estimated global area that in a non-used state would be covered in forests (Erb et al. 2017 <sup>[[#fn:r200|200]]</sup> ) has been converted to agriculture.
+| 0.34
+[0.19–0.54]
-Global forest area declined by 3% since 1990 (about –5% since 1960) and continues to do so (FAO 2015a <sup>[[#fn:r201|201]]</sup> ; Keenan et al. 2015 <sup>[[#fn:r202|202]]</sup> ; MacDicken et al. 2015 <sup>[[#fn:r203|203]]</sup> ; FAO 1963; Figure 1.1 <sup>[[#fn:r204|204]]</sup> ), but uncertainties are large. ''Low agreement'' relates to the concomitant trend of global tree cover. Some remote-sensing based assessments show global net-losses of forest or tree cover (Li et al. 2016 <sup>[[#fn:r205|205]]</sup> ; Nowosad et al. 2018 <sup>[[#fn:r206|206]]</sup> ; Hansen et al. 2013 <sup>[[#fn:r207|207]]</sup> ); others indicate a net gain (Song et al. 2018 <sup>[[#fn:r208|208]]</sup> ). Tree-cover gains would be in line with observed and modelled increases in photosynthetic active tissues (‘greening’; Chen et al. 2019 <sup>[[#fn:r209|209]]</sup> ; Zhu et al. 2016 <sup>[[#fn:r210|210]]</sup> ; Zhao et al. 2018 <sup>[[#fn:r211|211]]</sup> ; de Jong et al. 2013 <sup>[[#fn:r212|212]]</sup> ; Pugh et al. 2019 <sup>[[#fn:r213|213]]</sup> ; De Kauwe et al. 2016 <sup>[[#fn:r214|214]]</sup> ; Kolby Smith et al. 2015 <sup>[[#fn:r215|215]]</sup> ) (Box 2.3 in Chapter 2), but ''confidence'' remains ''low'' whether gross forest or tree-cover gains are as large, or larger, than losses. This uncertainty, together with poor information on forest management, affects estimates and attribution of the land carbon sink (Sections 2.3, 4.3 and 4.6). Discrepancies are caused by different classification schemes and applied thresholds (e.g., minimum tree height and tree-cover thresholds used to define a forest), the divergence of forest and tree cover, and differences in methods and spatiotemporal resolution (Keenan et al. 2015 <sup>[[#fn:r216|216]]</sup> ; Schepaschenko et al. 2015 <sup>[[#fn:r217|217]]</sup> ; Bastin et al. 2017 <sup>[[#fn:r218|218]]</sup> ; Sloan and Sayer 2015 <sup>[[#fn:r219|219]]</sup> ; Chazdon et al. 2016a <sup>[[#fn:r220|220]]</sup> ; Achard et al. 2014 <sup>[[#fn:r221|221]]</sup> ). However, there is ''robust evidence''  and ''high agreement'' that a net loss of forest and tree cover prevails in the tropics and a net gain, mainly of secondary, semi-natural and planted forests, in the temperate and boreal zones.
+| 0.48
+[0.26–0.79]
-The observed regional and global historical land-use trends result in regionally distinct patterns of C fluxes between land and the atmosphere (Figure 1.3B). They are also associated with declines in biodiversity, far above background rates (Ceballos et al. 2015 <sup>[[#fn:r222|222]]</sup> ; De Vos et al. 2015 <sup>[[#fn:r223|223]]</sup> ; Pimm et al. 2014 <sup>[[#fn:r224|224]]</sup> ; Newbold et al. 2015 <sup>[[#fn:r225|225]]</sup> ; Maxwell et al. 2016 <sup>[[#fn:r226|226]]</sup> ; Marques et al. 2019 <sup>[[#fn:r227|227]]</sup> ). Biodiversity losses from past global land-use change have been estimated to be about 8–14%, depending on the biodiversity indicator applied (Newbold et al. 2015 <sup>[[#fn:r228|228]]</sup> ; Wilting et al. 2017 <sup>[[#fn:r229|229]]</sup> ; Gossner et al. 2016 <sup>[[#fn:r230|230]]</sup> ; Newbold et al. 2018 <sup>[[#fn:r231|231]]</sup> ; Paillet et al. 2010 <sup>[[#fn:r232|232]]</sup> ). In future, climate warming has been projected to accelerate losses of species diversity rapidly (Settele et al. 2014 <sup>[[#fn:r|]]</sup> 233; Urban et al. 2016 <sup>[[#fn:r234|234]]</sup> ; Scholes et al. 2018 <sup>[[#fn:r235|235]]</sup> ; Fischer et al. 2018 <sup>[[#fn:r236|236]]</sup> ; Hoegh-Guldberg et al. 2018 <sup>[[#fn:r237|237]]</sup> ). The concomitance of land-use and climate change pressures render ecosystem restoration a key challenge (Anderson-Teixeira 2018 <sup>[[#fn:r238|238]]</sup> ; Yang et al. 2019 <sup>[[#fn:r240|240]]</sup> ) (Sections 4.8 and 4.9).
+| 0.75
+[0.52–1.11]
+| 0.68
+[0.45–1.08]
-</div>
+| 0.74
+[0.51–1.14]
-</div>
+|}
-</div>
-</div>
+Notes:
-</div>
+# Most recent reference period used in this report.
+# Most recent reference period used in AR5.
+# Difference between recent reference periods.
+# Current WMO standard reference periods.
+# Most recent 20-year period.
+# Linear trends estimated by a straight-line fit, expressed in degrees yr <sup>−1</sup> multiplied by 133 or 135 years respectively, with uncertainty ranges incorporating observational uncertainty only.
+# To estimate changes in the NOAAGlobalTemp and GISTEMP datasets relative to the 1850–1900 reference period, warming is computed relative to 1850–1900 using the HadCRUT4.6 dataset and scaled by the ratio of the linear trend 1880–2015 in the NOAAGlobalTemp or GISTEMP dataset with the corresponding linear trend computed from HadCRUT4.
+# Average of diagnostics derived – see (7) – from four peer-reviewed global datasets, HadCRUT4.6, NOAA, GISTEMP &amp; Cowtan-Way. Note that differences between averages may not coincide with average differences because of rounding.
+# No peer-reviewed publication available for these global combined land–sea datasets.
+# CMIP5 changes estimated relative to 1861–80 plus 0.02°C for the offset in HadCRUT4.6 from 1850–1900. CMIP5 values are the mean of the RCP8.5 ensemble, with 5–95% ensemble range. They are included to illustrate the difference between a complete global surface air temperature record (SAT) and a blended surface air and sea surface temperature (SST) record accounting for incomplete coverage (masked), following Richardson et al. (2016) <sup>[[#fn:r88|88]]</sup> . Note that 1986–2005 temperatures in CMIP5 appear to have been depressed more than observed temperatures by the eruption of Mount Pinatubo.
-</div>
-</div>
-<div>
-<div>
-<span id="key-challenges-related-to-land-use-change"></span>
-== 1.2 Key challenges related to land use change ==
-</div>
-<div class="section">
-<div>
-<div>
-<span id="land-system-change-land-degradation-desertification-and-food-security"></span>
-== 1.2.1 Land system change, land degradation, desertification and food security ==
-</div>
-<div id="section-1-2-1-1-future-trends-in-the-global-land-system">
-<div>
+== 1.2.1.3 Total versus human-induced warming and warming rates ==
-<div>
-<span id="future-trends-in-the-global-land-system"></span>
-== 1.2.1.1 Future trends in the global land system ==
-</div>
-<div>
-<div id="section-1-2-1-1-future-trends-in-the-global-land-system-block-1">
+Total warming refers to the actual temperature change, irrespective of cause, while human-induced warming refers to the component of that warming that is attributable to human activities. Mitigation studies focus on human-induced warming (that is not subject to internal climate variability), while studies of climate change impacts typically refer to total warming (often with the impact of internal variability minimised through the use of multi-decade averages).
-<div>
+In the absence of strong natural forcing due to changes in solar or volcanic activity, the difference between total and human-induced warming is small: assessing empirical studies quantifying solar and volcanic contributions to GMST from 1890 to 2010, AR5 (Figure 10.6 of Bindoff et al., 2013) <sup>[[#fn:r89|89]]</sup> found their net impact on warming over the full period to be less than plus or minus 0.1°C. Figure 1.2 shows that the level of human-induced warming has been indistinguishable from total observed warming since 2000, including over the decade 2006–2015. Bindoff et al. (2013) <sup>[[#fn:r90|90]]</sup> assessed the magnitude of human-induced warming over the period 1951–2010 to be 0.7°C ( ''likely'' between 0.6°C and 0.8°C), which is slightly greater than the 0.65°C observed warming over this period (Figures 10.4 and 10.5) with a ''likely'' range of ±14%. The key surface temperature attribution studies underlying this finding (Gillett et al., 2013; Jones et al., 2013; Ribes and Terray, 2013) <sup>[[#fn:r91|91]]</sup> used temperatures since the 19th century to constrain human-induced warming, and so their results are equally applicable to the attribution of causes of warming over longer periods. Jones et al. (2016) <sup>[[#fn:r92|92]]</sup> show (Figure 10) human-induced warming trends over the period 1905–2005 to be indistinguishable from the corresponding total observed warming trend accounting for natural variability using spatio-temporal detection patterns from 12 out of 15 CMIP5 models and from the multi-model average. Figures from Ribes and Terray (2013) <sup>[[#fn:r93|93]]</sup> , show the anthropogenic contribution to the observed linear warming trend 1880–2012 in the HadCRUT4 dataset (0.83°C in Table 1.1) to be 0.86°C using a multi-model average global diagnostic, with a 5–95% confidence interval of 0.72°C–1.00°C (see figure 1.SM.6). In all cases, since 2000 the estimated combined contribution of solar and volcanic activity to warming relative to 1850–1900 is found to be less than ±0.1°C (Gillett et al., 2013) <sup>[[#fn:r94|94]]</sup> , while anthropogenic warming is indistinguishable from, and if anything slightly greater than, the total observed warming, with 5–95% confidence intervals typically around ±20%.
-<div>
+Haustein et al. (2017) <sup>[[#fn:r95|95]]</sup> give a 5–95% confidence interval for human-induced warming in 2017 of 0.87°C–1.22°C, with a best estimate of 1.02°C, based on the HadCRUT4 dataset accounting for observational and forcing uncertainty and internal variability. Applying their method to the average of the four datasets shown in Figure 1.2 gives an average level of human-induced warming in 2017 of 1.04°C. They also estimate a human-induced warming trend over the past 20 years of 0.17°C (0.13°C–0.33°C) per decade, consistent with estimates of the total observed trend of Foster and Rahmstorf (2011) <sup>[[#fn:r96|96]]</sup> (0.17° ± 0.03°C per decade, uncertainty in linear trend only), Folland et al. (2018) <sup>[[#fn:r97|97]]</sup>
-<div>
+and Kirtman et al. (2013) <sup>[[#fn:r98|98]]</sup> (0.3°C–0.7°C over 30 years, or 0.1°C–0.23°C per decade, ''likely'' range), and a best-estimate warming rate over the past five years of 0.215°C/decade (Leach et al., 2018) <sup>[[#fn:r99|99]]</sup> . Drawing on these multiple lines of evidence, human-induced warming is assessed to have reached 1.0°C in 2017, having increased by 0.13°C from the mid-point of 2006–2015, with a ''likely'' range of 0.8°C to 1.2°C (reduced from 5–95% to account for additional forcing and model uncertainty), increasing at 0.2°C per decade (with a ''likely'' range of 0.1°C to 0.3°C per decade: estimates of human-induced warming given to 0.1°C precision only).
-Human population is projected to increase to nearly 9.8 (± 1) billion people by 2050 and 11.2 billion by 2100 (United Nations 2018 <sup>[[#fn:r241|241]]</sup> ). More people, a growing global middle class (Crist et al. 2017 <sup>[[#fn:r242|242]]</sup> ), economic growth, and continued urbanisation (Jiang and O’Neill 2017 <sup>[[#fn:r243|243]]</sup> ) increase the pressures on expanding crop and pasture area and intensifying land management. Changes in diets, efficiency and technology could reduce these pressures (Billen et al. 2015 <sup>[[#fn:r244|244]]</sup> ; Popp et al. 2016 <sup>[[#fn:r245|245]]</sup> ; Muller et al. 2017 <sup>[[#fn:r246|246]]</sup> ; Alexander et al. 2015 <sup>[[#fn:r247|247]]</sup> ; Springmann et al. 2018 <sup>[[#fn:r248|248]]</sup> ; Myers et al. 2017 <sup>[[#fn:r249|249]]</sup> ; Erb et al. 2016c <sup>[[#fn:r250|250]]</sup> ; FAO 2018b <sup>[[#fn:r251|251]]</sup> ) (Sections 5.3 and 6.2.2).
+Since warming is here defined in terms of a 30-year average, corrected for short-term natural fluctuations, when warming is considered to be at 1.5°C, global temperatures would fluctuate equally on either side of 1.5°C in the absence of a large cooling volcanic eruption (Bethke et al., 2017) <sup>[[#fn:r100|100]]</sup> . Figure 1.2 indicates there is a substantial chance of GMST in a single month fluctuating over 1.5°C between now and 2020 (or, by 2030, for a longer period: Henley and King, 2017) <sup>[[#fn:r101|101]]</sup> , but this would not constitute temperatures ‘reaching 1.5°C’ on our working definition. Rogelj et al. (2017) <sup>[[#fn:r102|102]]</sup> show limiting the probability of annual GMST exceeding 1.5°C to less than one-year-in-20 would require limiting warming, on the definition used here, to 1.31°C or lower.
-Given the large uncertainties underlying the many drivers of land use, as well as their complex relation to climate change and other biophysical constraints, future trends in the global land system are explored in scenarios and models that seek to span across these uncertainties (Cross-Chapter Box 1 in Chapter 1). Generally, these scenarios indicate a continued increase in global food demand, owing to population growth and increasing wealth. The associated land area needs are a key uncertainty, a function of the interplay between production, consumption, yields, and production efficiency (in particular for livestock and waste) (FAO 2018b; van Vuuren et al. 2017 <sup>[[#fn:r252|252]]</sup> ; Springmann et al. 2018 <sup>[[#fn:r253|253]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r254|254]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r255|255]]</sup> ; Ramankutty et al. 2018 <sup>[[#fn:r256|256]]</sup> ; Erb et al. 2016b <sup>[[#fn:r257|257]]</sup> ; Popp et al. 2016 <sup>[[#fn:r258|258]]</sup> ) (Section 1.3 and Cross-Chapter Box 1 in Chapter 1). Many factors, such as climate change, local contexts, education, human and social capital, policy-making, economic framework conditions, energy availability, degradation, and many more, affect this interplay, as discussed in all chapters of this report.
-</div>
-<div>
-Global telecouplings in the land system, the distal connections and multidirectional flows between regions and land systems, are expected to increase, due to urbanisation (Seto et al. 2012 <sup>[[#fn:r259|259]]</sup> ; van Vliet et al. 2017 <sup>[[#fn:r260|260]]</sup> ; Jiang and O’Neill 2017 <sup>[[#fn:r261|261]]</sup> ; Friis et al. 2016 <sup>[[#fn:r262|262]]</sup> ), and international trade (Konar et al. 2016 <sup>[[#fn:r263|263]]</sup> ; Erb et al. 2016b; Billen et al. 2015 <sup>[[#fn:r264|264]]</sup> ; Lassaletta et al. 2016 <sup>[[#fn:r265|265]]</sup> ). Telecoupling can support efficiency gains in production, but can also lead to complex cause–effect chains and indirect effects such as land competition or leakage (displacement of the environmental impacts; see Glossary), with governance challenges (Baldos and Hertel 2015 <sup>[[#fn:r266|266]]</sup> ; Kastner et al. 2014 <sup>[[#fn:r267|267]]</sup> ; Liu et al. 2013 <sup>[[#fn:r268|268]]</sup> ; Wood et al. 2018 <sup>[[#fn:r269|269]]</sup> ; Schröter et al. 2018 <sup>[[#fn:r270|270]]</sup> ; Lapola et al. 2010 <sup>[[#fn:r271|271]]</sup> ; Jadin et al. 2016 <sup>[[#fn:r272|272]]</sup> ; Erb et al. 2016b; Billen et al. 2015 <sup>[[#fn:r273|273]]</sup> ; Chaudhary and Kastner 2016 <sup>[[#fn:r274|274]]</sup> ; Marques et al. 2019 <sup>[[#fn:r275|275]]</sup> ; Seto and Ramankutty 2016 <sup>[[#fn:r276|276]]</sup> ) (Section 1.2.1.5). Furthermore, urban growth is anticipated to occur at the expense of fertile (crop)land, posing a food security challenge, in particular in regions of high population density and agrarian-dominated economies, with limited capacity to compensate for these losses (Seto et al. 2012 <sup>[[#fn:r277|277]]</sup> ; Güneralp et al. 2013 <sup>[[#fn:r278|278]]</sup> ; Aronson et al. 2014 <sup>[[#fn:r279|279]]</sup> ; Martellozzo et al. 2015 <sup>[[#fn:r280|280]]</sup> ; Bren d’Amour et al. 2016 <sup>[[#fn:r281|281]]</sup> ; Seto and Ramankutty 2016 <sup>[[#fn:r282|282]]</sup> ; van Vliet et al. 2017 <sup>[[#fn:r283|283]]</sup> ).
-Future climate change and increasing atmospheric CO <sub>2</sub> concentration are expected to accentuate existing challenges by, for example, shifting biomes or affecting crop yields (Baldos and Hertel 2015 <sup>[[#fn:r284|284]]</sup> ; Schlenker and Lobell 2010 <sup>[[#fn:r285|285]]</sup> ; Lipper et al. 2014 <sup>[[#fn:r286|286]]</sup> ; Challinor et al. 2014 <sup>[[#fn:r287|287]]</sup> ; Myers et al. 2017 <sup>[[#fn:r288|288]]</sup> ) (Section 5.2.2), as well as through land-based climate change mitigation. There is ''high confidence'' that large-scale implementation of bioenergy or afforestation can further exacerbate existing challenges (Smith et al. 2016 <sup>[[#fn:r289|289]]</sup> ) (Section 1.3.1 and Cross-Chapter Box 7 in Chapter 6).
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
+== 1.2.2 Global versus Regional and Seasonal Warming ==
-</div>
-<div id="section-1-2-1-2-land-degradation">
-<div>
-<div>
-<span id="land-degradation"></span>
-== 1.2.1.2 Land degradation ==
+Warming is not observed or expected to be spatially or seasonally uniform (Collins et al., 2013) <sup>[[#fn:r103|103]]</sup> . A 1.5°C increase in GMST will be associated with warming substantially greater than 1.5°C in many land regions, and less than 1.5°C in most ocean regions. This is illustrated by Figure 1.3, which shows an estimate of the observed change in annual and seasonal average temperatures between the 1850–1900 pre-industrial reference period and the decade 2006–2015 in the Cowtan-Way dataset. These regional changes are associated with an observed GMST increase of 0.91°C in the dataset shown here, or 0.87°C in the four-dataset average (Table 1.1). This observed pattern reflects an on-going transient warming: features such as enhanced warming over land may be less pronounced, but still present, in equilibrium (Collins et al., 2013) <sup>[[#fn:r104|104]]</sup> . This figure illustrates the magnitude of spatial and seasonal differences, with many locations, particularly in Northern Hemisphere mid-latitude winter (December–February), already experiencing regional warming more than double the global average. Individual seasons may be substantially warmer, or cooler, than these expected changes in the long-term average.
-</div>
-<div>
-<div id="section-1-2-1-2-land-degradation-block-1">
-<div>
-<div>
-<div>
-As discussed in Chapter 4, the concept of land degradation, including its definition, has been used in different ways in different communities and in previous assessments (such as the IPBES Land Degradation and Restoration Assessment). In the SRCCL, land degradation is defined as a ''negative trend in land condition, caused by direct or indirect human-induced processes including anthropogenic climate change, expressed as long-term reduction or loss of at least one of the following: biological productivity, ecological integrity or value to humans.'' This definition applies to forest and non-forest land (Chapter 4 and Glossary).
-Land degradation is a critical issue for ecosystems around the world due to the loss of actual or potential productivity or utility (Ravi et al. 2010 <sup>[[#fn:r291|291]]</sup> ; Mirzabaev et al. 2015 <sup>[[#fn:r292|292]]</sup> ; FAO and ITPS 2015 <sup>[[#fn:r293|293]]</sup> ; Cerretelli et al. 2018 <sup>[[#fn:r294|294]]</sup> ). Land degradation is driven to a large degree by unsustainable agriculture and forestry, socio-economic pressures, such as rapid urbanisation and population growth, and unsustainable production practices in combination with climatic factors (Field et al. 2014b <sup>[[#fn:r295|295]]</sup> ; Lal 2009 <sup>[[#fn:r296|296]]</sup> ; Beinroth et al. 1994 <sup>[[#fn:r297|297]]</sup> ; Abu Hammad and Tumeizi 2012 <sup>[[#fn:r298|298]]</sup> ; Ferreira et al. 2018 <sup>[[#fn:r299|299]]</sup> ; Franco and Giannini 2005 <sup>[[#fn:r300|300]]</sup> ; Abahussain et al. 2002 <sup>[[#fn:r301|301]]</sup> ).
+====== Figure 1.3 ======
-</div>
-</div>
-<div>
-<div>
+==== Spatial and seasonal pattern of present-day warming. ====
-<div>
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/Figure-1.3-1024x854.png]]
-<div>
+Regional warming for the 2006–2015 decade relative to 1850–1900 for the annual mean (top), the average of December, January, and February (bottom left) and for June, July, and August (bottom right). Warming is evaluated by regressing regional changes in the Cowtan and Way (2014) <sup>[[#fn:r105|105]]</sup> dataset onto the total (combined human and natural) externally forced warming (yellow line in Figure 1.2). See Supplementary Material 1.SM for further details and versions using alternative datasets. The definition of regions (green boxes and labels in top panel) is adopted from the AR5 (Christensen et al., 2013) <sup>[[#fn:r106|106]]</sup> .
-<div>
-Global estimates of the total degraded area vary from less than 10 million km <sup>2</sup> to over 60 million km <sup>2</sup> , with additionally large disagreement regarding the spatial distribution (Gibbs and Salmon 2015 <sup>[[#fn:r302|302]]</sup> ) (Section 4.3). The annual increase in the degraded land area has been estimated as 50,000–100,000 million km <sup>2</sup> yr <sup>–1</sup> (Stavi and Lal 2015 <sup>[[#fn:r303|303]]</sup> ), and the loss of total ecosystem services equivalent to about 10% of the world’s GDP in the year 2010 (Sutton et al. 2016 <sup>[[#fn:r304|304]]</sup> ). Although land degradation is a common risk across the globe, poor countries remain most vulnerable to its impacts. Soil degradation is of particular concern, due to the long period necessary to restore soils (Lal 2009; Stockmann et al. 2013 <sup>[[#fn:r305|305]]</sup> ; Lal 2015 <sup>[[#fn:r306|306]]</sup> ), as well as the rapid degradation of primary forests through fragmentation (Haddad et al. 2015 <sup>[[#fn:r307|307]]</sup> ). Among the most vulnerable ecosystems to degradation are high-carbon- stock wetlands (including peatlands). Drainage of natural wetlands for use in agriculture leads to high CO <sub>2</sub> emissions and degradation ( ''high confidence'' ) (Strack 2008 <sup>[[#fn:r308|308]]</sup> ; Limpens et al. 2008 <sup>[[#fn:r309|309]]</sup> ; Aich et al. 2014 <sup>[[#fn:r310|310]]</sup> ; Murdiyarso et al. 2015 <sup>[[#fn:r311|311]]</sup> ; Kauffman et al. 2016 <sup>[[#fn:r312|312]]</sup> ; Dohong et al. 2017 <sup>[[#fn:r313|313]]</sup> ; Arifanti et al. 2018 <sup>[[#fn:r314|314]]</sup> ; Evans et al. 2019 <sup>[[#fn:r315|315]]</sup> ). Land degradation is an important factor contributing to uncertainties in the mitigation potential of land-based ecosystems (Smith et al. 2014 <sup>[[#fn:r316|316]]</sup> ). Furthermore, degradation that reduces forest (and agricultural) biomass and soil organic carbon leads to higher rates of runoff ( ''high confidence'' ) (Molina et al. 2007 <sup>[[#fn:r317|317]]</sup> ; Valentin et al. 2008 <sup>[[#fn:r318|318]]</sup> ; Mateos et al. 2017 <sup>[[#fn:r319|319]]</sup> ; Noordwijk et al. 2017 <sup>[[#fn:r320|320]]</sup> ) and hence to increasing flood risk ( ''low confidence'' ) (Bradshaw et al. 2007 <sup>[[#fn:r321|321]]</sup> ; Laurance 2007 <sup>[[#fn:r322|322]]</sup> ; van Dijk et al. 2009 <sup>[[#fn:r323|323]]</sup> ).
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
+== 1.2.3 Definition of 1.5°C Pathways: Probability, Transience, Stabilization and Overshoot ==
-</div>
-<div id="section-1-2-1-3-desertification">
-<div>
-<div>
-<span id="desertification"></span>
-== 1.2.1.3 Desertification ==
+Pathways considered in this report, consistent with available literature on 1.5°C, primarily focus on the time scale up to 2100, recognising that the evolution of GMST after 2100 is also important. Two broad categories of 1.5°C pathways can be used to characterise mitigation options and impacts: pathways in which warming (defined as 30-year averaged GMST relative to pre-industrial levels, see Section 1.2.1) remains below 1.5°C throughout the 21st century, and pathways in which warming temporarily exceeds (‘overshoots’) 1.5°C and returns to 1.5°C either before or soon after 2100. Pathways in which warming exceeds 1.5°C before 2100, but might return to that level in some future century, are not considered 1.5°C pathways.
-</div>
+Because of uncertainty in the climate response, a ‘prospective’ mitigation pathway (see Cross-Chapter Box 1 in this chapter), in which emissions are prescribed, can only provide a level of probability of warming remaining below a temperature threshold. This probability cannot be quantified precisely since estimates depend on the method used (Rogelj et al., 2016b; Millar et al., 2017b; Goodwin et al., 2018; Tokarska and Gillett, 2018) <sup>[[#fn:r107|107]]</sup> . This report defines a ‘1.5°C pathway’ as a pathway of emissions and associated possible temperature responses in which the majority of approaches using presently available information assign a probability of approximately one-in-two to two-in-three to warming remaining below 1.5°C or, in the case of an overshoot pathway, to warming returning to 1.5°C by around 2100 or earlier. Recognizing the very different potential impacts and risks associated with high-overshoot pathways, this report singles out 1.5°C pathways with no or limited (&lt;0.1°C) overshoot in many instances and pursues efforts to ensure that when the term ‘1.5°C pathway’ is used, the associated overshoot is made explicit where relevant. In Chapter 2, the classification of pathways is based on one modelling approach to avoid ambiguity, but probabilities of exceeding 1.5°C are checked against other approaches to verify that they lie within this approximate range. All these absolute probabilities are imprecise, depend on the information used to constrain them, and hence are expected to evolve in the future. Imprecise probabilities can nevertheless be useful for decision-making, provided the imprecision is acknowledged (Hall et al., 2007; Kriegler et al., 2009; Simpson et al., 2016) <sup>[[#fn:r108|108]]</sup> . Relative and rank probabilities can be assessed much more consistently: approaches may differ on the absolute probability assigned to individual outcomes, but typically agree on which outcomes are more probable.
-<div>
-<div id="section-1-2-1-3-desertification-block-1">
+Importantly, 1.5°C pathways allow a substantial (up to one-in-two) chance of warming still exceeding 1.5°C. An ‘adaptive’ mitigation pathway in which emissions are continuously adjusted to achieve a specific temperature outcome (e.g., Millar et al., 2017b) <sup>[[#fn:r109|109]]</sup> reduces uncertainty in the temperature outcome while increasing uncertainty in the emissions required to achieve it. It has been argued (Otto et al., 2015; Xu and Ramanathan, 2017) <sup>[[#fn:r110|110]]</sup> that achieving very ambitious temperature goals will require such an adaptive approach to mitigation, but very few studies have been performed taking this approach (e.g., Jarvis et al., 2012) <sup>[[#fn:r111|111]]</sup> .
-<div>
+Figure 1.4 illustrates categories of (a) 1.5°C pathways and associated (b) annual and (c) cumulative emissions of CO <sub>2</sub> . It also shows (d) an example of a ‘time-integrated impact’ that continues to increase even after GMST has stabilised, such as sea level rise. This schematic assumes for the purposes of illustration that the fractional contribution of non-CO <sub>2</sub> climate forcers to total anthropogenic forcing (which is currently increasing, Myhre et al., 2017) <sup>[[#fn:r112|112]]</sup> is approximately constant from now on. Consequently, total human-induced warming is proportional to cumulative CO <sub>2</sub> emissions (solid line in c), and GMST stabilises when emissions reach zero. This is only the case in the most ambitious scenarios for non-CO <sub>2</sub> mitigation (Leach et al., 2018) <sup>[[#fn:r113|113]]</sup> . A simple way of accounting for varying non-CO <sub>2</sub> forcing in Figure 1.4 would be to note that every 1 W m <sup>−2</sup> increase in non-CO <sub>2</sub> forcing between now and the decade or two immediately prior to the time of peak warming reduces cumulative CO <sub>2</sub> emissions consistent with the same peak warming by approximately 1100 GtCO <sub>2</sub> , with a range of 900-1500 GtCO <sub>2</sub>  (using values from AR5: Myhre et al., 2013; Allen et al., 2018; Jenkins et al., 2018 <sup>[[#fn:r114|114]]</sup> ; Cross-Chapter Box 2 in this chapter).
-<div>
-<div>
-The SRCCL adopts the definition of the UNCCD of desertification, being land degradation in arid, semi-arid and dry sub-humid areas (drylands) (Glossary and Section 3.1.1). Desertification results from various factors, including climate variations and human activities, and is not limited to irreversible forms of land degradation (Tal 2010 <sup>[[#fn:r930|930]]</sup> ; Bai et al. 2008 <sup>[[#fn:r931|931]]</sup> ). A critical challenge in the assessment of desertification is to identify a ‘non-desertified’ reference state (Bestelmeyer et al. 2015 <sup>[[#fn:r324|324]]</sup> ). While climatic trends and variability can change the intensity of desertification processes, some authors exclude climate effects, arguing that desertification is a purely human-induced process of land degradation with different levels of severity and consequences (Sivakumar 2007 <sup>[[#fn:r325|325]]</sup> ).
-</div>
-<div>
-<div>
-<div>
-As a consequence of varying definitions and different methodologies, the area of desertification varies widely (D’Odorico et al. 2013 <sup>[[#fn:r326|326]]</sup> ; Bestelmeyer et al. 2015 <sup>[[#fn:r327|327]]</sup> ; and references therein). Arid regions of the world cover up to about 46% of the total terrestrial surface (about 60 million km <sup>2</sup> ) (Pravalie 2016 <sup>[[#fn:r328|328]]</sup> ; Koutroulis 2019 <sup>[[#fn:r329|329]]</sup> ). Around 3 billion people reside in dryland regions (D’Odorico et al. 2013 <sup>[[#fn:r330|330]]</sup> ; Maestre et al. 2016 <sup>[[#fn:r331|331]]</sup> ) (Section 3.1.1). In 2015, about 500 (360–620) million people lived within areas which experienced desertification between 1980s and 2000s (Figure 1.1and Section 3.1.1). The combination of low rainfall with frequently infertile soils renders these regions, and the people who rely on them, vulnerable to both climate change, and unsustainable land management ( ''high confidence'' ). In spite of the national, regional and international efforts to combat desertification, it remains one of the major environmental problems (Abahussain et al. 2002 <sup>[[#fn:r332|332]]</sup> ; Cherlet et al. 2018 <sup>[[#fn:r333|333]]</sup> ).
+== 1.2.3.1 Pathways remaining below 1.5°C ==
-</div>
-</div>
-</div>
-</div>
+In this category of 1.5°C pathways, human-induced warming either rises monotonically to stabilise at 1.5°C (Figure 1.4, brown lines) or peaks at or below 1.5°C and then declines (yellow lines). Figure 1.4b demonstrates that pathways remaining below 1.5°C require net annual CO <sub>2</sub> emissions to peak and decline to near zero or below, depending on the long-term adjustment of the carbon cycle and non-CO <sub>2</sub> emissions (Bowerman et al., 2013; Wigley, 2018) <sup>[[#fn:r115|115]]</sup> . Reducing emissions to zero corresponds to stabilizing cumulative CO <sub>2</sub> emissions (Figure 1.4c, solid lines) and falling concentrations of CO <sub>2</sub> in the atmosphere (panel c dashed lines) (Matthews and Caldeira, 2008; Solomon et al., 2009) <sup>[[#fn:r116|116]]</sup> , which is required to stabilize GMST if non-CO <sub>2</sub> climate forcings are constant and positive. Stabilizing atmospheric greenhouse gas concentrations would result in continued warming (see Section 1.2.4).
-</div>
+If emission reductions do not begin until temperatures are close to the proposed limit, pathways remaining below 1.5°C necessarily involve much faster rates of net CO <sub>2</sub> emission reductions (Figure 1.4, green lines), combined with rapid reductions in non-CO <sub>2</sub> forcing and these pathways also reach 1.5°C earlier. Note that the emissions associated with these schematic temperature pathways may not correspond to feasible emission scenarios, but they do illustrate the fact that the timing of net zero emissions does not in itself determine peak warming: what matters is total cumulative emissions up to that time. Hence every year’s delay before initiating emission reductions decreases by approximately two years the remaining time available to reach zero emissions on a pathway still remaining below 1.5°C (Allen and Stocker, 2013; Leach et al., 2018) <sup>[[#fn:r117|117]]</sup> .
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-2-1-4-food-security-food-systems-and-linkages-to-land-based-ecosystems">
-<div>
-<div>
-<span id="food-security-food-systems-and-linkages-to-land-based-ecosystems"></span>
-== 1.2.1.4 Food security, food systems and linkages to land-based ecosystems ==
-</div>
-<div>
-<div id="section-1-2-1-4-food-security-food-systems-and-linkages-to-land-based-ecosystems-block-1">
+== 1.2.3.2 Pathways temporarily exceeding 1.5°C ==
-<div>
-<div>
-<div>
-The High Level Panel of Experts of the Committee on Food Security define the food system as to “gather all the elements (environment, people, inputs, processes, infrastructures, institutions, etc.) and activities that relate to the production, processing, distribution, preparation and consumption of food, and the output of these activities, including socio-economic and environmental outcomes” (HLPE 2017 <sup>[[#fn:r334|334]]</sup> ). Likewise, food security has been defined as “a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (FAO 2017 <sup>[[#fn:r335|335]]</sup> ). By this definition, food security is characterised by food availability, economic and physical access to food, food utilisation and food stability over time. Food and nutrition security is one of the key outcomes of the food system (FAO 2018b <sup>[[#fn:r336|336]]</sup> ; Figure 1.4).
-After a prolonged decline, world hunger appears to be on the rise again, with the number of undernourished people having increased to an estimated 821 million in 2017, up from 804 million in 2016 and 784 million in 2015, although still below the 900 million reported in 2000 (FAO et al. 2018 <sup>[[#fn:r337|337]]</sup> ) (Section 5.1.2). Of the total undernourished in 2018, for example, 256.5 million lived in Africa, and 515.1 million in Asia (excluding Japan). The same FAO report also states that child undernourishment continues to decline, but levels of overweight populations and obesity are increasing. The total number of overweight children in 2017 was 38–40 million worldwide, and globally up to around two billion adults are by now overweight (Section 5.1.2). FAO also estimated that close to 2000 million people suffer from micronutrient malnutrition (FAO 2018b <sup>[[#fn:r338|338]]</sup> ).
+With the pathways in this category, also referred to as overshoot pathways, GMST rises above 1.5°C relative to pre-industrial before peaking and returning to 1.5°C around or before 2100 (Figure 1.4, blue lines), subsequently either stabilising or continuing to fall. This allows initially slower or delayed emission reductions, but lowering GMST requires net negative global CO <sub>2</sub> emissions (net anthropogenic removal of CO <sub>2</sub> ; Figure 1.4b). Cooling, or reduced warming, through sustained reductions of net non-CO <sub>2</sub> climate forcing (Cross-Chapter Box 2 in this chapter) is also required, but their role is limited because emissions of most non-CO <sub>2</sub> forcers cannot be reduced to below zero. Hence the feasibility and availability of large-scale CO <sub>2</sub> removal limits the possible rate and magnitude of temperature decline. In this report, overshoot pathways are referred to as 1.5°C pathways, but qualified by the amount of the temperature overshoot, which can have a substantial impact on irreversible climate change impacts (Mathesius et al., 2015; Tokarska and Zickfeld, 2015) <sup>[[#fn:r118|118]]</sup> .
-Food insecurity most notably occurs in situations of conflict, and conflict combined with droughts or floods (Cafiero et al. 2018 <sup>[[#fn:r339|339]]</sup> ; Smith et al. 2017 <sup>[[#fn:r340|340]]</sup> ). The close parallel between food insecurity prevalence and poverty means that tackling development priorities would enhance sustainable land use options for climate mitigation.
-<div>
-<div>
-<div>
-Climate change affects the food system as changes in trends and variability in rainfall and temperature variability impact crop and livestock productivity and total production (Osborne and Wheeler 2013 <sup>[[#fn:r341|341]]</sup> ; Tigchelaar et al. 2018 <sup>[[#fn:r342|342]]</sup> ; Iizumi and Ramankutty 2015 <sup>[[#fn:r343|343]]</sup> ), the nutritional quality of food (Loladze 2014 <sup>[[#fn:r344|344]]</sup> ; Myers et al. 2014 <sup>[[#fn:r345|345]]</sup> ; Ziska et al. 2016 <sup>[[#fn:r346|346]]</sup> ; Medek et al. 2017 <sup>[[#fn:r347|347]]</sup> ), water supply (Nkhonjera 2017 <sup>[[#fn:r348|348]]</sup> ), and incidence of pests and diseases (Curtis et al. 2018 <sup>[[#fn:r349|349]]</sup> ). These factors also impact on human health, increasing morbidity and affecting human ability to process ingested food (Franchini and Mannucci 2015 <sup>[[#fn:r350|350]]</sup> ; Wu et al. 2016 <sup>[[#fn:r351|351]]</sup> ; Raiten and Aimone 2017 <sup>[[#fn:r352|352]]</sup> ). At the same time, the food system generates negative externalities (the environmental effects of production and consumption) in the form of GHG emissions
-</div>
-<div>
-<div>
-<div>
-<div>
+== 1.2.3.3 Impacts at 1.5°C warming associated with different pathways: transience versus stabilisation ==
-<div>
-(Sections 1.1.2 and 2.3), pollution (van Noordwijk and Brussaard 2014 <sup>[[#fn:r353|353]]</sup> ; Thyberg and Tonjes 2016 <sup>[[#fn:r354|354]]</sup> ; Borsato et al. 2018 <sup>[[#fn:r355|355]]</sup> ; Kibler et al. 2018 <sup>[[#fn:r356|356]]</sup> ), water quality (Malone et al. 2014 <sup>[[#fn:r357|357]]</sup> ; Norse and Ju 2015 <sup>[[#fn:r358|358]]</sup> ), and ecosystem services loss (Schipper et al. 2014 <sup>[[#fn:r359|359]]</sup> ; Eeraerts et al. 2017 <sup>[[#fn:r360|360]]</sup> ) with direct and indirect impacts on climate change and reduced resilience to climate variability. As food systems are assessed in relation to their contribution to global warming and/or to land degradation (e.g., livestock systems) it is critical to evaluate their contribution to food security and livelihoods and to consider alternatives, especially for developing countries where food insecurity is prevalent (Röös et al. 2017 <sup>[[#fn:r361|361]]</sup> ; Salmon et al. 2018 <sup>[[#fn:r362|362]]</sup> ).
-</div>
-</div>
+Figure 1.4 also illustrates time scales associated with different impacts. While many impacts scale with the change in GMST itself, some (such as those associated with ocean acidification) scale with the change in atmospheric CO <sub>2</sub> concentration, indicated by the fraction of cumulative CO <sub>2</sub> emissions remaining in the atmosphere (dotted lines in Figure 1.4c). Others may depend on the rate of change of GMST, while ‘time-integrated impacts’, such as sea level rise, shown in Figure 1.4d continue to increase even after GMST has stabilised.
-</div>
+Hence impacts that occur when GMST reaches 1.5°C could be very different depending on the pathway to 1.5°C. CO <sub>2</sub> concentrations will be higher as GMST rises past 1.5°C (transient warming) than when GMST has stabilized at 1.5°C, while sea level and, potentially, global mean precipitation (Pendergrass et al., 2015) <sup>[[#fn:r119|119]]</sup> would both be lower (see Figure 1.4). These differences could lead to very different impacts on agriculture, on some forms of extreme weather (e.g., Baker et al., 2018) <sup>[[#fn:r120|120]]</sup> , and on marine and terrestrial ecosystems (e.g., Mitchell et al., 2017 <sup>[[#fn:r121|121]]</sup> and Boxes 3.1 and 3.2). Sea level would be higher still if GMST returns to 1.5°C after an overshoot (Figure 1.4 d), with potentially significantly different impacts in vulnerable regions. Temperature overshoot could also cause irreversible impacts (see Chapter 3).
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-2-1-4-food-security-food-systems-and-linkages-to-land-based-ecosystems-block-2">
-<div class="figure">
-<span id="figure-1.4"></span>
 ====== Figure 1.4 ======
-<span id="food-system-and-its-relations-to-land-and-climatethe-food-system-is-conceptualised-through-supply-production-processing-marketing-and-retailing-and-demand-consumption-and-diets-that-are-shaped-by-physical-economic-social-and-cultural-determinants-influencing-choices-access-utilisation-quality-safety-and-waste.-food-system-drivers-ecosystem-services-economics-and-technology-social-and-cultural-norms"></span>
-==== Food system (and its relations to land and climate):The food system is conceptualised through supply (production, processing, marketing and retailing) and demand (consumption and diets) that are shaped by physical, economic, social and cultural determinants influencing choices, access, utilisation, quality, safety and waste. Food system drivers (ecosystem services, economics and technology, social and cultural norms […] ====
-[[File:https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/Figure-1.4-1024x699.jpg]]
-<div>
-Food system (and its relations to land and climate):The food system is conceptualised through supply (production, processing, marketing and retailing) and demand (consumption and diets) that are shaped by physical, economic, social and cultural determinants influencing choices, access, utilisation, quality, safety and waste. Food system drivers (ecosystem services, economics and technology, social and cultural norms and traditions, and demographics) combine with the enabling conditions (policies, institutions and governance) to affect food system outcomes including food security, nutrition and health, livelihoods, economic and cultural benefits as well as environmental outcomes or side-effects (nutrient and soil loss, water use and quality, GHG emissions and other pollutants). Climate and climate change have direct impacts on the food system (productivity, variability, nutritional quality) while the latter contributes to local climate (albedo, evapotranspiration) and global warming (GHGs). The land system (function, structures, and processes) affects the food system directly (food production) and indirectly (ecosystem services) while food demand and supply processes affect land (land-use change) and land-related processes (e.g., land degradation, desertification) (Chapter 5).
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-2-1-5-challenges-arising-from-land-governance">
-<div>
-<div>
-<span id="challenges-arising-from-land-governance"></span>
-== 1.2.1.5 Challenges arising from land governance ==
-</div>
-<div>
-<div id="section-1-2-1-5-challenges-arising-from-land-governance-block-1">
-<div>
-<div>
+==== Different 1.5°C pathways Schematic <sup>[[#fn:1|1]]</sup> illustration of the relationship between (a) global mean surface temperature (GMST) change; (b) annual rates of CO <sub>2</sub> emissions, assuming constant fractional contribution of non-CO <sub>2</sub> forcing to total human-induced warming; (c) total cumulative CO <sub>2</sub> emissions (solid lines) and the fraction thereof remaining in the atmosphere (dashed lines; these also indicates changes in atmospheric CO <sub>2</sub> concentrations); and (d) a time-integrated impact, such as sea level rise, that continues to increase even after GMST has stabilized. ====
-<div>
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/figure-1.4-1024x717.png]]
-<div>
+Different 1.5°C pathways Schematic <sup>[[#fn:1|1]]</sup> illustration of the relationship between (a) global mean surface temperature (GMST) change; (b) annual rates of CO <sub>2</sub> emissions, assuming constant fractional contribution of non-CO <sub>2</sub> forcing to total human-induced warming; (c) total cumulative CO <sub>2</sub> emissions (solid lines) and the fraction thereof remaining in the atmosphere (dashed lines; these also indicates changes in atmospheric CO <sub>2</sub> concentrations); and (d) a time-integrated impact, such as sea level rise, that continues to increase even after GMST has stabilized. Colours indicate different 1.5°C pathways. Brown: GMST remaining below and stabilizing at 1.5°C in 2100; Green: a delayed start but faster emission reductions pathway with GMST remaining below and reaching 1.5°C earlier; Blue: a pathway temporarily exceeding 1.5°C, with temperatures reduced to 1.5°C by net negative CO <sub>2</sub> emissions after temperatures peak; and Yellow: a pathway peaking at 1.5°C and subsequently declining. Temperatures are anchored to 1°C above pre-industrial in 2017; emissions–temperature relationships are computed using a simple climate model (Myhre et al., 2013; Millar et al., 2017a; Jenkins et al., 2018) <sup>[[#fn:r122|122]]</sup> with a lower value of the Transient Climate Response (TCR) than used in the quantitative pathway assessments in Chapter 2 to illustrate qualitative differences between pathways: this figure is not intended to provide quantitative information. The time-integrated impact is illustrated by the semi-empirical sea level rise model of Kopp et al. (2016) <sup>[[#fn:r123|123]]</sup> .
-Land-use change has both positive and negative effects: it can lead to economic growth, but it can become a source of tension and social unrest leading to elite capture, and competition (Haberl 2015 <sup>[[#fn:r363|363]]</sup> ). Competition for land plays out continuously among different use types (cropland, pastureland, forests, urban spaces, and conservation and protected lands) and between different users within the same land-use category (subsistence vs commercial farmers) (Dell’Angelo et al. 2017b <sup>[[#fn:r364|364]]</sup> ). Competition is mediated through economic and market forces (expressed through land rental and purchases, as well as trade and investments). In the context of such transactions, power relations often disfavour disadvantaged groups such as small-scale farmers, indigenous communities or women (Doss et al. 2015 <sup>[[#fn:r365|365]]</sup> ; Ravnborg et al. 2016 <sup>[[#fn:r366|366]]</sup> ). These drivers are influenced to a large degree by policies, institutions and governance structures. Land governance determines not only who can access the land, but also the role of land ownership (legal, formal, customary or collective) which influences land use, land-use change and the resulting land competition (Moroni 2018 <sup>[[#fn:r367|367]]</sup> ).
-<div>
-<div>
-<div>
+<div id="section-1-2-3-3-block-3" class="box">
-<div>
-Globally, there is competition for land because it is a finite resource and because most of the highly productive land is already exploited by humans (Lambin and Meyfroidt 2011 <sup>[[#fn:r368|368]]</sup> ; Lambin 2012 <sup>[[#fn:r369|369]]</sup> ; Venter et al. 2016 <sup>[[#fn:r370|370]]</sup> ). Driven by growing population, urbanisation, demand for food and energy, as well as land degradation, competition for land is expected to accentuate land scarcity in the future (Tilman et al. 2011 <sup>[[#fn:r371|371]]</sup> ; Foley et al. 2011 <sup>[[#fn:r372|372]]</sup> ; Lambin 2012 <sup>[[#fn:r373|373]]</sup> ; Popp et al. 2016 <sup>[[#fn:r374|374]]</sup> ) ( ''robust evidence, high agreement'' ). Climate change influences land use both directly and indirectly, as climate policies can also a play a role in increasing land competition via forest conservation policies, afforestation, or energy crop production (Section 1.3.1), with the potential for implications for food security (Hussein et al. 2013 <sup>[[#fn:r375|375]]</sup> ) and local land-ownership.
-<div>
-<div>
+== Cross-Chapter Box 1: Scenarios and Pathways ==
-<div>
+====== Lead Authors ======
-An example of large-scale change in land ownership is the much-debated large-scale land acquisition (LSLA) by investors which peaked in 2008 during the food price crisis, the financial crisis, and has also been linked to the search for biofuel investments (Dell’Angelo et al. 2017a <sup>[[#fn:r376|376]]</sup> ). Since 2000, almost 50 million hectares of land have been acquired, and there are no signs of stagnation in the foreseeable future (Land Matrix 2018 <sup>[[#fn:r377|377]]</sup> ).The LSLA phenomenon, which largely targets agriculture, is widespread, including Sub-Saharan Africa, Southeast Asia, Eastern Europe and Latin America (Rulli et al. 2012 <sup>[[#fn:r378|378]]</sup> ; Nolte et al. 2016 <sup>[[#fn:r379|379]]</sup> ; Constantin et al. 2017 <sup>[[#fn:r380|380]]</sup> ). LSLAs are promoted by investors and host governments on economic grounds (infrastructure, employment, market development) (Deininger et al. 2011 <sup>[[#fn:r381|381]]</sup> ), but their social and environmental impacts can be negative and significant (Dell’Angelo et al. 2017a <sup>[[#fn:r382|382]]</sup> ).
+* Mikiko Kainuma (Japan)
+* Kristie L. Ebi (United States)
+* Sabine Fuss (Germany)
+* Elmar Kriegler (Germany)
+* Keywan Riahi (Austria)
+* Joeri Rogelj (Austria, Belgium)
+* Petra Tschakert (Australia, Austria)
+* Rachel Warren (United Kingdom)
-Much of the criticism of LSLA focuses on its social impacts, especially the threat to local communities’ land rights (especially indigenous people and women) (Anseeuw et al. 2011 <sup>[[#fn:r383|383]]</sup> ) and displaced communities creating secondary land expansion (Messerli et al. 2014 <sup>[[#fn:r384|384]]</sup> ; Davis et al. 2015 <sup>[[#fn:r385|385]]</sup> ). The promises that LSLAs would develop efficient agriculture on non-forested, unused land (Deininger et al. 2011 <sup>[[#fn:r386|386]]</sup> ) has so far not been fulfilled. However, LSLA is not the only outcome of weak land governance structures (Wang et al. 2016 <sup>[[#fn:r387|387]]</sup> ): other forms of inequitable or irregular land acquisition can also be home-grown, pitting one community against a more vulnerable group (Xu 2018 <sup>[[#fn:r388|388]]</sup> ) or land capture by urban elites (McDonnell 2017 <sup>[[#fn:r389|389]]</sup> ). As demands on land are increasing, building governance capacity and securing land tenure becomes essential to attain sustainable land use, which has the potential to mitigate climate change, promote food security, and potentially reduce risks of climate-induced migration and associated risks of conflicts (Section 7.6).
-</div>
-</div>
-</div>
+Climate change scenarios have been used in IPCC assessments since the First Assessment Report (Leggett et al., 1992) <sup>[[#fn:r124|124]]</sup> . The '''SRES scenarios''' (named after the IPCC Special Report on Emissions Scenarios published in 2000; IPCC, 2000) <sup>[[#fn:r125|125]]</sup> , consist of four scenarios that do not take into account any future measures to limit greenhouse gas (GHG) emissions. Subsequently, many policy scenarios have been developed based upon them (Morita et al., 2001) <sup>[[#fn:r126|126]]</sup> . The SRES scenarios are superseded by a set of scenarios based on the Representative Concentration Pathways (RCPs) and Shared Socio-Economic Pathways (SSPs) (Riahi et al., 2017) <sup>[[#fn:r127|127]]</sup> . The RCPs comprise a set of four GHG concentration trajectories that jointly span a large range of plausible human-caused climate forcing ranging from 2.6 W m <sup>−2</sup> (RCP2.6) to 8.5 W m <sup>−2</sup> (RCP8.5) by the end of the 21st century (van Vuuren et al., 2011) <sup>[[#fn:r128|128]]</sup> . They were used to develop climate projections in the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012) <sup>[[#fn:r129|129]]</sup> and were assessed in the IPCC Fifth Assessment Report (AR5). Based on the CMIP5 ensemble, RCP2.6, provides a better than two-in-three chance of staying below 2°C and a median warming of 1.6°C relative to 1850–1900 in 2100 (Collins et al., 2013) <sup>[[#fn:r130|130]]</sup> .
+The SSPs were developed to complement the RCPs with varying socio-economic challenges to adaptation and mitigation. SSP-based scenarios were developed for a range of climate forcing levels, including the end-of-century forcing levels of the RCPs (Riahi et al., 2017) <sup>[[#fn:r131|131]]</sup> and a level below RCP2.6 to explore pathways limiting warming to 1.5°C above pre-industrial levels (Rogelj et al., 2018) <sup>[[#fn:r132|132]]</sup> . The SSP-based 1.5°C pathways are assessed in Chapter 2 of this report. These scenarios offer an integrated perspective on socio-economic, energy-system (Bauer et al., 2017) <sup>[[#fn:r133|133]]</sup> , land use (Popp et al., 2017) <sup>[[#fn:r134|134]]</sup> , air pollution (Rao et al., 2017) <sup>[[#fn:r135|135]]</sup> and, GHG emissions developments (Riahi et al., 2017) <sup>[[#fn:r136|136]]</sup> . Because of their harmonised assumptions, scenarios developed with the SSPs facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation and mitigation.
-</div>
+'''Scenarios and Pathways in this Report'''
-</div>
+This report focuses on pathways that could limit the increase of global mean surface temperature (GMST) to 1.5°C above pre-industrial levels and pathways that align with the goals of sustainable development and poverty eradication. The pace and scale of mitigation and adaptation are assessed in the context of historical evidence to determine where unprecedented change is required (see Chapter 4). Other scenarios are also assessed, primarily as benchmarks for comparison of mitigation, impacts, and/or adaptation requirements. These include baseline scenarios that assume no climate policy; scenarios that assume some kind of continuation of current climate policy trends and plans, many of which are used to assess the implications of the nationally determined contributions (NDCs); and scenarios holding warming below 2°C above pre-industrial levels. This report assesses the spectrum from global mitigation scenarios to local adaptation choices – complemented by a bottom-up assessment of individual mitigation and adaptation options, and their implementation (policies, finance, institutions, and governance, see Chapter 4). Regional, national, and local scenarios, as well as decision-making processes involving values and difficult trade-offs are important for understanding the challenges of limiting GMST increase to 1.5°C and are thus indispensable when assessing implementation.
-</div>
+Different climate policies result in different temperature pathways, which result in different levels of climate risks and actual climate impacts with associated long-term implications. Temperature pathways are classified into continued warming pathways (in the cases of baseline and reference scenarios), pathways that keep the temperature increase below a specific limit (like 1.5°C or 2°C), and pathways that temporarily exceed and later fall to a specific limit (overshoot pathways). In the case of a temperature overshoot, net negative CO <sub>2</sub> emissions are required to remove excess CO <sub>2</sub> from the atmosphere (Section 1.2.3).
-</div>
+In a ‘prospective’ mitigation pathway, emissions (or sometimes concentrations) are prescribed, giving a range of GMST outcomes because of uncertainty in the climate response. Prospective pathways are considered ‘1.5°C pathways’ in this report if, based on current knowledge, the majority of available approaches assign an approximate probability of one-in-two to two-in-three to temperatures either remaining below 1.5°C or returning to 1.5°C either before or around 2100. Most pathways assessed in Chapter 2 are prospective pathways, and therefore even ‘1.5°C pathways’ are also associated with risks of warming higher than 1.5°C, noting that many risks increase non-linearly with increasing GMST. In contrast, the ‘risks of warming of 1.5°C’ assessed in Chapter 3 refer to risks in a world in which GMST is either passing through (transient) or stabilized at 1.5°C, without considering probabilities of different GMST levels (unless otherwise qualified). To stay below any desired temperature limit, mitigation measures and strategies would need to be adjusted as knowledge of the climate response is updated (Millar et al., 2017b; Emori et al., 2018) <sup>[[#fn:r137|137]]</sup> . Such pathways can be called ‘adaptive’ mitigation pathways. Given there is always a possibility of a greater-than-expected climate response (Xu and Ramanathan, 2017) <sup>[[#fn:r138|138]]</sup> , adaptive mitigation pathways are important to minimise climate risks, but need also to consider the risks and feasibility (see Cross-Chapter Box 3 in this chapter) of faster-than-expected emission reductions. Chapter 5 includes assessments of two related topics: aligning mitigation and adaptation pathways with sustainable development pathways, and transformative visions for the future that would support avoiding negative impacts on the poorest and most disadvantaged populations and vulnerable sectors.
-</div>
+'''Definitions of Scenarios and Pathways'''
-</div>
+Climate scenarios and pathways are terms that are sometimes used interchangeably, with a wide range of overlapping definitions (Rosenbloom, 2017) <sup>[[#fn:r139|139]]</sup> .
-</div>
+A ‘ '''scenario’''' is an internally consistent, plausible, and integrated description of a possible future of the human–environment system, including a narrative with qualitative trends and quantitative projections (IPCC, 2000) <sup>[[#fn:r140|140]]</sup> . Climate change scenarios provide a framework for developing and integrating projections of emissions, climate change, and climate impacts, including an assessment of their inherent uncertainties. The long-term and multi-faceted nature of climate change requires climate scenarios to describe how socio-economic trends in the 21st century could influence future energy and land use, resulting emissions and the evolution of human vulnerability and exposure. Such driving forces include population, GDP, technological innovation, governance and lifestyles. Climate change scenarios are used for analysing and contrasting climate policy choices.
-</div>
+The notion of a '''‘pathway’''' can have multiple meanings in the climate literature. It is often used to describe the temporal evolution of a set of scenario features, such as GHG emissions and socio-economic development. As such, it can describe individual scenario components or sometimes be used interchangeably with the word ‘scenario’. For example, the RCPs describe GHG concentration trajectories (van Vuuren et al., 2011) <sup>[[#fn:r141|141]]</sup> and the SSPs are a set of narratives of societal futures augmented by quantitative projections of socio-economic determinants such as population, GDP and urbanization (Kriegler et al., 2012; O’Neill et al., 2014) <sup>[[#fn:r142|142]]</sup> . Socio-economic driving forces consistent with any of the SSPs can be combined with a set of climate policy assumptions (Kriegler et al., 2014) <sup>[[#fn:r143|143]]</sup> that together would lead to emissions and concentration outcomes consistent with the RCPs (Riahi et al., 2017) <sup>[[#fn:r144|144]]</sup> . This is at the core of the scenario framework for climate change research that aims to facilitate creating scenarios integrating emissions and development pathways dimensions (Ebi et al., 2014; van Vuuren et al., 2014) <sup>[[#fn:r145|145]]</sup> .
-</div>
+In other parts of the literature, ‘pathway’ implies a solution-oriented trajectory describing a pathway from today’s world to achieving a set of future goals. '''Sustainable Development Pathways''' describe national and global pathways where climate policy becomes part of a larger sustainability transformation (Shukla and Chaturvedi, 2013; Fleurbaey et al., 2014; van Vuuren et al., 2015) <sup>[[#fn:r146|146]]</sup> . The AR5 presented '''c''' '''limate-''' '''r''' '''esilient pathways''' as sustainable development pathways that combine the goals of adaptation and mitigation (Denton et al., 2014) <sup>[[#fn:r147|147]]</sup> , more broadly defined as iterative processes for managing change within complex systems in order to reduce disruptions and enhance opportunities associated with climate change (IPCC, 2014a) <sup>[[#fn:r148|148]]</sup> . The AR5 also introduced the notion of '''climate-resilient development pathways,''' with a more explicit focus on dynamic livelihoods, multi-dimensional poverty, structural inequalities, and equity among poor and non-poor people (Olsson et al., 2014) <sup>[[#fn:r149|149]]</sup> . '''A''' '''daptation pathways''' are understood as a series of adaptation choices involving trade-offs between short-term and long-term goals and values (Reisinger et al., 2014) <sup>[[#fn:r150|150]]</sup> . They are decision-making processes sequenced over time with the purpose of deliberating and identifying socially salient solutions in specific places (Barnett et al., 2014; Wise et al., 2014; Fazey et al., 2016) <sup>[[#fn:r151|151]]</sup> . There is a range of possible pathways for transformational change, often negotiated through iterative and inclusive processes (Harris et al., 2017; Fazey et al., 2018; Tàbara et al., 2018) <sup>[[#fn:r152|152]]</sup> .
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="section">
-<div>
-<div>
-<span id="progress-in-dealing-with-uncertainties-in-assessing-land-processes-in-the-climate-system"></span>
-== 1.2.2 Progress in dealing with uncertainties in assessing land processes in the climate system ==
-</div>
-<div id="section-1-2-2-1-concepts-related-to-risk-uncertainty-and-confidence">
-<div>
-<div>
-<span id="concepts-related-to-risk-uncertainty-and-confidence"></span>
-== 1.2.2.1 Concepts related to risk, uncertainty and confidence ==
-</div>
+== 1.2.4 Geophysical Warming Commitment ==
-<div>
-<div id="section-1-2-2-1-concepts-related-to-risk-uncertainty-and-confidence-block-1">
-<div>
-<div>
-<div>
-In context of the SRCCL, risk refers to the potential for the adverse consequences for human or (land-based) ecological systems, arising from climate change or responses to climate change. Risk related to climate change impacts integrates across the hazard itself, the time of exposure and the vulnerability of the system; the assessment of all three of these components, their interactions and outcomes, is uncertain (see Glossary for expanded definition, and Section 7.1.2). For instance, a risk to human society is the continued loss of productive land which might arise from climate change, mismanagement, or a combination of both factors. However, risk can also arise from the potential for adverse consequences from responses to climate change, such as widespread deployment of bioenergy which is intended to reduce GHG emissions and thus limit climate change, but can present its own risks to food security (Chapters 5–7).
+It is frequently asked whether limiting warming to 1.5°C is ‘feasible’ (Cross-Chapter Box 3 in this chapter). There are many dimensions to this question, including the warming ‘commitment’ from past emissions of greenhouse gases and aerosol precursors. Quantifying commitment from past emissions is complicated by the very different behaviour of different climate forcers affected by human activity: emissions of long-lived greenhouse gases such as CO <sub>2</sub> and nitrous oxide (N <sub>2</sub> O) have a very persistent impact on radiative forcing (Myhre et al., 2013) <sup>[[#fn:r153|153]]</sup> , lasting from over a century (in the case of N <sub>2</sub> O) to hundreds of thousands of years (for CO <sub>2</sub> ). The radiative forcing impact of short-lived climate forcers (SLCFs) such as methane (CH <sub>4</sub> ) and aerosols, in contrast, persists for at most about a decade (in the case of methane) down to only a few days. These different behaviours must be taken into account in assessing the implications of any approach to calculating aggregate emissions (Cross-Chapter Box 2 in this chapter).
-<div>
+Geophysical warming commitment is defined as the unavoidable future warming resulting from physical Earth system inertia. Different variants are discussed in the literature, including (i) the ‘constant composition commitment’ (CCC), defined by Meehl et al. (2007) <sup>[[#fn:r154|154]]</sup> as the further warming that would result if atmospheric concentrations of GHGs and other climate forcers were stabilised at the current level; and (ii) and the ‘zero emissions commitment’ (ZEC), defined as the further warming that would still occur if all future anthropogenic emissions of greenhouse gases and aerosol precursors were eliminated instantaneously (Meehl et al., 2007; Collins et al., 2013) <sup>[[#fn:r155|155]]</sup> .
-<div>
+The CCC is primarily associated with thermal inertia of the ocean (Hansen et al., 2005) <sup>[[#fn:r156|156]]</sup> , and has led to the misconception that substantial future warming is inevitable (Matthews and Solomon, 2013) <sup>[[#fn:r157|157]]</sup> . The CCC takes into account the warming from past emissions, but also includes warming from future emissions (declining but still non-zero) that are required to maintain a constant atmospheric composition. It is therefore not relevant to the warming commitment from past emissions alone.
-<div>
+The ZEC, although based on equally idealised assumptions, allows for a clear separation of the response to past emissions from the effects of future emissions. The magnitude and sign of the ZEC depend on the mix of GHGs and aerosols considered. For CO <sub>2</sub> , which takes hundreds of thousands of years to be fully removed from the atmosphere by natural processes following its emission (Eby et al., 2009; Ciais et al., 2013) <sup>[[#fn:r158|158]]</sup> , the multi-century warming commitment from emissions to date in addition to warming already observed is estimated to range from slightly negative (i.e., a slight cooling relative to present-day) to slightly positive (Matthews and Caldeira, 2008; Lowe et al., 2009; Gillett et al., 2011; Collins et al., 2013) <sup>[[#fn:r159|159]]</sup> . Some studies estimate a larger ZEC from CO <sub>2</sub> , but for cumulative emissions much higher than those up to present day (Frölicher et al., 2014; Ehlert and Zickfeld, 2017) <sup>[[#fn:r160|160]]</sup> . The ZEC from past CO <sub>2</sub> emissions is small because the continued warming effect from ocean thermal inertia is approximately balanced by declining radiative forcing due to CO <sub>2</sub> uptake by the ocean (Solomon et al., 2009; Goodwin et al., 2015; Williams et al., 2017) <sup>[[#fn:r161|161]]</sup> . Thus, although present-day CO <sub>2</sub> -induced warming is irreversible on millennial time scales (without human intervention such as active carbon dioxide removal or solar radiation modification; Section 1.4.1), past CO <sub>2</sub> emissions do not commit to substantial further warming (Matthews and Solomon, 2013) <sup>[[#fn:r162|162]]</sup> .
-Demonstrating with some statistical certainty that the climate or the land system affected by climate or land use has changed (detection),
+Sustained net zero anthropogenic emissions of CO <sub>2</sub> and declining net anthropogenic non-CO <sub>2</sub> radiative forcing over a multi-decade period would halt anthropogenic global warming over that period, although it would not halt sea level rise or many other aspects of climate system adjustment. The rate of decline of non-CO <sub>2</sub> radiative forcing must be sufficient to compensate for the ongoing adjustment of the climate system to this forcing (assuming it remains positive) due to ocean thermal inertia. It therefore depends on deep ocean response time scales, which are uncertain but of order centuries, corresponding to decline rates of non-CO <sub>2</sub> radiative forcing of less than 1% per year. In the longer term, Earth system feedbacks such as the release of carbon from melting permafrost may require net negative CO <sub>2</sub> emissions to maintain stable temperatures (Lowe and Bernie, 2018) <sup>[[#fn:r163|163]]</sup> .
+For warming SLCFs, meaning those associated with positive radiative forcing such as methane, the ZEC is negative. Eliminating emissions of these substances results in an immediate cooling relative to the present (Figure 1.5, magenta lines) (Frölicher and Joos, 2010; Matthews and Zickfeld, 2012; Mauritsen and Pincus, 2017) <sup>[[#fn:r164|164]]</sup> . Cooling SLCFs (those associated with negative radiative forcing) such as sulphate aerosols create a positive ZEC, as elimination of these forcers results in rapid increase in radiative forcing and warming (Figure 1.5, green lines) (Matthews and Zickfeld, 2012; Mauritsen and Pincus, 2017; Samset et al., 2018) <sup>[[#fn:r165|165]]</sup> . Estimates of the warming commitment from eliminating aerosol emissions are affected by large uncertainties in net aerosol radiative forcing (Myhre et al., 2013, 2017) <sup>[[#fn:r166|166]]</sup> and the impact of other measures affecting aerosol loading (e.g., Fernández et al., 2017) <sup>[[#fn:r167|167]]</sup> . If present-day emissions of all GHGs (short- and long-lived) and aerosols (including sulphate, nitrate and carbonaceous aerosols) are eliminated (Figure 1.5, yellow lines) GMST rises over the following decade, driven by the removal of negative aerosol radiative forcing. This initial warming is followed by a gradual cooling driven by the decline in radiative forcing of short-lived greenhouse gases (Matthews and Zickfeld, 2012; Collins et al., 2013) <sup>[[#fn:r168|168]]</sup> . Peak warming following elimination of all emissions was assessed at a few tenths of a degree in AR5, and century-scale warming was assessed to change only slightly relative to the time emissions are reduced to zero (Collins et al., 2013) <sup>[[#fn:r169|169]]</sup> . New evidence since AR5 suggests a larger methane forcing (Etminan et al., 2016) <sup>[[#fn:r170|170]]</sup> but no revision in the range of aerosol forcing (although this remains an active field of research, e.g., Myhre et al., 2017) <sup>[[#fn:r171|171]]</sup> . This revised methane forcing estimate results in a smaller peak warming and a faster temperature decline than assessed in AR5 (Figure 1.5, yellow line).
-</div>
+Expert judgement based on the available evidence (including model simulations, radiative forcing and climate sensitivity) suggests that if all anthropogenic emissions were reduced to zero immediately, any further warming beyond the 1°C already experienced would ''likely'' be less than 0.5°C over the next two to three decades, and also ''likely'' less than 0.5°C on a century time scale.
-<div>
-and evaluating the relative contributions of multiple causal factors to that change (with a formal assessment of confidence (attribution); see Glossary) remain challenging aspects in both observations and models (Rosenzweig and Neofotis 2013 <sup>[[#fn:r390|390]]</sup> ; Gillett et al. 2016 <sup>[[#fn:r391|391]]</sup> ; Lean 2018 <sup>[[#fn:r392|392]]</sup> ). Uncertainties arising for example, from missing or imprecise data, ambiguous terminology, incomplete process representation in models, or human decision-making contribute to these challenges, and some examples are provided in this subsection. In order to reflect various sources of uncertainties in the state of scientific understanding, IPCC assessment reports provide estimates of confidence (Mastrandrea et al. 2011 <sup>[[#fn:r393|393]]</sup> ). This confidence language is also used in the SRCCL (Figure 1.5).
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-2-2-1-concepts-related-to-risk-uncertainty-and-confidence-block-2">
-<div class="figure">
-<span id="figure-1.5"></span>
 ====== Figure 1.5 ======
-<span id="use-of-confidence-language."></span>
-==== Use of confidence language. ====
-[[File:https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/Figure-1.5-1024x511.jpg]]
-<div>
-Use of confidence language.
+==== Warming commitment from past emissions of greenhouse gases and aerosols. ====
-</div>
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/figure-5-pdf-922x1024.jpg]]
-</div>
+Radiative forcing (top) and global mean surface temperature change (bottom) for scenarios with different combinations of greenhouse gas and aerosol precursor emissions reduced to zero in 2020. Variables were calculated using a simple climate–carbon cycle model (Millar et al., 2017a) <sup>[[#fn:r172|172]]</sup> with a simple representation of atmospheric chemistry (Smith et al., 2018) <sup>[[#fn:r173|173]]</sup> . The bars on the right-hand side indicate the median warming in 2100 and 5–95% uncertainty ranges (also indicated by the plume around the yellow line) taking into account one estimate of uncertainty in climate response, effective radiative forcing and carbon cycle sensitivity, and constraining simple model parameters with response ranges from AR5 combined with historical climate observations (Smith et al., 2018) <sup>[[#fn:r174|174]]</sup> . Temperatures continue to increase slightly after elimination of CO <sub>2</sub> emissions (blue line) in response to constant non-CO <sub>2</sub> forcing. The dashed blue line extrapolates one estimate of the current rate of warming, while dotted blue lines show a case where CO <sub>2</sub> emissions are reduced linearly to zero assuming constant non-CO <sub>2</sub> forcing after 2020. Under these highly idealized assumptions, the time to stabilize temperatures at 1.5°C is approximately double the time remaining to reach 1.5°C at the current warming rate.
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use">
-<div>
-<div>
+Since most sources of emissions cannot, in reality, be brought to zero instantaneously due to techno-economic inertia, the current rate of emissions also constitutes a conditional commitment to future emissions and consequent warming depending on achievable rates of emission reductions. The current level and rate of human-induced warming determines both the time left before a temperature threshold is exceeded if warming continues (dashed blue line in Figure 1.5) and the time over which the warming rate must be reduced to avoid exceeding that threshold (approximately indicated by the dotted blue line in Figure 1.5). Leach et al. (2018) <sup>[[#fn:r175|175]]</sup> use a central estimate of human-induced warming of 1.02°C in 2017, increasing at 0.215°C per decade (Haustein et al., 2017) <sup>[[#fn:r176|176]]</sup> , to argue that it will take 13–32 years (one-standard-error range) to reach 1.5°C if the current warming rate continues, allowing 25–64 years to stabilise temperatures at 1.5°C if the warming rate is reduced at a constant rate of deceleration starting immediately. Applying a similar approach to the multi-dataset average GMST used in this report gives an assessed ''likely'' range for the date at which warming reaches 1.5°C of 2030 to 2052. The lower bound on this range, 2030, is supported by multiple lines of evidence, including the AR5 assessment for the ''likely'' range of warming (0.3°C–0.7°C) for the period 2016–2035 relative to 1986–2005. The upper bound, 2052, is supported by fewer lines of evidence, so we have used the upper bound of the 5–95% confidence interval given by the Leach et al. (2018) <sup>[[#fn:r177|177]]</sup> method applied to the multi-dataset average GMST, expressed as the upper limit of the ''likely'' range, to reflect the reliance on a single approach. Results are sensitive both to the confidence level chosen and the number of years used to estimate the current rate of anthropogenic warming (5 years used here, to capture the recent acceleration due to rising non-CO <sub>2</sub> forcing). Since the rate of human-induced warming is proportional to the rate of CO <sub>2</sub> emissions (Matthews et al., 2009; Zickfeld et al., 2009) <sup>[[#fn:r178|178]]</sup> plus a term approximately proportional to the rate of increase in non-CO <sub>2</sub> radiative forcing (Gregory and Forster, 2008; Allen et al., 2018 <sup>[[#fn:r179|179]]</sup> ; Cross-Chapter Box 2 in this chapter), these time scales also provide an indication of minimum emission reduction rates required if a warming greater than 1.5°C is to be avoided (see Figure 1.5, Supplementary Material 1.SM.6 and FAQ 1.2).
-<span id="nature-and-scope-of-uncertainties-related-to-land-use"></span>
-== 1.2.2.2 Nature and scope of uncertainties related to land use ==
+<div id="section-1-2-4-block-4" class="box">
-</div>
-<div>
-<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use-block-1">
-<div>
-<div>
+== Cross-Chapter Box 2: Measuring Progress to Net Zero Emissions Combining Long-Lived and Short-Lived Climate Forcers ==
-<div>
+====== Lead Authors ======
-Identification and communication of uncertainties is crucial to support decision making towards sustainable land management. Providing a robust, and comprehensive understanding of uncertainties in observations, models and scenarios is a fundamental first step in the IPCC confidence framework (see above). This will remain a challenge in future, but some important progress has been made over recent years.
+* Piers Forster (United Kingdom)
+* Elmar Kriegler (Germany)
+* Joeri Rogelj (Austria, Belgium)
+* Seth Schultz (United States)
+* Drew Shindell (United States)
+* Kirsten Zickfeld (Canada, Germany)
-Uncertainties in observations
-The detection of changes in vegetation cover and structural properties underpins the assessment of land-use change, degradation and desertification. It is continuously improving by enhanced Earth observation capacity (Hansen et al. 2013 <sup>[[#fn:r394|394]]</sup> ; He et al. 2018 <sup>[[#fn:r395|395]]</sup> ; Ardö et al. 2018 <sup>[[#fn:r396|396]]</sup> ; Spennemann et al. 2018 <sup>[[#fn:r397|397]]</sup> ) (see also Table SM.1.1 in Supplementary Material). Likewise, the picture of how soil organic carbon, and GHG and water fluxes, respond to land-use change and land management continues to improve through advances in methodologies and sensors (Kostyanovsky et al. 2018 <sup>[[#fn:r398|398]]</sup> ; Brümmer et al. 2017 <sup>[[#fn:r399|399]]</sup> ; Iwata et al. 2017 <sup>[[#fn:r400|400]]</sup> ; Valayamkunnath et al. 2018 <sup>[[#fn:r401|401]]</sup> ). In both cases, the relative shortness of the record, data gaps, data treatment algorithms and – for remote sensing – differences in the definitions of major vegetation-cover classes limit the detection of trends (Alexander et al. 2016a <sup>[[#fn:r402|402]]</sup> ; Chen et al. 2014 <sup>[[#fn:r403|403]]</sup> ; Yu et al. 2014 <sup>[[#fn:r404|404]]</sup> ; Lacaze et al. 2015 <sup>[[#fn:r405|405]]</sup> ; Song 2018 <sup>[[#fn:r406|406]]</sup> ; Peterson et al. 2017 <sup>[[#fn:r407|407]]</sup> ). In many developing countries, the cost of satellite remote sensing remains a challenge, although technological advances are starting to overcome this problem (Santilli et al. 2018 <sup>[[#fn:r408|408]]</sup> ), while ground-based observations networks are often not available.
-Integration of multiple data sources in model and data assimilation schemes reduces uncertainties (Li et al. 2017 <sup>[[#fn:r409|409]]</sup> ; Clark et al. 2017 <sup>[[#fn:r410|410]]</sup> ; Lees et al. 2018 <sup>[[#fn:r411|411]]</sup> ), which might be important for the advancement of early warning systems. Early warning systems are a key feature of short-term (i.e. seasonal) decision-support systems and are becoming increasingly important for sustainable land management and food security (Shtienberg 2013 <sup>[[#fn:r412|412]]</sup> ; Jarroudi et al. 2015 <sup>[[#fn:r413|413]]</sup> ) (Sections 6.2.3 and 7.4.3). Early warning systems can help to optimise fertiliser and water use, aid disease suppression, and/or increase the economic benefit by enabling strategic farming decisions on when and what to plant (Caffi et al. 2012 <sup>[[#fn:r414|414]]</sup> ; Watmuff et al. 2013 <sup>[[#fn:r415|415]]</sup> ; Jarroudi et al. 2015 <sup>[[#fn:r416|416]]</sup> ; Chipanshi et al. 2015 <sup>[[#fn:r417|417]]</sup> ). Their suitability depends on the capability of the methods to accurately predict crop or pest developments, which in turn depends on expert agricultural knowledge, and the accuracy of the weather data used to run phenological models (Caffi et al. 2012 <sup>[[#fn:r418|418]]</sup> ; Shtienberg 2013 <sup>[[#fn:r419|419]]</sup> ).
-<div>
-<div>
+Emissions of many different climate forcers will affect the rate and magnitude of climate change over the next few decades (Myhre et al., 2013) <sup>[[#fn:r180|180]]</sup> . Since these decades will determine when 1.5°C is reached or whether a warming greater than 1.5°C is avoided, understanding the aggregate impact of different forcing agents is particularly important in the context of 1.5°C pathways. Paragraph 17 of Decision 1 of the 21st Conference of the Parties on the adoption of the Paris Agreement specifically states that this report is to identify aggregate greenhouse gas emission levels compatible with holding the increase in global average temperatures to 1.5°C above pre-industrial levels (see Chapter 2). This request highlights the need to consider the implications of different methods of aggregating emissions of different gases, both for future temperatures and for other aspects of the climate system (Levasseur et al., 2016; Ocko et al., 2017) <sup>[[#fn:r181|181]]</sup> .
