Test/Reports/SRCCL/Chapter1

Chapter 1 Framing and context

From Report SRCCL
Report	Special Report on Climate Change and Land
Info	More Stuff

ES Executive Summary

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}

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}

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 ₂ , 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}

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}

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}

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 ₂ emissions from deforestation, CH ₄ emissions from rice and ruminant livestock and N ₂ 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}

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}

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}

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}

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}

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}

1.1.1 Objectives and scope of the assessment

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 ¹ ; Mace et al. 2012 ² ; Newbold et al. 2015 ³ ; Runting et al. 2017 ⁴ ; Isbell et al. 2017 ⁵ ) (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 ⁶ ; IMF 2018 ⁷ ). Land also supports non-material ecosystem services such as cognitive and spiritual enrichment and aesthetic values (Hernández-Morcillo et al. 2013 ⁸ ; Fish et al. 2016 ⁹ ), 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 ¹⁰ ; Terraube et al. 2017 ¹¹ ). 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 ¹² ; IPBES 2018a ¹³ ). 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 ¹⁴ ; FAO et al. 2018 ¹⁵ ).

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 ¹⁶ ; Bertram et al. 2015 ¹⁷ ; Riahi et al. 2015 ¹⁸ ; Millar et al. 2017 ¹⁹ ; Rogelj et al.   2018a ²⁰ ). 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 ²¹ ; Le Quere et al. 2018 ²² ). 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 ²³ ; Tubiello et al. 2015 ²⁴ ; Le Quere et al. 2018 ²⁵ ; Ciais et al. 2013a ²⁶ ). 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 ²⁷ ,b ²⁸ ; Grassi et al. 2017 ²⁹ ; Forsell et al. 2016 ³⁰ ), 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 ³¹ ; Bonsch et al. 2016 ³² ; Crist et al. 2017 ^[[#fn:r|]] 33 ; Humpenoder et al. 2014 ³⁴ ; Harvey and Pilgrim 2011 ³⁵ ; Mouratiadou et al. 2016 ³⁶ ; Zhang et al. 2015 ³⁷ ; Sanz-Sanchez et al. 2017 ³⁸ ; Pereira et al. 2010 ³⁹ ; Griscom et al. 2017 ⁴⁰ ; Rogelj et al. 2018a ⁴¹ ) (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 ⁴² ) (Box 1.1). The main objectives of the SRCCL are to:

1. 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;
2. 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;
3. 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;
4. 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.====== Figure 1.1 ========== 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.pngA 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 . N ₂ O and CH ₄ from agriculture are from FAOSTAT; Net land-use change emissions of CO ₂ 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 ₂ -eq are based on AR5 100-year Global Warming Potential values without climate-carbon feedbacks (N ₂ O = 265; CH ₄ = 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) >25 kg m ^–2 (source: Abarca-Gómez et al. 2017 ⁴³ ); underweight is defined as BMI <18.5 kg m ^–2 . (Population density, source: United Nations, Department of Economic and Social Affairs 2017 ⁴⁴ ) (Sections 5.1 and 5.2). F . Dryland areas were estimated using TerraClimate precipitation and potential evapotranspiration (1980–2015) (Abatzoglou et al. 2018 ⁴⁵ ) to identify areas where the Aridity Index is below 0.65. Areas experiencing human caused desertification, after accounting for precipitation variability and CO ₂ 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 ⁴⁶ ). Areas in drought are based on the 12-month accumulation Global Precipitation Climatology Centre Drought Index (Ziese et al. 2014 ⁴⁷ ). 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 ⁴⁸ ; Darrah et al. 2019 ⁴⁹ ) (Sections 3.1, 4.2 and 4.6).== Box 1.1 Land in previous IPCC and other relevant reports ==

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 ⁵⁰ ). 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 ⁵¹ ; Edenhofer et al. 2014 ⁵² ).

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 ⁵³ ). 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 ⁵⁴ ). 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 ⁵⁵ , b ⁵⁶ , c ⁵⁷ , d ⁵⁸ , e ⁵⁹ ) 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 ⁹²⁹ ), which potentially can lead to different conclusions for restoration and ecosystem management.

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 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).====== Figure 1.2 ========== Overview over the SRCCL. ====

File:Https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/Figure-1.2-1024x301.jpgOverview over the SRCCL.== 1.1.2 Status and dynamics of the (global) land system ==

1.1.2.1 1.1.2.1 Land ecosystems and climate change

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 ⁶⁰ ). 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 ⁶¹ ; Tubiello et al. 2015 ⁶² ; Le Quere et al. 2018 ⁶³ ; Ciais et al. 2013a ⁶⁴ ), and especially methane (CH ₄ ) and nitrous oxide (N ₂ O) emissions from agriculture have been rapidly increasing over the last decades (Hoesly et al. 2018 ⁶⁵ ; Tian et al. 2019 ⁶⁶ ) (Figure 1.1 and Sections 2.3.2–2.3.3).

Globally, land also serves as a large CO ₂ sink, which was estimated for the period 2008–2017 to be nearly 30% of total anthropogenic emissions (Le Quere et al. 2015 ⁶⁷ ; Canadell and Schulze 2014 ⁶⁸ ; Ciais et al. 2013a ⁶⁹ ; Zhu et al. 2016 ⁷⁰ ) (Section 2.3.1). This sink has been attributed to increasing atmospheric CO ₂ concentration, a prolonged growing season in cool environments, or forest regrowth (Le Quéré et al. 2013 ⁷¹ ; Pugh et al. 2019 ⁷² ; Le Quéré et al. 2018 ⁷³ ; Ciais et al. 2013a ⁷⁴ ; Zhu et al. 2016 ⁷⁵ ). 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 ⁷⁶ ; Bloom et al. 2016 ⁷⁷ ; Friend et al. 2014 ⁷⁸ ; Le Quere et al. 2018 ⁷⁹ ). 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 ⁸⁰ ; Zhang et al. 2014 ⁸¹ ; Alkama and Cescatti 2016 ⁸² ) (Section 2.6). Due to the current magnitude of GHG emissions and CO ₂ 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 ⁸⁴ ; Smith et al. 2014 ⁸⁵ ; Griscom et al. 2017 ⁸⁶ ) (Sections 2.6 and 6.3).

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 ⁸⁷ ; Wärlind et al. 2014 ⁸⁸ ; Davies-Barnard et al. 2015 ⁸⁹ ; Nakamura et al. 2017 ⁹⁰ ). Atmospheric CO ₂ increases have been attributed to underlie, at least partially, observed woody plant cover increase in grasslands and savannahs (Donohue et al. 2013 ⁹¹ ). Climate change-induced shifts in habitats, together with warmer temperatures, cause pressure on plants and animals (Pimm et al. 2014 ⁹² ; Urban et al. 2016 ⁹³ ). 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 ⁹⁴ ; Lesk et al. 2016 ⁹⁵ ). Climate change is expected to reduce yields in areas that are already under heat and water stress (Schlenker and Lobell 2010 ⁹⁶ ; Lobell et al. 2011 ⁹⁷ , 2012 ⁹⁸ ; Challinor et al. 2014 ⁹⁹ ) (Section 5.2.2). At the same time, warmer temperatures can increase productivity in cooler regions (Moore and Lobell 2015 ¹⁰⁰ ) 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 ¹⁰¹ ; Di Paola et al. 2018 ¹⁰² ). Increasing atmospheric CO ₂ is expected to increase productivity and water use efficiency in crops and in forests (Muller et al. 2015 ¹⁰³ ; Nakamura et al. 2017 ¹⁰⁴ ; Kimball 2016 ¹⁰⁵ ). 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 ¹⁰⁶ ; Fasullo et al. 2018 ¹⁰⁷ ) (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 ¹⁰⁸ ; Anderegg et al. 2012 ¹⁰⁹ ), whilst weather extremes also impact local infrastructure and hence transportation and trade in land-related goods (Schweikert et al. 2014 ¹¹⁰ ; Chappin and van der Lei 2014 ¹¹¹ ). Thus, adaptation is a key challenge to reduce adverse impacts on land systems (Section 1.3.6).

1.1.2.2 Current patterns of land use and land cover

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 ¹¹² ; Luyssaert et al. 2014 ¹¹³ ; Venter et al. 2016 ¹¹⁴ ) (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 ¹¹⁵ ). 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 ¹¹⁶ ; Birdsey and Pan 2015 ¹¹⁷ ; Morales-Hidalgo et al. 2015 ¹¹⁸ ; Potapov et al. 2017 ¹¹⁹ ; Erb et al. 2017 ¹²⁰ ), 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 ² ) and grazing land (39–62 million km ² ), due to discrepancies in definitions and observation methods (Luyssaert et al. 2014 ¹²¹ ; Erb et al. 2017; Putz and Redford 2010 ¹²² ; Schepaschenko et al. 2015 ¹²³ ; Birdsey and Pan 2015 ¹²⁴ ; FAO 2015a ¹²⁵ ; Chazdon et al. 2016a ¹²⁶ ; FAO 2018a ¹²⁷ ). 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 ¹²⁸ ; Laurance et al. 2014 ¹²⁹ ).

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 ¹³⁰ ; Ellis et al. 2010 ¹³¹ ; Cherlet et al. 2018 ¹³² ; Ellis et al. 2010 ¹³³ ) (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 ¹³⁴ ). 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 ¹³⁵ ; Chaturvedi et al. 2015 ¹³⁶ ; Siebert et al. 2015 ¹³⁷ ; FAOSTAT 2018 ¹³⁸ ). 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 ^–1 ; Bajželj et al. 2014 ¹³⁹ ; Haberl et al. 2014 ¹⁴⁰ ), 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 ^–1 , around 50–60% of this is consumed by livestock. Forestry harvest for timber and wood fuel amounts to about 1 GtC yr ^–1 (Alexander et al. 2017 ¹⁴¹ ; Bodirsky and Müller 2014 ¹⁴² ; Lassaletta et al. 2014 ¹⁴³ , 2016; Mottet et al. 2017 ¹⁴⁴ ; Haberl et al. 2014 ¹⁴⁵ ; Smith et al. 2014 ¹⁴⁶ ; Bais et al. 2015 ¹⁴⁷ ; Bajželj et al. 2014 ¹⁴⁸ ) (Cross-Chapter Box 7 in Chapter 6).====== Table 1.1 ========== 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]]

1.1.2.3 Past and ongoing trends

Globally, cropland area changed by +15% and the area of permanent pastures by +8% since the early 1960s (FAOSTAT 2018 ¹⁴⁹ ), 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 ¹⁵⁰ ) (Figure 1.3). Per capita calorie supply increased by 17% since 1970 (Kastner et al. 2012 ¹⁵¹ ), 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 ¹⁵² , 2018b ¹⁵³ ; Tilman and Clark 2014 ¹⁵⁴ ; Marques et al. 2019 ¹⁵⁵ ). 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 ¹⁵⁶ ). 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 ¹⁵⁷ ; Ray et al. 2012 ¹⁵⁸ ; Elbehri, Aziz, Joshua Elliott 2015 ¹⁵⁹ ) (Section 5.2.2).

