IPCC:AR6/SRCCL/TS: Difference between revisions

Revision as of 03:27, 4 May 2026

(Content)

TS

Technical Summary

Editors:

Priyadarshi R. Shukla

       (India), Jim Skea (United Kingdom), Raphael Slade (United Kingdom) Renée van Diemen (The Netherlands/United
       Kingdom), Eamon Haughey (Ireland), Juliette Malley (United Kingdom), Minal Pathak (India), Joana Portugal
       Pereira (United Kingdom)

Drafting Authors:

Fahmuddin Agus (Indonesia),

       Almut Arneth (Germany), Paulo Artaxo (Brazil), Humberto Barbosa (Brazil), Luis G. Barioni (Brazil), Tim G.
       Benton (United Kingdom), Suruchi Bhadwal (India), Katherine Calvin (The United States of America), Eduardo Calvo
       (Peru), Donovan Campbell (Jamaica), Francesco Cherubini (Italy), Sarah Connors (France/United Kingdom), Annette
       Cowie (Australia), Edouard Davin (France/Switzerland), Kenel Delusca (Haiti), Fatima Denton (The Gambia), Aziz
       Elbehri (Morocco), Karlheinz Erb (Italy), Jason Evans (Australia), Dulce Flores-Renteria (Mexico), Felipe
       Garcia-Oliva (Mexico), Giacomo Grassi (Italy/European Union), Kathleen Hermans (Germany), Mario Herrero
       (Australia/Costa Rica), Richard Houghton (The United States of America), Joanna House (United Kingdom), Mark
       Howden (Australia), Margot Hurlbert (Canada), Ismail Abdel Galil Hussein (Egypt), Muhammad Mohsin Iqbal
       (Pakistan), Gensuo Jia (China), Esteban Jobbagy (Argentina), Francis

X. Johnson (Sweden), Joyce

       Kimutai (Kenya), Kaoru Kitajima (Japan), Tony Knowles (South Africa), Vladimir Korotkov (The Russian
       Federation), Murukesan V. Krishnapillai (Micronesia/ India), Jagdish Krishnaswamy (India), Werner Kurz (Canada),
       Anh Le Hoang (Viet Nam), Christopher Lennard (South Africa), Diqiang Li (China), Emma Liwenga (The United
       Republic of Tanzania), Shuaib Lwasa (Uganda), Nagmeldin Mahmoud (Sudan), Valérie Masson-Delmotte (France),
       Cheikh Mbow (Senegal), Pamela McElwee (The United States of America), Carlos Fernando Mena (Ecuador), Francisco
       Meza (Chile), Alisher Mirzabaev (Germany/Uzbekistan), John Morton (United Kingdom), Wilfran Moufouma-Okia
       (France), Soojeong Myeong (The Republic of Korea), Dalila Nedjraoui (Algeria), Johnson Nkem (Cameroon), Ephraim
       Nkonya (The United Republic of Tanzania), Nathalie De Noblet-Ducoudré (France), Lennart Olsson (Sweden), Balgis
       Osman Elasha (Côte d’Ivoire), Jan Petzold (Germany), Ramón Pichs-Madruga (Cuba), Elvira Poloczanska (United
       Kingdom), Alexander Popp (Germany), Hans-Otto Pörtner (Germany), Prajal Pradhan (Germany/Nepal), Mohammad Rahimi
       (Iran), Andy Reisinger (New Zealand), Marta G. Rivera-Ferre (Spain), Debra C. Roberts (South Africa), Cynthia
       Rosenzweig

[Image: image]

TS

(The United States of

       America), Mark Rounsevell (United Kingdom), Nobuko Saigusa (Japan), Tek Sapkota (Canada/Nepal), Elena
       Shevliakova (The United States of America), Andrey Sirin (The Russian Federation), Pete Smith (United Kingdom),
       Youba Sokona (Mali), Denis Jean Sonwa (Cameroon), Jean-Francois Soussana (France), Adrian Spence (Jamaica),
       Lindsay Stringer (United Kingdom), Raman Sukumar (India), Miguel Angel Taboada (Argentina), Fasil Tena
       (Ethiopia), Francesco N. Tubiello (The United States of America/Italy), Murat Türkeş (Turkey), Riccardo
       Valentini (Italy), Ranses José Vázquez Montenegro (Cuba), Louis Verchot (Colombia/The United States of America),
       David Viner (United Kingdom), Koko Warner (The United States of America), Mark Weltz (The United States of
       America), Nora M. Weyer (Germany), Anita Wreford (New Zealand), Jianguo Wu (China), Yinlong Xu (China),
       Noureddine Yassaa (Algeria), Sumaya Zakieldeen (Sudan), Panmao Zhai (China), Zinta Zommers (Latvia)