-<div>
+To date, reporting of GHG emissions under the UNFCCC has used Global Warming Potentials (GWPs) evaluated over a 100-year time horizon (GWP <sub>100</sub> ) to combine multiple climate forcers. IPCC Working Group 3 reports have also used GWP <sub>100</sub> to represent multi-gas pathways (Clarke et al., 2014) <sup>[[#fn:r182|182]]</sup> . For reasons of comparability and consistency with current practice, Chapter 2 in this Special Report continues to use this aggregation method. Numerous other methods of combining different climate forcers have been proposed, such as the Global Temperature-change Potential (GTP; Shine et al., 2005) <sup>[[#fn:r183|183]]</sup> and the Global Damage Potential (Tol et al., 2012; Deuber et al., 2013) <sup>[[#fn:r184|184]]</sup> .
-<div>
-Uncertainties in models
-Model intercomparison is a widely used approach to quantify some sources of uncertainty in climate change, land-use change and ecosystem modelling, often associated with the calculation of model-ensemble medians or means (see e.g., Sections 2.2 and 5.2). Even models of broadly similar structure differ in their projected outcome for the same input, as seen for instance in the spread in climate change projections from Earth System Models (ESMs) to similar future anthropogenic GHG emissions (Parker 2013 <sup>[[#fn:r932|932]]</sup> ; Stocker et al. 2013a <sup>[[#fn:r933|933]]</sup> ). These uncertainties arise, for instance, from different parameter values, different processes represented in models, or how these processes are mathematically described. If the outputs of ESM simulations are used as input to impact models, these uncertainties can propagate to projected impacts (Ahlstrom et al. 2013 <sup>[[#fn:r420|420]]</sup> ).
-<div>
-<div>
-<div>
-<div>
-Thus, the increased quantification of model performance in benchmarking exercises (the repeated confrontation of models with observations to establish a track-record of model developments and performance) is an important development to support the design and the interpretation of the outcomes of model ensemble studies (Randerson et al. 2009 <sup>[[#fn:r421|421]]</sup> ; Luo et al. 2012 <sup>[[#fn:r422|422]]</sup> ; Kelley et al. 2013 <sup>[[#fn:r423|423]]</sup> ). Since observational datasets in themselves are uncertain, benchmarking benefits from transparent information on the observations that are used, and the inclusion of multiple, regularly updated data sources (Luo et al. 2012 <sup>[[#fn:r424|424]]</sup> ; Kelley et al. 2013 <sup>[[#fn:r425|425]]</sup> ). Improved benchmarking approaches and the associated scoring of models may support weighted model means contingent on model performance. This could be an important step forward when calculating ensemble means across a range of models (Buisson et al. 2009 <sup>[[#fn:r426|426]]</sup> ; Parker 2013 <sup>[[#fn:r427|427]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r428|428]]</sup> ).
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-<div>
-Uncertainties arising from unknown futures
-Large differences exist in projections of future land-cover change, both between and within scenario projections (Fuchs et al. 2015 <sup>[[#fn:r429|429]]</sup> ; Eitelberg et al. 2016 <sup>[[#fn:r430|430]]</sup> ; Popp et al. 2016 <sup>[[#fn:r431|431]]</sup> ; Krause et al. 2017 <sup>[[#fn:r432|432]]</sup> ; Alexander et al. 2016a <sup>[[#fn:r433|433]]</sup> ). These differences reflect the uncertainties associated with baseline data, thematic classifications, different model structures and model parameter estimation (Alexander et al. 2017a <sup>[[#fn:r434|434]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r435|435]]</sup> ; Cross-Chapter Box 1 in Chapter 1). Likewise, projections of future land-use change are also highly uncertain, reflecting – among other factors – the absence of important crop, pasture and management processes in Integrated Assessment Models (Rose 2014 <sup>[[#fn:r436|436]]</sup> ) (Cross-Chapter Box 1 in Chapter 1 ) and in models of the terrestrial carbon cycle (Arneth et al. 2017 <sup>[[#fn:r437|437]]</sup> ). These processes have been shown to have large impacts on carbon stock changes (Arneth et al. 2017 <sup>[[#fn:r438|438]]</sup> ). Common scenario frameworks are used to capture the range of future uncertainties in scenarios. The most commonly used recent framework in climate change studies is based on the Representative Concentration Pathways (RCPs) and the Shared Socio-economic Pathways (SSPs) (Popp et al. 2016 <sup>[[#fn:r439|439]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r440|440]]</sup> ). The RCPs prescribe levels of radiative forcing (W m <sup>–2</sup> ) arising from different atmospheric concentrations of GHGs that lead to different levels of climate change. For example, RCP2.6 (2.6 W m <sup>–2</sup> ) is projected to lead to global mean temperature changes of about 0.9°C–2.3°C, and RCP8.5 (8.5 W m <sup>–2</sup> ) to global mean temperature changes of about 3.2°C–5.4°C (van Vuuren et al. 2014 <sup>[[#fn:r441|441]]</sup> ).
-<div>
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-<div>
-<div>
-The SSPs describe alternative trajectories of future socio-economic development with a focus on challenges to climate mitigation and challenges to climate adaptation (O’Neill et al. 2014 <sup>[[#fn:r442|442]]</sup> ). SSP1 represents a sustainable and cooperative society with a low-carbon economy and high capacity to adapt to climate change. SSP3 has social inequality that entrenches reliance on fossil fuels and limits adaptive capacity. SSP4 has large differences in income within and across world regions; it facilitates low-carbon economies in places, but limits adaptive capacity everywhere. SSP5 is a technologically advanced world with a strong economy that is heavily dependent on fossil fuels, but with high adaptive capacity. SSP2 is an intermediate case between SSP1 and SSP3 (O’Neill et al. 2014 <sup>[[#fn:r443|443]]</sup> ). The SSPs are commonly used with models to project future land-use change (Cross-Chapter Box 1 in Chapter 1).
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+Climate forcers fall into two broad categories in terms of their impact on global temperature (Smith et al., 2012) <sup>[[#fn:r185|185]]</sup> : long-lived GHGs, such as CO <sub>2</sub> and nitrous oxide (N <sub>2</sub> O), whose warming impact depends primarily on the total cumulative amount emitted over the past century or the entire industrial epoch; and short-lived climate forcers (SLCFs), such as methane and black carbon, whose warming impact depends primarily on current and recent annual emission rates (Reisinger et al., 2012; Myhre et al., 2013; Smith et al., 2013; Strefler et al., 2014) <sup>[[#fn:r186|186]]</sup> . These different dependencies affect the emissions reductions required of individual forcers to limit warming to 1.5°C or any other level.
-</div>
+Natural processes that remove CO <sub>2</sub> permanently from the climate system are so slow that reducing the rate of CO <sub>2</sub> -induced warming to zero requires net zero global anthropogenic CO <sub>2</sub> emissions (Archer and Brovkin, 2008; Matthews and Caldeira, 2008; Solomon et al., 2009) <sup>[[#fn:r187|187]]</sup> , meaning almost all remaining anthropogenic CO <sub>2</sub> emissions must be compensated for by an equal rate of anthropogenic carbon dioxide removal (CDR). Cumulative CO <sub>2</sub> emissions are therefore an accurate indicator of CO <sub>2</sub> -induced warming, except in periods of high negative CO <sub>2</sub> emissions (Zickfeld et al., 2016) <sup>[[#fn:r188|188]]</sup> , and potentially in century-long periods of near-stable temperatures (Bowerman et al., 2011; Wigley, 2018) <sup>[[#fn:r189|189]]</sup> . In contrast, sustained constant emissions of a SLCF such as methane, would (after a few decades) be consistent with constant methane concentrations and hence very little additional methane-induced warming (Allen et al., 2018; Fuglestvedt et al., 2018) <sup>[[#fn:r190|190]]</sup> . Both GWP and GTP would equate sustained SLCF emissions with sustained constant CO <sub>2</sub> emissions, which would continue to accumulate in the climate system, warming global temperatures indefinitely. Hence nominally ‘equivalent’ emissions of CO <sub>2</sub> and SLCFs, if equated conventionally using GWP or GTP, have very different temperature impacts, and these differences are particularly evident under ambitious mitigation characterizing 1.5°C pathways.
-</div>
+Since the AR5, a revised usage of GWP has been proposed (Lauder et al., 2013; Allen et al., 2016) <sup>[[#fn:r191|191]]</sup> , denoted GWP* (Allen et al., 2018) <sup>[[#fn:r192|192]]</sup> , that addresses this issue by equating a permanently sustained change in the emission ''rate'' of an SLCF or SLCF-precursor (in tonnes-per-year), or other non-CO <sub>2</sub> forcing (in watts per square metre), with a one-off ''pulse'' emission (in tonnes) of a fixed amount of CO <sub>2</sub> . Specifically, GWP* equates a 1 tonne-per-year increase in emission rate of an SLCF with a pulse emission of GWP ''<sub>H</sub>'' x ''H'' tonnes of CO <sub>2</sub> , where  is the conventional GWP <sub>''H''</sub> of that SLCF evaluated over time GWP ''<sub>H</sub>'' for SLCFs decreases with increasing time H, GWP <sub>''H''</sub> x ''H'' for SLCFs is less dependent on the choice of time horizon. Similarly, a permanent 1 W m <sup>−2</sup> increase in radiative forcing has a similar temperature impact as the cumulative emission of ''H'' /AGWP <sub>''H''</sub> tonnes of CO <sub>2</sub> , where AGWP ''<sub>H</sub>'' is the Absolute Global Warming Potential of CO <sub>2</sub> (Shine et al., 2005; Myhre et al., 2013; Allen et al., 2018) <sup>[[#fn:r193|193]]</sup> . This indicates approximately how future changes in non-CO <sub>2</sub> radiative forcing affect cumulative CO <sub>2</sub> emissions consistent with any given level of peak warming.
-</div>
+When combined using GWP*, cumulative aggregate GHG emissions are closely proportional to total GHG-induced warming, while the annual rate of GHG-induced warming is proportional to the annual rate of aggregate GHG emissions (see Cross-Chapter Box 2, Figure 1). This is not the case when emissions are aggregated using GWP or GTP, with discrepancies particularly pronounced when SLCF emissions are falling. Persistent net zero CO <sub>2</sub> -equivalent emissions containing a residual positive forcing contribution from SLCFs and aggregated using GWP <sub>100</sub> or GTP would result in a steady decline of GMST. Net zero global emissions aggregated using GWP* (which corresponds to zero net emissions of CO <sub>2</sub> and other long-lived GHGs like nitrous oxide, combined with near-constant SLCF forcing – see Figure 1.5) results in approximately stable GMST (Allen et al., 2018; Fuglestvedt et al., 2018 <sup>[[#fn:r194|194]]</sup> and Cross-Chapter Box 2, Figure 1, below).
-<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use-block-2" class="box">
-<div>
+Whatever method is used to relate emissions of different greenhouse gases, scenarios achieving stable GMST well below 2°C require both near-zero net emissions of long-lived greenhouse gases and deep reductions in warming SLCFs (Chapter 2), in part to compensate for the reductions in cooling SLCFs that are expected to accompany reductions in CO <sub>2</sub> emissions (Rogelj et al., 2016b; Hienola et al., 2018) <sup>[[#fn:r195|195]]</sup> . Understanding the implications of different methods of combining emissions of different climate forcers is, however, helpful in tracking progress towards temperature stabilisation and ‘balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases’ as stated in Article 4 of the Paris Agreement. Fuglestvedt et al. (2018) <sup>[[#fn:r196|196]]</sup> and Tanaka and O’Neill (2018) <sup>[[#fn:r197|197]]</sup> show that when, and even whether, aggregate GHG emissions need to reach net zero before 2100 to limit warming to 1.5°C depends on the scenario, aggregation method and mix of long-lived and short-lived climate forcers.
-<div>
+The comparison of the impacts of different climate forcers can also consider more than their effects on GMST (Johansson, 2012; Tol et al., 2012; Deuber et al., 2013; Myhre et al., 2013; Cherubini and Tanaka, 2016) <sup>[[#fn:r198|198]]</sup> . Climate impacts arise from both magnitude and rate of climate change, and from other variables such as precipitation (Shine et al., 2015) <sup>[[#fn:r199|199]]</sup> . Even if GMST is stabilised, sea level rise and associated impacts will continue to increase (Sterner et al., 2014) <sup>[[#fn:r200|200]]</sup> , while impacts that depend on CO <sub>2</sub> concentrations such as ocean acidification may begin to reverse. From an economic perspective, comparison of different climate forcers ideally reflects the ratio of marginal economic damages if used to determine the exchange ratio of different GHGs under multi-gas regulation (Tol et al., 2012; Deuber et al., 2013; Kolstad et al., 2014) <sup>[[#fn:r201|201]]</sup> .
-<span id="ccb1-scenarios-and-other-methods-to-characterise-the-future-of-land"></span>
+Emission reductions can interact with other dimensions of sustainable development (see Chapter 5). In particular, early action on some SLCFs (including actions that may warm the climate, such as reducing sulphur dioxide emissions) may have considerable societal co-benefits, such as reduced air pollution and improved public health with associated economic benefits (OECD, 2016; Shindell et al., 2016) <sup>[[#fn:r202|202]]</sup> . Valuation of broadly defined social costs attempts to account for many of these additional non-climate factors along with climate-related impacts (Shindell, 2015; Sarofim et al., 2017; Shindell et al., 2017) <sup>[[#fn:r203|203]]</sup> . See Chapter 4, Section 4.3.6, for a discussions of mitigation options, noting that mitigation priorities for different climate forcers depend on multiple economic and social criteria that vary between sectors, regions and countries.
-== CCB1 Scenarios and other methods to characterise the future of land ==
-</div>
-<div>
-<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use-block-1">
-<div>
-<div>
-<div>
-<div>
+====== Cross Chapter Box 2: Figure 1 ======
-Mark Rounsevell (United Kingdom/Germany), Almut Arneth (Germany), Katherine Calvin (The United States of America), Edouard Davin (France/Switzerland), Jan Fuglestvedt (Norway), Joanna House (United Kingdom), Alexander Popp (Germany), Joana Portugal Pereira (United Kingdom), Prajal Pradhan (Nepal/Germany), Jim Skea (United Kingdom), David Viner (United Kingdom).
-'''About this box'''
+==== Implications of different approaches to calculating aggregate greenhouse gas emissions on a pathway to net zero. ====
-The land-climate system is complex and future changes are uncertain, but methods exist (collectively known as futures analysis) to help decision-makers in navigating through this uncertainty. Futures analysis comprises a number of different and widely used methods, such as scenario analysis (Rounsevell and Metzger 2010 <sup>[[#fn:r444|444]]</sup> ), envisioning or target setting (Kok et al. 2018 <sup>[[#fn:r445|445]]</sup> ), pathways analysis (IPBES 2016 <sup>[[#fn:r446|446]]</sup> ; IPCC 2018 <sup>[[#fn:r447|447]]</sup> ) <sup>[[#fn:1|1]]</sup> , and conditional probabilistic futures (Vuuren et al. 2018 <sup>[[#fn:r448|448]]</sup> ; Engstrom et al. 2016 <sup>[[#fn:r449|449]]</sup> ; Henry et al. 2018 <sup>[[#fn:r450|450]]</sup> ) (Table 1 in this Cross-Chapter Box). Scenarios and other methods to characterise the future can support a discourse with decision-makers about the sustainable development options that are available to them. All chapters of this assessment draw conclusions from futures analysis and so, the purpose of this box is to outline the principal methods used, their application domains, their uncertainties and their limitations.
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/box-2-figure-1-1024x461.jpg]]
-'''Exploratory scenario analysis'''
+(a) Aggregate emissions of well-mixed greenhouse gases (WMGHGs) under the RCP2.6 mitigation scenario expressed as CO2-equivalent using GWP100 (blue); GTP100 (green) and GWP* (yellow). Aggregate WMGHG emissions appear to fall more rapidly if calculated using GWP* than using either GWP or GTP, primarily because GWP* equates a falling methane emission rate with negative CO <sub>2</sub> emissions, as only active CO <sub>2</sub> removal would have the same impact on radiative forcing and GMST as a reduction in methane emission rate. (b) Cumulative emissions of WMGHGs combined as in panel (a) (blue, green and yellow lines &amp; left hand axis) and warming response to combined emissions (black dotted line and right hand axis, Millar et al. (2017a) <sup>[[#fn:r204|204]]</sup> . The temperature response under ambitious mitigation is closely correlated with cumulative WMGHG emissions aggregated using GWP*, but with neither emission rate nor cumulative emissions if aggregated using GWP or GTP.
-Many exploratory scenarios are reported in climate and land system studies on climate change (Dokken 2014 <sup>[[#fn:r451|451]]</sup> ), such as related to land-based, climate change mitigation via reforestation/afforestation, avoided deforestation or bioenergy (Kraxner et al. 2013 <sup>[[#fn:r452|452]]</sup> ; Humpenoder et al. 2014 <sup>[[#fn:r453|453]]</sup> ; Krause et al. 2017 <sup>[[#fn:r454|454]]</sup> ) and climate change impacts and adaptation (Warszawski et al. 2014 <sup>[[#fn:r455|455]]</sup> ). There are global-scale scenarios of food security (Foley et al. 2011 <sup>[[#fn:r456|456]]</sup> ; Pradhan et al. 2013 <sup>[[#fn:r457|457]]</sup> , 2014 <sup>[[#fn:r458|458]]</sup> ), but fewer scenarios of desertification, land degradation and restoration (Wolff et al. 2018 <sup>[[#fn:r459|459]]</sup> ). Exploratory scenarios combine qualitative ‘storylines’ or descriptive narratives of the underlying causes (or drivers) of change (Nakicenovic and Swart 2000 <sup>[[#fn:r460|460]]</sup> ; Rounsevell and Metzger 2010 <sup>[[#fn:r461|461]]</sup> ; O’Neill et al. 2014 <sup>[[#fn:r462|462]]</sup> ) with quantitative projections from computer models. Different types of models are used for this purpose based on very different modelling paradigms, baseline data and underlying assumptions (Alexander et al. 2016a <sup>[[#fn:r463|463]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r464|464]]</sup> ). Figure 1 in this Cross-Chapter Box below outlines how a combination of models can quantify these components as well as the interactions between them.
-Exploratory scenarios often show that socio-economic drivers have a larger effect on land-use change than climate drivers (Harrison et al. 2014 <sup>[[#fn:r465|465]]</sup> , 2016 <sup>[[#fn:r466|466]]</sup> ). Of these, technological development is critical in affecting the production potential (yields) of food and bioenergy and the feed conversion efficiency of livestock (Rounsevell et al. 2006 <sup>[[#fn:r467|467]]</sup> ; Wise et al. 2014 <sup>[[#fn:r|]]</sup> 468; Kreidenweis et al. 2018 <sup>[[#fn:r469|469]]</sup> ), as well as the area of land needed for food production (Foley et al. 2011 <sup>[[#fn:r470|470]]</sup> ; Weindl et al. 2017 <sup>[[#fn:r471|471]]</sup> ; Kreidenweis et al. 2018 <sup>[[#fn:r472|472]]</sup> ). Trends in consumption, for example, diets or waste reduction, are also fundamental in affecting land-use change (Pradhan et al. 2013 <sup>[[#fn:r473|473]]</sup> ; Alexander et al. 2016b <sup>[[#fn:r474|474]]</sup> ; Weindl et al. 2017 <sup>[[#fn:r475|475]]</sup> ; Alexander et al. 2017 <sup>[[#fn:r476|476]]</sup> ; Vuuren et al. 2018 <sup>[[#fn:r477|477]]</sup> ; Bajželj et al. 2014 <sup>[[#fn:r478|478]]</sup> ). Scenarios of land-based mitigation through large-scale bioenergy production and afforestation often lead to negative trade-offs with food security (food prices), water resources and biodiversity (Cross-Chapter Box 7 in Chapter 6).
-Many exploratory scenarios are based on common frameworks such as the Shared Socio-economic Pathways (SSPs) (Popp et al. 2016 <sup>[[#fn:r479|479]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r480|480]]</sup> ; Doelman et al. 2018 <sup>[[#fn:r481|481]]</sup> )) (Section 1.2). However, other methods are used. Stylised scenarios prescribe assumptions about climate and land-use change solutions, for example, dietary change, food waste reduction and afforestation areas (Pradhan et al. 2013 <sup>[[#fn:r482|482]]</sup> , 2014 <sup>[[#fn:r483|483]]</sup> ; Kreidenweis et al. 2016 <sup>[[#fn:r484|484]]</sup> ; Rogelj et al. 2018b <sup>[[#fn:r485|485]]</sup> ; Seneviratne et al. 2018 <sup>[[#fn:r486|486]]</sup> ; Vuuren et al. 2018 <sup>[[#fn:r487|487]]</sup> ). These scenarios provide useful thought experiments, but the feasibility of achieving the stylised assumptions is often unknown. Shock scenarios explore the consequences of low probability, high-impact events such as pandemic diseases, cyber-attacks and failures in food supply chains (Challinor et al. 2018 <sup>[[#fn:r488|488]]</sup> ), often in food security studies. Because of the diversity of exploratory scenarios, attempts have been made to categorise them into ‘archetypes’ based on the similarity between their assumptions in order to facilitate communication (IPBES 2018a <sup>[[#fn:r489|489]]</sup> ).
-<div>
-<div>
-<div>
-<div>
-Conditional probabilistic futures explore the consequences of model parameter uncertainty in which these uncertainties are conditional on scenario assumptions (Neill 2004 <sup>[[#fn:r490|490]]</sup> ). Only a few studies have applied the conditional probabilistic approach to land-use futures (Brown et al. 2014 <sup>[[#fn:r491|491]]</sup> ; Engstrom et al. 2016 <sup>[[#fn:r492|492]]</sup> ; Henry et al. 2018 <sup>[[#fn:r493|493]]</sup> ). By accounting for uncertainties in key drivers these studies show large ranges in land-use change, for example, global cropland areas of 893–2380 Mha by the end of the 21st century (Engstrom et al. 2016 <sup>[[#fn:r494|494]]</sup> ). They also find that land-use targets may not be achieved, even across a wide range of scenario parameter settings, because of trade-offs arising from the competition for land (Henry et al. 2018 <sup>[[#fn:r495|495]]</sup> ; Heck et al. 2018 <sup>[[#fn:r496|496]]</sup> ). Accounting for uncertainties across scenario assumptions can lead to convergent outcomes for land-use change, which implies that certain outcomes are more robust across a wide range of uncertain scenario assumptions (Brown et al. 2014 <sup>[[#fn:r497|497]]</sup> ).
-In addition to global scale scenario studies, sub-national studies demonstrate that regional climate change impacts on the land system are highly variable geographically because of differences in the spatial patterns of both climate and socio-economic change (Harrison et al. 2014 <sup>[[#fn:r498|498]]</sup> ). Moreover, the capacity to adapt to these impacts is strongly dependent on the regional, socio-economic context and coping capacity (Dunford et al. 2014 <sup>[[#fn:r499|499]]</sup> ); processes that are difficult to capture in global scale scenarios. Regional scenarios are often co-created with stakeholders through participatory approaches (Kok et al. 2014 <sup>[[#fn:r500|500]]</sup> ), which are powerful in reflecting diverse worldviews and stakeholder values. Stakeholder participatory methods provide additional richness and context to storylines, as well as providing salience and legitimacy for local stakeholders (Kok et al. 2014).
-</div>
-</div>
-</div>
-</div>
+== 1.3 Impacts at 1.5°C and Beyond ==
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use-block-2">
-<div class="figure">
-<span id="cross-chapter-box-1-table-1"></span>
+== 1.3.1 Definitions ==
-====== Cross-Chapter Box 1, Table 1 ======
-<span id="description-of-the-principal-methods-used-in-land-and-climate-futures-analysis."></span>
-==== Description of the principal methods used in land and climate futures analysis. ====
-<div>
-<div>
-{| class="wikitable"
-|-
-| <div>
-<div>
+Consistent with the AR5 (IPCC, 2014a) <sup>[[#fn:r205|205]]</sup> , ‘impact’ in this report refers to the effects of climate change on human and natural systems. Impacts may include the effects of changing hazards, such as the frequency and intensity of heat waves. ‘Risk’ refers to potential negative impacts of climate change where something of value is at stake, recognizing the diversity of values. Risks depend on hazards, exposure, vulnerability (including sensitivity and capacity to respond) and likelihood. Climate change risks can be managed through efforts to mitigate climate change forcers, adaptation of impacted systems, and remedial measures (Section 1.4.1).
-Futures method
+In the context of this report, ''regional'' impacts of ''global'' warming at 1.5°C and 2°C are assessed in Chapter 3. The ‘ ''warming experience at 1.5°C'' ’ is that of regional climate change (temperature, rainfall, and other changes) at the time when global average temperatures, as defined in Section 1.2.1, reach 1.5°C above pre-industrial (the same principle applies to impacts at any other global mean temperature). Over the decade 2006–2015, many regions have experienced higher than average levels of warming and some are already now 1.5°C or more warmer with respect to the pre-industrial period (Figure 1.3). At a global warming of 1.5°C, some seasons will be substantially warmer than 1.5°C above pre-industrial (Seneviratne et al., 2016) <sup>[[#fn:r206|206]]</sup> . Therefore, most regional impacts of a global mean warming of 1.5°C will be different from those of a regional warming by 1.5°C.
+The impacts of 1.5°C global warming will vary in both space and time (Ebi et al., 2016) <sup>[[#fn:r207|207]]</sup> . For many regions, an increase in global mean temperature by 1.5°C or 2°C implies substantial increases in the occurrence and/or intensity of some extreme events (Fischer and Knutti, 2015; Karmalkar and Bradley, 2017; King et al., 2017; Chevuturi et al., 2018) <sup>[[#fn:r208|208]]</sup> , resulting in different impacts (see Chapter 3). By comparing impacts at 1.5°C versus those at 2°C, this report discusses the ‘avoided impacts’ by maintaining global temperature increase at or below 1.5°C as compared to 2°C, noting that these also depend on the pathway taken to 1.5°C (see Section 1.2.3 and Cross-Chapter Box 8 in Chapter 3 on 1.5°C warmer worlds). Many impacts take time to observe, and because of the warming trend, impacts over the past 20 years were associated with a level of human-induced warming that was, on average, 0.1°C–0.23°C colder than its present level, based on the AR5 estimate of the warming trend over this period (Section 1.2.1 and Kirtman et al., 2013) <sup>[[#fn:r209|209]]</sup> . Attribution studies (e.g., van Oldenborgh et al., 2017) <sup>[[#fn:r210|210]]</sup> can address this bias, but informal estimates of ‘recent impact experience’ in a rapidly warming world necessarily understate the temperature-related impacts of the current level of warming.
-</div>
-</div>
-| <div>
-<div>
-Description and subtypes
-</div>
-</div>
-| <div>
-<div>
-Application domain
+== 1.3.2 Drivers of Impacts ==
-</div>
-</div>
-| <div>
-<div>
-Time horizon
+Impacts of climate change are due to multiple environmental drivers besides rising temperatures, such as rising atmospheric CO <sub>2</sub> , shifting rainfall patterns (Lee et al., 2018) <sup>[[#fn:r211|211]]</sup> , rising sea levels, increasing ocean acidification, and extreme events, such as floods, droughts, and heat waves (IPCC, 2014a) <sup>[[#fn:r212|212]]</sup> . Changes in rainfall affect the hydrological cycle and water availability (Schewe et al., 2014; Döll et al., 2018; Saeed et al., 2018) <sup>[[#fn:r213|213]]</sup> . Several impacts depend on atmospheric composition, increasing atmospheric carbon dioxide levels leading to changes in plant productivity (Forkel et al., 2016) <sup>[[#fn:r214|214]]</sup> , but also to ocean acidification (Hoegh-Guldberg et al., 2007) <sup>[[#fn:r215|215]]</sup> . Other impacts are driven by changes in ocean heat content such as the destabilization of coastal ice sheets and sea level rise (Bindoff et al., 2007; Chen et al., 2017) <sup>[[#fn:r216|216]]</sup> , whereas impacts due to heat waves depend directly on ambient air or ocean temperature (Matthews et al., 2017) <sup>[[#fn:r217|217]]</sup> . Impacts can be direct, such as coral bleaching due to ocean warming, and indirect, such as reduced tourism due to coral bleaching. Indirect impacts can also arise from mitigation efforts such as changed agricultural management (Section 3.6.2) or remedial measures such as solar radiation modification (Section 4.3.8, Cross-Chapter Box 10 in Chapter 4).
-</div>
+Impacts may also be triggered by combinations of factors, including ‘impact cascades’ (Cramer et al., 2014) <sup>[[#fn:r218|218]]</sup> through secondary consequences of changed systems. Changes in agricultural water availability caused by upstream changes in glacier volume are a typical example. Recent studies also identify compound events (e.g., droughts and heat waves), that is, when impacts are induced by the combination of several climate events (AghaKouchak et al., 2014; Leonard et al., 2014; Martius et al., 2016; Zscheischler and Seneviratne, 2017) <sup>[[#fn:r219|219]]</sup> .
-</div>
+There are now techniques to attribute impacts formally to anthropogenic global warming and associated rainfall changes (Rosenzweig et al., 2008; Cramer et al., 2014; Hansen et al., 2016) <sup>[[#fn:r220|220]]</sup> , taking into account other drivers such as land-use change (Oliver and Morecroft, 2014) <sup>[[#fn:r221|221]]</sup> and pollution (e.g., tropospheric ozone; Sitch et al., 2007) <sup>[[#fn:r222|222]]</sup> . There are multiple lines of evidence that climate change has observable and often severely negative effects on people, especially where climate-sensitive biophysical conditions and socio-economic and political constraints on adaptive capacities combine to create high vulnerabilities (IPCC, 2012a; 2014a; World Bank, 2013) <sup>[[#fn:r223|223]]</sup> . The character and severity of impacts depend not only on the hazards (e.g., changed climate averages and extremes) but also on the vulnerability (including sensitivities and adaptive capacities) of different communities and their exposure to climate threats. These impacts also affect a range of natural and human systems, such as terrestrial, coastal and marine ecosystems and their services; agricultural production; infrastructure; the built environment; human health; and other socio-economic systems (Rosenzweig et al., 2017) <sup>[[#fn:r224|224]]</sup> .
-| <div>
-<div>
+Sensitivity to changing drivers varies markedly across systems and regions. Impacts of climate change on natural and managed ecosystems can imply loss or increase in growth, biomass or diversity at the level of species populations, interspecific relationships such as pollination, landscapes or entire biomes. Impacts occur in addition to the natural variation in growth, ecosystem dynamics, disturbance, succession and other processes, rendering attribution of impacts at lower levels of warming difficult in certain situations. The same magnitude of warming can be lethal during one phase of the life of an organism and irrelevant during another. Many ecosystems (notably forests, coral reefs and others) undergo long-term successional processes characterised by varying levels of resilience to environmental change over time. Organisms and ecosystems may adapt to environmental change to a certain degree, through changes in physiology, ecosystem structure, species composition or evolution. Large-scale shifts in ecosystems may cause important feedbacks, in terms of changing water and carbon fluxes through impacted ecosystems – these can amplify or dampen atmospheric change at regional to continental scale. Of particular concern is the response of most of the world’s forests and seagrass ecosystems, which play key roles as carbon sinks (Settele et al., 2014; Marbà et al., 2015) <sup>[[#fn:r225|225]]</sup> .
-Examples in this assessment
+Some ambitious efforts to constrain atmospheric greenhouse gas concentrations may themselves impact ecosystems. In particular, changes in land use, potentially required for massively enhanced production of biofuels (either as simple replacement of fossil fuels, or as part of bioenergy with carbon capture and storage, BECCS) impact all other land ecosystems through competition for land (e.g., Creutzig, 2016) <sup>[[#fn:r226|226]]</sup> (see Cross-Chapter Box 7 in Chapter 3, Section 3.6.2.1).
+Human adaptive capacity to a 1.5°C warmer world varies markedly for individual sectors and across sectors such as water supply, public health, infrastructure, ecosystems and food supply. For example, density and risk exposure, infrastructure vulnerability and resilience, governance, and institutional capacity all drive different impacts across a range of human settlement types (Dasgupta et al., 2014; Revi et al., 2014; Rosenzweig et al., 2018) <sup>[[#fn:r227|227]]</sup> . Additionally, the adaptive capacity of communities and human settlements in both rural and urban areas, especially in highly populated regions, raises equity, social justice and sustainable development issues. Vulnerabilities due to gender, age, level of education and culture act as compounding factors (Arora-Jonsson, 2011; Cardona et al., 2012; Resurrección, 2013; Olsson et al., 2014; Vincent et al., 2014) <sup>[[#fn:r228|228]]</sup> .
-</div>
-</div>
-|-
-| rowspan="6"| <div>
-<div>
-Exploratory scenarios.
-Trajectories of change in system components from the present to contrasting, alterna- tive futures based on plausible and internally consistent assumptions about the underlying drivers of change
-</div>
-</div>
-| <div>
-<div>
-Long-term projections quantified with models
+== 1.3.3 Uncertainty and Non-Linearity of Impacts ==
-</div>
-</div>
-| <div>
-<div>
-Climate system, land system and other components of the environment (e.g., biodiversity, ecosystem function- ing, water resources and quality), for example the SSPs
+Uncertainties in projections of future climate change and impacts come from a variety of different sources, including the assumptions made regarding future emission pathways (Moss et al., 2010) <sup>[[#fn:r229|229]]</sup> , the inherent limitations and assumptions of the climate models used for the projections, including limitations in simulating regional climate variability (James et al., 2017) <sup>[[#fn:r230|230]]</sup> , downscaling and bias-correction methods (Ekström et al., 2015) <sup>[[#fn:r231|231]]</sup> , the assumption of a linear scaling of impacts with GMST used in many studies (Lewis et al., 2017; King et al., 2018b) <sup>[[#fn:r232|232]]</sup> , and in impact models (e.g., Asseng et al., 2013) <sup>[[#fn:r233|233]]</sup> . The evolution of climate change also affects uncertainty with respect to impacts. For example, the impacts of overshooting 1.5°C and stabilization at a later stage compared to stabilization at 1.5°C without overshoot may differ in magnitude (Schleussner et al., 2016) <sup>[[#fn:r234|234]]</sup> .
+AR5 (IPCC, 2013b) <sup>[[#fn:r235|235]]</sup> and World Bank (2013) <sup>[[#fn:r236|236]]</sup> underscored the non-linearity of risks and impacts as temperature rises from 2°C to 4°C of warming, particularly in relation to water availability, heat extremes, bleaching of coral reefs, and more. Recent studies (Schleussner et al., 2016; James et al., 2017; Barcikowska et al., 2018; King et al., 2018a) <sup>[[#fn:r237|237]]</sup> assess the impacts of 1.5°C versus 2°C warming, with the same message of non-linearity. The resilience of ecosystems, meaning their ability either to resist change or to recover after a disturbance, may change, and often decline, in a non-linear way. An example are reef ecosystems, with some studies suggesting that reefs will change, rather than disappear entirely, and with particular species showing greater tolerance to coral bleaching than others (Pörtner et al., 2014) <sup>[[#fn:r238|238]]</sup> . A key issue is therefore whether ecosystems such as coral reefs survive an overshoot scenario, and to what extent they would be able to recover after stabilization at 1.5°C or higher levels of warming (see Box 3.4).
-</div>
-</div>
-| <div>
-<div>
-–100 years
-</div>
-</div>
-| <div>
-<div>
-.3, 2.6.2, 5.2.3, 6.1.4, 6.4.4, 7.2
+== 1.4 Strengthening the Global Response ==
-</div>
-</div>
-|-
-| <div>
-<div>
-Business-as-usual scenarios
+This section frames the implementation options, enabling conditions (discussed further in Cross-Chapter Box 3 on feasibility in this chapter), capacities and types of knowledge and their availability (Blicharska et al., 2017) <sup>[[#fn:r239|239]]</sup> that can allow institutions, communities and societies to respond to the 1.5°C challenge in the context of sustainable development and the Sustainable Development Goals (SDGs). It also addresses other relevant international agreements such as the Sendai Framework for Disaster Risk Reduction. Equity and ethics are recognised as issues of importance in reducing vulnerability and eradicating poverty.
-(including ‘outlooks’)
+The connection between the enabling conditions for limiting global warming to 1.5°C and the ambitions of the SDGs are complex across scale and multi-faceted (Chapter 5). Climate mitigation–adaptation linkages, including synergies and trade-offs, are important when considering opportunities and threats for sustainable development. The IPCC AR5 acknowledged that ‘adaptation and mitigation have the potential to both contribute to and impede sustainable development, and sustainable development strategies and choices have the potential to both contribute to and impede climate change responses’ (Denton et al., 2014) <sup>[[#fn:r240|240]]</sup> . Climate mitigation and adaptation measures and actions can reflect and enforce specific patterns of development and governance that differ amongst the world’s regions (Gouldson et al., 2015; Termeer et al., 2017) <sup>[[#fn:r241|241]]</sup> . The role of limited adaptation and mitigation capacity, limits to adaptation and mitigation, and conditions of mal-adaptation and mal-mitigation are assessed in this report (Chapters 4 and 5).
-</div>
-</div>
-| <div>
-<div>
-A continuation into the future of current trends<br />
-in key drivers to explore the consequences of these in the near term
-</div>
-</div>
+== 1.4.1 Classifying Response Options ==
-| <div>
-<div>
-–10 years, 20–30 years for outlooks
-</div>
-</div>
+Key broad categories of responses to the climate change problem are framed here. '''Mitigation''' refers to efforts to reduce or prevent the emission of greenhouse gases, or to enhance the absorption of gases already emitted, thus limiting the magnitude of future warming (IPCC, 2014b) <sup>[[#fn:r242|242]]</sup> . Mitigation requires the use of new technologies, clean energy sources, reduced deforestation, improved sustainable agricultural methods, and changes in individual and collective behaviour. Many of these may provide substantial co-benefits for air quality, biodiversity and sustainable development. Mal-mitigation includes changes that could reduce emissions in the short-term but could lock in technology choices or practices that include significant trade-offs for effectiveness of future adaptation and other forms of mitigation (Chapters 2 and 4).
-| <div>
-<div>
+'''Carbon dioxide removal''' (CDR) or ‘negative emissions’ activities are considered in this report as distinct from the above mitigation activities. While most mitigation activities focus on reducing the amount of carbon dioxide or other greenhouse gases emitted, CDR aims to reduce concentrations already in the atmosphere. Technologies for CDR are mostly in their infancy despite their importance to ambitious climate change mitigation pathways (Minx et al., 2017) <sup>[[#fn:r243|243]]</sup> . Although some CDR activities such as reforestation and ecosystem restoration are well understood, the feasibility of massive-scale deployment of many CDR technologies remains an open question (IPCC, 2014d; Leung et al., 2014) <sup>[[#fn:r244|244]]</sup> (Chapters 2 and 4). Technologies for the active removal of other greenhouse gases, such as methane, are even less developed, and are briefly discussed in Chapter 4.
-.2.1, 2.6.2, 5.3.4, 6.1.4
+Climate change adaptation refers to the actions taken to manage the impacts of climate change (IPCC, 2014a) <sup>[[#fn:r245|245]]</sup> . The aim is to reduce vulnerability and exposure to the harmful effects of climate change (e.g., sea level rise, more intense extreme weather events or food insecurity). It also includes exploring the potential beneficial opportunities associated with climate change (for example, longer growing seasons or increased yields in some regions). Different adaptation pathways can be undertaken. Adaptation can be incremental, or transformational, meaning fundamental attributes of the system are changed (Chapter 3 and 4). There can be limits to ecosystem-based adaptation or the ability of humans to adapt (Chapter 4). If there is no possibility for adaptive actions that can be applied to avoid an intolerable risk, these are referred to as hard adaptation limits, while soft adaptation limits are identified when there are currently no options to avoid intolerable risks, but they are theoretically possible (Chapter 3 and 4). While climate change is a global issue, impacts are experienced locally. Cities and municipalities are at the frontline of adaptation (Rosenzweig et al., 2018) <sup>[[#fn:r246|246]]</sup> , focusing on reducing and managing disaster risks due to extreme and slow-onset weather and climate events, installing flood and drought early warning systems, and improving water storage and use (Chapters 3 and 4 and Cross-Chapter Box 12 in Chapter 5). Agricultural and rural areas, including often highly vulnerable remote and indigenous communities, also need to address climate-related risks by strengthening and making more resilient agricultural and other natural resource extraction systems.
+'''Remedial measures''' are distinct from mitigation or adaptation, as the aim is to temporarily reduce or offset warming (IPCC, 2012b) <sup>[[#fn:r247|247]]</sup> . One such measure is solar radiation modification (SRM), also referred to as solar radiation management in the literature, which involves deliberate changes to the albedo of the Earth system, with the net effect of increasing the amount of solar radiation reflected from the Earth to reduce the peak temperature from climate change (The Royal Society, 2009; Smith and Rasch, 2013; Schäfer et al., 2015) <sup>[[#fn:r248|248]]</sup> . It should be noted that while some radiation modification measures, such as cirrus cloud thinning (Kristjánsson et al., 2016) <sup>[[#fn:r249|249]]</sup> , aim at enhancing outgoing long-wave radiation, SRM is used in this report to refer to all direct interventions on the planetary radiation budget. This report does not use the term ‘geo-engineering’ because of inconsistencies in the literature, which uses this term to cover SRM, CDR or both, whereas this report explicitly differentiates between CDR and SRM. Large-scale SRM could potentially be used to supplement mitigation in overshoot scenarios to keep the global mean temperature below 1.5°C and temporarily reduce the severity of near-term impacts (e.g., MacMartin et al., 2018) <sup>[[#fn:r250|250]]</sup> . The impacts of SRM (both biophysical and societal), costs, technical feasibility, governance and ethical issues associated need to be carefully considered (Schäfer et al., 2015 <sup>[[#fn:r251|251]]</sup> ; Section 4.3.8 and Cross-Chapter Box 10 in Chapter 4).
-</div>
-</div>
-|-
-| <div>
-<div>
-Policy and planning scenarios
-(including business planning)
-</div>
-</div>
-| <div>
-<div>
-Ex ante analysis of the consequences of alternative policies or decisions based on known policy options or already implemented policy and planning measures
+== 1.4.2 Governance, Implementation and Policies ==
-</div>
-</div>
-| <div>
-<div>
-–30 years
+A challenge in creating the enabling conditions of a 1.5°C warmer world is the governance capacity of institutions to develop, implement and evaluate the changes needed within diverse and highly interlinked global social-ecological systems (Busby, 2016) <sup>[[#fn:r252|252]]</sup> (Chapter 4). Policy arenas, governance structures and robust institutions are key enabling conditions for transformative climate action (Chapter 4). It is through governance that justice, ethics and equity within the adaptation–mitigation–sustainable development nexus can be addressed (Von Stechow et al., 2016) <sup>[[#fn:r253|253]]</sup> (Chapter 5).
+Governance capacity includes a wide range of activities and efforts needed by different actors to develop coordinated climate mitigation and adaptation strategies in the context of sustainable development, taking into account equity, justice and poverty eradication. Significant governance challenges include the ability to incorporate multiple stakeholder perspectives in the decision-making process to reach meaningful and equitable decisions, interactions and coordination between different levels of government, and the capacity to raise financing and support for both technological and human resource development. For example, Lövbrand et al. (2017) <sup>[[#fn:r254|254]]</sup> , argue that the voluntary pledges submitted by states and non-state actors to meet the conditions of the Paris Agreement will need to be more firmly coordinated, evaluated and upscaled.
-</div>
+Barriers for transitioning from climate change mitigation and adaptation planning to practical policy implementation include finance, information, technology, public attitudes, social values and practices (Whitmarsh et al., 2011; Corner and Clarke, 2017) <sup>[[#fn:r255|255]]</sup> , and human resource constraints. Institutional capacity to deploy available knowledge and resources is also needed (Mimura et al., 2014) <sup>[[#fn:r256|256]]</sup> . Incorporating strong linkages across sectors, devolution of power and resources to sub-national and local governments with the support of national government, and facilitating partnerships among public, civic, private sectors and higher education institutions (Leal Filho et al., 2018) <sup>[[#fn:r257|257]]</sup> can help in the implementation of identified response options (Chapter 4). Implementation challenges of 1.5°C pathways are larger than for those that are consistent with limiting warming to well below 2°C, particularly concerning scale and speed of the transition and the distributional impacts on ecosystems and socio-economic actors. Uncertainties in climate change at different scales and capacities to respond combined with the complexities of coupled social and ecological systems point to a need for diverse and adaptive implementation options within and among different regions involving different actors. The large regional diversity between highly carbon-invested economies and emerging economies are important considerations for sustainable development and equity in pursuing efforts to limit warming to 1.5°C. Key sectors, including energy, food systems, health, and water supply, also are critical to understanding these connections.
-</div>
-| <div>
-<div>
+<div id="section-1-4-2-block-2" class="box">
-.6.3, 5.5.2, 5.6.2, 6.4.4
-</div>
-</div>
+== Cross-Chapter Box 3: Framing Feasibility: Key Concepts and Conditions for Limiting Global Temperature Increases to 1.5°C ==
-|-
-| <div>
-<div>
+====== Lead Authors ======
-Stylised scenarios (with single and multiple options)
+* William Solecki (United States)
+* Anton Cartwright (South Africa)
+* Wolfgang Cramer (France, Germany)
+* James Ford (United Kingdom, Canada)
+* Kejun Jiang (China)
+* Joana Portugal Pereira (United Kingdom, Portugal)
+* Joeri Rogelj (Austria, Belgium)
+* Linda Steg (Netherlands)
+* Henri Waisman (France)
-</div>
-</div>
-| <div>
-<div>
-Afforestation/reforestation areas, bioenergy areas, protected areas for conservation, consumption patterns (e.g., diets, food waste)
+This Cross-Chapter Box describes the concept of feasibility in relation to efforts to limit global warming to 1.5°C in the context of sustainable development and efforts to eradicate poverty and draws from the understanding of feasibility emerging within the IPCC (IPCC, 2017) <sup>[[#fn:r258|258]]</sup> . Feasibility can be assessed in different ways, and no single answer exists as to the question of whether it is feasible to limit warming to 1.5°C. This implies that an assessment of feasibility would go beyond a ‘yes’ or a ‘no’. Rather, feasibility provides a frame to understand the different conditions and potential responses for implementing adaptation and mitigation pathways, and options compatible with a 1.5°C warmer world. This report assesses the overall feasibility of limiting warming to 1.5°C, and the feasibility of adaptation and mitigation options compatible with a 1.5°C warmer world, in six dimensions:
+'''Geophysical''' : What global emission pathways could be consistent with conditions of a 1.5°C warmer world? What are the physical potentials for adaptation?
-</div>
+'''Environmental-ecological''' : What are the ecosystem services and resources, including geological storage capacity and related rate of needed land-use change, available to promote transformations, and to what extent are they compatible with enhanced resilience?
-</div>
+'''Technological''' : What technologies are available to support transformation?
-| <div>
-<div>
+'''Economic''' : What economic conditions could support transformation?
-–100 years
+'''Socio-cultural''' : What conditions could support transformations in behaviour and lifestyles? To what extent are the transformations socially acceptable and consistent with equity?
+'''Institutional''' : What institutional conditions are in place to support transformations, including multi-level governance, institutional capacity, and political support?
-</div>
+Assessment of feasibility in this report starts by evaluating the unavoidable warming from past emissions (Section 1.2.4) and identifying mitigation pathways that would lead to a 1.5°C world, which indicates that rapid and deep deviations from current emission pathways are necessary (Chapter 2). In the case of adaptation, an assessment of feasibility starts from an evaluation of the risks and impacts of climate change (Chapter 3). To mitigate and adapt to climate risks, system-wide technical, institutional and socio-economic transitions would be required, as well as the implementation of a range of specific mitigation and adaptation options. Chapter 4 applies various indicators categorised in these six dimensions to assess the feasibility of illustrative examples of relevant mitigation and adaptation options (Section 4.5.1). Such options and pathways have different effects on sustainable development, poverty eradication and adaptation capacity (Chapter 5).
-</div>
+The six feasibility dimensions interact in complex and place-specific ways. Synergies and trade-offs may occur between the feasibility dimensions, and between specific mitigation and adaptation options (Section 4.5.4). The presence or absence of enabling conditions would affect the options that comprise feasibility pathways (Section 4.4), and can reduce trade-offs and amplify synergies between options.
-| <div>
-<div>
+Sustainable development, eradicating poverty and reducing inequalities are not only preconditions for feasible transformations, but the interplay between climate action (both mitigation and adaptation options) and the development patterns to which they apply may actually enhance the feasibility of particular options (see Chapter 5).
-.6.1, 5.5.1, 5.5.2, 5.6.1, 5.6.2, 6.4.4, 7.2
+The connections between the feasibility dimensions can be specified across three types of effects (discussed below). Each of these dimensions presents challenges and opportunities in realizing conditions consistent with a 1.5°C warmer world.
+'''Systemic effects:''' Conditions that have embedded within them system-level functions that could include linear and non-linear connections and feedbacks. For example, the deployment of technology and large installations (e.g., renewable or low carbon energy mega-projects) depends upon economic conditions (costs, capacity to mobilize investments for R&amp;D), social or cultural conditions (acceptability), and institutional conditions (political support; e.g., Sovacool et al., 2015) <sup>[[#fn:r259|259]]</sup> . Case studies can demonstrate system-level interactions and positive or negative feedback effects between the different conditions (Jacobson et al., 2015; Loftus et al., 2015) <sup>[[#fn:r260|260]]</sup> . This suggests that each set of conditions and their interactions need to be considered to understand synergies, inequities and unintended consequences.
-</div>
+'''Dynamic effects:''' Conditions that are highly dynamic and vary over time, especially under potential conditions of overshoot or no overshoot. Some dimensions might be more time sensitive or sequential than others (i.e., if conditions are such that it is no longer geophysically feasible to avoid overshooting 1.5°C, the social and institutional feasibility of avoiding overshoot will be no longer relevant). Path dependencies, risks of legacy lock-ins related to existing infrastructures, and possibilities of acceleration permitted by cumulative effects (e.g., dramatic cost decreases driven by learning-by-doing) are all key features to be captured. The effects can play out over various time scales and thus require understanding the connections between near-term (meaning within the next several years to two decades) and long-term implications (meaning over the next several decades) when assessing feasibility conditions.
-</div>
+'''Spatial effects''' : Conditions that are spatially variable and scale dependent, according to context-specific factors such as regional-scale environmental resource limits and endowment; economic wealth of local populations; social organisation, cultural beliefs, values and worldviews; spatial organisation, including conditions of urbanisation; and financial and institutional and governance capacity. This means that the conditions for achieving the global transformation required for a 1.5°C world will be heterogeneous and vary according to the specific context. On the other hand, the satisfaction of these conditions may depend upon global-scale drivers, such as international flows of finance, technologies or capacities. This points to the need for understanding feasibility to capture the interplay between the conditions at different scales.
-|-
-| <div>
-<div>
+With each effect, the interplay between different conditions influences the feasibility of both pathways (Chapter 2) and options (Chapter 4), which in turn affect the likelihood of limiting warming to 1.5°C. The complexity of these interplays triggers unavoidable uncertainties, requiring transformations that remain robust under a range of possible futures that limit warming to 1.5°C.
-Shock scenarios (high impact single events)
-</div>
-</div>
-| <div>
-<div>
-Food supply chain collapses, cyberattacks, pandemic diseases (humans, crops and livestock)
-</div>
-</div>
-| <div>
-<div>
-Near-term events<br />
-(up to 10 years) leading to long-term impacts (10–100 years)
-</div>
+== 1.4.3 Transformation, Transformation Pathways, and Transition: Evaluating Trade-Offs and Synergies Between Mitigation, Adaptation and Sustainable Development Goals ==
-</div>
-| <div>
-<div>
-.8.1
-</div>
+Embedded in the goal of limiting warming to 1.5°C is the opportunity for intentional societal transformation (see Box 1.1 on the Anthropocene). The form and process of transformation are varied and multifaceted (Pelling, 2011; O’Brien et al., 2012; O’Brien and Selboe, 2015; Pelling et al., 2015) <sup>[[#fn:r261|261]]</sup> . Fundamental elements of 1.5°C-related transformation include a decoupling of economic growth from energy demand and CO <sub>2</sub> emissions; leap-frogging development to new and emerging low-carbon, zero-carbon and carbon-negative technologies; and synergistically linking climate mitigation and adaptation to global scale trends (e.g., global trade and urbanization) that will enhance the prospects for effective climate action, as well as enhanced poverty reduction and greater equity (Tschakert et al., 2013; Rogelj et al., 2015; Patterson et al., 2017) <sup>[[#fn:r262|262]]</sup> (Chapters 4 and 5). The connection between transformative climate action and sustainable development illustrates a complex coupling of systems that have important spatial and time scale lag effects and implications for process and procedural equity, including intergenerational equity and for non-human species (Cross-Chapter Box 4 in this chapter, Chapter 5). Adaptation and mitigation transition pathways highlight the importance of cultural norms and values, sector-specific context, and proximate (i.e., occurrence of an extreme event) drivers that when acting together enhance the conditions for societal transformation (Solecki et al., 2017; Rosenzweig et al., 2018) <sup>[[#fn:r263|263]]</sup> (Chapters 4 and 5).
-</div>
+Diversity and flexibility in implementation choices exist for adaptation, mitigation (including carbon dioxide removal, CDR) and remedial measures (such as solar radiation modification, SRM), and a potential for trade-offs and synergies between these choices and sustainable development (IPCC, 2014d; Olsson et al., 2014) <sup>[[#fn:r264|264]]</sup> . The responses chosen could act to synergistically enhance mitigation, adaptation and sustainable development, or they may result in trade-offs which positively impact some aspects and negatively impact others. Climate change is expected to decrease the likelihood of achieving the Sustainable Development Goals (SDGs). While some strategies limiting warming towards 1.5°C are expected to significantly increase the likelihood of meeting those goals while also providing synergies for climate adaptation and mitigation (Chapter 5).
-|-
-| <div>
-<div>
+Dramatic transformations required to achieve the enabling conditions for a 1.5°C warmer world could impose trade-offs on dimensions of development (IPCC, 2014c; Olsson et al., 2014) <sup>[[#fn:r265|265]]</sup> . Some choices of adaptation methods also could adversely impact development (Olsson et al., 2014) <sup>[[#fn:r266|266]]</sup> . This report recognizes the potential for adverse impacts and focuses on finding the synergies between limiting warming, sustainable development, and eradicating poverty, thus highlighting pathways that do not constrain other goals, such as sustainable development and eradicating poverty.
-Conditional probabilistic futures
+The report is framed to address these multiple goals simultaneously and assesses the conditions to achieve a cost-effective and socially acceptable solution, rather than addressing these goals piecemeal (von Stechow et al., 2016) <sup>[[#fn:r267|267]]</sup> (Section 4.5.4 and Chapter 5), although there may be different synergies and trade-offs between a 2°C (von Stechow et al., 2016) <sup>[[#fn:r268|268]]</sup> and 1.5°C warmer world (Kainuma et al., 2017) <sup>[[#fn:r269|269]]</sup> . Climate-resilient development pathways (see Cross-Chapter Box 12 in Chapter 5 and Glossary) are trajectories that strengthen sustainable development, including mitigating and adapting to climate change and efforts to eradicate poverty while promoting fair and cross-scalar resilience in a changing climate. They take into account dynamic livelihoods; the multiple dimensions of poverty, structural inequalities; and equity between and among poor and non-poor people (Olsson et al., 2014) <sup>[[#fn:r270|270]]</sup> . Climate-resilient development pathways can be considered at different scales, including cities, rural areas, regions or at global level (Denton et al., 2014 <sup>[[#fn:r271|271]]</sup> ; Chapter 5).
-ascribe probabilities to uncertain drivers that are conditional on scenario assumptions
+<div id="section-1-4-3-block-2" class="box">
-</div>
-</div>
-| <div>
-<div>
-Where some knowledge is known about driver uncertainties, for example, population, economic growth, land-use change
+== Cross-Chapter Box 4: Sustainable Development and the Sustainable Development Goals ==
+====== Lead Authors ======
-</div>
+* Diana Liverman (United States)
+* Mustafa Babiker (Sudan)
+* Purnamita Dasgupta (India)
+* Riyanti Djalante (Japan, Indonesia)
+* Stephen Humphreys (United Kingdom, Ireland)
+* Natalie Mahowald (United States)
+* Yacob Mulugetta (United Kingdom, Ethiopia)
+* Maria Virginia Vilariño (Argentina)
+* Henri Waisman (France)
-</div>
-| <div>
-<div>
-–100 years
-</div>
+Sustainable development is most often defined as ‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (WCED, 1987) <sup>[[#fn:r272|272]]</sup> and includes balancing social well-being, economic prosperity and environmental protection. The AR5 used this definition and linked it to climate change (Denton et al., 2014) <sup>[[#fn:r273|273]]</sup> . The most significant step since AR5 is the adoption of the UN Sustainable Development Goals, and the emergence of literature that links them to climate (von Stechow et al., 2015; Wright et al., 2015; Epstein and Theuer, 2017; Hammill and Price-Kelly, 2017; Kelman, 2017; Lofts et al., 2017; Maupin, 2017; Gomez-Echeverri, 2018) <sup>[[#fn:r274|274]]</sup> .
-</div>
+In September 2015, the UN endorsed a universal agenda – ‘Transforming our World: the 2030 Agenda for Sustainable Development’ – which aims ‘to take the bold and transformative steps which are urgently needed to shift the world onto a sustainable and resilient path’. Based on a participatory process, the resolution in support of the 2030 agenda adopted 17 non-legally-binding Sustainable Development Goals (SDGs) and 169 targets to support people, prosperity, peace, partnerships and the planet (Kanie and Biermann, 2017) <sup>[[#fn:r275|275]]</sup> .
-| <div>
-<div>
+The SDGs expanded efforts to reduce poverty and other deprivations under the UN Millennium Development Goals (MDGs). There were improvements under the MDGs between 1990 and 2015, including reducing overall poverty and hunger, reducing infant mortality, and improving access to drinking water (United Nations, 2015a) <sup>[[#fn:r276|276]]</sup> . However, greenhouse gas emissions increased by more than 50% from 1990 to 2015, and 1.6 billion people were still living in multidimensional poverty with persistent inequalities in 2015 (Alkire et al., 2015) <sup>[[#fn:r277|277]]</sup> .
-.2
+The SDGs raise the ambition for eliminating poverty, hunger, inequality and other societal problems while protecting the environment. They have been criticised: as too many and too complex, needing more realistic targets, overly focused on 2030 at the expense of longer-term objectives, not embracing all aspects of sustainable development, and even contradicting each other (Horton, 2014; Death and Gabay, 2015; Biermann et al., 2017; Weber, 2017; Winkler and Satterthwaite, 2017) <sup>[[#fn:r278|278]]</sup> .
+Climate change is an integral influence on sustainable development, closely related to the economic, social and environmental dimensions of the SDGs. The IPCC has woven the concept of sustainable development into recent assessments, showing how climate change might undermine sustainable development, and the synergies between sustainable development and responses to climate change (Denton et al., 2014) <sup>[[#fn:r279|279]]</sup> . Climate change is also explicit in the SDGs. SDG13 specifically requires ‘urgent action to address climate change and its impacts’. The targets include strengthening resilience and adaptive capacity to climate-related hazards and natural disasters; integrating climate change measures into national policies, strategies and planning; and improving education, awareness-raising and human and institutional capacity.
-</div>
+Targets also include implementing the commitment undertaken by developed-country parties to the UNFCCC to the goal of mobilizing jointly 100 billion USD annually by 2020 and operationalizing the Green Climate Fund, as well as promoting mechanisms for raising capacity for effective climate change-related planning and management in least developed countries and Small Island Developing States, including focusing on women, youth and local and marginalised communities. SDG13 also acknowledges that the UNFCCC is the primary international, intergovernmental forum for negotiating the global response to climate change.
-</div>
+Climate change is also mentioned in SDGs beyond SDG13, for example in goal targets 1.5, 2.4, 11.B, 12.8.1 related to poverty, hunger, cities and education respectively. The UNFCCC addresses other SDGs in commitments to ‘control, reduce or prevent anthropogenic emissions of greenhouse gases […] in all relevant sectors, including the energy, transport, industry, agriculture, forestry and waste management sectors’ (Art4, 1(c)) and to work towards ‘the conservation and enhancement, as appropriate, of […] biomass, forests and oceans as well as other terrestrial, coastal and marine ecosystems’ (Art4, 1(d)). This corresponds to SDGs that seek clean energy for all (Goal 7), sustainable industry (Goal 9) and cities (Goal 11) and the protection of life on land and below water (14 and 15).
-|-
-| rowspan="2"| <div>
-<div>
+The SDGs and UNFCCC also differ in their time horizons. The SDGs focus primarily on 2030 whereas the Paris Agreement sets out that ‘Parties aim […] to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century’.
-Normative scenarios.
+The IPCC decision to prepare this report on the impacts of 1.5°C and associated emission pathways explicitly asked for the assessment to be in the context of sustainable development and efforts to eradicate poverty. Chapter 1 frames the interaction between sustainable development, poverty eradication and ethics and equity. Chapter 2 assesses how risks and synergies of individual mitigation measures interact with 1.5°C pathways within the context of the SDGs and how these vary according to the mix of measures in alternative mitigation portfolios (Section 2.5). Chapter 3 examines the impacts of 1.5°C global warming on natural and human systems with comparison to 2°C and provides the basis for considering the interactions of climate change with sustainable development in Chapter 5. Chapter 4 analyses strategies for strengthening the response to climate change, many of which interact with sustainable development. Chapter 5 takes sustainable development, eradicating poverty and reducing inequalities as its focal point for the analysis of pathways to 1.5°C and discusses explicitly the linkages between achieving SDGs while eradicating poverty and reducing inequality.
-Desired futures or outcomes that are aspirational and how to achieve them
-</div>
-</div>
-| <div>
-<div>
-Visions, goal-seeking or target-seeking scenarios
+====== Cross-Chapter Box 4: Figure 1 Climate action is number 13 of the UN Sustainable Development Goals ======
-</div>
-</div>
+====  ====
-| <div>
-<div>
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/box-4-fig-1-1024x584.jpg]]
-Environmental quality, societal development, human well-being, the Representative Concentration Pathways (RCPs,) 1.5°C scenarios
-</div>
-</div>
-| <div>
-<div>
-–10 years to 10–100 years
-</div>
-</div>
-| <div>
-<div>
-.6.2, 6.4.4, 7.2, 5.5.2
+== 1.5 Assessment Frameworks and Emerging Methodologies that Integrate Climate Change Mitigation and Adaptation with Sustainable Development ==
-</div>
-</div>
-|-
-| <div>
-<div>
-Pathways as alternative sets<br />
-of choices, actions or behaviours that lead to a future vision<br />
-(goal or target)
+This report employs information and data that are global in scope and include region-scale analysis. It also includes syntheses of municipal, sub-national, and national case studies. Global level statistics including physical and social science data are used, as well as detailed and illustrative case study material of particular conditions and contexts. The assessment provides the state of knowledge, including an assessment of confidence and uncertainty. The main time scale of the assessment is the 21st century and the time is separated into the near-, medium-, and long-term. Near-term refers to the coming decade, medium-term to the period 2030–2050, while long-term refers to 2050–2100. Spatial and temporal contexts are illustrated throughout, including: assessment tools that include dynamic projections of emission trajectories and the underlying energy and land transformation (Chapter 2); methods for assessing observed impacts and projected risks in natural and managed ecosystems and at 1.5°C and higher levels of warming in natural and managed ecosystems and human systems (Chapter 3); assessments of the feasibility of mitigation and adaptation options (Chapter 4); and linkages of the Shared Socioeconomic Pathways (SSPs) and Sustainable Development Goals (SDGs) (Cross-Chapter Boxes 1 and 4 in this chapter, Chapter 2 and Chapter 5).
-</div>
-</div>
-| <div>
-<div>
-Socio-economic systems, governance and policy actions
-</div>
-</div>
-| <div>
-<div>
+== 1.5.1 Knowledge Sources and Evidence Used in the Report ==
-–10 years to 10–100 years
-</div>
-</div>
-| <div>
-<div>
+This report is based on a comprehensive assessment of documented evidence of the enabling conditions to pursuing efforts to limit the global average temperature rise to 1.5°C and adapting to this level of warming in the overarching context of the Anthropocene (Delanty and Mota, 2017) <sup>[[#fn:r280|280]]</sup> . Two sources of evidence are used: peer-reviewed scientific literature and ‘grey’ literature in accordance with procedure on the use of literature in IPCC reports (IPCC, 2013a <sup>[[#fn:r281|281]]</sup> , Annex 2 to Appendix A), with the former being the dominant source. Grey literature is largely used on key issues not covered in peer-reviewed literature.
-.5.2, 6.4.4, 7.2
+The peer-reviewed literature includes the following sources: 1) knowledge regarding the physical climate system and human-induced changes, associated impacts, vulnerabilities, and adaptation options, established from work based on empirical evidence, simulations, modelling, and scenarios, with emphasis on new information since the publication of the IPCC AR5 to the cut-off date for this report (15th of May 2018); 2) humanities and social science theory and knowledge from actual human experiences of climate change risks and vulnerability in the context of social-ecological systems, development, equity, justice, and governance, and from indigenous knowledge systems; and 3) mitigation pathways based on climate projections into the future.
-</div>
+The grey literature category extends to empirical observations, interviews, and reports from government, industry, research institutes, conference proceedings and international or other organisations. Incorporating knowledge from different sources, settings and information channels while building awareness at various levels will advance decision-making and motivate implementation of context-specific responses to 1.5°C warming (Somanathan et al., 2014) <sup>[[#fn:r282|282]]</sup> . The assessment does not assess non-written evidence and does not use oral evidence, media reports or newspaper publications. With important exceptions, such as China, published knowledge from the most vulnerable parts of the world to climate change is limited (Czerniewicz et al., 2017) <sup>[[#fn:r283|283]]</sup> .
-</div>
-|}
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use-block-3">
-<div class="figure">
-<span id="cross-chapter-box-figure-1"></span>
-====== Cross-Chapter-Box-Figure-1 ======
-<span id="interactions-between-land-and-climate-system-components-and-models-in-scenario-analysis.-the-blue-text-describes-selected-model-inputs-and-outputs."></span>
-==== Interactions between land and climate system components and models in scenario analysis. The blue text describes selected model inputs and outputs. ====
-[[File:https://www.ipcc.ch/site/assets/uploads/sites/4/2020/01/C1_Cross-Chapter-Box-Figure-1_Raw.jpg]]
+== 1.5.2 Assessment Frameworks and Methodologies ==
-<div>
-Interactions between land and climate system components and models in scenario analysis. The blue text describes selected model inputs and outputs.
-</div>
-</div>
-</div>
+''Climate models and associated simulations''
-<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use-block-4">
-<div>
+The multiple sources of climate model information used in this assessment are provided in Chapter 2 (Section 2.2) and Chapter 3 (Section 3.2). Results from global simulations, which have also been assessed in previous IPCC reports and that are conducted as part of the World Climate Research Programme (WCRP) Coupled Models Intercomparison Project (CMIP) are used. The IPCC AR4 and Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) reports were mostly based on simulations from the CMIP3 experiment, while the AR5 was mostly based on simulations from the CMIP5 experiment. The simulations of the CMIP3 and CMIP5 experiments were found to be very similar (e.g., Knutti and Sedláček, 2012; Mueller and Seneviratne, 2014) <sup>[[#fn:r284|284]]</sup> . In addition to the CMIP3 and CMIP5 experiments, results from coordinated regional climate model experiments (e.g., the Coordinated Regional Climate Downscaling Experiment, CORDEX) have been assessed and are available for different regions (Giorgi and Gutowski, 2015) <sup>[[#fn:r285|285]]</sup> . For instance, assessments based on publications from an extension of the IMPACT2C project (Vautard et al., 2014; Jacob and Solman, 2017) <sup>[[#fn:r286|286]]</sup> are newly available for 1.5°C projections. Recently, simulations from the ‘Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) multimodel experiment have been performed to specifically assess climate changes at 1.5°C vs 2°C global warming (Mitchell et al., 2016) <sup>[[#fn:r287|287]]</sup> . The HAPPI protocol consists of coupled land–atmosphere initial condition ensemble simulations with prescribed sea surface temperatures (SSTs); sea ice, GHG and aerosol concentrations; and solar and volcanic activity that coincide with three forced climate states: present-day (2006–2015) (see Section 1.2.1) and future (2091–2100) either with 1.5°C or 2°C global warming (prescribed by modified SSTs).
-<div>
+''Detection and attribution of change in climate and impacted systems''
-<div>
+Formalized scientific methods are available to detect and attribute impacts of greenhouse gas forcing on observed changes in climate (e.g., Hegerl et al., 2007; Seneviratne et al., 2012; Bindoff et al., 2013) <sup>[[#fn:r288|288]]</sup> and impacts of climate change on natural and human systems (e.g., Stone et al., 2013; Hansen and Cramer, 2015; Hansen et al., 2016) <sup>[[#fn:r289|289]]</sup> . The reader is referred to these sources, as well as to the AR5 for more background on these methods.
-<div>
+Global climate warming has already reached approximately 1°C (see Section 1.2.1) relative to pre-industrial conditions, and thus ‘climate at 1.5°C global warming’ corresponds to approximately the addition of only half a degree of warming compared to the present day, comparable to the warming that has occurred since the 1970s (Bindoff et al., 2013) <sup>[[#fn:r290|290]]</sup> . Methods used in the attribution of observed changes associate with this recent warming are therefore also applicable to assessments of future changes in climate at 1.5°C warming, especially in cases where no climate model simulations or analyses are available.
-Normative scenarios: visions and pathways analysis
+Impacts of 1.5°C global warming can be assessed in part from regional and global climate changes that have already been detected and attributed to human influence (e.g., Schleussner et al., 2017) <sup>[[#fn:r291|291]]</sup> and are components of the climate system that are most responsive to current and projected future forcing. For this reason, when specific projections are missing for 1.5°C global warming, some of the assessments of climate change provided in Chapter 3 (Section 3.3) build upon joint assessments of (i) changes that were observed and attributed to human influence up to the present, that is, for 1°C global warming and (ii) projections for higher levels of warming (e.g., 2°C, 3°C or 4°C) to assess the changes at 1.5°C. Such assessments are for transient changes only (see Chapter 3, Section 3.3).
-Normative scenarios reflect a desired or target-seeking future. Pathways analysis is important in moving beyond the ‘what if?’ perspective of exploratory scenarios to evaluate how normative futures might be achieved in practice, recognising that multiple pathways may achieve the same future vision. Pathways analysis focuses on consumption and behavioural changes through transitions and transformative solutions (IPBES 2018a <sup>[[#fn:r501|501]]</sup> ). Pathways analysis is highly relevant in support of policy, since it outlines sets of time-dependent actions and decisions to achieve future targets, especially with respect to sustainable development goals, as well as highlighting trade-offs and co-benefits (IPBES 2018a <sup>[[#fn:r502|502]]</sup> ). Multiple, alternative pathways have been shown to exist that mitigate trade-offs whilst achieving the priorities for future sustainable development outlined by governments and societal actors. Of these alternatives, the most promising focus on long-term societal transformations through education, awareness raising, knowledge sharing and participatory decision-making (IPBES 2018a <sup>[[#fn:r503|503]]</sup> ).
+Besides quantitative detection and attribution methods, assessments can also be based on indigenous and local knowledge (see Chapter 4, Box 4.3). While climate observations may not be available to assess impacts from a scientific perspective, local community knowledge can also indicate actual impacts (Brinkman et al., 2016; Kabir et al., 2016) <sup>[[#fn:r292|292]]</sup> . The challenge is that a community’s perception of loss due to the impacts of climate change is an area that requires further research (Tschakert et al., 2017) <sup>[[#fn:r293|293]]</sup> .
-What are the limitations of land-use scenarios?
+''Costs and benefits analysis''
-Applying a common scenario framework (e.g., RCPs/SSPs) supports the comparison and integration of climate- and land-system scenarios, but a ‘climate-centric’ perspective can limit the capacity of these scenarios to account for a wider range of land-relevant drivers (Rosa et al. 2017 <sup>[[#fn:r504|504]]</sup> ). For example, in climate mitigation scenarios it is important to assess the impact of mitigation actions on the broader environment such as biodiversity, ecosystem functioning, air quality, food security, desertification/degradation and water cycles (Rosa et al. 2017 <sup>[[#fn:r505|505]]</sup> ). This implies the need for a more encompassing and flexible approach to creating scenarios that considers other environmental aspects, not only as a part of impact assessment, but also during the process of creating the scenarios themselves.
+Cost–benefit analyses are common tools used for decision-making, whereby the costs of impacts are compared to the benefits from different response actions (IPCC, 2014a, b) <sup>[[#fn:r294|294]]</sup> . However, for the case of climate change, recognising the complex inter-linkages of the Anthropocene, cost–benefit analysis tools can be difficult to use because of disparate impacts versus costs and complex interconnectivity within the global social-ecological system (see Box 1.1 and Cross-Chapter Box 5 in Chapter 2). Some costs are relatively easily quantifiable in monetary terms but not all. Climate change impacts human lives and livelihoods, culture and values, and whole ecosystems. It has unpredictable feedback loops and impacts on other regions (IPCC, 2014a) <sup>[[#fn:r295|295]]</sup> , giving rise to indirect, secondary, tertiary and opportunity costs that are typically extremely difficult to quantify. Monetary quantification is further complicated by the fact that costs and benefits can occur in different regions at very different times, possibly spanning centuries, while it is extremely difficult if not impossible to meaningfully estimate discount rates for future costs and benefits. Thus standard cost–benefit analyses become difficult to justify (IPCC, 2014a; Dietz et al., 2016) <sup>[[#fn:r296|296]]</sup> and are not used as an assessment tool in this report.