The past increases in agricultural production have been associated with strong increases in agricultural inputs (Foley et al. 2011 ¹⁶⁰ ; Siebert et al. 2015 ¹⁶¹ ; Lassaletta et al. 2016 ¹⁶² ) (Figures 1.1 and 1.3). Irrigation area doubled, total nitrogen fertiliser use increased by 800% (FAOSTAT 2018 ¹⁶³ ; IFASTAT 2018 ¹⁶⁴ ) since the early 1960s. Biomass trade volumes grew by a factor of nine (in tonnes dry matter yr ^–1 ) in this period, which is much stronger than production (FAOSTAT 2018 ¹⁶⁵ ), resulting in a growing spatial disconnect between regions of production and consumption (Friis et al. 2016 ¹⁶⁶ ; Friis and Nielsen 2017 ¹⁶⁷ ; Schröter et al. 2018 ¹⁶⁸ ; Liu et al. 2013 ¹⁶⁹ ; Krausmann and Langthaler 2019 ¹⁷⁰ ). Urban and other infrastructure areas expanded by a factor of two since 1960 (Krausmann et al. 2013 ¹⁷¹ ), resulting in disproportionally large losses of highly fertile cropland (Seto and Reenberg 2014 ¹⁷² ; Martellozzo et al. 2015 ¹⁷³ ; Bren d’Amour et al. 2016 ¹⁷⁴ ; Seto and Ramankutty 2016 ¹⁷⁵ ; van Vliet et al. 2017 ¹⁷⁶ ). World regions show distinct patterns of change (Figure 1.3).====== Figure 1.3 ========== 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 […] ====

File:Https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/Figure-1.3-724x1024.pngStatus 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) ¹⁷⁷ ; Ellis et al. (2010) ¹⁷⁸ ; Potapov et al. (2017) ¹⁷⁹ ; 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) ¹⁸⁰ and FAO (1963) ¹⁸¹ for forest area 1961). (d) drivers of cropland for food production between 1994 and 2011 (Alexander et al. 2015 ¹⁸² ). 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 ^–2 displayed; Erb et al. 2017 ¹⁸³ ), 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 ^–2 yr ^–1 (Krausmann et al. 2013 ¹⁸⁴ ). The sum of the three components represents the NPP of the potential vegetation and consist of: (i) NPP _eco , i.e. the amount of NPP remaining in ecosystem after harvest, (ii) HANPP _harv , i.e. NPP harvested or killed during harvest, and (iii) HANPP _luc , i.e. NPP foregone due to land-use change. The sum of NPP _eco and HANPP _harv is the NPP of the actual vegetation (Haberl et al. 2014 ¹⁸⁵ ; Krausmann et al. 2013 ¹⁸⁶ ). 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 ₂ fluxes between land and the atmosphere between 2000 and 2014. LUC: annual CO ₂ land use flux due to changes in land cover and forest management; Sink _land : the annual CO ₂ land sink caused mainly by the indirect anthropogenic effects of environmental change (e.g, climate change and the fertilising effects of rising CO ₂ and N concentrations), excluding impacts of land-use change (Le Quéré et al. 2018 ¹⁸⁷ ) (Section 2.3) While most pastureland expansion replaced natural grasslands, cropland expansion replaced mainly forests (Ramankutty et al. 2018 ¹⁸⁸ ; Ordway et al. 2017 ¹⁸⁹ ; Richards and Friess 2016 ¹⁹⁰ ). Noteworthy large conversions occurred in tropical dry woodlands and savannahs, for example, in the Brazilian Cerrado (Lehmann and Parr 2016 ¹⁹¹ ; Strassburg et al. 2017 ¹⁹² ), the South American Caatinga and Chaco regions (Parr et al. 2014 ¹⁹³ ; Lehmann and Parr 2016 ¹⁹⁴ ) or African savannahs (Ryan et al. 2016 ¹⁹⁵ ). More than half of the original 4.3–12.6 million km ² global wetlands (Erb et al. 2016a ¹⁹⁶ ; Davidson 2014 ¹⁹⁷ ; Dixon et al. 2016 ¹⁹⁸ ) 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 ¹⁹⁹ ) (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 ²⁰⁰ ) has been converted to agriculture.

Global forest area declined by 3% since 1990 (about –5% since 1960) and continues to do so (FAO 2015a ²⁰¹ ; Keenan et al. 2015 ²⁰² ; MacDicken et al. 2015 ²⁰³ ; FAO 1963; Figure 1.1 ²⁰⁴ ), 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 ²⁰⁵ ; Nowosad et al. 2018 ²⁰⁶ ; Hansen et al. 2013 ²⁰⁷ ); others indicate a net gain (Song et al. 2018 ²⁰⁸ ). Tree-cover gains would be in line with observed and modelled increases in photosynthetic active tissues (‘greening’; Chen et al. 2019 ²⁰⁹ ; Zhu et al. 2016 ²¹⁰ ; Zhao et al. 2018 ²¹¹ ; de Jong et al. 2013 ²¹² ; Pugh et al. 2019 ²¹³ ; De Kauwe et al. 2016 ²¹⁴ ; Kolby Smith et al. 2015 ²¹⁵ ) (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 ²¹⁶ ; Schepaschenko et al. 2015 ²¹⁷ ; Bastin et al. 2017 ²¹⁸ ; Sloan and Sayer 2015 ²¹⁹ ; Chazdon et al. 2016a ²²⁰ ; Achard et al. 2014 ²²¹ ). 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.

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 ²²² ; De Vos et al. 2015 ²²³ ; Pimm et al. 2014 ²²⁴ ; Newbold et al. 2015 ²²⁵ ; Maxwell et al. 2016 ²²⁶ ; Marques et al. 2019 ²²⁷ ). 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 ²²⁸ ; Wilting et al. 2017 ²²⁹ ; Gossner et al. 2016 ²³⁰ ; Newbold et al. 2018 ²³¹ ; Paillet et al. 2010 ²³² ). In future, climate warming has been projected to accelerate losses of species diversity rapidly (Settele et al. 2014 ^[[#fn:r|]] 233; Urban et al. 2016 ²³⁴ ; Scholes et al. 2018 ²³⁵ ; Fischer et al. 2018 ²³⁶ ; Hoegh-Guldberg et al. 2018 ²³⁷ ). The concomitance of land-use and climate change pressures render ecosystem restoration a key challenge (Anderson-Teixeira 2018 ²³⁸ ; Yang et al. 2019 ²⁴⁰ ) (Sections 4.8 and 4.9).== 1.2 Key challenges related to land use change ==

1.2.1.1 Future trends in the global land system

Human population is projected to increase to nearly 9.8 (± 1) billion people by 2050 and 11.2 billion by 2100 (United Nations 2018 ²⁴¹ ). More people, a growing global middle class (Crist et al. 2017 ²⁴² ), economic growth, and continued urbanisation (Jiang and O’Neill 2017 ²⁴³ ) 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 ²⁴⁴ ; Popp et al. 2016 ²⁴⁵ ; Muller et al. 2017 ²⁴⁶ ; Alexander et al. 2015 ²⁴⁷ ; Springmann et al. 2018 ²⁴⁸ ; Myers et al. 2017 ²⁴⁹ ; Erb et al. 2016c ²⁵⁰ ; FAO 2018b ²⁵¹ ) (Sections 5.3 and 6.2.2).

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 ²⁵² ; Springmann et al. 2018 ²⁵³ ; Riahi et al. 2017 ²⁵⁴ ; Prestele et al. 2016 ²⁵⁵ ; Ramankutty et al. 2018 ²⁵⁶ ; Erb et al. 2016b ²⁵⁷ ; Popp et al. 2016 ²⁵⁸ ) (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.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 ²⁵⁹ ; van Vliet et al. 2017 ²⁶⁰ ; Jiang and O’Neill 2017 ²⁶¹ ; Friis et al. 2016 ²⁶² ), and international trade (Konar et al. 2016 ²⁶³ ; Erb et al. 2016b; Billen et al. 2015 ²⁶⁴ ; Lassaletta et al. 2016 ²⁶⁵ ). 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 ²⁶⁶ ; Kastner et al. 2014 ²⁶⁷ ; Liu et al. 2013 ²⁶⁸ ; Wood et al. 2018 ²⁶⁹ ; Schröter et al. 2018 ²⁷⁰ ; Lapola et al. 2010 ²⁷¹ ; Jadin et al. 2016 ²⁷² ; Erb et al. 2016b; Billen et al. 2015 ²⁷³ ; Chaudhary and Kastner 2016 ²⁷⁴ ; Marques et al. 2019 ²⁷⁵ ; Seto and Ramankutty 2016 ²⁷⁶ ) (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 ²⁷⁷ ; Güneralp et al. 2013 ²⁷⁸ ; Aronson et al. 2014 ²⁷⁹ ; Martellozzo et al. 2015 ²⁸⁰ ; Bren d’Amour et al. 2016 ²⁸¹ ; Seto and Ramankutty 2016 ²⁸² ; van Vliet et al. 2017 ²⁸³ ).

Future climate change and increasing atmospheric CO ₂ concentration are expected to accentuate existing challenges by, for example, shifting biomes or affecting crop yields (Baldos and Hertel 2015 ²⁸⁴ ; Schlenker and Lobell 2010 ²⁸⁵ ; Lipper et al. 2014 ²⁸⁶ ; Challinor et al. 2014 ²⁸⁷ ; Myers et al. 2017 ²⁸⁸ ) (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 ²⁸⁹ ) (Section 1.3.1 and Cross-Chapter Box 7 in Chapter 6).