-A limited number of models can quantify global scale, land-use change scenarios, and there is large variance in the outcomes of these models (Alexander et al. 2016a <sup>[[#fn:r506|506]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r507|507]]</sup> ). In some cases, there is greater variability between the models themselves than between the scenarios that they are quantifying, and these differences vary geographically (Prestele et al. 2016 <sup>[[#fn:r508|508]]</sup> ). These differences arise from variations in baseline datasets, thematic classes and modelling paradigms (Alexander et al. 2016a <sup>[[#fn:r509|509]]</sup> ; Popp et al. 2016 <sup>[[#fn:r510|510]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r511|511]]</sup> ). Model evaluation is critical in establishing confidence in the outcomes of modelled futures (Ahlstrom et al. 2012 <sup>[[#fn:r512|512]]</sup> ; Kelley et al. 2013 <sup>[[#fn:r513|513]]</sup> ). Some, but not all, land-use models are evaluated against observational data and model evaluation is rarely reported. Hence, there is a need for more transparency in land-use modelling, especially in evaluation and testing, as well as making model code available with complete sets of scenario outputs (e.g., Dietrich et al. 2018 <sup>[[#fn:r514|514]]</sup> ).
-<div>
-<div>
-<div>
-<div>
-There is a small, but growing literature on quantitative pathways to achieve normative visions and their associated trade-offs (IPBES 2018a <sup>[[#fn:r515|515]]</sup> ). Whilst the visions themselves may be clearly articulated, the societal choices, behaviours and transitions needed to attain them, are not. Better accounting for human behaviour and decision-making processes in global scale land-use models would improve the capacity to quantify pathways to sustainable futures (Rounsevell et al. 2014 <sup>[[#fn:r516|516]]</sup> ; Arneth et al. 2014 <sup>[[#fn:r517|517]]</sup> ; Calvin and Bond-Lamberty 2018 <sup>[[#fn:r518|518]]</sup> ). It is, however, difficult to understand and represent human behaviour and social interaction processes at global scales. Decision-making in global models is commonly represented through economic processes (Arneth et al. 2014 <sup>[[#fn:r519|519]]</sup> ). Other important human processes for land systems including equity, fairness, land tenure and the role of institutions and governance, receive less attention, and this limits the use of global models to quantify transformative pathways, adaptation and mitigation (Arneth et al. 2014 <sup>[[#fn:r520|520]]</sup> ; Rounsevell et al. 2014 <sup>[[#fn:r521|521]]</sup> ; Wang et al. 2016 <sup>[[#fn:r|]]</sup> 522). No model exists at present to represent complex human behaviours at the global scale, although the need has been highlighted (Rounsevell et al. 2014 <sup>[[#fn:r523|523]]</sup> ; Arneth et al. 2014 <sup>[[#fn:r524|524]]</sup> ; Robinson et al. 2017 <sup>[[#fn:r525|525]]</sup> ; Brown et al. 2017 <sup>[[#fn:r526|526]]</sup> ; Calvin and Bond-Lamberty 2018 <sup>[[#fn:r527|527]]</sup> ).
-</div>
-</div>
+== 1.6 Confidence, Uncertainty and Risk ==
-</div>
-</div>
-</div>
-</div>
-</div>
+This report relies on the IPCC’s uncertainty guidance provided in Mastrandrea et al. (2011) <sup>[[#fn:r297|297]]</sup> and sources given therein. Two metrics for qualifying key findings are used:
-</div>
+'''Confidence:''' Five qualifiers are used to express levels of confidence in key findings, ranging from ''very low'' , through ''low'' , ''medium'' , ''high'' , to ''very high'' . The assessment of confidence involves at least two dimensions, one being the type, quality, amount or internal consistency of individual lines of evidence, and the second being the level of agreement between different lines of evidence. Very high confidence findings must either be supported by a high level of agreement across multiple lines of mutually independent and individually robust lines of evidence or, if only a single line of evidence is available, by a very high level of understanding underlying that evidence. Findings of low or very low confidence are presented only if they address a topic of major concern.
-</div>
+'''Likelihood:''' A calibrated language scale is used to communicate assessed probabilities of outcomes, ranging from ''exceptionally unlikely'' (&lt;1%), ''extremely unlikely'' (&lt;5%), ''very unlikely'' (&lt;10%), ''unlikely'' (&lt;33%), ''about as likely as not'' (33–66%), ''likely'' (&gt;66%), ''very likely'' (&gt;90%), ''extremely likely'' (&gt;95%) to ''virtually certain'' (&gt;99%). These terms are normally only applied to findings associated with high or very high confidence. Frequency of occurrence within a model ensemble does not correspond to actual assessed probability of outcome unless the ensemble is judged to capture and represent the full range of relevant uncertainties.
-</div>
+Three specific challenges arise in the treatment of uncertainty and risk in this report. First, the current state of the scientific literature on 1.5°C means that findings based on multiple lines of robust evidence for which quantitative probabilistic results can be expressed may be few in number, and those that do exist may not be the most policy-relevant. Hence many key findings are expressed using confidence qualifiers alone.
-</div>
+Second, many of the most important findings of this report are conditional because they refer to ambitious mitigation scenarios, potentially involving large-scale technological or societal transformation. Conditional probabilities often depend strongly on how conditions are specified, such as whether temperature goals are met through early emission reductions, reliance on negative emissions, or through a low climate response. Whether a certain risk is considered high at 1.5°C may therefore depend strongly on how 1.5°C is specified, whereas a statement that a certain risk may be substantially higher at 2°C relative to 1.5°C may be much more robust.
-</div>
+Third, achieving ambitious mitigation goals will require active, goal-directed efforts aiming explicitly for specific outcomes and incorporating new information as it becomes available (Otto et al., 2015) <sup>[[#fn:r298|298]]</sup> . This shifts the focus of uncertainty from the climate outcome itself to the level of mitigation effort that may be required to achieve it. Probabilistic statements about human decisions are always problematic, but in the context of robust decision-making, many near-term policies that are needed to keep open the option of limiting warming to 1.5°C may be the same, regardless of the actual probability that the goal will be met (Knutti et al., 2015) <sup>[[#fn:r299|299]]</sup> .
-</div>
-</div>
-</div>
-<div id="section-1-2-2-3-uncertainties-in-decision-making">
-<div>
-<div>
-<span id="uncertainties-in-decision-making"></span>
-== 1.2.2.3 Uncertainties in decision-making ==
+== 1.7 Storyline of the Report ==
-</div>
-<div>
-<div id="section-1-2-2-3-uncertainties-in-decision-making-block-1">
-<div>
-<div>
-<div>
+The storyline of this report (Figure 1.6) includes a set of interconnected components. The report consists of five chapters (plus Supplementary Material for Chapters 1 through 4), a Technical Summary and a Summary for Policymakers. It also includes a set of boxes to elucidate specific or cross-cutting themes, as well as Frequently Asked Questions for each chapter, a Glossary, and several other Annexes.
-Decision-makers develop and implement policy in the face of many uncertainties (Rosenzweig and Neofotis 2013 <sup>[[#fn:r528|528]]</sup> ; Anav et al. 2013 <sup>[[#fn:r529|529]]</sup> ; Ciais et al. 2013a <sup>[[#fn:r530|530]]</sup> ; Stocker et al. 2013b <sup>[[#fn:r531|531]]</sup> ) (Section 7.5). In context of climate change, the term ‘deep uncertainty’ is frequently used to denote situations in which either the analysis of a situation is inconclusive, or parties to a decision cannot agree on a number of criteria that would help to rank model results in terms of likelihood (e.g., Hallegatte and Mach 2016 <sup>[[#fn:r532|532]]</sup> ; Maier et al. 2016 <sup>[[#fn:r533|533]]</sup> ) (Sections 7.1 and 7.5, and Table SM.1.2 in Supplementary Material). However, existing uncertainty does not support societal and political inaction.
+At a time of unequivocal and rapid global warming, this report emerges from the long-term temperature goal of the Paris Agreement – strengthening the global response to the threat of climate change by pursuing efforts to limit warming to 1.5°C through reducing emissions to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases. The assessment focuses first, in Chapter 1, on how 1.5°C is defined and understood, what is the current level of warming to date, and the present trajectory of change. The framing presented in Chapter 1 provides the basis through which to understand the enabling conditions of a 1.5°C warmer world and connections to the SDGs, poverty eradication, and equity and ethics.
-The many ways of dealing with uncertainty in decision-making can be summarised by two decision approaches: (economic) cost-benefit analysis, and the precautionary approach. A typical variant of cost-benefit analysis is the minimisation of negative consequences. This approach needs reliable probability estimates (Gleckler et al. 2016 <sup>[[#fn:r534|534]]</sup> ; Parker 2013 <sup>[[#fn:r535|535]]</sup> ) and tends to focus on the short term. The precautionary approach does not take account of probability estimates (cf. Raffensperger and Tickner 1999 <sup>[[#fn:r536|536]]</sup> ), but instead focuses on avoiding the worst outcome (Gardiner 2006 <sup>[[#fn:r537|537]]</sup> ).
+In Chapter 2, scenarios of a 1.5°C warmer world and the associated pathways are assessed. The pathways assessment builds upon the AR5 with a greater emphasis on sustainable development in mitigation pathways. All pathways begin now and involve rapid and unprecedented societal transformation. An important framing device for this report is the recognition that choices that determine emissions pathways, whether ambitious mitigation or ‘no policy’ scenarios, do not occur independently of these other changes and are, in fact, highly interdependent.
-<div>
+Projected impacts that emerge in a 1.5°C warmer world and beyond are dominant narrative threads of the report and are assessed in Chapter 3. The chapter focuses on observed and attributable global and regional climate changes and impacts and vulnerabilities. The projected impacts have diverse and uneven spatial, temporal, human, economic, and ecological system-level manifestations. Central to the assessment is the reporting of impacts at 1.5°C and 2°C, potential impacts avoided through limiting warming to 1.5°C, and, where possible, adaptation potential and limits to adaptive capacity.
-<div>
+Response options and associated enabling conditions emerge next, in Chapter 4. Attention is directed to exploring questions of adaptation and mitigation implementation, integration, and transformation in a highly interdependent world, with consideration of synergies and trade-offs. Emission pathways, in particular, are broken down into policy options and instruments. The role of technological choices, institutional capacity and global-scale trends like urbanization and changes in ecosystems are assessed.
-<div>
+Chapter 5 covers linkages between achieving the SDGs and a 1.5°C warmer world and turns toward identifying opportunities and challenges of transformation. This is assessed within a transition to climate-resilient development pathways and connection between the evolution towards 1.5°C, associated impacts, and emission pathways. Positive and negative effects of adaptation and mitigation response measures and pathways for a 1.5°C warmer world are examined. Progress along these pathways involves inclusive processes, institutional integration, adequate finance and technology, and attention to issues of power, values, and inequalities to maximize the benefits of pursuing climate stabilisation at 1.5°C and the goals of sustainable development at multiple scales of human and natural systems from global, regional, national to local and community levels.
-Between these two extremes, various decision approaches seek to address uncertainties in a more reflective manner that avoids the limitations of cost-benefit analysis and the precautionary approach. Climate-informed decision analysis combines various approaches to explore options and the vulnerabilities and sensitivities of certain decisions. Such an approach includes stakeholder involvement (e.g., elicitation methods), and can be combined with, for example, analysis of climate or land-use change modelling (Hallegatte and Rentschler 2015 <sup>[[#fn:r538|538]]</sup> ; Luedeling and Shepherd 2016 <sup>[[#fn:r539|539]]</sup> ).
-Flexibility is facilitated by political decisions that are not set in stone and can change over time (Walker et al. 2013 <sup>[[#fn:r540|540]]</sup> ; Hallegatte and Rentschler 2015 <sup>[[#fn:r541|541]]</sup> ). Generally, within the research community that investigates deep uncertainty, a paradigm is emerging that requires the development of a strategic vision of the long – or mid-term future, while committing to short-term actions and establishing a framework to guide future actions, including revisions and flexible adjustment of decisions (Haasnoot 2013 <sup>[[#fn:r542|542]]</sup> ) (Section 7.5).
-</div>
-</div>
-</div>
-</div>
+====== Figure 1.6. Schematic of report storyline ======
-</div>
-</div>
+====  ====
-</div>
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/figure-6-1024x1009.jpg]]
-</div>
+Original Creation for this Report
-</div>
-</div>
-</div>
-</div>
-</div>
-<div>
-<div>
-<span id="response-options-to-the-key-challenges"></span>
-== 1.3 Response options to the key challenges ==
-</div>
+== FAQs Frequently Asked Questions ==
-<div>
-<div id="article-1-3-response-options-to-the-key-challenges-block-1">
-<div>
-<div>
+<div id="article-faq-chapter-1-block-1" class="box">
-<div>
-A number of response options underpin solutions to the challenges arising from GHG emissions from land, and the loss of productivity arising from degradation and desertification. These options are discussed in Sections 2.5 and 6.2 and rely on (i) land management, (ii) value chain management, and (iii) risk management (Table 1.2). None of these response options are mutually exclusive, and it is their combination in a regionally, context-specific manner that is most likely to achieve co-benefits between climate change mitigation, adaptation and other environmental challenges in a cost-effective way (Griscom et al. 2017 <sup>[[#fn:r543|543]]</sup> ; Kok et al. 2018 <sup>[[#fn:r544|544]]</sup> ). Sustainable solutions affecting both demand and supply are expected to yield most co-benefits if these rely not only on the carbon footprint, but are extended to other vital ecosystems such as water, nutrients and biodiversity footprints (van Noordwijk and Brussaard 2014 <sup>[[#fn:r545|545]]</sup> ; Cremasch 2016 <sup>[[#fn:r546|546]]</sup> ). As an entry point to the discussion in Chapter 6, we introduce here a selected number of examples that cut across climate change mitigation, food security, desertification, and degradation issues, including potential trade-offs and co-benefits.
-</div>
+== FAQ 1.1 Why are we talking about 1.5°C? ==
-</div>
-</div>
-</div>
-<div id="article-1-3-response-options-to-the-key-challenges-block-2">
-<div class="figure">
-<span id="table-1.2"></span>
+''Summary: Climate change represents an urgent and potentially irreversible threat to human societies and the planet. In recognition of this, the overwhelming majority of countries around the world adopted the Paris Agreement in December 2015, the central aim of which includes pursuing efforts to limit global temperature rise to 1.5°C. In doing so, these countries, through the United Nations Framework Convention on Climate Change (UNFCCC), also invited the IPCC to provide a Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emissions pathways.''
-====== Table 1.2 ======
-<span id="broad-categorisation-of-response-options-into-three-main-classes-and-eight-sub-classes."></span>
+At the 21st Conference of the Parties (COP21) in December 2015, 195 nations adopted the Paris Agreement <sup>[[#fn:2|2]]</sup> . The first instrument of its kind, the landmark agreement includes the aim to strengthen the global response to the threat of climate change by ‘holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels’.
-==== Broad categorisation of response options into three main classes and eight sub-classes. ====
-<div>
+The first UNFCCC document to mention a limit to global warming of 1.5°C was the Cancun Agreement, adopted at the sixteenth COP (COP16) in 2010. The Cancun Agreement established a process to periodically review the ‘adequacy of the long-term global goal (LTGG) in the light of the ultimate objective of the Convention and the overall progress made towards achieving the LTGG, including a consideration of the implementation of the commitments under the Convention’. The definition of LTGG in the Cancun Agreement was ‘to hold the increase in global average temperature below 2°C above pre-industrial levels’. The agreement also recognised the need to consider ‘strengthening the long-term global goal on the basis of the best available scientific knowledge…to a global average temperature rise of 1.5°C’.
-<div>
+Beginning in 2013 and ending at the COP21 in Paris in 2015, the first review period of the long-term global goal largely consisted of the Structured Expert Dialogue (SED). This was a fact-finding, face-to-face exchange of views between invited experts and UNFCCC delegates. The final report of the SED <sup>[[#fn:3|3]]</sup> concluded that ‘in some regions and vulnerable ecosystems, high risks are projected even for warming above 1.5°C’. The SED report also suggested that Parties would profit from restating the temperature limit of the long-term global goal as a ‘defence line’ or ‘buffer zone’, instead of a ‘guardrail’ up to which all would be safe, adding that this new understanding would ‘probably also favour emission pathways that will limit warming to a range of temperatures below 2°C’. Specifically on strengthening the temperature limit of 2°C, the SED’s key message was: ‘While science on the 1.5°C warming limit is less robust, efforts should be made to push the defence line as low as possible’. The findings of the SED, in turn, fed into the draft decision adopted at COP21.
-<div>
+With the adoption of the Paris Agreement, the UNFCCC invited the IPCC to provide a Special Report in 2018 on ‘the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emissions pathways’. The request was that the report, known as SR1.5, should not only assess what a 1.5°C warmer world would look like but also the different pathways by which global temperature rise could be limited to 1.5°C. In 2016, the IPCC accepted the invitation, adding that the Special Report would also look at these issues in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty.
-<div>
+The combination of rising exposure to climate change and the fact that there is a limited capacity to adapt to its impacts amplifies the risks posed by warming of 1.5°C and 2°C. This is particularly true for developing and island countries in the tropics and other vulnerable countries and areas. The risks posed by global warming of 1.5°C are greater than for present-day conditions but lower than at 2°C.
-For illustration, the table includes examples of individual response options. A complete list and description is provided in Chapter 6.
-</div>
-</div>
-</div>
-</div>
-<div>
-<div>
+====== FAQ1.1, Figure 1 ======
-{| class="wikitable"
-|-
-| colspan="2"| <div>
-<div>
+==== A timeline of notable dates in preparing the IPCC Special Report on Global Warming of 1.5°C (blue) embedded within processes and milestones of the United Nations Framework Convention on Climate Change (UNFCCC; grey), including events that may be relevant for discussion of temperature limits. ====
-Response options based on land management
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/FAQ1.1_IPCC-1024x658.jpg]]
-</div>
-</div>
-|-
-| <div>
-<div>
-in agriculture
+<div id="article-faq-chapter-1-block-4" class="box">
-</div>
-</div>
-| <div>
-<div>
+== FAQ 1.2 How close are we to 1.5°C? ==
-Improved management of: cropland, grazing land, livestock; agro-forestry; avoidance of conversion of grassland to cropland; integrated water management
-</div>
-</div>
-|-
-| <div>
-<div>
-in forests
-</div>
+'''''Summary:''''' ''Human-induced warming has already reached about'' ''1°C above pre-industrial levels at the time of writing of this Special Report.'' ''By the decade 2006–2015, human activity had warmed the world by 0.87°C (±0.12°C) compared to pre-industrial times (1850–1900). If the current warming rate continues, the world would reach human-induced global warming of 1.5°C around 2040.''
-</div>
+Under the 2015 Paris Agreement, countries agreed to cut greenhouse gas emissions with a view to ‘holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels’. While the overall intention of strengthening the global response to climate change is clear, the Paris Agreement does not specify precisely what is meant by ‘global average temperature’, or what period in history should be considered ‘pre-industrial’. To answer the question of how close are we to 1.5°C of warming, we need to first be clear about how both terms are defined in this Special Report.
-| <div>
-<div>
+The choice of pre-industrial reference period, along with the method used to calculate global average temperature, can alter scientists’ estimates of historical warming by a couple of tenths of a degree Celsius. Such differences become important in the context of a global temperature limit just half a degree above where we are now. But provided consistent definitions are used, they do not affect our understanding of how human activity is influencing the climate.
-Improved management of forests and forest restoration; reduced deforestation and degradation; afforestation
+In principle, ‘pre-industrial levels’ could refer to any period of time before the start of the industrial revolution. But the number of direct temperature measurements decreases as we go back in time. Defining a ‘pre-industrial’ reference period is, therefore, a compromise between the reliability of the temperature information and how representative it is of truly pre-industrial conditions. Some pre-industrial periods are cooler than others for purely natural reasons. This could be because of spontaneous climate variability or the response of the climate to natural perturbations, such as volcanic eruptions and variations in the sun’s activity. This IPCC Special Report on Global Warming of 1.5°C uses the reference period 1850–1900 to represent pre-industrial temperature. This is the earliest period with near-global observations and is the reference period used as an approximation of pre-industrial temperatures in the IPCC Fifth Assessment Report.
+Once scientists have defined ‘pre-industrial’, the next step is to calculate the amount of warming at any given time relative to that reference period. In this report, warming is defined as the increase in the 30-year global average of combined air temperature over land and water temperature at the ocean surface. The 30-year timespan accounts for the effect of natural variability, which can cause global temperatures to fluctuate from one year to the next. For example, 2015 and 2016 were both affected by a strong El Niño event, which amplified the underlying human-caused warming.
-</div>
+In the decade 2006–2015, warming reached 0.87°C (±0.12°C) relative to 1850–1900, predominantly due to human activity increasing the amount of greenhouse gases in the atmosphere. Given that global temperature is currently rising by 0.2°C (±0.1°C) per decade, human-induced warming reached 1°C above pre-industrial levels around 2017 and, if this pace of warming continues, would reach 1.5°C around 2040.
-</div>
+While the change in global average temperature tells researchers about how the planet as a whole is changing, looking more closely at specific regions, countries and seasons reveals important details. Since the 1970s, most land regions have been warming faster than the global average, for example. This means that warming in many regions has already exceeded 1.5°C above pre-industrial levels. Over a fifth of the global population live in regions that have already experienced warming in at least one season that is greater than 1.5°C above pre-industrial levels.
-|-
-| <div>
-<div>
-of soils
-</div>
-</div>
-| <div>
-<div>
-Increased soil organic carbon content; reduced soil erosion; reduced soil salinisation
+====== FAQ1.2, Figure 1 ======
-</div>
+==== Human-induced warming reached approximately 1°C above pre-industrial levels in 2017. ====
-</div>
+[[File:https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/FAQ1.2_IPCC-1024x1003.jpg]]
-|-
-| <div>
-<div>
+At the present rate, global temperatures would reach 1.5°C around 2040. Stylized 1.5°C pathway shown here involves emission reductions beginning immediately, and CO <sub>2</sub> emissions reaching zero by 2055.
-across all/other ecosystems
-</div>
-</div>
-| <div>
-<div>
-Reduced landslides and natural hazards; reduced pollution including acidification; biodiversity conservation; restoration and reduced conversion of peatlands
-</div>
-</div>
-|-
-| <div>
-<div>
-specifically for CO <sub>2</sub> removal
+== SM Supplementary Material ==
-</div>
-</div>
-| <div>
-<div>
-Enhanced weathering of minerals; bioenergy and BECCS
+To view the  Supplementary Material  for Chapter 1 click on the image below
-</div>
+[https://www.ipcc.ch/site/assets/uploads/sites/2/2019/10/SR15_1SM_Low_Res.pdf [[File:../../../site/assets/uploads/sites/2/2019/10/chapter_1_SM.jpg]]]
-</div>
-|-
-| colspan="2"| <div>
-<div>
+To download the high res version of the Chapter 1 Supplementary Material  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/10/SR15_1SM_High_Res.pdf click here]
-Response options based on value chain management
-</div>
-</div>
-|-
-| <div>
-<div>
-through demand management
+== CD Chapter Downloads ==
-</div>
-</div>
-| <div>
-<div>
-Dietary change; reduced post-harvest losses; reduced food waste
+In this section, you will find all of the documents available to download for Chapter 1.
-</div>
-</div>
+==== Final Report ====
-|-
-| <div>
-<div>
+CHAPTER 1: FRAMING AND CONTEXT  [https://www.ipcc.ch/site/assets/uploads/sites/2/2022/06/SR15_Chapter_1_HR.pdf Download High Res] ,  [https://www.ipcc.ch/site/assets/uploads/sites/2/2022/06/SR15_Chapter_1_LR.pdf Download Low Res]
-through supply management
+Chapter 1 – Supplementary Material [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/10/SR15_1SM_High_Res.pdf Download High Res] , [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/10/SR15_1SM_Low_Res.pdf Download Low Res]
-</div>
+==== Draft submitted to 48th Session of IPCC ====
-</div>
+CHAPTER 1: FRAMING AND CONTEXT –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/05/SR15_Approval_Chapter_1.pdf Download]
-| <div>
-<div>
+CHAPTER 1: FRAMING AND CONTEXT – Supplementary Material –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/05/SR15_Approval_Chapter_1_SM.pdf Download]
-Sustainable sourcing; improved energy use in food systems; improved food processing and retailing
+==== Final Government Draft ====
-</div>
+CHAPTER 1: FRAMING AND CONTEXT –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/03/SR15_FGD_Chapter_1.pdf Download]
-</div>
+CHAPTER 1: FRAMING AND CONTEXT – Annex –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/03/SR15_FGD_Chapter_1_Annex.pdf Download]
-|-
-| colspan="2"| <div>
-<div>
-Response options based on risk management
+==== Second Order Draft ====
+CHAPTER 1: FRAMING AND CONTEXT –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/03/SR15_SOD_Chapter1.pdf Download]
-</div>
+CHAPTER 1: FRAMING AND CONTEXT – Technical Appendix –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/03/SR15_SOD_Chapter1_Technical_Appendix.pdf Download]
-</div>
+Review Comments and Responses –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/09/SR15SOD_Chapter1_Comments_and_Responses.pdf Download]
-|-
-| <div>
-<div>
-Risk management
+==== First Order Draft ====
+CHAPTER 1: FRAMING AND CONTEXT –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/03/SR15_FOD_Chapter1.pdf Download]
-</div>
+Review Comments and Responses –  [https://www.ipcc.ch/site/assets/uploads/sites/2/2019/09/SR15FOD_Chapter1_Comments_and_Responses.pdf Download]
-</div>
-| <div>
-<div>
+To view all the downloads for the report please go to the  [[IPCC:Sr15:Download|Download Report]]  page.
-Risk-sharing instruments; use of local seeds; disaster risk management
-</div>
-</div>
-|}
-</div>
-</div>
-</div>
+=== Footnotes ===
-</div>
-</div>
-<div class="section">
-<div>
-<div>
+# <span id="fn:1">An animated version of Figure 1.4 will be embedded in the web-based version of this Special Report</span>
+# <span id="fn:2">Paris Agreement FCCC/CP/2015/10/Add.1 https://unfccc.int/documents/9097</span>
+# <span id="fn:3">Structured Expert Dialogue (SED) final report FCCC/SB/2015/INF.1 https://unfccc.int/documents/8707</span>
-<span id="targeted-decarbonisation-relying-on-large-land-area-need"></span>
-== 1.3.1 Targeted decarbonisation relying on large land-area need ==
-</div>
-<div>
-<div id="section-1-3-1-targeted-decarbonisation-relying-on-large-land-area-need-block-1">
+=== References ===
-<div>
-<div>
-<div>
-Most global future scenarios that aim to achieve global warming of 2°C or well below rely on bioenergy (BE; BECCS, with carbon capture and storage; Cross-Chapter Box 7 in Chapter 6) or afforestation and reforestation (de Coninck et al. 2018 <sup>[[#fn:r547|547]]</sup> ; Rogelj et al. 2018b <sup>[[#fn:r548|548]]</sup> ,a <sup>[[#fn:r549|549]]</sup> ; Anderson and Peters 2016 <sup>[[#fn:r550|550]]</sup> ; Popp et al. 2016 <sup>[[#fn:r551|551]]</sup> ; Smith et al. 2016 <sup>[[#fn:r552|552]]</sup> ) (Cross-Chapter Box 2 in Chapter 1). In addition to the very large area requirements projected for 2050 or 2100, several other aspects of these scenarios have also been criticised. For instance, they simulate very rapid technological and societal uptake rates for the land-related mitigation measures, when compared with historical observations (Turner et al. 2018 <sup>[[#fn:r553|553]]</sup> ; Brown et al. 2019 <sup>[[#fn:r554|554]]</sup> ; Vaughan and Gough 2016 <sup>[[#fn:r555|555]]</sup> ). Furthermore, ''confidence'' in the projected bioenergy or BECCS net carbon uptake potential is ''low'' , because of many diverging assumptions. This includes assumptions about bioenergy crop yields, the possibly large energy demand for CCS, which diminishes the net-GHG-saving of bioenergy systems, or the incomplete accounting for ecosystem processes and of the cumulative carbon-loss arising from natural vegetation clearance for bioenergy crops or bioenergy forests and subsequent harvest regimes (Anderson and Peters 2016 <sup>[[#fn:r556|556]]</sup> ; Bentsen 2017 <sup>[[#fn:r557|557]]</sup> ; Searchinger et al. 2017 <sup>[[#fn:r558|558]]</sup> ; Bayer et al. 2017 <sup>[[#fn:r559|559]]</sup> ; Fuchs et al. 2017 <sup>[[#fn:r560|560]]</sup> ; Pingoud et al. 2018 <sup>[[#fn:r561|561]]</sup> ; Schlesinger 2018 <sup>[[#fn:r562|562]]</sup> ). Bioenergy provision under politically unstable conditions may also be a problem (Erb et al. 2012 <sup>[[#fn:r563|563]]</sup> ; Searle and Malins 2015 <sup>[[#fn:r564|564]]</sup> ).
+<ol>
+<li><span id="fn:r1">IPCC, 2013b: Summary for Policymakers. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–29.</span></li>
-<div>
+<li><span id="fn:r2">IPCC, 2012a: Summary for Policymakers. In: ''Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation'' [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–21.</span>
-<div>
-<div>
-Large-scale bioenergy plantations and forests may compete for the same land area (Harper et al. 2018 <sup>[[#fn:r565|565]]</sup> ). Both potentially have adverse side effects on biodiversity and ecosystem services, as well as socio-economic trade-offs such as higher food prices due to land-area competition (Shi et al. 2013 <sup>[[#fn:r566|566]]</sup> ; Bárcena et al. 2014 <sup>[[#fn:r567|567]]</sup> ; Fernandez-Martinez et al. 2014 <sup>[[#fn:r568|568]]</sup> ; Searchinger et al. 2015 <sup>[[#fn:r569|569]]</sup> ; Bonsch et al. 2016 <sup>[[#fn:r570|570]]</sup> ; Creutzig et al. 2015 <sup>[[#fn:r571|571]]</sup> ; Kreidenweis et al. 2016 <sup>[[#fn:r572|572]]</sup> ; Santangeli et al. 2016 <sup>[[#fn:r573|573]]</sup> ; Williamson 2016 <sup>[[#fn:r574|574]]</sup> ; Graham et al. 2017 <sup>[[#fn:r575|575]]</sup> ; Krause et al. 2017 <sup>[[#fn:r576|576]]</sup> ; Hasegawa et al. 2018 <sup>[[#fn:r577|577]]</sup> ; Humpenoeder et al. 2018 <sup>[[#fn:r578|578]]</sup> ). Although forest-based mitigation could have co-benefits for biodiversity and many ecosystem services, this depends on the type of forest planted and the vegetation cover it replaces (Popp et al. 2014 <sup>[[#fn:r579|579]]</sup> ; Searchinger et al. 2015 <sup>[[#fn:r580|580]]</sup> ) (Cross-Chapter Box 2 in Chapter 1).
-There is ''high confidence'' that scenarios with large land requirements for climate change mitigation may not achieve SDGs, such as no poverty, zero hunger and life on land, if competition for land and the need for agricultural intensification are greatly enhanced (Creutzig et al. 2016 <sup>[[#fn:r581|581]]</sup> ; Dooley and Kartha 2018 <sup>[[#fn:r582|582]]</sup> ; Hasegawa et al. 2015 <sup>[[#fn:r583|583]]</sup> ; Hof et al. 2018 <sup>[[#fn:r584|584]]</sup> ; Roy et al. 2018 <sup>[[#fn:r585|585]]</sup> ; Santangeli et al. 2016 <sup>[[#fn:r586|586]]</sup> ; Boysen et al. 2017 <sup>[[#fn:r587|587]]</sup> ; Henry et al. 2018 <sup>[[#fn:r588|588]]</sup> ; Kreidenweis et al. 2016 <sup>[[#fn:r589|589]]</sup> ; UN 2015 <sup>[[#fn:r590|590]]</sup> ). This does not mean that smaller-scale land-based climate mitigation could not have positive outcomes for then achieving these goals (e.g., Sections 6.2, and 4.5, Cross-Chapter Box 7 in Chapter 6).
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div id="section-1-3-1-targeted-decarbonisation-relying-on-large-land-area-need-block-2" class="box">
-<div>
-<div>
-<span id="ccb2-implications-of-large-scale-conversion-from-non-forest-to-forest-land"></span>
-== CCB2 Implications of large-scale conversion from non-forest to forest land ==
-</div>
-<div>
-<div id="section-1-3-1-targeted-decarbonisation-relying-on-large-land-area-need-block-1">
-<div>
-{| class="wikitable"
-|-
-|
-|-
-| <div>
-<div>
-Baldur Janz (Germany), Almut Arneth (Germany), Francesco Cherubini (Norway/Italy), Edouard Davin (Switzerland/France), Aziz Elbehri (Morocco), Kaoru Kitajima (Japan), Werner Kurz (Canada).
-Efforts to increase forest area
-While deforestation continues in many world regions, especially in the tropics, large expansion of mostly managed forest area has taken place in some countries. In the IPCC context, reforestation (conversion to forest of land that previously contained forests but has been converted to some other use) is distinguished from afforestation (conversion to forest of land that historically has not contained forests; see Glossary). Past expansion of managed forest area occurred in many world-regions for a variety of reasons, from meeting needs for wood fuel or timber (Vadell et al. 2016 <sup>[[#fn:r591|591]]</sup> ; Joshi et al. 2011 <sup>[[#fn:r592|592]]</sup> ; Zaloumis and Bond 2015 <sup>[[#fn:r593|593]]</sup> ; Payn et al. 2015 <sup>[[#fn:r594|594]]</sup> ; Shoyama 2008 <sup>[[#fn:r595|595]]</sup> ; Miyamoto et al. 2011 <sup>[[#fn:r596|596]]</sup> ) to restoration-driven efforts, with the aim of enhancing ecological function (Filoso et al. 2017 <sup>[[#fn:r597|597]]</sup> ; Salvati and Carlucci 2014 <sup>[[#fn:r598|598]]</sup> ; Ogle et al. 2018 <sup>[[#fn:r599|599]]</sup> ; Crouzeilles et al. 2016 <sup>[[#fn:r600|600]]</sup> ; FAO 2016 <sup>[[#fn:r601|601]]</sup> ) (Sections 3.7 and 4.9).
-In many regions, net forest area increase includes deforestation (often of native forests) alongside increasing forest area (often managed forest, but also more natural forest restoration efforts) (Heilmayr et al. 2016 <sup>[[#fn:r602|602]]</sup> ; Scheidel and Work 2018 <sup>[[#fn:r603|603]]</sup> ; Hua et al. 2018 <sup>[[#fn:r604|604]]</sup> ; Crouzeilles et al. 2016 <sup>[[#fn:r605|605]]</sup> ; Chazdon et al. 2016b <sup>[[#fn:r606|606]]</sup> ). China and India have seen the largest net forest area increase, aiming to alleviate soil erosion, desertification and overgrazing (Ahrends et al. 2017 <sup>[[#fn:r607|607]]</sup> ; Cao et al. 2016 <sup>[[#fn:r608|608]]</sup> ; Deng et al. 2015 <sup>[[#fn:r609|609]]</sup> ; Chen et al. 2019 <sup>[[#fn:r610|610]]</sup> ) (Sections 3.7 and 4.9) but uncertainties in exact forest area changes remain large, mostly due to differences in methodology and forest classification (FAO 2015a <sup>[[#fn:r611|611]]</sup> ; Song et al. 2018 <sup>[[#fn:r612|612]]</sup> ; Hansen et al. 2013 <sup>[[#fn:r613|613]]</sup> ; MacDicken et al. 2015 <sup>[[#fn:r614|614]]</sup> ).
-'''What are the implications for ecosystems?'''
-''1. Implications for biogeochemical and biophysical processes''
-There is robust evidence and medium agreement that whilst forest area expansion increases ecosystem carbon storage, the magnitude of the increased stock depends on the type and length of former land use, forest type planted, and climatic regions (Bárcena et al. 2014 <sup>[[#fn:r615|615]]</sup> ; Poeplau et al. 2011 <sup>[[#fn:r616|616]]</sup> ; Shi et al. 2013 <sup>[[#fn:r617|617]]</sup> ; Li et al. 2012 <sup>[[#fn:r618|618]]</sup> ) (Section 4.3). While reforestation of former croplands increases net ecosystem carbon storage (Bernal et al. 2018 <sup>[[#fn:r619|619]]</sup> ; Lamb 2018 <sup>[[#fn:r620|620]]</sup> ), afforestation on native grassland results in reduction of soil carbon stocks, which can reduce or negate the net carbon benefits which are dominated by increases in biomass, dead wood and litter carbon pools (Veldman et al. 2015, 2017 <sup>[[#fn:r621|621]]</sup> ).
-Forest vs non-forest lands differ in land surface reflectiveness of shortwave radiation and evapotranspiration (Anderson et al. 2011 <sup>[[#fn:r622|622]]</sup> ; Perugini et al. 2017 <sup>[[#fn:r623|623]]</sup> ) (Section 2.4). Evapotranspiration from forests during the growing season regionally cools the land surface and enhances cloud cover that reduces shortwave radiation reaching the land, an impact that is especially pronounced in the tropics. However, dark evergreen conifer-dominated forests have low surface reflectance, and tend to cause warming of the near-surface atmosphere compared to non-forest land, especially when snow cover is present such as in boreal regions (Duveiller et al. 2018 <sup>[[#fn:r624|624]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r625|625]]</sup> ; Perugini et al. 2017 <sup>[[#fn:r626|626]]</sup> ) (medium evidence, high agreement).
-''2. Implications for water balance''
-Evapotranspiration by forests reduces surface runoff and erosion of soil and nutrients (Salvati et al. 2014 <sup>[[#fn:r627|627]]</sup> ). Planting of fast-growing species in semi-arid regions or replacing natural grasslands with forest plantations can divert soil water resources to evapotranspiration from groundwater recharge (Silveira et al. 2016 <sup>[[#fn:r628|628]]</sup> ; Zheng et al. 2016 <sup>[[#fn:r629|629]]</sup> ; Cao et al. 2016 <sup>[[#fn:r630|630]]</sup> ). Multiple cases are reported from China where afforestation programs, some with irrigation, without having been tailored to local precipitation conditions, resulted in water shortages and tree mortality (Cao et al. 2016; Yang et al. 2014 <sup>[[#fn:r631|631]]</sup> ; Li et al. 2014 <sup>[[#fn:r632|632]]</sup> ; Feng et al. 2016 <sup>[[#fn:r633|633]]</sup> ). Water shortages may create long-term water conflicts (Zheng et al. 2016 <sup>[[#fn:r634|634]]</sup> ). However, reforestation (in particular for restoration) is also associated with improved water filtration, groundwater recharge (Ellison et al. 2017 <sup>[[#fn:r635|635]]</sup> ) and can reduce risk of soil erosion, flooding, and associated disasters (Lee et al. 2018 <sup>[[#fn:r636|636]]</sup> ) (Section 4.9).
-''3. Implications for biodiversity''
-Impacts of forest area expansion on biodiversity depend mostly on the vegetation cover that is replaced: afforestation on natural non-tree-dominated ecosystems can have negative impacts on biodiversity (Abreu et al. 2017 <sup>[[#fn:r637|637]]</sup> ; Griffith et al. 2017 <sup>[[#fn:r638|638]]</sup> ; Veldman et al. 2015 <sup>[[#fn:r639|639]]</sup> ; Parr et al. 2014 <sup>[[#fn:r640|640]]</sup> ; Wilson et al. 2017 <sup>[[#fn:r641|641]]</sup> ; Hua et al. 2016 <sup>[[#fn:r642|642]]</sup> ; see also IPCC 1.5° report (2018)). Reforestation with monocultures of fast-growing, non-native trees has little benefit to biodiversity (Shimamoto et al. 2018 <sup>[[#fn:r643|643]]</sup> ; Hua et al. 2016). There are also concerns regarding some commonly used plantation species (e.g., Acacia and Pinus species) to become invasive (Padmanaba and Corlett 2014 <sup>[[#fn:r644|644]]</sup> ; Cunningham et al. 2015b <sup>[[#fn:r645|645]]</sup> ).
-</div>
-</div>
-|}
-<div>
-<div>
-<div>
-<div>
-<div>
-<div>
-Reforestation with mixes of native species, especially in areas that retain fragments of native forest, can support ecosystem services and biodiversity recovery, with positive social and environmental co-benefits (Cunningham et al. 2015a <sup>[[#fn:r646|646]]</sup> ; Dendy et al. 2015 <sup>[[#fn:r647|647]]</sup> ; Chaudhary and Kastner 2016 <sup>[[#fn:r648|648]]</sup> ; Huang et al. 2018 <sup>[[#fn:r649|649]]</sup> ; Locatelli et al. 2015b <sup>[[#fn:r650|650]]</sup> ) (Section 4.5). Even though species diversity in re-growing forests is typically lower than in primary forests, planting native or mixed species can have positive effects on biodiversity (Brockerhoff et al. 2013 <sup>[[#fn:r651|651]]</sup> ; Pawson et al. 2013 <sup>[[#fn:r652|652]]</sup> ; Thompson et al. 2014 <sup>[[#fn:r653|653]]</sup> ). Reforestation has been shown to improve links among existing remnant forest patches, increasing species movement, and fostering gene flow between otherwise isolated populations (Gilbert-Norton et al. 2010 <sup>[[#fn:r654|654]]</sup> ; Barlow et al. 2007 <sup>[[#fn:r655|655]]</sup> ; Lindenmayer and Hobbs 2004 <sup>[[#fn:r656|656]]</sup> ).
-''4. Implications for other ecosystem services and societies''
-Forest area expansion could benefit recreation and health, preservation of cultural heritage and local values and knowledge, livelihood support (via reduced resource conflicts, restoration of local resources). These social benefits could be most successfully achieved if local communities’ concerns are considered (Le et al. 2012 <sup>[[#fn:r657|657]]</sup> ). However, these co-benefits have rarely been assessed due to a lack of suitable frameworks and evaluation tools (Baral et al. 2016 <sup>[[#fn:r658|658]]</sup> ).
-Industrial forest management can be in conflict with the needs of forest-dependent people and community-based forest management over access to natural resources (Gerber 2011 <sup>[[#fn:r659|659]]</sup> ; Baral et al. 2016 <sup>[[#fn:r660|660]]</sup> ) and/or loss of customary rights over land use (Malkamäki et al. 2018 <sup>[[#fn:r661|661]]</sup> ; Cotula et al. 2014 <sup>[[#fn:r662|662]]</sup> ). A common result is out-migration from rural areas and diminishing local uses of ecosystems (Gerber 2011 <sup>[[#fn:r663|663]]</sup> ). Policies promoting large-scale tree plantations gain traction if these are reappraised in view of potential co-benefits with several ecosystem services and local societies (Bull et al. 2006 <sup>[[#fn:r664|664]]</sup> ; Le et al. 2012 <sup>[[#fn:r665|665]]</sup> ).
-'''Scenarios of forest area expansion for land-based climate change mitigation'''
-Conversion of non-forest to forest land has been discussed as a relatively cost-effective climate change mitigation option when compared to options in the energy and transport sectors (medium evidence, medium agreement) (de Coninck et al. 2018 <sup>[[#fn:r666|666]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r667|667]]</sup> ; Fuss et al. 2018 <sup>[[#fn:r668|668]]</sup> ), and can have co-benefits with adaptation.
-Sequestration of CO <sub>2</sub> from the atmosphere through forest area expansion has become a fundamental part of stringent climate change mitigation scenarios (Rogelj et al. 2018a <sup>[[#fn:r669|669]]</sup> ; Fuss et al. 2018 <sup>[[#fn:r670|670]]</sup> ) (e.g., Sections 2.5, 4.5 and 6.2). The estimated mitigation potential ranges from about 0.5 to 10 GtCO <sub>2</sub> yr–1 (robust evidence, medium agreement), and depends on assumptions regarding available land and forest carbon uptake potential (Houghton 2013 <sup>[[#fn:r671|671]]</sup> ; Houghton and Nassikas 2017 <sup>[[#fn:r672|672]]</sup> ; Griscom et al. 2017 <sup>[[#fn:r673|673]]</sup> ; Lenton 2014 <sup>[[#fn:r674|674]]</sup> ; Fuss et al. 2018 <sup>[[#fn:r675|675]]</sup> ; Smith 2016 <sup>[[#fn:r676|676]]</sup> ) (Section 2.5.1). In climate change mitigation scenarios, typically, no differentiation is made between reforestation and afforestation despite different overall environmental impacts between these two measures. Likewise, biodiversity conservation, impacts on water balances, other ecosystem services, or land-ownership – as constraints when simulating forest area expansion (Cross-Chapter Box 1 in Chapter 1) – tend not to be included as constraints when simulating forest area expansion.
-Projected forest area increases, relative to today’s forest area, range from approximately 25% in 2050 and increase to nearly 50% by 2100 (Rogelj et al. 2018a <sup>[[#fn:r677|677]]</sup> ; Kreidenweis et al. 2016 <sup>[[#fn:r678|678]]</sup> ; Humpenoder et al. 2014 <sup>[[#fn:r679|679]]</sup> ). Potential adverse side-effects of such large-scale measures, especially for low-income countries, could be increasing food prices from the increased competition for land (Kreidenweis et al. 2016 <sup>[[#fn:r680|680]]</sup> ; Hasegawa et al. 2015 <sup>[[#fn:r681|681]]</sup> , 2018 <sup>[[#fn:r682|682]]</sup> ; Boysen et al. 2017 <sup>[[#fn:r683|683]]</sup> ) (Section 5.5). Forests also emit large amounts of biogenic volatile compounds that under some conditions contribute to the formation of atmospherically short-lived climate forcing compounds, which are also detrimental to health (Ashworth et al. 2013 <sup>[[#fn:r684|684]]</sup> ; Harrison et al. 2013 <sup>[[#fn:r685|685]]</sup> ). Recent analyses argued for an upper limit of about 5 million km2 of land globally available for climate change mitigation through reforestation, mostly in the tropics (Houghton 2013 <sup>[[#fn:r686|686]]</sup> ) – with potential regional co-benefits.
-Since forest growth competes for land with bioenergy crops (Harper et al. 2018 <sup>[[#fn:r687|687]]</sup> ) (Cross-Chapter Box 7 in Chapter 6), global area estimates need to be assessed in light of alternative mitigation measures at a given location. In all forest-based mitigation efforts, the sequestration potential will eventually saturate unless the area keeps expanding, or harvested wood is either used for long-term storage products or for carbon capture and storage (Fuss et al. 2018 <sup>[[#fn:r688|688]]</sup> ; Houghton et al. 2015 <sup>[[#fn:r689|689]]</sup> ) (Section 2.5.1). Considerable uncertainty in forest carbon uptake estimates is further introduced by potential forest losses from fire or pest outbreaks (Allen et al. 2010 <sup>[[#fn:r690|690]]</sup> ; Anderegg et al. 2015 <sup>[[#fn:r691|691]]</sup> ) (Cross-Chapter Box 3 in Chapter 2). And like all land-based mitigation measures, benefits may be diminshed by land-use displacement, and through trade of land-based products, especially in poor countries that experience forest loss (e.g., Africa) (Bhojvaid et al. 2016 <sup>[[#fn:r692|692]]</sup> ; Jadin et al. 2016 <sup>[[#fn:r693|693]]</sup> ).
-<div>
-<div>
-<div>
+-----
-<div>
+IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.
-'''Conclusion'''
+-----
-Reforestation is a mitigation measure with potential co-benefits for conservation and adaptation, including biodiversity habitat, air and water filtration, flood control, enhanced soil fertility and reversal of land degradation. Potential adverse side-effects of forest area expansion depend largely on the state of the land it displaces as well as tree species selections. Active governance and planning contribute to maximising co-benefits while minimising adverse side-effects (Laestadius et al. 2011 <sup>[[#fn:r694|694]]</sup> ; Dinerstein et al. 2015 <sup>[[#fn:r695|695]]</sup> ; Veldman et al. 2017 <sup>[[#fn:r696|696]]</sup> ) (Section 4.8 and Chapter 7). At large spatial scales, forest expansion is expected to lead to increased competition for land, with potentially undesirable impacts on food prices, biodiversity, non-forest ecosystems and water availability (Bryan and Crossman 2013 <sup>[[#fn:r697|697]]</sup> ; Boysen et al. 2017 <sup>[[#fn:r698|698]]</sup> ; Kreidenweis et al. 2016 <sup>[[#fn:r699|699]]</sup> ; Egginton et al. 2014 <sup>[[#fn:r700|700]]</sup> ; Cao et al. 2016 <sup>[[#fn:r701|701]]</sup> ; Locatelli et al. 2015a <sup>[[#fn:r702|702]]</sup> ; Smith et al. 2013 <sup>[[#fn:r703|703]]</sup> ).
+Mysiak, J., S. Surminski, A. Thieken, R. Mechler, and J. Aerts, 2016: Brief communication: Sendai framework for disaster risk reduction – Success or warning sign for Paris? ''Natural Hazards and Earth System Sciences'' , '''16(10)''' , 2189–2193, doi: [https://dx.doi.org/10.5194/nhess-16-2189-2016 10.5194/nhess-16-2189-2016] .</li>
+<li><span id="fn:r3">IPCC, 2012a: Summary for Policymakers. In: ''Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation'' [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–21.</span></li>
+<li><span id="fn:r4">Albert, S. et al., 2017: Heading for the hills: climate-driven community relocations in the Solomon Islands and Alaska provide insight for a 1.5°C future. ''Regional Environmental Change'' , 1–12, doi: [https://dx.doi.org/10.1007/s10113-017-1256-8 10.1007/s10113-017-1256-8] .</span></li>
+<li><span id="fn:r5">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r6">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r7">Dryzek, J.S., 2016: Institutions for the Anthropocene: Governance in a Changing Earth System. ''British Journal of Political Science'' , '''46(04)''' , 937–956, doi: [https://dx.doi.org/10.1017/s0007123414000453 10.1017/s0007123414000453] .</span>
+-----
-</div>
+Pattberg, P. and F. Zelli (eds.), 2016: ''Environmental politics and governance in the anthropocene: Institutions and legitimacy in a complex world'' . Routledge, London, UK, 268 pp., doi: [https://dx.doi.org/10.4324/9781315697468 10.4324/9781315697468] .
-</div>
+-----
-</div>
+Bäckstrand, K., J.W. Kuyper, B.-O. Linnér, and E. Lövbrand, 2017: Non-state actors in global climate governance: from Copenhagen to Paris and beyond. ''Environmental Politics'' , '''26(4)''' , 561–579, doi: [https://dx.doi.org/10.1080/09644016.2017.1327485 10.1080/09644016.2017.1327485] .
-</div>
+-----
-</div>
+Lövbrand, E., M. Hjerpe, and B.-O. Linnér, 2017: Making climate governance global: how UN climate summitry comes to matter in a complex climate regime. ''Environmental Politics'' , '''26(4)''' , 580–599, doi: [https://dx.doi.org/10.1080/09644016.2017.1319019 10.1080/09644016.2017.1319019] .</li>
+<li><span id="fn:r8">Birkmann, J., T. Welle, W. Solecki, S. Lwasa, and M. Garschagen, 2016: Boost resilience of small and mid-sized cities. ''Nature'' , '''537(7622)''' , 605–608, doi: [https://dx.doi.org/10.1038/537605a 10.1038/537605a] .</span></li>
+<li><span id="fn:r9">Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.</span></li>
+<li><span id="fn:r10">Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. ''Quarterly Journal of the Royal Meteorological Society'' , '''140(683)''' , 1935–1944, doi: [https://dx.doi.org/10.1002/qj.2297 10.1002/qj.2297] .</span></li>
+<li><span id="fn:r11">Steffen, W. et al., 2016: Stratigraphic and Earth System approaches to defining the Anthropocene. ''Earth’s Future'' , '''4(8)''' , 324–345, doi: [https://dx.doi.org/10.1002/2016ef000379 10.1002/2016ef000379] .</span>
-</div>
+-----
-</div>
+Waters, C.N. et al., 2016: The Anthropocene is functionally and stratigraphically distinct from the Holocene. ''Science'' , '''351(6269)''' , aad2622–aad2622, doi: [https://dx.doi.org/10.1126/science.aad2622 10.1126/science.aad2622] .</li>
+<li><span id="fn:r12">Crutzen, P.J. and E.F. Stoermer, 2000: The Anthropocene. ''Global Change Newsletter'' , '''41''' , 17–18, http://www.igbp.net/download/18.316f18321323470177580001401/1376383088452/nl41.pdf .</span>
-</div>
+-----
-</div>
+Crutzen, P.J., 2002: Geology of mankind. ''Nature'' , '''415(6867)''' , 23, doi: [https://dx.doi.org/10.1038/415023a 10.1038/415023a] .
-</div>
+-----
-</div>
+Gradstein, F.M., J.G. Ogg, M.D. Schmitz, and G.M. Ogg (eds.), 2012: ''The Geologic Time Scale'' . Elsevier BV, Boston, MA, USA, 1144 pp., doi: [https://dx.doi.org/10.1016/b978-0-444-59425-9.01001-5 10.1016/b978-0-444-59425-9.01001-5] .</li>
+<li><span id="fn:r13">Lüthi, D. et al., 2008: High-resolution carbon dioxide concentration record 650,000–800,000 years before present. ''Nature'' , '''453(7193)''' , 379–382, doi: [https://dx.doi.org/10.1038/nature06949 10.1038/nature06949] .</span>
-</div>
+-----
-</div>
+Bereiter, B. et al., 2015: Revision of the EPICA Dome C CO <sub>2</sub> record from 800 to 600-kyr before present. ''Geophysical Research Letters'' , '''42(2)''' , 542–549, doi: [https://dx.doi.org/10.1002/2014gl061957 10.1002/2014gl061957] .</li>
+<li><span id="fn:r14">Masson-Delmotte, V. et al., 2013: Information from Paleoclimate Archives. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 383–464.</span></li>
+<li><span id="fn:r15">Zalasiewicz, J. et al., 2017: Making the case for a formal Anthropocene Epoch: an analysis of ongoing critiques. ''Newsletters on Stratigraphy'' , '''50(2)''' , 205–226, doi: [https://dx.doi.org/10.1127/nos/2017/0385 10.1127/nos/2017/0385] .</span></li>
+<li><span id="fn:r16">Waters, C.N. et al., 2016: The Anthropocene is functionally and stratigraphically distinct from the Holocene. ''Science'' , '''351(6269)''' , aad2622–aad2622, doi: [https://dx.doi.org/10.1126/science.aad2622 10.1126/science.aad2622] .</span></li>
+<li><span id="fn:r17">Waters, C.N. et al., 2016: The Anthropocene is functionally and stratigraphically distinct from the Holocene. ''Science'' , '''351(6269)''' , aad2622–aad2622, doi: [https://dx.doi.org/10.1126/science.aad2622 10.1126/science.aad2622] .</span></li>
+<li><span id="fn:r18">Brondizio, E.S. et al., 2016: Re-conceptualizing the Anthropocene: A call for collaboration. ''Global Environmental Change'' , '''39''' , 318–327, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.02.006 10.1016/j.gloenvcha.2016.02.006] .</span></li>
+<li><span id="fn:r19">Pattberg, P. and F. Zelli (eds.), 2016: ''Environmental politics and governance in the anthropocene: Institutions and legitimacy in a complex world'' . Routledge, London, UK, 268 pp., doi: [https://dx.doi.org/10.4324/9781315697468 10.4324/9781315697468] .</span>
-</div>
+-----
-</div>
+Delanty, G. and A. Mota, 2017: Governing the Anthropocene. ''European Journal of Social Theory'' , '''20(1)''' , 9–38, doi: [https://dx.doi.org/10.1177/1368431016668535 10.1177/1368431016668535] .</li>
+<li><span id="fn:r20">Harrington, C., 2016: The Ends of the World: International Relations and the Anthropocene. ''Millennium: Journal of International Studies'' , '''44(3)''' , 478–498, doi: [https://dx.doi.org/10.1177/0305829816638745 10.1177/0305829816638745] .</span></li>
+<li><span id="fn:r21">Biermann, F. et al., 2016: Down to Earth: Contextualizing the Anthropocene. ''Global Environmental Change'' , '''39''' , 341–350, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2015.11.004 10.1016/j.gloenvcha.2015.11.004] .</span></li>
+<li><span id="fn:r22">Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.</span>
-</div>
+-----
-</div>
+Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
-</div>
+-----
-<div class="section">
-<div>
+Klein, R.J.T. et al., 2014: Adaptation opportunities, constraints, and limits. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 899–943.
-<div>
+-----
-<span id="land-management"></span>
+Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
-== 1.3.2 Land management ==
+-----
-</div>
+Porter, J.R. et al., 2014: Food security and food production systems. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485–533.
-<div id="section-1-3-2-1-agricultural-forest-and-soil-management">
-<div>
+-----
-<div>
+Stavins, R. et al., 2014: International Cooperation: Agreements and Instruments. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1001–1082.</li>
+<li><span id="fn:r23">Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.</span>
-<span id="agricultural-forest-and-soil-management"></span>
+-----
-== 1.3.2.1 Agricultural, forest and soil management ==
+Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.</li>
+<li><span id="fn:r24">Shelton, D., 2008: Equity. In: ''The Oxford Handbook of International Environmental Law'' [Bodansky, D., J. Brunnée, and E. Hey (eds.)]. Oxford University Press, Oxford, UK, pp. 639–662, doi: [https://dx.doi.org/10.1093/oxfordhb/9780199552153.013.0027 10.1093/oxfordhb/9780199552153.013.0027] .</span>
-</div>
+-----
-<div>
-<div id="section-1-3-2-1-agricultural-forest-and-soil-management-block-1">
+Bodansky, D., J. Brunnée, and L. Rajamani, 2017: ''International Climate Change Law'' . Oxford University Press, Oxford, UK, 416 pp.</li>
+<li><span id="fn:r25">Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.</span></li>
+<li><span id="fn:r26">Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.</span>
-<div>
+-----
-<div>
+Savaresi, A., 2016: The Paris Agreement: a new beginning? ''Journal of Energy &amp; Natural Resources Law'' , '''34(1)''' , 16–26, doi: [https://dx.doi.org/10.1080/02646811.2016.1133983 10.1080/02646811.2016.1133983] .
-<div>
+-----
-Sustainable land management (SLM) describes “the stewardship and use of land resources, including soils, water, animals and plants, to meet changing human needs while simultaneously assuring the long-term productive potential of these resources and the maintenance of their environmental functions” (Alemu 2016 <sup>[[#fn:r704|704]]</sup> ; Altieri and Nicholls 2017 <sup>[[#fn:r705|705]]</sup> ) (e.g., Section 4.1.5), and includes ecological, technological and governance aspects.
+Reckien, D. et al., 2017: Climate change, equity and the Sustainable Development Goals: an urban perspective. ''Environment &amp; Urbanization'' , '''29(1)''' , 159–182, doi: [https://dx.doi.org/10.1177/0956247816677778 10.1177/0956247816677778] .</li>
+<li><span id="fn:r27">Caney, S., 2005: Cosmopolitan Justice, Responsibility, and Global Climate Change. ''Leiden Journal of International Law'' , '''18(04)''' , 747–75, doi: [https://dx.doi.org/10.1017/s0922156505002992 10.1017/s0922156505002992] .</span>
-The choice of SLM strategy is a function of regional context and land-use types, with ''high agreement'' on (a combination of) choices such as agroecology (including agroforestry), conservation agriculture and forestry practices, crop and forest species diversity, appropriate crop and forest rotations, organic farming, integrated pest management, the preservation and protection of pollination services, rainwater harvesting, range and pasture management, and precision agriculture systems (Stockmann et al. 2013 <sup>[[#fn:r706|706]]</sup> ; Ebert, 2014 <sup>[[#fn:r707|707]]</sup> ; Schulte et al. 2014 <sup>[[#fn:r708|708]]</sup> ; Zhang et al. 2015 <sup>[[#fn:r709|709]]</sup> ; Sunil and Pandravada 2015 <sup>[[#fn:r710|710]]</sup> ; Poeplau and Don 2015 <sup>[[#fn:r711|711]]</sup> ; Agus et al. 2015 <sup>[[#fn:r712|712]]</sup> ; Keenan 2015 <sup>[[#fn:r713|713]]</sup> ; MacDicken et al. 2015 <sup>[[#fn:r714|714]]</sup> ; Abberton et al. 2016 <sup>[[#fn:r715|715]]</sup> ). Conservation agriculture and forestry uses management practices with minimal soil disturbance such as no tillage or minimum tillage, permanent soil cover with mulch, combined with rotations to ensure a permanent soil surface, or rapid regeneration of forest following harvest (Hobbs et al. 2008 <sup>[[#fn:r716|716]]</sup> ; Friedrich et al. 2012 <sup>[[#fn:r717|717]]</sup> ). Vegetation and soils in forests and woodland ecosystems play a crucial role in regulating critical ecosystem processes, therefore reduced deforestation together with sustainable forest management are integral to SLM (FAO 2015b <sup>[[#fn:r718|718]]</sup> ) (Section 4.8). In some circumstances, increased demand for forest products can also lead to increased management of carbon storage in forests (Favero and Mendelsohn 2014 <sup>[[#fn:r719|719]]</sup> ). Precision agriculture is characterised by a “management system that is information and technology based, is site specific and uses one or more of the following sources of data: soils, crops, nutrients, pests, moisture, or yield, for optimum profitability, sustainability, and protection of the environment” (USDA 2007 <sup>[[#fn:r720|720]]</sup> ) (Cross-Chapter Box 6 in Chapter 5). The management of protected areas that reduce deforestation also plays an important role in climate change mitigation and adaptation while delivering numerous ecosystem services and sustainable development benefits (Bebber and Butt 2017 <sup>[[#fn:r721|721]]</sup> ). Similarly, when managed in an integrated and sustainable way, peatlands are also known to provide numerous ecosystem services, as well as socio-economic and mitigation and adaptation benefits (Ziadat et al. 2018 <sup>[[#fn:r722|722]]</sup> ).
+-----
-<div>
+Schroeder, H., M.T. Boykoff, and L. Spiers, 2012: Equity and state representations in climate negotiations. ''Nature Climate Change'' , '''2''' , 834–836, doi: [https://dx.doi.org/10.1038/nclimate1742 10.1038/nclimate1742] .
-<div>
+-----
-<div>
+Ajibade, I., 2016: Distributive justice and human rights in climate policy: the long road to Paris. ''Journal of Sustainable Development Law and Policy (The)'' , '''7(2)''' , 65–80, doi: [https://dx.doi.org/10.4314/jsdlp.v7i2.4 10.4314/jsdlp.v7i2.4] .
-Biochar is an organic compound used as soil amendment and is believed to be potentially an important global resource for mitigation. Enhancing the carbon content of soil and/or use of biochar (Chapter 4) have become increasingly important as a climate change mitigation option with possibly large co-benefits for other ecosystem services. Enhancing soil carbon storage and the addition of biochar can be practiced with limited competition for land, provided no productivity/ yield loss and abundant unused biomass, but evidence is limited and impacts of large scale application of biochar on the full GHG balance of soils, or human health are yet to be explored (Gurwick et al. 2013 <sup>[[#fn:r723|723]]</sup> ; Lorenz and Lal 2014 <sup>[[#fn:r724|724]]</sup> ; Smith 2016 <sup>[[#fn:r725|725]]</sup> ).
+-----
+Reckien, D. et al., 2017: Climate change, equity and the Sustainable Development Goals: an urban perspective. ''Environment &amp; Urbanization'' , '''29(1)''' , 159–182, doi: [https://dx.doi.org/10.1177/0956247816677778 10.1177/0956247816677778] .
-</div>
+-----
-</div>
+Shue, H., 2018: Mitigation gambles: uncertainty, urgency and the last gamble possible. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2017.0105 10.1098/rsta.2017.0105] .</li>
+<li><span id="fn:r28">Schleussner, C.-F. et al., 2016: Differential climate impacts for policy relevant limits to global warming: the case of 1.5°C and 2°C. ''Earth System Dynamics'' , '''7(2)''' , 327–351, doi: [https://dx.doi.org/10.5194/esd-7-327-2016 10.5194/esd-7-327-2016] .</span>
-</div>
+-----
-</div>
+Aaheim, A., T. Wei, and B. Romstad, 2017: Conflicts of economic interests by limiting global warming to +3°C. ''Mitigation and Adaptation Strategies for Global Change'' , '''22(8)''' , 1131–1148, doi: [https://dx.doi.org/10.1007/s11027-016-9718-8 10.1007/s11027-016-9718-8] .</li>
+<li><span id="fn:r29">Okereke, C., 2010: Climate justice and the international regime. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''1(3)''' , 462–474, doi: [https://dx.doi.org/10.1002/wcc.52 10.1002/wcc.52] .</span>
-</div>
+-----
-</div>
+Harlan, S.L. et al., 2015: Climate Justice and Inequality: Insights from Sociology. In: ''Climate Change and Society: Sociological Perspectives'' [Dunlap, R.E. and R.J. Brulle (eds.)]. Oxford University Press, New York, NY, USA, pp. 127–163, doi: [https://dx.doi.org/10.1093/acprof:oso/9780199356102.003.0005 10.1093/acprof:oso/9780199356102.003.0005] .
-</div>
+-----
-</div>
+Ajibade, I., 2016: Distributive justice and human rights in climate policy: the long road to Paris. ''Journal of Sustainable Development Law and Policy (The)'' , '''7(2)''' , 65–80, doi: [https://dx.doi.org/10.4314/jsdlp.v7i2.4 10.4314/jsdlp.v7i2.4] .
-</div>
+-----
-</div>
+Savaresi, A., 2016: The Paris Agreement: a new beginning? ''Journal of Energy &amp; Natural Resources Law'' , '''34(1)''' , 16–26, doi: [https://dx.doi.org/10.1080/02646811.2016.1133983 10.1080/02646811.2016.1133983] .
-</div>
+-----
-</div>
+Reckien, D. et al., 2017: Climate change, equity and the Sustainable Development Goals: an urban perspective. ''Environment &amp; Urbanization'' , '''29(1)''' , 159–182, doi: [https://dx.doi.org/10.1177/0956247816677778 10.1177/0956247816677778] .</li>
-<div class="section">
+<li><span id="fn:r30">Shue, H., 2013: Climate Hope: Implementing the Exit Strategy. ''Chicago Journal of International Law'' , '''13(2)''' , 381–402, https://chicagounbound.uchicago.edu/cjil/vol13/iss2/6/ .</span>
-<div>
+-----
-<div>
+McKinnon, C., 2015: Climate justice in a carbon budget. ''Climatic Change'' , '''133(3)''' , 375–384, doi: [https://dx.doi.org/10.1007/s10584-015-1382-6 10.1007/s10584-015-1382-6] .
-<span id="value-chain-management"></span>
+-----
-== 1.3.3 Value chain management ==
+Otto, F.E.L., R.B. Skeie, J.S. Fuglestvedt, T. Berntsen, and M.R. Allen, 2017: Assigning historic responsibility for extreme weather events. ''Nature Climate Change'' , '''7(11)''' , 757–759, doi: [https://dx.doi.org/10.1038/nclimate3419 10.1038/nclimate3419] .
-</div>
+-----
-<div id="section-1-3-3-1-supply-management">
-<div>
+Skeie, R.B. et al., 2017: Perspective has a strong effect on the calculation of historical contributions to global warming. ''Environmental Research Letters'' , '''12(2)''' , 024022, doi: [https://dx.doi.org/10.1088/1748-9326/aa5b0a 10.1088/1748-9326/aa5b0a] .</li>
+<li><span id="fn:r31">Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.</span>
-<div>
+-----
-<span id="supply-management"></span>
+Shue, H., 2014: ''Climate Justice: Vulnerability and Protection'' . Oxford University Press, Oxford, UK, 368 pp.
-== 1.3.3.1 Supply management ==
+-----
-</div>
+Ionesco, D., D. Mokhnacheva, and F. Gemenne, 2016: ''Atlas de Migrations Environnmentales (in French)'' . Presses de Sciences Po, Paris, France, 152 pp.</li>
-<div>
+<li><span id="fn:r32">Robinson, M. and T. Shine, 2018: Achieving a climate justice pathway to 1.5°C. ''Nature Climate Change'' , '''8(7)''' , 564–569, doi: [https://dx.doi.org/10.1038/s41558-018-0189-7 10.1038/s41558-018-0189-7] .</span></li>
+<li><span id="fn:r33">Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.</span>
-<div id="section-1-3-3-1-supply-management-block-1">
+-----
-<div>
+Shue, H., 2014: ''Climate Justice: Vulnerability and Protection'' . Oxford University Press, Oxford, UK, 368 pp.
-<div>
+-----
-<div>
+Humphreys, S., 2017: Climate, Technology, ‘Justice’. In: ''Protecting the Environment for Future Generations – Principles and Actors in International Environmental Law'' [Proelß, A. (ed.)]. Erich Schmidt Verlag, Berlin, Germany, pp. 171–190.</li>
+<li><span id="fn:r34">OHCHR, 2009: ''Report of the Office of the United Nations High Commissioner for Human Rights on the relationship between climate change and human rights'' . A/HRC/10/61, Office of the United Nations High Commissioner for Human Rights (OHCHR), 32 pp.</span>
-Food losses from harvest to retailer. Approximately one-third of losses and waste in the food system occurs between crop production and food consumption, increasing substantially if losses in livestock production and overeating are included (Gustavsson et al. 2011 <sup>[[#fn:r726|726]]</sup> ; Alexander et al. 2017 <sup>[[#fn:r727|727]]</sup> ). This includes on-farm losses, farm to retailer losses, as well retailer and consumer losses (Section 1.3.3.2).
+-----
-Post-harvest food loss – on farm and from farm to retailer – is a widespread problem, especially in developing countries (Xue et al. 2017 <sup>[[#fn:r728|728]]</sup> ), but are challenging to quantify. For instance, averaged for eastern and southern Africa an estimated 10–17% of annual grain production is lost (Zorya et al. 2011 <sup>[[#fn:r729|729]]</sup> ). Across 84 countries and different time periods, annual median losses in the supply chain before retailing were estimated at about 28 kg per capita for cereals or about 12 kg per capita for eggs and dairy products (Xue et al. 2017 <sup>[[#fn:r730|730]]</sup> ). For the year 2013, losses prior to the reaching retailers were estimated at 20% (dry weight) of the production amount (22% wet weight) (Gustavsson et al. 2011 <sup>[[#fn:r731|731]]</sup> ; Alexander et al. 2017 <sup>[[#fn:r732|732]]</sup> ). While losses of food cannot be realistically reduced to zero, advancing harvesting technologies (Bradford et al. 2018 <sup>[[#fn:r733|733]]</sup> ; Affognon et al. 2015 <sup>[[#fn:r734|734]]</sup> ), storage capacity (Chegere 2018 <sup>[[#fn:r735|735]]</sup> ) and efficient transportation could all contribute to reducing these losses with co-benefits for food availability, the land area needed for food production and related GHG emissions.
+Caney, S., 2010: Climate change and the duties of the advantaged. ''Critical Review of International Social and Political Philosophy'' , '''13(1)''' , 203–228, doi: [https://dx.doi.org/10.1080/13698230903326331 10.1080/13698230903326331] .
-<div>
+-----
-<div>
+Adger, W.N. et al., 2014: Human Security. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 755–791.
-<div>
+-----
-'''Stability of food supply, transport and distribution.''' Increased climate variability enhances fluctuations in world food supply and price variability (Warren 2014 <sup>[[#fn:r736|736]]</sup> ; Challinor et al. 2015 <sup>[[#fn:r737|737]]</sup> ; Elbehri et al. 2017 <sup>[[#fn:r738|738]]</sup> ). ‘Food price shocks’ need to be understood regarding their transmission across sectors and borders and impacts on poor and food insecure populations, including urban poor subject to food deserts and inadequate food accessibility (Widener et al. 2017 <sup>[[#fn:r739|739]]</sup> ; Lehmann et al. 2013 <sup>[[#fn:r740|740]]</sup> ; Le 2016 <sup>[[#fn:r741|741]]</sup> ; FAO 2015b <sup>[[#fn:r742|742]]</sup> ). Trade can play an important stabilising role in food supply, especially for regions with agro-ecological limits to production, including water scarce regions, as well as regions that experience short-term production variability due to climate, conflicts or other economic shocks (Gilmont 2015 <sup>[[#fn:r743|743]]</sup> ; Marchand et al. 2016 <sup>[[#fn:r744|744]]</sup> ). Food trade can either increase or reduce the overall environmental impacts of agriculture (Kastner et al. 2014 <sup>[[#fn:r745|745]]</sup> ). Embedded in trade are virtual transfers of water, land area, productivity, ecosystem services, biodiversity, or nutrients (Marques et al. 2019 <sup>[[#fn:r746|746]]</sup> ; Wiedmann and Lenzen 2018 <sup>[[#fn:r747|747]]</sup> ; Chaudhary and Kastner 2016 <sup>[[#fn:r748|748]]</sup> ) with either positive or negative implications (Chen et al. 2018 <sup>[[#fn:r749|749]]</sup> ; Yu et al. 2013 <sup>[[#fn:r750|750]]</sup> ). Detrimental consequences in countries in which trade dependency may accentuate the risk of food shortages from foreign production shocks could be reduced by increasing domestic reserves or importing food from a diversity of suppliers (Gilmont 2015 <sup>[[#fn:r751|751]]</sup> ; Marchand et al. 2016 <sup>[[#fn:r752|752]]</sup> ).
+Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
-<div>
+-----
-<div>
+IBA, 2014: ''Achieving Justice and Human Rights in an Era of Climate Disruption'' . International Bar Association (IBA), London, UK, 240 pp.
-<div>
+-----
-Climate mitigation policies could create new trade opportunities (e.g., biomass) (Favero and Massetti 2014 <sup>[[#fn:r753|753]]</sup> ) or alter existing trade patterns. The transportation GHG footprints of supply chains may be causing a differentiation between short and long supply chains (Schmidt et al. 2017 <sup>[[#fn:r754|754]]</sup> ) that may be influenced by both economics and policy measures (Section 5.4). In the absence of sustainable practices and when the ecological footprint is not valued through the market system, trade can also exacerbate resource exploitation and environmental leakages, thus weakening trade mitigation contributions (Dalin and Rodríguez-Iturbe 2016 <sup>[[#fn:r755|755]]</sup> ; Mosnier et al. 2014 <sup>[[#fn:r756|756]]</sup> ; Elbehri et al. 2017 <sup>[[#fn:r757|757]]</sup> ). Ensuring stable food supply while pursuing climate mitigation and adaptation will benefit from evolving trade rules and policies that allow internalisation of the cost of carbon (and costs of other vital resources such as water, nutrients). Likewise, future climate change mitigation policies would gain from measures designed to internalise the environmental costs of resources and the benefits of ecosystem services (Elbehri et al. 2017 <sup>[[#fn:r758|758]]</sup> ; Brown et al. 2007 <sup>[[#fn:r759|759]]</sup> ).
+Knox, J.H., 2015: Human Rights Principles and Climate Change. In: ''Oxford Handbook of International Climate Change Law'' [Carlarne, C., K.R. Gray, and R. Tarasofsky (eds.)]. Oxford University Press, Oxford, UK, pp. 213–238, doi: [https://dx.doi.org/10.1093/law/9780199684601.003.0011 10.1093/law/9780199684601.003.0011] .
+-----
-</div>
+Duyck, S., S. Jodoin, and A. Johl (eds.), 2018: ''Routledge Handbook of Human Rights and Climate Governance'' . Routledge, Abingdon, UK, 430 pp.
-</div>
+-----
-</div>
+Robinson, M. and T. Shine, 2018: Achieving a climate justice pathway to 1.5°C. ''Nature Climate Change'' , '''8(7)''' , 564–569, doi: [https://dx.doi.org/10.1038/s41558-018-0189-7 10.1038/s41558-018-0189-7] .</li>
+<li><span id="fn:r35">Caney, S., 2010: Climate change and the duties of the advantaged. ''Critical Review of International Social and Political Philosophy'' , '''13(1)''' , 203–228, doi: [https://dx.doi.org/10.1080/13698230903326331 10.1080/13698230903326331] .</span>
-</div>
+-----
-</div>
+Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
-</div>
+-----
-</div>
+OHCHR, 2015: ''Understanding Human Rights and Climate Change'' . Submission of the Office of the High Commissioner for Human Rights to the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change, Office of the United Nations High Commissioner for Human Rights (OHCHR), 28 pp.</li>
+<li><span id="fn:r36">OHCHR, 2017: ''Analytical study on the relationship between climate change and the full and effective enjoyment of the rights of the child'' . A/HRC/35/13, Office of the United Nations High Commissioner for Human Rights (OHCHR), 18 pp.</span></li>
+<li><span id="fn:r37">Knox, J.H., 2015: Human Rights Principles and Climate Change. In: ''Oxford Handbook of International Climate Change Law'' [Carlarne, C., K.R. Gray, and R. Tarasofsky (eds.)]. Oxford University Press, Oxford, UK, pp. 213–238, doi: [https://dx.doi.org/10.1093/law/9780199684601.003.0011 10.1093/law/9780199684601.003.0011] .</span>
-</div>
+-----
-</div>
+OHCHR, 2015: ''Understanding Human Rights and Climate Change'' . Submission of the Office of the High Commissioner for Human Rights to the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change, Office of the United Nations High Commissioner for Human Rights (OHCHR), 28 pp.
-</div>
+-----
-</div>
+Humphreys, S., 2017: Climate, Technology, ‘Justice’. In: ''Protecting the Environment for Future Generations – Principles and Actors in International Environmental Law'' [Proelß, A. (ed.)]. Erich Schmidt Verlag, Berlin, Germany, pp. 171–190.</li>
+<li><span id="fn:r38">Holz, C., S. Kartha, and T. Athanasiou, 2017: Fairly sharing 1.5: national fair shares of a 1.5°C-compliant global mitigation effort. ''International Environmental Agreements: Politics, Law and Economics'' , '''18(1)''' , 1–18, doi: [https://dx.doi.org/10.1007/s10784-017-9371-z 10.1007/s10784-017-9371-z] .</span>
-</div>
+-----
-</div>
+Dooley, K., J. Gupta, and A. Patwardhan, 2018: INEA editorial: Achieving 1.5°C and climate justice. ''International Environmental Agreements: Politics, Law and Economics'' , '''18(1)''' , 1–9, doi: [https://dx.doi.org/10.1007/s10784-018-9389-x 10.1007/s10784-018-9389-x] .
-<div id="section-1-3-3-2-demand-management">
-<div>
+-----
-<div>
+Klinsky, S. and H. Winkler, 2018: Building equity in: strategies for integrating equity into modelling for a 1.5°C world. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2016.0461 10.1098/rsta.2016.0461] .</li>
+<li><span id="fn:r39">Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.</span></li>
+<li><span id="fn:r40">Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</span></li>
+<li><span id="fn:r41">UNDP, 2016: ''Human Development Report 2016: Human Development for Everyone'' . United Nations Development Programme (UNDP), New York, NY, USA, 286 pp.</span></li>
+<li><span id="fn:r42">Leichenko, R. and J.A. Silva, 2014: Climate change and poverty: Vulnerability, impacts, and alleviation strategies. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''5(4)''' , 539–556, doi: [https://dx.doi.org/10.1002/wcc.287 10.1002/wcc.287] .</span></li>
+<li><span id="fn:r43">Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</span></li>
+<li><span id="fn:r44">Shiferaw, B. et al., 2014: Managing vulnerability to drought and enhancing livelihood resilience in sub-Saharan Africa: Technological, institutional and policy options. ''Weather and Climate Extremes'' , '''3''' , 67–79, doi: [https://dx.doi.org/10.1016/j.wace.2014.04.004 10.1016/j.wace.2014.04.004] .</span>
-<span id="demand-management"></span>
+-----
-== 1.3.3.2 Demand management ==
+Miyan, M.A., 2015: Droughts in Asian Least Developed Countries: Vulnerability and sustainability. ''Weather and Climate Extremes'' , '''7''' , 8–23, doi: [https://dx.doi.org/10.1016/j.wace.2014.06.003 10.1016/j.wace.2014.06.003] .</li>
+<li><span id="fn:r45">Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</span></li>
+<li><span id="fn:r46">IPCC, 2014c: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.</span></li>
+<li><span id="fn:r47">Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.</span></li>
+<li><span id="fn:r48">UN, 2015b: ''Transforming our world: The 2030 agenda for sustainable development'' . A/RES/70/1, United Nations General Assembly (UNGA), 35 pp.</span></li>
+<li><span id="fn:r49">Rogelj, J. et al., 2016a: Paris Agreement climate proposals need boost to keep warming well below 2°C. ''Nature Climate Change'' , '''534''' , 631–639, doi: [https://dx.doi.org/10.1038/nature18307 10.1038/nature18307] .</span>
-</div>
+-----
-<div>
-<div id="section-1-3-3-2-demand-management-block-1">
+UNFCCC, 2016: ''Aggregate effect of the intended nationally determined contributions: an update'' . FCCC/CP/2016/2, United Nations Framework Convention on Climate Change (UNFCCC), 75 pp.</li>
+<li><span id="fn:r50">Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.</span></li>
+<li><span id="fn:r51">Hawkins, E. et al., 2017: Estimating changes in global temperature since the pre-industrial period. ''Bulletin of the American Meteorological Society'' , BAMS–D–16–0007.1, doi: [https://dx.doi.org/10.1175/bams-d-16-0007.1 10.1175/bams-d-16-0007.1] .</span>
-<div>
+-----
-<div>
+Pfleiderer, P., C.-F. Schleussner, M. Mengel, and J. Rogelj, 2018: Global mean temperature indicators linked to warming levels avoiding climate risks. ''Environmental Research Letters'' , '''13(6)''' , 064015, doi: [https://dx.doi.org/10.1088/1748-9326/aac319 10.1088/1748-9326/aac319] .</li>
+<li><span id="fn:r52">Deser, C., R. Knutti, S. Solomon, and A.S. Phillips, 2012: Communication of the Role of Natural Variability in Future North American Climate. ''Nature Climate Change'' , '''2(11)''' , 775–779, doi: [https://dx.doi.org/10.1038/nclimate1562 10.1038/nclimate1562] .</span></li>
+<li><span id="fn:r53">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r54">Foster, G. and S. Rahmstorf, 2011: Global temperature evolution 1979–2010. ''Environmental Research Letters'' , '''6(4)''' , 044022, doi: [https://dx.doi.org/10.1088/1748-9326/6/4/044022 10.1088/1748-9326/6/4/044022] .</span>
-<div>
+-----
-'''Dietary change.''' Demand-side solutions to climate mitigation are an essential complement to supply-side, technology and productivity driven solutions ( ''high confidence'' ) (Creutzig et al. 2016 <sup>[[#fn:r760|760]]</sup> ; Bajželj et al. 2014 <sup>[[#fn:r761|761]]</sup> ; Erb et al. 2016b <sup>[[#fn:r762|762]]</sup> ; Creutzig et al. 2018 <sup>[[#fn:r763|763]]</sup> ) (Sections 5.5.1 and 5.5.2). The environmental impacts of the animal-rich ‘western diets’ are being examined critically in the scientific literature (Hallström et al. 2015 <sup>[[#fn:r764|764]]</sup> ; Alexander et al. 2016b <sup>[[#fn:r765|765]]</sup> ; Alexander et al. 2015 <sup>[[#fn:r766|766]]</sup> ; Tilman and Clark 2014 <sup>[[#fn:r767|767]]</sup> ; Aleksandrowicz et al. 2016 <sup>[[#fn:r768|768]]</sup> ; Poore and Nemecek 2018 <sup>[[#fn:r769|769]]</sup> ) (Section 5.4.6). For example, if the average diet of each country were consumed globally, the agricultural land area needed to supply these diets would vary 14-fold, due to country differences in ruminant protein and calorific intake (–55% to +178% compared to existing cropland areas). Given the important role enteric fermentation plays in methane (CH4) emissions, a number of studies have examined the implications of lower animal-protein diets (Swain et al. 2018 <sup>[[#fn:r770|770]]</sup> ; Röös et al. 2017 <sup>[[#fn:r771|771]]</sup> ; Rao et al. 2018 <sup>[[#fn:r772|772]]</sup> ). Reduction of animal protein intake has been estimated to reduce global green water (from precipitation) use by 11% and blue water (from rivers, lakes, groundwater) use by 6% (Jalava et al. 2014 <sup>[[#fn:r773|773]]</sup> ). By avoiding meat from producers with above-median GHG emissions and halving animal-product intake, consumption change could free-up 21 million km <sup>2</sup> of agricultural land and reduce GHG emissions by nearly 5 GtCO <sub>2</sub> -eq yr <sup>–1</sup> or up to 10.4 GtCO <sub>2</sub> -eq yr <sup>–1</sup> when vegetation carbon uptake is considered on the previously agricultural land (Poore and Nemecek 2018 <sup>[[#fn:r774|774]]</sup> , 2019).
+Haustein, K. et al., 2017: A real-time Global Warming Index. ''Scientific Reports'' , '''7(1)''' , 15417, doi: [https://dx.doi.org/10.1038/s41598-017-14828-5 10.1038/s41598-017-14828-5] .
-<div>
+-----
-<div>
+Medhaug, I., M.B. Stolpe, E.M. Fischer, and R. Knutti, 2017: Reconciling controversies about the ‘global warming hiatus’. ''Nature'' , '''545(7652)''' , 41–47, doi: [https://dx.doi.org/10.1038/nature22315 10.1038/nature22315] .
-<div>
+-----
-Diets can be location and community specific, are rooted in culture and traditions while responding to changing lifestyles driven for instance by urbanisation and changing income. Changing dietary and consumption habits would require a combination of non-price (government procurement, regulations, education and awareness raising) and price incentives (Juhl and Jensen 2014 <sup>[[#fn:r775|775]]</sup> ) to induce consumer behavioural change with potential synergies between climate, health and equity (addressing growing global nutrition imbalances that emerge as undernutrition, malnutrition, and obesity) (FAO 2018b <sup>[[#fn:r776|776]]</sup> ).
+Folland, C.K., O. Boucher, A. Colman, and D.E. Parker, 2018: Causes of irregularities in trends of global mean surface temperature since the late 19th century. ''Science Advances'' , '''4(6)''' , eaao5297, doi: [https://dx.doi.org/10.1126/sciadv.aao5297 10.1126/sciadv.aao5297] .
-'''Reduced waste and losses in the food demand system.''' Global averaged per capita food waste and loss (FWL) have increased by 44% between 1961 and 2011 (Porter et al. 2016 <sup>[[#fn:r777|777]]</sup> ) and are now around 25–30% of global food produced (Kummu et al. 2012 <sup>[[#fn:r778|778]]</sup> ; Alexander et al. 2017 <sup>[[#fn:r779|779]]</sup> ). Food waste occurs at all stages of the food supply chain from the household to the marketplace (Parfitt et al. 2010 <sup>[[#fn:r780|780]]</sup> ) and is found to be larger at household than at supply chain levels. A meta-analysis of 55 studies showed that the highest share of food waste was at the consumer stage (43.9% of total) with waste increasing with per capita GDP for high-income countries until a plateaux at about 100 kg cap <sup>–1</sup> yr <sup>–1</sup> (around 16% of food consumption) above about 70,000 USD cap <sup>–1</sup> (van der Werf and Gilliland 2017 <sup>[[#fn:r781|781]]</sup> ; Xue et al. 2017 <sup>[[#fn:r782|782]]</sup> ). Food loss from supply chains tends to be more prevalent in less developed countries where inadequate technologies, limited infrastructure, and imperfect markets combine to raise the share of the food production lost before use.
+-----
-There are several causes behind food waste including economics (cheap food), food policies (subsidies) as well as individual behaviour (Schanes et al. 2018 <sup>[[#fn:r783|783]]</sup> ). Household level food waste arises from overeating or overbuying (Thyberg and Tonjes 2016 <sup>[[#fn:r784|784]]</sup> ). Globally, overconsumption was found to waste 9–10% of food bought (Alexander et al. 2017 <sup>[[#fn:r785|785]]</sup> ).
+Visser, H., S. Dangendorf, D.P. van Vuuren, B. Bregman, and A.C. Petersen, 2018: Signal detection in global mean temperatures after “Paris”: an uncertainty and sensitivity analysis. ''Climate of the Past'' , '''14(2)''' , 139–155, doi: [https://dx.doi.org/10.5194/cp-14-139-2018 10.5194/cp-14-139-2018] .</li>
+<li><span id="fn:r55">Bethke, I. et al., 2017: Potential volcanic impacts on future climate variability. ''Nature Climate Change'' , '''7(11)''' , 799–805, doi: [https://dx.doi.org/10.1038/nclimate3394 10.1038/nclimate3394] .</span></li>
+<li><span id="fn:r56">Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.</span></li>
+<li><span id="fn:r57">Folland, C.K., O. Boucher, A. Colman, and D.E. Parker, 2018: Causes of irregularities in trends of global mean surface temperature since the late 19th century. ''Science Advances'' , '''4(6)''' , eaao5297, doi: [https://dx.doi.org/10.1126/sciadv.aao5297 10.1126/sciadv.aao5297] .</span></li>
+<li><span id="fn:r58">Morice, C.P., J.J. Kennedy, N.A. Rayner, and P.D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. ''Journal of Geophysical Research: Atmospheres'' , '''117(D8)''' , D08101, doi: [https://dx.doi.org/10.1029/2011jd017187 10.1029/2011jd017187] .</span>
-Solutions to FWL thus need to address technical and economic aspects. Such solutions would benefit from more accurate data on the loss-source, loss-magnitude and causes along the food supply chain. In the long run, internalising the cost of food waste into the product price would more likely induce a shift in consumer behaviour towards less waste and more nutritious, or alternative, food intake (FAO 2018b <sup>[[#fn:r786|786]]</sup> ). Reducing FWL would bring a range of benefits for health, reducing pressures on land, water and nutrients, lowering emissions and safeguarding food security. Reducing food waste by 50% would generate net emissions reductions in the range of 20 to 30% of total food-sourced GHGs (Bajželj et al. 2014 <sup>[[#fn:r787|787]]</sup> ). SDG 12 (“Ensure sustainable consumption and production patterns”) calls for per capita global food waste to be reduced by one-half at the retail and consumer level, and reducing food losses along production and supply chains by 2030.
+-----
+Hartmann, D.J. et al., 2013: Observations: Atmosphere and Surface. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.</li>
+<li><span id="fn:r59">Berger, A., Q. Yin, H. Nifenecker, and J. Poitou, 2017: Slowdown of global surface air temperature increase and acceleration of ice melting. ''Earth’s Future'' , '''5(7)''' , 811–822, doi: [https://dx.doi.org/10.1002/2017ef000554 10.1002/2017ef000554] .</span></li>
+<li><span id="fn:r60">Cowtan, K. et al., 2015: Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures. ''Geophysical Research Letters'' , '''42(15)''' , 6526–6534, doi: [https://dx.doi.org/10.1002/2015gl064888 10.1002/2015gl064888] .</span></li>
+<li><span id="fn:r61">Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. ''Nature Climate Change'' , '''6(10)''' , 931–935, doi: [https://dx.doi.org/10.1038/nclimate3066 10.1038/nclimate3066] .</span></li>
+<li><span id="fn:r62">Stocker, T.F. et al., 2013: Technical Summary. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115.</span></li>
+<li><span id="fn:r63">Richardson, M., K. Cowtan, and R.J. Millar, 2018: Global temperature definition affects achievement of long-term climate goals. ''Environmental Research Letters'' , '''13(5)''' , 054004, doi: [https://dx.doi.org/10.1088/1748-9326/aab305 10.1088/1748-9326/aab305] .</span></li>
+<li><span id="fn:r64">Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. ''Reviews of Geophysics'' , '''48(4)''' , RG4004, doi: [https://dx.doi.org/10.1029/2010rg000345 10.1029/2010rg000345] .</span></li>
+<li><span id="fn:r65">Vose, R.S. et al., 2012: NOAA’s merged land-ocean surface temperature analysis. ''Bulletin of the American Meteorological Society'' , '''93(11)''' , 1677–1685, doi: [https://dx.doi.org/10.1175/bams-d-11-00241.1 10.1175/bams-d-11-00241.1] .</span></li>
+<li><span id="fn:r66">Morice, C.P., J.J. Kennedy, N.A. Rayner, and P.D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. ''Journal of Geophysical Research: Atmospheres'' , '''117(D8)''' , D08101, doi: [https://dx.doi.org/10.1029/2011jd017187 10.1029/2011jd017187] .</span></li>
+<li><span id="fn:r67">Rohde, R. et al., 2013: Berkeley Earth Temperature Averaging Process. ''Geoinformatics &amp; Geostatistics: An Overview'' , '''1(2)''' , 1–13, doi: [https://dx.doi.org/10.4172/2327-4581.1000103 10.4172/2327-4581.1000103] .</span>
-</div>
+-----
-</div>
+Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. ''Quarterly Journal of the Royal Meteorological Society'' , '''140(683)''' , 1935–1944, doi: [https://dx.doi.org/10.1002/qj.2297 10.1002/qj.2297] .
-</div>
+-----
-</div>
+Jones, P., 2016: The reliability of global and hemispheric surface temperature records. ''Advances in Atmospheric Sciences'' , '''33(3)''' , 269–282, doi: [https://dx.doi.org/10.1007/s00376-015-5194-4 10.1007/s00376-015-5194-4] .</li>
+<li><span id="fn:r68">Richardson, M., K. Cowtan, and R.J. Millar, 2018: Global temperature definition affects achievement of long-term climate goals. ''Environmental Research Letters'' , '''13(5)''' , 054004, doi: [https://dx.doi.org/10.1088/1748-9326/aab305 10.1088/1748-9326/aab305] .</span></li>
+<li><span id="fn:r69">Karl, T.R. et al., 2015: Possible artifacts of data biases in the recent global surface warming hiatus. ''Science'' , '''348(6242)''' , 1469–1472, doi: [https://dx.doi.org/10.1126/science.aaa5632 10.1126/science.aaa5632] .</span></li>
+<li><span id="fn:r70">Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. ''Quarterly Journal of the Royal Meteorological Society'' , '''140(683)''' , 1935–1944, doi: [https://dx.doi.org/10.1002/qj.2297 10.1002/qj.2297] .</span></li>
+<li><span id="fn:r71">Rohde, R. et al., 2013: Berkeley Earth Temperature Averaging Process. ''Geoinformatics &amp; Geostatistics: An Overview'' , '''1(2)''' , 1–13, doi: [https://dx.doi.org/10.4172/2327-4581.1000103 10.4172/2327-4581.1000103] .</span></li>
+<li><span id="fn:r72">Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. ''Nature Climate Change'' , '''6(10)''' , 931–935, doi: [https://dx.doi.org/10.1038/nclimate3066 10.1038/nclimate3066] .</span></li>
+<li><span id="fn:r73">Otto, F.E.L., D.J. Frame, A. Otto, and M.R. Allen, 2015: Embracing uncertainty in climate change policy. ''Nature Climate Change'' , '''5''' , 1–5, doi: [https://dx.doi.org/10.1038/nclimate2716 10.1038/nclimate2716] .</span></li>
+<li><span id="fn:r74">Haustein, K. et al., 2017: A real-time Global Warming Index. ''Scientific Reports'' , '''7(1)''' , 15417, doi: [https://dx.doi.org/10.1038/s41598-017-14828-5 10.1038/s41598-017-14828-5] .</span></li>
+<li><span id="fn:r75">Cowtan, K. et al., 2015: Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures. ''Geophysical Research Letters'' , '''42(15)''' , 6526–6534, doi: [https://dx.doi.org/10.1002/2015gl064888 10.1002/2015gl064888] .</span>
-</div>
+-----
-</div>
+Richardson, M., K. Cowtan, and R.J. Millar, 2018: Global temperature definition affects achievement of long-term climate goals. ''Environmental Research Letters'' , '''13(5)''' , 054004, doi: [https://dx.doi.org/10.1088/1748-9326/aab305 10.1088/1748-9326/aab305] .</li>
+<li><span id="fn:r76">Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A reconstruction of regional and global temperature for the past 11,300 years. ''Science'' , '''339(6124)''' , 1198–201, doi: [https://dx.doi.org/10.1126/science.1228026 10.1126/science.1228026] .</span></li>
+<li><span id="fn:r77">Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.</span></li>
+<li><span id="fn:r78">Field, C.B. et al., 2014: Technical Summary. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 35–94.</span></li>
+<li><span id="fn:r79">Hawkins, E. et al., 2017: Estimating changes in global temperature since the pre-industrial period. ''Bulletin of the American Meteorological Society'' , BAMS–D–16–0007.1, doi: [https://dx.doi.org/10.1175/bams-d-16-0007.1 10.1175/bams-d-16-0007.1] .</span></li>
+<li><span id="fn:r80">Abram, N.J. et al., 2016: Early onset of industrial-era warming across the oceans and continents. ''Nature'' , '''536''' , 411–418, doi: [https://dx.doi.org/10.1038/nature19082 10.1038/nature19082] .</span>
-</div>
+-----
-</div>
+Schurer, A.P., M.E. Mann, E. Hawkins, S.F.B. Tett, and G.C. Hegerl, 2017: Importance of the pre-industrial baseline for likelihood of exceeding Paris goals. ''Nature Climate Change'' , '''7(8)''' , 563–567, doi: [https://dx.doi.org/10.1038/nclimate3345 10.1038/nclimate3345] .</li>
+<li><span id="fn:r81">Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A reconstruction of regional and global temperature for the past 11,300 years. ''Science'' , '''339(6124)''' , 1198–201, doi: [https://dx.doi.org/10.1126/science.1228026 10.1126/science.1228026] .</span>
-</div>
+-----
-</div>
+Lüning, S. and F. Vahrenholt, 2017: Paleoclimatological Context and Reference Level of the 2°C and 1.5°C Paris Agreement Long-Term Temperature Limits. ''Frontiers in Earth Science'' , '''5''' , 104, doi: [https://dx.doi.org/10.3389/feart.2017.00104 10.3389/feart.2017.00104] .
-</div>
+-----
-</div>
+Marsicek, J., B.N. Shuman, P.J. Bartlein, S.L. Shafer, and S. Brewer, 2018: Reconciling divergent trends and millennial variations in Holocene temperatures. ''Nature'' , '''554(7690)''' , 92–96, doi: [https://dx.doi.org/10.1038/nature25464 10.1038/nature25464] .</li>
-<div class="section">
+<li><span id="fn:r82">Hawkins, E. et al., 2017: Estimating changes in global temperature since the pre-industrial period. ''Bulletin of the American Meteorological Society'' , BAMS–D–16–0007.1, doi: [https://dx.doi.org/10.1175/bams-d-16-0007.1 10.1175/bams-d-16-0007.1] .</span>
-<div>
+-----
-<div>
+Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. ''Nature Geoscience'' , '''10(10)''' , 741–747, doi: [https://dx.doi.org/10.1038/ngeo3031 10.1038/ngeo3031] .
-<span id="risk-management"></span>
+-----
-== 1.3.4 Risk management ==
+Simmons, A.J. et al., 2017: A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. ''Quarterly Journal of the Royal Meteorological Society'' , '''143(702)''' , 101–119, doi: [https://dx.doi.org/10.1002/qj.2949 10.1002/qj.2949] .</li>
+<li><span id="fn:r83">Kosaka, Y. and S.P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. ''Nature'' , '''501(7467)''' , 403–407, doi: [https://dx.doi.org/10.1038/nature12534 10.1038/nature12534] .</span>
-</div>
+-----
-<div>
-<div id="section-1-3-4-risk-management-block-1">
+Medhaug, I., M.B. Stolpe, E.M. Fischer, and R. Knutti, 2017: Reconciling controversies about the ‘global warming hiatus’. ''Nature'' , '''545(7652)''' , 41–47, doi: [https://dx.doi.org/10.1038/nature22315 10.1038/nature22315] .</li>
+<li><span id="fn:r84">England, M.H. et al., 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. ''Nature Climate Change'' , '''4(3)''' , 222–227, doi: [https://dx.doi.org/10.1038/nclimate2106 10.1038/nclimate2106] .</span></li>
+<li><span id="fn:r85">Foster, G. and S. Rahmstorf, 2011: Global temperature evolution 1979–2010. ''Environmental Research Letters'' , '''6(4)''' , 044022, doi: [https://dx.doi.org/10.1088/1748-9326/6/4/044022 10.1088/1748-9326/6/4/044022] .</span></li>
+<li><span id="fn:r86">Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.</span></li>
+<li><span id="fn:r87">Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.</span>
-<div>
+-----
-<div>
+Jones, G.S., P.A. Stott, and J.F.B. Mitchell, 2016: Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction. ''Journal of Geophysical Research: Atmospheres'' , '''121(12)''' , 6969–6992, doi: [https://dx.doi.org/10.1002/2015jd024337 10.1002/2015jd024337] .
-<div>
+-----
-Risk management refers to plans, actions, strategies or policies to reduce the likelihood and/or magnitude of adverse potential consequences, based on assessed or perceived risks. Insurance and early warning systems are examples of risk management, but risk can also be reduced (or resilience enhanced) through a broad set of options ranging from seed sovereignty, livelihood diversification, to reducing land loss through urban sprawl. Early warning systems support farmer decision-making on management strategies (Section 1.2) and are a good example of an adaptation measure with mitigation co-benefits such as reducing carbon losses (Section 1.3.6). Primarily designed to avoid yield losses, early warning systems also support fire management strategies in forest ecosystems, which prevents financial as well as carbon losses (de Groot et al. 2015 <sup>[[#fn:r788|788]]</sup> ). Given that over recent decades on average around 10% of cereal production was lost through extreme weather events (Lesk et al. 2016 <sup>[[#fn:r790|790]]</sup> ), where available and affordable, insurance can buffer farmers and foresters against the financial losses incurred through such weather and other (fire, pests) extremes (Falco et al. 2014 <sup>[[#fn:r791|791]]</sup> ) (Sections 7.2 and 7.4). Decisions to take up insurance are influenced by a range of factors such as the removal of subsidies or targeted education (Falco et al. 2014). Enhancing access and affordability of insurance in low-income countries is a specific objective of the UNFCCC (Linnerooth-Bayer and Mechler 2006 <sup>[[#fn:r792|792]]</sup> ). A global mitigation co-benefit of insurance schemes may also include incentives for future risk reduction (Surminski and Oramas-Dorta 2014 <sup>[[#fn:r793|793]]</sup> ).
+Haustein, K. et al., 2017: A real-time Global Warming Index. ''Scientific Reports'' , '''7(1)''' , 15417, doi: [https://dx.doi.org/10.1038/s41598-017-14828-5 10.1038/s41598-017-14828-5] .
+-----
-</div>
+Ribes, A., F.W. Zwiers, J.-M. Azaïs, and P. Naveau, 2017: A new statistical approach to climate change detection and attribution. ''Climate Dynamics'' , '''48(1)''' , 367–386, doi: [https://dx.doi.org/10.1007/s00382-016-3079-6 10.1007/s00382-016-3079-6] .</li>
+<li><span id="fn:r88">Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. ''Nature Climate Change'' , '''6(10)''' , 931–935, doi: [https://dx.doi.org/10.1038/nclimate3066 10.1038/nclimate3066] .</span></li>
+<li><span id="fn:r89">Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.</span></li>
+<li><span id="fn:r90">Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.</span></li>
+<li><span id="fn:r91">Gillett, N.P., V.K. Arora, D. Matthews, and M.R. Allen, 2013: Constraining the ratio of global warming to cumulative CO <sub>2</sub> emissions using CMIP5 simulations. ''Journal of Climate'' , '''26(18)''' , 6844–6858, doi: [https://dx.doi.org/10.1175/jcli-d-12-00476.1 10.1175/jcli-d-12-00476.1] .</span>
-</div>
+-----
-</div>
+Jones, G.S., P.A. Stott, and N. Christidis, 2013: Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. ''Journal of Geophysical Research: Atmospheres'' , '''118(10)''' , 4001–4024, doi: [https://dx.doi.org/10.1002/jgrd.50239 10.1002/jgrd.50239] .
-</div>
+-----
-</div>
+Ribes, A. and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature. ''Climate Dynamics'' , '''41(11)''' , 2837–2853, doi: [https://dx.doi.org/10.1007/s00382-013-1736-6 10.1007/s00382-013-1736-6] .</li>
+<li><span id="fn:r92">Jones, G.S., P.A. Stott, and J.F.B. Mitchell, 2016: Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction. ''Journal of Geophysical Research: Atmospheres'' , '''121(12)''' , 6969–6992, doi: [https://dx.doi.org/10.1002/2015jd024337 10.1002/2015jd024337] .</span></li>
+<li><span id="fn:r93">Ribes, A. and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature. ''Climate Dynamics'' , '''41(11)''' , 2837–2853, doi: [https://dx.doi.org/10.1007/s00382-013-1736-6 10.1007/s00382-013-1736-6] .</span></li>
+<li><span id="fn:r94">Gillett, N.P., V.K. Arora, D. Matthews, and M.R. Allen, 2013: Constraining the ratio of global warming to cumulative CO <sub>2</sub> emissions using CMIP5 simulations. ''Journal of Climate'' , '''26(18)''' , 6844–6858, doi: [https://dx.doi.org/10.1175/jcli-d-12-00476.1 10.1175/jcli-d-12-00476.1] .</span></li>
+<li><span id="fn:r95">Haustein, K. et al., 2017: A real-time Global Warming Index. ''Scientific Reports'' , '''7(1)''' , 15417, doi: [https://dx.doi.org/10.1038/s41598-017-14828-5 10.1038/s41598-017-14828-5] .</span></li>
+<li><span id="fn:r96">Foster, G. and S. Rahmstorf, 2011: Global temperature evolution 1979–2010. ''Environmental Research Letters'' , '''6(4)''' , 044022, doi: [https://dx.doi.org/10.1088/1748-9326/6/4/044022 10.1088/1748-9326/6/4/044022] .</span></li>
+<li><span id="fn:r97">Folland, C.K., O. Boucher, A. Colman, and D.E. Parker, 2018: Causes of irregularities in trends of global mean surface temperature since the late 19th century. ''Science Advances'' , '''4(6)''' , eaao5297, doi: [https://dx.doi.org/10.1126/sciadv.aao5297 10.1126/sciadv.aao5297] .</span></li>
+<li><span id="fn:r98">Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.</span></li>
+<li><span id="fn:r99">Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. ''Nature Geoscience'' , '''11(8)''' , 574–579, doi: [https://dx.doi.org/10.1038/s41561-018-0156-y 10.1038/s41561-018-0156-y] .</span></li>
+<li><span id="fn:r100">Bethke, I. et al., 2017: Potential volcanic impacts on future climate variability. ''Nature Climate Change'' , '''7(11)''' , 799–805, doi: [https://dx.doi.org/10.1038/nclimate3394 10.1038/nclimate3394] .</span></li>
+<li><span id="fn:r101">Henley, B.J. and A.D. King, 2017: Trajectories toward the 1.5°C Paris target: Modulation by the Interdecadal Pacific Oscillation. ''Geophysical Research Letters'' , '''44(9)''' , 4256–4262, doi: [https://dx.doi.org/10.1002/2017gl073480 10.1002/2017gl073480] .</span></li>
+<li><span id="fn:r102">Rogelj, J., C.-F. Schleussner, and W. Hare, 2017: Getting It Right Matters: Temperature Goal Interpretations in Geoscience Research. ''Geophysical Research Letters'' , '''44(20)''' , 10,662–10,665, doi: [https://dx.doi.org/10.1002/2017gl075612 10.1002/2017gl075612] .</span></li>
+<li><span id="fn:r103">Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.</span></li>
+<li><span id="fn:r104">Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.</span></li>
+<li><span id="fn:r105">Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. ''Quarterly Journal of the Royal Meteorological Society'' , '''140(683)''' , 1935–1944, doi: [https://dx.doi.org/10.1002/qj.2297 10.1002/qj.2297] .</span></li>
+<li><span id="fn:r106">Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change Supplementary Material. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.</span></li>
+<li><span id="fn:r107">Rogelj, J. et al., 2016b: Differences between carbon budget estimates unravelled. ''Nature Climate Change'' , '''6(3)''' , 245–252, doi: [https://dx.doi.org/10.1038/nclimate2868 10.1038/nclimate2868] .</span>
-</div>
+-----
-</div>
+Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. ''Nature Geoscience'' , '''10(10)''' , 741–747, doi: [https://dx.doi.org/10.1038/ngeo3031 10.1038/ngeo3031] .
-<div class="section">
-<div>
+-----
-<div>
+Goodwin, P. et al., 2018: Pathways to 1.5°C and 2°C warming based on observational and geological constraints. ''Nature Geoscience'' , '''11(2)''' , 102–107, doi: [https://dx.doi.org/10.1038/s41561-017-0054-8 10.1038/s41561-017-0054-8] .
-<span id="economics-of-land-based-mitigation-pathways-costs-versus-benefits-of-early-action-under-uncertainty"></span>
+-----
-== 1.3.5 Economics of land-based mitigation pathways: Costs versus benefits of early action under uncertainty ==
+Tokarska, K.B. and N.P. Gillett, 2018: Cumulative carbon emissions budgets consistent with 1.5°C global warming. ''Nature Climate Change'' , '''8(4)''' , 296–299, doi: [https://dx.doi.org/10.1038/s41558-018-0118-9 10.1038/s41558-018-0118-9] .</li>
+<li><span id="fn:r108">Hall, J., G. Fu, and J. Lawry, 2007: Imprecise probabilities of climate change: Aggregation of fuzzy scenarios and model uncertainties. ''Climatic Change'' , '''81(3–4)''' , 265–281, doi: [https://dx.doi.org/10.1007/s10584-006-9175-6 10.1007/s10584-006-9175-6] .</span>
-</div>
+-----
-<div>
-<div id="section-1-3-5-economics-of-land-based-mitigation-pathways-costs-versus-benefits-of-early-action-under-uncertainty-block-1">
+Kriegler, E., J.W. Hall, H. Held, R. Dawson, and H.J. Schellnhuber, 2009: Imprecise probability assessment of tipping points in the climate system. ''Proceedings of the National Academy of Sciences'' , '''106(13)''' , 5041–5046, doi: [https://dx.doi.org/10.1073/pnas.0809117106 10.1073/pnas.0809117106] .
-<div>
+-----
-<div>
+Simpson, M. et al., 2016: Decision Analysis for Management of Natural Hazards. ''Annual Review of Environment and Resources'' , '''41(1)''' , 489–516, doi: [https://dx.doi.org/10.1146/annurev-environ-110615-090011 10.1146/annurev-environ-110615-090011] .</li>
+<li><span id="fn:r109">Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. ''Nature Geoscience'' , '''10(10)''' , 741–747, doi: [https://dx.doi.org/10.1038/ngeo3031 10.1038/ngeo3031] .</span></li>
+<li><span id="fn:r110">Otto, F.E.L., D.J. Frame, A. Otto, and M.R. Allen, 2015: Embracing uncertainty in climate change policy. ''Nature Climate Change'' , '''5''' , 1–5, doi: [https://dx.doi.org/10.1038/nclimate2716 10.1038/nclimate2716] .</span>
-<div>
+-----
-The overarching societal costs associated with GHG emissions and the potential implications of mitigation activities can be measured by various metrics (cost-benefit analysis, cost effectiveness analysis) at different scales (project, technology, sector or the economy) (IPCC 2018 <sup>[[#fn:r794|794]]</sup> ) (Section 1.4). The social cost of carbon (SCC) measures the total net damages of an extra metric tonne of CO <sub>2</sub>  emissions due to the associated climate change (Nordhaus 2014 <sup>[[#fn:r795|795]]</sup> ; Pizer et al. 2014 <sup>[[#fn:r796|796]]</sup> ). Both negative and positive impacts are monetised and discounted to arrive at the net value of consumption loss. As the SCC depends on discount rate assumptions and value judgements (e.g., relative weight given to current vs future generations), it is not a straightforward policy tool to compare alternative options. At the sectoral level, marginal abatement cost curves (MACCs) are widely used for the assessment  of costs related to GHG emissions reduction. MACCs measure the cost of reducing one more GHG unit and are either expert-based or model-derived and offer a range of approaches and assumptions on discount rates or available abatement technologies (Kesicki 2013 <sup>[[#fn:r797|797]]</sup> ). In land-based sectors, Gillingham and Stock (2018) <sup>[[#fn:r798|798]]</sup> reported short-term static abatement costs for afforestation of between 1 and 10 USD2017 per tCO <sub>2</sub> , soil management at 57 and livestock management at 71 USD2017 per tCO <sub>2</sub> . MACCs are more reliable when used to rank alternative options compared to a baseline (or business as usual) rather than offering absolute numerical measures (Huang et al. 2016 <sup>[[#fn:r799|799]]</sup> ). The economics of land-based mitigation options encompass also the “costs of inaction” that arise either from the economic damages due to continued accumulation of GHGs in the atmosphere and from the diminution in value of ecosystem services or the cost of their restoration where feasible (Rodriguez-Labajos 2013 <sup>[[#fn:r800|800]]</sup> ; Ricke et al. 2018 <sup>[[#fn:r801|801]]</sup> ). Overall, it remains challenging to estimate the costs of alternative mitigation options owing to the context – and scale-specific interplay between multiple drivers (technological, economic, and socio-cultural) and enabling policies and institutions (IPCC 2018 <sup>[[#fn:r802|802]]</sup> ) (Section 1.4).
+Xu, Y. and V. Ramanathan, 2017: Well below 2°C: Mitigation strategies for avoiding dangerous to catastrophic climate changes. ''Proceedings of the National Academy of Sciences'' , 1–9, doi: [https://dx.doi.org/10.1073/pnas.1618481114 10.1073/pnas.1618481114] .</li>
+<li><span id="fn:r111">Jarvis, A.J., D.T. Leedal, and C.N. Hewitt, 2012: Climate-society feedbacks and the avoidance of dangerous climate change. ''Nature Climate Change'' , '''2(9)''' , 668–671, doi: [https://dx.doi.org/10.1038/nclimate1586 10.1038/nclimate1586] .</span></li>
+<li><span id="fn:r112">Myhre, G. et al., 2017: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015. ''Atmospheric Chemistry and Physics'' , '''17(4)''' , 2709–2720, doi: [https://dx.doi.org/10.5194/acp-17-2709-2017 10.5194/acp-17-2709-2017] .</span></li>
+<li><span id="fn:r113">Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. ''Nature Geoscience'' , '''11(8)''' , 574–579, doi: [https://dx.doi.org/10.1038/s41561-018-0156-y 10.1038/s41561-018-0156-y] .</span></li>
+<li><span id="fn:r114">Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.</span>
-<div>
+-----
-<div>
+Allen, M.R. et al., 2018: A solution to the misrepresentations of CO <sub>2</sub> -equivalent emissions of short-lived climate pollutants under ambitious mitigation. ''npj Climate and Atmospheric Science'' , '''1(1)''' , 16, doi: [https://dx.doi.org/10.1038/s41612-018-0026-8 10.1038/s41612-018-0026-8] .
-<div>
+-----
-The costs associated with mitigation (both project-linked such as capital costs or land rental rates, or sometimes social costs) generally increase with stringent mitigation targets and over time. Sources of uncertainty include the future availability, cost and performance of technologies (Rosen and Guenther 2015 <sup>[[#fn:r803|803]]</sup> ; Chen et al. 2016 <sup>[[#fn:r804|804]]</sup> ) or lags in decision-making, which have been demonstrated by the uptake of land use and land utilisation policies (Alexander et al. 2013 <sup>[[#fn:r805|805]]</sup> ; Hull et al. 2015 <sup>[[#fn:r806|806]]</sup> ; Brown et al. 2018b <sup>[[#fn:r807|807]]</sup> ). There is growing evidence of significant mitigation gains through conservation, restoration and improved land management practices (Griscom et al. 2017 <sup>[[#fn:r808|808]]</sup> ; Kindermann et al. 2008 <sup>[[#fn:r809|809]]</sup> ; Golub et al. 2013 <sup>[[#fn:r810|810]]</sup> ; Favero et al. 2017 <sup>[[#fn:r811|811]]</sup> ) (Chapters 4 and 6), but the mitigation cost efficiency can vary according to region and specific ecosystem (Albanito et al. 2016 <sup>[[#fn:r812|812]]</sup> ). Recent model developments that treat process-based, human–environment interactions have recognised feedbacks that reinforce or dampen the original stimulus for land-use change (Robinson et al. 2017 <sup>[[#fn:r813|813]]</sup> ; Walters and Scholes 2017 <sup>[[#fn:r814|814]]</sup> ). For instance, land mitigation interventions that rely on large-scale, land-use change (e.g., afforestation) would need to account for the rebound effect (which dampens initial impacts due to feedbacks) in which raising land prices also raises the cost of land-based mitigation (Vivanco et al. 2016 <sup>[[#fn:r815|815]]</sup> ). Although there are few direct estimates, indirect assessments strongly point to much higher costs if action is delayed or limited in scope ( ''medium confidence'' ). Quicker response options are also needed to avoid loss of high-carbon ecosystems and other vital ecosystem services that provide multiple services that are difficult to replace (peatlands, wetlands, mangroves, forests) (Yirdaw et al. 2017 <sup>[[#fn:r816|816]]</sup> ; Pedrozo-Acuña et al. 2015 <sup>[[#fn:r817|817]]</sup> ). Delayed action would raise relative costs in the future or could make response options less feasible ( ''medium confidence'' ) (Goldstein et al. 2019 <sup>[[#fn:r818|818]]</sup> ; Butler et al. 2014 <sup>[[#fn:r819|819]]</sup> ).
+Jenkins, S., R.J. Millar, N. Leach, and M.R. Allen, 2018: Framing Climate Goals in Terms of Cumulative CO <sub>2</sub> -Forcing-Equivalent Emissions. ''Geophysical Research Letters'' , '''45(6)''' , 2795–2804, doi: [https://dx.doi.org/10.1002/2017gl076173 10.1002/2017gl076173] .</li>
+<li><span id="fn:r115">Bowerman, N.H.A. et al., 2013: The role of short-lived climate pollutants in meeting temperature goals. ''Nature Climate Change'' , '''3(12)''' , 1021–1024, doi: [https://dx.doi.org/10.1038/nclimate2034 10.1038/nclimate2034] .</span>
+-----
-</div>
+Wigley, T.M.L., 2018: The Paris warming targets: emissions requirements and sea level consequences. ''Climatic Change'' , '''147(1–2)''' , 31–45, doi: [https://dx.doi.org/10.1007/s10584-017-2119-5 10.1007/s10584-017-2119-5] .</li>
+<li><span id="fn:r116">Matthews, H.D. and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. ''Geophysical Research Letters'' , '''35(4)''' , L04705, doi: [https://dx.doi.org/10.1029/2007gl032388 10.1029/2007gl032388] .</span>
-</div>
+-----
-</div>
+Solomon, S., G.-K.G. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate change due to carbon dioxide emissions. ''Proceedings of the National Academy of Sciences'' , '''106(6)''' , 1704–9, doi: [https://dx.doi.org/10.1073/pnas.0812721106 10.1073/pnas.0812721106] .</li>
+<li><span id="fn:r117">Allen, M.R. and T.F. Stocker, 2013: Impact of delay in reducing carbon dioxide emissions. ''Nature Climate Change'' , '''4(1)''' , 23–26, doi: [https://dx.doi.org/10.1038/nclimate2077 10.1038/nclimate2077] .</span>
-</div>
+-----
-</div>
+Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. ''Nature Geoscience'' , '''11(8)''' , 574–579, doi: [https://dx.doi.org/10.1038/s41561-018-0156-y 10.1038/s41561-018-0156-y] .</li>
+<li><span id="fn:r118">Mathesius, S., M. Hofmann, K. Caldeira, and H.J. Schellnhuber, 2015: Long-term response of oceans to CO <sub>2</sub> removal from the atmosphere. ''Nature Climate Change'' , '''5(12)''' , 1107–1113, doi: [https://dx.doi.org/10.1038/nclimate2729 10.1038/nclimate2729] .</span>
-</div>
+-----
-</div>
+Tokarska, K.B. and K. Zickfeld, 2015: The effectiveness of net negative carbon dioxide emissions in reversing anthropogenic climate change. ''Environmental Research Letters'' , '''10(9)''' , 094013, doi: [https://dx.doi.org/10.1088/1748-9326/10/9/094013 10.1088/1748-9326/10/9/094013] .</li>
+<li><span id="fn:r119">Pendergrass, A.G., F. Lehner, B.M. Sanderson, and Y. Xu, 2015: Does extreme precipitation intensity depend on the emissions scenario? ''Geophysical Research Letters'' , '''42(20)''' , 8767–8774, doi: [https://dx.doi.org/10.1002/2015gl065854 10.1002/2015gl065854] .</span></li>
+<li><span id="fn:r120">Baker, H.S. et al., 2018: Higher CO <sub>2</sub> concentrations increase extreme event risk in a 1.5°C world. ''Nature Climate Change'' , '''8(7)''' , 604–608, doi: [https://dx.doi.org/10.1038/s41558-018-0190-1 10.1038/s41558-018-0190-1] .</span></li>
+<li><span id="fn:r121">Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. ''Geoscientific Model Development'' , '''10(2)''' , 571–583, doi: [https://dx.doi.org/10.5194/gmd-10-571-2017 10.5194/gmd-10-571-2017] .</span></li>
+<li><span id="fn:r122">Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.</span>
-</div>
+-----
-</div>
+Millar, R.J., Z.R. Nicholls, P. Friedlingstein, and M.R. Allen, 2017a: A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. ''Atmospheric Chemistry and Physics'' , '''17(11)''' , 7213–7228, doi: [https://dx.doi.org/10.5194/acp-17-7213-2017 10.5194/acp-17-7213-2017] .
-</div>
+-----
-<div class="section">
-<div>
+Jenkins, S., R.J. Millar, N. Leach, and M.R. Allen, 2018: Framing Climate Goals in Terms of Cumulative CO <sub>2</sub> -Forcing-Equivalent Emissions. ''Geophysical Research Letters'' , '''45(6)''' , 2795–2804, doi: [https://dx.doi.org/10.1002/2017gl076173 10.1002/2017gl076173] .</li>
+<li><span id="fn:r123">Kopp, R.E. et al., 2016: Temperature-driven global sea-level variability in the Common Era. ''Proceedings of the National Academy of Sciences'' , '''113(11)''' , 1–8, doi: [https://dx.doi.org/10.1073/pnas.1517056113 10.1073/pnas.1517056113] .</span></li>
+<li><span id="fn:r124">Leggett, J. et al., 1992: Emissions scenarios for the IPCC: an update. In: ''Climate change 1992: The Supplementary Report to the IPCC Scientific Assessment'' [Houghton, J.T., B.A. Callander, and S.K. Varney (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 69–95.</span></li>
+<li><span id="fn:r125">IPCC, 2000: Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. [Nakićenović, N. and R. Swart (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 570 pp.</span></li>
+<li><span id="fn:r126">Morita, T. et al., 2001: Greenhouse Gas Emission Mitigation Scenarios and Implications. In: ''Climate Change 2001: Mitigation. Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change'' [B. Metz, O. Davidson, R. Swart, and J. Pan (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 115–164.</span></li>
+<li><span id="fn:r127">Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. ''Global Environmental Change'' , '''42''' , 153–168, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.05.009 10.1016/j.gloenvcha.2016.05.009] .</span></li>
+<li><span id="fn:r128">van Vuuren, D.P. et al., 2011: The representative concentration pathways: An overview. ''Climatic Change'' , '''109(1)''' , 5–31, doi: [https://dx.doi.org/10.1007/s10584-011-0148-z 10.1007/s10584-011-0148-z] .</span></li>
+<li><span id="fn:r129">Taylor, K.E., R.J. Stouffer, and G.A. Meehl, 2012: An overview of CMIP5 and the experiment design. ''Bulletin of the American Meteorological Society'' , '''93(4)''' , 485–498, doi: [https://dx.doi.org/10.1175/bams-d-11-00094.1 10.1175/bams-d-11-00094.1] .</span></li>
+<li><span id="fn:r130">Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.</span></li>
+<li><span id="fn:r131">Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. ''Global Environmental Change'' , '''42''' , 153–168, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.05.009 10.1016/j.gloenvcha.2016.05.009] .</span></li>
+<li><span id="fn:r132">Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°C. ''Nature Climate Change'' , '''8(4)''' , 325–332, doi: [https://dx.doi.org/10.1038/s41558-018-0091-3 10.1038/s41558-018-0091-3] .</span></li>
+<li><span id="fn:r133">Bauer, N. et al., 2017: Shared Socio-Economic Pathways of the Energy Sector – Quantifying the Narratives. ''Global Environmental Change'' , '''42''' , 316–330, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.07.006 10.1016/j.gloenvcha.2016.07.006] .</span></li>
+<li><span id="fn:r134">Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. ''Global Environmental Change'' , '''42''' , 331–345, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.10.002 10.1016/j.gloenvcha.2016.10.002] .</span></li>
+<li><span id="fn:r135">Rao, S. et al., 2017: Future Air Pollution in the Shared Socio-Economic Pathways. ''Global Environmental Change'' , '''42''' , 346–358, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.05.012 10.1016/j.gloenvcha.2016.05.012] .</span></li>
+<li><span id="fn:r136">Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. ''Global Environmental Change'' , '''42''' , 153–168, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.05.009 10.1016/j.gloenvcha.2016.05.009] .</span></li>
+<li><span id="fn:r137">Millar, R.J. et al., 2017b: Emission budgets and pathways consistent with limiting warming to 1.5°C. ''Nature Geoscience'' , '''10(10)''' , 741–747, doi: [https://dx.doi.org/10.1038/ngeo3031 10.1038/ngeo3031] .</span>
-<div>
+-----
-<span id="adaptation-measures-and-scope-for-co-benefits-with-mitigation"></span>
+Emori, S. et al., 2018: Risk implications of long-term global climate goals: overall conclusions of the ICA-RUS project. ''Sustainability Science'' , '''13(2)''' , 279–289, doi: [https://dx.doi.org/10.1007/s11625-018-0530-0 10.1007/s11625-018-0530-0] .</li>
-== 1.3.6 Adaptation measures and scope for co-benefits with mitigation ==
+<li><span id="fn:r138">Xu, Y. and V. Ramanathan, 2017: Well below 2°C: Mitigation strategies for avoiding dangerous to catastrophic climate changes. ''Proceedings of the National Academy of Sciences'' , 1–9, doi: [https://dx.doi.org/10.1073/pnas.1618481114 10.1073/pnas.1618481114] .</span></li>
+<li><span id="fn:r139">Rosenbloom, D., 2017: Pathways: An emerging concept for the theory and governance of low-carbon transitions. ''Global Environmental Change'' , '''43''' , 37–50, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.12.011 10.1016/j.gloenvcha.2016.12.011] .</span></li>
+<li><span id="fn:r140">IPCC, 2000: Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. [Nakićenović, N. and R. Swart (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 570 pp.</span></li>
+<li><span id="fn:r141">van Vuuren, D.P. et al., 2011: The representative concentration pathways: An overview. ''Climatic Change'' , '''109(1)''' , 5–31, doi: [https://dx.doi.org/10.1007/s10584-011-0148-z 10.1007/s10584-011-0148-z] .</span></li>
+<li><span id="fn:r142">Kriegler, E. et al., 2012: The need for and use of socio-economic scenarios for climate change analysis: A new approach based on shared socio-economic pathways. ''Global Environmental Change'' , '''22(4)''' , 807–822, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2012.05.005 10.1016/j.gloenvcha.2012.05.005] .</span>
+-----
-</div>
+O’Neill, B.C. et al., 2014: A new scenario framework for climate change research: The concept of shared socioeconomic pathways. ''Climatic Change'' , '''122(3)''' , 387–400, doi: [https://dx.doi.org/10.1007/s10584-013-0905-2 10.1007/s10584-013-0905-2] .</li>
-<div>
+<li><span id="fn:r143">Kriegler, E. et al., 2014: A new scenario framework for climate change research: The concept of shared climate policy assumptions. ''Climatic Change'' , '''122(3)''' , 401–414, doi: [https://dx.doi.org/10.1007/s10584-013-0971-5 10.1007/s10584-013-0971-5] .</span></li>
+<li><span id="fn:r144">Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. ''Global Environmental Change'' , '''42''' , 153–168, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2016.05.009 10.1016/j.gloenvcha.2016.05.009] .</span></li>
+<li><span id="fn:r145">Ebi, K.L. et al., 2014: A new scenario framework for climate change research: Background, process, and future directions. ''Climatic Change'' , '''122(3)''' , 363–372, doi: [https://dx.doi.org/10.1007/s10584-013-0912-3 10.1007/s10584-013-0912-3] .</span>
-<div id="section-1-3-6-adaptation-measures-and-scope-for-co-benefits-with-mitigation-block-1">
+-----
-<div>
+van Vuuren, D.P. et al., 2014: A new scenario framework for Climate Change Research: Scenario matrix architecture. ''Climatic Change'' , '''122(3)''' , 373–386, doi: [https://dx.doi.org/10.1007/s10584-013-0906-1 10.1007/s10584-013-0906-1] .</li>
+<li><span id="fn:r146">Shukla, P.R. and V. Chaturvedi, 2013: Sustainable energy transformations in India under climate policy. ''Sustainable Development'' , '''21(1)''' , 48–59, doi: [https://dx.doi.org/10.1002/sd.516 10.1002/sd.516] .</span>
-<div>
+-----
-<div>
+Fleurbaey, M. et al., 2014: Sustainable Development and Equity. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, P.E. K. Seyboth, A. Adler, I. Baum, S. Brunner, and T.Z.J.C.M. B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow (eds.)]. Cambridge University Press, Cambridge, Cambridge, United Kingdom and New York, NY, USA, pp. 283–350.
-Adaptation and mitigation have generally been treated as two separate discourses, both in policy and practice, with mitigation addressing cause and adaptation dealing with the consequences of climate change (Hennessey et al. 2017 <sup>[[#fn:r820|820]]</sup> ). While adaptation (e.g., reducing flood risks) and mitigation (e.g., reducing non-CO <sub>2</sub> emissions from agriculture) may have different objectives and operate at different scales, they can also generate joint outcomes (Locatelli et al. 2015b <sup>[[#fn:r821|821]]</sup> ) with adaptation generating mitigation co-benefits. Seeking to integrate strategies for achieving adaptation and mitigation goals is attractive in order to reduce competition for limited resources and trade-offs (Lobell et al. 2013 <sup>[[#fn:r822|822]]</sup> ; Berry et al. 2015 <sup>[[#fn:r823|823]]</sup> ; Kongsager and Corbera 2015 <sup>[[#fn:r824|824]]</sup> ). Moreover, determinants that can foster adaptation and mitigation practices are similar. These tend to include available technology and resources, and credible information for policymakers to act on (Yohe 2001 <sup>[[#fn:r825|825]]</sup> ).
+-----
-<div>
+van Vuuren, D.P. et al., 2015: Pathways to achieve a set of ambitious global sustainability objectives by 2050: Explorations using the IMAGE integrated assessment model. ''Technological Forecasting and Social Change'' , '''98''' , 303–323, doi: [https://dx.doi.org/10.1016/j.techfore.2015.03.005 10.1016/j.techfore.2015.03.005] .</li>
+<li><span id="fn:r147">Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.</span></li>
+<li><span id="fn:r148">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r149">Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</span></li>
+<li><span id="fn:r150">Reisinger, A. et al., 2014: Australasia. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1371–1438.</span></li>
+<li><span id="fn:r151">Barnett, J. et al., 2014: A local coastal adaptation pathway. ''Nature Climate Change'' , '''4(12)''' , 1103–1108, doi: [https://dx.doi.org/10.1038/nclimate2383 10.1038/nclimate2383] .</span>
-<div>
+-----
-<div>
+Wise, R.M. et al., 2014: Reconceptualising adaptation to climate change as part of pathways of change and response. ''Global Environmental Change'' , '''28''' , 325–336, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2013.12.002 10.1016/j.gloenvcha.2013.12.002] .
-Four sets of mitigation–adaptation interrelationships can be distinguished: (i) mitigation actions that can result in adaptation benefits; (ii) adaptation actions that have mitigation benefits; (iii) processes that have implications for both adaptation and mitigation; and (iv) strategies and policy processes that seek to promote an integrated set of responses for both adaptation and mitigation (Klein et al. 2007). A high level of adaptive capacity is a key ingredient to developing successful mitigation policy. Implementing mitigation action can result in increasing resilience especially if it is able to reduce risks. Yet, mitigation and adaptation objectives, scale of implementation, sector and even metrics to identify impacts tend to differ (Ayers and Huq 2009 <sup>[[#fn:r826|826]]</sup> ), and institutional setting, often does not enable an environment where synergies are sought (Kongsager et al. 2016 <sup>[[#fn:r827|827]]</sup> ). Trade-offs between adaptation and mitigation exist as well and need to be understood (and avoided) to establish win-win situations (Porter et al. 2014 <sup>[[#fn:r828|828]]</sup> ; Kongsager et al. 2016 <sup>[[#fn:r829|829]]</sup> ).
+-----
-<div>
+Fazey, I. et al., 2016: Past and future adaptation pathways. ''Climate and Development'' , '''8(1)''' , 26–44, doi: [https://dx.doi.org/10.1080/17565529.2014.989192 10.1080/17565529.2014.989192] .</li>
+<li><span id="fn:r152">Harris, L.M., E.K. Chu, and G. Ziervogel, 2017: Negotiated resilience. ''Resilience'' , '''3293''' , 1–19, doi: [https://dx.doi.org/10.1080/21693293.2017.1353196 10.1080/21693293.2017.1353196] .</span>
-<div>
+-----
-<div>
+Fazey, I. et al., 2018: Community resilience for a 1.5°C world. ''Current Opinion in Environmental Sustainability'' , '''31''' , 30–40, doi: [https://dx.doi.org/10.1016/j.cosust.2017.12.006 10.1016/j.cosust.2017.12.006] .
-Forestry and agriculture offer a wide range of lessons for the integration of adaptation and mitigation actions given the vulnerability of forest ecosystems or cropland to climate variability and change (Keenan 2015 <sup>[[#fn:r830|830]]</sup> ; Gaba et al. 2015 <sup>[[#fn:r831|831]]</sup> ) (Sections 5.6 and 4.8). Increasing adaptive capacity in forested areas has the potential to prevent deforestation and forest degradation (Locatelli et al. 2011 <sup>[[#fn:r832|832]]</sup> ). Reforestation projects, if well managed, can increase community economic opportunities that encourage conservation (Nelson and de Jong 2003 <sup>[[#fn:r833|833]]</sup> ), build capacity through training of farmers and installation of multifunctional plantations with income generation (Reyer et al. 2009 <sup>[[#fn:r834|834]]</sup> ), strengthen local institutions (Locatelli et al. 2015a <sup>[[#fn:r835|835]]</sup> ) and increase cash-flow to local forest stakeholders from foreign donors (West 2016 <sup>[[#fn:r836|836]]</sup> ). A forest plantation that sequesters carbon for mitigation can also reduce water availability to downstream populations and heighten their vulnerability to drought. Inversely, not recognising mitigation in adaptation projects may yield adaptation measures that increase greenhouse gas emissions, a prime example of ‘maladaptation’. Analogously, ‘mal-mitigation’ would result in reducing GHG emissions, but increasing vulnerability (Barnett and O’Neill 2010 <sup>[[#fn:r837|837]]</sup> ; Porter et al. 2014 <sup>[[#fn:r838|838]]</sup> ). For instance, the cost of pursuing large-scale adaptation and mitigation projects has been associated with higher failure risks, onerous transactions costs and the complexity of managing big projects (Swart and Raes 2007 <sup>[[#fn:r839|839]]</sup> ).
+-----
-<div>
+Tàbara, J.D. et al., 2018: Positive tipping points in a rapidly warming world. ''Current Opinion in Environmental Sustainability'' , '''31''' , 120–129, doi: [https://dx.doi.org/10.1016/j.cosust.2018.01.012 10.1016/j.cosust.2018.01.012] .</li>
+<li><span id="fn:r153">Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.</span></li>
+<li><span id="fn:r154">Meehl, G.A. et al., 2007: Global Climate Projections. In: ''Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change'' [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 747–845.</span></li>
+<li><span id="fn:r155">Meehl, G.A. et al., 2007: Global Climate Projections. In: ''Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change'' [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 747–845.</span>
-<div>
+-----
-<div>
+Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.</li>
+<li><span id="fn:r156">Hansen, J. et al., 2005: Earth’s energy imbalance: confirmation and implications. ''Science'' , '''308''' , 1431–1435, doi: [https://dx.doi.org/10.1126/science.1110252 10.1126/science.1110252] .</span></li>
+<li><span id="fn:r157">Matthews, H.D. and S. Solomon, 2013: Irreversible Does Not Mean Unavoidable. ''Science'' , '''340(6131)''' , 438–439, doi: [https://dx.doi.org/10.1126/science.1236372 10.1126/science.1236372] .</span></li>
+<li><span id="fn:r158">Eby, M. et al., 2009: Lifetime of anthropogenic climate change: Millennial time scales of potential CO <sub>2</sub> and surface temperature perturbations. ''Journal of Climate'' , '''22(10)''' , 2501–2511, doi: [https://dx.doi.org/10.1175/2008jcli2554.1 10.1175/2008jcli2554.1] .</span>
-Adaptation encompasses both biophysical and socio-economic vulnerability and underlying causes (informational, capacity, financial, institutional, and technological; Huq et al. 2014 <sup>[[#fn:r840|840]]</sup> ) and it is increasingly linked to resilience and to broader development goals (Huq et al. 2014 <sup>[[#fn:r841|841]]</sup> ). Adaptation measures can increase performance of mitigation projects under climate change and legitimise mitigation measures through the more immediately felt effects of adaptation (Locatelli et al. 2011 <sup>[[#fn:r842|842]]</sup> ; Campbell et al. 2014 <sup>[[#fn:r843|843]]</sup> ; Locatelli et al. 2015b <sup>[[#fn:r844|844]]</sup> ). Effective climate policy integration in the land sector is expected to gain from (i) internal policy coherence between adaptation and mitigation objectives, (ii) external climate coherence between climate change and development objectives, (iii) policy integration that favours vertical governance structures to foster effective mainstreaming of climate change into sectoral policies, and (iv) horizontal policy integration through overarching governance structures to enable cross-sectoral coordination (Sections 1.4 and 7.4).
+-----
+Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.</li>
+<li><span id="fn:r159">Matthews, H.D. and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. ''Geophysical Research Letters'' , '''35(4)''' , L04705, doi: [https://dx.doi.org/10.1029/2007gl032388 10.1029/2007gl032388] .</span>
-</div>
+-----
-</div>
+Lowe, J.A. et al., 2009: How difficult is it to recover from dangerous levels of global warming? ''Environmental Research Letters'' , '''4(1)''' , 014012, doi: [https://dx.doi.org/10.1088/1748-9326/4/1/014012 10.1088/1748-9326/4/1/014012] .
-</div>
+-----
-</div>
+Gillett, N.P., V.K. Arora, K. Zickfeld, S.J. Marshall, and W.J. Merryfield, 2011: Ongoing climate change following a complete cessation of carbon dioxide emissions. ''Nature Geoscience'' , '''4''' , 83–87, doi: [https://dx.doi.org/10.1038/ngeo1047 10.1038/ngeo1047] .
-</div>
+-----
-</div>
+Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.</li>
+<li><span id="fn:r160">Frölicher, T.L., M. Winton, and J.L. Sarmiento, 2014: Continued global warming after CO <sub>2</sub> emissions stoppage. ''Nature Climate Change'' , '''4(1)''' , 40–44, doi: [https://dx.doi.org/10.1038/nclimate2060 10.1038/nclimate2060] .</span>
-</div>
+-----
-</div>
+Ehlert, D. and K. Zickfeld, 2017: What determines the warming commitment after cessation of CO <sub>2</sub> emissions? ''Environmental Research Letters'' , '''12(1)''' , 015002, doi: [https://dx.doi.org/10.1088/1748-9326/aa564a 10.1088/1748-9326/aa564a] .</li>
+<li><span id="fn:r161">Solomon, S., G.-K.G. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate change due to carbon dioxide emissions. ''Proceedings of the National Academy of Sciences'' , '''106(6)''' , 1704–9, doi: [https://dx.doi.org/10.1073/pnas.0812721106 10.1073/pnas.0812721106] .</span>
-</div>
+-----
-</div>
+Goodwin, P., R.G. Williams, and A. Ridgwell, 2015: Sensitivity of climate to cumulative carbon emissions due to compensation of ocean heat and carbon uptake. ''Nature Geoscience'' , '''8(1)''' , 29–34, doi: [https://dx.doi.org/10.1038/ngeo2304 10.1038/ngeo2304] .
-</div>
+-----
-</div>
+Williams, R.G., V. Roussenov, T.L. Frölicher, and P. Goodwin, 2017: Drivers of Continued Surface Warming After Cessation of Carbon Emissions. ''Geophysical Research Letters'' , '''44(20)''' , 10,633–10,642, doi: [https://dx.doi.org/10.1002/2017gl075080 10.1002/2017gl075080] .</li>
+<li><span id="fn:r162">Matthews, H.D. and S. Solomon, 2013: Irreversible Does Not Mean Unavoidable. ''Science'' , '''340(6131)''' , 438–439, doi: [https://dx.doi.org/10.1126/science.1236372 10.1126/science.1236372] .</span></li>
+<li><span id="fn:r163">Lowe, J.A. and D. Bernie, 2018: The impact of Earth system feedbacks on carbon budgets and climate response. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2017.0263 10.1098/rsta.2017.0263] .</span></li>
+<li><span id="fn:r164">Frölicher, T.L. and F. Joos, 2010: Reversible and irreversible impacts of greenhouse gas emissions in multi-century projections with the NCAR global coupled carbon cycle-climate model. ''Climate Dynamics'' , '''35(7)''' , 1439–1459, doi: [https://dx.doi.org/10.1007/s00382-009-0727-0 10.1007/s00382-009-0727-0] .</span>
-</div>
+-----
-</div>
+Matthews, H.D. and K. Zickfeld, 2012: Climate response to zeroed emissions of greenhouse gases and aerosols. ''Nature Climate Change'' , '''2(5)''' , 338–341, doi: [https://dx.doi.org/10.1038/nclimate1424 10.1038/nclimate1424] .
-</div>
+-----
-</div>
+Mauritsen, T. and R. Pincus, 2017: Committed warming inferred from observations. ''Nature Climate Change'' , '''2''' , 1–5, doi: [https://dx.doi.org/10.1038/nclimate3357 10.1038/nclimate3357] .</li>
+<li><span id="fn:r165">Matthews, H.D. and K. Zickfeld, 2012: Climate response to zeroed emissions of greenhouse gases and aerosols. ''Nature Climate Change'' , '''2(5)''' , 338–341, doi: [https://dx.doi.org/10.1038/nclimate1424 10.1038/nclimate1424] .</span>
-</div>
+-----
-<div>
-<div>
+Mauritsen, T. and R. Pincus, 2017: Committed warming inferred from observations. ''Nature Climate Change'' , '''2''' , 1–5, doi: [https://dx.doi.org/10.1038/nclimate3357 10.1038/nclimate3357] .
-<span id="enabling-the-response"></span>
+-----
-== 1.4 Enabling the response ==
+Samset, B.H. et al., 2018: Climate Impacts From a Removal of Anthropogenic Aerosol Emissions. ''Geophysical Research Letters'' , '''45(2)''' , 1020–1029, doi: [https://dx.doi.org/10.1002/2017gl076079 10.1002/2017gl076079] .</li>
+<li><span id="fn:r166">Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.</span>
-</div>
+-----
-<div>
-<div id="article-1-4-enabling-the-response-block-1">
+Myhre, G. et al., 2017: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015. ''Atmospheric Chemistry and Physics'' , '''17(4)''' , 2709–2720, doi: [https://dx.doi.org/10.5194/acp-17-2709-2017 10.5194/acp-17-2709-2017] .</li>
+<li><span id="fn:r167">Fernández, A.J. et al., 2017: Aerosol optical, microphysical and radiative forcing properties during variable intensity African dust events in the Iberian Peninsula. ''Atmospheric Research'' , '''196''' , 129–141, doi: [https://dx.doi.org/10.1016/j.atmosres.2017.06.019 10.1016/j.atmosres.2017.06.019] .</span></li>
+<li><span id="fn:r168">Matthews, H.D. and K. Zickfeld, 2012: Climate response to zeroed emissions of greenhouse gases and aerosols. ''Nature Climate Change'' , '''2(5)''' , 338–341, doi: [https://dx.doi.org/10.1038/nclimate1424 10.1038/nclimate1424] .</span>
-<div>
+-----
-<div>
+Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.</li>
+<li><span id="fn:r169">Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.</span></li>
+<li><span id="fn:r170">Etminan, M., G. Myhre, E.J. Highwood, and K.P. Shine, 2016: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing. ''Geophysical Research Letters'' , '''43(24)''' , 12,614–12,623, doi: [https://dx.doi.org/10.1002/2016gl071930 10.1002/2016gl071930] .</span></li>
+<li><span id="fn:r171">Myhre, G. et al., 2017: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015. ''Atmospheric Chemistry and Physics'' , '''17(4)''' , 2709–2720, doi: [https://dx.doi.org/10.5194/acp-17-2709-2017 10.5194/acp-17-2709-2017] .</span></li>
+<li><span id="fn:r172">Millar, R.J., Z.R. Nicholls, P. Friedlingstein, and M.R. Allen, 2017a: A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. ''Atmospheric Chemistry and Physics'' , '''17(11)''' , 7213–7228, doi: [https://dx.doi.org/10.5194/acp-17-7213-2017 10.5194/acp-17-7213-2017] .</span></li>
+<li><span id="fn:r173">Smith, C.J. et al., 2018: FAIR v1.3: a simple emissions-based impulse response and carbon cycle model. ''Geoscientific Model Development'' , '''11(6)''' , 2273–2297, doi: [https://dx.doi.org/10.5194/gmd-11-2273-2018 10.5194/gmd-11-2273-2018] .</span></li>
+<li><span id="fn:r174">Smith, C.J. et al., 2018: FAIR v1.3: a simple emissions-based impulse response and carbon cycle model. ''Geoscientific Model Development'' , '''11(6)''' , 2273–2297, doi: [https://dx.doi.org/10.5194/gmd-11-2273-2018 10.5194/gmd-11-2273-2018] .</span></li>
+<li><span id="fn:r175">Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. ''Nature Geoscience'' , '''11(8)''' , 574–579, doi: [https://dx.doi.org/10.1038/s41561-018-0156-y 10.1038/s41561-018-0156-y] .</span></li>
+<li><span id="fn:r176">Haustein, K. et al., 2017: A real-time Global Warming Index. ''Scientific Reports'' , '''7(1)''' , 15417, doi: [https://dx.doi.org/10.1038/s41598-017-14828-5 10.1038/s41598-017-14828-5] .</span></li>
+<li><span id="fn:r177">Leach, N.J. et al., 2018: Current level and rate of warming determine emissions budgets under ambitious mitigation. ''Nature Geoscience'' , '''11(8)''' , 574–579, doi: [https://dx.doi.org/10.1038/s41561-018-0156-y 10.1038/s41561-018-0156-y] .</span></li>
+<li><span id="fn:r178">Matthews, H.D., N.P. Gillett, P. Stott, and K. Zickfeld, 2009: The proportionality of global warming to cumulative carbon emissions. ''Nature'' , '''459(7248)''' , 829–32, doi: [https://dx.doi.org/10.1038/nature08047 10.1038/nature08047] .</span>
-<div>
+-----
-Climate change and sustainable development are challenges to society that require action at local, national, transboundary and global scales. Different time-perspectives are also important in decision-making, ranging from immediate actions to long-term planning and investment. Acknowledging the systemic link between food production and consumption, and land-resources more broadly is expected to enhance the success of actions (Bazilian et al. 2011 <sup>[[#fn:r845|845]]</sup> ; Hussey and Pittock 2012 <sup>[[#fn:r846|846]]</sup> ). Because of the complexity of challenges and the diversity of actors involved in addressing these challenges, decision-making would benefit from a portfolio of policy instruments. Decision-making would also be facilitated by overcoming barriers such as inadequate education and funding mechanisms, as well as integrating international decisions into all relevant (sub)national sectoral policies (Section 7.4).
+Zickfeld, K., M. Eby, H.D. Matthews, and A.J. Weaver, 2009: Setting cumulative emissions targets to reduce the risk of dangerous climate change. ''Proceedings of the National Academy of Sciences'' , '''106(38)''' , 16129–16134, doi: [https://dx.doi.org/10.1073/pnas.0805800106 10.1073/pnas.0805800106] .</li>
+<li><span id="fn:r179">Gregory, J.M. and P.M. Forster, 2008: Transient climate response estimated from radiative forcing and observed temperature change. ''Journal of Geophysical Research: Atmospheres'' , '''113(D23)''' , D23105, doi: [https://dx.doi.org/10.1029/2008jd010405 10.1029/2008jd010405] .</span>
-<div>
+-----
-<div>
+Allen, M.R. et al., 2018: A solution to the misrepresentations of CO <sub>2</sub> -equivalent emissions of short-lived climate pollutants under ambitious mitigation. ''npj Climate and Atmospheric Science'' , '''1(1)''' , 16, doi: [https://dx.doi.org/10.1038/s41612-018-0026-8 10.1038/s41612-018-0026-8] .</li>
+<li><span id="fn:r180">Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.</span></li>
+<li><span id="fn:r181">Levasseur, A. et al., 2016: Enhancing life cycle impact assessment from climate science: Review of recent findings and recommendations for application to LCA. ''Ecological Indicators'' , '''71''' , 163–174, doi: [https://dx.doi.org/10.1016/j.ecolind.2016.06.049 10.1016/j.ecolind.2016.06.049] .</span>
-<div>
+-----
-‘Nexus thinking’ emerged as an alternative to the sector-specific governance of natural resource use to achieve global securities of water (D’Odorico et al. 2018 <sup>[[#fn:r847|847]]</sup> ), food and energy (Hoff 2011 <sup>[[#fn:r848|848]]</sup> ; Allan et al. 2015 <sup>[[#fn:r849|849]]</sup> ), and also to address biodiversity concerns (Fischer et al. 2017 <sup>[[#fn:r850|850]]</sup> ). Yet, there is no agreed definition of “nexus” nor a uniform framework to approach the concept, which may be land-focused (Howells et al. 2013 <sup>[[#fn:r851|851]]</sup> ), water-focused (Hoff 2011 <sup>[[#fn:r852|852]]</sup> ) or food-centred (Ringler and Lawford 2013 <sup>[[#fn:r853|853]]</sup> ; Biggs et al. 2015 <sup>[[#fn:r854|854]]</sup> ). Significant barriers remain to establish nexus approaches as part of a wider repertoire of responses to global environmental change, including challenges to cross-disciplinary collaboration, complexity, political economy and the incompatibility of current institutional structures (Hayley et al. 2015 <sup>[[#fn:r855|855]]</sup> ; Wichelns 2017 <sup>[[#fn:r856|856]]</sup> ) (Sections 7.5.6 and 7.6.2).
+Ocko, I.B. et al., 2017: Unmask temporal trade-offs in climate policy debates. ''Science'' , '''356(6337)''' , 492–493, doi: [https://dx.doi.org/10.1126/science.aaj2350 10.1126/science.aaj2350] .</li>
+<li><span id="fn:r182">Clarke, L.E. et al., 2014: Assessing transformation pathways. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 413–510.</span></li>
+<li><span id="fn:r183">Shine, K.P., J.S. Fuglestvedt, K. Hailemariam, and N. Stuber, 2005: Alternatives to the Global Warming Potential for comparing climate impacts of emissions of greenhouse gases. ''Climatic Change'' , '''68(3)''' , 281–302, doi: [https://dx.doi.org/10.1007/s10584-005-1146-9 10.1007/s10584-005-1146-9] .</span></li>
+<li><span id="fn:r184">Tol, R.S.J., T.K. Berntsen, B.C. O’Neill, Fuglestvedt, and P.S. Keith, 2012: A unifying framework for metrics for aggregating the climate effect of different emissions. ''Environmental Research Letters'' , '''7(4)''' , 044006.</span>
+-----
-</div>
+Deuber, O., G. Luderer, and O. Edenhofer, 2013: Physico-economic evaluation of climate metrics: A conceptual framework. ''Environmental Science &amp; Policy'' , '''29(0)''' , 37–45, doi: [https://dx.doi.org/10.1016/j.envsci.2013.01.018 10.1016/j.envsci.2013.01.018] .</li>
+<li><span id="fn:r185">Smith, S.M. et al., 2012: Equivalence of greenhouse-gas emissions for peak temperature limits. ''Nature Climate Change'' , '''2(7)''' , 535–538, doi: [https://dx.doi.org/10.1038/nclimate1496 10.1038/nclimate1496] .</span></li>
+<li><span id="fn:r186">Reisinger, A. et al., 2012: Implications of alternative metrics for global mitigation costs and greenhouse gas emissions from agriculture. ''Climatic Change'' , 1–14, doi: [https://dx.doi.org/10.1007/s10584-012-0593-3 10.1007/s10584-012-0593-3] .</span>
-</div>
+-----
-</div>
+Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
-</div>
+-----
-</div>
+Smith, S.J., J. Karas, J. Edmonds, J. Eom, and A. Mizrahi, 2013: Sensitivity of multi-gas climate policy to emission metrics. ''Climatic Change'' , '''117(4)''' , 663–675, doi: [https://dx.doi.org/10.1007/s10584-012-0565-7 10.1007/s10584-012-0565-7] .
-</div>
+-----
-</div>
+Strefler, J., G. Luderer, T. Aboumahboub, and E. Kriegler, 2014: Economic impacts of alternative greenhouse gas emission metrics: a model-based assessment. ''Climatic Change'' , '''125(3–4)''' , 319–331, doi: [https://dx.doi.org/10.1007/s10584-014-1188-y 10.1007/s10584-014-1188-y] .</li>
+<li><span id="fn:r187">Archer, D. and V. Brovkin, 2008: The millennial atmospheric lifetime of anthropogenic CO <sub>2</sub> . ''Climatic Change'' , '''90(3)''' , 283–297, doi: [https://dx.doi.org/10.1007/s10584-008-9413-1 10.1007/s10584-008-9413-1] .</span>
-</div>
+-----
-<div class="section">
-<div>
+Matthews, H.D. and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. ''Geophysical Research Letters'' , '''35(4)''' , L04705, doi: [https://dx.doi.org/10.1029/2007gl032388 10.1029/2007gl032388] .
-<div>
+-----
-<span id="governance-to-enable-the-response"></span>
+Solomon, S., G.-K.G. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate change due to carbon dioxide emissions. ''Proceedings of the National Academy of Sciences'' , '''106(6)''' , 1704–9, doi: [https://dx.doi.org/10.1073/pnas.0812721106 10.1073/pnas.0812721106] .</li>
-== 1.4.1 Governance to enable the response ==
+<li><span id="fn:r188">Zickfeld, K., A.H. MacDougall, and H.D. Matthews, 2016: On the proportionality between global temperature change and cumulative CO <sub>2</sub> emissions during periods of net negative CO <sub>2</sub> emissions. ''Environmental Research Letters'' , '''11(5)''' , 055006, doi: [https://dx.doi.org/10.1088/1748-9326/11/5/055006 10.1088/1748-9326/11/5/055006] .</span></li>
+<li><span id="fn:r189">Bowerman, N.H.A., D.J. Frame, C. Huntingford, J.A. Lowe, and M.R. Allen, 2011: Cumulative carbon emissions, emissions floors and short-term rates of warming: implications for policy. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''369(1934)''' , 45–66, doi: [https://dx.doi.org/10.1098/rsta.2010.0288 10.1098/rsta.2010.0288] .</span>
+-----
-</div>
+Wigley, T.M.L., 2018: The Paris warming targets: emissions requirements and sea level consequences. ''Climatic Change'' , '''147(1–2)''' , 31–45, doi: [https://dx.doi.org/10.1007/s10584-017-2119-5 10.1007/s10584-017-2119-5] .</li>
-<div>
+<li><span id="fn:r190">Allen, M.R. et al., 2018: A solution to the misrepresentations of CO <sub>2</sub> -equivalent emissions of short-lived climate pollutants under ambitious mitigation. ''npj Climate and Atmospheric Science'' , '''1(1)''' , 16, doi: [https://dx.doi.org/10.1038/s41612-018-0026-8 10.1038/s41612-018-0026-8] .</span>
-<div id="section-1-4-1-governance-to-enable-the-response-block-1">
+-----
-<div>
+Fuglestvedt, J. et al., 2018: Implications of possible interpretations of ‘greenhouse gas balance’ in the Paris Agreement. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2016.0445 10.1098/rsta.2016.0445] .</li>
+<li><span id="fn:r191">Lauder, A.R. et al., 2013: Offsetting methane emissions – An alternative to emission equivalence metrics. ''International Journal of Greenhouse Gas Control'' , '''12''' , 419–429, doi: [https://dx.doi.org/10.1016/j.ijggc.2012.11.028 10.1016/j.ijggc.2012.11.028] .</span>
-<div>
+-----
-<div>
+Allen, M.R. et al., 2016: New use of global warming potentials to compare cumulative and short-lived climate pollutants. ''Nature Climate Change'' , '''6''' , 1–5, doi: [https://dx.doi.org/10.1038/nclimate2998 10.1038/nclimate2998] .</li>
+<li><span id="fn:r192">Allen, M.R. et al., 2018: A solution to the misrepresentations of CO <sub>2</sub> -equivalent emissions of short-lived climate pollutants under ambitious mitigation. ''npj Climate and Atmospheric Science'' , '''1(1)''' , 16, doi: [https://dx.doi.org/10.1038/s41612-018-0026-8 10.1038/s41612-018-0026-8] .</span></li>
+<li><span id="fn:r193">Shine, K.P., J.S. Fuglestvedt, K. Hailemariam, and N. Stuber, 2005: Alternatives to the Global Warming Potential for comparing climate impacts of emissions of greenhouse gases. ''Climatic Change'' , '''68(3)''' , 281–302, doi: [https://dx.doi.org/10.1007/s10584-005-1146-9 10.1007/s10584-005-1146-9] .</span>
-Governance includes the processes, structures, rules and traditions applied by formal and informal actors including governments, markets, organisations, and their interactions with people. Land governance actors include those affecting policies and markets, and those directly changing land use (Hersperger et al. 2010 <sup>[[#fn:r858|858]]</sup> ). The former includes governments and administrative entities, large companies investing in land, non-governmental institutions and international institutions. It also includes UN agencies that are working at the interface between climate change and land management, such as the FAO and the World Food Programme that have inter alia worked on advancing knowledge to support food security through the improvement of techniques and strategies for more resilient farm systems. Farmers and foresters directly act on land (actors in proximate causes) (Hersperger et al. 2010) (Chapter 7).
+-----
-<div>
+Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
-<div>
+-----
-<div>
+Allen, M.R. et al., 2018: A solution to the misrepresentations of CO <sub>2</sub> -equivalent emissions of short-lived climate pollutants under ambitious mitigation. ''npj Climate and Atmospheric Science'' , '''1(1)''' , 16, doi: [https://dx.doi.org/10.1038/s41612-018-0026-8 10.1038/s41612-018-0026-8] .</li>
+<li><span id="fn:r194">Allen, M.R. et al., 2018: A solution to the misrepresentations of CO <sub>2</sub> -equivalent emissions of short-lived climate pollutants under ambitious mitigation. ''npj Climate and Atmospheric Science'' , '''1(1)''' , 16, doi: [https://dx.doi.org/10.1038/s41612-018-0026-8 10.1038/s41612-018-0026-8] .</span>
-Policy design and formulation has often been strongly sectoral. For example, agricultural policy might be concerned with food security, but have little concern for environmental protection or human health. As food, energy and water security and the conservation of biodiversity rank highly on the Agenda 2030 for Sustainable Development, the promotion of synergies between and across sectoral policies is important (IPBES 2018a <sup>[[#fn:r859|859]]</sup> ). This can also reduce the risks of anthropogenic climate forcing through mitigation, and bring greater collaboration between scientists, policymakers, the private sector and land managers in adapting to climate change (FAO 2015a <sup>[[#fn:r860|860]]</sup> ). Polycentric governance (Section 7.6) has emerged as an appropriate way of handling resource management problems, in which the decision-making centres take account of one another in competitive and cooperative relationships and have recourse to conflict resolution mechanisms (Carlisle and Gruby 2017 <sup>[[#fn:r861|861]]</sup> ). Polycentric governance is also multi-scale and allows the interaction between actors at different levels (local, regional, national and global) in managing common pool resources such as forests or aquifers.
+-----
-Implementation of systemic, nexus approaches has been achieved through socio-ecological systems (SES) frameworks that emerged from studies of how institutions affect human incentives, actions and outcomes (Ostrom and Cox 2010 <sup>[[#fn:r862|862]]</sup> ). Recognition of the importance of SES laid the basis for alternative formulations to tackle the sustainable management of land resources focusing specifically on institutional and governance outcomes (Lebel et al. 2006 <sup>[[#fn:r863|863]]</sup> ; Bodin 2017 <sup>[[#fn:r864|864]]</sup> ). The SES approach also addresses the multiple scales in which the social and ecological dimensions interact (Veldkamp et al. 2011 <sup>[[#fn:r865|865]]</sup> ; Myers et al. 2016 <sup>[[#fn:r866|866]]</sup> ; Azizi et al. 2017 <sup>[[#fn:r867|867]]</sup> ) (Section 6.1).
+Fuglestvedt, J. et al., 2018: Implications of possible interpretations of ‘greenhouse gas balance’ in the Paris Agreement. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2016.0445 10.1098/rsta.2016.0445] .</li>
+<li><span id="fn:r195">Rogelj, J. et al., 2016b: Differences between carbon budget estimates unravelled. ''Nature Climate Change'' , '''6(3)''' , 245–252, doi: [https://dx.doi.org/10.1038/nclimate2868 10.1038/nclimate2868] .</span>
-<div>
+-----
-<div>
+Hienola, A. et al., 2018: The impact of aerosol emissions on the 1.5°C pathways. ''Environmental Research Letters'' , '''13(4)''' , 044011.</li>
+<li><span id="fn:r196">Fuglestvedt, J. et al., 2018: Implications of possible interpretations of ‘greenhouse gas balance’ in the Paris Agreement. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2016.0445 10.1098/rsta.2016.0445] .</span></li>
+<li><span id="fn:r197">Tanaka, K. and B.C. O’Neill, 2018: The Paris Agreement zero-emissions goal is not always consistent with the 1.5°C and 2°C temperature targets. ''Nature Climate Change'' , '''8(4)''' , 319–324, doi: [https://dx.doi.org/10.1038/s41558-018-0097-x 10.1038/s41558-018-0097-x] .</span></li>
+<li><span id="fn:r198">Johansson, D.J.A., 2012: Economics- and physical-based metrics for comparing greenhouse gases. ''Climatic Change'' , '''110(1–2)''' , 123–141, doi: [https://dx.doi.org/10.1007/s10584-011-0072-2 10.1007/s10584-011-0072-2] .</span>
-<div>
+-----
-Adaptation or resilience pathways within the SES frameworks require several attributes, including indigenous and local knowledge (ILK) and trust building for deliberative decision-making and effective collective action, polycentric and multi-layered institutions and responsible authorities that pursue just distributions of benefits to enhance the adaptive capacity of vulnerable groups and communities (Lebel et al. 2006 <sup>[[#fn:r868|868]]</sup> ). The nature, source and mode of knowledge generation are critical to ensure that sustainable solutions are community-owned and fully integrated within the local context (Mistry and Berardi 2016 <sup>[[#fn:r869|869]]</sup> ; Schneider and Buser 2018 <sup>[[#fn:r870|870]]</sup> ). Integrating ILK with scientific information is a prerequisite for such community-owned solutions (Cross-Chapter Box 13 in Chapter 7). ILK is context-specific, transmitted orally or through imitation and demonstration, adaptive to changing environments, and collectivised through a shared social memory (Mistry and Berardi 2016 <sup>[[#fn:r871|871]]</sup> ). ILK is also holistic since indigenous people do not seek solutions aimed at adapting to climate change alone, but instead look for solutions to increase their resilience to a wide range of shocks and stresses (Mistry and Berardi 2016 <sup>[[#fn:r872|872]]</sup> ). ILK can be deployed in the practice of climate governance, especially at the local level where actions are informed by the principles of decentralisation and autonomy (Chanza and de Wit 2016 <sup>[[#fn:r873|873]]</sup> ). ILK need not be viewed as needing confirmation or disapproval by formal science, but rather it can complement scientific knowledge (Klein et al. 2014 <sup>[[#fn:r874|874]]</sup> ).
+Tol, R.S.J., T.K. Berntsen, B.C. O’Neill, Fuglestvedt, and P.S. Keith, 2012: A unifying framework for metrics for aggregating the climate effect of different emissions. ''Environmental Research Letters'' , '''7(4)''' , 044006.
-<div>
+-----
-<div>
+Deuber, O., G. Luderer, and O. Edenhofer, 2013: Physico-economic evaluation of climate metrics: A conceptual framework. ''Environmental Science &amp; Policy'' , '''29(0)''' , 37–45, doi: [https://dx.doi.org/10.1016/j.envsci.2013.01.018 10.1016/j.envsci.2013.01.018] .
-<div>
+-----
-The capacity to apply individual policy instruments and policy mixes is influenced by governance modes. These modes include hierarchical governance that is centralised and imposes policy through top-down measures, decentralised governance in which public policy is devolved to regional or local government, public-private partnerships that aim for mutual benefits for the public and private sectors and self or private governance that involves decisions beyond the realms of the public sector (IPBES 2018a <sup>[[#fn:r875|875]]</sup> ). These governance modes provide both constraints and opportunities for key actors that impact the effectiveness, efficiency and equity of policy implementation.
+Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
+-----
-</div>
+Cherubini, F. and K. Tanaka, 2016: Amending the Inadequacy of a Single Indicator for Climate Impact Analyses. ''Environmental Science &amp; Technology'' , '''50(23)''' , 12530–12531, doi: [https://dx.doi.org/10.1021/acs.est.6b05343 10.1021/acs.est.6b05343] .</li>
+<li><span id="fn:r199">Shine, K.P., R.P. Allan, W.J. Collins, and J.S. Fuglestvedt, 2015: Metrics for linking emissions of gases and aerosols to global precipitation changes. ''Earth System Dynamics'' , '''6(2)''' , 525–540, doi: [https://dx.doi.org/10.5194/esd-6-525-2015 10.5194/esd-6-525-2015] .</span></li>
+<li><span id="fn:r200">Sterner, E., D.J.A. Johansson, and C. Azar, 2014: Emission metrics and sea level rise. ''Climatic Change'' , '''127(2)''' , 335–351, doi: [https://dx.doi.org/10.1007/s10584-014-1258-1 10.1007/s10584-014-1258-1] .</span></li>
+<li><span id="fn:r201">Tol, R.S.J., T.K. Berntsen, B.C. O’Neill, Fuglestvedt, and P.S. Keith, 2012: A unifying framework for metrics for aggregating the climate effect of different emissions. ''Environmental Research Letters'' , '''7(4)''' , 044006.</span>
-</div>
+-----
-</div>
+Deuber, O., G. Luderer, and O. Edenhofer, 2013: Physico-economic evaluation of climate metrics: A conceptual framework. ''Environmental Science &amp; Policy'' , '''29(0)''' , 37–45, doi: [https://dx.doi.org/10.1016/j.envsci.2013.01.018 10.1016/j.envsci.2013.01.018] .