1.2.1.2 Land degradation

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 ²⁹¹ ; Mirzabaev et al. 2015 ²⁹² ; FAO and ITPS 2015 ²⁹³ ; Cerretelli et al. 2018 ²⁹⁴ ). 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 ²⁹⁵ ; Lal 2009 ²⁹⁶ ; Beinroth et al. 1994 ²⁹⁷ ; Abu Hammad and Tumeizi 2012 ²⁹⁸ ; Ferreira et al. 2018 ²⁹⁹ ; Franco and Giannini 2005 ³⁰⁰ ; Abahussain et al. 2002 ³⁰¹ ). Global estimates of the total degraded area vary from less than 10 million km ² to over 60 million km ² , with additionally large disagreement regarding the spatial distribution (Gibbs and Salmon 2015 ³⁰² ) (Section 4.3). The annual increase in the degraded land area has been estimated as 50,000–100,000 million km ² yr ^–1 (Stavi and Lal 2015 ³⁰³ ), and the loss of total ecosystem services equivalent to about 10% of the world’s GDP in the year 2010 (Sutton et al. 2016 ³⁰⁴ ). 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 ³⁰⁵ ; Lal 2015 ³⁰⁶ ), as well as the rapid degradation of primary forests through fragmentation (Haddad et al. 2015 ³⁰⁷ ). 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 ₂ emissions and degradation ( high confidence ) (Strack 2008 ³⁰⁸ ; Limpens et al. 2008 ³⁰⁹ ; Aich et al. 2014 ³¹⁰ ; Murdiyarso et al. 2015 ³¹¹ ; Kauffman et al. 2016 ³¹² ; Dohong et al. 2017 ³¹³ ; Arifanti et al. 2018 ³¹⁴ ; Evans et al. 2019 ³¹⁵ ). Land degradation is an important factor contributing to uncertainties in the mitigation potential of land-based ecosystems (Smith et al. 2014 ³¹⁶ ). Furthermore, degradation that reduces forest (and agricultural) biomass and soil organic carbon leads to higher rates of runoff ( high confidence ) (Molina et al. 2007 ³¹⁷ ; Valentin et al. 2008 ³¹⁸ ; Mateos et al. 2017 ³¹⁹ ; Noordwijk et al. 2017 ³²⁰ ) and hence to increasing flood risk ( low confidence ) (Bradshaw et al. 2007 ³²¹ ; Laurance 2007 ³²² ; van Dijk et al. 2009 ³²³ ).

1.2.1.3 Desertification

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 ⁹³⁰ ; Bai et al. 2008 ⁹³¹ ). A critical challenge in the assessment of desertification is to identify a ‘non-desertified’ reference state (Bestelmeyer et al. 2015 ³²⁴ ). 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 ³²⁵ ). As a consequence of varying definitions and different methodologies, the area of desertification varies widely (D’Odorico et al. 2013 ³²⁶ ; Bestelmeyer et al. 2015 ³²⁷ ; and references therein). Arid regions of the world cover up to about 46% of the total terrestrial surface (about 60 million km ² ) (Pravalie 2016 ³²⁸ ; Koutroulis 2019 ³²⁹ ). Around 3 billion people reside in dryland regions (D’Odorico et al. 2013 ³³⁰ ; Maestre et al. 2016 ³³¹ ) (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 ³³² ; Cherlet et al. 2018 ³³³ ).== 1.2.1.4 Food security, food systems and linkages to land-based ecosystems ==

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 ³³⁴ ). 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 ³³⁵ ). 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 ³³⁶ ; 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 ³³⁷ ) (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 ³³⁸ ).

Food insecurity most notably occurs in situations of conflict, and conflict combined with droughts or floods (Cafiero et al. 2018 ³³⁹ ; Smith et al. 2017 ³⁴⁰ ). The close parallel between food insecurity prevalence and poverty means that tackling development priorities would enhance sustainable land use options for climate mitigation.

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 ³⁴¹ ; Tigchelaar et al. 2018 ³⁴² ; Iizumi and Ramankutty 2015 ³⁴³ ), the nutritional quality of food (Loladze 2014 ³⁴⁴ ; Myers et al. 2014 ³⁴⁵ ; Ziska et al. 2016 ³⁴⁶ ; Medek et al. 2017 ³⁴⁷ ), water supply (Nkhonjera 2017 ³⁴⁸ ), and incidence of pests and diseases (Curtis et al. 2018 ³⁴⁹ ). These factors also impact on human health, increasing morbidity and affecting human ability to process ingested food (Franchini and Mannucci 2015 ³⁵⁰ ; Wu et al. 2016 ³⁵¹ ; Raiten and Aimone 2017 ³⁵² ). At the same time, the food system generates negative externalities (the environmental effects of production and consumption) in the form of GHG emissions

(Sections 1.1.2 and 2.3), pollution (van Noordwijk and Brussaard 2014 ³⁵³ ; Thyberg and Tonjes 2016 ³⁵⁴ ; Borsato et al. 2018 ³⁵⁵ ; Kibler et al. 2018 ³⁵⁶ ), water quality (Malone et al. 2014 ³⁵⁷ ; Norse and Ju 2015 ³⁵⁸ ), and ecosystem services loss (Schipper et al. 2014 ³⁵⁹ ; Eeraerts et al. 2017 ³⁶⁰ ) 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 ³⁶¹ ; Salmon et al. 2018 ³⁶² ).

== Figure 1.4 ========== 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.jpgFood 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).== 1.2.1.5 Challenges arising from land governance == 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 ³⁶³ ). 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 ³⁶⁴ ). 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 ³⁶⁵ ; Ravnborg et al. 2016 ³⁶⁶ ). 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 ³⁶⁷ ). 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 ³⁶⁸ ; Lambin 2012 ³⁶⁹ ; Venter et al. 2016 ³⁷⁰ ). 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 ³⁷¹ ; Foley et al. 2011 ³⁷² ; Lambin 2012 ³⁷³ ; Popp et al. 2016 ³⁷⁴ ) ( 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 ³⁷⁵ ) and local land-ownership.

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 ³⁷⁶ ). 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 ³⁷⁷ ).The LSLA phenomenon, which largely targets agriculture, is widespread, including Sub-Saharan Africa, Southeast Asia, Eastern Europe and Latin America (Rulli et al. 2012 ³⁷⁸ ; Nolte et al. 2016 ³⁷⁹ ; Constantin et al. 2017 ³⁸⁰ ). LSLAs are promoted by investors and host governments on economic grounds (infrastructure, employment, market development) (Deininger et al. 2011 ³⁸¹ ), but their social and environmental impacts can be negative and significant (Dell’Angelo et al. 2017a ³⁸² ).

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 ³⁸³ ) and displaced communities creating secondary land expansion (Messerli et al. 2014 ³⁸⁴ ; Davis et al. 2015 ³⁸⁵ ). The promises that LSLAs would develop efficient agriculture on non-forested, unused land (Deininger et al. 2011 ³⁸⁶ ) has so far not been fulfilled. However, LSLA is not the only outcome of weak land governance structures (Wang et al. 2016 ³⁸⁷ ): other forms of inequitable or irregular land acquisition can also be home-grown, pitting one community against a more vulnerable group (Xu 2018 ³⁸⁸ ) or land capture by urban elites (McDonnell 2017 ³⁸⁹ ). 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).

 

1.2.2.1 Concepts related to risk, uncertainty and confidence

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).

Demonstrating with some statistical certainty that the climate or the land system affected by climate or land use has changed (detection),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 ³⁹⁰ ; Gillett et al. 2016 ³⁹¹ ; Lean 2018 ³⁹² ). 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 ³⁹³ ). This confidence language is also used in the SRCCL (Figure 1.5).====== Figure 1.5 ========== Use of confidence language. ====

File:Https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/Figure-1.5-1024x511.jpgUse of confidence language.== 1.2.2.2 Nature and scope of uncertainties related to land use ==

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.

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 ³⁹⁴ ; He et al. 2018 ³⁹⁵ ; Ardö et al. 2018 ³⁹⁶ ; Spennemann et al. 2018 ³⁹⁷ ) (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 ³⁹⁸ ; Brümmer et al. 2017 ³⁹⁹ ; Iwata et al. 2017 ⁴⁰⁰ ; Valayamkunnath et al. 2018 ⁴⁰¹ ). 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 ⁴⁰² ; Chen et al. 2014 ⁴⁰³ ; Yu et al. 2014 ⁴⁰⁴ ; Lacaze et al. 2015 ⁴⁰⁵ ; Song 2018 ⁴⁰⁶ ; Peterson et al. 2017 ⁴⁰⁷ ). 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 ⁴⁰⁸ ), 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 ⁴⁰⁹ ; Clark et al. 2017 ⁴¹⁰ ; Lees et al. 2018 ⁴¹¹ ), 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 ⁴¹² ; Jarroudi et al. 2015 ⁴¹³ ) (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 ⁴¹⁴ ; Watmuff et al. 2013 ⁴¹⁵ ; Jarroudi et al. 2015 ⁴¹⁶ ; Chipanshi et al. 2015 ⁴¹⁷ ). 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 ⁴¹⁸ ; Shtienberg 2013 ⁴¹⁹ ). 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 ⁹³² ; Stocker et al. 2013a ⁹³³ ). 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 ⁴²⁰ ). 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 ⁴²¹ ; Luo et al. 2012 ⁴²² ; Kelley et al. 2013 ⁴²³ ). 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 ⁴²⁴ ; Kelley et al. 2013 ⁴²⁵ ). 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 ⁴²⁶ ; Parker 2013 ⁴²⁷ ; Prestele et al. 2016 ⁴²⁸ ). 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 ⁴²⁹ ; Eitelberg et al. 2016 ⁴³⁰ ; Popp et al. 2016 ⁴³¹ ; Krause et al. 2017 ⁴³² ; Alexander et al. 2016a ⁴³³ ). These differences reflect the uncertainties associated with baseline data, thematic classifications, different model structures and model parameter estimation (Alexander et al. 2017a ⁴³⁴ ; Prestele et al. 2016 ⁴³⁵ ; 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 ⁴³⁶ ) (Cross-Chapter Box 1 in Chapter 1 ) and in models of the terrestrial carbon cycle (Arneth et al. 2017 ⁴³⁷ ). These processes have been shown to have large impacts on carbon stock changes (Arneth et al. 2017 ⁴³⁸ ). 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 ⁴³⁹ ; Riahi et al. 2017 ⁴⁴⁰ ). The RCPs prescribe levels of radiative forcing (W m ^–2 ) arising from different atmospheric concentrations of GHGs that lead to different levels of climate change. For example, RCP2.6 (2.6 W m ^–2 ) is projected to lead to global mean temperature changes of about 0.9°C–2.3°C, and RCP8.5 (8.5 W m ^–2 ) to global mean temperature changes of about 3.2°C–5.4°C (van Vuuren et al. 2014 ⁴⁴¹ ). 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 ⁴⁴² ). 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 ⁴⁴³ ). The SSPs are commonly used with models to project future land-use change (Cross-Chapter Box 1 in Chapter 1). == CCB1 Scenarios and other methods to characterise the future of land == 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