-</div>
+-----
-</div>
+Kolstad, C. et al., 2014: Social, Economic, and Ethical Concepts and Methods. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 207–282.</li>
+<li><span id="fn:r202">OECD, 2016: ''The OECD supporting action on climate change'' . Organisation for Economic Co-operation and Development (OECD), Paris, France, 18 pp.</span>
-</div>
+-----
-</div>
+Shindell, D.T., Y. Lee, and G. Faluvegi, 2016: Climate and health impacts of US emissions reductions consistent with 2°C. ''Nature Climate Change'' , '''6''' , 503–507, doi: [https://dx.doi.org/10.1038/nclimate2935 10.1038/nclimate2935] .</li>
+<li><span id="fn:r203">Shindell, D.T., 2015: The social cost of atmospheric release. ''Climatic Change'' , '''130(2)''' , 313–326, doi: [https://dx.doi.org/10.1007/s10584-015-1343-0 10.1007/s10584-015-1343-0] .</span>
-</div>
+-----
-</div>
+Sarofim, M.C., S.T. Waldhoff, and S.C. Anenberg, 2017: Valuing the Ozone-Related Health Benefits of Methane Emission Controls. ''Environmental and Resource Economics'' , '''66(1)''' , 45–63, doi: [https://dx.doi.org/10.1007/s10640-015-9937-6 10.1007/s10640-015-9937-6] .
-</div>
+-----
-</div>
+Shindell, D.T., J.S. Fuglestvedt, and W.J. Collins, 2017: The social cost of methane: theory and applications. ''Faraday Discussions'' , '''200''' , 429–451, doi: [https://dx.doi.org/10.1039/c7fd00009j 10.1039/c7fd00009j] .</li>
+<li><span id="fn:r204">Millar, R.J., Z.R. Nicholls, P. Friedlingstein, and M.R. Allen, 2017a: A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. ''Atmospheric Chemistry and Physics'' , '''17(11)''' , 7213–7228, doi: [https://dx.doi.org/10.5194/acp-17-7213-2017 10.5194/acp-17-7213-2017] .</span></li>
+<li><span id="fn:r205">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r206">Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO <sub>2</sub> emissions based on regional and impact-related climate targets. ''Nature'' , '''529(7587)''' , 477–483, doi: [https://dx.doi.org/10.1038/nature16542 10.1038/nature16542] .</span></li>
+<li><span id="fn:r207">Ebi, K.L., L.H. Ziska, and G.W. Yohe, 2016: The shape of impacts to come: lessons and opportunities for adaptation from uneven increases in global and regional temperatures. ''Climatic Change'' , '''139(3)''' , 341–349, doi: [https://dx.doi.org/10.1007/s10584-016-1816-9 10.1007/s10584-016-1816-9] .</span></li>
+<li><span id="fn:r208">Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. ''Nature Climate Change'' , '''5(6)''' , 560–564, doi: [https://dx.doi.org/10.1038/nclimate2617 10.1038/nclimate2617] .</span>
-</div>
+-----
-</div>
+Karmalkar, A. and R.S. Bradley, 2017: Consequences of Global Warming of 1.5°C and 2°C for Regional Temperature and Precipitation Changes in the Contiguous United States. ''PLOS ONE'' , '''12(1)''' , e0168697, doi: [https://dx.doi.org/10.1371/journal.pone.0168697 10.1371/journal.pone.0168697] .
-</div>
+-----
-</div>
+King, A.D., D.J. Karoly, and B.J. Henley, 2017: Australian climate extremes at 1.5°C and 2°C of global warming. ''Nature Climate Change'' , '''7(6)''' , 412–416, doi: [https://dx.doi.org/10.1038/nclimate3296 10.1038/nclimate3296] .
-</div>
+-----
-<div class="section">
-<div>
+Chevuturi, A., N.P. Klingaman, A.G. Turner, and S. Hannah, 2018: Projected Changes in the Asian-Australian Monsoon Region in 1.5°C and 2.0°C Global-Warming Scenarios. ''Earth’s Future'' , '''6(3)''' , 339–358, doi: [https://dx.doi.org/10.1002/2017ef000734 10.1002/2017ef000734] .</li>
+<li><span id="fn:r209">Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028.</span></li>
+<li><span id="fn:r210">van Oldenborgh, G.J. et al., 2017: Attribution of extreme rainfall from Hurricane Harvey, August 2017. ''Environmental Research Letters'' , '''12(12)''' , 124009, doi: [https://dx.doi.org/10.1088/1748-9326/aa9ef2 10.1088/1748-9326/aa9ef2] .</span></li>
+<li><span id="fn:r211">Lee, D. et al., 2018: Impacts of half a degree additional warming on the Asian summer monsoon rainfall characteristics. ''Environmental Research Letters'' , '''13(4)''' , 044033, doi: [https://dx.doi.org/10.1088/1748-9326/aab55d 10.1088/1748-9326/aab55d] .</span></li>
+<li><span id="fn:r212">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r213">Schewe, J. et al., 2014: Multimodel assessment of water scarcity under climate change. ''Proceedings of the National Academy of Sciences'' , '''111(9)''' , 3245–3250, doi: [https://dx.doi.org/10.1073/pnas.1222460110 10.1073/pnas.1222460110] .</span>
-<div>
+-----
-<span id="gender-agency-as-a-critical-factor-in-climate-and-land-sustainability-outcomes"></span>
+Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. ''Environmental Research Letters'' , '''13(4)''' , 044038, doi: [https://dx.doi.org/10.1088/1748-9326/aab792 10.1088/1748-9326/aab792] .
-== 1.4.2 Gender agency as a critical factor in climate and land sustainability outcomes ==
+-----
-</div>
+Saeed, F. et al., 2018: Robust changes in tropical rainy season length at 1.5°C and 2°C. ''Environmental Research Letters'' , '''13(6)''' , 064024, doi: [https://dx.doi.org/10.1088/1748-9326/aab797 10.1088/1748-9326/aab797] .</li>
-<div>
+<li><span id="fn:r214">Forkel, M. et al., 2016: Enhanced seasonal CO <sub>2</sub> exchange caused by amplified plant productivity in northern ecosystems. ''Science'' , '''351(6274)''' , 696–699, doi: [https://dx.doi.org/10.1126/science.aac4971 10.1126/science.aac4971] .</span></li>
+<li><span id="fn:r215">Hoegh-Guldberg, O. et al., 2007: Coral Reefs Under Rapid Climate Change and Ocean Acidification. ''Science'' , '''318(5857)''' , 1737–1742, doi: [https://dx.doi.org/10.1126/science.1152509 10.1126/science.1152509] .</span></li>
+<li><span id="fn:r216">Bindoff, N.L. et al., 2007: Observations: Oceanic Climate Change and Sea Level. In: ''Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change'' [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 385–432.</span>
-<div id="section-1-4-2-gender-agency-as-a-critical-factor-in-climate-and-land-sustainability-outcomes-block-1">
+-----
-<div>
+Chen, X. et al., 2017: The increasing rate of global mean sea-level rise during 1993-2014. ''Nature Climate Change'' , '''7(7)''' , 492–495, doi: [https://dx.doi.org/10.1038/nclimate3325 10.1038/nclimate3325] .</li>
+<li><span id="fn:r217">Matthews, T.K.R., R.L. Wilby, and C. Murphy, 2017: Communicating the deadly consequences of global warming for human heat stress. ''Proceedings of the National Academy of Sciences'' , '''114(15)''' , 3861–3866, doi: [https://dx.doi.org/10.1073/pnas.1617526114 10.1073/pnas.1617526114] .</span></li>
+<li><span id="fn:r218">Cramer, W. et al., 2014: Detection and attribution of observed impacts. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037.</span></li>
+<li><span id="fn:r219">AghaKouchak, A., L. Cheng, O. Mazdiyasni, and A. Farahmand, 2014: Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. ''Geophysical Research Letters'' , '''41(24)''' , 8847–8852, doi: [https://dx.doi.org/10.1002/2014gl062308 10.1002/2014gl062308] .</span>
-<div>
+-----
-<div>
+Leonard, M. et al., 2014: A compound event framework for understanding extreme impacts. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''5(1)''' , 113–128, doi: [https://dx.doi.org/10.1002/wcc.252 10.1002/wcc.252] .
-Environmental resource management is not gender neutral. Gender is an essential variable in shaping ecological processes and change, building better prospects for livelihoods and sustainable development (Resurrección 2013 <sup>[[#fn:r876|876]]</sup> ) (Cross-Chapter Box 11 in Chapter 7). Entrenched legal and social structures and power relations constitute additional stressors that render women’s experience of natural resources disproportionately negative when compared to men. Socio-economic drivers and entrenched gender inequalities affect land-based management (Agarwal 2010 <sup>[[#fn:r877|877]]</sup> ). The intersections between climate change, gender and climate adaptation takes place at multiple scales: household, national and international, and adaptive capacities are shaped through power and knowledge.
+-----
-<div>
+Martius, O., S. Pfahl, and C. Chevalier, 2016: A global quantification of compound precipitation and wind extremes. ''Geophysical Research Letters'' , '''43(14)''' , 7709–7717, doi: [https://dx.doi.org/10.1002/2016gl070017 10.1002/2016gl070017] .
-<div>
+-----
-<div>
+Zscheischler, J. and S.I. Seneviratne, 2017: Dependence of drivers affects risks associated with compound events. ''Science Advances'' , '''3(6)''' , e1700263, doi: [https://dx.doi.org/10.1126/sciadv.1700263 10.1126/sciadv.1700263] .</li>
+<li><span id="fn:r220">Rosenzweig, C. et al., 2008: Attributing physical and biological impacts to anthropogenic climate change. ''Nature'' , '''453(7193)''' , 353–357, doi: [https://dx.doi.org/10.1038/nature06937 10.1038/nature06937] .</span>
-Germaine to the gender inequities is the unequal access to land-based resources. Women play a significant role in agriculture (Boserup 1989 <sup>[[#fn:r878|878]]</sup> ; Darity 1980 <sup>[[#fn:r879|879]]</sup> ) and rural economies globally (FAO 2011 <sup>[[#fn:r880|880]]</sup> ), but are well below their share of labour in agriculture globally (FAO 2011). In 59% of 161 surveyed countries, customary, traditional and religious practices hinder women’s land rights (OECD 2014 <sup>[[#fn:r881|881]]</sup> ). Moreover, women typically shoulder disproportionate responsibility for unpaid domestic work including care-giving activities (Beuchelt and Badstue 2013 <sup>[[#fn:r882|882]]</sup> ) and the provision of water and firewood (UNEP 2016 <sup>[[#fn:r883|883]]</sup> ). Exposure to violence restricts, in large regions, their mobility for capacity-building activities and productive work outside the home (Day et al. 2005 <sup>[[#fn:r884|884]]</sup> ; UNEP 2016 <sup>[[#fn:r885|885]]</sup> ). Large-scale development projects can erode rights, and lead to over-exploitation of natural resources. Hence, there are cases where reforms related to land-based management, instead of enhancing food security, have tended to increase the vulnerability of both women and men and reduce their ability to adapt to climate change (Pham et al. 2016 <sup>[[#fn:r886|886]]</sup> ). Access to, and control over, land and land-based resources is essential in taking concrete action on land-based mitigation, and inadequate access can affect women’s rights and participation in land governance and management of productive assets.
+-----
-Timely information, such as from early warning systems, is critical in managing risks, disasters, and land degradation, and in enabling land-based adaptation. Gender, household resources and social status, are all determinants that influence the adoption of land-based strategies (Theriault et al. 2017 <sup>[[#fn:r887|887]]</sup> ). Climate change is not a lone driver in the marginalisation of women; their ability to respond swiftly to its impacts will depend on other socio-economic drivers that may help or hinder action towards adaptive governance. Empowering women and removing gender-based inequities constitutes a mechanism for greater participation in the adoption of sustainable practices of land management (Mello and Schmink 2017 <sup>[[#fn:r888|888]]</sup> ). Improving women’s access to land (Arora-Jonsson 2014 <sup>[[#fn:r889|889]]</sup> ) and other resources (water) and means of economic livelihoods (such as credit and finance) are the prerequisites to enable women to participate in governance and decision-making structures (Namubiru-Mwaura 2014 <sup>[[#fn:r890|890]]</sup> ). Still, women are not a homogenous group, and distinctions through elements of ethnicity, class, age and social status, require a more nuanced approach and not a uniform treatment through vulnerability lenses only. An intersectional approach that accounts for various social identifiers under different situations of power (Rao 2017 <sup>[[#fn:r891|891]]</sup> ) is considered suitable to integrate gender into climate change research and helps to recognise overlapping and interdependent systems of power (Djoudi et al. 2016 <sup>[[#fn:r892|892]]</sup> ; Kaijser and Kronsell 2014 <sup>[[#fn:r893|893]]</sup> ; Moosa and Tuana 2014 <sup>[[#fn:r894|894]]</sup> ; Thompson-Hall et al. 2016 <sup>[[#fn:r895|895]]</sup> ).
+Cramer, W. et al., 2014: Detection and attribution of observed impacts. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037.
+-----
-</div>
+Hansen, G., D. Stone, M. Auffhammer, C. Huggel, and W. Cramer, 2016: Linking local impacts to changes in climate: a guide to attribution. ''Regional Environmental Change'' , '''16(2)''' , 527–541, doi: [https://dx.doi.org/10.1007/s10113-015-0760-y 10.1007/s10113-015-0760-y] .</li>
+<li><span id="fn:r221">Oliver, T.H. and M.D. Morecroft, 2014: Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''5(3)''' , 317–335, doi: [https://dx.doi.org/10.1002/wcc.271 10.1002/wcc.271] .</span></li>
+<li><span id="fn:r222">Sitch, S., P.M. Cox, W.J. Collins, and C. Huntingford, 2007: Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. ''Nature'' , '''448(7155)''' , 791–794, doi: [https://dx.doi.org/10.1038/nature06059 10.1038/nature06059] .</span></li>
+<li><span id="fn:r223">IPCC, 2012a: Summary for Policymakers. In: ''Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation'' [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–21.</span>
-</div>
+-----
-</div>
+World Bank, 2013: ''Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience'' . The World Bank, Washington DC, USA, 254 pp.
-</div>
+-----
-</div>
+IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</li>
+<li><span id="fn:r224">Rosenzweig, C. et al., 2017: Assessing inter-sectoral climate change risks: the role of ISIMIP. ''Environmental Research Letters'' , '''12(1)''' , 010301.</span></li>
+<li><span id="fn:r225">Settele, J. et al., 2014: Terrestrial and inland water systems. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359.</span>
-</div>
+-----
-</div>
+Marbà, N. et al., 2015: Impact of seagrass loss and subsequent revegetation on carbon sequestration and stocks. ''Journal of Ecology'' , '''103(2)''' , 296–302, doi: [https://dx.doi.org/10.1111/1365-2745.12370 10.1111/1365-2745.12370] .</li>
+<li><span id="fn:r226">Creutzig, F., 2016: Economic and ecological views on climate change mitigation with bioenergy and negative emissions. ''GCB Bioenergy'' , '''8(1)''' , 4–10, doi: [https://dx.doi.org/10.1111/gcbb.12235 10.1111/gcbb.12235] .</span></li>
+<li><span id="fn:r227">Dasgupta, P. et al., 2014: Rural areas. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 613–657.</span>
-</div>
+-----
-</div>
+Revi, A. et al., 2014: Urban areas. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 535–612.
-</div>
+-----
-<div class="section">
-<div>
+Rosenzweig, C., W. Solecki, P. Romeo-Lankao, M. Shagun, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: ''Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network'' . Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp.</li>
+<li><span id="fn:r228">Arora-Jonsson, S., 2011: Virtue and vulnerability: Discourses on women, gender and climate change. ''Global Environmental Change'' , '''21(2)''' , 744–751, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2011.01.005 10.1016/j.gloenvcha.2011.01.005] .</span>
-<div>
+-----
-<span id="policy-instruments"></span>
+Cardona, O.D. et al., 2012: Determinants of Risk: Exposure and Vulnerablity. In: ''Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation'' [Field, C.B., V.R. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 65–108.
-== 1.4.3 Policy instruments ==
+-----
-</div>
+Resurrección, B.P., 2013: Persistent women and environment linkages in climate change and sustainable development agendas. ''Women’s Studies International Forum'' , '''40''' , 33–43, doi: [https://dx.doi.org/10.1016/j.wsif.2013.03.011 10.1016/j.wsif.2013.03.011] .
-<div>
-<div id="section-1-4-3-policy-instruments-block-1">
+-----
-<div>
+Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.
-<div>
+-----
-<div>
+Vincent, K.E., P. Tschakert, J. Barnett, M.G. Rivera-Ferre, and A. Woodward, 2014: Cross-chapter box on gender and climate change. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 105–107.</li>
+<li><span id="fn:r229">Moss, R.H. et al., 2010: The next generation of scenarios for climate change research and assessment. ''Nature'' , '''463(7282)''' , 747–756, doi: [https://dx.doi.org/10.1038/nature08823 10.1038/nature08823] .</span></li>
+<li><span id="fn:r230">James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''8(2)''' , e457, doi: [https://dx.doi.org/10.1002/wcc.457 10.1002/wcc.457] .</span></li>
+<li><span id="fn:r231">Ekström, M., M.R. Grose, and P.H. Whetton, 2015: An appraisal of downscaling methods used in climate change research. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''6(3)''' , 301–319, doi: [https://dx.doi.org/10.1002/wcc.339 10.1002/wcc.339] .</span></li>
+<li><span id="fn:r232">Lewis, S.C., A.D. King, and D.M. Mitchell, 2017: Australia’s Unprecedented Future Temperature Extremes Under Paris Limits to Warming. ''Geophysical Research Letters'' , '''44(19)''' , 9947–9956, doi: [https://dx.doi.org/10.1002/2017gl074612 10.1002/2017gl074612] .</span>
-Policy instruments enable governance actors to respond to environmental and societal challenges through policy action. Examples of the range of policy instruments available to public policymakers are discussed below based on four categories of instruments: (i) legal and regulatory instruments, (ii) rights-based instruments and customary norms, (iii) economic and financial instruments, and (iv) social and cultural instruments.
+-----
+King, A.D. et al., 2018b: On the Linearity of Local and Regional Temperature Changes from 1.5°C to 2°C of Global Warming. ''Journal of Climate'' , '''31(18)''' , 7495–7514, doi: [https://dx.doi.org/10.1175/jcli-d-17-0649.1 10.1175/jcli-d-17-0649.1] .</li>
+<li><span id="fn:r233">Asseng, S. et al., 2013: Uncertainty in simulating wheat yields under climate change. ''Nature Climate Change'' , '''3(9)''' , 827–832, doi: [https://dx.doi.org/10.1038/nclimate1916 10.1038/nclimate1916] .</span></li>
+<li><span id="fn:r234">Schleussner, C.-F. et al., 2016: Differential climate impacts for policy relevant limits to global warming: the case of 1.5°C and 2°C. ''Earth System Dynamics'' , '''7(2)''' , 327–351, doi: [https://dx.doi.org/10.5194/esd-7-327-2016 10.5194/esd-7-327-2016] .</span></li>
+<li><span id="fn:r235">IPCC, 2013b: Summary for Policymakers. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–29.</span></li>
+<li><span id="fn:r236">World Bank, 2013: ''Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience'' . The World Bank, Washington DC, USA, 254 pp.</span></li>
+<li><span id="fn:r237">Schleussner, C.-F. et al., 2016: Differential climate impacts for policy relevant limits to global warming: the case of 1.5°C and 2°C. ''Earth System Dynamics'' , '''7(2)''' , 327–351, doi: [https://dx.doi.org/10.5194/esd-7-327-2016 10.5194/esd-7-327-2016] .</span>
-</div>
+-----
-</div>
+James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''8(2)''' , e457, doi: [https://dx.doi.org/10.1002/wcc.457 10.1002/wcc.457] .
-</div>
+-----
-</div>
+Barcikowska, M.J. et al., 2018: Euro-Atlantic winter storminess and precipitation extremes under 1.5°C vs. 2°C warming scenarios. ''Earth System Dynamics'' , '''9(2)''' , 679–699, doi: [https://dx.doi.org/10.5194/esd-9-679-2018 10.5194/esd-9-679-2018] .
-</div>
+-----
-<div id="section-1-4-3-1-legal-and-regulatory-instruments">
-<div>
+King, A.D. et al., 2018a: Reduced heat exposure by limiting global warming to 1.5°C. ''Nature Climate Change'' , '''8(7)''' , 549–551, doi: [https://dx.doi.org/10.1038/s41558-018-0191-0 10.1038/s41558-018-0191-0] .</li>
+<li><span id="fn:r238">Pörtner, H.-O. et al., 2014: Ocean systems. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.</span></li>
+<li><span id="fn:r239">Blicharska, M. et al., 2017: Steps to overcome the North–South divide in research relevant to climate change policy and practice. ''Nature Climate Change'' , '''7(1)''' , 21–27, doi: [https://dx.doi.org/10.1038/nclimate3163 10.1038/nclimate3163] .</span></li>
+<li><span id="fn:r240">Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.</span></li>
+<li><span id="fn:r241">Gouldson, A. et al., 2015: Exploring the economic case for climate action in cities. ''Global Environmental Change'' , '''35''' , 93–105, doi: [https://dx.doi.org/10.1016/j.gloenvcha.2015.07.009 10.1016/j.gloenvcha.2015.07.009] .</span>
-<div>
+-----
-<span id="legal-and-regulatory-instruments"></span>
+Termeer, C.J.A.M., A. Dewulf, and G.R. Biesbroek, 2017: Transformational change: governance interventions for climate change adaptation from a continuous change perspective. ''Journal of Environmental Planning and Management'' , '''60(4)''' , 558–576, doi: [https://dx.doi.org/10.1080/09640568.2016.1168288 10.1080/09640568.2016.1168288] .</li>
-== 1.4.3.1 Legal and regulatory instruments ==
+<li><span id="fn:r242">IPCC, 2014b: Summary for Policymakers. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–30.</span></li>
+<li><span id="fn:r243">Minx, J.C., W.F. Lamb, M.W. Callaghan, L. Bornmann, and S. Fuss, 2017: Fast growing research on negative emissions. ''Environmental Research Letters'' , '''12(3)''' , 035007, doi: [https://dx.doi.org/10.1088/1748-9326/aa5ee5 10.1088/1748-9326/aa5ee5] .</span></li>
+<li><span id="fn:r244">IPCC, 2014b: Summary for Policymakers. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–30.</span>
+-----
-</div>
+Leung, D.Y.C., G. Caramanna, and M.M. Maroto-Valer, 2014: An overview of current status of carbon dioxide capture and storage technologies. ''Renewable and Sustainable Energy Reviews'' , '''39''' , 426–443, doi: [https://dx.doi.org/10.1016/j.rser.2014.07.093 10.1016/j.rser.2014.07.093] .</li>
-<div>
+<li><span id="fn:r245">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r246">Rosenzweig, C., W. Solecki, P. Romeo-Lankao, M. Shagun, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: ''Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network'' . Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp.</span></li>
+<li><span id="fn:r247">IPCC, 2012b: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Geoengineering. [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, C. Field, V. Barros, T.F. Stocker, Q. Dahe, J. Minx, K. Mach, G.-K. Plattner, S. Schlömer, G. Hansen, and M. Mastrandrea (eds.)]. IPCC Working Group III Technical Support Unit, Potsdam Institute for Climate Impact Research, Potsdam, Germany, 99 pp.</span></li>
+<li><span id="fn:r248">The Royal Society, 2009: ''Geoengineering the climate: science, governance and uncertainty'' . RS Policy document 10/09, The Royal Society, London, UK, 82 pp.</span>
-<div id="section-1-4-3-1-legal-and-regulatory-instruments-block-1">
+-----
-<div>
+Smith, S.J. and P.J. Rasch, 2013: The long-term policy context for solar radiation management. ''Climatic Change'' , '''121(3)''' , 487–497, doi: [https://dx.doi.org/10.1007/s10584-012-0577-3 10.1007/s10584-012-0577-3] .
-<div>
+-----
-<div>
+Schäfer, S., M. Lawrence, H. Stelzer, W. Born, and S. Low (eds.), 2015: ''The European Transdisciplinary Assessment of Climate Engineering (EuTRACE): Removing Greenhouse Gases from the Atmosphere and Reflecting Sunlight away from Earth'' . The European Transdisciplinary Assessment of Climate Engineering (EuTRACE), 170 pp.</li>
+<li><span id="fn:r249">Kristjánsson, J.E., M. Helene, and S. Hauke, 2016: The hydrological cycle response to cirrus cloud thinning. ''Geophysical Research Letters'' , '''42(24)''' , 10,807–810,815, doi: [https://dx.doi.org/10.1002/2015gl066795 10.1002/2015gl066795] .</span></li>
+<li><span id="fn:r250">MacMartin, D.G., K.L. Ricke, and D.W. Keith, 2018: Solar geoengineering as part of an overall strategy for meeting the 1.5°C Paris target. ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2016.0454 10.1098/rsta.2016.0454] .</span></li>
+<li><span id="fn:r251">Schäfer, S., M. Lawrence, H. Stelzer, W. Born, and S. Low (eds.), 2015: ''The European Transdisciplinary Assessment of Climate Engineering (EuTRACE): Removing Greenhouse Gases from the Atmosphere and Reflecting Sunlight away from Earth'' . The European Transdisciplinary Assessment of Climate Engineering (EuTRACE), 170 pp.</span></li>
+<li><span id="fn:r252">Busby, J., 2016: After Paris: Good enough climate governance. ''Current History'' , '''15(777)''' , 3–9, http://www.currenthistory.com/busby_currenthistory.pdf .</span></li>
+<li><span id="fn:r253">von Stechow, C. et al., 2016: 2°C and the SDGs: United they stand, divided they fall? ''Environmental Research Letters'' , '''11(3)''' , 034022, doi: [https://dx.doi.org/10.1088/1748-9326/11/3/034022 10.1088/1748-9326/11/3/034022] .</span></li>
+<li><span id="fn:r254">Lövbrand, E., M. Hjerpe, and B.-O. Linnér, 2017: Making climate governance global: how UN climate summitry comes to matter in a complex climate regime. ''Environmental Politics'' , '''26(4)''' , 580–599, doi: [https://dx.doi.org/10.1080/09644016.2017.1319019 10.1080/09644016.2017.1319019] .</span></li>
+<li><span id="fn:r255">Whitmarsh, L., S. O’Neill, and I. Lorenzoni (eds.), 2011: ''Engaging the Public with Climate Change: Behaviour Change and Communication'' . Earthscan, London, UK and Washington, DC, USA, 289 pp.</span>
-Legal and regulatory instruments deal with all aspects of intervention by public policy organisations to correct market failures, expand market reach, or intervene in socially relevant areas with inexistent markets. Such instruments can include legislation to limit the impacts of intensive land management, for example, protecting areas that are susceptible to nitrate pollution or soil erosion. Such instruments can also set standards or threshold values, for example, mandated water quality limits, organic production standards, or geographically defined regional food products. Legal and regulatory instruments may also define liability rules, for example, where environmental standards are not met, as well as establishing long-term agreements for land resource protection with land owners and land users.
+-----
+Corner, A. and J. Clarke, 2017: ''Talking Climate – From Research to Practice in Public Engagement'' . Palgrave Macmillan, Oxford, UK, 146 pp., doi: [https://dx.doi.org/10.1007/978-3-319-46744-3 10.1007/978-3-319-46744-3] .</li>
+<li><span id="fn:r256">Mimura, N. et al., 2014: Adaptation planning and implementation. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 869–898.</span></li>
+<li><span id="fn:r257">Leal Filho, W. et al., 2018: Implementing climate change research at universities: Barriers, potential and actions. ''Journal of Cleaner Production'' , '''170''' , 269–277, doi: [https://dx.doi.org/10.1016/j.jclepro.2017.09.105 10.1016/j.jclepro.2017.09.105] .</span></li>
+<li><span id="fn:r258">IPCC, 2017: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Mitigation, Sustainability and Climate Stabilization Scenarios. [Shukla, P.R., J. Skea, R. Diemen, E. Huntley, M. Pathak, J. Portugal-Pereira, J. Scull, and R. Slade (eds.)]. IPCC Working Group III Technical Support Unit, Imperial College London, London, UK, 44 pp.</span></li>
+<li><span id="fn:r259">Sovacool, B.K., B.-O. Linnér, and M.E. Goodsite, 2015: The political economy of climate adaptation. ''Nature Climate Change'' , '''5(7)''' , 616–618, doi: [https://dx.doi.org/10.1038/nclimate2665 10.1038/nclimate2665] .</span></li>
+<li><span id="fn:r260">Jacobson, M.Z. et al., 2015: 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States. ''Energy &amp; Environmental Science'' , '''8(7)''' , 2093–2117, doi: [https://dx.doi.org/10.1039/c5ee01283j 10.1039/c5ee01283j] .</span>
-</div>
+-----
-</div>
+Loftus, P.J., A.M. Cohen, J.C.S. Long, and J.D. Jenkins, 2015: A critical review of global decarbonization scenarios: What do they tell us about feasibility? ''Wiley Interdisciplinary Reviews: Climate Change'' , '''6(1)''' , 93–112, doi: [https://dx.doi.org/10.1002/wcc.324 10.1002/wcc.324] .</li>
+<li><span id="fn:r261">Pelling, M., 2011: ''Adaptation to Climate Change: From Resilience to Transformation'' . Routledge, Abingdon, Oxon, UK and New York, NY, USA, 224 pp.</span>
-</div>
+-----
-</div>
+O’Brien, K. et al., 2012: Toward a sustainable and resilient future. In: ''Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation'' [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 437–486.
-</div>
+-----
-</div>
+O’Brien, K. and E. Selboe, 2015: Social transformation. In: ''The Adaptive Challenge of Climate Change'' [O’Brien, K. and E. Selboe (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY , USA, pp. 311–324, doi: [https://dx.doi.org/10.1017/cbo9781139149389.018 10.1017/cbo9781139149389.018] .
-</div>
+-----
-<div id="section-1-4-3-2-economic-and-financial-instruments">
-<div>
+Pelling, M., K. O’Brien, and D. Matyas, 2015: Adaptation and transformation. ''Climatic Change'' , '''133(1)''' , 113–127, doi: [https://dx.doi.org/10.1007/s10584-014-1303-0 10.1007/s10584-014-1303-0] .</li>
+<li><span id="fn:r262">Tschakert, P., B. van Oort, A.L. St. Clair, and A. LaMadrid, 2013: Inequality and transformation analyses: a complementary lens for addressing vulnerability to climate change. ''Climate and Development'' , '''5(4)''' , 340–350, doi: [https://dx.doi.org/10.1080/17565529.2013.828583 10.1080/17565529.2013.828583] .</span>
-<div>
+-----
-<span id="economic-and-financial-instruments"></span>
+Rogelj, J. et al., 2015: Energy system transformations for limiting end-of-century warming to below 1.5°C. ''Nature Climate Change'' , '''5(6)''' , 519–527, doi: [https://dx.doi.org/10.1038/nclimate2572 10.1038/nclimate2572] .
-== 1.4.3.2 Economic and financial instruments ==
+-----
-</div>
+Patterson, J. et al., 2017: Exploring the governance and politics of transformations towards sustainability. ''Environmental Innovation and Societal Transitions'' , '''24''' , 1–16, doi: [https://dx.doi.org/10.1016/j.eist.2016.09.001 10.1016/j.eist.2016.09.001] .</li>
-<div>
+<li><span id="fn:r263">Solecki, W., M. Pelling, and M. Garschagen, 2017: Transitions between risk management regimes in cities. ''Ecology and Society'' , '''22(2)''' , 38, doi: [https://dx.doi.org/10.5751/es-09102-220238 10.5751/es-09102-220238] .</span>
-<div id="section-1-4-3-2-economic-and-financial-instruments-block-1">
+-----
-<div>
+Rosenzweig, C., W. Solecki, P. Romeo-Lankao, M. Shagun, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: ''Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network'' . Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp.</li>
+<li><span id="fn:r264">IPCC, 2014d: Summary for Policymakers. In: ''Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, pp. 2–34.</span>
-<div>
+-----
-<div>
+Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</li>
+<li><span id="fn:r265">IPCC, 2014d: Summary for Policymakers. In: ''Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Core Writing Team, R.K. Pachauri, and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, pp. 2–34.</span>
-Economic (such as taxes, subsidies) and financial (weather-index insurance) instruments deal with the many ways in which public policy organisations can intervene in markets. A number of instruments are available to support climate mitigation actions including public provision, environmental regulations, creating property rights and markets (Sterner 2003 <sup>[[#fn:r896|896]]</sup> ). Market-based policies such as carbon taxes, fuel taxes, cap and trade systems or green payments have been promoted (mostly in industrial economies) to encourage markets and businesses to contribute to climate mitigation, but their effectiveness to date has not always matched expectations (Grolleau et al. 2016 <sup>[[#fn:r897|897]]</sup> ) (Section 7.4.4). Market-based instruments in ecosystem services generate both positive (incentives for conservation), but also negative environmental impacts, and also push food prices up or increase price instability (Gómez-Baggethun and Muradian 2015 <sup>[[#fn:r898|898]]</sup> ; Farley and Voinov 2016 <sup>[[#fn:r899|899]]</sup> ). Footprint labels can be an effective means of shifting consumer behaviour. However, private labels focusing on a single metric (e.g., carbon) may give misleading signals if they target a portion of the life cycle (e.g., transport) (Appleton 2009 <sup>[[#fn:r900|900]]</sup> ) or ignore other ecological indicators (water, nutrients, biodiversity) (van Noordwijk and Brussaard 2014 <sup>[[#fn:r901|901]]</sup> ).
+-----
-<div>
+Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</li>
+<li><span id="fn:r266">Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</span></li>
+<li><span id="fn:r267">von Stechow, C. et al., 2016: 2°C and the SDGs: United they stand, divided they fall? ''Environmental Research Letters'' , '''11(3)''' , 034022, doi: [https://dx.doi.org/10.1088/1748-9326/11/3/034022 10.1088/1748-9326/11/3/034022] .</span></li>
+<li><span id="fn:r268">von Stechow, C. et al., 2016: 2°C and the SDGs: United they stand, divided they fall? ''Environmental Research Letters'' , '''11(3)''' , 034022, doi: [https://dx.doi.org/10.1088/1748-9326/11/3/034022 10.1088/1748-9326/11/3/034022] .</span></li>
+<li><span id="fn:r269">Kainuma, M., R. Pandey, T. Masui, and S. Nishioka, 2017: Methodologies for leapfrogging to low carbon and sustainable development in Asia. ''Journal of Renewable and Sustainable Energy'' , '''9(2)''' , 021406, doi: [https://dx.doi.org/10.1063/1.4978469 10.1063/1.4978469] .</span></li>
+<li><span id="fn:r270">Olsson, L. et al., 2014: Livelihoods and poverty. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 798–832.</span></li>
+<li><span id="fn:r271">Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.</span></li>
+<li><span id="fn:r272">WCED, 1987: ''Our Common Future'' . World Commission on Environment and Development (WCED), Geneva, Switzerland, 383 pp., doi: [https://dx.doi.org/10.2307/2621529 10.2307/2621529] .</span></li>
+<li><span id="fn:r273">Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.</span></li>
+<li><span id="fn:r274">von Stechow, C. et al., 2015: Integrating Global Climate Change Mitigation Goals with Other Sustainability Objectives: A Synthesis. ''Annual Review of Environment and Resources'' , '''40(1)''' , 363–394, doi: [https://dx.doi.org/10.1146/annurev-environ-021113-095626 10.1146/annurev-environ-021113-095626] .</span>
-<div>
+-----
-<div>
+Wright, H., S. Huq, and J. Reeves, 2015: ''Impact of climate change on least developed countries: are the SDGs possible?'' IIED Briefing May 2015, International Institute for Environment and Development (IIED), London, UK, 4 pp.
-Effective and durable, market-led responses for climate mitigation depend on business models that internalise the cost of emissions into economic calculations. Such ‘business transformation’ would itself require integrated policies and strategies that aim to account for emissions in economic activities (Biagini and Miller 2013 <sup>[[#fn:r902|902]]</sup> ; Weitzman 2014 <sup>[[#fn:r903|903]]</sup> ; Eidelwein et al. 2018 <sup>[[#fn:r904|904]]</sup> ). International initiatives such as REDD+ and agricultural commodity roundtables (beef, soybeans, palm oil, sugar) are expanding the scope of private sector participation in climate mitigation (Nepstad et al. 2013 <sup>[[#fn:r905|905]]</sup> ), but their impacts have not always been effective (Denis et al. 2014 <sup>[[#fn:r906|906]]</sup> ). Payments for environmental services (PES) defined as “voluntary transactions between service users and service providers that are conditional on agreed rules of natural resource management for generating offsite services” (Wunder 2015 <sup>[[#fn:r907|907]]</sup> ) have not been widely adopted and have not yet been demonstrated to deliver as effectively as originally hoped (Börner et al. 2017 <sup>[[#fn:r908|908]]</sup> ) (Sections 7.4 and 7.5). PES in forestry were shown to be effective only when coupled with appropriate regulatory measures (Alix-Garcia and Wolff 2014 <sup>[[#fn:r909|909]]</sup> ). Better designed and expanded PES schemes would encourage integrated soil–water–nutrient management packages (Stavi et al. 2016 <sup>[[#fn:r910|910]]</sup> ), services for pollinator protection (Nicole 2015 <sup>[[#fn:r911|911]]</sup> ), water use governance under scarcity, and engage both public and private actors (Loch et al. 2013 <sup>[[#fn:r912|912]]</sup> ). Effective PES also requires better economic metrics to account for human- directed losses in terrestrial ecosystems and to food potential, and to address market failures or externalities unaccounted for in market valuation of ecosystem services.
+-----
-<div>
+Epstein, A.H. and S.L.H. Theuer, 2017: Sustainable development and climate action: thoughts on an integrated approach to SDG and climate policy implementation. In: ''Papers from Interconnections 2017'' . Interconnections 2017, pp. 50.
-<div>
+-----
-<div>
+Hammill, A. and H. Price-Kelly, 2017: ''Using NDCs , NAPs and the SDGs to Advance Climate-Resilient Development'' . NDC Expert perspectives for the NDC Partnership, NDC Partnership, Washington, DC, USA and Bonn, Germany, 10 pp.
-Resilient strategies for climate adaptation can rely on the construction of markets through social networks as in the case of livestock systems (Denis et al. 2014 <sup>[[#fn:r913|913]]</sup> ) or when market signals encourage adaptation through land markets or supply chain incentives for sustainable land management practices (Anderson et al. 2018 <sup>[[#fn:r914|914]]</sup> ). Adequate policy (through regulations, investments in research and development or support to social capabilities) can support private initiatives for effective solutions to restore degraded lands (Reed and Stringer 2015 <sup>[[#fn:r915|915]]</sup> ), or mitigate against risk and to avoid shifting risks to the public (Biagini and Miller 2013 <sup>[[#fn:r916|916]]</sup> ). Governments, private business, and community groups could also partner to develop sustainable production codes (Chartres and Noble 2015 <sup>[[#fn:r917|917]]</sup> ), and in co-managing land-based resources (Baker and Chapin 2018 <sup>[[#fn:r918|918]]</sup> ), while public-private partnerships can be effective mechanisms in deploying infrastructure to cope with climatic events (floods) and for climate-indexed insurance (Kunreuther 2015 <sup>[[#fn:r919|919]]</sup> ). Private initiatives that depend on trade for climate adaptation and mitigation require reliable trading systems that do not impede climate mitigation objectives (Elbehri et al. 2015 <sup>[[#fn:r920|920]]</sup> ; Mathews 2017 <sup>[[#fn:r921|921]]</sup> ).
+-----
+Kelman, I., 2017: Linking disaster risk reduction, climate change, and the sustainable development goals. ''Disaster Prevention and Management: An International Journal'' , '''26(3)''' , 254–258, doi: [https://dx.doi.org/10.1108/dpm-02-2017-0043 10.1108/dpm-02-2017-0043] .
-</div>
+-----
-</div>
+Lofts, K., S. Shamin, T.S. Zaman, and R. Kibugi, 2017: Brief on Sustainable Development Goal 13 on Taking Action on Climate Change and Its Impacts: Contributions of International Law, Policy and Governance,. ''McGill Journal of Sustainable Development Law'' , '''11(1)''' , 183–192, doi: [https://dx.doi.org/10.3868/s050-004-015-0003-8 10.3868/s050-004-015-0003-8] .
-</div>
+-----
-</div>
+Maupin, A., 2017: The SDG13 to combat climate change: an opportunity for Africa to become a trailblazer? ''African Geographical Review'' , '''36(2)''' , 131–145, doi: [https://dx.doi.org/10.1080/19376812.2016.1171156 10.1080/19376812.2016.1171156] .
-</div>
+-----
-</div>
+Gomez-Echeverri, L., 2018: Climate and development: enhancing impact through stronger linkages in the implementation of the Paris Agreement and the Sustainable Development Goals (SDGs). ''Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences'' , '''376(2119)''' , doi: [https://dx.doi.org/10.1098/rsta.2016.0444 10.1098/rsta.2016.0444] .</li>
+<li><span id="fn:r275">Kanie, N. and F. Biermann (eds.), 2017: ''Governing through Goals: Sustainable Development Goals as Governance Innovation'' . MIT Press, Cambridge, MA, USA, 352 pp.</span></li>
+<li><span id="fn:r276">UN, 2015a: ''The Millennium Development Goals Report 2015'' . United Nations (UN), New York, NY, USA, 75 pp.</span></li>
+<li><span id="fn:r277">Alkire, S., C. Jindra, G. Robles Aguilar, S. Seth, and A. Vaz, 2015: ''Global Multidimensional Poverty Index 2015'' . Briefing 31, Oxford Poverty &amp; Human Development Initiative, University of Oxford, Oxford, UK, 8 pp.</span></li>
+<li><span id="fn:r278">Horton, R., 2014: Why the sustainable development goals will fail. ''The Lancet'' , '''383(9936)''' , 2196, doi: [https://dx.doi.org/10.1016/s0140-6736(14)61046-1 10.1016/s0140-6736(14)61046-1] .</span>
-</div>
+-----
-</div>
+Death, C. and C. Gabay, 2015: Doing biopolitics differently? Radical potential in the post-2015 MDG and SDG debates. ''Globalizations'' , '''12(4)''' , 597–612, doi: [https://dx.doi.org/10.1080/14747731.2015.1033172 10.1080/14747731.2015.1033172] .
-</div>
+-----
-</div>
+Biermann, F., N. Kanie, and R.E. Kim, 2017: Global governance by goal-setting: the novel approach of the UN Sustainable Development Goals. ''Current Opinion in Environmental Sustainability'' , '''26–27''' , 26–31, doi: [https://dx.doi.org/10.1016/j.cosust.2017.01.010 10.1016/j.cosust.2017.01.010] .
-</div>
+-----
-</div>
+Weber, H., 2017: Politics of ‘Leaving No One Behind’: Contesting the 2030 Sustainable Development Goals Agenda. ''Globalizations'' , '''14(3)''' , 399–414, doi: [https://dx.doi.org/10.1080/14747731.2016.1275404 10.1080/14747731.2016.1275404] .
-</div>
+-----
-<div id="section-1-4-3-3-rights-based-instruments-and-customary-norms">
-<div>
+Winkler, I.T. and M.L. Satterthwaite, 2017: Leaving no one behind? Persistent inequalities in the SDGs. ''The International Journal of Human Rights'' , '''21(8)''' , 1073–1097, doi: [https://dx.doi.org/10.1080/13642987.2017.1348702 10.1080/13642987.2017.1348702] .</li>
+<li><span id="fn:r279">Denton, F. et al., 2014: Climate-Resilient Pathways: Adaptation, Mitigation, and Sustainable Development. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1101–1131.</span></li>
+<li><span id="fn:r280">Delanty, G. and A. Mota, 2017: Governing the Anthropocene. ''European Journal of Social Theory'' , '''20(1)''' , 9–38, doi: [https://dx.doi.org/10.1177/1368431016668535 10.1177/1368431016668535] .</span></li>
+<li><span id="fn:r281">IPCC, 2013a: ''Principles Governing IPCC Work'' . Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, 2 pp.</span></li>
+<li><span id="fn:r282">Somanathan, E. et al., 2014: National and Sub-national Policies and Institutions. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1141–1205.</span></li>
+<li><span id="fn:r283">Czerniewicz, L., S. Goodier, and R. Morrell, 2017: Southern knowledge online? Climate change research discoverability and communication practices. ''Information, Communication &amp; Society'' , '''20(3)''' , 386–405, doi: [https://dx.doi.org/10.1080/1369118x.2016.1168473 10.1080/1369118x.2016.1168473] .</span></li>
+<li><span id="fn:r284">Knutti, R. and J. Sedláček, 2012: Robustness and uncertainties in the new CMIP5 climate model projections. ''Nature Climate Change'' , '''3(4)''' , 369–373, doi: [https://dx.doi.org/10.1038/nclimate1716 10.1038/nclimate1716] .</span>
-<div>
+-----
-<span id="rights-based-instruments-and-customary-norms"></span>
+Mueller, B. and S.I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. ''Geophysical Research Letters'' , '''41(1)''' , 128–134, doi: [https://dx.doi.org/10.1002/2013gl058055 10.1002/2013gl058055] .</li>
-== 1.4.3.3 Rights-based instruments and customary norms ==
+<li><span id="fn:r285">Giorgi, F. and W.J. Gutowski, 2015: Regional Dynamical Downscaling and the CORDEX Initiative. ''Annual Review of Environment and Resources'' , '''40(1)''' , 467–490, doi: [https://dx.doi.org/10.1146/annurev-environ-102014-021217 10.1146/annurev-environ-102014-021217] .</span></li>
+<li><span id="fn:r286">Vautard, R. et al., 2014: The European climate under a 2°C global warming. ''Environmental Research Letters'' , '''9(3)''' , 034006, doi: [https://dx.doi.org/10.1088/1748-9326/9/3/034006 10.1088/1748-9326/9/3/034006] .</span>
+-----
-</div>
+Jacob, D. and S. Solman, 2017: IMPACT2C – An introduction. ''Climate Services'' , '''7''' , 1–2, doi: [https://dx.doi.org/10.1016/j.cliser.2017.07.006 10.1016/j.cliser.2017.07.006] .</li>
-<div>
+<li><span id="fn:r287">Mitchell, D. et al., 2016: Realizing the impacts of a 1.5°C warmer world. ''Nature Climate Change'' , '''6(8)''' , 735–737, doi: [https://dx.doi.org/10.1038/nclimate3055 10.1038/nclimate3055] .</span></li>
+<li><span id="fn:r288">Hegerl, G.C. et al., 2007: Understanding and Attributing Climate Change. In: ''Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change'' [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 663–745.</span>
-<div id="section-1-4-3-3-rights-based-instruments-and-customary-norms-block-1">
+-----
-<div>
+Seneviratne, S.I. et al., 2012: Changes in climate extremes and their impacts on the natural physical environment. In: ''Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation'' [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230.
-<div>
+-----
-<div>
+Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.</li>
+<li><span id="fn:r289">Stone, D. et al., 2013: The challenge to detect and attribute effects of climate change on human and natural systems. ''Climatic Change'' , '''121(2)''' , 381–395, doi: [https://dx.doi.org/10.1007/s10584-013-0873-6 10.1007/s10584-013-0873-6] .</span>
-Rights-based instruments and customary norms deal with the equitable and fair management of land resources for all people (IPBES 2018a <sup>[[#fn:r922|922]]</sup> ). These instruments emphasise the rights in particular of indigenous peoples and local communities, including for example, recognition of the rights embedded in the access to, and use of, common land. Common land includes situations without legal ownership (e.g., hunter-gathering communities in South America or Africa, and bushmeat), where the legal ownership is distinct from usage rights (Mediterranean transhumance grazing systems), or mixed ownership-common grazing systems (e.g., crofting in Scotland). A lack of formal (legal) ownership has often led to the loss of access rights to land, where these rights were also not formally enshrined in law, which especially effects indigenous communities, for example, deforestation in the Amazon basin. Overcoming the constraints associated with common-pool resources (forestry, fisheries, water) are often of economic and institutional nature (Hinkel et al. 2014 <sup>[[#fn:r923|923]]</sup> ) and require tackling the absence or poor functioning of institutions and the structural constraints that they engender through access and control levers using policies and markets and other mechanisms (Schut et al. 2016 <sup>[[#fn:r924|924]]</sup> ). Other examples of rights-based instruments include the protection of heritage sites, sacred sites and peace parks (IPBES 2018a <sup>[[#fn:r925|925]]</sup> ). Rights-based instruments and customary norms are consistent with the aims of international and national human rights, and the critical issue of liability in the climate change problem.
+-----
+Hansen, G. and W. Cramer, 2015: Global distribution of observed climate change impacts. ''Nature Climate Change'' , '''5(3)''' , 182–185, doi: [https://dx.doi.org/10.1038/nclimate2529 10.1038/nclimate2529] .
-</div>
+-----
-</div>
+Hansen, G., D. Stone, M. Auffhammer, C. Huggel, and W. Cramer, 2016: Linking local impacts to changes in climate: a guide to attribution. ''Regional Environmental Change'' , '''16(2)''' , 527–541, doi: [https://dx.doi.org/10.1007/s10113-015-0760-y 10.1007/s10113-015-0760-y] .</li>
+<li><span id="fn:r290">Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: ''Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 426–488.</span></li>
+<li><span id="fn:r291">Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. ''Nature Climate Change'' , '''7(7)''' , 460–462, doi: [https://dx.doi.org/10.1038/nclimate3320 10.1038/nclimate3320] .</span></li>
+<li><span id="fn:r292">Brinkman, T.J. et al., 2016: Arctic communities perceive climate impacts on access as a critical challenge to availability of subsistence resources. ''Climatic Change'' , '''139(3–4)''' , 413–427, doi: [https://dx.doi.org/10.1007/s10584-016-1819-6 10.1007/s10584-016-1819-6] .</span>
-</div>
+-----
-</div>
+Kabir, M.I. et al., 2016: Knowledge and perception about climate change and human health: findings from a baseline survey among vulnerable communities in Bangladesh. ''BMC Public Health'' , '''16(1)''' , 266, doi: [https://dx.doi.org/10.1186/s12889-016-2930-3 10.1186/s12889-016-2930-3] .</li>
+<li><span id="fn:r293">Tschakert, P. et al., 2017: Climate change and loss, as if people mattered: values, places, and experiences. ''Wiley Interdisciplinary Reviews: Climate Change'' , '''8(5)''' , e476, doi: [https://dx.doi.org/10.1002/wcc.476 10.1002/wcc.476] .</span></li>
+<li><span id="fn:r294">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span>
-</div>
+-----
-</div>
+IPCC, 2014b: Summary for Policymakers. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–30.</li>
+<li><span id="fn:r295">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span></li>
+<li><span id="fn:r296">IPCC, 2014a: Summary for Policymakers. In: ''Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–32.</span>
-</div>
+-----
-<div id="section-1-4-3-4-social-and-cultural-norms">
-<div>
+Dietz, S., B. Groom, and W.A. Pizer, 2016: Weighing the Costs and Benefits of Climate Change to Our Children. ''The Future of Children'' , '''26(1)''' , 133–155, http://www.jstor.org/stable/43755234 .</li>
+<li><span id="fn:r297">Mastrandrea, M.D. et al., 2011: The IPCC AR5 guidance note on consistent treatment of uncertainties: a common approach across the working groups. ''Climatic Change'' , '''108(4)''' , 675–691, doi: [https://dx.doi.org/10.1007/s10584-011-0178-6 10.1007/s10584-011-0178-6] .</span></li>
+<li><span id="fn:r298">Otto, F.E.L., D.J. Frame, A. Otto, and M.R. Allen, 2015: Embracing uncertainty in climate change policy. ''Nature Climate Change'' , '''5''' , 1–5, doi: [https://dx.doi.org/10.1038/nclimate2716 10.1038/nclimate2716] .</span></li>
+<li><span id="fn:r299">Knutti, R., J. Rogelj, J. Sedláček, and E.M. Fischer, 2015: A scientific critique of the two-degree climate change target. ''Nature Geoscience'' , '''9(1)''' , 13–18, doi: [https://dx.doi.org/10.1038/ngeo2595 10.1038/ngeo2595] .</span></li>
+<li><span id="fn:r300">Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A reconstruction of regional and global temperature for the past 11,300 years. ''Science'' , '''339(6124)''' , 1198–201, doi: [https://dx.doi.org/10.1126/science.1228026 10.1126/science.1228026] .</span>
-<div>
+-----
-<span id="social-and-cultural-norms"></span>
+NOAA, 2016: State of the Climate: Global Climate Report for Annual 2015. National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI). Retrieved from: [https://www.ncdc.noaa.gov/sotc/global/201513 http://www.ncdc.noaa.gov/sotc/global/201513] .</li>
-== 1.4.3.4 Social and cultural norms ==
+<li><span id="fn:r301">Summerhayes, C.P., 2015: ''Earth’s Climate Evolution'' . John Wiley &amp; Sons Ltd, Chichester, UK, 394 pp., doi: [https://dx.doi.org/10.1002/9781118897362 10.1002/9781118897362] .</span>
+-----
-</div>
+Foster, G.L., D.L. Royer, and D.J. Lunt, 2017: Future climate forcing potentially without precedent in the last 420 million years. ''Nature Communications'' , '''8''' , 14845, doi: [https://dx.doi.org/10.1038/ncomms14845 10.1038/ncomms14845] .</li></ol>
-<div>
-<div id="section-1-4-3-4-social-and-cultural-norms-block-1">
-<div>
-<div>
-<div>
-Social and cultural instruments are concerned with the communication of knowledge about conscious consumption patterns and resource-effective ways of life through awareness raising, education and communication of the quality and the provenance of land-based products. Examples of the latter include consumption choices aided by ecolabelling (Section 1.4.3.2) and certification. Cultural indicators (such as social capital, cooperation, gender equity, women’s knowledge, socio-ecological mobility) contribute to the resilience of social-ecological systems (Sterling et al. 2017 <sup>[[#fn:r926|926]]</sup> ). Indigenous communities (such as the Inuit and Tsleil Waututh Nation in Canada) that continue to maintain traditional foods exhibit greater dietary quality and adequacy (Sheehy et al. 2015 <sup>[[#fn:r927|927]]</sup> ). Social and cultural instruments also include approaches to self-regulation and voluntary agreements, especially with respect to environmental management and land resource use. This is becoming especially irrelevant for the increasingly important domain of corporate social responsibility (Halkos and Skouloudis 2016 <sup>[[#fn:r928|928]]</sup> ).
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div>
-<div>
-<span id="the-interdisciplinary-nature-of-the-srccl"></span>
-== 1.5 The interdisciplinary nature of the SRCCL ==
-</div>
-<div>
-<div id="article-1-5-the-interdisciplinary-nature-of-the-srccl-block-1">
-<div>
-<div>
-<div>
-Assessing the land system in view of the multiple challenges that are covered by the SRCCL requires a broad, inter-disciplinary perspective. Methods, core concepts and definitions are used differently in different sectors, geographic regions, and across academic communities addressing land systems, and these concepts and approaches to research are also undergoing a change in their interpretation through time. These differences reflect varying perspectives, in nuances or emphasis, on land as components of the climate and socio-economic systems. Because of its inter-disciplinary nature, the SRCCL can take advantage of these varying perspectives and the diverse methods that accompany them. That way, the report aims to support decision- makers across sectors and world regions in the interpretation of its main findings and support the implementation of solutions.
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-<div class="section">
-<div>
-<div>
-<span id="section-2"></span>
-==  ==
-<span id="footnotes"></span>
-=== Footnotes ===
-</div>
-<div>
-<div>
-# <span id="fn:1">Different communities have a different understanding of the concept of pathways (IPCC 2018). Here, we refer to pathways as a description of the time-dependent actions required to move from today’s world to a set of future visions (IPCC 2018). However, the term pathways is commonly used in the climate change literature as a synonym for projections or trajectories (e.g., shared socio-economic pathways).</span>
-# <span id="fn:2">Uncertainty here is defined as the coefficient of variation CV. In the case of micrometeorological fluxes they refer to random errors and CV of daily average.</span>
-# <span id="fn:3">&gt;100 for fluxes less than 5 gN <sub>2</sub> O-N ha <sup>–1</sup> d <sup>–1</sup> .</span>
-</div>
-</div>
-<div>
-<span id="section-3"></span>
-==  ==
-<span id="references"></span>
-=== References ===
-</div>
-<div>
-<div>
-<ol>
-<li><span id="fn:r1">Hoekstra, A.Y. and T.O. Wiedmann, 2014: Humanity’s unsustainable environmental footprint. Science, 344, 1114–1117, doi:10.1126/science.1248365.</span></li>
-<li><span id="fn:r2">Mace, G.M., K. Norris, and A.H. Fitter, 2012: Biodiversity and ecosystem services: A multilayered relationship. Trends Ecol. Evol., 27, 19–25, doi:10.1016/j.tree.2011.08.006.</span></li>
-<li><span id="fn:r3">Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50, doi:10.1038/nature14324.</span></li>
-<li><span id="fn:r4">Runting, R.K. et al., 2017: Incorporating climate change into ecosystem service assessments and decisions: a review. Glob. Chang. Biol., 23, 28–41, doi:10.1111/gcb.13457.</span></li>
-<li><span id="fn:r5">Isbell, F. et al., 2017: Linking the influence and dependence of people on biodiversity across scales. Nature, 546, 65–72, doi:10.1038/nature22899.</span></li>
-<li><span id="fn:r6">Costanza, R. et al., 2014: Changes in the global value of ecosystem services. Glob. Environ. Chang., 26, 152–158, doi:10.1016/j.gloenvcha.2014.04.002.</span></li>
-<li><span id="fn:r7">IMF, 2018: World Economic Outlook. World Economic Outlook Database, International Monetary Fund, Washington D.C., USA.</span></li>
-<li><span id="fn:r8">Hernández-Morcillo, M., T. Plieninger and C. Bieling, 2013: An empirical review of cultural ecosystem service indicators. Ecol. Indic., 29, 434–444, doi:10.1016/j.ecolind.2013.01.013.</span></li>
-<li><span id="fn:r9">Fish, R., A. Church, and M. Winter, 2016: Conceptualising cultural ecosystem services: A novel framework for research and critical engagement. Ecosyst. Serv., 21, 208–217, doi:10.1016/j.ecoser.2016.09.002.</span></li>
-<li><span id="fn:r10">Rook, G.A., 2013: Regulation of the immune system by biodiversity from the natural environment: An ecosystem service essential to health. Proc. Natl. Acad. Sci., 110, 18360–18367, doi:10.1073/pnas.1313731110.</span></li>
-<li><span id="fn:r11">Terraube, J., A. Fernandez-Llamazares, and M. Cabeza, 2017: The role of protected areas in supporting human health: A call to broaden the assessment of conservation outcomes. Curr. Opin. Environ. Sustain., 25, 50–58, doi.org/10.1016/j.cosust.2017.08.005.</span></li>
-<li><span id="fn:r12">Fischer, M. et al., 2018: IPBES: Summary for Policymakers of the Regional Assessment Report on Biodiversity and Ecosystem Services for Europe and Central Asia of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Bonn, Germany, 48 pp.</span></li>
-<li><span id="fn:r13">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r14">Kongsager, R., B. Locatelli, and F. Chazarin, 2016: Addressing climate change mitigation and adaptation together: A global assessment of agriculture and forestry projects. Environ. Manage., 57, 271–282, doi:10.1007/s00267-015-0605-y.</span></li>
-<li><span id="fn:r15">FAO, IFAD, UNICEF, WFP and WHO, 2018: The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r16">Schaeffer, M. et al.., 2015: Mid – and long-term climate projections for fragmented and delayed-action scenarios. Technol. Forecast. Soc. Change, 90, 257–268, doi:10.1016/j.techfore.2013.09.013.</span></li>
-<li><span id="fn:r17">Bertram, C. et al., 2015: Carbon lock-in through capital stock inertia associated with weak near-term climate policies. Technol. Forecast. Soc. Change, 90, 62–72, doi:10.1016/j.techfore.2013.10.001.</span></li>
-<li><span id="fn:r18">Riahi, K. et al., 2015: Locked into Copenhagen pledges – implications of short-term emission targets for the cost and feasibility of long-term climate goals. Technol. Forecast. Soc. Change, 90, 8–23, doi:10.1016/j.techfore.2013.09.016.</span></li>
-<li><span id="fn:r19">Millar, R.J. et al., 2017: Emission budgets and pathways consistent with limiting warming to 1.5°C. Nat. Geosci., 10, 741–747, doi:10.1038/NGEO3031.</span></li>
-<li><span id="fn:r20">Rogelj, J.D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian and M.V.Vilariño, 2018a: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 93–174.</span></li>
-<li><span id="fn:r21">Wynes, S. and K.A. Nicholas, 2017: The climate mitigation gap: Education and government recommendations miss the most effective individual actions. Environ. Res. Lett., 12, 74024, doi:10.1088/1748-9326/aa7541.</span></li>
-<li><span id="fn:r22">Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018.</span></li>
-<li><span id="fn:r23">Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E.A. Elsiddig, H. Haberl, R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N.H. Ravindranath, C.W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</span></li>
-<li><span id="fn:r24">Tubiello, F.N. et al., 2015: The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012. Glob. Chang. Biol., 21, 2655–2660, doi:10.1111/gcb.12865.</span></li>
-<li><span id="fn:r25">Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data Discuss., 1–3, doi:10.5194/essd-2018-120.</span></li>
-<li><span id="fn:r26">Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.</span></li>
-<li><span id="fn:r27">Rogelj, J.D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian and M.V.Vilariño, 2018a: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 93–174.</span></li>
-<li><span id="fn:r28">Rogelj, J. et al., 2018b: Scenarios towards limiting global mean temperature increase below 1.5 degrees C. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.</span></li>
-<li><span id="fn:r29">Grassi, G., et al., 2017: The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang., 7, 220–226, doi:10.1038/nclimate3227.</span></li>
-<li><span id="fn:r30">Forsell, N., O. Turkovska, M. Gusti, M. Obersteiner, M. Elzen and P. Havlík, 2016: Assessing the INDCs’ land use, land use change and forest emission projections. Carbon Balance Manage., 11, 1–17, doi:10.1186/s13021-016-0068-3.</span></li>
-<li><span id="fn:r31">Meyfroidt, P., 2018: Trade-offs between environment and livelihoods: Bridging the global land use and food security discussions. Glob. Food Sec., 16, 9–16, doi:10.1016/J.GFS.2017.08.001.</span></li>
-<li><span id="fn:r32">Bonsch, M. et al., 2016: Trade-offs between land and water requirements for large-scale bioenergy production. GCB Bioenergy, 8, 11–24, doi:10.1111/gcbb.12226.</span></li>
-<li><span id="fn:r33">Crist, E., C. Mora, and R. Engelman, 2017: The interaction of human population, food production, and biodiversity protection. Science, 356, 260–264, doi:10.1126/science.aal2011.</span></li>
-<li><span id="fn:r34">Humpenoder, F. et al., 2014: Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environ. Res. Lett., 9, 064029, doi:10.1088/1748-9326/9/6/064029.</span></li>
-<li><span id="fn:r35">Harvey, M. and S. Pilgrim, 2011: The new competition for land: Food, energy and climate change. Food Policy, 36, S40–S51, doi:10.1016/J.FOODPOL.2010.11.009.</span></li>
-<li><span id="fn:r36">Mouratiadou, I. et al., 2016: The impact of climate change mitigation on water demand for energy and food: An integrated analysis based on the Shared Socioeconomic Pathways. Environ. Sci. Policy, 64, 48–58, doi:10.1016/J.ENVSCI.2016.06.007.</span></li>
-<li><span id="fn:r37">Zhang, X. et al., 2015: Managing nitrogen for sustainable development. Nature, 528, 51–59, doi:10.1038/nature15743.</span></li>
-<li><span id="fn:r38">Sanz-Sanchez, M.-J. et al., 2017: Sustainable Land Management Contribution to Successful Land-based Climate Change Adaptation and Mitigation. A Report of the Science-Policy Interface, United Nations Convention to Combat Desertification (UNCCD), Bonn, Germany, 170 pp.</span></li>
-<li><span id="fn:r39">Pereira, H.M. et al., 2010: Scenarios for global biodiversity in the 21st century. Science, 330, 1496–1501, doi:10.1126/science.1196624.</span></li>
-<li><span id="fn:r40">Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114.</span></li>
-<li><span id="fn:r41">Rogelj, J.D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian and M.V.Vilariño, 2018a: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 93–174.</span></li>
-<li><span id="fn:r42">IPCC, 2000: Land Use, Land-Use Change and Forestry: A special report of the Intergovernmental Panel on Climate Change [Watson, R.T., I.R. Noble, B. Bolin, N.H. Ravindranath, D.J. Verardo and D.J. Dokken (eds.).]. Cambridge University Press Cambridge, United Kingdom, pp 375.</span></li>
-<li><span id="fn:r43">Abarca-Gómez, L. et al., 2017: Worldwide trends in body-mass index, underweight, overweight and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents and adults. Lancet, 390, 2627–2642, doi:10.1016/S0140-6736(17)32129-3.</span></li>
-<li><span id="fn:r44">United Nations Department of Economic and Social Affairs, 2017: World Population Prospects: The 2017 Revision, DVD Edition.</span></li>
-<li><span id="fn:r45">Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks and K.C. Hegewisch, 2018: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data, 5, 170191, doi:10.1038/sdata.2017.191.</span></li>
-<li><span id="fn:r46">Goldewijk, K.K., A. Beusen, J. Doelman, and E. Stehfest, 2017: Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data, 9, 927–953, doi:10.5194/essd-9-927-2017.</span></li>
-<li><span id="fn:r47">Ziese, M. et al., 2014: The GPCC Drought Index – A new, combined and gridded global drought index. Earth Syst. Sci. Data, 6, 285–295, doi:10.5194/essd-6-285-2014.</span></li>
-<li><span id="fn:r48">Dixon, M.J.R. et al., 2016: Tracking global change in ecosystem area: The Wetland Extent Trends index. Biol. Conserv., doi:10.1016/j.biocon.2015.10.023.</span></li>
-<li><span id="fn:r49">Darrah, S.E. et al., 2019: Improvements to the Wetland Extent Trends (WET) index as a tool for monitoring natural and human-made wetlands. Ecol. Indic., 99, 294–298, doi:10.1016/j.ecolind.2018.12.032.</span></li>
-<li><span id="fn:r50">IPCC, 2000: Land Use, Land-Use Change and Forestry: A special report of the Intergovernmental Panel on Climate Change [Watson, R.T., I.R. Noble, B. Bolin, N.H. Ravindranath, D.J. Verardo and D.J. Dokken (eds.).]. Cambridge University Press Cambridge, United Kingdom, pp 375.</span></li>
-<li><span id="fn:r51">Settele, J., R. Scholes, R. Betts, S. Bunn, P. Leadley, D. Nepstad, J.T. Overpeck, and M.A. Taboada, 2014: Terrestrial and inland water systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359.</span></li>
-<li><span id="fn:r52">Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E.A. Elsiddig, H. Haberl, R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N.H. Ravindranath, C.W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</span></li>
-<li><span id="fn:r53">IPCC, 2018: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. In press, 1552 pp.</span></li>
-<li><span id="fn:r54">FAO and ITPS, 2015: Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r55">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r56">IPBES, 2018b: The IPBES Assessment Report on Land Degradation and Restoration [Montanarella, L., Scholes, R. and Brainich, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 744 pp.</span></li>
-<li><span id="fn:r57">IPBES, 2018c: The Regional Assessment Report on Biodiversity and Ecosystem Services for Africa [Archer, E. Dziba, L., Mulongoy, K.J., Maoela, M.A. and Walters, M. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 492 pp.</span></li>
-<li><span id="fn:r58">IPBES, 2018d: The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas [Rice, J., Seixas, C.S., Zaccagnini, M.E., Bedoya-Gaitán, M. and Valderrama N. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 656 pp.</span></li>
-<li><span id="fn:r59">IPBES, 2018e: The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for Asia and the Pacific [Karki, M., Senaratna Sellamuttu, S., Okayasu, S. and Suzuki, W. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 612 pp.</span></li>
-<li><span id="fn:r60">Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.</span></li>
-<li><span id="fn:r61">Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E.A. Elsiddig, H. Haberl, R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N.H. Ravindranath, C.W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</span></li>
-<li><span id="fn:r62">Tubiello, F.N. et al., 2015: The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012. Glob. Chang. Biol., 21, 2655–2660, doi:10.1111/gcb.12865.</span></li>
-<li><span id="fn:r63">Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data Discuss., 1–3, doi:10.5194/essd-2018-120.</span></li>
-<li><span id="fn:r64">Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.</span></li>
-<li><span id="fn:r65">Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev., 11, 369–408, doi:10.5194/gmd-11-369-2018.</span></li>
-<li><span id="fn:r66">Tian, H. et al., 2019: Global soil nitrous oxide emissions since the preindustrial era estimated by an ensemble of terrestrial biosphere models: Magnitude, attribution and uncertainty. Glob. Chang. Biol., 25, 640–659, doi:10.1111/gcb.14514.</span></li>
-<li><span id="fn:r67">Le Quéré, C. et al., 2015: Global Carbon Budget 2015. Earth Syst. Sci. Data, 7, 349–396, doi:10.5194/essd-7-349-2015.</span></li>
-<li><span id="fn:r68">Canadell, J.G., and E.D. Schulze, 2014: Global potential of biospheric carbon management for climate mitigation. Nat. Commun., 5, doi:528210.1038/ncomms6282.</span></li>
-<li><span id="fn:r69">Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.</span></li>
-<li><span id="fn:r70">Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.</span></li>
-<li><span id="fn:r71">Le Quéré, C. et al., 2013: The global carbon budget 1959–2011. Earth Syst. Sci. Data, 5, 165–185, doi:10.5194/essd-5-165-2013.</span></li>
-<li><span id="fn:r72">Pugh, T.A.M. et al., 2019: Role of forest regrowth in global carbon sink dynamics. Proc. Natl. Acad. Sci., 201810512, doi:10.1073/pnas.1810512116.</span></li>
-<li><span id="fn:r73">Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018.</span></li>
-<li><span id="fn:r74">Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.</span></li>
-<li><span id="fn:r75">Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.</span></li>
-<li><div id="fn:r76">
-</div></li>
-<li><span id="fn:r77">Bloom, A.A., J.-F. Exbrayat, I.R. van der Velde, L. Feng, and M. Williams, 2016: The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times. Proc. Natl. Acad. Sci., 113, 1285–1290, doi:10.1073/pnas.1515160113.</span></li>
-<li><span id="fn:r78">Friend, A.D. et al., 2014: Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl. Acad. Sci., 111, 3280–3285, doi:10.1073/pnas.1222477110.</span></li>
-<li><span id="fn:r79">Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018.</span></li>
-<li><span id="fn:r80">Lee, X. et al., 2011: Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384–387, doi:10.1038/nature10588.</span></li>
-<li><span id="fn:r81">Zhang, M. et al., 2014: Response of surface air temperature to small-scale land clearing across latitudes. Environ. Res. Lett., 9, 34002, doi:10.1088/1748-9326/9/3/034002.</span></li>
-<li><span id="fn:r82">Alkama, R. and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/science.aac8083.</span></li>
-<li><span id="fn:r83">Smith, P. and P.J. Gregory, 2013: Climate change and sustainable food production. Proceedings of the Nutrition Society, Vol. 72 of, 21–28, doi:10.1017/S0029665112002832.</span></li>
-<li><span id="fn:r84">Smith, P. and P.J. Gregory, 2013: Climate change and sustainable food production. Proceedings of the Nutrition Society, Vol. 72 of, 21–28, doi:10.1017/S0029665112002832.</span></li>
-<li><span id="fn:r85">Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E.A. Elsiddig, H. Haberl, R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N.H. Ravindranath, C.W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</span></li>
-<li><span id="fn:r86">Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114.</span></li>
-<li><span id="fn:r87">Gonzalez, P., R.P. Neilson, J.M. Lenihan, and R.J. Drapek, 2010: Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Glob. Ecol. Biogeogr., 19, 755–768, doi:10.1111/j.1466-8238.2010.00558.x.</span></li>
-<li><span id="fn:r88">Wärlind, D. et al., 2014: Nitrogen feedbacks increase future terrestrial ecosystem carbon uptake in an individual-based dynamic vegetation model. Biogeosciences, 11, 6131–6146, doi:10.5194/bg-11-6131-2014.</span></li>
-<li><span id="fn:r89">Davies-Barnard, T., P.J. Valdes, J.S. Singarayer, A.J. Wiltshire and C.D. Jones, 2015: Quantifying the relative importance of land cover change from climate and land-use in the representative concentration pathway. Global Biogeochem. Cycles, 842–853, doi:10.1002/2014GB004949.</span></li>
-<li><span id="fn:r90">Nakamura, A. et al., 2017: Forests and their canopies: Achievements and horizons in canopy science, Trends Ecol. Evol., 32, 438–451, doi:10.1016/j.tree.2017.02.020.</span></li>
-<li><span id="fn:r91">Donohue, R.J., M.L. Roderick, T.R. McVicar, and G.D. Farquhar, 2013: Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett., 40, 3031–3035, doi:10.1002/grl.50563.</span></li>
-<li><span id="fn:r92">Pimm, S.L. et al., 2014: The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344, 1246752–1246752, doi:10.1126/science.1246752.</span></li>
-<li><span id="fn:r93">Urban, M.C. et al., 2016: Improving the forecast for biodiversity under climate change. Science, 353, aad8466, doi:10.1126/science.aad8466.</span></li>
-<li><span id="fn:r94">Deryng, D. et al., 2016b: Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat. Clim. Chang., 6, 786–790, doi:10.1038/nclimate2995.</span></li>
-<li><span id="fn:r95">Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84, doi:10.1038/nature16467.</span></li>
-<li><span id="fn:r96">Schlenker, W. and D.B. Lobell, 2010: Robust negative impacts of climate change on African agriculture. Environ. Res. Lett., 5, 14010, doi:10.1186/s13021-018-0095-3.</span></li>
-<li><span id="fn:r97">Lobell, D.B., W. Schlenker, and J. Costa-Roberts, 2011: Climate trends and global crop production since 1980. Science, 333, 616–620, doi:10.1126/science.1204531.</span></li>
-<li><span id="fn:r98">Lobell, D.B., A. Sibley, and J. Ivan Ortiz-Monasterio, 2012: Extreme heat effects on wheat senescence in India. Nat. Clim. Chang., 2, 186–189, doi:10.1038/nclimate1356.</span></li>
-<li><span id="fn:r99">Challinor, A.J. et al., 2014: A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang., 4, 287–291, doi:10.1038/nclimate2153.</span></li>
-<li><span id="fn:r100">Moore, F.C. and D.B. Lobell, 2015: The fingerprint of climate trends on European crop yields. Proc. Natl. Acad. Sci., 112, 2670–2675, doi:10.1073/pnas.1409606112.</span></li>
-<li><span id="fn:r101">Pugh, T.A.M. et al., 2016: Climate analogues suggest limited potential for intensification of production on current croplands under climate change. Nat. Commun., 7, doi:1260810.1038/ncomms12608.</span></li>
-<li><span id="fn:r102">Di Paola, A. et al., 2018: The expansion of wheat thermal suitability of Russia in response to climate change. Land use policy, 78, 70–77, doi:10.1016/J.LANDUSEPOL.2018.06.035.</span></li>
-<li><span id="fn:r103">Muller, C. et al., 2015: Implications of climate mitigation for future agricultural production. Environ. Res. Lett., 10, doi:12500410.1088/1748-9326/10/12/125004.</span></li>
-<li><span id="fn:r104">Nakamura, A. et al., 2017: Forests and their canopies: Achievements and horizons in canopy science, Trends Ecol. Evol., 32, 438–451, doi:10.1016/j.tree.2017.02.020.</span></li>
-<li><span id="fn:r105">Kimball, B.A., 2016: Crop responses to elevated CO2 and interactions with H2O, N, and temperature. Curr. Opin. Plant Biol., 31, 36–43, doi:10.1016/j.pbi.2016.03.006.</span></li>
-<li><span id="fn:r106">Seidl, R. et al., 2017: Forest disturbances under climate change. Nat. Clim. Chang., doi:10.1038/nclimate3303.</span></li>
-<li><span id="fn:r107">Fasullo, J.T., B.L. Otto-Bliesner and S. Stevenson, 2018: ENSO’s changing influence on temperature, precipitation, and wildfire in a warming climate. Geophys. Res. Lett., 0, doi:10.1029/2018GL079022.</span></li>
-<li><span id="fn:r108">Allen, C.D. et al., 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage., 259, 660–684, doi:10.1016/j.foreco.2009.09.001.</span></li>
-<li><span id="fn:r109">Anderegg, W.R.L., J.M. Kane, and L.D.L. Anderegg, 2012: Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Chang., 3, 30.</span></li>
-<li><span id="fn:r110">Schweikert, A., P. Chinowsky, X. Espinet, and M. Tarbert, 2014: Climate change and infrastructure impacts: Comparing the impact on roads in ten countries through 2100. Procedia Eng., 78, 306–316, doi:10.1016/j.proeng.2014.07.072.</span></li>
-<li><span id="fn:r111">Chappin, E.J.L. and T. van der Lei, 2014: Adaptation of interconnected infrastructures to climate change: A socio-technical systems perspective. Util. Policy, 31, 10–17, doi: 10.1016/j.jup.2014.07.003.</span></li>
-<li><span id="fn:r112">Erb, K.-H. et al., 2016a: Land management: Data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.</span></li>
-<li><span id="fn:r113">Luyssaert, S. et al., 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Clim. Chang., 4, 389–393, doi:10.1038/nclimate2196.</span></li>
-<li><span id="fn:r114">Venter, O. et al., 2016: Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun., 7, doi:10.1038/ncomms12558.</span></li>
-<li><span id="fn:r115">Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452.</span></li>
-<li><span id="fn:r116">Luyssaert, S. et al., 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Clim. Chang., 4, 389–393, doi:10.1038/nclimate2196.</span></li>
-<li><span id="fn:r117">Birdsey, R. and Y. Pan, 2015: Trends in management of the world’s forests and impacts on carbon stocks. For. Ecol. Manage., 355, 83–90, doi:10.1016/j.foreco.2015.04.031.</span></li>
-<li><span id="fn:r118">Morales-Hidalgo, D., S.N. Oswalt, and E. Somanathan, 2015: Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. For. Ecol. Manage., 352, 68–77, doi:10.1016/j.foreco.2015.06.011.</span></li>
-<li><span id="fn:r119">Potapov, P. et al., 2017: The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv., 3, e1600821, doi:10.1126/sciadv.1600821.</span></li>
-<li><span id="fn:r120">Erb, K.-H. et al., 2017: Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature, 553, 73–76, doi:10.1038/nature25138.</span></li>
-<li><span id="fn:r121">Luyssaert, S. et al., 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Clim. Chang., 4, 389–393, doi:10.1038/nclimate2196.</span></li>
-<li><span id="fn:r122">Putz, F.E. and K.H. Redford, 2010: The importance of defining “Forest”: Tropical forest degradation, deforestation, long-term phase shifts and further transitions. Biotropica, doi:10.1111/j.1744-7429.2009.00567.x.</span></li>
-<li><span id="fn:r123">Schepaschenko, D. et al., 2015: Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sens. Environ., 162, 208–220, doi:10.1016/j.rse.2015.02.011.</span></li>
-<li><span id="fn:r124">Birdsey, R. and Y. Pan, 2015: Trends in management of the world’s forests and impacts on carbon stocks. For. Ecol. Manage., 355, 83–90, doi:10.1016/j.foreco.2015.04.031.</span></li>
-<li><span id="fn:r125">FAO, 2015a: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations, Rome.</span></li>
-<li><span id="fn:r126">Chazdon, R.L. et al., 2016a: When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration. Ambio, doi:10.1007/s13280-016-0772-y.</span></li>
-<li><span id="fn:r127">FAO, 2018a: The State of the World’s Forests 2018 – Forest Pathways to Sustainable Development. Food and Agriculture Organization of the United Nations, Rome, Italy, 139 pp.</span></li>
-<li><span id="fn:r128">Cherlet, M. et al., (eds.), 2018: World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management (3rd edition). Publication Office of the European Union, Luxembourg, 247 pp.</span></li>
-<li><span id="fn:r129">Laurance, W.F., J. Sayer and K.G. Cassman, 2014: Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol., 29, 107–116, doi:10.1016/J.TREE.2013.12.001.</span></li>
-<li><span id="fn:r130">Ellis, E.C. and N. Ramankutty, 2008: Putting people in the map: Anthropogenic biomes of the world. Front. Ecol. Environ., 6, 439–447, doi:10.1890/070062.</span></li>
-<li><span id="fn:r131">Ellis, E.C., K.K. Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty, 2010: Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr., doi:10.1111/j.1466-8238.2010.00540.x.</span></li>
-<li><span id="fn:r132">Cherlet, M. et al., (eds.), 2018: World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management (3rd edition). Publication Office of the European Union, Luxembourg, 247 pp.</span></li>
-<li><span id="fn:r133">Ellis, E.C., K.K. Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty, 2010: Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr., doi:10.1111/j.1466-8238.2010.00540.x.</span></li>
-<li><span id="fn:r134">Erb, K.-H. et al., 2016a: Land management: Data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.</span></li>
-<li><span id="fn:r135">Wisser, D., S. Frolking, E.M. Douglas, B.M. Fekete, C.J. Vörösmarty and A.H. Schumann, 2008: Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets. Geophys. Res. Lett., doi:10.1029/2008GL035296.</span></li>
-<li><span id="fn:r136">Chaturvedi, V. et al., 2015: Climate mitigation policy implications for global irrigation water demand. Mitig. Adapt. Strateg. Glob. Chang., 20, 389–407, doi:10.1007/s11027-013-9497-4.</span></li>
-<li><span id="fn:r137">Siebert, S., M. Kummu, M. Porkka, P. Döll, N. Ramankutty and B.R. Scanlon, 2015: A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci., doi:10.5194/hess-19-1521-2015.</span></li>
-<li><span id="fn:r138">FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org .</span></li>
-<li><span id="fn:r139">Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353.</span></li>
-<li><span id="fn:r140">Haberl, H., K.-H. Erb and F. Krausmann, 2014: Human appropriation of net primary production: Patterns, trends and planetary boundaries. Annu. Rev. Environ. Resour., 39, 363–391, doi:10.1146/annurev-environ-121912-094620.</span></li>
-<li><span id="fn:r141">Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.</span></li>
-<li><span id="fn:r142">Bodirsky, B.L. and C. Müller, 2014: Robust relationship between yields and nitrogen inputs indicates three ways to reduce nitrogen pollution. Environ. Res. Lett., doi:10.1088/1748-9326/9/11/111005.</span></li>
-<li><span id="fn:r143">Lassaletta, L., G. Billen, B. Grizzetti, J. Anglade, and J. Garnier, 2014: 50 year trends in nitrogen use efficiency of world cropping systems: The relationship between yield and nitrogen input to cropland. Environ. Res. Lett., doi:10.1088/1748-9326/9/10/105011.</span></li>
-<li><span id="fn:r144">Mottet, A. et al., 2017: Livestock: On our plates or eating at our table? A new analysis of the feed/food debate. Glob. Food Sec., 14, 1–8, doi:10.1016/J.GFS.2017.01.001.</span></li>
-<li><span id="fn:r145">Haberl, H., K.-H. Erb and F. Krausmann, 2014: Human appropriation of net primary production: Patterns, trends and planetary boundaries. Annu. Rev. Environ. Resour., 39, 363–391, doi:10.1146/annurev-environ-121912-094620.</span></li>
-<li><span id="fn:r146">Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E.A. Elsiddig, H. Haberl, R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N.H. Ravindranath, C.W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</span></li>
-<li><span id="fn:r147">Bais, A.L.S., C. Lauk, T. Kastner, and K. Erb, 2015: Global patterns and trends of wood harvest and use between 1990 and 2010. Ecol. Econ., 119, 326–337, doi:10.1016/j.ecolecon.2015.09.011.</span></li>
-<li><span id="fn:r148">Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353.</span></li>
-<li><span id="fn:r149">FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org .</span></li>
-<li><span id="fn:r150">FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org .</span></li>
-<li><span id="fn:r151">Kastner, T., M.J.I. Rivas, W. Koch, and S. Nonhebel, 2012: Global changes in diets and the consequences for land requirements for food. Proc. Natl. Acad. Sci., doi:10.1073/pnas.1117054109.</span></li>
-<li><span id="fn:r152">FAO, 2017: The Future of Food and Agriculture: Trends and Challenges. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r153">FAO, 2018a: The State of the World’s Forests 2018 – Forest Pathways to Sustainable Development. Food and Agriculture Organization of the United Nations, Rome, Italy, 139 pp.</span></li>
-<li><span id="fn:r154">Tilman, D. and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13959.</span></li>
-<li><span id="fn:r155">Marques, A. et al., 2019: Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol., 3, 628–637, doi:10.1038/s41559-019-0824-3.</span></li>
-<li><span id="fn:r156">Alexander, P. et al., 2015: Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy. Glob. Environ. Chang., doi:10.1016/j.gloenvcha.2015.08.011.</span></li>
-<li><span id="fn:r157">Lin, M. and P. Huybers, 2012: Reckoning wheat yield trends. Environ. Res. Lett., 7, 24016, doi:10.1088/1748-9326/7/2/024016.</span></li>
-<li><span id="fn:r158">Ray, D.K., N. Ramankutty, N.D. Mueller, P.C. West and J.A. Foley, 2012: Recent patterns of crop yield growth and stagnation. Nat. Commun., 3, doi:10.1038/ncomms2296.</span></li>
-<li><span id="fn:r159">Elbehri, A., J. Elliott, and T. Wheeler, 2015: Climate change, food security and trade: An overview of global assessments and policy insights. In: Climate Change and Food Systems: Global assessments and implications for food security and trade [Elbehri, A. (ed.)]. FAO, Rome, Italy, pp. 1–27.</span></li>
-<li><span id="fn:r160">Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452.</span></li>
-<li><span id="fn:r161">Siebert, S., M. Kummu, M. Porkka, P. Döll, N. Ramankutty and B.R. Scanlon, 2015: A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci., doi:10.5194/hess-19-1521-2015.</span></li>
-<li><span id="fn:r162">Lassaletta, L. et al., 2016: Nitrogen use in the global food system: Past trends and future trajectories of agronomic performance, pollution, trade and dietary demand. Environ. Res. Lett., 11, 095007, doi:10.1088/1748-9326/11/9/095007.</span></li>
-<li><span id="fn:r163">FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org .</span></li>
-<li><span id="fn:r164">IFASTAT, 2018: Statistical Databases. http://www.ifastat.org/ .</span></li>
-<li><span id="fn:r165">FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org .</span></li>
-<li><span id="fn:r166">Friis, C. et al., 2016: From teleconnection to telecoupling: Taking stock of an emerging framework in land system science. J. Land Use Sci., doi:10.1080/1747423X.2015.1096423.</span></li>
-<li><span id="fn:r167">Friis, C. and J.Ø. Nielsen, 2017: Land-use change in a telecoupled world: The relevance and applicability of the telecoupling framework in the case of banana plantation expansion in Laos. Ecol. Soc., doi:10.5751/ES-09480-220430.</span></li>
-<li><span id="fn:r168">Schröter, M. et al., 2018: Interregional flows of ecosystem services: Concepts, typology and four cases. Ecosyst. Serv., doi:10.1016/j.ecoser.2018.02.003.</span></li>
-<li><span id="fn:r169">Liu, J. et al., 2013: Framing Sustainability in a Telecoupled World. Ecol. Soc., 2, doi:10.5751/ES-05873-180226.</span></li>
-<li><span id="fn:r170">Krausmann, F. and E. Langthaler, 2019: Food regimes and their trade links: A socio-ecological perspective. Ecol. Econ., 160, 87–95, doi:10.1016/J.ECOLECON.2019.02.011.</span></li>
-<li><span id="fn:r171">Krausmann, F. et al., 2013: Global human appropriation of net primary production doubled in the 20th century. Proc. Natl. Acad. Sci. U.S.A., 110, 10324–10329, doi:10.1073/pnas.1211349110.</span></li>
-<li><span id="fn:r172">Seto, K.C. and A. Reenberg (eds.), 2014: Rethinking Global Land Use in an Urban Era. The MIT Press, Cambridge, Massachusetts, USA, 408 pp.</span></li>
-<li><span id="fn:r173">Martellozzo, F. et al., 2015: Urbanization and the loss of prime farmland: A case study in the Calgary–Edmonton corridor of Alberta. Reg. Environ. Chang., 15, 881–893, doi:10.1007/s10113-014-0658-0.</span></li>
-<li><span id="fn:r174">Bren d’Amour, C. et al., 2016: Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci., 114, 201606036, doi:10.1073/pnas.1606036114.</span></li>
-<li><span id="fn:r175">Seto, K.C. and N. Ramankutty, 2016: Hidden linkages between urbanization and food systems. Science, 352, 943–945, doi:10.1126/science.aaf7439.</span></li>
-<li><span id="fn:r176">van Vliet, J., D.A. Eitelberg, and P.H. Verburg, 2017: A global analysis of land take in cropland areas and production displacement from urbanization. Glob. Environ. Chang., 43, 107–115, doi:10.1016/j.gloenvcha.2017.02.001.</span></li>
-<li><span id="fn:r177">Nachtergaele, F., 2008: Mapping Land Use Systems at Global and Regional Scales for Land Degradation Assessment Analysis Version 1.0, Food and Agriculture Organization of the United Nations, Rome, Italy, 77 pp.</span></li>
-<li><span id="fn:r178">Ellis, E.C., K.K. Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty, 2010: Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr., doi:10.1111/j.1466-8238.2010.00540.x.</span></li>
-<li><span id="fn:r179">Potapov, P. et al., 2017: The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv., 3, e1600821, doi:10.1126/sciadv.1600821.</span></li>
-<li><span id="fn:r180">FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org .</span></li>
-<li><span id="fn:r181">FAO, 1963: World Forest Inventory 1963. Food and Agriculture Organization of the United Nations, Rome, 113 pp.</span></li>
-<li><span id="fn:r182">Alexander, P. et al., 2015: Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy. Glob. Environ. Chang., doi:10.1016/j.gloenvcha.2015.08.011.</span></li>
-<li><span id="fn:r183">Erb, K.-H. et al., 2017: Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature, 553, 73–76, doi:10.1038/nature25138.</span></li>
-<li><span id="fn:r184">Krausmann, F. et al., 2013: Global human appropriation of net primary production doubled in the 20th century. Proc. Natl. Acad. Sci. U.S.A., 110, 10324–10329, doi:10.1073/pnas.1211349110.</span></li>
-<li><span id="fn:r185">Haberl, H., K.-H. Erb and F. Krausmann, 2014: Human appropriation of net primary production: Patterns, trends and planetary boundaries. Annu. Rev. Environ. Resour., 39, 363–391, doi:10.1146/annurev-environ-121912-094620.</span></li>
-<li><span id="fn:r186">Krausmann, F. et al., 2013: Global human appropriation of net primary production doubled in the 20th century. Proc. Natl. Acad. Sci. U.S.A., 110, 10324–10329, doi:10.1073/pnas.1211349110.</span></li>
-<li><span id="fn:r187">Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018.</span></li>
-<li><span id="fn:r188">Ramankutty, N. et al., 2018: Trends in global agricultural land use: Implications for environmental health and food security. Annu. Rev. Plant Biol., 69, 789–815, doi:10.1146/annurev-arplant-042817-040256.</span></li>
-<li><span id="fn:r189">Ordway, E.M., G.P. Asner and E.F. Lambin, 2017: Deforestation risk due to commodity crop expansion in sub-Saharan Africa. Environ. Res. Lett., 12, 044015, doi:10.1088/1748-9326/aa6509.</span></li>
-<li><span id="fn:r190">Richards, D.R. and D.A. Friess, 2016: Rates and drivers of mangrove deforestation in Southeast Asia, 2000-2012. Proc. Natl. Acad. Sci. U.S.A., 113, 344–349, doi:10.1073/pnas, 1510272113.</span></li>
-<li><span id="fn:r191">Lehmann, C.E.R. and C.L. Parr, 2016: Tropical grassy biomes: Linking ecology, human use and conservation. Philos. Trans. R. Soc. B-Biological Sci., 371, 20160329, doi:20160329 10.1098/rstb.2016.0329.</span></li>
-<li><span id="fn:r192">Strassburg, B.B.N. et al., 2017: Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol., 1, 0099, doi:10.1038/s41559-017-0099.</span></li>
-<li><span id="fn:r193">Parr, C.L., C.E.R.R. Lehmann, W.J. Bond, W.A. Hoffmann A.N. Andersen, 2014: Tropical grassy biomes: misunderstood, neglected and under threat. Trends Ecol. Evol., 29, 205–213, doi:10.1016/j.tree.2014.02.004.</span></li>
-<li><span id="fn:r194">Lehmann, C.E.R. and C.L. Parr, 2016: Tropical grassy biomes: Linking ecology, human use and conservation. Philos. Trans. R. Soc. B-Biological Sci., 371, 20160329, doi:20160329 10.1098/rstb.2016.0329.</span></li>
-<li><span id="fn:r195">Ryan, C.M. et al., 2016: Ecosystem services from southern African woodlands and their future under global change. Philos. Trans. R. Soc. B-Biological Sci., 371, doi:2015031210.1098/rstb.2015.0312.</span></li>
-<li><span id="fn:r196">Erb, K.-H. et al., 2016a: Land management: Data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.</span></li>
-<li><span id="fn:r197">Davidson, N.C., 2014: How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar. Freshw. Res., 65, 934–941, doi:10.1071/MF14173.</span></li>
-<li><span id="fn:r198">Dixon, M.J.R. et al., 2016: Tracking global change in ecosystem area: The Wetland Extent Trends index. Biol. Conserv., doi:10.1016/j.biocon.2015.10.023.</span></li>
-<li><span id="fn:r199">Darrah, S.E. et al., 2019: Improvements to the Wetland Extent Trends (WET) index as a tool for monitoring natural and human-made wetlands. Ecol. Indic., 99, 294–298, doi:10.1016/j.ecolind.2018.12.032.</span></li>
-<li><span id="fn:r200">Erb, K.-H. et al., 2017: Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature, 553, 73–76, doi:10.1038/nature25138.</span></li>
-<li><span id="fn:r201">FAO, 2015a: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations, Rome.</span></li>
-<li><span id="fn:r202">Keenan, R.J. et al., 2015: Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage., 352, 9–20, doi:10.1016/j.foreco.2015.06.014.</span></li>
-<li><span id="fn:r203">MacDicken, K.G. et al., 2015: Global progress toward sustainable forest management. For. Ecol. Manage., 352, 47–56, doi:10.1016/j.foreco.2015.02.005.</span></li>
-<li><span id="fn:r204">FAO, 1963: World Forest Inventory 1963. Food and Agriculture Organization of the United Nations, Rome, 113 pp.</span></li>
-<li><span id="fn:r205">Li, W. et al., 2016: Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product. Int. J. Appl. Earth Obs. Geoinf., 47, 30–39, doi:10.1016/J.JAG.2015.12.006.</span></li>
-<li><span id="fn:r206">Nowosad, J., T.F. Stepinski and P. Netzel, 2018: Global assessment and mapping of changes in mesoscale landscapes: 1992–2015. Int. J. Appl. Earth Obs. Geoinf., 78, 332–40, doi:10.1016/j.jag.2018.09.013.</span></li>
-<li><span id="fn:r207">Hansen, M.C. et al., 2013: High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853, doi:10.1126/science.1244693.</span></li>
-<li><span id="fn:r208">Song, X.-P. et al., 2018: Global land change from 1982 to 2016. Nature, 560, 639–643, doi:10.1038/s41586-018-0411-9.</span></li>
-<li><span id="fn:r209">Chen, C. et al., 2019: China and India lead in greening of the world through land-use management. Nat. Sustain., 2, 122–129, doi:10.1038/s41893-019-0220-7.</span></li>
-<li><span id="fn:r210">Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.</span></li>
-<li><span id="fn:r211">Zhao, L., A. Dai and B. Dong, 2018: Changes in global vegetation activity and its driving factors during 1982–2013. Agric. For. Meteorol., doi:10.1016/j.agrformet.2017.11.013.</span></li>
-<li><span id="fn:r212">de Jong, R., M.E. Schaepman, R. Furrer, S. de Bruin, and P.H. Verburg, 2013: Spatial relationship between climatologies and changes in global vegetation activity. Glob. Chang. Biol., 19, 1953–1964, doi:10.1111/gcb.12193.</span></li>
-<li><span id="fn:r213">Pugh, T.A.M. et al., 2019: Role of forest regrowth in global carbon sink dynamics. Proc. Natl. Acad. Sci., 201810512, doi:10.1073/pnas.1810512116.</span></li>
-<li><span id="fn:r214">De Kauwe, M.G., T.F. Keenan, B.E. Medlyn, I.C. Prentice, and C. Terrer, 2016: Satellite-based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat. Clim. Chang., 6, pages 892–893, doi:10.1038/nclimate3105.</span></li>
-<li><span id="fn:r215">Kolby Smith, W. et al., 2015: Large divergence of satellite and earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Chang., 6, 306–310, doi:10.1038/nclimate2879.</span></li>
-<li><span id="fn:r216">Keenan, R.J. et al., 2015: Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage., 352, 9–20, doi:10.1016/j.foreco.2015.06.014.</span></li>
-<li><span id="fn:r217">Schepaschenko, D. et al., 2015: Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sens. Environ., 162, 208–220, doi:10.1016/j.rse.2015.02.011.</span></li>
-<li><span id="fn:r218">Bastin, J.-F. et al., 2017: The extent of forest in dryland biomes. Science, 356, 635–638, doi:10.1126/science.aam6527.</span></li>
-<li><span id="fn:r219">Sloan, S. and J.A. Sayer, 2015: Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. For. Ecol. Manage., 352, 134–145, doi:10.1016/j.foreco.2015.06.013.</span></li>
-<li><span id="fn:r220">Chazdon, R.L. et al., 2016a: When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration. Ambio, doi:10.1007/s13280-016-0772-y.</span></li>
-<li><span id="fn:r221">Achard, F. et al., 2014: Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Chang. Biol., 20, 2540–2554, doi:10.1111/GCB.12605.</span></li>
-<li><span id="fn:r222">Ceballos, G. et al., 2015: Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv., doi:10.1126/sciadv.1400253.</span></li>
-<li><span id="fn:r223">De Vos, J.M., L.N. Joppa, J.L. Gittleman, P.R. Stephens, and S.L. Pimm, 2015: Estimating the normal background rate of species extinction. Conserv. Biol., 29, 452–462, doi:10.1111/cobi.12380.</span></li>
-<li><span id="fn:r224">Pimm, S.L. et al., 2014: The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344, 1246752–1246752, doi:10.1126/science.1246752.</span></li>
-<li><span id="fn:r225">Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50, doi:10.1038/nature14324.</span></li>
-<li><span id="fn:r226">Maxwell, S.L., R.A. Fuller, T.M. Brooks, and J.E.M. Watson, 2016: Biodiversity: The ravages of guns, nets and bulldozers. Nature, 536, 143–145, doi:10.1038/536143a.</span></li>
-<li><span id="fn:r227">Marques, A. et al., 2019: Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol., 3, 628–637, doi:10.1038/s41559-019-0824-3.</span></li>
-<li><span id="fn:r228">Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50, doi:10.1038/nature14324.</span></li>
-<li><span id="fn:r229">Wilting, H.C., A.M. Schipper, M. Bakkenes, J.R. Meijer and M.A.J. Huijbregts, 2017: Quantifying biodiversity losses due to human consumption: A global-scale footprint analysis. Environ. Sci. Technol., 51, 3298–3306, doi:10.1021/acs.est.6b05296.</span></li>
-<li><span id="fn:r230">Gossner, M.M. et al., 2016: Land-use intensification causes multitrophic homogenization of grassland communities. Nature, 540, 266–269, doi:10.1038/nature20575.</span></li>
-<li><span id="fn:r231">Newbold, T., D.P. Tittensor, M.B.J. Harfoot, J.P.W. Scharlemann, and D.W. Purves, 2018: Non-linear changes in modelled terrestrial ecosystems subjected to perturbations. bioRxiv, doi:10.1101/439059.</span></li>
-<li><span id="fn:r232">Paillet, Y. et al., 2010: Biodiversity differences between managed and unmanaged forests: Meta-analysis of species richness in Europe, Conserv. Biol., 24(1), 101–112, doi:10.1111/j.1523-1739.2009.01399.x.</span></li>
-<li><span id="fn:r233">Settele, J., R. Scholes, R. Betts, S. Bunn, P. Leadley, D. Nepstad, J.T. Overpeck, and M.A. Taboada, 2014: Terrestrial and inland water systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359.</span></li>
-<li><span id="fn:r234">Urban, M.C. et al., 2016: Improving the forecast for biodiversity under climate change. Science, 353, aad8466, doi:10.1126/science.aad8466.</span></li>
-<li><span id="fn:r235">Scholes, R. et al., 2018: IPBES: Summary for policymakers of the thematic assessment report on land degradation and restoration of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany, 44 pp.</span></li>
-<li><span id="fn:r236">Fischer, M. et al., 2018: IPBES: Summary for Policymakers of the Regional Assessment Report on Biodiversity and Ecosystem Services for Europe and Central Asia of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Bonn, Germany, 48 pp.</span></li>
-<li><span id="fn:r237">Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J. Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5°C Global Warming on Natural and Human Systems. In: Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [Masson-Delmotte, V.P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. In press.</span></li>
-<li><span id="fn:r238">Anderson-Teixeira, K.J., 2018: Prioritizing biodiversity and carbon. Nat. Clim. Chang., 8, 667–668, doi:10.1038/s41558-018-0242-6.</span></li>
-<li><span id="fn:r239">Anderson, S.E. et al., 2018: The Critical Role of Markets in Climate Change Adaptation. National Bureau of Economic Research.</span></li>
-<li><span id="fn:r240">Yang, Y., D. Tilman, G. Furey and C. Lehman, 2019: Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nat. Commun., 10, 718, doi:10.1038/s41467-019-08636-w.</span></li>
-<li><span id="fn:r241">United Nations, 2018: 2018 Revision of World Urbanization Prospects. http://www.un.org/development/desa/publications/2018-revision-of-world-</span></li>
-<li><span id="fn:r242">Crist, E., C. Mora, and R. Engelman, 2017: The interaction of human population, food production, and biodiversity protection. Science, 356, 260–264, doi:10.1126/science.aal2011.</span></li>
-<li><span id="fn:r243">Jiang, L. and B.C. O’Neill, 2017: Global urbanization projections for the shared socioeconomic pathways. Glob. Environ. Chang., 42, 193–199, doi:10.1016/J.GLOENVCHA.2015.03.008.</span></li>
-<li><span id="fn:r244">Billen, G., L. Lassaletta, and J. Garnier, 2015: A vast range of opportunities for feeding the world in 2050: Trade-off between diet, N contamination and international trade. Environ. Res. Lett., 10, doi:10.1088/1748-9326/10/2/025001.</span></li>
-<li><span id="fn:r245">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r246">Muller, A. et al., 2017: Strategies for feeding the world more sustainably with organic agriculture. Nat. Commun., 8, doi:10.1038/s41467-017-01410-w.</span></li>
-<li><span id="fn:r247">Alexander, P. et al., 2015: Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy. Glob. Environ. Chang., doi:10.1016/j.gloenvcha.2015.08.011.</span></li>
-<li><span id="fn:r248">Springmann, M. et al., 2018: Options for keeping the food system within environmental limits. Nature, 562, 1, doi:10.1038/s41586-018-0594-0.</span></li>
-<li><span id="fn:r249">Myers, S.S. et al., 2017: Climate change and global food systems: Potential impacts on food security and undernutrition. Annu. Rev. Public Health, 38, 259–277, doi:10.1146/annurev-publhealth-031816-044356.</span></li>
-<li><span id="fn:r250">Erb, K.-H. et al., 2016c: Biomass turnover time in terrestrial ecosystems halved by land use. Nat. Geosci., 9, 674–678, doi:10.1038/ngeo2782.</span></li>
-<li><span id="fn:r251">FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp.</span></li>
-<li><span id="fn:r252">van Vuuren, D.P. et al., 2017: Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Glob. Environ. Chang., 42, 237–250, doi:10.1016/J.GLOENVCHA.2016.05.008.</span></li>
-<li><span id="fn:r253">Springmann, M. et al., 2018: Options for keeping the food system within environmental limits. Nature, 562, 1, doi:10.1038/s41586-018-0594-0.</span></li>
-<li><span id="fn:r254">Riahi, K. et al., 2017: The shared socioeconomic pathways and their energy, land use and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.</span></li>
-<li><span id="fn:r255">Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337.</span></li>
-<li><span id="fn:r256">Ramankutty, N. et al., 2018: Trends in global agricultural land use: Implications for environmental health and food security. Annu. Rev. Plant Biol., 69, 789–815, doi:10.1146/annurev-arplant-042817-040256.</span></li>
-<li><span id="fn:r257">Erb, K.-H. et al., 2016b: Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun., 7.</span></li>
-<li><span id="fn:r258">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r259">Seto, K.C., B. Guneralp and L.R. Hutyra, 2012: Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci., 109, 16083–16088, doi:10.1073/pnas.1211658109.</span></li>
-<li><span id="fn:r260">van Vliet, J., D.A. Eitelberg, and P.H. Verburg, 2017: A global analysis of land take in cropland areas and production displacement from urbanization. Glob. Environ. Chang., 43, 107–115, doi:10.1016/j.gloenvcha.2017.02.001.</span></li>
-<li><span id="fn:r261">Jiang, L. and B.C. O’Neill, 2017: Global urbanization projections for the shared socioeconomic pathways. Glob. Environ. Chang., 42, 193–199, doi:10.1016/J.GLOENVCHA.2015.03.008.</span></li>
-<li><span id="fn:r262">Friis, C. et al., 2016: From teleconnection to telecoupling: Taking stock of an emerging framework in land system science. J. Land Use Sci., doi:10.1080/1747423X.2015.1096423.</span></li>
-<li><span id="fn:r263">Konar, M., J.J. Reimer, Z. Hussein, and N. Hanasaki, 2016: The water footprint of staple crop trade under climate and policy scenarios. Environ. Res. Lett., 11, 035006, doi:10.1088/1748-9326/11/3/035006.</span></li>
-<li><span id="fn:r264">Billen, G., L. Lassaletta, and J. Garnier, 2015: A vast range of opportunities for feeding the world in 2050: Trade-off between diet, N contamination and international trade. Environ. Res. Lett., 10, doi:10.1088/1748-9326/10/2/025001.</span></li>
-<li><span id="fn:r265">Lassaletta, L. et al., 2016: Nitrogen use in the global food system: Past trends and future trajectories of agronomic performance, pollution, trade and dietary demand. Environ. Res. Lett., 11, 095007, doi:10.1088/1748-9326/11/9/095007.</span></li>
-<li><span id="fn:r266">Baldos, U.L.C. and T.W. Hertel, 2015: The role of international trade in managing food security risks from climate change. Food Secur., 7, 275–290, doi:10.1007/s12571-015-0435-z.</span></li>
-<li><span id="fn:r267">Kastner, T., K.H. Erb, and H. Haberl, 2014: Rapid growth in agricultural trade: Effects on global area efficiency and the role of management. Environ. Res. Lett., 9, doi:10.1088/1748-9326/9/3/034015.</span></li>
-<li><span id="fn:r268">Liu, J. et al., 2013: Framing Sustainability in a Telecoupled World. Ecol. Soc., 2, doi:10.5751/ES-05873-180226.</span></li>
-<li><span id="fn:r269">Wood, S.A., M.R. Smith, J. Fanzo, R. Remans and R.S. DeFries, 2018: Trade and the equitability of global food nutrient distribution. Nat. Sustain., 1, 34–37, doi:10.1038/s41893-017-0008-6.</span></li>
-<li><span id="fn:r270">Schröter, M. et al., 2018: Interregional flows of ecosystem services: Concepts, typology and four cases. Ecosyst. Serv., doi:10.1016/j.ecoser.2018.02.003.</span></li>
-<li><span id="fn:r271">Lapola, D.M. et al., 2010: Indirect land-use changes can overcome carbon savings from biofuels in Brazil. Proc. Natl. Acad. Sci. U.S.A., 107, 3388–3393, doi:10.1073/pnas.0907318107.</span></li>
-<li><span id="fn:r272">Jadin, I., P. Meyfroidt and E.F. Lambin, 2016: International trade and land use intensification and spatial reorganization explain Costa Rica’s forest transition. Environ. Res. Lett., 11, 035005, doi:10.1088/1748-9326/11/3/035005.</span></li>
-<li><span id="fn:r273">Billen, G., L. Lassaletta, and J. Garnier, 2015: A vast range of opportunities for feeding the world in 2050: Trade-off between diet, N contamination and international trade. Environ. Res. Lett., 10, doi:10.1088/1748-9326/10/2/025001.</span></li>
-<li><span id="fn:r274">Chaudhary, A. and T. Kastner, 2016: Land use biodiversity impacts embodied in international food trade. Glob. Environ. Chang., 38, 195–204, doi:10.1016/J.GLOENVCHA.2016.03.013.</span></li>
-<li><span id="fn:r275">Marques, A. et al., 2019: Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol., 3, 628–637, doi:10.1038/s41559-019-0824-3.</span></li>
-<li><span id="fn:r276">Seto, K.C. and N. Ramankutty, 2016: Hidden linkages between urbanization and food systems. Science, 352, 943–945, doi:10.1126/science.aaf7439.</span></li>
-<li><span id="fn:r277">Seto, K.C., B. Guneralp and L.R. Hutyra, 2012: Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci., 109, 16083–16088, doi:10.1073/pnas.1211658109.</span></li>
-<li><span id="fn:r278">Güneralp, B., K.C. Seto, B. Gueneralp, and K.C. Seto, 2013: Futures of global urban expansion: Uncertainties and implications for biodiversity conservation. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/1/014025.</span></li>
-<li><span id="fn:r279">Aronson, M.F.J. et al., 2014: A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proc. R. Soc. B Biol. Sci., 281, 20133330–20133330, doi:10.1098/rspb.2013.3330.</span></li>
-<li><span id="fn:r280">Martellozzo, F. et al., 2015: Urbanization and the loss of prime farmland: A case study in the Calgary–Edmonton corridor of Alberta. Reg. Environ. Chang., 15, 881–893, doi:10.1007/s10113-014-0658-0.</span></li>
-<li><span id="fn:r281">Bren d’Amour, C. et al., 2016: Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci., 114, 201606036, doi:10.1073/pnas.1606036114.</span></li>
-<li><span id="fn:r282">Seto, K.C. and N. Ramankutty, 2016: Hidden linkages between urbanization and food systems. Science, 352, 943–945, doi:10.1126/science.aaf7439.</span></li>
-<li><span id="fn:r283">van Vliet, J., D.A. Eitelberg, and P.H. Verburg, 2017: A global analysis of land take in cropland areas and production displacement from urbanization. Glob. Environ. Chang., 43, 107–115, doi:10.1016/j.gloenvcha.2017.02.001.</span></li>
-<li><span id="fn:r284">Baldos, U.L.C. and T.W. Hertel, 2015: The role of international trade in managing food security risks from climate change. Food Secur., 7, 275–290, doi:10.1007/s12571-015-0435-z.</span></li>
-<li><span id="fn:r285">Schlenker, W. and D.B. Lobell, 2010: Robust negative impacts of climate change on African agriculture. Environ. Res. Lett., 5, 14010, doi:10.1186/s13021-018-0095-3.</span></li>
-<li><span id="fn:r286">Lipper, L. et al., 2014: Climate-smart agriculture for food security. Nat. Clim. Chang., 4, 1068–1072, doi:10.1038/nclimate2437.</span></li>
-<li><span id="fn:r287">Challinor, A.J. et al., 2014: A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang., 4, 287–291, doi:10.1038/nclimate2153.</span></li>
-<li><span id="fn:r288">Myers, S.S. et al., 2017: Climate change and global food systems: Potential impacts on food security and undernutrition. Annu. Rev. Public Health, 38, 259–277, doi:10.1146/annurev-publhealth-031816-044356.</span></li>
-<li><span id="fn:r289">Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178.</span></li>
-<li><span id="fn:r290">Smith, P. et al., 2016: Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Chang., 6, 42–50, doi:DOI: 10.1038/NCLIMATE2870.</span></li>
-<li><span id="fn:r291">Ravi, S., D.D. Breshears, T.E. Huxman, and P. D’Odorico, 2010: Land degradation in drylands: Interactions among hydrologic–aeolian erosion and vegetation dynamics. Geomorphology, 116, 236–245, doi:10.1016/j.geomorph.2009.11.023.</span></li>
-<li><span id="fn:r292">Mirzabaev, A., E. Nkonya and J. von Braun, 2015: Economics of sustainable land management. Elsevier, 15, 9–19, doi:10.1016/j.cosust.2015.07.004.</span></li>
-<li><span id="fn:r293">FAO and ITPS, 2015: Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r294">Cerretelli, S. et al., 2018: Spatial assessment of land degradation through key ecosystem services: The role of globally available data. Sci. Total Environ., 628–629, 539–555, doi:10.1016/J.SCITOTENV.2018.02.085.</span></li>
-<li><span id="fn:r295">Field, C.B., V.R. Barros, K.J. Mach, M.D. Mastrandrea, M. van Aalst,W.N. Adger, D.J. Arent, J. Barnett, R. Betts, T.E. Bilir, J. Birkmann, J. Carmin, D.D. Chadee, A.J. Challinor, M. Chatterjee,W. Cramer, D.J. Davidson, Y.O. Estrada, J. P. Gattuso, Y. Hijioka, O. Hoegh-Guldberg, H.Q. Huang, G.E. Insarov, R.N. Jones, R.S. Kovats, P. Romero-Lankao, J.N. Larsen, I.J. Losada, J.A. Marengo, R.F. McLean, L.O. Mearns, R. Mechler, J.F. Morton, I. Niang, T. Oki, J.M. Olwoch, M. Opondo, E.S. Poloczanska, H.-O. Pörtner, M.H. Redsteer, A. Reisinger, A. Revi, D.N. Schmidt, M.R. Shaw, W. Solecki, D.A. Stone, J.M.R. Stone, K.M. Strzepek, A.G. Suarez, P. Tschakert, R. Valentini, S. Vicuña, A. Villamizar, K.E. Vincent, R. Warren, L.L. White, T.J. Wilbanks, P.P. Wong and G.W. Yohe., 2014b: Technical Summary. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 35–94 pp.</span></li>
-<li><span id="fn:r296">Lal, R., 2009: Soils and world food security. Soil and Tillage Research, 102, 1–4, doi:10.1016/j.still.2008.08.001.</span></li>
-<li><span id="fn:r297">Beinroth, F.H., H. Eswaran, P.F. Reich and E. Van Den Berg, 1994: Land related stresses. In: Stressed Ecosystems and Sustainable Agriculture [Virmani, S.M., J.C. Katyal, H. Eswaran and I.P. Abrol, (eds.)]. Oxford and IBH, New Delhi, India.</span></li>
-<li><span id="fn:r298">Abu Hammad, A. and A. Tumeizi, 2012: Land degradation: Socioeconomic and environmental causes and consequences in the eastern Mediterranean. L. Degrad. Dev., 23, 216–226, doi:10.1002/ldr.1069.</span></li>
-<li><span id="fn:r299">Ferreira, C.S.S., R.P.D. Walsh and A.J.D. Ferreira, 2018: Degradation in urban areas. Curr. Opin. Environ. Sci. Heal., 5, 19–25, doi:10.1016/j.coesh.2018.04.001.</span></li>
-<li><span id="fn:r300">Franco, A. and N. Giannini, 2005: Perspectives for the use of biomass as fuel in combined cycle power plants. Int. J. Therm. Sci., 44, 163–177, doi:10.1016/J.IJTHERMALSCI.2004.07.005.</span></li>
-<li><span id="fn:r301">Abahussain, A.A., A.S. Abdu, W.K. Al-Zubari, N.A. El-Deen and M. Abdul-Raheem, 2002: Desertification in the Arab region: Analysis of current status and trends. J. Arid Environ., 51, 521–545, doi:10.1006/jare.2002.0975.</span></li>
-<li><span id="fn:r302">Gibbs, H.K. and J.M. Salmon, 2015: Mapping the world’s degraded lands. Appl. Geogr., 57, 12–21, doi:10.1016/j.apgeog.2014.11.024.</span></li>
-<li><span id="fn:r303">Stavi, I. and R. Lal, 2015: Achieving zero net land degradation: Challenges and opportunities. J. Arid Environ., 112, 44–51, doi:10.1016/j.jaridenv.2014.01.016.</span></li>
-<li><span id="fn:r304">Sutton, P.C., S.J. Anderson, R. Costanza, and I. Kubiszewski, 2016: The ecological economics of land degradation: Impacts on ecosystem service values. Ecol. Econ., 129, 182–192, doi:10.1016/j.ecolecon.2016.06.016.</span></li>
-<li><span id="fn:r305">Stockmann, U. et al., 2013: The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ., 164, 80–99, doi:10.1016/J.AGEE.2012.10.001.</span></li>
-<li><span id="fn:r306">Lal, R., 2015: Restoring soil quality to mitigate soil degradation. Sustainability, 7, 5875, doi:10.3390/su7055875.</span></li>
-<li><span id="fn:r307">Haddad, N.M. et al., 2015: Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv., 1, doi:10.1126/sciadv.1500052.</span></li>
-<li><span id="fn:r308">Strack, M., 2008: Peatland and Climate Change. International Peat Society and Saarijärven Offset Oy, Jyväskylä, Finland, 223 pp.</span></li>
-<li><span id="fn:r309">Limpens, J. et al., 2008: Peatlands and the carbon cycle: from local processes to global implications – a synthesis. Biogeosciences, 5, 1475–1491, doi:10.5194/bg-5-1475-2008.</span></li>
-<li><span id="fn:r310">Aich, S., S.M.L. Ewe, B. Gu, T.W. Dreschel, 2014: An evaluation of peat loss from an Everglades tree island, Florida, USA. Mires Peat, 14, 1–15.</span></li>
-<li><span id="fn:r311">Murdiyarso, D. et al., 2015: The potential of Indonesian mangrove forests for global climate change mitigation. Nat. Clim. Chang., 5, 1089–1092, doi:10.1038/NCLIMATE2734.</span></li>
-<li><span id="fn:r312">Kauffman, J.B., H. Hernandez Trejo, M. del Carmen Jesus Garcia, C. Heider, and W.M. Contreras, 2016: Carbon stocks of mangroves and losses arising from their conversion to cattle pastures in the Pantanos de Centla, Mexico. Wetl. Ecol. Manag., 24, 203–216, doi:10.1007/s11273-015-9453-z.</span></li>
-<li><span id="fn:r313">Dohong, A., A.A. Aziz, and P. Dargusch, 2017: A review of the drivers of tropical peatland degradation in South-East Asia. Land use policy, 69, 349–360, doi:10.1016/j.landusepol.2017.09.035.</span></li>
-<li><span id="fn:r314">Arifanti, V.B., J.B. Kauffman, D. Hadriyanto, D. Murdiyarso, and R. Diana, 2018: Carbon dynamics and land use carbon footprints in mangrove-converted aquaculture: The case of the Mahakam Delta, Indonesia. For. Ecol. Manage., 432, 17–29, doi:10.1016/j.foreco.2018.08.047.</span></li>
-<li><span id="fn:r315">Evans, C.D. et al., 2019: Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma, 338, 410–421, doi:10.1016/j.geoderma.2018.12.028.</span></li>
-<li><span id="fn:r316">Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E.A. Elsiddig, H. Haberl, R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N.H. Ravindranath, C.W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</span></li>
-<li><span id="fn:r317">Molina, A., G. Govers, V. Vanacker, and J. Poesen, 2007: Runoff generation in a degraded Andean ecosystem: Interaction of vegetation cover and land use. Catena, 71, 357–370, doi:10.1016/j.catena.2007.04.002.</span></li>
-<li><span id="fn:r318">Valentin, C. et al., 2008: Agriculture, ecosystems and environment runoff and sediment losses from 27 upland catchments in Southeast Asia: Impact of rapid land use changes and conservation practices. Agric. Ecosyst. Environ., 128, 225–238, doi:10.1016/j.agee.2008.06.004.</span></li>
-<li><span id="fn:r319">Mateos, E., J.M. Edeso, and L. Ormaetxea, 2017: Soil erosion and forests biomass as energy resource in the basin of the Oka River in Biscay. Forests, 8, 1–20, doi:10.3390/f8070258.</span></li>
-<li><span id="fn:r320">Noordwijk, M. Van, L. Tanika and B. Lusiana, 2017: Flood risk reduction and flow buffering as ecosystem services – Part 2: Land use and rainfall intensity effects in Southeast Asia. Hydrol. Earth Syst. Sci., 2341–2360, doi:10.5194/hess-21-2341-2017.</span></li>
-<li><span id="fn:r321">Bradshaw, C.J.A., N.S. Sodhi, K.S.-H. Peh, and B.W. Brook, 2007: Global evidence that deforestation amplifies flood risk and severity in the developing world. Glob. Chang. Biol., 13, 2379–2395, doi:10.1111/j.1365-2486.2007.01446.x.</span></li>
-<li><span id="fn:r322">Laurance, W.F., 2007: Forests and floods. Nature, 449, 409–410, doi: 10.1038/449409a.</span></li>
-<li><span id="fn:r323">van Dijk, A.I.J.M. et al., 2009: Forest – flood relation still tenuous – comment on ‘Global evidence that deforestation amplifies flood risk and severity in the developing world’ by C.J.A. Bradshaw, N.S. Sodi, K.S.-H. Peh and B.W. Brook. Glob. Chang. Biol., 15, 110–115, doi:10.1111/j.1365-2486.2008.01708.x.</span></li>
-<li><span id="fn:r324">Bestelmeyer, B.T. et al., 2015: Desertification, land use and the transformation of global drylands. Front. Ecol. Environ., 13, 28–36, doi:10.1890/140162.</span></li>
-<li><span id="fn:r325">Sivakumar, M.V.K., 2007: Interactions between climate and desertification. Agric. For. Meteorol.142, 143–155, doi:10.1016/j.agrformet.2006.03.025.</span></li>
-<li><span id="fn:r326">D’Odorico, P., A. Bhattachan, K.F. Davis, S. Ravi, and C.W. Runyan, 2013: Global desertification: Drivers and feedbacks. Adv. Water Resour., 51, 326–344, doi:10.1016/j.advwatres.2012.01.013.</span></li>
-<li><span id="fn:r327">Bestelmeyer, B.T. et al., 2015: Desertification, land use and the transformation of global drylands. Front. Ecol. Environ., 13, 28–36, doi:10.1890/140162.</span></li>
-<li><span id="fn:r328">Pravalie, R., 2016: Drylands extent and environmental issues. A global approach. Earth-Science Rev., 161, 259–278, doi:10.1016/j.earscirev.2016.08.003.</span></li>
-<li><span id="fn:r329">Koutroulis, A.G., 2019: Dryland changes under different levels of global warming. Sci. Total Environ., 655, 482–511, doi:10.1016/J.SCITOTENV.2018.11.215.</span></li>
-<li><span id="fn:r330">D’Odorico, P., A. Bhattachan, K.F. Davis, S. Ravi, and C.W. Runyan, 2013: Global desertification: Drivers and feedbacks. Adv. Water Resour., 51, 326–344, doi:10.1016/j.advwatres.2012.01.013.</span></li>
-<li><span id="fn:r331">Maestre F.T. et al., 2016: Structure and functioning of dryland ecosystems in a changing world. Annual Review of Ecology, Evolution and Systematics, 47, 215–237, doi:10.1146/annurev-ecolsys-121415-032311.</span></li>
-<li><span id="fn:r332">Abahussain, A.A., A.S. Abdu, W.K. Al-Zubari, N.A. El-Deen and M. Abdul-Raheem, 2002: Desertification in the Arab region: Analysis of current status and trends. J. Arid Environ., 51, 521–545, doi:10.1006/jare.2002.0975.</span></li>
-<li><span id="fn:r333">Cherlet, M. et al., (eds.), 2018: World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management (3rd edition). Publication Office of the European Union, Luxembourg, 247 pp.</span></li>
-<li><span id="fn:r334">HLPE, 2017: Nutrition and Food Systems. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, Rome, Italy, 151 pp.</span></li>
-<li><span id="fn:r335">FAO, 2017: The Future of Food and Agriculture: Trends and Challenges. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r336">FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp.</span></li>
-<li><span id="fn:r337">FAO, IFAD, UNICEF, WFP and WHO, 2018: The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r338">FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp.</span></li>
-<li><span id="fn:r339">Cafiero, C., S. Viviani, and M. Nord, 2018: Food security measurement in a global context: The food insecurity experience scale. Measurement, 116, 146–152, doi:10.1016/J.MEASUREMENT.2017.10.065.</span></li>
-<li><span id="fn:r340">Smith, M.D., M.P. Rabbitt and A. Coleman – Jensen, 2017: Who are the world’s food insecure? New evidence from the food and agriculture organization’s food insecurity experience scale. World Dev., 93, 402–412, doi:10.1016/J.WORLDDEV.2017.01.006.</span></li>
-<li><span id="fn:r341">Osborne, T.M. and T.R. Wheeler, 2013: Evidence for a climate signal in trends of global crop yield variability over the past 50 years. Environ. Res. Lett., 8, 024001, doi:10.1088/1748-9326/8/2/024001.</span></li>
-<li><span id="fn:r342">Tigchelaar, M., D.S. Battisti, R.L. Naylor and D.K. Ray, 2018: Future warming increases probability of globally synchronized maize production shocks. Proc. Natl. Acad. Sci., 115, 6644–6649, doi:10.1073/pnas.1718031115.</span></li>
-<li><span id="fn:r343">Iizumi, T. and N. Ramankutty, 2015: How do weather and climate influence cropping area and intensity? Glob. Food Sec., 4, 46–50, doi:10.1016/j.gfs.2014.11.003.</span></li>
-<li><span id="fn:r344">Loladze, I., 2014: Hidden shift of the ionome of plants exposed to elevated CO2 depletes minerals at the base of human nutrition. Elife, 3, e02245, doi:10.7554/eLife.02245.</span></li>
-<li><span id="fn:r345">Myers, S.S., Zanobetti, A., Kloog, I., Huybers, P., Leakey, A.D., Bloom, A.J., 2014: Increasing CO2 threatens human nutrition. Nature, 510, 139, doi:10.1038/nature13179.</span></li>
-<li><span id="fn:r346">Ziska, L.H. et al., 2016: Rising atmospheric CO2 is reducing the protein concentration of a floral pollen source essential for North American bees. Proceedings. Biol. Sci., 283, 20160414, doi:10.1098/rspb.2016.0414.</span></li>
-<li><span id="fn:r347">Medek, Danielle E., Joel Schwartz, S.S.M., 2017: Estimated effects of future atmospheric CO2 concentrations on protein intake and the risk of protein deficiency by country and region. Env. Heal. Perspect, 125, 087002. doi:10.1289/EHP41.</span></li>
-<li><span id="fn:r348">Nkhonjera, G.K., 2017: Understanding the impact of climate change on the dwindling water resources of South Africa, focusing mainly on Olifants River basin: A review. Environ. Sci. Policy, 71, 19–29, doi:10.1016/J.ENVSCI.2017.02.004.</span></li>
-<li><span id="fn:r349">Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen, 2018: Classifying drivers of global forest loss. Science, 361, 1108–1111, doi:10.1126/science.aau3445.</span></li>
-<li><span id="fn:r350">Franchini, M. and P.M. Mannucci, 2015: Impact on human health of climate changes. Eur. J. Intern. Med., 26, 1–5, doi:10.1016/j.ejim.2014.12.008.</span></li>
-<li><span id="fn:r351">Wu, X., Y. Lu, S. Zhou, L. Chen and B. Xu, 2016: Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ. Int., 86, 14–23, doi:10.1016/J.ENVINT.2015.09.007.</span></li>
-<li><span id="fn:r352">Raiten, D.J. and A.M. Aimone, 2017: The intersection of climate/environment, food, nutrition and health: Crisis and opportunity. Curr. Opin. Biotechnol., 44, 52–62, doi:10.1016/J.COPBIO.2016.10.006.</span></li>
-<li><span id="fn:r353">van Noordwijk, M. and L. Brussaard, 2014: Minimizing the ecological footprint of food: Closing yield and efficiency gaps simultaneously? Curr. Opin. Environ. Sustain., 8, 62–70, doi:10.1016/J.COSUST.2014.08.008.</span></li>
-<li><span id="fn:r354">Thyberg, K.L. and D.J. Tonjes, 2016: Drivers of food waste and their implications for sustainable policy development. Resour. Conserv. Recycl., 106, 110–123, doi:10.1016/J.RESCONREC.2015.11.016.</span></li>
-<li><span id="fn:r355">Borsato, E., P. Tarolli, and F. Marinello, 2018: Sustainable patterns of main agricultural products combining different footprint parameters. J. Clean. Prod., 179, 357–367, doi:10.1016/J.JCLEPRO.2018.01.044.</span></li>
-<li><span id="fn:r356">Kibler, K.M., D. Reinhart, C. Hawkins, A.M. Motlagh, and J. Wright, 2018: Food waste and the food-energy-water nexus: A review of food waste management alternatives. Waste Manag., 74, 52–62, doi:10.1016/J.WASMAN.2018.01.014.</span></li>
-<li><span id="fn:r357">Malone, R.W. et al., 2014: Cover crops in the upper midwestern United States: Simulated effect on nitrate leaching with artificial drainage. J. Soil Water Conserv., 69, 292–305, doi:10.2489/jswc.69.4.292.</span></li>
-<li><span id="fn:r358">Norse, D. and X. Ju, 2015: Environmental costs of China’s food security. Agric. Ecosyst. Environ., 209, 5–14, doi:10.1016/J.AGEE.2015.02.014.</span></li>
-<li><span id="fn:r359">Schipper, L.A., R.L. Parfitt, S. Fraser, R.A. Littler, W.T. Baisden and C. Ross, 2014: Soil order and grazing management effects on changes in soil C and N in New Zealand pastures. Agric. Ecosyst. Environ., 184, 67–75, doi:10.1016/J.AGEE.2013.11.012.</span></li>
-<li><span id="fn:r360">Eeraerts, M., I. Meeus, S. Van Den Berge, and G. Smagghe, 2017: Landscapes with high intensive fruit cultivation reduce wild pollinator services to sweet cherry. Agric. Ecosyst. Environ., 239, 342–348, doi:10.1016/J.AGEE.2017.01.031.</span></li>
-<li><span id="fn:r361">Röös, E. et al., 2017: Greedy or needy? Land use and climate impacts of food in 2050 under different livestock futures. Glob. Environ. Chang., 47, 1–12, doi:10.1016/J.GLOENVCHA.2017.09.001.</span></li>
-<li><span id="fn:r362">Salmon, G. et al., 2018: Trade-offs in livestock development at farm level: Different actors with different objectives. Glob. Food Sec., doi:10.1016/J.GFS.2018.04.002.</span></li>
-<li><span id="fn:r363">Haberl, H., 2015: Competition for land: A sociometabolic perspective. Elsevier, 119, 424–431, doi:10.1016/j.ecolecon.2014.10.002.</span></li>
-<li><span id="fn:r364">Dell’Angelo, J., P. D’Odorico, M.C. Rulli, and P. Marchand, 2017b: The tragedy of the grabbed commons: Coercion and dispossession in the global land rush. World Dev., 92, 1–12, doi:10.1016/J.WORLDDEV.2016.11.005.</span></li>
-<li><span id="fn:r365">Doss, C., C. Kovarik, A. Peterman, A. Quisumbing, and M. van den Bold, 2015: Gender inequalities in ownership and control of land in Africa: Myth and reality. Agric. Econ., 46, 403–434, doi:10.1111/agec.12171.</span></li>
-<li><span id="fn:r366">Ravnborg, H.M., R. Spichiger, R.B. Broegaard, and R.H. Pedersen, 2016: Land governance, gender equality and development: Past achievements and remaining challenges. J. Int. Dev., 28, 412–427, doi:10.1002/jid.3215.</span></li>
-<li><span id="fn:r367">Moroni, S., 2018: Property as a human right and property as a special title. Rediscussing private ownership of land. Land use policy, 70, 273–280, doi:10.1016/J.LANDUSEPOL.2017.10.037.</span></li>
-<li><span id="fn:r368">Lambin, E.F. and P. Meyfroidt, 2011: Global land use change, economic globalization and the looming land scarcity. Proc Natl Acad Sci U S A, 108, 3465–3472, doi:10.1073/pnas.1100480108.</span></li>
-<li><span id="fn:r369">Lambin, E.F., 2012: Global land availability: Malthus versus Ricardo.Global Food Security, 1, 83–87, doi:10.1016/j.gfs.2012.11.002.</span></li>
-<li><span id="fn:r370">Venter, O. et al., 2016: Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun., 7, doi:10.1038/ncomms12558.</span></li>
-<li><span id="fn:r371">Tilman, D., C. Balzer, J. Hill, and B.L. Befort, 2011: Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci., 108, 20260–20264, doi:10.1073/pnas.1116437108.</span></li>
-<li><span id="fn:r372">Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452.</span></li>
-<li><span id="fn:r373">Lambin, E.F., 2012: Global land availability: Malthus versus Ricardo.Global Food Security, 1, 83–87, doi:10.1016/j.gfs.2012.11.002.</span></li>
-<li><span id="fn:r374">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r375">Hussein, Z., T. Hertel and A. Golub, 2013: Climate change mitigation policies and poverty in developing countries. Environ. Res. Lett., 8, 035009, [PAGE CITATION?] doi:10.1088/1748-9326/8/3/035009.</span></li>
-<li><span id="fn:r376">Dell’Angelo, J., P. D’Odorico, and M.C. Rulli, 2017a: Threats to sustainable development posed by land and water grabbing. Curr. Opin. Environ. Sustain., 26–27, 120–128, doi:10.1016/j.cosust.2017.07.007.</span></li>
-<li><span id="fn:r377">Land Matrix, 2018: Land Matrix Global Observatory. http://www.landmatrix.org .</span></li>
-<li><span id="fn:r378">Rulli, M.C., A. Saviori, and P. D’Odorico, 2012: Global land and water grabbing. Pnas, 110, 892–897, doi:10.1073/pnas.1213163110/-/DCSupplemental.</span></li>
-<li><span id="fn:r379">Nolte, K., W. Chamberlain and M. Giger, 2016: International Land Deals for Agriculture: Fresh insights from the Land Matrix: Analytical Report II. Centre for Development and Environment, University of Bern; Centre de coopération internationale en recherche agronomique pour le développement; German Institute of Global and Area Studies; University of Pretoria; Bern Open Publishing, Bern, Montpellier, Hamburg, Pretoria, 1–56 pp.</span></li>
-<li><span id="fn:r380">Constantin, C., C. Luminița, and A.J. Vasile, 2017: Land grabbing: A review of extent and possible consequences in Romania. Land use policy, 62, 143–150, doi:10.1016/j.landusepol.2017.01.001.</span></li>
-<li><span id="fn:r381">Deininger, K. et al., 2011: Rising Global Interest in Farmland: Can it Yield Sustainable and Equitable Benefits? 1st ed. The World Bank, Washington D.C., 164 pp. doi:10.1596/978-0-8213-8591-3.</span></li>
-<li><span id="fn:r382">Dell’Angelo, J., P. D’Odorico, and M.C. Rulli, 2017a: Threats to sustainable development posed by land and water grabbing. Curr. Opin. Environ. Sustain., 26–27, 120–128, doi:10.1016/j.cosust.2017.07.007.</span></li>
-<li><span id="fn:r383">Anseeuw, W., L.A. Wily, L. Cotula, and M. Taylor, 2011: Land Rights and the Rush for Land: Findings of the Global Commercial Pressures on Land Research Project. International Land Coalition, Rome, Italy, 72 pp.</span></li>
-<li><span id="fn:r384">Messerli, P., M. Giger, M.B. Dwyer, T. Breu and S. Eckert, 2014: The geography of large-scale land acquisitions: Analysing socio-ecological patterns of target contexts in the global South. Appl. Geogr., 53, 449–459, doi:10.1016/j.apgeog.2014.07.005.</span></li>
-<li><span id="fn:r385">Davis, K.F., K. Yu, M.C. Rulli, L. Pichdara and P. D’Odorico, 2015: Accelerated deforestation driven by large-scale land acquisitions in Cambodia. Nat. Geosci., 8, 772–775, doi:10.1038/ngeo2540.</span></li>
-<li><span id="fn:r386">Deininger, K. et al., 2011: Rising Global Interest in Farmland: Can it Yield Sustainable and Equitable Benefits? 1st ed. The World Bank, Washington D.C., 164 pp. doi:10.1596/978-0-8213-8591-3.</span></li>
-<li><span id="fn:r387">Wang, X. et al., 2016: Taking account of governance: Implications for land-use dynamics, food prices and trade patterns. Ecol. Econ., 122, 12–24, doi:10.1016/j.ecolecon.2015.11.018.</span></li>
-<li><span id="fn:r388">Xu, Y., 2018: Political economy of land grabbing inside China involving foreign investors. Third World Q., 39(11), 2069–2084, doi:10.1080/01436597.2018.1447372.</span></li>
-<li><span id="fn:r389">McDonnell, S., 2017: Urban land grabbing by political elites: Exploring the political economy of land and the challenges of regulation. In: Kastom, property and ideology: Land transformations in Melanesia [McDonnell, S., M.G. Allen, C. Filer (Eds.)]. Australian National University Press, Canberra, Australia, pp. 283–304.</span></li>
-<li><span id="fn:r390">Rosenzweig, C. and P. Neofotis, 2013: Detection and attribution of anthropogenic climate change impacts. Wiley Interdiscip. Rev. Chang., 4, 121–150, doi:10.1002/wcc.209.</span></li>
-<li><span id="fn:r391">Gillett, N.P. et al., 2016: The detection and attribution model intercomparison project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev., 9, 3685–3697, doi:10.5194/gmd-9-3685-2016.</span></li>
-<li><span id="fn:r392">Lean, J.L., 2018: Observation-based detection and attribution of 21st century climate change. Wiley Interdiscip. Rev. Chang., 9, doi:0.1002/wcc.511.</span></li>
-<li><span id="fn:r393">Mastrandrea, M.D. et al., 2011: The IPCC AR5 guidance note on consistent treatment of uncertainties: A common approach across the working groups. Clim. Change, 108, 675, doi:10.1007/s10584-011-0178-6.</span></li>
-<li><span id="fn:r394">Hansen, M.C. et al., 2013: High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853, doi:10.1126/science.1244693.</span></li>
-<li><span id="fn:r395">He, T. et al., 2018: Evaluating land surface albedo estimation from Landsat MSS, TM, ETM + and OLI data based on the unified direct estimation approach. Remote Sens. Environ., 204, 181–196, doi:10.1016/j.rse.2017.10.031.</span></li>
-<li><span id="fn:r396">Ardö, J., T. Tagesson, S. Jamali, and A. Khatir, 2018: MODIS EVI-based net primary production in the Sahel 2000–2014. Int. J. Appl. Earth Obs. Geoinf., 65, 35–45, doi:10.1016/j.jag.2017.10.002.</span></li>
-<li><span id="fn:r397">Spennemann, P.C. et al., 2018: Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models. Int. J. Appl. Earth Obs. Geoinf., 64, 96–103, doi:10.1016/j.jag.2017.08.016.</span></li>
-<li><span id="fn:r398">Kostyanovsky, K.I., D.R. Huggins, C.O. Stockle, S. Waldo, and B. Lamb, 2018: Developing a flow through chamber system for automated measurements of soil N2O and CO2 emissions. Meas. J. Int. Meas. Confed., 113, 172–180, doi:10.1016/j.measurement.2017.05.040.</span></li>
-<li><span id="fn:r399">Brümmer, C. et al., 2017: Gas chromatography vs. quantum cascade laser-based N2O flux measurements using a novel chamber design. Biogeosciences, 14, 1365–1381, doi:10.5194/bg-14-1365-2017.</span></li>
-<li><span id="fn:r400">Iwata, Y., T. Miyamoto, K. Kameyama and M. Nishiya, 2017: Effect of sensor installation on the accurate measurement of soil water content. Eur. J. Soil Sci., 68, 817–828, doi:10.1111/ejss.12493.</span></li>
-<li><span id="fn:r401">Valayamkunnath, P., V. Sridhar, W. Zhao, and R.G. Allen, 2018: Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate. Agric. For. Meteorol., 248, 22–47, doi:10.1016/j.agrformet.2017.08.025.</span></li>
-<li><span id="fn:r402">Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447.</span></li>
-<li><span id="fn:r403">Chen, J. et al., 2014: Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens., 103, 7–27, doi:10.1016/j.isprsjprs.2014.09.002.</span></li>
-<li><span id="fn:r404">Yu, L. et al., 2014: Meta-discoveries from a synthesis of satellite-based land-cover mapping research. Int. J. Remote Sens., 35, 4573–4588, doi:10.1080/01431161.2014.930206.</span></li>
-<li><span id="fn:r405">Lacaze, R. et al., 2015: Operational 333m biophysical products of the copernicus global land service for agriculture monitoring. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – ISPRS Arch., 40, 53–56, doi:10.5194/isprsarchives-XL-7-W3-53-2015.</span></li>
-<li><span id="fn:r406">Song, X.-P., 2018: Global estimates of ecosystem service value and change: Taking into account uncertainties in satellite-based land cover data. Ecol. Econ., 143, 227–235, doi:10.1016/j.ecolecon.2017.07.019.</span></li>
-<li><span id="fn:r407">Peterson, E.E., S.A. Cunningham, M. Thomas, S. Collings, G.D. Bonnett and B. Harch, 2017: An assessment framework for measuring agroecosystem health. Ecol. Indic., 79, 265–275, doi:10.1016/j.ecolind.2017.04.002.</span></li>
-<li><span id="fn:r408">Santilli, G., C. Vendittozzi, C. Cappelletti, S. Battistini, and P. Gessini, 2018: CubeSat constellations for disaster management in remote areas. Acta Astronaut., 145, 11–17, doi:10.1016/j.actaastro.2017.12.050.</span></li>
-<li><span id="fn:r409">Li, W. et al., 2017: Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences, 1 145194, 5053–5067, doi:10.5194/bg-14-5053-2017.</span></li>
-<li><span id="fn:r410">Clark, D.A. et al., 2017: Reviews and syntheses: Field data to benchmark the carbon cycle models for tropical forests. Biogeosciences, 14, 4663–4690, doi:10.5194/bg-14-4663-2017.</span></li>
-<li><span id="fn:r411">Lees, K.J., T. Quaife, R.R.E. Artz, M. Khomik, and J.M. Clark, 2018: Potential for using remote sensing to estimate carbon fluxes across northern peatlands – A review. Sci. Total Environ., 615, 857–874, doi:10.1016/j.scitotenv.2017.09.103.</span></li>
-<li><span id="fn:r412">Shtienberg, D., 2013: Will decision-support systems be widely used for the management of plant diseases? Annu. Rev. Phytopathol., 51, 1–16, doi:10.1146/annurev-phyto-082712-102244.</span></li>
-<li><span id="fn:r413">EL Jarroudi, M. et al., 2015: Economics of a decision-support system for managing the main fungal diseases of winter wheat in the Grand-Duchy of Luxembourg. F. Crop. Res., 172, 32–41, doi:10.1016/J.FCR.2014.11.012.</span></li>
-<li><span id="fn:r414">Caffi, T., S.E. Legler, V. Rossi, and R. Bugiani, 2012: Evaluation of a warning system for early-season control of grapevine powdery mildew. Plant Dis., 96, 104–110, doi:10.1094/PDIS-06-11-0484.</span></li>
-<li><span id="fn:r415">Watmuff, G., D.J. Reuter and S.D. Speirs, 2013: Methodologies for assembling and interrogating N, P, K, and S soil test calibrations for Australian cereal, oilseed and pulse crops. Crop Pasture Sci., 64, 424, doi:10.1071/CP12424.</span></li>
-<li><span id="fn:r416">EL Jarroudi, M. et al., 2015: Economics of a decision-support system for managing the main fungal diseases of winter wheat in the Grand-Duchy of Luxembourg. F. Crop. Res., 172, 32–41, doi:10.1016/J.FCR.2014.11.012.</span></li>
-<li><span id="fn:r417">Chipanshi, A. et al., 2015: Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agric. For. Meteorol., 206, 137–150, doi:10.1016/J.AGRFORMET.2015.03.007.</span></li>
-<li><span id="fn:r418">Caffi, T., S.E. Legler, V. Rossi, and R. Bugiani, 2012: Evaluation of a warning system for early-season control of grapevine powdery mildew. Plant Dis., 96, 104–110, doi:10.1094/PDIS-06-11-0484.</span></li>
-<li><span id="fn:r419">Shtienberg, D., 2013: Will decision-support systems be widely used for the management of plant diseases? Annu. Rev. Phytopathol., 51, 1–16, doi:10.1146/annurev-phyto-082712-102244.</span></li>
-<li><span id="fn:r420">Ahlstrom, A., B. Smith, J. Lindstrom, M. Rummukainen and C.B. Uvo, 2013: GCM characteristics explain the majority of uncertainty in projected 21st century terrestrial ecosystem carbon balance. Biogeosciences, 10, 1517–1528, doi:10.5194/bg-10-1517-2013.</span></li>
-<li><span id="fn:r421">Randerson, J.T. et al., 2009: Systematic assessment of terrestrial biogeochemistry in coupled climate-carbon models. Glob. Chang. Biol., 15, 2462–2484, doi:10.1111/j.1365-2486.2009.01912.x.</span></li>
-<li><span id="fn:r422">Luo, Y.Q. et al., 2012: A framework of benchmarking land models. Biogeosciences, 10, 3857–3874, doi:10.5194/bgd-9-1899-2012.</span></li>
-<li><span id="fn:r423">Kelley, D.I. et al., 2013: A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences, 10, 3313–3340, doi:10.5194/bg-10-3313-2013.</span></li>
-<li><span id="fn:r424">Luo, Y.Q. et al., 2012: A framework of benchmarking land models. Biogeosciences, 10, 3857–3874, doi:10.5194/bgd-9-1899-2012.</span></li>
-<li><span id="fn:r425">Kelley, D.I. et al., 2013: A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences, 10, 3313–3340, doi:10.5194/bg-10-3313-2013.</span></li>
-<li><span id="fn:r426">Buisson, L., W. Thuiller, N. Casajus, S. Lek, and G. Grenouillet, 2009: Uncertainty in ensemble forecasting of species distribution. Glob. Chang. Biol., 16, 1145–1157, doi:10.1111/j.1365-2486.2009.02000.x.</span></li>
-<li><span id="fn:r427">Parker, W.S., 2013: Ensemble modeling, uncertainty and robust predictions. Wiley Interdiscip. Rev. Chang., 4, 213–223, doi:10.1002/wcc.220.</span></li>
-<li><span id="fn:r428">Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337.</span></li>
-<li><span id="fn:r429">Fuchs, R., M. Herold, P.H. Verburg, J.G.P.W. Clevers, and J. Eberle, 2015: Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010. Glob. Chang. Biol., 21, 299–313, doi:10.1111/gcb.12714.</span></li>
-<li><span id="fn:r430">Eitelberg, D.A., J. van Vliet, J.C. Doelman, E. Stehfest, and P.H. Verburg, 2016: Demand for biodiversity protection and carbon storage as drivers of global land change scenarios. Glob. Environ. Chang., 40, 101–111, doi:10.1016/j.gloenvcha.2016.06.014.</span></li>
-<li><span id="fn:r431">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r432">Krause, A. et al., 2017: Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators. Biogeosciences, 4829–4850, doi:10.5194/bg-2017-160.</span></li>
-<li><span id="fn:r433">Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447.</span></li>
-<li><span id="fn:r434">Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.</span></li>
-<li><span id="fn:r435">Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337.</span></li>
-<li><span id="fn:r436">Rose, S.K., 2014: Integrated assessment modeling of climate change adaptation in forestry and pasture land use: A review. Energy Econ., 46, 548–554, doi:10.1016/J.ENECO.2014.09.018.</span></li>
-<li><span id="fn:r437">Arneth, A. et al., 2017: Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci., 10, 79, doi:10.1038/ngeo2882.</span></li>
-<li><span id="fn:r438">Arneth, A. et al., 2017: Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci., 10, 79, doi:10.1038/ngeo2882.</span></li>
-<li><span id="fn:r439">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r440">Riahi, K. et al., 2017: The shared socioeconomic pathways and their energy, land use and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.</span></li>
-<li><span id="fn:r441">van Vuuren, D.P. and T.R. Carter, 2014: Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old. Clim. Change, 122, 415–429, doi:10.1007/s10584-013-0974-2.</span></li>
-<li><span id="fn:r442">O’Neill, B.C. et al., 2014: A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Clim. Change, 122, 387–400, doi:10.1007/s10584-013-0905-2.</span></li>
-<li><span id="fn:r443">O’Neill, B.C. et al., 2014: A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Clim. Change, 122, 387–400, doi:10.1007/s10584-013-0905-2.</span></li>
-<li><span id="fn:r444">Rounsevell, M.D.A. and M.J. Metzger, 2010: Developing qualitative scenario storylines for environmental change assessment. Wiley Interdiscip. Rev. Clim. Chang., 1, 606–619, doi:10.1002/wcc.63.</span></li>
-<li><span id="fn:r445">Kok, M.T.J. et al., 2018: Pathways for agriculture and forestry to contribute to terrestrial biodiversity conservation: A global scenario-study. Biol. Conserv., 221, 137–150, doi:10.1016/j.biocon.2018.03.003.</span></li>
-<li><span id="fn:r446">IPBES, 2016: The Methodological Assessment Report on Scenarios and Models of Biodiversity and Ecosystem Services [S. Ferrier et al., (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 348 pp.</span></li>
-<li><span id="fn:r447">IPCC, 2018: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. In press, 1552 pp.</span></li>
-<li><span id="fn:r448">Vuuren, D.P. Van et al., 2018: The need for negative emission technologies. Nat. Clim. Chang., 8, 391–397, doi:10.1038/s41558-018-0119-8.</span></li>
-<li><span id="fn:r449">Engstrom, K. et al., 2016: Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework. Earth Syst. Dyn., 7, 893–915, doi:10.5194/esd-7-893-2016.</span></li>
-<li><span id="fn:r450">Henry, R.C. et al., 2018: Food supply and bioenergy production within the global cropland planetary boundary. PLoS One, 13, e0194695–e0194695, doi:10.1371/journal.pone.0194695.</span></li>
-<li><span id="fn:r451">IPCC, 2014: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp</span></li>
-<li><span id="fn:r452">Kraxner, F. et al., 2013: Global bioenergy scenarios – Future forest development, land-use implications and trade-offs. Biomass and Bioenergy, 57, 86–96, doi:10.1016/j.biombioe.2013.02.003.</span></li>
-<li><span id="fn:r453">Humpenoder, F. et al., 2014: Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environ. Res. Lett., 9, 064029, doi:10.1088/1748-9326/9/6/064029.</span></li>
-<li><span id="fn:r454">Krause, A. et al., 2017: Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators. Biogeosciences, 4829–4850, doi:10.5194/bg-2017-160.</span></li>
-<li><span id="fn:r455">Warszawski, L., K. Frieler, V. Huber, F. Piontek, O. Serdeczny and J. Schewe, 2014: The inter-sectoral impact model intercomparison project (ISI–MIP): Project framework. Proc. Natl. Acad. Sci., 111, 3228–3232, doi:10.1073/pnas.1312330110.</span></li>
-<li><span id="fn:r456">Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452.</span></li>
-<li><span id="fn:r457">Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2013: Embodied crop calories in animal products. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/4/044044.</span></li>
-<li><span id="fn:r458">Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2014: Food Self-Sufficiency across Scales: How Local Can We Go? 15, 9779, doi:10.1021/es5005939.</span></li>
-<li><span id="fn:r459">Wolff, S., E.A. Schrammeijer, C. Schulp and P.H. Verburg, 2018: Meeting global land restoration and protection targets: What would the world look like in 2050? Glob. Environ. Chang., 52, 259–272, doi:10.1016/j.gloenvcha.2018.08.002.</span></li>
-<li><span id="fn:r460">Nakicenovic and Swart 2000 à IPCC, 2000: Special Report on Emissions Scenarios. Nature Publishing Group [Nakićenović, N. and R. Swart (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 612 pp.</span></li>
-<li><span id="fn:r461">Rounsevell, M.D.A. and M.J. Metzger, 2010: Developing qualitative scenario storylines for environmental change assessment. Wiley Interdiscip. Rev. Clim. Chang., 1, 606–619, doi:10.1002/wcc.63.</span></li>
-<li><span id="fn:r462">O’Neill, B.C. et al., 2014: A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Clim. Change, 122, 387–400, doi:10.1007/s10584-013-0905-2.</span></li>
-<li><span id="fn:r463">Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447.</span></li>
-<li><span id="fn:r464">Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337.</span></li>
-<li><span id="fn:r465">Harrison, P.A., R. Dunford, C. Savin, M.D.A. Rounsevell, I.P. Holman, A.S. Kebede and B. Stuch, 2014: Cross-sectoral impacts of climate change and socio-economic change for multiple, European land – and water-based sectors. Clim. Change, 128, 279–292, doi:10.1007/s10584-014-1239-4.</span></li>
-<li><span id="fn:r466">Harrison, P.A., R.W. Dunford, I.P. Holman, and M.D.A. Rounsevell, 2016: Climate change impact modelling needs to include cross-sectoral interactions. Nat. Clim. Chang., 6, 885–890, doi:10.1038/nclimate3039.</span></li>
-<li><span id="fn:r467">Rounsevell, M.D.A. et al., 2006: A coherent set of future land use change scenarios for Europe. Agric. Ecosyst. Environ., 114, 57–68, doi:10.1016/j.agee.2005.11.027.</span></li>
-<li><span id="fn:r468">Wise, R.M., I. Fazey, M.S. Smith, S.E. Park, H.C. Eakin, E.R.M.A. Van Garderen and B. Campbell, 2014: Reconceptualising adaptation to climate change as part of pathways of change and response. Glob. Environ. Chang., 28, 325–336, doi:10.1016/j.gloenvcha.2013.12.002.</span></li>
-<li><span id="fn:r469">Kreidenweis, U. et al., 2018: Pasture intensification is insufficient to relieve pressure on conservation priority areas in open agricultural markets. Glob. Chang. Biol., 24, 3199–3213, doi:10.1111/gcb.14272.</span></li>
-<li><span id="fn:r470">Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452.</span></li>
-<li><span id="fn:r471">Weindl, I. et al., 2017: Livestock and human use of land: Productivity trends and dietary choices as drivers of future land and carbon dynamics. Glob. Planet. Change, 159, 1–10, doi:10.1016/j.gloplacha.2017.10.002.</span></li>
-<li><span id="fn:r472">Kreidenweis, U. et al., 2018: Pasture intensification is insufficient to relieve pressure on conservation priority areas in open agricultural markets. Glob. Chang. Biol., 24, 3199–3213, doi:10.1111/gcb.14272.</span></li>
-<li><span id="fn:r473">Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2013: Embodied crop calories in animal products. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/4/044044.</span></li>
-<li><span id="fn:r474">Alexander, P., C. Brown, A. Arneth, J. Finnigan, and M.D.A. Rounsevell, 2016b: Human appropriation of land for food: The role of diet. Glob. Environ. Chang. Policy Dimens., 41, 88–98, doi:10.1016/j.gloenvcha.2016.09.005.</span></li>
-<li><span id="fn:r475">Weindl, I. et al., 2017: Livestock and human use of land: Productivity trends and dietary choices as drivers of future land and carbon dynamics. Glob. Planet. Change, 159, 1–10, doi:10.1016/j.gloplacha.2017.10.002.</span></li>
-<li><span id="fn:r476">Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.</span></li>
-<li><span id="fn:r477">Vuuren, D.P. Van et al., 2018: The need for negative emission technologies. Nat. Clim. Chang., 8, 391–397, doi:10.1038/s41558-018-0119-8.</span></li>
-<li><span id="fn:r478">Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353.</span></li>
-<li><span id="fn:r479">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r480">Riahi, K. et al., 2017: The shared socioeconomic pathways and their energy, land use and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.</span></li>
-<li><span id="fn:r481">Doelman, J.C. et al., 2018: Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Chang., 48, 119–135, doi:10.1016/j.gloenvcha.2017.11.014.</span></li>
-<li><span id="fn:r482">Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2013: Embodied crop calories in animal products. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/4/044044.</span></li>
-<li><span id="fn:r483">Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2014: Food Self-Sufficiency across Scales: How Local Can We Go? 15, 9779, doi:10.1021/es5005939.</span></li>
-<li><span id="fn:r484">Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 1–12, doi:10.1088/1748-9326/11/8/085001.</span></li>
-<li><span id="fn:r485">Rogelj, J. et al., 2018b: Scenarios towards limiting global mean temperature increase below 1.5 degrees C. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.</span></li>
-<li><span id="fn:r486">Seneviratne, S.I. et al., 2018: Climate extremes, land-climate feedbacks and land-use forcing at 1.5 degrees C. Philos. Trans. R. Soc. a-Mathematical Phys. Eng. Sci., 376, doi:2016045010.1098/rsta.2016.0450.</span></li>
-<li><span id="fn:r487">Vuuren, D.P. Van et al., 2018: The need for negative emission technologies. Nat. Clim. Chang., 8, 391–397, doi:10.1038/s41558-018-0119-8.</span></li>
-<li><span id="fn:r488">Challinor, A.J. et al., 2018: Transmission of climate risks across sectors and borders Subject Areas.</span></li>
-<li><span id="fn:r489">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r490">O’Neill, B. C, 2004: Conditional Probabilistic Population Projections: An Application to Climate Change. International Statistical Institute, 72(2), 167–184.</span></li>
-<li><span id="fn:r491">Brown, C. et al., 2014: Analysing uncertainties in climate change impact assessment across sectors and scenarios. Clim. Change, 128, 293–306, doi:10.1007/s10584-014-1133-0.</span></li>
-<li><span id="fn:r492">Engstrom, K. et al., 2016: Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework. Earth Syst. Dyn., 7, 893–915, doi:10.5194/esd-7-893-2016.</span></li>
-<li><span id="fn:r493">Henry, R.C. et al., 2018: Food supply and bioenergy production within the global cropland planetary boundary. PLoS One, 13, e0194695–e0194695, doi:10.1371/journal.pone.0194695.</span></li>
-<li><span id="fn:r494">Engstrom, K. et al., 2016: Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework. Earth Syst. Dyn., 7, 893–915, doi:10.5194/esd-7-893-2016.</span></li>
-<li><span id="fn:r495">Henry, R.C. et al., 2018: Food supply and bioenergy production within the global cropland planetary boundary. PLoS One, 13, e0194695–e0194695, doi:10.1371/journal.pone.0194695.</span></li>
-<li><span id="fn:r496">Heck, V., D. Gerten, W. Lucht and A. Popp, 2018: Biomass-based negative emissions difficult to reconcile with planetary boundaries. Nat. Clim. Chang., 8, 151–155, doi:10.1038/s41558-017-0064-y.</span></li>
-<li><span id="fn:r497">Brown, C. et al., 2014: Analysing uncertainties in climate change impact assessment across sectors and scenarios. Clim. Change, 128, 293–306, doi:10.1007/s10584-014-1133-0.</span></li>
-<li><span id="fn:r498">Harrison, P.A., R. Dunford, C. Savin, M.D.A. Rounsevell, I.P. Holman, A.S. Kebede and B. Stuch, 2014: Cross-sectoral impacts of climate change and socio-economic change for multiple, European land – and water-based sectors. Clim. Change, 128, 279–292, doi:10.1007/s10584-014-1239-4.</span></li>
-<li><span id="fn:r499">Dunford, R., P.A. Harrison, J. Jäger, M.D.A. Rounsevell, and R. Tinch, 2014: Exploring climate change vulnerability across sectors and scenarios using indicators of impacts and coping capacity. Clim. Change, 128, 339–354, doi:10.1007/s10584-014-1162-8.</span></li>
-<li><span id="fn:r500">Kok, K. et al., 2014: European participatory scenario development: Strengthening the link between stories and models. Clim. Change, 128, 187–200, doi:10.1007/s10584-014-1143-y.</span></li>
-<li><span id="fn:r501">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r502">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r503">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r504">Rosa, I.M.D.I.M.D. et al., 2017: Multiscale scenarios for nature futures. Nat. Ecol. Evol., 1, 1416–1419, doi:10.1038/s41559-017-0273-9.</span></li>
-<li><span id="fn:r505">Rosa, I.M.D.I.M.D. et al., 2017: Multiscale scenarios for nature futures. Nat. Ecol. Evol., 1, 1416–1419, doi:10.1038/s41559-017-0273-9.</span></li>
-<li><span id="fn:r506">Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447.</span></li>
-<li><span id="fn:r507">Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337.</span></li>
-<li><span id="fn:r508">Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337.</span></li>
-<li><span id="fn:r509">Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447.</span></li>
-<li><span id="fn:r510">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r511">Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337.</span></li>
-<li><span id="fn:r512">Ahlstrom, A., G. Schurgers, A. Arneth and B. Smith, 2012: Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environ. Res. Lett., 7, doi:04400810.1088/1748-9326/7/4/044008.</span></li>
-<li><span id="fn:r513">Kelley, D.I. et al., 2013: A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences, 10, 3313–3340, doi:10.5194/bg-10-3313-2013.</span></li>
-<li><span id="fn:r514">Dietrich, J.P. et al., 2018: MAgPIE 4 – A modular open source framework for modeling global land-systems. Geosci. Model Dev. 12(4) 1299–1317, doi:10.5194/gmd-2018-295.</span></li>
-<li><span id="fn:r515">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r516">Rounsevell, M.D.A. et al., 2014: Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dyn., 5, 117–137, doi:10.5194/esd-5-117-2014.</span></li>
-<li><span id="fn:r517">Arneth, A., C. Brown, and M.D.A. Rounsevell, 2014: Global models of human decision-making for land-based mitigation and adaptation assessment. Nat. Clim. Chang., 4, 550–557, doi:10.1038/nclimate2250.</span></li>
-<li><span id="fn:r518">Calvin, K. and B. Bond-Lamberty, 2018: Integrated human-earth system modeling – State of the science and future directions. Environ. Res. Lett., 13, doi:10.1088/1748-9326/aac642.</span></li>
-<li><span id="fn:r519">Arneth, A., C. Brown, and M.D.A. Rounsevell, 2014: Global models of human decision-making for land-based mitigation and adaptation assessment. Nat. Clim. Chang., 4, 550–557, doi:10.1038/nclimate2250.</span></li>
-<li><span id="fn:r520">Arneth, A., C. Brown, and M.D.A. Rounsevell, 2014: Global models of human decision-making for land-based mitigation and adaptation assessment. Nat. Clim. Chang., 4, 550–557, doi:10.1038/nclimate2250.</span></li>
-<li><span id="fn:r521">Rounsevell, M.D.A. et al., 2014: Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dyn., 5, 117–137, doi:10.5194/esd-5-117-2014.</span></li>
-<li><span id="fn:r522">Wang, X. et al., 2016: Taking account of governance: Implications for land-use dynamics, food prices and trade patterns. Ecol. Econ., 122, 12–24, doi:10.1016/j.ecolecon.2015.11.018.</span></li>
-<li><span id="fn:r523">Rounsevell, M.D.A. et al., 2014: Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dyn., 5, 117–137, doi:10.5194/esd-5-117-2014.</span></li>
-<li><span id="fn:r524">Arneth, A., C. Brown, and M.D.A. Rounsevell, 2014: Global models of human decision-making for land-based mitigation and adaptation assessment. Nat. Clim. Chang., 4, 550–557, doi:10.1038/nclimate2250.</span></li>
-<li><span id="fn:r525">Robinson, D.A. et al., 2017: Modelling feedbacks between human and natural processes in the land system. Earth Syst. Dyn. Discuss., doi:10.5194/esd-2017-68.</span></li>
-<li><span id="fn:r526">Brown, C., P. Alexander, S. Holzhauer, and M.D.A. Rounsevell, 2017: Behavioral models of climate change adaptation and mitigation in land-based sectors. Wiley Interdiscip. Rev. Clim. Chang., 8, e448, doi:10.1002/wcc.448.</span></li>
-<li><span id="fn:r527">Calvin, K. and B. Bond-Lamberty, 2018: Integrated human-earth system modeling – State of the science and future directions. Environ. Res. Lett., 13, doi:10.1088/1748-9326/aac642.</span></li>
-<li><span id="fn:r528">Rosenzweig, C. and P. Neofotis, 2013: Detection and attribution of anthropogenic climate change impacts. Wiley Interdiscip. Rev. Chang., 4, 121–150, doi:10.1002/wcc.209.</span></li>
-<li><span id="fn:r529">Anav, A. et al., 2013: Evaluating the land and ocean components of the global carbon cycle in the CMIP5 Earth system models. J. Clim., 26, 6801–6843, doi:10.1175/jcli-d-12-00417.1.</span></li>
-<li><span id="fn:r530">Ciais, P. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao, and P. Thornton, 2013a: Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.</span></li>
-<li><span id="fn:r531">Stocker, T.F. et al., 2013b: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 33–115 pp.</span></li>
-<li><span id="fn:r532">Hallegatte, S. and K.J. Mach, 2016: Make climate-change assessments more relevant. Nature, 534, 613–615, doi:10.1038/534613a.</span></li>
-<li><span id="fn:r533">Maier, H.R. et al., 2016: An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together? Environ. Model. Softw., 81, 154–164, doi:10.1016/j.envsoft.2016.03.014.</span></li>
-<li><span id="fn:r534">Gleckler P.J., et al., 2016: A more powerful reality test for climate models. Eos (Washington. DC)., 97, doi:10.1029/2016EO051663.</span></li>
-<li><span id="fn:r535">Parker, W.S., 2013: Ensemble modeling, uncertainty and robust predictions. Wiley Interdiscip. Rev. Chang., 4, 213–223, doi:10.1002/wcc.220.</span></li>
-<li><span id="fn:r536">Raffensperger, C. and J.A. Tickner, 1999: Introduction: To Foresee and Forestall. In: Protecting Public Health &amp; The Environment: Implementing The Precautionary Principle, [Raffensperger, C. and J.A. Tickner (eds.)]. Island Press, Washington, DC, USA, pp. 1–11.</span></li>
-<li><span id="fn:r537">Gardiner, S.M., 2006: A Core Precautionary Principle. J. Polit. Philos., 14, 33–60, doi:10.1111/j.1467-9760.2006.00237.x.</span></li>
-<li><span id="fn:r538">Hallegatte, S. and J. Rentschler, 2015: Risk management for development-assessing obstacles and prioritizing action. Risk Anal., 35, 193–210, doi:10.1111/risa.12269.</span></li>
-<li><span id="fn:r539">Luedeling, E. and E. Shepherd, 2016: Decision-Focused Agricultural Research. Solutions, 7, 46–54.</span></li>
-<li><span id="fn:r540">Walker, W.E., M. Haasnoot and J.H. Kwakkel, 2013: Adapt or perish: A review of planning approaches for adaptation under deep uncertainty. Sustainability, 5, 955–979, doi:10.3390/su5030955.</span></li>
-<li><span id="fn:r541">Hallegatte, S. and J. Rentschler, 2015: Risk management for development-assessing obstacles and prioritizing action. Risk Anal., 35, 193–210, doi:10.1111/risa.12269.</span></li>
-<li><span id="fn:r542">Haasnoot, M., 2013: Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. Glob. Environ. Chang., 23, 485–498, doi:10.1016/j.gloenvcha.2012.12.006.</span></li>
-<li><span id="fn:r543">Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114.</span></li>
-<li><span id="fn:r544">Kok, M.T.J. et al., 2018: Pathways for agriculture and forestry to contribute to terrestrial biodiversity conservation: A global scenario-study. Biol. Conserv., 221, 137–150, doi:10.1016/j.biocon.2018.03.003.</span></li>
-<li><span id="fn:r545">van Noordwijk, M. and L. Brussaard, 2014: Minimizing the ecological footprint of food: Closing yield and efficiency gaps simultaneously? Curr. Opin. Environ. Sustain., 8, 62–70, doi:10.1016/J.COSUST.2014.08.008.</span></li>
-<li><span id="fn:r546">Cremasch, G.D., 2016: Sustainability Metrics for Agri-food Supply Chains. PhD Thesis, Wageningen School of Social Sciences (WASS), Wageningen, Netherlands, doi:10.18174/380247.</span></li>
-<li><span id="fn:r547">de Coninck, H., A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, 2018: Strengthening and implementing the global response. In: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press.</span></li>