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 ⁴⁴⁴ ), envisioning or target setting (Kok et al. 2018 ⁴⁴⁵ ), pathways analysis (IPBES 2016 ⁴⁴⁶ ; IPCC 2018 ⁴⁴⁷ ) ¹ , and conditional probabilistic futures (Vuuren et al. 2018 ⁴⁴⁸ ; Engstrom et al. 2016 ⁴⁴⁹ ; Henry et al. 2018 ⁴⁵⁰ ) (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.

Exploratory scenario analysis

Many exploratory scenarios are reported in climate and land system studies on climate change (Dokken 2014 ⁴⁵¹ ), such as related to land-based, climate change mitigation via reforestation/afforestation, avoided deforestation or bioenergy (Kraxner et al. 2013 ⁴⁵² ; Humpenoder et al. 2014 ⁴⁵³ ; Krause et al. 2017 ⁴⁵⁴ ) and climate change impacts and adaptation (Warszawski et al. 2014 ⁴⁵⁵ ). There are global-scale scenarios of food security (Foley et al. 2011 ⁴⁵⁶ ; Pradhan et al. 2013 ⁴⁵⁷ , 2014 ⁴⁵⁸ ), but fewer scenarios of desertification, land degradation and restoration (Wolff et al. 2018 ⁴⁵⁹ ). Exploratory scenarios combine qualitative ‘storylines’ or descriptive narratives of the underlying causes (or drivers) of change (Nakicenovic and Swart 2000 ⁴⁶⁰ ; Rounsevell and Metzger 2010 ⁴⁶¹ ; O’Neill et al. 2014 ⁴⁶² ) 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 ⁴⁶³ ; Prestele et al. 2016 ⁴⁶⁴ ). 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 ⁴⁶⁵ , 2016 ⁴⁶⁶ ). 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 ⁴⁶⁷ ; Wise et al. 2014 ^[[#fn:r|]] 468; Kreidenweis et al. 2018 ⁴⁶⁹ ), as well as the area of land needed for food production (Foley et al. 2011 ⁴⁷⁰ ; Weindl et al. 2017 ⁴⁷¹ ; Kreidenweis et al. 2018 ⁴⁷² ). Trends in consumption, for example, diets or waste reduction, are also fundamental in affecting land-use change (Pradhan et al. 2013 ⁴⁷³ ; Alexander et al. 2016b ⁴⁷⁴ ; Weindl et al. 2017 ⁴⁷⁵ ; Alexander et al. 2017 ⁴⁷⁶ ; Vuuren et al. 2018 ⁴⁷⁷ ; Bajželj et al. 2014 ⁴⁷⁸ ). 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 ⁴⁷⁹ ; Riahi et al. 2017 ⁴⁸⁰ ; Doelman et al. 2018 ⁴⁸¹ )) (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 ⁴⁸² , 2014 ⁴⁸³ ; Kreidenweis et al. 2016 ⁴⁸⁴ ; Rogelj et al. 2018b ⁴⁸⁵ ; Seneviratne et al. 2018 ⁴⁸⁶ ; Vuuren et al. 2018 ⁴⁸⁷ ). 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 ⁴⁸⁸ ), 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 ⁴⁸⁹ ). Conditional probabilistic futures explore the consequences of model parameter uncertainty in which these uncertainties are conditional on scenario assumptions (Neill 2004 ⁴⁹⁰ ). Only a few studies have applied the conditional probabilistic approach to land-use futures (Brown et al. 2014 ⁴⁹¹ ; Engstrom et al. 2016 ⁴⁹² ; Henry et al. 2018 ⁴⁹³ ). 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 ⁴⁹⁴ ). 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 ⁴⁹⁵ ; Heck et al. 2018 ⁴⁹⁶ ). 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 ⁴⁹⁷ ).

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 ⁴⁹⁸ ). Moreover, the capacity to adapt to these impacts is strongly dependent on the regional, socio-economic context and coping capacity (Dunford et al. 2014 ⁴⁹⁹ ); 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 ⁵⁰⁰ ), 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). ====== Cross-Chapter Box 1, Table 1 ========== Description of the principal methods used in land and climate futures analysis. ===={| class="wikitable" |- | Futures method| Description and subtypes| Application domain| Time horizon| Examples in this assessment|- | rowspan="6"| 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| Long-term projections quantified with models| Climate system, land system and other components of the environment (e.g., biodiversity, ecosystem function- ing, water resources and quality), for example the SSPs| 10–100 years| 2.3, 2.6.2, 5.2.3, 6.1.4, 6.4.4, 7.2|- | Business-as-usual scenarios

(including ‘outlooks’)| A continuation into the future of current trends
in key drivers to explore the consequences of these in the near term| 5–10 years, 20–30 years for outlooks| 1.2.1, 2.6.2, 5.3.4, 6.1.4|- | Policy and planning scenarios

(including business planning)| Ex ante analysis of the consequences of alternative policies or decisions based on known policy options or already implemented policy and planning measures| 5–30 years| 2.6.3, 5.5.2, 5.6.2, 6.4.4|- | Stylised scenarios (with single and multiple options)| Afforestation/reforestation areas, bioenergy areas, protected areas for conservation, consumption patterns (e.g., diets, food waste)| 10–100 years| 2.6.1, 5.5.1, 5.5.2, 5.6.1, 5.6.2, 6.4.4, 7.2|- | Shock scenarios (high impact single events)| Food supply chain collapses, cyberattacks, pandemic diseases (humans, crops and livestock)| Near-term events
(up to 10 years) leading to long-term impacts (10–100 years)| 5.8.1|- | Conditional probabilistic futures

ascribe probabilities to uncertain drivers that are conditional on scenario assumptions| Where some knowledge is known about driver uncertainties, for example, population, economic growth, land-use change| 10–100 years| 1.2|- | rowspan="2"| Normative scenarios.

Desired futures or outcomes that are aspirational and how to achieve them| Visions, goal-seeking or target-seeking scenarios| Environmental quality, societal development, human well-being, the Representative Concentration Pathways (RCPs,) 1.5°C scenarios| 5–10 years to 10–100 years| 2.6.2, 6.4.4, 7.2, 5.5.2|- | Pathways as alternative sets
of choices, actions or behaviours that lead to a future vision
(goal or target)| Socio-economic systems, governance and policy actions| 5–10 years to 10–100 years| 5.5.2, 6.4.4, 7.2|}====== Cross-Chapter-Box-Figure-1 ========== 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.jpgInteractions between land and climate system components and models in scenario analysis. The blue text describes selected model inputs and outputs.Normative scenarios: visions and pathways analysis

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 ⁵⁰¹ ). 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 ⁵⁰² ). 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 ⁵⁰³ ).

What are the limitations of land-use scenarios?

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 ⁵⁰⁴ ). 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 ⁵⁰⁵ ). 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.

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 ⁵⁰⁶ ; Prestele et al. 2016 ⁵⁰⁷ ). 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 ⁵⁰⁸ ). These differences arise from variations in baseline datasets, thematic classes and modelling paradigms (Alexander et al. 2016a ⁵⁰⁹ ; Popp et al. 2016 ⁵¹⁰ ; Prestele et al. 2016 ⁵¹¹ ). Model evaluation is critical in establishing confidence in the outcomes of modelled futures (Ahlstrom et al. 2012 ⁵¹² ; Kelley et al. 2013 ⁵¹³ ). 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 ⁵¹⁴ ). There is a small, but growing literature on quantitative pathways to achieve normative visions and their associated trade-offs (IPBES 2018a ⁵¹⁵ ). 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 ⁵¹⁶ ; Arneth et al. 2014 ⁵¹⁷ ; Calvin and Bond-Lamberty 2018 ⁵¹⁸ ). 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 ⁵¹⁹ ). 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 ⁵²⁰ ; Rounsevell et al. 2014 ⁵²¹ ; Wang et al. 2016 ^[[#fn:r|]] 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 ⁵²³ ; Arneth et al. 2014 ⁵²⁴ ; Robinson et al. 2017 ⁵²⁵ ; Brown et al. 2017 ⁵²⁶ ; Calvin and Bond-Lamberty 2018 ⁵²⁷ ).== 1.2.2.3 Uncertainties in decision-making ==

Decision-makers develop and implement policy in the face of many uncertainties (Rosenzweig and Neofotis 2013 ⁵²⁸ ; Anav et al. 2013 ⁵²⁹ ; Ciais et al. 2013a ⁵³⁰ ; Stocker et al. 2013b ⁵³¹ ) (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 ⁵³² ; Maier et al. 2016 ⁵³³ ) (Sections 7.1 and 7.5, and Table SM.1.2 in Supplementary Material). However, existing uncertainty does not support societal and political inaction.

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 ⁵³⁴ ; Parker 2013 ⁵³⁵ ) and tends to focus on the short term. The precautionary approach does not take account of probability estimates (cf. Raffensperger and Tickner 1999 ⁵³⁶ ), but instead focuses on avoiding the worst outcome (Gardiner 2006 ⁵³⁷ ).

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 ⁵³⁸ ; Luedeling and Shepherd 2016 ⁵³⁹ ).

Flexibility is facilitated by political decisions that are not set in stone and can change over time (Walker et al. 2013 ⁵⁴⁰ ; Hallegatte and Rentschler 2015 ⁵⁴¹ ). 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 ⁵⁴² ) (Section 7.5).== 1.3 Response options to the key challenges ==

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 ⁵⁴³ ; Kok et al. 2018 ⁵⁴⁴ ). 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 ⁵⁴⁵ ; Cremasch 2016 ⁵⁴⁶ ). 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.====== Table 1.2 ========== Broad categorisation of response options into three main classes and eight sub-classes. ==== For illustration, the table includes examples of individual response options. A complete list and description is provided in Chapter 6.

Response options based on land management|-

in agriculture| Improved management of: cropland, grazing land, livestock; agro-forestry; avoidance of conversion of grassland to cropland; integrated water management|-

in forests| Improved management of forests and forest restoration; reduced deforestation and degradation; afforestation|-

of soils| Increased soil organic carbon content; reduced soil erosion; reduced soil salinisation|-

across all/other ecosystems| Reduced landslides and natural hazards; reduced pollution including acidification; biodiversity conservation; restoration and reduced conversion of peatlands|-

specifically for CO 2 removal| Enhanced weathering of minerals; bioenergy and BECCS|-

Response options based on value chain management|-

through demand management| Dietary change; reduced post-harvest losses; reduced food waste|-

through supply management| Sustainable sourcing; improved energy use in food systems; improved food processing and retailing|-