-<li><span id="fn:r548">Rogelj, J.D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian and M.V.Vilariño, 2018a: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 93–174.</span></li>
-<li><span id="fn:r549">Rogelj, J. et al., 2018b: Scenarios towards limiting global mean temperature increase below 1.5 degrees C. Nat. Clim. Chang., 8, 325–332, doi:10.1038/s41558-018-0091-3.</span></li>
-<li><span id="fn:r550">Anderson, K. and G.P. Peters, 2016: The trouble with negative emissions. Science, 354, 182–183, doi:10.1126/science.aah4567.</span></li>
-<li><span id="fn:r551">Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.</span></li>
-<li><span id="fn:r552">Smith, P. et al., 2016: Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Chang., 6, 42–50, doi:DOI: 10.1038/NCLIMATE2870.</span></li>
-<li><span id="fn:r553">Turner, P.A., C.B. Field, D.B. Lobell, D.L. Sanchez, and K.J. Mach, 2018: Unprecedented rates of land-use transformation in modelled climate change mitigation pathways. Nature Sust., 1, 240–245, doi: 10.1038/s41893-018-0063-7.</span></li>
-<li><span id="fn:r554">Brown, C., P. Alexander, A. Arneth, I. Holman, and M. Rounsevell, 2019: Achievement of Paris climate goals unlikely due to time lags in the land system. Nat. Clim. Chang., 9, 203–208, doi:10.1038/s41558-019-0400-5.</span></li>
-<li><span id="fn:r555">Vaughan, N.E. and C. Gough, 2016: Expert assessment concludes negative emissions scenarios may not deliver. Environ. Res. Lett., 11, 95003, doi:10.1088/1748-9326/11/9/095003.</span></li>
-<li><span id="fn:r556">Anderson, K. and G.P. Peters, 2016: The trouble with negative emissions. Science, 354, 182–183, doi:10.1126/science.aah4567.</span></li>
-<li><span id="fn:r557">Bentsen, N.S., 2017: Carbon debt and payback time – Lost in the forest? Renew. Sustain. Energy Rev., 73, 1211–1217, doi:10.1016/j.rser.2017.02.004.</span></li>
-<li><span id="fn:r558">Searchinger, T.D., T. Beringer, and A. Strong, 2017: Does the world have low-carbon bioenergy potential from the dedicated use of land? Energy Policy, 110, 434–446, doi:10.1016/j.enpol.2017.08.016.</span></li>
-<li><span id="fn:r559">Bayer, A.D. et al., 2017: Uncertainties in the land-use flux resulting from land-use change reconstructions and gross land transitions. Earth Syst. Dyn., 8, 91–111, doi:10.5194/esd-8-91-2017.</span></li>
-<li><span id="fn:r560">Fuchs, R., R. Prestele, and P.H. Verburg, 2017: A global assessment of gross and net land change dynamics for current conditions and future scenarios. Earth Syst. Dyn. Discuss., 1–29, doi:10.5194/esd-2017-121.</span></li>
-<li><span id="fn:r561">Pingoud, K., T. Ekholm, R. Sievänen, S. Huuskonen, and J. Hynynen, 2018: Trade-offs between forest carbon stocks and harvests in a steady state – A multi-criteria analysis. J. Environ. Manage., 210, 96–103, doi:10.1016/J.JENVMAN.2017.12.076.</span></li>
-<li><span id="fn:r562">Schlesinger, W.H., 2018: Are wood pellets a green fuel? Science, 359, 1328–1329, doi:10.1126/science.aat2305.</span></li>
-<li><span id="fn:r563">Erb, K.-H., H. Haberl, and C. Plutzar, 2012: Dependency of global primary bioenergy crop potentials in 2050 on food systems, yields, biodiversity conservation and political stability. Energy Policy, 47, 260–269, doi:10.1016/j.enpol.2012.04.066.</span></li>
-<li><span id="fn:r564">Searle, S. and C. Malins, 2015: A reassessment of global bioenergy potential in 2050. GCB Bioenergy, 7, 328–336, doi:10.1111/gcbb.12141.</span></li>
-<li><span id="fn:r565">Harper, A.B. et al., 2018: Land-use emissions play a critical role in land-based mitigation for Paris climate targets. Nat. Commun., 9, doi:10.1038/s41467-018-05340-z.</span></li>
-<li><span id="fn:r566">Shi, S., W. Zhang, P. Zhang, Y. Yu, and and F. Ding, 2013: A synthesis of change in deep soil organic carbon stores with afforestation of agricultural soils. For. Ecol. Manage., 296, 53–63, doi:10.1016/j.foreco.2013.01.026.</span></li>
-<li><span id="fn:r567">Bárcena, T.G. et al., 2014: Soil carbon stock change following afforestation in Northern Europe: a meta-analysis. Glob. Chang. Biol., 20, 2393–2405, doi:10.1111/gcb.12576.</span></li>
-<li><span id="fn:r568">Fernandez-Martinez, M. et al., 2014: Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Chang., 4, 471–476, doi:10.1038/nclimate2177.</span></li>
-<li><span id="fn:r569">Searchinger, T.D. et al., 2015: High carbon and biodiversity costs from converting Africa’s wet savannahs to cropland. Nat. Clim. Chang., 5, 481–486, doi:10.1038/nclimate2584.</span></li>
-<li><span id="fn:r570">Bonsch, M. et al., 2016: Trade-offs between land and water requirements for large-scale bioenergy production. GCB Bioenergy, 8, 11–24, doi:10.1111/gcbb.12226.</span></li>
-<li><span id="fn:r571">Creutzig, F. et al., 2015: Bioenergy and climate change mitigation: an assessment. Glob. Chang. Biol. Bioenergy, 7, 916–944, doi:10.1111/gcbb.12205.</span></li>
-<li><span id="fn:r572">Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 1–12, doi:10.1088/1748-9326/11/8/085001.</span></li>
-<li><span id="fn:r573">Santangeli, A. et al., 2016: Global change synergies and trade-offs between renewable energy and biodiversity. Glob. Chang. Biol. Bioenergy, 8, doi:10.1111/gcbb.12299.</span></li>
-<li><span id="fn:r574">Williamson, P., 2016: Emissions reduction: Scrutinize CO2 removal methods. Nature, 530, 153–155, doi:10.1038/530153a.</span></li>
-<li><span id="fn:r575">Graham, C.T., et al., 2017: Implications of afforestation for bird communities: the importance of preceding land-use type. Biodivers. Conserv., 26, 3051–3071, doi:10.1007/s10531-015-0987-4.</span></li>
-<li><span id="fn:r576">Krause, A. et al., 2017: Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators. Biogeosciences, 4829–4850, doi:10.5194/bg-2017-160.</span></li>
-<li><span id="fn:r577">Hasegawa, T. et al., 2018: Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Chang., 8, 699–703, doi:10.1038/s41558-018-0230-x.</span></li>
-<li><span id="fn:r578">Humpenoeder, F. et al., 2018: Large-scale bioenergy production: How to resolve sustainability trade-offs? Environ. Res. Lett., 13, doi:10.1088/1748-9326/aa9e3b.</span></li>
-<li><span id="fn:r579">Popp, A. et al., 2014: Land-use protection for climate change mitigation. Nat. Clim. Chang., 4, 1095–1098, doi:10.1038/nclimate2444.</span></li>
-<li><span id="fn:r580">Searchinger, T.D. et al., 2015: High carbon and biodiversity costs from converting Africa’s wet savannahs to cropland. Nat. Clim. Chang., 5, 481–486, doi:10.1038/nclimate2584.</span></li>
-<li><span id="fn:r581">Creutzig, F. et al., 2016: Beyond technology: Demand-side solutions for climate change mitigation. Annu. Rev. Environ. Resour., 41, 173–198, doi:10.1146/annurev-environ-110615-085428.</span></li>
-<li><span id="fn:r582">Dooley, K. and S. Kartha, 2018: Land-based negative emissions: risks for climate mitigation and impacts on sustainable development. Int. Environ. Agreements Polit. Law Econ., 18, 79–98, doi:10.1007/s10784-017-9382-9.</span></li>
-<li><span id="fn:r583">Hasegawa, T. et al., 2015: Consequence of climate mitigation on the risk of hunger. Environ. Sci. Technol., 49, 7245–7253, doi:10.1021/es5051748.</span></li>
-<li><span id="fn:r584">Hof, C., et al., 2018: Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. Proc. Natl. Acad. Sci., 115, 13294–13299, doi:10.1073/pnas.1807745115.</span></li>
-<li><span id="fn:r585">Roy, J., P. Tschakert, H. Waisman, S. Abdul Halim, P. Antwi-Agyei, P. Dasgupta, B. Hayward, M. Kanninen, D. Liverman, C. Okereke, P.F. Pinho, K. Riahi, and A.G. Suarez Rodriguez, 2018: Sustainable Development, Poverty Eradication and Reducing Inequalities. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 445–538.</span></li>
-<li><span id="fn:r586">Santangeli, A. et al., 2016: Global change synergies and trade-offs between renewable energy and biodiversity. Glob. Chang. Biol. Bioenergy, 8, doi:10.1111/gcbb.12299.</span></li>
-<li><span id="fn:r587">Boysen, L.R., W. Lucht, and D. Gerten, 2017: Trade-offs for food production, nature conservation and climate limit the terrestrial carbon dioxide removal potential. Glob. Chang. Biol., 23, 4303–4317, doi:10.1111/gcb.13745.</span></li>
-<li><span id="fn:r588">Henry, R.C. et al., 2018: Food supply and bioenergy production within the global cropland planetary boundary. PLoS One, 13, e0194695–e0194695, doi:10.1371/journal.pone.0194695.</span></li>
-<li><span id="fn:r589">Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 1–12, doi:10.1088/1748-9326/11/8/085001.</span></li>
-<li><span id="fn:r590">United Nations, 2015: Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations, New York, NY, USA, 41 pp.</span></li>
-<li><span id="fn:r591">Vadell, E., S. De-Miguel, and J. Pemán, 2016: Large-scale reforestation and afforestation policy in Spain: A historical review of its underlying ecological, socioeconomic and political dynamics. Land use policy, 55, 37–48, doi:10.1016/J.LANDUSEPOL.2016.03.017.</span></li>
-<li><span id="fn:r592">Joshi, A.K., P. Pant, P. Kumar, A. Giriraj, and P.K. Joshi, 2011: National forest policy in India: Critique of targets and implementation. Small-scale For., 10, 83–96, doi:10.1007/s11842-010-9133-z.</span></li>
-<li><span id="fn:r593">Zaloumis, N.P. and W.J. Bond, 2015: Reforestation of afforestation? The attributes of old growth grasslands in South Africa. Philos. Trans. R. Soc. B, 371, 1–9, doi:10.1098/rstb.2015.0310.</span></li>
-<li><span id="fn:r594">Payn, T. et al., 2015: Changes in planted forests and future global implications. For. Ecol. Manage., 352, 57–67, doi:10.1016/J.FORECO.2015.06.021.</span></li>
-<li><span id="fn:r595">Shoyama, K., 2008: Reforestation of abandoned pasture on Hokkaido, northern Japan: Effect of plantations on the recovery of conifer-broadleaved mixed forest. Landsc. Ecol. Eng., 4, 11–23, doi:10.1007/s11355-008-0034-7.</span></li>
-<li><span id="fn:r596">Miyamoto, A., M. Sano, H. Tanaka and K. Niiyama, 2011: Changes in forest resource utilization and forest landscapes in the southern Abukuma Mountains, Japan during the twentieth century. J. For. Res., 16, 87–97, doi:10.1007/s10310-010-0213-x.</span></li>
-<li><span id="fn:r597">Filoso, S., M.O. Bezerra, K.C.B. Weiss and M.A. Palmer, 2017: Impacts of forest restoration on water yield: A systematic review. PLoS One, 12, e0183210, doi:10.1371/journal.pone.0183210.</span></li>
-<li><span id="fn:r598">Salvati, L. and M. Carlucci, 2014: Zero net land degradation in Italy: The role of socioeconomic and agro-forest factors. J. Environ. Manage., 145, 299–306, doi:10.1016/J.JENVMAN.2014.07.006.</span></li>
-<li><span id="fn:r599">Ogle, S.M. et al., 2018: Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change. Carbon Balance Manag., 13, doi:10.1186/s13021-018-0095-3.</span></li>
-<li><span id="fn:r600">Crouzeilles, R. et al., 2016: A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun., 7, 1–8, doi:1166610.1038/ncomms11666.</span></li>
-<li><span id="fn:r601">FAO, 2016: State of the World’s Forests 2016. Forests and agriculture: Land-use challenges and opportunities. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r602">Heilmayr, R., C. Echeverría, R. Fuentes, and E.F. Lambin, 2016: A plantation-dominated forest transition in Chile. Appl. Geogr., 75, 71–82, doi:10.1016/j.apgeog.2016.07.014.</span></li>
-<li><span id="fn:r603">Hua, F. et al., 2018: Tree plantations displacing native forests: The nature and drivers of apparent forest recovery on former croplands in Southwestern China from 2000 to 2015. Biol. Conserv., 222, 113–124, doi:10.1016/j.biocon.2018.03.034.</span></li>
-<li><span id="fn:r604">Scheidel, A. and C. Work, 2018: Forest plantations and climate change discourses: New powers of ‘green’ grabbing in Cambodia. Land use policy, 77, 9–18, doi:10.1016/j.landusepol.2018.04.057.</span></li>
-<li><span id="fn:r605">Crouzeilles, R. et al., 2016: A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun., 7, 1–8, doi:1166610.1038/ncomms11666.</span></li>
-<li><span id="fn:r606">Chazdon, R.L. et al., 2016b: Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv., 2, e1501639–e1501639, doi:10.1126/sciadv.1501639.</span></li>
-<li><span id="fn:r607">Ahrends, A. et al., 2017: China’s fight to halt tree cover loss. Proc. R. Soc. B Biol. Sci., 284, 1–10, doi:10.1098/rspb.2016.2559.</span></li>
-<li><span id="fn:r608">Cao, S., J. Zhang, L. Chen, and T. Zhao, 2016: Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manage., 183, 843–849, doi:10.1016/j.jenvman.2016.07.096.</span></li>
-<li><span id="fn:r609">Deng, L., Z. Shangguan, and S. Sweeney, 2015: “Grain for Green” driven land use change and carbon sequestration on the Loess Plateau, China. Sci. Rep., 4, 7039, doi:10.1038/srep07039.</span></li>
-<li><span id="fn:r610">Chen, C. et al., 2019: China and India lead in greening of the world through land-use management. Nat. Sustain., 2, 122–129, doi:10.1038/s41893-019-0220-7.</span></li>
-<li><span id="fn:r611">Song, X.-P., 2018: Global estimates of ecosystem service value and change: Taking into account uncertainties in satellite-based land cover data. Ecol. Econ., 143, 227–235, doi:10.1016/j.ecolecon.2017.07.019.</span></li>
-<li><span id="fn:r612">Song, X.-P. et al., 2018: Global land change from 1982 to 2016. Nature, 560, 639–643, doi:10.1038/s41586-018-0411-9.</span></li>
-<li><span id="fn:r613">Hansen, M.C. et al., 2013: High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853, doi:10.1126/science.1244693.</span></li>
-<li><span id="fn:r614">MacDicken, K.G. et al., 2015: Global progress toward sustainable forest management. For. Ecol. Manage., 352, 47–56, doi:10.1016/j.foreco.2015.02.005.</span></li>
-<li><span id="fn:r615">Bárcena, T.G. et al., 2014: Soil carbon stock change following afforestation in Northern Europe: a meta-analysis. Glob. Chang. Biol., 20, 2393–2405, doi:10.1111/gcb.12576.</span></li>
-<li><span id="fn:r616">Poeplau, C. et al., 2011: Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach. Glob. Chang. Biol., 17, 2415–2427, doi:10.1111/j.1365-2486.2011.02408.x.</span></li>
-<li><span id="fn:r617">Shi, S., W. Zhang, P. Zhang, Y. Yu, and and F. Ding, 2013: A synthesis of change in deep soil organic carbon stores with afforestation of agricultural soils. For. Ecol. Manage., 296, 53–63, doi:10.1016/j.foreco.2013.01.026.</span></li>
-<li><span id="fn:r618">Li, D., S. Niu, and Y. Luo, 2012: Global patterns of the dynamics of soil carbon and nitrogen stocks following afforestation: A meta-analysis. New Phytol., 195, 172–181, doi:10.1111/j.1469-8137.2012.04150.x.</span></li>
-<li><span id="fn:r619">Bernal, B., L.T. Murray, and T.R.H. Pearson, 2018: Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag., 13, doi:10.1186/s13021-018-0110-8.</span></li>
-<li><span id="fn:r620">Lamb, D., 2018: Undertaking large-scale forest restoration to generate ecosystem services. Restor. Ecol., 26, 657–666, doi:10.1111/rec.12706.</span></li>
-<li><span id="fn:r621">Veldman, J.W., F.A.O. Silveira, F.D. Fleischman, N.L. Ascarrunz and G. Durigan, 2017: Grassy biomes: An inconvenient reality for large-scale forest restoration? A comment on the essay by Chazdon and Laestadius. Am. J. Bot., 104, 649–651, doi:10.3732/ajb.1600427.</span></li>
-<li><span id="fn:r622">Anderson, R.G. et al., 2011: Biophysical considerations in forestry for climate protection. Front. Ecol. Environ., 9, 174–182, doi:10.1890/090179.</span></li>
-<li><span id="fn:r623">Perugini, L. et al., 2017: Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett., 12, 1–21, doi:10.1088/1748-9326/aa6b3f.</span></li>
-<li><span id="fn:r624">Duveiller, G., J. Hooker, and A. Cescatti, 2018: The mark of vegetation change on Earth’s surface energy balance. Nat. Commun., 9, 679, doi:10.1038/s41467-017-02810-8.</span></li>
-<li><span id="fn:r625">Alkama, R. and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/science.aac8083.</span></li>
-<li><span id="fn:r626">Perugini, L. et al., 2017: Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett., 12, 1–21, doi:10.1088/1748-9326/aa6b3f.</span></li>
-<li><span id="fn:r627">Salvati, L. and M. Carlucci, 2014: Zero net land degradation in Italy: The role of socioeconomic and agro-forest factors. J. Environ. Manage., 145, 299–306, doi:10.1016/J.JENVMAN.2014.07.006.</span></li>
-<li><span id="fn:r628">Silveira, L., P. Gamazo, J. Alonso and L. Martínez, 2016: Effects of afforestation on groundwater recharge and water budgets in the western region of Uruguay. Hydrol. Process., 30, 3596–3608, doi:10.1002/hyp.10952.</span></li>
-<li><span id="fn:r629">Zheng, H., Y. Wang, Y. Chen and T. Zhao, 2016: Effects of large-scale afforestation project on the ecosystem water balance in humid areas: An example for southern China. Ecol. Eng., 89, 103–108, doi:10.1016/j.ecoleng.2016.01.013.</span></li>
-<li><span id="fn:r630">Cao, S., J. Zhang, L. Chen, and T. Zhao, 2016: Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manage., 183, 843–849, doi:10.1016/j.jenvman.2016.07.096.</span></li>
-<li><span id="fn:r631">Yang, L., L. Chen, W. Wei, Y. Yu and H. Zhang, 2014: Comparison of deep soil moisture in two re-vegetation watersheds in semi-arid regions. J. Hydrol., 513, 314–321, doi:10.1016/j.jhydrol.2014.03.049.</span></li>
-<li><span id="fn:r632">Li, S., M. Xu and B. Sun, 2014: Long-term hydrological response to reforestation in a large watershed in southeastern China. Hydrol. Process., 28, 5573–5582, doi:10.1002/hyp.10018.</span></li>
-<li><span id="fn:r633">Feng, X. et al., 2016: Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang., 6, 1019–1022, doi:10.1038/nclimate3092.</span></li>
-<li><span id="fn:r634">Zheng, H., Y. Wang, Y. Chen and T. Zhao, 2016: Effects of large-scale afforestation project on the ecosystem water balance in humid areas: An example for southern China. Ecol. Eng., 89, 103–108, doi:10.1016/j.ecoleng.2016.01.013.</span></li>
-<li><span id="fn:r635">Ellison, D. et al., 2017: Trees, forests and water: Cool insights for a hot world. Glob. Environ. Chang., 43, 51–61, doi:10.1016/j.gloenvcha.2017.01.002.</span></li>
-<li><span id="fn:r636">Lee, J. et al., 2018: Economic viability of the national-scale forestation program: The case of success in the Republic of Korea. Ecosyst. Serv., 29, 40–46, doi:10.1016/j.ecoser.2017.11.001.</span></li>
-<li><span id="fn:r637">Abreu, R.C.R. et al., 2017: The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv., 3, e1701284, doi:10.1126/sciadv.1701284.</span></li>
-<li><span id="fn:r638">Griffith, D.M. et al., 2017: Comment on “The extent of forest in dryland biomes”. Science, 358, eaao1309, doi:10.1126/science.aao1309.</span></li>
-<li><span id="fn:r639">Veldman, J.W. et al., 2015: Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience, 65, 1011–1018, doi:10.1093/biosci/biv118.</span></li>
-<li><span id="fn:r640">Parr, C.L., C.E.R.R. Lehmann, W.J. Bond, W.A. Hoffmann A.N. Andersen, 2014: Tropical grassy biomes: misunderstood, neglected and under threat. Trends Ecol. Evol., 29, 205–213, doi:10.1016/j.tree.2014.02.004.</span></li>
-<li><span id="fn:r641">Wilson, S.J., J. Schelhas, R. Grau, A.S. Nanni and S. Sloan, 2017: Forest ecosystem-service transitions: The ecological dimensions of the forest transition. Ecol. Soc., 22, doi:10.5751/es-09615-220438.</span></li>
-<li><span id="fn:r642">Hua, F. et al., 2016: Opportunities for biodiversity gains under the world’s largest reforestation programme. Nat. Commun., 7, 1–11, doi:10.1038/ncomms12717.</span></li>
-<li><span id="fn:r643">Shimamoto, C.Y., A.A. Padial, C.M. Da Rosa, and M.C.M.M. Marques, 2018: Restoration of ecosystem services in tropical forests: A global meta-analysis. PLoS One, 13, 1–16, doi:10.1371/journal.pone.0208523.</span></li>
-<li><span id="fn:r644">Padmanaba, M. and R.T. Corlett, 2014: Minimizing risks of invasive alien plant species in tropical production forest management. Forests, 5, 1982–1998, doi:10.3390/f5081982.</span></li>
-<li><span id="fn:r645">Cunningham, S.C. et al., 2015b: Balancing the environmental benefits of reforestation in agricultural regions. Perspect. Plant Ecol. Evol. Syst., 17, 301–317, doi:10.1016/J.PPEES.2015.06.001.</span></li>
-<li><span id="fn:r646">Dendy, J., S. Cordell, C.P. Giardina, B. Hwang, E. Polloi and K. Rengulbai, 2015: The role of remnant forest patches for habitat restoration in degraded areas of Palau. Restor. Ecol., 23, 872–881, doi:10.1111/rec.12268.</span></li>
-<li><span id="fn:r647">Chaudhary, A. and T. Kastner, 2016: Land use biodiversity impacts embodied in international food trade. Glob. Environ. Chang., 38, 195–204, doi:10.1016/J.GLOENVCHA.2016.03.013.</span></li>
-<li><span id="fn:r648">Huang, Y. et al., 2018: Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science, 362, 80–83, doi:10.1126/science.aat6405.</span></li>
-<li><span id="fn:r649">Locatelli, B., C. Pavageau, E. Pramova, and M. Di Gregorio, 2015b: Integrating climate change mitigation and adaptation in agriculture and forestry: Opportunities and trade-offs. Wiley Interdiscip. Rev. Clim. Chang., 6, 585-598, doi:10.1002/wcc.357.</span></li>
-<li><span id="fn:r650">Brockerhoff, E.G., H. Jactel, J.A. Parrotta, and S.F.B. Ferraz, 2013: Role of eucalypt and other planted forests in biodiversity conservation and the provision of biodiversity-related ecosystem services. For. Ecol. Manage., 301, 43–50, doi:10.1016/j.foreco.2012.09.018.</span></li>
-<li><span id="fn:r651">Pawson, S.M. et al., 2013: Plantation forests, climate change and biodiversity. Biodivers. Conserv., 22, 1203–1227, doi:10.1007/s10531-013-0458-8.</span></li>
-<li><span id="fn:r652">Thompson, I.D. et al., 2014: Biodiversity and ecosystem services: Lessons from nature to improve management of planted forests for REDD-plus. Biodivers. Conserv., 23, 2613–2635, doi:10.1007/s10531-014-0736-0.</span></li>
-<li><span id="fn:r653">Gilbert, M. et al., 2018: Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Scientific Data, 5, doi: 10.1038/sdata.2018.227.</span></li>
-<li><span id="fn:r654">Gilbert-Norton, L., R. Wilson, J.R. Stevens and K.H. Beard, 2010: A meta-analytic review of corridor effectiveness. Conserv. Biol., 24, 660–668, doi:10.1111/j.1523-1739.2010.01450.x.</span></li>
-<li><span id="fn:r655">Barlow, J. et al., 2007: Quantifying the biodiversity value of tropical primary, secondary, and plantation forests. Proc. Natl. Acad. Sci. U.S.A., 104, 18555–18560, doi:10.1073/pnas.0703333104.</span></li>
-<li><span id="fn:r656">Lindenmayer, D.B. and R.J. Hobbs, 2004: Fauna conservation in Australian plantation forests – a review. Biol. Conserv., 119, 151–168, doi:10.1016/J.BIOCON.2003.10.028.</span></li>
-<li><span id="fn:r657">Le, H.D., C. Smith, J. Herbohn and S. Harrison, 2012: More than just trees: Assessing reforestation success in tropical developing countries. J. Rural Stud., 28, 5–19, doi:10.1016/j.jrurstud.2011.07.006.</span></li>
-<li><span id="fn:r658">Baral, H., M.R. Guariguata, and R.J. Keenan, 2016: A proposed framework for assessing ecosystem goods and services from planted forests. Ecosyst. Serv., 22, 260–268, doi:10.1016/j.ecoser.2016.10.002.</span></li>
-<li><span id="fn:r659">Gerber, J.F., 2011: Conflicts over industrial tree plantations in the South: Who, how and why? Glob. Environ. Chang., 21, 165–176, doi:10.1016/j.gloenvcha.2010.09.005.</span></li>
-<li><span id="fn:r660">Baral, H., M.R. Guariguata, and R.J. Keenan, 2016: A proposed framework for assessing ecosystem goods and services from planted forests. Ecosyst. Serv., 22, 260–268, doi:10.1016/j.ecoser.2016.10.002.</span></li>
-<li><span id="fn:r661">Malkamäki, A. et al., 2018: A systematic review of the socio-economic impacts of large-scale tree plantations, worldwide. Glob. Environ. Chang., 53, 90–103, doi:10.1016/j.gloenvcha.2018.09.001.</span></li>
-<li><span id="fn:r662">Cotula, L. et al., 2014: Testing claims about large land deals in Africa: Findings from a multi-country study. J. Dev. Stud., 50, 903–925, doi:10.1080/00220388.2014.901501.</span></li>
-<li><span id="fn:r663">Gerber, J.F., 2011: Conflicts over industrial tree plantations in the South: Who, how and why? Glob. Environ. Chang., 21, 165–176, doi:10.1016/j.gloenvcha.2010.09.005.</span></li>
-<li><span id="fn:r664">Bull, G.Q. et al., 2006: Industrial forest plantation subsidies: Impacts and implications. For. Policy Econ., 9, 13–31, doi:10.1016/j.forpol.2005.01.004.</span></li>
-<li><span id="fn:r665">Le, H.D., C. Smith, J. Herbohn and S. Harrison, 2012: More than just trees: Assessing reforestation success in tropical developing countries. J. Rural Stud., 28, 5–19, doi:10.1016/j.jrurstud.2011.07.006.</span></li>
-<li><span id="fn:r666">de Coninck, H., A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, 2018: Strengthening and implementing the global response. In: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press.</span></li>
-<li><span id="fn:r667">Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114.</span></li>
-<li><span id="fn:r668">Fuss, S. et al., 2018: Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett., 13, 063002, doi:10.1088/1748-9326/aabf9f.</span></li>
-<li><span id="fn:r669">Rogelj, J.D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian and M.V.Vilariño, 2018a: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 93–174.</span></li>
-<li><span id="fn:r670">Fuss, S. et al., 2018: Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett., 13, 063002, doi:10.1088/1748-9326/aabf9f.</span></li>
-<li><span id="fn:r671">Houghton, R.A., 2013: The emissions of carbon from deforestation and degradation in the tropics: Past trends and future potential. Carbon Manag., 4, 539–546, doi:10.4155/cmt.13.41.</span></li>
-<li><span id="fn:r672">Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.</span></li>
-<li><span id="fn:r673">Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114.</span></li>
-<li><span id="fn:r674">Lenton, T.M., 2014: The global potential for carbon dioxide removal. In: Geoengineering of the Climate System [Hester, R.E. and R.M. Harrison, (eds.)]. Royal Society of Chemistry, pp. 52–79.</span></li>
-<li><span id="fn:r675">Fuss, S. et al., 2018: Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett., 13, 063002, doi:10.1088/1748-9326/aabf9f.</span></li>
-<li><span id="fn:r676">Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178.</span></li>
-<li><span id="fn:r677">Rogelj, J.D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian and M.V.Vilariño, 2018a: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 93–174.</span></li>
-<li><span id="fn:r678">Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 1–12, doi:10.1088/1748-9326/11/8/085001.</span></li>
-<li><span id="fn:r679">Humpenoder, F. et al., 2014: Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environ. Res. Lett., 9, 064029, doi:10.1088/1748-9326/9/6/064029.</span></li>
-<li><span id="fn:r680">Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 1–12, doi:10.1088/1748-9326/11/8/085001.</span></li>
-<li><span id="fn:r681">Hasegawa, T. et al., 2015: Consequence of climate mitigation on the risk of hunger. Environ. Sci. Technol., 49, 7245–7253, doi:10.1021/es5051748.</span></li>
-<li><span id="fn:r682">Hasegawa, T. et al., 2018: Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Chang., 8, 699–703, doi:10.1038/s41558-018-0230-x.</span></li>
-<li><span id="fn:r683">Boysen, L.R., W. Lucht, and D. Gerten, 2017: Trade-offs for food production, nature conservation and climate limit the terrestrial carbon dioxide removal potential. Glob. Chang. Biol., 23, 4303–4317, doi:10.1111/gcb.13745.</span></li>
-<li><span id="fn:r684">Ashworth, K., O. Wild, and C.N. Hewitt, 2013: Impacts of biofuel cultivation on mortality and crop yields. Nat. Clim. Chang., 3, 492–496, doi:10.1038/nclimate1788.</span></li>
-<li><span id="fn:r685">Harrison, S.P. et al., 2013: Volatile isoprenoid emissions from plastid to planet. New Phytol., 197, 49–57, doi:10.1111/nph.12021.</span></li>
-<li><span id="fn:r686">Houghton, R.A., 2013: The emissions of carbon from deforestation and degradation in the tropics: Past trends and future potential. Carbon Manag., 4, 539–546, doi:10.4155/cmt.13.41.</span></li>
-<li><span id="fn:r687">Harper, A.B. et al., 2018: Land-use emissions play a critical role in land-based mitigation for Paris climate targets. Nat. Commun., 9, doi:10.1038/s41467-018-05340-z.</span></li>
-<li><span id="fn:r688">Fuss, S. et al., 2018: Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett., 13, 063002, doi:10.1088/1748-9326/aabf9f.</span></li>
-<li><span id="fn:r689">Houghton, R.A., B. Byers, and A.A. Nassikas, 2015: A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Chang., 5, 1022–1023, doi:10.1038/nclimate2869.</span></li>
-<li><span id="fn:r690">Allen, C.D. et al., 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage., 259, 660–684, doi:10.1016/j.foreco.2009.09.001.</span></li>
-<li><span id="fn:r691">Anderegg, W.R.L. et al., 2015: Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol., 208, 674–683, doi:10.1111/nph.13477.</span></li>
-<li><span id="fn:r692">Bhojvaid, P.P., M.P. Singh, S.R. Reddy, and J. Ashraf, 2016: Forest transition curve of India and related policies, acts and other major factors. Trop. Ecol., 57, 133–141.</span></li>
-<li><span id="fn:r693">Jadin, I., P. Meyfroidt and E.F. Lambin, 2016: International trade and land use intensification and spatial reorganization explain Costa Rica’s forest transition. Environ. Res. Lett., 11, 035005, doi:10.1088/1748-9326/11/3/035005.</span></li>
-<li><span id="fn:r694">Laestadius, L. et al., 2011: Mapping opportunities for forest landscape restoration. Unasylva, 62, 47–48.</span></li>
-<li><span id="fn:r695">Dinerstein, E. et al., 2015: Guiding agricultural expansion to spare tropical forests. Conserv. Lett., 8, 262–271, doi:10.1111/conl.12149.</span></li>
-<li><span id="fn:r696">Veldman, J.W., F.A.O. Silveira, F.D. Fleischman, N.L. Ascarrunz and G. Durigan, 2017: Grassy biomes: An inconvenient reality for large-scale forest restoration? A comment on the essay by Chazdon and Laestadius. Am. J. Bot., 104, 649–651, doi:10.3732/ajb.1600427.</span></li>
-<li><span id="fn:r697">Bryan, B.A. and N.D. Crossman, 2013: Impact of multiple interacting financial incentives on land use change and the supply of ecosystem services. Ecosyst. Serv., 4, 60–72, doi:10.1016/j.ecoser.2013.03.004.</span></li>
-<li><span id="fn:r698">Boysen, L.R., W. Lucht, and D. Gerten, 2017: Trade-offs for food production, nature conservation and climate limit the terrestrial carbon dioxide removal potential. Glob. Chang. Biol., 23, 4303–4317, doi:10.1111/gcb.13745.</span></li>
-<li><span id="fn:r699">Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 1–12, doi:10.1088/1748-9326/11/8/085001.</span></li>
-<li><span id="fn:r700">Egginton, P., F. Beall, and J. Buttle, 2014: Reforestation – Climate change and water resource implications. For. Chron., 90, 516–524, doi:10.5558/tfc2014-102.</span></li>
-<li><span id="fn:r701">Cao, S., J. Zhang, L. Chen, and T. Zhao, 2016: Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manage., 183, 843–849, doi:10.1016/j.jenvman.2016.07.096.</span></li>
-<li><span id="fn:r702">Locatelli, B. et al., 2015a: Tropical reforestation and climate change: Beyond carbon. Restor. Ecol., 23, 337–343, doi:10.1111/rec.12209.</span></li>
-<li><span id="fn:r703">Smith, P. and P.J. Gregory, 2013: Climate change and sustainable food production. Proceedings of the Nutrition Society, Vol. 72 of, 21–28, doi:10.1017/S0029665112002832.</span></li>
-<li><span id="fn:r704">Alemu, M.M., 2016: Sustainable land management. J. Environ. Prot., 7, 502–506, doi:10.4236/jep.2016.74045.</span></li>
-<li><span id="fn:r705">Altieri, M.A., and C.I. Nicholls, 2017: The adaptation and mitigation potential of traditional agriculture in a changing climate. Clim. Change, 140, 33–45, doi:10.1007/s10584-013-0909-y.</span></li>
-<li><span id="fn:r706">Stockmann, U. et al., 2013: The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ., 164, 80–99, doi:10.1016/J.AGEE.2012.10.001.</span></li>
-<li><span id="fn:r707">Ebert, A.W., 2014: Potential of underutilized traditional vegetables and legume crops to contribute to food and nutritional security, income and more sustainable production systems. Sustainability, 6, 319–335, doi:10.3390/su6010319.</span></li>
-<li><span id="fn:r708">Schulte, R.P.O. et al., 2014: Functional land management: A framework for managing soil-based ecosystem services for the sustainable intensification of agriculture. Environ. Sci. Policy, 38, 45–58, doi:10.1016/J.ENVSCI.2013.10.002.</span></li>
-<li><span id="fn:r709">Zhang, X. et al., 2015: Managing nitrogen for sustainable development. Nature, 528, 51–59, doi:10.1038/nature15743.</span></li>
-<li><span id="fn:r710">Sunil, N. and S.R. Pandravada, 2015: Alien Crop Resources and Underutilized Species for Food and Nutritional Security of India. In: Plant Biology and Biotechnology, Springer India, New Delhi, pp. 757–775.</span></li>
-<li><span id="fn:r711">Poeplau, C. and A. Don, 2015: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agric. Ecosyst. Environ., 200, 33–41, doi:10.1016/J.AGEE.2014.10.024.</span></li>
-<li><span id="fn:r712">Agus, F., H. Husnain and R.D. Yustika, 2015: Improving agricultural resilience to climate change through soil management. J. Penelit. dan Pengemb. Pertan., 34, 147–158. doi:10.21082/jp3.v34n4.2015. pp. 147–158.</span></li>
-<li><span id="fn:r713">Keenan, R.J., 2015: Climate change impacts and adaptation in forest management: A review. Ann. For. Sci., 72, 145–167, doi:10.1007/s13595-014-0446-5.</span></li>
-<li><span id="fn:r714">MacDicken, K.G. et al., 2015: Global progress toward sustainable forest management. For. Ecol. Manage., 352, 47–56, doi:10.1016/j.foreco.2015.02.005.</span></li>
-<li><span id="fn:r715">Abberton, M., 2016: Global agricultural intensification during climate change: a role for genomics. Plant Biotechnol. J., 14, 1095–1098, doi:10.1111/pbi.12467.</span></li>
-<li><span id="fn:r716">Hobbs, P.R., K. Sayre, and R. Gupta, 2008: The role of conservation agriculture in sustainable agriculture. Philos. Trans. R. Soc. B Biol. Sci., 363, 543–555, doi:10.1098/rstb.2007.2169.</span></li>
-<li><span id="fn:r717">Friedrich, T., R. Derpsch, and A. Kassam, 2012: Overview of the global spread of conservation agriculture. F. Actions Sci. Reports, 1–7, doi:10.1201/9781315365800-4.</span></li>
-<li><span id="fn:r718">FAO, 2015b: Learning Tool on Nationally Appropriate Mitigation Actions (NAMAs) in the agriculture, forestry and other land use (AFOLU) sector. Food and Agriculture Organization of the United Nations, Rome, Italy, 162 pp.</span></li>
-<li><span id="fn:r719">Favero, A. and R. Mendelsohn, 2014: Using markets for woody biomass energy to sequester carbon in forests. J. Assoc. Environ. Resour. Econ., 1, 75–95, doi:10.1086/676033.</span></li>
-<li><span id="fn:r720">USDA, 2007: Precision Agriculture: NRCS Support for Emerging Technologies. Agronomy Technical Note No. 1, Soil Quality National Technology Development Team, East National Technology Support Center, Natural Resources Conservation Service, Greensboro, North Carolina, USA, 9 pp.</span></li>
-<li><span id="fn:r721">Bebber, D.P. and N. Butt, 2017: Tropical protected areas reduced deforestation carbon emissions by one third from 2000–2012. Sci. Rep., 7, doi:1400510.1038/s41598-017-14467-w.</span></li>
-<li><span id="fn:r722">Ziadat, F., S. Bunning, S. Corsi, and R. Vargas, 2018: Sustainable soil and land management for climate smart agriculture. In: Climate Smart Agriculture Sourcebook [Ziadat, F., S. Bunning, S. Corsi and R. Vargas (eds.)]. Food and Agriculture Organization of the United Nations, Rome, Italy, pp 1–33.</span></li>
-<li><span id="fn:r723">Gurwick, N.P., L.A. Moore, C. Kelly, and P. Elias, 2013: A systematic review of biochar research, with a focus on its stability in situ and its promise as a climate mitigation strategy. PLoS One, 8, doi:10.1371/journal.pone.0075932.</span></li>
-<li><span id="fn:r724">Lorenz, K. and R. Lal, 2014: Biochar application to soil for climate change mitigation by soil organic carbon sequestration. J. Plant Nutr. Soil Sci., 177, 651–670, doi:10.1002/jpln.201400058.</span></li>
-<li><span id="fn:r725">Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178.</span></li>
-<li><span id="fn:r726">Gustavsson, J., C. Cederberg, U. Sonesson, R. van Otterdijk and A. Meybeck, 2011: Global Food Losses and Food Waste – Extent, Causes and Prevention. Study conducted for the International Congress, Swedish Institute for Food and Biotechnology (SIK), Gothenburg, Sweden.</span></li>
-<li><span id="fn:r727">Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.</span></li>
-<li><span id="fn:r728">Xue, L. et al., 2017: Missing food, missing data? A critical review of global food losses and food waste data. Environ. Sci. Technol., 51, 6618–6633, doi:10.1021/acs.est.7b00401.</span></li>
-<li><span id="fn:r729">Zorya, S. et al., 2011: Missing food: The Case of Postharvest Grain Losses in Sub-Saharan Africa. The International Bank for Reconstruction and Development/The World Bank Report No. 60371-AFR, Washington, DC, USA, 96 pp.</span></li>
-<li><span id="fn:r730">Xue, L. et al., 2017: Missing food, missing data? A critical review of global food losses and food waste data. Environ. Sci. Technol., 51, 6618–6633, doi:10.1021/acs.est.7b00401.</span></li>
-<li><span id="fn:r731">Gustavsson, J., C. Cederberg, U. Sonesson, R. van Otterdijk and A. Meybeck, 2011: Global Food Losses and Food Waste – Extent, Causes and Prevention. Study conducted for the International Congress, Swedish Institute for Food and Biotechnology (SIK), Gothenburg, Sweden.</span></li>
-<li><span id="fn:r732">Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.</span></li>
-<li><span id="fn:r733">Bradford, K.J. et al., 2018: The dry chain: Reducing postharvest losses and improving food safety in humid climates. Trends Food Sci. Technol., 71, 84–93, doi:10.1016/J.TIFS.2017.11.002.</span></li>
-<li><span id="fn:r734">Affognon, H. et al., 2015: Unpacking postharvest losses in Sub-Saharan Africa: A meta-analysis. World Dev., 66, 49–68, doi:10.1016/J.WORLDDEV.2014.08.002.</span></li>
-<li><span id="fn:r735">Chegere, M.J., 2018: Post-harvest losses reduction by small-scale maize farmers: The role of handling practices. Food Policy, 77, 103–115, doi:10.1016/J.FOODPOL.2018.05.001.</span></li>
-<li><span id="fn:r736">Warren, D.D. and D.C. and N.R. and J.P. and R., 2014: Global crop yield response to extreme heat stress under multiple climate change futures. Environ. Res. Lett., 9, 34011.</span></li>
-<li><span id="fn:r737">Challinor, A.J., B. Parkes, and J. Ramirez-Villegas, 2015: Crop yield response to climate change varies with cropping intensity. Glob. Chang. Biol., 21, 1679–1688, doi:10.1111/gcb.12808.</span></li>
-<li><span id="fn:r738">Elbehri, A. et al., 2017: FAO-IPCC Expert Meeting on Climate Change, Land Use and Food Security: Final Meeting Report; January 23–25, 2017, FAO and IPCC, Food and Agriculture Organization of the United Nations, Rome, Italy, pp. 1–27.</span></li>
-<li><span id="fn:r739">Widener, M.J., L. Minaker, S. Farber, J. Allen, B. Vitali, P.C. Coleman and B. Cook, 2017: How do changes in the daily food and transportation environments affect grocery store accessibility? Appl. Geogr., 83, 46–62, doi:10.1016/J.APGEOG.2017.03.018.</span></li>
-<li><span id="fn:r740">Lehmann, N., S. Briner, and R. Finger, 2013: The impact of climate and price risks on agricultural land use and crop management decisions. Land use policy, 35, 119–130, doi:10.1016/J.LANDUSEPOL.2013.05.008.</span></li>
-<li><span id="fn:r741">Le, T.T.H. Trang, 2016: Effects of climate change on rice yield and rice market in Vietnam. J. Agric. Appl. Econ., 48, 366–382, doi:10.1017/aae.2016.21.</span></li>
-<li><span id="fn:r742">FAO, 2015b: Learning Tool on Nationally Appropriate Mitigation Actions (NAMAs) in the agriculture, forestry and other land use (AFOLU) sector. Food and Agriculture Organization of the United Nations, Rome, Italy, 162 pp.</span></li>
-<li><span id="fn:r743">Gilmont, M., 2015: Water resource decoupling in the MENA through food trade as a mechanism for circumventing national water scarcity. Food Secur., 7, 1113–1131, doi:10.1007/s12571-015-0513-2.</span></li>
-<li><span id="fn:r744">Marchand, P. et al., 2016: Reserves and trade jointly determine exposure to food supply shocks. Environ. Res. Lett., 11, 095009, doi:10.1088/1748-9326/11/9/095009.</span></li>
-<li><span id="fn:r745">Kastner, T., K.H. Erb, and H. Haberl, 2014: Rapid growth in agricultural trade: Effects on global area efficiency and the role of management. Environ. Res. Lett., 9, doi:10.1088/1748-9326/9/3/034015.</span></li>
-<li><span id="fn:r746">Marques, A. et al., 2019: Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol., 3, 628–637, doi:10.1038/s41559-019-0824-3.</span></li>
-<li><span id="fn:r747">Wiedmann, T. and M. Lenzen, 2018: Environmental and social footprints of international trade. Nat. Geosci., 11, 314–321, doi:10.1038/s41561-018-0113-9.</span></li>
-<li><span id="fn:r748">Chaudhary, A. and T. Kastner, 2016: Land use biodiversity impacts embodied in international food trade. Glob. Environ. Chang., 38, 195–204, doi:10.1016/J.GLOENVCHA.2016.03.013.</span></li>
-<li><span id="fn:r749">Chen, B. et al., 2018: Global land-water nexus: Agricultural land and freshwater use embodied in worldwide supply chains. Sci. Total Environ., 613–614, 931–943, doi:10.1016/J.SCITOTENV.2017.09.138.</span></li>
-<li><span id="fn:r750">Yu, Y., K. Feng and K. Hubacek, 2013: Tele-connecting local consumption to global land use. Glob. Environ. Chang., 23, 1178–1186, doi:10.1016/J.GLOENVCHA.2013.04.006.</span></li>
-<li><span id="fn:r751">Gilmont, M., 2015: Water resource decoupling in the MENA through food trade as a mechanism for circumventing national water scarcity. Food Secur., 7, 1113–1131, doi:10.1007/s12571-015-0513-2.</span></li>
-<li><span id="fn:r752">Marchand, P. et al., 2016: Reserves and trade jointly determine exposure to food supply shocks. Environ. Res. Lett., 11, 095009, doi:10.1088/1748-9326/11/9/095009.</span></li>
-<li><span id="fn:r753">Favero, A. and E. Massetti, 2014: Trade of woody biomass for electricity generation under climate mitigation policy. Resour. Energy Econ., 36, 166–190, doi:10.1016/J.RESENEECO.2013.11.00.</span></li>
-<li><span id="fn:r754">Schmidt, C.G., K. Foerstl, and B. Schaltenbrand, 2017: The supply chain position paradox: Green practices and firm performance. J. Supply Chain Manag., 53, 3–25, doi:10.1111/jscm.12113.</span></li>
-<li><span id="fn:r755">Dalin, C. and I. Rodríguez-Iturbe, 2016: Environmental impacts of food trade via resource use and greenhouse gas emissions. Environ. Res. Lett., 11, 035012, doi:10.1088/1748-9326/11/3/035012.</span></li>
-<li><span id="fn:r756">Mosnier, A. et al., 2014: Global food markets, trade and the cost of climate change adaptation. Food Secur., 6, 29–44, doi:10.1007/s12571-013-0319-z.</span></li>
-<li><span id="fn:r757">Elbehri, A. et al., 2017: FAO-IPCC Expert Meeting on Climate Change, Land Use and Food Security: Final Meeting Report; January 23–25, 2017, FAO and IPCC, Food and Agriculture Organization of the United Nations, Rome, Italy, pp. 1–27.</span></li>
-<li><span id="fn:r758">Elbehri, A. et al., 2017: FAO-IPCC Expert Meeting on Climate Change, Land Use and Food Security: Final Meeting Report; January 23–25, 2017, FAO and IPCC, Food and Agriculture Organization of the United Nations, Rome, Italy, pp. 1–27.</span></li>
-<li><span id="fn:r759">Brown, C., P. Alexander, S. Holzhauer, and M.D.A. Rounsevell, 2017: Behavioral models of climate change adaptation and mitigation in land-based sectors. Wiley Interdiscip. Rev. Clim. Chang., 8, e448, doi:10.1002/wcc.448.</span></li>
-<li><span id="fn:r760">Creutzig, F. et al., 2016: Beyond technology: Demand-side solutions for climate change mitigation. Annu. Rev. Environ. Resour., 41, 173–198, doi:10.1146/annurev-environ-110615-085428.</span></li>
-<li><span id="fn:r761">Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353.</span></li>
-<li><span id="fn:r762">Erb, K.-H. et al., 2016b: Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun., 7.</span></li>
-<li><span id="fn:r763">Creutzig, F. et al., 2018: Towards demand-side solutions for mitigating climate change. Nat. Clim. Chang., 8, 260–263, doi:10.1038/s41558-018-0121-1.</span></li>
-<li><span id="fn:r764">Hallegatte, S. and J. Rentschler, 2015: Risk management for development-assessing obstacles and prioritizing action. Risk Anal., 35, 193–210, doi:10.1111/risa.12269.</span></li>
-<li><span id="fn:r765">Hallström, E., A. Carlsson-Kanyama and P. Börjesson, 2015: Environmental impact of dietary change: A systematic review. J. Clean. Prod., 91, 1–11, doi:10.1016/J.JCLEPRO.2014.12.008.</span></li>
-<li><span id="fn:r766">Alexander, P. et al., 2015: Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy. Glob. Environ. Chang., doi:10.1016/j.gloenvcha.2015.08.011.</span></li>
-<li><span id="fn:r767">Tilman, D. and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13959.</span></li>
-<li><span id="fn:r768">Aleksandrowicz, L., R. Green, E.J.M. Joy, P. Smith, and A. Haines, 2016: The impacts of dietary change on greenhouse gas emissions, land use, water use and health: A systematic review. PLoS One, 11, e0165797, doi:10.1371/journal.pone.0165797.</span></li>
-<li><span id="fn:r769">Poore, J. and T. Nemecek, 2018: Reducing food’s environmental impacts through producers and consumers. Science, 360, 987–992, doi:10.1126/science.aaq0216.</span></li>
-<li><span id="fn:r770">Swain, M., L. Blomqvist, J. McNamara, and W.J. Ripple, 2018: Reducing the environmental impact of global diets. Sci. Total Environ., 610–611, 1207–1209, doi:10.1016/J.SCITOTENV.2017.08.125.</span></li>
-<li><span id="fn:r771">Röös, E. et al., 2017: Greedy or needy? Land use and climate impacts of food in 2050 under different livestock futures. Glob. Environ. Chang., 47, 1–12, doi:10.1016/J.GLOENVCHA.2017.09.001.</span></li>
-<li><span id="fn:r772">Rao, Y. et al., 2018: Integrating ecosystem services value for sustainable land-use management in semi-arid region. J. Clean. Prod., 186, 662–672, doi:10.1016/J.JCLEPRO.2018.03.119.</span></li>
-<li><span id="fn:r773">Jalava, M., M. Kummu, M. Porkka, S. Siebert, and O. Varis, 2014: Diet change—a solution to reduce water use? Environ. Res. Lett., 9, 74016.</span></li>
-<li><span id="fn:r774">Poore, J. and T. Nemecek, 2018: Reducing food’s environmental impacts through producers and consumers. Science, 360, 987–992, doi:10.1126/science.aaq0216.</span></li>
-<li><span id="fn:r775">Juhl, H.J. and M.B. Jensen, 2014: Relative price changes as a tool to stimulate more healthy food choices – A Danish household panel study. Food Policy, 46, 178–182, doi:10.1016/J.FOODPOL.2014.03.008.</span></li>
-<li><span id="fn:r776">FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp.</span></li>
-<li><span id="fn:r777">Porter, S.D., D.S. Reay, P. Higgins and E. Bomberg, 2016: A half-century of production-phase greenhouse gas emissions from food loss and waste in the global food supply chain. Sci. Total Environ., 571, 721–729, doi:10.1016/J.SCITOTENV.2016.07.041.</span></li>
-<li><span id="fn:r778">Kummu, M. et al., 2012: Lost food, wasted resources: Global food supply chain losses and their impacts on freshwater, cropland and fertiliser use. Sci. Total Environ., 438, 477–489, doi:10.1016/J.SCITOTENV.2012.08.092.</span></li>
-<li><span id="fn:r779">Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.</span></li>
-<li><span id="fn:r780">Parfitt, J., M. Barthel, and S. Macnaughton, 2010: Food waste within food supply chains: quantification and potential for change to 2050. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 365, 3065–3081, doi:10.1098/rstb.2010.0126.</span></li>
-<li><span id="fn:r781">van der Werf, P. and J.A. Gilliland, 2017: A systematic review of food losses and food waste generation in developed countries. Proc. Inst. Civ. Eng. – Waste Resour. Manag., 170, 66–77, doi:10.1680/jwarm.16.00026.</span></li>
-<li><span id="fn:r782">Xue, L. et al., 2017: Missing food, missing data? A critical review of global food losses and food waste data. Environ. Sci. Technol., 51, 6618–6633, doi:10.1021/acs.est.7b00401.</span></li>
-<li><span id="fn:r783">Schanes, K., K. Dobernig and B. Gözet, 2018: Food waste matters – A systematic review of household food waste practices and their policy implications. J. Clean. Prod., 182, 978–991, doi:10.1016/J.JCLEPRO.2018.02.030.</span></li>
-<li><span id="fn:r784">Thyberg, K.L. and D.J. Tonjes, 2016: Drivers of food waste and their implications for sustainable policy development. Resour. Conserv. Recycl., 106, 110–123, doi:10.1016/J.RESCONREC.2015.11.016.</span></li>
-<li><span id="fn:r785">Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.</span></li>
-<li><span id="fn:r786">FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp.</span></li>
-<li><span id="fn:r787">Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353.</span></li>
-<li><span id="fn:r788">de Groot, W.J., B.M. Wotton, and M.D. Flannigan, 2015: Chapter 11 – Wildland Fire Danger Rating and Early Warning Systems. In:Wildfire Hazards, Risks and Disasters [Shroder, J.F. and D. Paton (eds.)], Elsevier, Oxford, pp. 207–228, doi:10.1016/B978-0-12-410434-1.00011-7.</span></li>
-<li><span id="fn:r789">Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84, doi:10.1038/ nature16467.</span></li>
-<li><span id="fn:r790">Falco, S. Di, F. Adinolfi, M. Bozzola, and F. Capitanio, 2014: Crop Insurance as a Strategy for Adapting to Climate Change. J. Agric. Econ., 65, 485–504, doi:10.1111/1477-9552.12053.</span></li>
-<li><span id="fn:r791">Falco, S. Di, F. Adinolfi, M. Bozzola, and F. Capitanio, 2014: Crop Insurance as a Strategy for Adapting to Climate Change. J. Agric. Econ., 65, 485–504, doi:10.1111/1477-9552.12053.</span></li>
-<li><span id="fn:r792">Linnerooth-Bayer, J. and R. Mechler, 2006: Insurance for assisting adaptation to climate change in developing countries: A proposed strategy. Clim. Policy, 6, 621–636, doi:10.1080/14693062.2006.9685628.</span></li>
-<li><span id="fn:r793">Surminski, S. and D. Oramas-Dorta, 2014: Flood insurance schemes and climate adaptation in developing countries. Int. J. Disaster Risk Reduct., 7, 154–164, doi: 10.1016/j.ijdrr.2013.10.005.</span></li>
-<li><span id="fn:r794">IPCC, 2018: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. In press, 1552 pp.</span></li>
-<li><span id="fn:r795">Nordhaus, W., 2014: Estimates of the social cost of carbon: Concepts and results from the DICE-2013R model and alternative approaches. J. Assoc. Environ. Resour. Econ., 1, 273–312, doi:10.1086/676035.</span></li>
-<li><span id="fn:r796">Pizer, W. et al., 2014: Using and improving the social cost of carbon. Science, 346, 1189–1190, doi:10.1126/science.1259774.</span></li>
-<li><span id="fn:r797">Kesicki, F., 2013: What are the key drivers of MAC curves? A partial-equilibrium modelling approach for the UK. Energy Policy, 58, 142–151, doi:10.1016/J.ENPOL.2013.02.043.</span></li>
-<li><span id="fn:r798">Gillingham, K. and J.H. Stock, 2018: The cost of reducing greenhouse gas emissions. J. Econ. Perspect., 32, 53–72, doi:10.1257/jep.32.4.53.</span></li>
-<li><span id="fn:r799">Huang, S.K., L. Kuo, and K.-L. Chou, 2016: The applicability of marginal abatement cost approach: A comprehensive review. J. Clean. Prod., 127, 59–71, doi:10.1016/J.JCLEPRO.2016.04.013.</span></li>
-<li><span id="fn:r800">Rodriguez-Labajos, B., 2013: Climate change, ecosystem services and costs of action and inaction: Scoping the interface. Wiley Interdiscip. Rev. Chang., 4, 555–573, doi:10.1002/wcc.247.</span></li>
-<li><span id="fn:r801">Ricke, K., L. Drouet, K. Caldeira, and M. Tavoni, 2018: Country-level social cost of carbon. Nat. Clim. Chang., 8, 895–900, doi:10.1038/s41558-018-0282-y.</span></li>
-<li><span id="fn:r802">IPCC, 2018: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. In press, 1552 pp.</span></li>
-<li><span id="fn:r803">Rosen, R.A. and E. Guenther, 2015: The economics of mitigating climate change: What can we know? Technol. Forecast. Soc. Change, 91, 93–106, doi:10.1016/J.TECHFORE.2014.01.013.</span></li>
-<li><span id="fn:r804">Chen, Y.-H., M. Babiker, S. Paltsev, and J. Reilly, 2016: Costs of Climate Mitigation Policies. MIT Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA, 22 pp.</span></li>
-<li><span id="fn:r805">Alexander, P., D. Moran, M.D.A. Rounsevell, and P. Smith, 2013: Modelling the perennial energy crop market: The role of spatial diffusion. J.R. Soc. Interface, 10, in press, doi:10.1098/rsif.2013.0656.</span></li>
-<li><span id="fn:r806">Hull, V., M.-N. Tuanniu and J. Liu, 2015: Synthesis of human-nature feedbacks. Ecol. Soc., 20(3), doi.org/10.5751/ES-07404-200317.</span></li>
-<li><span id="fn:r807">Brown, C., P. Alexander, and M. Rounsevell, 2018: Empirical evidence for the diffusion of knowledge in land use change. J. Land Use Sci., 13(3), 269–283, doi:10.1080/1747423X.2018.1515995.</span></li>
-<li><span id="fn:r808">Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114.</span></li>
-<li><span id="fn:r809">Kindermann, G., I. McCallum, S. Fritz, and M. Obersteiner, 2008: A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fenn., 42, doi:10.14214/sf.244.</span></li>
-<li><span id="fn:r810">Golub, A.A., et al., 2013: Global climate policy impacts on livestock, land use, livelihoods and food security. Proc. Natl. Acad. Sci. U.S.A., 110, 20894–20899, doi:10.1073/pnas.1108772109.</span></li>
-<li><span id="fn:r811">Favero, A., R. Mendelsohn, and B. Sohngen, 2017: Using forests for climate mitigation: Sequester carbon or produce woody biomass? Clim. Change, 144, 195–206, doi:10.1007/s10584-017-2034-9.</span></li>
-<li><span id="fn:r812">Albanito, F. et al., 2016: Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: A global assessment. GCB Bioenergy, 8, 81–95, doi:10.1111/gcbb.12242.</span></li>
-<li><span id="fn:r813">Robinson, D.A. et al., 2017: Modelling feedbacks between human and natural processes in the land system. Earth Syst. Dyn. Discuss., doi:10.5194/esd-2017-68.</span></li>
-<li><span id="fn:r814">Walters, M. and R.J. Scholes (eds.), 2017: The GEO handbook on biodiversity observation networks. Springer International Publishing, Cham, Switzerland, 326 pp. doi:10.1007/978-3-319-27288-7.</span></li>
-<li><span id="fn:r815">Font Vivanco, D., R. Kemp, and E. van der Voet, 2016: How to deal with the rebound effect? A policy-oriented approach. Energy Policy, 94, 114–125, doi:10.1016/J.ENPOL.2016.03.054.</span></li>
-<li><span id="fn:r816">Yirdaw, E., M. Tigabu and A. Monge, 2017: Rehabilitation of degraded dryland ecosystems – Review. Silva Fenn., 51, doi:10.14214/sf.1673.</span></li>
-<li><span id="fn:r817">Pedrozo-Acuña, A., R. Damania, M.A. Laverde-Barajas, and D. Mira-Salama, 2015: Assessing the consequences of sea-level rise in the coastal zone of Quintana Roo, México: The costs of inaction. J. Coast. Conserv., 19, 227–240, doi:10.1007/s11852-015-0383-y.</span></li>
-<li><span id="fn:r818">Goldstein, A., W.R. Turner, J. Gladstone, and D.G. Hole, 2019: The private sector’s climate change risk and adaptation blind spots. Nat. Clim. Chang., 9, 18–25, doi:10.1038/s41558-018-0340-5.</span></li>
-<li><span id="fn:r819">Butler, M.P., P.M. Reed, K. Fisher-Vanden, K. Keller, and T. Wagener, 2014: Inaction and climate stabilization uncertainties lead to severe economic risks. Clim. Change, 127, 463–474, doi:10.1007/s10584-014-1283-0.</span></li>
-<li><span id="fn:r820">Hennessey, R., J. Pittman, A. Morand, and A. Douglas, 2017: Co-benefits of integrating climate change adaptation and mitigation in the Canadian energy sector. Energy Policy, 111, 214–221, doi:10.1016/J.ENPOL.2017.09.025.</span></li>
-<li><span id="fn:r821">Locatelli, B., C. Pavageau, E. Pramova, and M. Di Gregorio, 2015b: Integrating climate change mitigation and adaptation in agriculture and forestry: Opportunities and trade-offs. Wiley Interdiscip. Rev. Clim. Chang., 6, 585-598, doi:10.1002/wcc.357.</span></li>
-<li><span id="fn:r822">Lobell, D.B., C.B. Uris Lantz, and T.W. Hertel, 2013: Climate adaptation as mitigation: The case of agricultural investments. Environ. Res. Lett., 8, 15012, doi:10.1088/1748-9326/8/1/015012.</span></li>
-<li><span id="fn:r823">Berry, P.M. et al., 2015: Cross-sectoral interactions of adaptation and mitigation measures. Clim. Change, 128, 381–393, doi:10.1007/s10584-014-1214-0.</span></li>
-<li><span id="fn:r824">Kongsager, R. and E. Corbera, 2015: Linking mitigation and adaptation in carbon forestry projects: Evidence from Belize. World Dev., 76, 132–146, doi:10.1016/J.WORLDDEV.2015.07.003.</span></li>
-<li><span id="fn:r825">Yohe, G.W., 2001: Mitigative capacity – The mirror image of adaptive capacity on the emissions side. Clim. Change, 49, 247–262, doi:10.1023/A:1010677916703.</span></li>
-<li><span id="fn:r826">Ayers, J.M. and S. Huq, 2009: The value of linking mitigation and adaptation: A case study of Bangladesh. Environ. Manage., 43, 753–764, doi:10.1007/s00267-008-9223-2.</span></li>
-<li><span id="fn:r827">Kongsager, R., B. Locatelli, and F. Chazarin, 2016: Addressing climate change mitigation and adaptation together: A global assessment of agriculture and forestry projects. Environ. Manage., 57, 271–282, doi:10.1007/s00267-015-0605-y.</span></li>
-<li><span id="fn:r828">Porter, J.R., L. Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, and M.I. Travasso, 2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom, pp. 485–533, doi:10.1017/CBO9781107415379.</span></li>
-<li><span id="fn:r829">Kongsager, R., B. Locatelli, and F. Chazarin, 2016: Addressing climate change mitigation and adaptation together: A global assessment of agriculture and forestry projects. Environ. Manage., 57, 271–282, doi:10.1007/s00267-015-0605-y.</span></li>
-<li><span id="fn:r830">Keenan, R.J., 2015: Climate change impacts and adaptation in forest management: A review. Ann. For. Sci., 72, 145–167, doi:10.1007/s13595-014-0446-5.</span></li>
-<li><span id="fn:r831">Gaba, S. et al., 2015: Multiple cropping systems as drivers for providing multiple ecosystem services: From concepts to design. Agron. Sustain. Dev., 35, 607–623, doi:10.1007/s13593-014-0272-z.</span></li>
-<li><span id="fn:r832">Locatelli, B., V. Evans, A. Wardell, A. Andrade, and R. Vignola, 2011: Forests and climate change in Latin America: Linking adaptation and mitigation. Forests, 2, doi:10.3390/f2010431.</span></li>
-<li><span id="fn:r833">Nelson, K.C. and B.H.J. de Jong, 2003: Making global initiatives local realities: Carbon mitigation projects in Chiapas, Mexico. Glob. Environ. Chang., 13, 19–30, doi: 10.1016/S0959-3780(02)00088-2.</span></li>
-<li><span id="fn:r834">Reyer, C., M. Guericke, and P.L. Ibisch, 2009: Climate change mitigation via afforestation, reforestation and deforestation avoidance: and what about adaptation to environmental change? New For., 38, 15–34, doi:10.1007/s11056-008-9129-0.</span></li>
-<li><span id="fn:r835">Locatelli, B. et al., 2015a: Tropical reforestation and climate change: Beyond carbon. Restor. Ecol., 23, 337–343, doi:10.1111/rec.12209.</span></li>
-<li><span id="fn:r836">West, T.A.P., 2016: Indigenous community benefits from a de-centralized approach to REDD+ in Brazil. Clim. Policy, 16, 924–939, doi:10.1080/14693062.2015.1058238.</span></li>
-<li><span id="fn:r837">Barnett, J., and S. O’Neill, 2010: Maladaptation. Glob. Environ. Chang., 2, 211–213, doi:10.1016/j.gloenvcha.2009.11.004.</span></li>
-<li><span id="fn:r838">Porter, J.R., L. Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, and M.I. Travasso, 2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom, pp. 485–533, doi:10.1017/CBO9781107415379.</span></li>
-<li><span id="fn:r839">Swart, R.O.B. and F. Raes, 2007: Making integration of adaptation and mitigation work: mainstreaming into sustainable development policies? Clim. Policy, 7, 288–303, doi:10.1080/14693062.2007.9685657.</span></li>
-<li><span id="fn:r840">Noble, I.R., S. Huq, Y.A. Anokhin, J. Carmin, D. Goudou, F.P. Lansigan, B. Osman-Elasha and A. Villamizar, 2014: Adaptation needs and options. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 833–868.</span></li>
-<li><span id="fn:r841">Noble, I.R., S. Huq, Y.A. Anokhin, J. Carmin, D. Goudou, F.P. Lansigan, B. Osman-Elasha and A. Villamizar, 2014: Adaptation needs and options. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 833–868.</span></li>
-<li><span id="fn:r842">Locatelli, B., V. Evans, A. Wardell, A. Andrade, and R. Vignola, 2011: Forests and climate change in Latin America: Linking adaptation and mitigation. Forests, 2, doi:10.3390/f2010431.</span></li>
-<li><span id="fn:r843">Campbell, B.M., P. Thornton, R. Zougmoré, P. van Asten, and L. Lipper, 2014: Sustainable intensification: What is its role in climate smart agriculture? Curr. Opin. Environ. Sustain., 8, 39–43, doi:10.1016/j.cosust.2014.07.002.</span></li>
-<li><span id="fn:r844">Locatelli, B., C. Pavageau, E. Pramova, and M. Di Gregorio, 2015b: Integrating climate change mitigation and adaptation in agriculture and forestry: Opportunities and trade-offs. Wiley Interdiscip. Rev. Clim. Chang., 6, 585-598, doi:10.1002/wcc.357.</span></li>
-<li><span id="fn:r845">Bazilian, M. et al., 2011: Considering the energy, water and food nexus: Towards an integrated modelling approach. Energy Policy, 39, 7896–7906, doi:10.1016/J.ENPOL.2011.09.039.</span></li>
-<li><span id="fn:r846">Hussey, K. and J. Pittock, 2012: The energy–water nexus: Managing the links between energy and water for a sustainable future. Ecol. Soc., 17, doi:10.5751/ES-04641-170131.</span></li>
-<li><span id="fn:r847">D’Odorico, P. et al., 2018: The Global Food-Energy-Water Nexus. Rev. Geophys., 56, 456–531, doi:10.1029/2017RG000591.</span></li>
-<li><span id="fn:r848">Hoff, H., 2011: Bonn 2011 Conference: The Water, Energy and Food Security Nexus – Solutions for the Green Economy. Stockholm, 1–52 pp.</span></li>
-<li><span id="fn:r849">Allan, T., M. Keulertz, and E. Woertz, 2015: The water–food–energy nexus: An introduction to nexus concepts and some conceptual and operational problems (vol 31, pg 301, 2015). Int. J. Water Resour. Dev., 31, 800, doi:10.1080/07900627.2015.1060725.</span></li>
-<li><span id="fn:r850">Fischer, J. et al., 2017: Reframing the food–biodiversity challenge. Trends Ecol. Evol., 32, 335–345, doi:10.1016/j.tree.2017.02.009.</span></li>
-<li><span id="fn:r851">Howells, M. et al., 2013: Integrated analysis of climate change, land-use, energy and water strategies. Nat. Clim. Chang., 3, 621–626. doi:10.1038/nclimate1789.</span></li>
-<li><span id="fn:r852">Hoff, H., 2011: Bonn 2011 Conference: The Water, Energy and Food Security Nexus – Solutions for the Green Economy. Stockholm, 1–52 pp.</span></li>
-<li><span id="fn:r853">Ringler, C. and R. Lawford, 2013: The nexus across water, energy, land and food (WELF): Potential for improved resource use efficiency? Curr. Opin. Environ. Sustain., 5, 617–624, doi:10.1016/J.COSUST.2013.11.002.</span></li>
-<li><span id="fn:r854">Biggs, E.M. et al., 2015: Sustainable development and the water–energy–food nexus: A perspective on livelihoods. Environ. Sci. Policy, 54, 389–397, doi:10.1016/J.ENVSCI.2015.08.002.</span></li>
-<li><span id="fn:r855">Hayley, L., C. Declan, B. Michael, and R. Judith, 2015: Tracing the water–energy–food nexus: Description, theory and practice. Geogr. Compass, 9, 445–460, doi:10.1111/gec3.12222.</span></li>
-<li><span id="fn:r856">Wichelns, D., 2017: The water-energy-food nexus: Is the increasing attention warranted, from either a research or policy perspective? Environ. Sci. Policy, 69, 113–123, doi:10.1016/J.ENVSCI.2016.12.018.</span></li>
-<li><span id="fn:r857">Hersperger, A.M., M.-P. Gennaio, P.H. Verburg and M. Bürgi, 2010: Linking land change with driving forces and actors: Four conceptual models. Ecol. Soc., 15, doi:10.5751/ES-03562-150401.</span></li>
-<li><span id="fn:r858">Hersperger, A.M., M.-P. Gennaio, P.H. Verburg and M. Bürgi, 2010: Linking land change with driving forces and actors: Four conceptual models. Ecol. Soc., 15, doi:10.5751/ES-03562-150401.</span></li>
-<li><span id="fn:r859">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r860">FAO, 2015a: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations, Rome.</span></li>
-<li><span id="fn:r861">Carlisle, K., and R.L. Gruby, 2017: Polycentric systems of governance: A theoretical model for the commons. Policy Stud. J., doi:10.1111/psj.12212.</span></li>
-<li><span id="fn:r862">Ostrom, E. and M. Cox, 2010: Moving beyond panaceas: A multi-tiered diagnostic approach for social-ecological analysis. Environ. Conserv., 37, 451–463, doi:10.1017/S0376892910000834.</span></li>
-<li><span id="fn:r863">Lebel, L. et al., 2006: Governance and the capacity to manage resilience in regional social-ecological systems. Ecol. Soc., 11, 19, doi:10.5751/ES-01606-110119.</span></li>
-<li><span id="fn:r864">Bodin, Ö., 2017: Collaborative environmental governance: Achieving collective action in social-ecological systems. Science, 357, eaan1114, doi:10.1126/science.aan1114.</span></li>
-<li><span id="fn:r865">Tom Veldkamp, Nico Polman, Stijn Reinhard, M.S., 2011: From scaling to governance of the land system: Bridging ecological and economic perspectives. Ecol. Soc., 16, 1, doi: 10.5751/ES-03691-160101.</span></li>
-<li><span id="fn:r866">Myers, R., A.J.P. Sanders, A.M. Larson, R.D. Prasti, A. Ravikumar, 2016: Analyzing multilevel governance in Indonesia: Lessons for REDD+ from the study of landuse change in Central and West Kalimantan, CIFOR Working Paper no. 202, Center for International Forestry Research (CIFOR), Bogor, Indonesia, 69 pp.</span></li>
-<li><span id="fn:r867">Azizi, A., A. Ghorbani, B. Malekmohammadi, and H.R. Jafari, 2017: Government management and overexploitation of groundwater resources: absence of local community initiatives in Ardabil plain-Iran. J. Environ. Plan. Manag., 60, 1785–1808, doi:10.1080/09640568.2016.1257975.</span></li>
-<li><span id="fn:r868">Lebel, L. et al., 2006: Governance and the capacity to manage resilience in regional social-ecological systems. Ecol. Soc., 11, 19, doi:10.5751/ES-01606-110119.</span></li>
-<li><span id="fn:r869">Mistry, J. and A. Berardi, 2016: Bridging indigenous and scientific knowledge. Science, 352, 1274–1275, doi:10.1126/science.aaf1160.</span></li>
-<li><span id="fn:r870">Schneider, F. and T. Buser, 2018: Promising degrees of stakeholder interaction in research for sustainable development. Sustain. Sci., 13, 129–142, doi:10.1007/s11625-017-0507-4.</span></li>
-<li><span id="fn:r871">Mistry, J. and A. Berardi, 2016: Bridging indigenous and scientific knowledge. Science, 352, 1274–1275, doi:10.1126/science.aaf1160.</span></li>
-<li><span id="fn:r872">Mistry, J. and A. Berardi, 2016: Bridging indigenous and scientific knowledge. Science, 352, 1274–1275, doi:10.1126/science.aaf1160.</span></li>
-<li><span id="fn:r873">Chanza, N. and A. de Wit, 2016: Enhancing climate governance through indigenous knowledge: Case in sustainability science. S. Afr. J. Sci., 112, 1–7, doi:10.17159/sajs.2016/20140286.</span></li>
-<li><span id="fn:r874">Klein, J.A. et al., 2014: Unexpected climate impacts on the Tibetan Plateau: Local and scientific knowledge in findings of delayed summer. Glob. Environ. Chang., 28, 141–152, doi:10.1016/J.GLOENVCHA.2014.03.007.</span></li>
-<li><span id="fn:r875">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r876">Resurrección, B.P., 2013: Persistent women and environment linkages in climate change and sustainable development agendas. Womens. Stud. Int. Forum, 40, 33–43, doi:10.1016/J.WSIF.2013.03.011.</span></li>
-<li><span id="fn:r877">Agarwal, B., 2010: Gender and Green Governance. Oxford University Press, Oxford, UK.</span></li>
-<li><span id="fn:r878">Boserup, E., 1989: Population, the status of women, and rural development. Popul. Dev. Rev., 15, 45–60, doi:10.2307/2807921.</span></li>
-<li><span id="fn:r879">Darity, W.A., 1980: The Boserup theory of agricultural growth: A model for anthropological economics. J. Dev. Econ., 7, 137–157, doi:10.1016/0304-3878(80)90001-2.</span></li>
-<li><span id="fn:r880">FAO, 2011: The State of Food and Agriculture: Women in agriculture – Closing the gender gap for development. Food and Agriculture Organization of the United Nations, Rome, Italy.</span></li>
-<li><span id="fn:r881">OECD, 2014: Social Institutions and Gender Index (SIGI). OECD Development Centre’s Social Cohesion Unit, Paris, France, http://www.oecd.org/dev/development-</span></li>
-<li><span id="fn:r882">Beuchelt, T.D. and L. Badstue, 2013: Gender, nutrition – and climate-smart food production: Opportunities and trade-offs. Food Secur., 5, 709–721, doi:10.1007/s12571-013-0290-8.</span></li>
-<li><span id="fn:r883">UNEP, 2016: Global Gender and Environment Outlook, UN Environment, Nairobi, Kenya, 222 pp.</span></li>
-<li><span id="fn:r884">Day, T., K. McKenna, and A. Bowlus, 2005: The Economic Costs of Violence Against Women: An Evaluation of the Literature. Expert brief compiled in preparation for the Secretary-General’s in-depth study on all forms of violence against women. NY: United Nations. Barzman, New York City, pp. 1–66.</span></li>
-<li><span id="fn:r885">UNEP, 2016: Global Gender and Environment Outlook, UN Environment, Nairobi, Kenya, 222 pp.</span></li>
-<li><span id="fn:r886">Pham, P., P. Doneys, and D.L. Doane, 2016: Changing livelihoods, gender roles and gender hierarchies: The impact of climate, regulatory and socio-economic changes on women and men in a Co Tu community in Vietnam. Women’s Stud. Int. Forum, 54, 48–56, doi:10.1016/J.WSIF.2015.10.001.</span></li>
-<li><span id="fn:r887">Theriault, V., M. Smale, and H. Haider, 2017: How does gender affect sustainable intensification of cereal production in the West African Sahel? Evidence from Burkina Faso. World Dev., 92, 177–191, doi:10.1016/J.WORLDDEV.2016.12.003.</span></li>
-<li><span id="fn:r888">Mello, D. and M. Schmink, 2017: Amazon entrepreneurs: Women’s economic empowerment and the potential for more sustainable land use practices. Womens. Stud. Int. Forum, 65, 28–36, doi:10.1016/J.WSIF.2016.11.008.</span></li>
-<li><span id="fn:r889">Arora-Jonsson, S., 2014: Forty years of gender research and environmental policy: Where do we stand? Womens. Stud. Int. Forum, 47, 295–308, doi:10.1016/J.WSIF.2014.02.009.</span></li>
-<li><span id="fn:r890">Namubiru-Mwaura, E., 2014: Land tenure and gender: Approaches and challenges for strengthening rural women’s land rights. Women’s Voice, Agency, &amp; Participation Research Series No. 06, World Bank, Washington, DC, USA, 32 pp.</span></li>
-<li><span id="fn:r891">Rao, N., 2017: Assets, agency and legitimacy: Towards a relational understanding of gender equality policy and practice. World Dev., 95, 43–54, doi:10.1016/J.WORLDDEV.2017.02.018.</span></li>
-<li><span id="fn:r892">Djoudi, H. et al., 2016: Beyond dichotomies: Gender and intersecting inequalities in climate change studies. Ambio, 45, 248–262, doi:10.1007/s13280-016-0825-2.</span></li>
-<li><span id="fn:r893">Kaijser, A. and A. Kronsell, 2014: Climate change through the lens of intersectionality. Env. Polit., 23, 417–433, doi:10.1080/09644016.2013.835203.</span></li>
-<li><span id="fn:r894">Moosa, C.S. and N. Tuana, 2014: Mapping a research agenda concerning gender and climate change: A review of the literature. Hypatia, 29, 677–694, doi:10.1111/hypa.12085.</span></li>
-<li><span id="fn:r895">Thompson-Hall, M., E.R. Carr, and U. Pascual, 2016: Enhancing and expanding intersectional research for climate change adaptation in agrarian settings. Ambio, 45, 373–382, doi:10.1007/s13280-016-0827-0.</span></li>
-<li><span id="fn:r896">Sterner, T. and Coria, J. (eds.), 2003: Policy Instruments for Environmental and Natural Resource Management. Resources for the Future Press, Washington, DC, USA, 504 pp.</span></li>
-<li><span id="fn:r897">Grolleau, G., L. Ibanez, N. Mzoughi, and M. Teisl, 2016: Helping eco-labels to fulfil their promises. Clim. Policy, 16, 792–802, doi:10.1080/14693062.2015.1033675.</span></li>
-<li><span id="fn:r898">Gómez-Baggethun, E. and R. Muradian, 2015: In markets we trust? Setting the boundaries of market-based instruments in ecosystem services governance. Ecol. Econ., 117, 217–224, doi:10.1016/J.ECOLECON.2015.03.016.</span></li>
-<li><span id="fn:r899">Farley, J. and A. Voinov, 2016: Economics, socio-ecological resilience and ecosystem services. J. Environ. Manage., 183, 389–398, doi:10.1016/J.JENVMAN.2016.07.065.</span></li>
-<li><span id="fn:r900">Appleton, A.E., 2009: Private climate change standards and labelling schemes under the WTO agreement on technical barriers to trade. In: International trade regulation and the mitigation of climate change: World Trade Forum. Cambridge University Press, Cambridge, United Kingdom, pp. 131–152.</span></li>
-<li><span id="fn:r901">van Noordwijk, M. and L. Brussaard, 2014: Minimizing the ecological footprint of food: Closing yield and efficiency gaps simultaneously? Curr. Opin. Environ. Sustain., 8, 62–70, doi:10.1016/J.COSUST.2014.08.008.</span></li>
-<li><span id="fn:r902">Biagini, B. and A. Miller, 2013: Engaging the private sector in adaptation to climate change in developing countries: importance, status and challenges. Clim. Dev., 5, 242–252, doi:10.1080/17565529.2013.821053.</span></li>
-<li><span id="fn:r903">Weitzman, M.L., 2014: Can negotiating a uniform carbon price help to internalize the global warming externality? J. Assoc. Environ. Resour. Econ., 1, 29–49, doi:10.3386/w19644.</span></li>
-<li><span id="fn:r904">Eidelwein, F., D.C. Collatto, L.H. Rodrigues, D.P. Lacerda, and F.S. Piran, 2018: Internalization of environmental externalities: Development of a method for elaborating the statement of economic and environmental results. J. Clean. Prod., 170, 1316–1327, doi:10.1016/J.JCLEPRO.2017.09.208.</span></li>
-<li><span id="fn:r905">Nepstad, D.C., W. Boyd, C.M. Stickler, T. Bezerra, and A.A. Azevedo, 2013: Responding to climate change and the global land crisis: REDD+, market transformation and low-emissions rural development. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 368, 20120167, doi:10.1098/rstb.2012.0167.</span></li>
-<li><span id="fn:r906">Denis, G. et al., 2014: Global changes, livestock and vulnerability: The social construction of markets as an adaptive strategy. Geogr. J., 182, 153–164, doi:10.1111/geoj.12115.</span></li>
-<li><span id="fn:r907">Wunder, S., 2015: Revisiting the concept of payments for environmental services. Ecol. Econ., 117, 234–243, doi:10.1016/J.ECOLECON.2014.08.016.</span></li>
-<li><span id="fn:r908">Börner, J. et al., 2017: The effectiveness of payments for environmental services. World Dev., 96, 359–374, doi:10.1016/J.WORLDDEV.2017.03.020.</span></li>
-<li><span id="fn:r909">Alix-Garcia, J. and H. Wolff, 2014: Payment for ecosystem services from forests. Annu. Rev. Resour. Econ., 6, 361–380, doi:10.1146/annurev-resource-100913-012524.</span></li>
-<li><span id="fn:r910">Stavi, I., G. Bel and E. Zaady, 2016: Soil functions and ecosystem services in conventional, conservation and integrated agricultural systems. A review. Agron. Sustain. Dev., 36, 32, doi:10.1007/s13593-016-0368-8.</span></li>
-<li><span id="fn:r911">Nicole, W., 2015: Pollinator power: Nutrition security benefits of an ecosystem service. Environ. Health Perspect., 123, A210–A215, doi:10.1289/ehp.123-A210.</span></li>
-<li><span id="fn:r912">Loch, A. et al., 2013: The Role of Water Markets in Climate Change Adaptation. National Climate Change Adaptation Research Facility, Gold Coast, Australia, 125 pp.</span></li>
-<li><span id="fn:r913">Denis, G. et al., 2014: Global changes, livestock and vulnerability: The social construction of markets as an adaptive strategy. Geogr. J., 182, 153–164, doi:10.1111/geoj.12115.</span></li>
-<li><span id="fn:r914">Anderson, S.E. et al., 2018: The Critical Role of Markets in Climate Change Adaptation. National Bureau of Economic Research.</span></li>
-<li><span id="fn:r915">Reed, M. and L.C. Stringer, 2015: Climate change and desertification: Anticipating, assessing &amp; adapting to future change in drylands. Impulse Report for the 3rd UNCCD Scientific Conference, Agropolis International, Montpellier, France, 1–140 pp.</span></li>
-<li><span id="fn:r916">Biagini, B. and A. Miller, 2013: Engaging the private sector in adaptation to climate change in developing countries: importance, status and challenges. Clim. Dev., 5, 242–252, doi:10.1080/17565529.2013.821053.</span></li>
-<li><span id="fn:r917">Chartres, C.J. and A. Noble, 2015: Sustainable intensification: Overcoming land and water constraints on food production. Food Secur., 7, 235–245, doi:10.1007/s12571-015-0425-1.</span></li>
-<li><span id="fn:r918">Baker, S. and F.S. Chapin III, 2018: Going beyond “it depends:” the role of context in shaping participation in natural resource management. Ecol. Soc., 23, doi:10.5751/ES-09868-230120.</span></li>
-<li><span id="fn:r919">Kunreuther, H., 2015: The role of insurance in reducing losses from extreme events: The need for public–private partnerships. Geneva Pap. Risk Insur. Issues Pract., 40, 741–762, doi:10.1057/gpp.2015.14.</span></li>
-<li><span id="fn:r920">Elbehri, A., J. Elliott, and T. Wheeler, 2015: Climate change, food security and trade: An overview of global assessments and policy insights. In: Climate Change and Food Systems: Global assessments and implications for food security and trade [Elbehri, A. (ed.)]. FAO, Rome, Italy, pp. 1–27.</span></li>
-<li><span id="fn:r921">Mathews, J.A., 2017: Global trade and promotion of cleantech industry: A post-Paris agenda. Clim. Policy, 17, 102–110, doi:10.1080/14693062.2016.1215286.</span></li>
-<li><span id="fn:r922">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r923">Hinkel, J., P.W.G. Bots and M. Schlüter, 2014: Enhancing the Ostrom social-ecological system framework through formalization. Ecol. Soc., 19(3), doi:10.5751/ES-06475-190351.</span></li>
-<li><span id="fn:r924">Schut, M. et al., 2016: Sustainable intensification of agricultural systems in the Central African Highlands: The need for institutional innovation. Agric. Syst., 145, 165–176, doi:10.1016/J.AGSY.2016.03.005.</span></li>
-<li><span id="fn:r925">IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.</span></li>
-<li><span id="fn:r926">Sterling, E. et al., 2017: Culturally grounded indicators of resilience in social-ecological systems. Environ. Soc., 8, 63–95, doi:10.3167/ares.2017.080104.</span></li>
-<li><span id="fn:r927">Sheehy, T., F. Kolahdooz, C. Roache, and S. Sharma, 2015: Traditional food consumption is associated with better diet quality and adequacy among Inuit adults in Nunavut, Canada. Int. J. Food Sci. Nutr., 66, 445–451, doi:</span></li>
-<li><span id="fn:r928">Halkos, G. and A. Skouloudis, 2016: Cultural dimensions and corporate social responsibility: A cross-country analysis. MPRA Paper 6922, University Library of Munich, Germany.</span></li>
-<li><div id="fn:r929">
-</div></li>
-<li><span id="fn:r930">IPBES, 2018b: The IPBES Assessment Report on Land Degradation and Restoration [Montanarella, L., Scholes, R. and Brainich, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 744 pp.</span></li>
-<li><span id="fn:r931">Tal, A., 2010: Desertification. In: The Turning Points of Environmental History [Uekoetter, F. (ed.)]. University of Pittsburgh Press, Pittsburgh, Pennsylvania, USA, pp. 146–161.</span></li>
-<li><span id="fn:r932">Bai, Z.G., D.L. Dent, L. Olsson, and M.E. Schaepman, 2008: Proxy global assessment of land degradation. Soil Use Manag., 24, 223–234, doi:10.1111/j.1475-2743.2008.00169.x.</span></li>
-<li><span id="fn:r933">Parker, W.S., 2013: Ensemble modeling, uncertainty and robust predictions. Wiley Interdiscip. Rev. Chang., 4, 213–223, doi:10.1002/wcc.220.</span></li>
-<li><span id="fn:r934">Stocker, T.F. et al., 2013b: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 33–115 pp.</span></li></ol>
+=== Mitigation pathways compatible with 1.5°C in the context of sustainable development ===
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+[[IPCC:Sr15:Chapter:Chapter-2|Next Chapter]]
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 == Contributors ==
 '''Coordinating Lead Authors:'''<br>
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 Yuping Bai (China), Baldur Janz (Germany)
+|-|Chapter info=
 '''This chapter should be cited as:'''<br>
 Arneth, A., F. Denton, F. Agus, A. Elbehri, K. Erb, B. Osman Elasha, M. Rahimi, M. Rounsevell, A. Spence, R. Valentini, 2019: Framing and Context. In: ''Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems'' [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D.C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley,
 K. Kissick, M. Belkacemi, J. Malley, (eds.)]. https://doi.org/10.1017/9781009157988.003
+</tabber>