Response options based on risk management|-

Risk management| Risk-sharing instruments; use of local seeds; disaster risk management|} 1.3.1 Targeted decarbonisation relying on large land-area need 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 547 ; Rogelj et al. 2018b 548 ,a 549 ; Anderson and Peters 2016 550 ; Popp et al. 2016 551 ; Smith et al. 2016 552 ) (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 553 ; Brown et al. 2019 554 ; Vaughan and Gough 2016 555 ). 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 556 ; Bentsen 2017 557 ; Searchinger et al. 2017 558 ; Bayer et al. 2017 559 ; Fuchs et al. 2017 560 ; Pingoud et al. 2018 561 ; Schlesinger 2018 562 ). Bioenergy provision under politically unstable conditions may also be a problem (Erb et al. 2012 563 ; Searle and Malins 2015 564 ). Large-scale bioenergy plantations and forests may compete for the same land area (Harper et al. 2018 565 ). 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 566 ; Bárcena et al. 2014 567 ; Fernandez-Martinez et al. 2014 568 ; Searchinger et al. 2015 569 ; Bonsch et al. 2016 570 ; Creutzig et al. 2015 571 ; Kreidenweis et al. 2016 572 ; Santangeli et al. 2016 573 ; Williamson 2016 574 ; Graham et al. 2017 575 ; Krause et al. 2017 576 ; Hasegawa et al. 2018 577 ; Humpenoeder et al. 2018 578 ). 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 579 ; Searchinger et al. 2015 580 ) (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 581 ; Dooley and Kartha 2018 582 ; Hasegawa et al. 2015 583 ; Hof et al. 2018 584 ; Roy et al. 2018 585 ; Santangeli et al. 2016 586 ; Boysen et al. 2017 587 ; Henry et al. 2018 588 ; Kreidenweis et al. 2016 589 ; UN 2015 590 ). 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). CCB2 Implications of large-scale conversion from non-forest to forest land 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 591 ; Joshi et al. 2011 592 ; Zaloumis and Bond 2015 593 ; Payn et al. 2015 594 ; Shoyama 2008 595 ; Miyamoto et al. 2011 596 ) to restoration-driven efforts, with the aim of enhancing ecological function (Filoso et al. 2017 597 ; Salvati and Carlucci 2014 598 ; Ogle et al. 2018 599 ; Crouzeilles et al. 2016 600 ; FAO 2016 601 ) (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 602 ; Scheidel and Work 2018 603 ; Hua et al. 2018 604 ; Crouzeilles et al. 2016 605 ; Chazdon et al. 2016b 606 ). China and India have seen the largest net forest area increase, aiming to alleviate soil erosion, desertification and overgrazing (Ahrends et al. 2017 607 ; Cao et al. 2016 608 ; Deng et al. 2015 609 ; Chen et al. 2019 610 ) (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 611 ; Song et al. 2018 612 ; Hansen et al. 2013 613 ; MacDicken et al. 2015 614 ). 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 615 ; Poeplau et al. 2011 616 ; Shi et al. 2013 617 ; Li et al. 2012 618 ) (Section 4.3). While reforestation of former croplands increases net ecosystem carbon storage (Bernal et al. 2018 619 ; Lamb 2018 620 ), 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 621 ). Forest vs non-forest lands differ in land surface reflectiveness of shortwave radiation and evapotranspiration (Anderson et al. 2011 622 ; Perugini et al. 2017 623 ) (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 624 ; Alkama and Cescatti 2016 625 ; Perugini et al. 2017 626 ) (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 627 ). 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 628 ; Zheng et al. 2016 629 ; Cao et al. 2016 630 ). 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 631 ; Li et al. 2014 632 ; Feng et al. 2016 633 ). Water shortages may create long-term water conflicts (Zheng et al. 2016 634 ). However, reforestation (in particular for restoration) is also associated with improved water filtration, groundwater recharge (Ellison et al. 2017 635 ) and can reduce risk of soil erosion, flooding, and associated disasters (Lee et al. 2018 636 ) (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 637 ; Griffith et al. 2017 638 ; Veldman et al. 2015 639 ; Parr et al. 2014 640 ; Wilson et al. 2017 641 ; Hua et al. 2016 642 ; 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 643 ; 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 644 ; Cunningham et al. 2015b 645 ).|} 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 646 ; Dendy et al. 2015 647 ; Chaudhary and Kastner 2016 648 ; Huang et al. 2018 649 ; Locatelli et al. 2015b 650 ) (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 651 ; Pawson et al. 2013 652 ; Thompson et al. 2014 653 ). 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 654 ; Barlow et al. 2007 655 ; Lindenmayer and Hobbs 2004 656 ). 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 657 ). However, these co-benefits have rarely been assessed due to a lack of suitable frameworks and evaluation tools (Baral et al. 2016 658 ). 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 659 ; Baral et al. 2016 660 ) and/or loss of customary rights over land use (Malkamäki et al. 2018 661 ; Cotula et al. 2014 662 ). A common result is out-migration from rural areas and diminishing local uses of ecosystems (Gerber 2011 663 ). 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 664 ; Le et al. 2012 665 ). 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 666 ; Griscom et al. 2017 667 ; Fuss et al. 2018 668 ), and can have co-benefits with adaptation. Sequestration of CO 2 from the atmosphere through forest area expansion has become a fundamental part of stringent climate change mitigation scenarios (Rogelj et al. 2018a 669 ; Fuss et al. 2018 670 ) (e.g., Sections 2.5, 4.5 and 6.2). The estimated mitigation potential ranges from about 0.5 to 10 GtCO 2 yr–1 (robust evidence, medium agreement), and depends on assumptions regarding available land and forest carbon uptake potential (Houghton 2013 671 ; Houghton and Nassikas 2017 672 ; Griscom et al. 2017 673 ; Lenton 2014 674 ; Fuss et al. 2018 675 ; Smith 2016 676 ) (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 677 ; Kreidenweis et al. 2016 678 ; Humpenoder et al. 2014 679 ). 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 680 ; Hasegawa et al. 2015 681 , 2018 682 ; Boysen et al. 2017 683 ) (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 684 ; Harrison et al. 2013 685 ). 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 686 ) – with potential regional co-benefits. Since forest growth competes for land with bioenergy crops (Harper et al. 2018 687 ) (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 688 ; Houghton et al. 2015 689 ) (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 690 ; Anderegg et al. 2015 691 ) (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 692 ; Jadin et al. 2016 693 ). 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 694 ; Dinerstein et al. 2015 695 ; Veldman et al. 2017 696 ) (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 697 ; Boysen et al. 2017 698 ; Kreidenweis et al. 2016 699 ; Egginton et al. 2014 700 ; Cao et al. 2016 701 ; Locatelli et al. 2015a 702 ; Smith et al. 2013 703 ).== 1.3.2 Land management == 1.3.2.1 Agricultural, forest and soil management 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 704 ; Altieri and Nicholls 2017 705 ) (e.g., Section 4.1.5), and includes ecological, technological and governance aspects. 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 706 ; Ebert, 2014 707 ; Schulte et al. 2014 708 ; Zhang et al. 2015 709 ; Sunil and Pandravada 2015 710 ; Poeplau and Don 2015 711 ; Agus et al. 2015 712 ; Keenan 2015 713 ; MacDicken et al. 2015 714 ; Abberton et al. 2016 715 ). 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 716 ; Friedrich et al. 2012 717 ). 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 718 ) (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 719 ). 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 720 ) (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 721 ). 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 722 ). 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 723 ; Lorenz and Lal 2014 724 ; Smith 2016 725 ).== 1.3.3 Value chain management == 1.3.3.1 Supply management 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 726 ; Alexander et al. 2017 727 ). 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 728 ), 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 729 ). 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 730 ). 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 731 ; Alexander et al. 2017 732 ). While losses of food cannot be realistically reduced to zero, advancing harvesting technologies (Bradford et al. 2018 733 ; Affognon et al. 2015 734 ), storage capacity (Chegere 2018 735 ) 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. Stability of food supply, transport and distribution. Increased climate variability enhances fluctuations in world food supply and price variability (Warren 2014 736 ; Challinor et al. 2015 737 ; Elbehri et al. 2017 738 ). ‘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 739 ; Lehmann et al. 2013 740 ; Le 2016 741 ; FAO 2015b 742 ). 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 743 ; Marchand et al. 2016 744 ). Food trade can either increase or reduce the overall environmental impacts of agriculture (Kastner et al. 2014 745 ). Embedded in trade are virtual transfers of water, land area, productivity, ecosystem services, biodiversity, or nutrients (Marques et al. 2019 746 ; Wiedmann and Lenzen 2018 747 ; Chaudhary and Kastner 2016 748 ) with either positive or negative implications (Chen et al. 2018 749 ; Yu et al. 2013 750 ). 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 751 ; Marchand et al. 2016 752 ). Climate mitigation policies could create new trade opportunities (e.g., biomass) (Favero and Massetti 2014 753 ) 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 754 ) 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 755 ; Mosnier et al. 2014 756 ; Elbehri et al. 2017 757 ). 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 758 ; Brown et al. 2007 759 ). 1.3.3.2 Demand management 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 760 ; Bajželj et al. 2014 761 ; Erb et al. 2016b 762 ; Creutzig et al. 2018 763 ) (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 764 ; Alexander et al. 2016b 765 ; Alexander et al. 2015 766 ; Tilman and Clark 2014 767 ; Aleksandrowicz et al. 2016 768 ; Poore and Nemecek 2018 769 ) (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 770 ; Röös et al. 2017 771 ; Rao et al. 2018 772 ). 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 773 ). By avoiding meat from producers with above-median GHG emissions and halving animal-product intake, consumption change could free-up 21 million km 2 of agricultural land and reduce GHG emissions by nearly 5 GtCO 2 -eq yr –1 or up to 10.4 GtCO 2 -eq yr –1 when vegetation carbon uptake is considered on the previously agricultural land (Poore and Nemecek 2018 774 , 2019). 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 775 ) 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 776 ). 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 777 ) and are now around 25–30% of global food produced (Kummu et al. 2012 778 ; Alexander et al. 2017 779 ). Food waste occurs at all stages of the food supply chain from the household to the marketplace (Parfitt et al. 2010 780 ) 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 –1 yr –1 (around 16% of food consumption) above about 70,000 USD cap –1 (van der Werf and Gilliland 2017 781 ; Xue et al. 2017 782 ). 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 783 ). Household level food waste arises from overeating or overbuying (Thyberg and Tonjes 2016 784 ). Globally, overconsumption was found to waste 9–10% of food bought (Alexander et al. 2017 785 ). 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 786 ). 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 787 ). 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.== 1.3.4 Risk management == 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 788 ). Given that over recent decades on average around 10% of cereal production was lost through extreme weather events (Lesk et al. 2016 790 ), 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 791 ) (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 792 ). A global mitigation co-benefit of insurance schemes may also include incentives for future risk reduction (Surminski and Oramas-Dorta 2014 793 ). 1.3.5 Economics of land-based mitigation pathways: Costs versus benefits of early action under uncertainty 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 794 ) (Section 1.4). The social cost of carbon (SCC) measures the total net damages of an extra metric tonne of CO 2  emissions due to the associated climate change (Nordhaus 2014 795 ; Pizer et al. 2014 796 ). 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 797 ). In land-based sectors, Gillingham and Stock (2018) 798 reported short-term static abatement costs for afforestation of between 1 and 10 USD2017 per tCO 2 , soil management at 57 and livestock management at 71 USD2017 per tCO 2 . 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 799 ). 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 800 ; Ricke et al. 2018 801 ). 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 802 ) (Section 1.4). 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 803 ; Chen et al. 2016 804 ) or lags in decision-making, which have been demonstrated by the uptake of land use and land utilisation policies (Alexander et al. 2013 805 ; Hull et al. 2015 806 ; Brown et al. 2018b 807 ). There is growing evidence of significant mitigation gains through conservation, restoration and improved land management practices (Griscom et al. 2017 808 ; Kindermann et al. 2008 809 ; Golub et al. 2013 810 ; Favero et al. 2017 811 ) (Chapters 4 and 6), but the mitigation cost efficiency can vary according to region and specific ecosystem (Albanito et al. 2016 812 ). 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 813 ; Walters and Scholes 2017 814 ). 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 815 ). 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 816 ; Pedrozo-Acuña et al. 2015 817 ). Delayed action would raise relative costs in the future or could make response options less feasible ( medium confidence ) (Goldstein et al. 2019 818 ; Butler et al. 2014 819 ). 1.3.6 Adaptation measures and scope for co-benefits with mitigation 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 820 ). While adaptation (e.g., reducing flood risks) and mitigation (e.g., reducing non-CO 2 emissions from agriculture) may have different objectives and operate at different scales, they can also generate joint outcomes (Locatelli et al. 2015b 821 ) 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 822 ; Berry et al. 2015 823 ; Kongsager and Corbera 2015 824 ). 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 825 ). 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 826 ), and institutional setting, often does not enable an environment where synergies are sought (Kongsager et al. 2016 827 ). 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 828 ; Kongsager et al. 2016 829 ). 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 830 ; Gaba et al. 2015 831 ) (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 832 ). Reforestation projects, if well managed, can increase community economic opportunities that encourage conservation (Nelson and de Jong 2003 833 ), build capacity through training of farmers and installation of multifunctional plantations with income generation (Reyer et al. 2009 834 ), strengthen local institutions (Locatelli et al. 2015a 835 ) and increase cash-flow to local forest stakeholders from foreign donors (West 2016 836 ). 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 837 ; Porter et al. 2014 838 ). 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 839 ). Adaptation encompasses both biophysical and socio-economic vulnerability and underlying causes (informational, capacity, financial, institutional, and technological; Huq et al. 2014 840 ) and it is increasingly linked to resilience and to broader development goals (Huq et al. 2014 841 ). 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 842 ; Campbell et al. 2014 843 ; Locatelli et al. 2015b 844 ). 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). 1.4 Enabling the response 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 845 ; Hussey and Pittock 2012 846 ). 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). ‘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 847 ), food and energy (Hoff 2011 848 ; Allan et al. 2015 849 ), and also to address biodiversity concerns (Fischer et al. 2017 850 ). 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 851 ), water-focused (Hoff 2011 852 ) or food-centred (Ringler and Lawford 2013 853 ; Biggs et al. 2015 854 ). 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 855 ; Wichelns 2017 856 ) (Sections 7.5.6 and 7.6.2). 1.4.1 Governance to enable the response 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 858 ). 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). 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 859 ). 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 860 ). 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 861 ). 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 862 ). 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 863 ; Bodin 2017 864 ). The SES approach also addresses the multiple scales in which the social and ecological dimensions interact (Veldkamp et al. 2011 865 ; Myers et al. 2016 866 ; Azizi et al. 2017 867 ) (Section 6.1). 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 868 ). 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 869 ; Schneider and Buser 2018 870 ). 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 871 ). 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 872 ). 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 873 ). ILK need not be viewed as needing confirmation or disapproval by formal science, but rather it can complement scientific knowledge (Klein et al. 2014 874 ). 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 875 ). These governance modes provide both constraints and opportunities for key actors that impact the effectiveness, efficiency and equity of policy implementation. 1.4.2 Gender agency as a critical factor in climate and land sustainability outcomes 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 876 ) (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 877 ). 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. Germaine to the gender inequities is the unequal access to land-based resources. Women play a significant role in agriculture (Boserup 1989 878 ; Darity 1980 879 ) and rural economies globally (FAO 2011 880 ), 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 881 ). Moreover, women typically shoulder disproportionate responsibility for unpaid domestic work including care-giving activities (Beuchelt and Badstue 2013 882 ) and the provision of water and firewood (UNEP 2016 883 ). Exposure to violence restricts, in large regions, their mobility for capacity-building activities and productive work outside the home (Day et al. 2005 884 ; UNEP 2016 885 ). 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 886 ). 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 887 ). 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 888 ). Improving women’s access to land (Arora-Jonsson 2014 889 ) 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 890 ). 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 891 ) is considered suitable to integrate gender into climate change research and helps to recognise overlapping and interdependent systems of power (Djoudi et al. 2016 892 ; Kaijser and Kronsell 2014 893 ; Moosa and Tuana 2014 894 ; Thompson-Hall et al. 2016 895 ). 1.4.3 Policy instruments 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. 1.4.3.1 Legal and regulatory instruments 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. 1.4.3.2 Economic and financial instruments 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 896 ). 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 897 ) (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 898 ; Farley and Voinov 2016 899 ). 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 900 ) or ignore other ecological indicators (water, nutrients, biodiversity) (van Noordwijk and Brussaard 2014 901 ). 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 902 ; Weitzman 2014 903 ; Eidelwein et al. 2018 904 ). 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 905 ), but their impacts have not always been effective (Denis et al. 2014 906 ). 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 907 ) have not been widely adopted and have not yet been demonstrated to deliver as effectively as originally hoped (Börner et al. 2017 908 ) (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 909 ). Better designed and expanded PES schemes would encourage integrated soil–water–nutrient management packages (Stavi et al. 2016 910 ), services for pollinator protection (Nicole 2015 911 ), water use governance under scarcity, and engage both public and private actors (Loch et al. 2013 912 ). 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. 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 913 ) or when market signals encourage adaptation through land markets or supply chain incentives for sustainable land management practices (Anderson et al. 2018 914 ). 