Test/Reports/SRCCL/Chapter1: Difference between revisions

Latest revision as of 04:44, 11 March 2026

Chapter 1 Framing and context

ES Executive Summary

1.1 Assessing the Knowledge Base for a 1.5°C Warmer World

Figure 1.1

Box 1.1 The Anthropocene: Strengthening the Global Response to 1.5°C Global Warming

1.1.1 Equity and a 1.5°C Warmer World

1.1.2 Eradication of Poverty

1.1.3 Sustainable Development and a 1.5°C Warmer World

1.2 Understanding 1.5°C: Reference Levels, Probability, Transience, Overshoot, and Stabilization

1.2.1 Working Definitions of 1.5°C and 2°C Warming Relative to Pre-Industrial Levels

1.2.1.1 Definition of global average temperature

Figure 1.2

Evolution of global mean surface temperature (GMST) over the period of instrumental observations.

1.2.1.2 Choice of reference period

Table 1.1

Observed increase in global average surface temperature in various datasets. Numbers in square brackets correspond to 5–95% uncertainty ranges from individual datasets, encompassing known sources of observational uncertainty only.

1.2.1.3 Total versus human-induced warming and warming rates

1.2.2 Global versus Regional and Seasonal Warming

Figure 1.3

Spatial and seasonal pattern of present-day warming.

1.2.3 Definition of 1.5°C Pathways: Probability, Transience, Stabilization and Overshoot

1.2.3.1 Pathways remaining below 1.5°C

1.2.3.2 Pathways temporarily exceeding 1.5°C

1.2.3.3 Impacts at 1.5°C warming associated with different pathways: transience versus stabilisation

Figure 1.4

Cross-Chapter Box 1: Scenarios and Pathways

Lead Authors

1.2.4 Geophysical Warming Commitment

Figure 1.5

Warming commitment from past emissions of greenhouse gases and aerosols.

Cross-Chapter Box 2: Measuring Progress to Net Zero Emissions Combining Long-Lived and Short-Lived Climate Forcers

Lead Authors

Cross Chapter Box 2: Figure 1

Implications of different approaches to calculating aggregate greenhouse gas emissions on a pathway to net zero.

1.3 Impacts at 1.5°C and Beyond

1.3.1 Definitions

1.3.2 Drivers of Impacts

1.3.3 Uncertainty and Non-Linearity of Impacts

1.4 Strengthening the Global Response

1.4.1 Classifying Response Options

1.4.2 Governance, Implementation and Policies

Cross-Chapter Box 3: Framing Feasibility: Key Concepts and Conditions for Limiting Global Temperature Increases to 1.5°C

Lead Authors

1.4.3 Transformation, Transformation Pathways, and Transition: Evaluating Trade-Offs and Synergies Between Mitigation, Adaptation and Sustainable Development Goals

Cross-Chapter Box 4: Sustainable Development and the Sustainable Development Goals

Lead Authors

Cross-Chapter Box 4: Figure 1 Climate action is number 13 of the UN Sustainable Development Goals

1.5 Assessment Frameworks and Emerging Methodologies that Integrate Climate Change Mitigation and Adaptation with Sustainable Development

1.5.1 Knowledge Sources and Evidence Used in the Report

1.5.2 Assessment Frameworks and Methodologies

1.6 Confidence, Uncertainty and Risk

1.7 Storyline of the Report

Figure 1.6. Schematic of report storyline

FAQs Frequently Asked Questions

FAQ 1.1 Why are we talking about 1.5°C?

FAQ1.1, Figure 1

A timeline of notable dates in preparing the IPCC Special Report on Global Warming of 1.5°C (blue) embedded within processes and milestones of the United Nations Framework Convention on Climate Change (UNFCCC; grey), including events that may be relevant for discussion of temperature limits.

FAQ 1.2 How close are we to 1.5°C?

FAQ1.2, Figure 1

Human-induced warming reached approximately 1°C above pre-industrial levels in 2017.

SM Supplementary Material

CD Chapter Downloads

Final Report

Draft submitted to 48th Session of IPCC

Final Government Draft

Second Order Draft

First Order Draft

Footnotes

References

Mitigation pathways compatible with 1.5°C in the context of sustainable development

Contributors

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