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 915 ), or mitigate against risk and to avoid shifting risks to the public (Biagini and Miller 2013 916 ). Governments, private business, and community groups could also partner to develop sustainable production codes (Chartres and Noble 2015 917 ), and in co-managing land-based resources (Baker and Chapin 2018 918 ), while public-private partnerships can be effective mechanisms in deploying infrastructure to cope with climatic events (floods) and for climate-indexed insurance (Kunreuther 2015 919 ). 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 920 ; Mathews 2017 921 ). 1.4.3.3 Rights-based instruments and customary norms Rights-based instruments and customary norms deal with the equitable and fair management of land resources for all people (IPBES 2018a 922 ). 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 923 ) 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 924 ). Other examples of rights-based instruments include the protection of heritage sites, sacred sites and peace parks (IPBES 2018a 925 ). 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. 1.4.3.4 Social and cultural norms 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 926 ). 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 927 ). 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 928 ).== 1.5 The interdisciplinary nature of the SRCCL == 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.== ===== Footnotes === 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). 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. >100 for fluxes less than 5 gN 2 O-N ha –1 d –1 .== ===== References === Hoekstra, A.Y. and T.O. Wiedmann, 2014: Humanity’s unsustainable environmental footprint. Science, 344, 1114–1117, doi:10.1126/science.1248365. 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. Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50, doi:10.1038/nature14324. 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. Isbell, F. et al., 2017: Linking the influence and dependence of people on biodiversity across scales. Nature, 546, 65–72, doi:10.1038/nature22899. 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. IMF, 2018: World Economic Outlook. World Economic Outlook Database, International Monetary Fund, Washington D.C., USA. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018. 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. 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. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data Discuss., 1–3, doi:10.5194/essd-2018-120. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Zhang, X. et al., 2015: Managing nitrogen for sustainable development. Nature, 528, 51–59, doi:10.1038/nature15743. 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. Pereira, H.M. et al., 2010: Scenarios for global biodiversity in the 21st century. Science, 330, 1496–1501, doi:10.1126/science.1196624. Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114. 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. 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. 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. United Nations Department of Economic and Social Affairs, 2017: World Population Prospects: The 2017 Revision, DVD Edition. 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. 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. 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. 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. 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. 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. 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. 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. 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. FAO and ITPS, 2015: Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations, Rome, Italy. 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. 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. 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. 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. 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. 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. 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. 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. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data Discuss., 1–3, doi:10.5194/essd-2018-120. 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. 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. 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. Le Quéré, C. et al., 2015: Global Carbon Budget 2015. Earth Syst. Sci. Data, 7, 349–396, doi:10.5194/essd-7-349-2015. Canadell, J.G., and E.D. Schulze, 2014: Global potential of biospheric carbon management for climate mitigation. Nat. Commun., 5, doi:528210.1038/ncomms6282. 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. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004. 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. 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. Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018. 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. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004. 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. 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. Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018. Lee, X. et al., 2011: Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384–387, doi:10.1038/nature10588. 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. 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. 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. 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. 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. Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114. 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. 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. 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. 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. 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. 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. Urban, M.C. et al., 2016: Improving the forecast for biodiversity under climate change. Science, 353, aad8466, doi:10.1126/science.aad8466. 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. Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84, doi:10.1038/nature16467. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Seidl, R. et al., 2017: Forest disturbances under climate change. Nat. Clim. Chang., doi:10.1038/nclimate3303. 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. 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. 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. 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. 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. 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. 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. 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. Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452. 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. 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. 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. 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. 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. 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. 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. 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. 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. FAO, 2015a: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations, Rome. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org . Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353. 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. 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. 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. 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. 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. 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. 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. 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. Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353. FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org . FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org . 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. FAO, 2017: The Future of Food and Agriculture: Trends and Challenges. Food and Agriculture Organization of the United Nations, Rome, Italy. 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. Tilman, D. and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13959. 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. 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. Lin, M. and P. Huybers, 2012: Reckoning wheat yield trends. Environ. Res. Lett., 7, 24016, doi:10.1088/1748-9326/7/2/024016. 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. 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. Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452. 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. 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. FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org . IFASTAT, 2018: Statistical Databases. http://www.ifastat.org/ . FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org . 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. 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. 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. Liu, J. et al., 2013: Framing Sustainability in a Telecoupled World. Ecol. Soc., 2, doi:10.5751/ES-05873-180226. 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. 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. Seto, K.C. and A. Reenberg (eds.), 2014: Rethinking Global Land Use in an Urban Era. The MIT Press, Cambridge, Massachusetts, USA, 408 pp. 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. 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. Seto, K.C. and N. Ramankutty, 2016: Hidden linkages between urbanization and food systems. Science, 352, 943–945, doi:10.1126/science.aaf7439. 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. 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. 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. 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. FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org . FAO, 1963: World Forest Inventory 1963. Food and Agriculture Organization of the United Nations, Rome, 113 pp. 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. 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. 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. 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. 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. Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. FAO, 2015a: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations, Rome. 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. 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. FAO, 1963: World Forest Inventory 1963. Food and Agriculture Organization of the United Nations, Rome, 113 pp. 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. 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. 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. Song, X.-P. et al., 2018: Global land change from 1982 to 2016. Nature, 560, 639–643, doi:10.1038/s41586-018-0411-9. 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. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004. 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. 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. 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. 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. 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. 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. 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. Bastin, J.-F. et al., 2017: The extent of forest in dryland biomes. Science, 356, 635–638, doi:10.1126/science.aam6527. 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. 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. 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. Ceballos, G. et al., 2015: Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv., doi:10.1126/sciadv.1400253. 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. 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. Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50, doi:10.1038/nature14324. 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. 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. Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50, doi:10.1038/nature14324. 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. Gossner, M.M. et al., 2016: Land-use intensification causes multitrophic homogenization of grassland communities. Nature, 540, 266–269, doi:10.1038/nature20575. 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. 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. 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. Urban, M.C. et al., 2016: Improving the forecast for biodiversity under climate change. Science, 353, aad8466, doi:10.1126/science.aad8466. 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. 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. 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. Anderson-Teixeira, K.J., 2018: Prioritizing biodiversity and carbon. Nat. Clim. Chang., 8, 667–668, doi:10.1038/s41558-018-0242-6. Anderson, S.E. et al., 2018: The Critical Role of Markets in Climate Change Adaptation. National Bureau of Economic Research. 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. United Nations, 2018: 2018 Revision of World Urbanization Prospects. http://www.un.org/development/desa/publications/2018-revision-of-world- 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. 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. 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. 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. 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. 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. Springmann, M. et al., 2018: Options for keeping the food system within environmental limits. Nature, 562, 1, doi:10.1038/s41586-018-0594-0. 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. 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. FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp. 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. Springmann, M. et al., 2018: Options for keeping the food system within environmental limits. Nature, 562, 1, doi:10.1038/s41586-018-0594-0. 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. 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. 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. Erb, K.-H. et al., 2016b: Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun., 7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Liu, J. et al., 2013: Framing Sustainability in a Telecoupled World. Ecol. Soc., 2, doi:10.5751/ES-05873-180226. 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. 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. 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. 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. 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. 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. 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. Seto, K.C. and N. Ramankutty, 2016: Hidden linkages between urbanization and food systems. Science, 352, 943–945, doi:10.1126/science.aaf7439. 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. 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. 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. 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. 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. Seto, K.C. and N. Ramankutty, 2016: Hidden linkages between urbanization and food systems. Science, 352, 943–945, doi:10.1126/science.aaf7439. 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. 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. 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. Lipper, L. et al., 2014: Climate-smart agriculture for food security. Nat. Clim. Chang., 4, 1068–1072, doi:10.1038/nclimate2437. 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. 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. Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178. Smith, P. et al., 2016: Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Chang., 6, 42–50, doi:DOI: 10.1038/NCLIMATE2870. 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. 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. FAO and ITPS, 2015: Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations, Rome, Italy. 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. 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. Lal, R., 2009: Soils and world food security. Soil and Tillage Research, 102, 1–4, doi:10.1016/j.still.2008.08.001. 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. 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. 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. 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. 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. 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. 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. 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. 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. Lal, R., 2015: Restoring soil quality to mitigate soil degradation. Sustainability, 7, 5875, doi:10.3390/su7055875. Haddad, N.M. et al., 2015: Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv., 1, doi:10.1126/sciadv.1500052. Strack, M., 2008: Peatland and Climate Change. International Peat Society and Saarijärven Offset Oy, Jyväskylä, Finland, 223 pp. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Laurance, W.F., 2007: Forests and floods. Nature, 449, 409–410, doi: 10.1038/449409a. 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. 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. Sivakumar, M.V.K., 2007: Interactions between climate and desertification. Agric. For. Meteorol.142, 143–155, doi:10.1016/j.agrformet.2006.03.025. 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. 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. 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. 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. 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. 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. 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. 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. 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. FAO, 2017: The Future of Food and Agriculture: Trends and Challenges. Food and Agriculture Organization of the United Nations, Rome, Italy. FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp. 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. FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Haberl, H., 2015: Competition for land: A sociometabolic perspective. Elsevier, 119, 424–431, doi:10.1016/j.ecolecon.2014.10.002. 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. 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. 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. 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. 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. Lambin, E.F., 2012: Global land availability: Malthus versus Ricardo.Global Food Security, 1, 83–87, doi:10.1016/j.gfs.2012.11.002. 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. 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. Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452. Lambin, E.F., 2012: Global land availability: Malthus versus Ricardo.Global Food Security, 1, 83–87, doi:10.1016/j.gfs.2012.11.002. 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. 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. 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. Land Matrix, 2018: Land Matrix Global Observatory. http://www.landmatrix.org . 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Lean, J.L., 2018: Observation-based detection and attribution of 21st century climate change. Wiley Interdiscip. Rev. Chang., 9, doi:0.1002/wcc.511. 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. 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. 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. 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. 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. 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. 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. 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. 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. Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Luo, Y.Q. et al., 2012: A framework of benchmarking land models. Biogeosciences, 10, 3857–3874, doi:10.5194/bgd-9-1899-2012. 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. Luo, Y.Q. et al., 2012: A framework of benchmarking land models. Biogeosciences, 10, 3857–3874, doi:10.5194/bgd-9-1899-2012. 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. 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. Parker, W.S., 2013: Ensemble modeling, uncertainty and robust predictions. Wiley Interdiscip. Rev. Chang., 4, 213–223, doi:10.1002/wcc.220. 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. 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. 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. 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. 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. Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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. 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. 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. 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. Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452. 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. 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. 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. 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. 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. 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. Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447. 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. 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. 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. 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. 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. 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. Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337–342, doi:10.1038/nature10452. 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. 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. 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. 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. 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. 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. 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. Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353. 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. 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. 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. 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. 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. 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. 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. 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. 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. Challinor, A.J. et al., 2018: Transmission of climate risks across sectors and borders Subject Areas. 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. O’Neill, B. C, 2004: Conditional Probabilistic Population Projections: An Application to Climate Change. International Statistical Institute, 72(2), 167–184. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447. 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. 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. Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Hallegatte, S. and K.J. Mach, 2016: Make climate-change assessments more relevant. Nature, 534, 613–615, doi:10.1038/534613a. 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. Gleckler P.J., et al., 2016: A more powerful reality test for climate models. Eos (Washington. DC)., 97, doi:10.1029/2016EO051663. Parker, W.S., 2013: Ensemble modeling, uncertainty and robust predictions. Wiley Interdiscip. Rev. Chang., 4, 213–223, doi:10.1002/wcc.220. Raffensperger, C. and J.A. Tickner, 1999: Introduction: To Foresee and Forestall. In: Protecting Public Health & The Environment: Implementing The Precautionary Principle, [Raffensperger, C. and J.A. Tickner (eds.)]. Island Press, Washington, DC, USA, pp. 1–11. Gardiner, S.M., 2006: A Core Precautionary Principle. J. Polit. Philos., 14, 33–60, doi:10.1111/j.1467-9760.2006.00237.x. 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. Luedeling, E. and E. Shepherd, 2016: Decision-Focused Agricultural Research. Solutions, 7, 46–54. 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. 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. 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. Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114. 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. 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. 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. 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. 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. 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. Anderson, K. and G.P. Peters, 2016: The trouble with negative emissions. Science, 354, 182–183, doi:10.1126/science.aah4567. 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. Smith, P. et al., 2016: Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Chang., 6, 42–50, doi:DOI: 10.1038/NCLIMATE2870. 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. 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. 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. Anderson, K. and G.P. Peters, 2016: The trouble with negative emissions. Science, 354, 182–183, doi:10.1126/science.aah4567. 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. 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. 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. 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. 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. Schlesinger, W.H., 2018: Are wood pellets a green fuel? Science, 359, 1328–1329, doi:10.1126/science.aat2305. 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. Searle, S. and C. Malins, 2015: A reassessment of global bioenergy potential in 2050. GCB Bioenergy, 7, 328–336, doi:10.1111/gcbb.12141. 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. 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. 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. 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. 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. 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. Creutzig, F. et al., 2015: Bioenergy and climate change mitigation: an assessment. Glob. Chang. Biol. Bioenergy, 7, 916–944, doi:10.1111/gcbb.12205. 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. 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. Williamson, P., 2016: Emissions reduction: Scrutinize CO2 removal methods. Nature, 530, 153–155, doi:10.1038/530153a. 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. 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. 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. 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. Popp, A. et al., 2014: Land-use protection for climate change mitigation. Nat. Clim. Chang., 4, 1095–1098, doi:10.1038/nclimate2444. 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. 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. 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. Hasegawa, T. et al., 2015: Consequence of climate mitigation on the risk of hunger. Environ. Sci. Technol., 49, 7245–7253, doi:10.1021/es5051748. 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. 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. 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. 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. 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. 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. United Nations, 2015: Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations, New York, NY, USA, 41 pp. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Song, X.-P. et al., 2018: Global land change from 1982 to 2016. Nature, 560, 639–643, doi:10.1038/s41586-018-0411-9. 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. 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. 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. 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. 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. 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. 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. Lamb, D., 2018: Undertaking large-scale forest restoration to generate ecosystem services. Restor. Ecol., 26, 657–666, doi:10.1111/rec.12706. 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. Anderson, R.G. et al., 2011: Biophysical considerations in forestry for climate protection. Front. Ecol. Environ., 9, 174–182, doi:10.1890/090179. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Griffith, D.M. et al., 2017: Comment on “The extent of forest in dryland biomes”. Science, 358, eaao1309, doi:10.1126/science.aao1309. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Pawson, S.M. et al., 2013: Plantation forests, climate change and biodiversity. Biodivers. Conserv., 22, 1203–1227, doi:10.1007/s10531-013-0458-8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114. 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. 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. 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. 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. 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. Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114. 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. 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. Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178. 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. 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. 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. 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. Hasegawa, T. et al., 2015: Consequence of climate mitigation on the risk of hunger. Environ. Sci. Technol., 49, 7245–7253, doi:10.1021/es5051748. 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. 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. 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. Harrison, S.P. et al., 2013: Volatile isoprenoid emissions from plastid to planet. New Phytol., 197, 49–57, doi:10.1111/nph.12021. 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. 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. 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. 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. 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. 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. 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. 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. Laestadius, L. et al., 2011: Mapping opportunities for forest landscape restoration. Unasylva, 62, 47–48. Dinerstein, E. et al., 2015: Guiding agricultural expansion to spare tropical forests. Conserv. Lett., 8, 262–271, doi:10.1111/conl.12149. 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. 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. 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. 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. 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. 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. Locatelli, B. et al., 2015a: Tropical reforestation and climate change: Beyond carbon. Restor. Ecol., 23, 337–343, doi:10.1111/rec.12209. 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. Alemu, M.M., 2016: Sustainable land management. J. Environ. Prot., 7, 502–506, doi:10.4236/jep.2016.74045. 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. 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. 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. 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. Zhang, X. et al., 2015: Managing nitrogen for sustainable development. Nature, 528, 51–59, doi:10.1038/nature15743. 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. 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. 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. 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. 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. Abberton, M., 2016: Global agricultural intensification during climate change: a role for genomics. Plant Biotechnol. J., 14, 1095–1098, doi:10.1111/pbi.12467. 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. 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. 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. 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. 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. 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. 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. 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. 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. Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353. Erb, K.-H. et al., 2016b: Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun., 7. 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. 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. 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. 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. Tilman, D. and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13959. 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. Poore, J. and T. Nemecek, 2018: Reducing food’s environmental impacts through producers and consumers. Science, 360, 987–992, doi:10.1126/science.aaq0216. 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. 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. 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. 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. Poore, J. and T. Nemecek, 2018: Reducing food’s environmental impacts through producers and consumers. Science, 360, 987–992, doi:10.1126/science.aaq0216. 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. FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp. 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. 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. 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. 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. 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. 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. 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. 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. 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. FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp. Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353. 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. Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84, doi:10.1038/ nature16467. 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. 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. 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. 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. 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. 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. Pizer, W. et al., 2014: Using and improving the social cost of carbon. Science, 346, 1189–1190, doi:10.1126/science.1259774. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114. 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. 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. 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. 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. 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. 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. 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. Yirdaw, E., M. Tigabu and A. Monge, 2017: Rehabilitation of degraded dryland ecosystems – Review. Silva Fenn., 51, doi:10.14214/sf.1673. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Locatelli, B. et al., 2015a: Tropical reforestation and climate change: Beyond carbon. Restor. Ecol., 23, 337–343, doi:10.1111/rec.12209. 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. Barnett, J., and S. O’Neill, 2010: Maladaptation. Glob. Environ. Chang., 2, 211–213, doi:10.1016/j.gloenvcha.2009.11.004. 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. 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. 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. 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. 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. 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. 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. 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. 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. D’Odorico, P. et al., 2018: The Global Food-Energy-Water Nexus. Rev. Geophys., 56, 456–531, doi:10.1029/2017RG000591. Hoff, H., 2011: Bonn 2011 Conference: The Water, Energy and Food Security Nexus – Solutions for the Green Economy. Stockholm, 1–52 pp. 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. Fischer, J. et al., 2017: Reframing the food–biodiversity challenge. Trends Ecol. Evol., 32, 335–345, doi:10.1016/j.tree.2017.02.009. 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. Hoff, H., 2011: Bonn 2011 Conference: The Water, Energy and Food Security Nexus – Solutions for the Green Economy. Stockholm, 1–52 pp. 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. 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. 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. 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. 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. 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. 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. FAO, 2015a: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations, Rome. 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. 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. 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. Bodin, Ö., 2017: Collaborative environmental governance: Achieving collective action in social-ecological systems. Science, 357, eaan1114, doi:10.1126/science.aan1114. 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. 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. 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. 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. Mistry, J. and A. Berardi, 2016: Bridging indigenous and scientific knowledge. Science, 352, 1274–1275, doi:10.1126/science.aaf1160. 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. Mistry, J. and A. Berardi, 2016: Bridging indigenous and scientific knowledge. Science, 352, 1274–1275, doi:10.1126/science.aaf1160. Mistry, J. and A. Berardi, 2016: Bridging indigenous and scientific knowledge. Science, 352, 1274–1275, doi:10.1126/science.aaf1160. 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. 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. 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. 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. Agarwal, B., 2010: Gender and Green Governance. Oxford University Press, Oxford, UK. Boserup, E., 1989: Population, the status of women, and rural development. Popul. Dev. Rev., 15, 45–60, doi:10.2307/2807921. 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. 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. OECD, 2014: Social Institutions and Gender Index (SIGI). OECD Development Centre’s Social Cohesion Unit, Paris, France, http://www.oecd.org/dev/development- 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. UNEP, 2016: Global Gender and Environment Outlook, UN Environment, Nairobi, Kenya, 222 pp. 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. UNEP, 2016: Global Gender and Environment Outlook, UN Environment, Nairobi, Kenya, 222 pp. 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. 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. 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. 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. Namubiru-Mwaura, E., 2014: Land tenure and gender: Approaches and challenges for strengthening rural women’s land rights. Women’s Voice, Agency, & Participation Research Series No. 06, World Bank, Washington, DC, USA, 32 pp. 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. 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. Kaijser, A. and A. Kronsell, 2014: Climate change through the lens of intersectionality. Env. Polit., 23, 417–433, doi:10.1080/09644016.2013.835203. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Wunder, S., 2015: Revisiting the concept of payments for environmental services. Ecol. Econ., 117, 234–243, doi:10.1016/J.ECOLECON.2014.08.016. 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. 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. 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. Nicole, W., 2015: Pollinator power: Nutrition security benefits of an ecosystem service. Environ. Health Perspect., 123, A210–A215, doi:10.1289/ehp.123-A210. 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. 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. Anderson, S.E. et al., 2018: The Critical Role of Markets in Climate Change Adaptation. National Bureau of Economic Research. Reed, M. and L.C. Stringer, 2015: Climate change and desertification: Anticipating, assessing & adapting to future change in drylands. Impulse Report for the 3rd UNCCD Scientific Conference, Agropolis International, Montpellier, France, 1–140 pp. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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: Halkos, G. and A. Skouloudis, 2016: Cultural dimensions and corporate social responsibility: A cross-country analysis. MPRA Paper 6922, University Library of Munich, Germany. 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. Tal, A., 2010: Desertification. In: The Turning Points of Environmental History [Uekoetter, F. (ed.)]. University of Pittsburgh Press, Pittsburgh, Pennsylvania, USA, pp. 146–161. 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. Parker, W.S., 2013: Ensemble modeling, uncertainty and robust predictions. Wiley Interdiscip. Rev. Chang., 4, 213–223, doi:10.1002/wcc.220. 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. 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