Chapter Scientists:

Yuping Bai (China), Aliyu

       Salisu Barau (Nigeria), Abdoul Aziz Diouf (Senegal), Baldur Janz (Germany), Frances Manning (United Kingdom),
       Erik Mencos Contreras (The United States of America/Mexico), Dorothy Nampanzira (Uganda), Chuck Chuan Ng
       (Malaysia), Helen Berga Paulos (Ethiopia), Xiyan Xu (China), Thobekile Zikhali (Zimbabwe)

This Technical Summary should be

       cited as:

P.R. Shukla, J. Skea, R. Slade, R. van Diemen, E. Haughey, J. Malley, M. Pathak, J. Portugal Pereira (eds.)

       Technical Summary, 2019. 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/10.1017/9781009157988.002

(Content)

Table of Contents

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TS

TS.0 Introduction��40TS.1 Framing and context��40TS.2 Land–climate interactions��44TS.3 Desertification��50TS.4 Land degradation��53TS.5 Food security��56

TS.6 Interlinkages

       between desertification, land degradation, food security and greenhouse

gas fluxes��61

TS.7 Risk management and decision

       making

in relation to sustainable development��67

TS.0 Introduction

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TS

This

Technical Summary to the IPCC Special Report on Climate Change and Land (SRCCL)1comprises a compilation of the chapter executive summaries illustrated with figures from the report. It
follows the structure of the SRCCL (Figure TS.1) and is presented in seven parts. TS.1 (Chapter 1) provides a
synopsis of the main issues addressed in the Special Report, introducing key concepts and definitions and
highlighting where the report builds on previous publications. TS.2 (Chapter 2) focuses on the dynamics of the
land–climate system (Figure TS.2). It assesses recent progress towards understanding the impacts of climate
change on land, and the feedbacks land has on climate and which arise from altered biogeochemical and
biophysical fluxes between the atmosphere and the land surface. TS.3 (Chapter 3) examines how the world’s
dryland populations are uniquely vulnerable to desertification and climate change, but also have significant
knowledge in adapting to climate variability and addressing desertification. TS.4 (Chapter 4) assesses the
urgency of tackling land degradation across all land ecosystems. Despite accelerating trends of land
degradation, reversing these trends is attainable through restoration efforts and improved land management,
which is expected to improve resilience to climate change, mitigate climate change, and ensure food security for
generations to come. TS.5 (Chapter 5) focuses on food security, with an assessment of the risks and
opportunities that climate change presents to food systems. It considers how mitigation and adaptation can
contribute to both human and planetary health. TS.6 (Chapter 6) introduces options for responding to the
challenges of desertification, land degradation and food security and evaluates the trade-offs for sustainable
land management, climate adaptation and mitigation, and the sustainable development goals. TS.7 (Chapter 7)
further assesses decision making and policy responses to risks in the climate-land-human system.

TS.1 Framing and

       context

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). (Figure TS.2). {1.1.2}

[Image: image]

Desertification(Chapter 3)

Degradation(Chapter 4)

Synergies, trade-offs, integrated

       response options(Chapter 6)

Food security(Chapter 5)

Natural system dynamics(Chapter 2)

Framing and context(Chapter 1)

[Image: image]

...

Risks and

       decision-making

(Chapter 7)

Figure TS.1 | Overview of the IPCC

       Special Report on Climate Change and Land (SRCCL).

[Image: image]

1The full title of the report is theIPCC special report on climate

           change, desertification, land degradation, sustainable land management, food security, and greenhouse gas
           fluxes in terrestrial ecosystems

Land use and observed

       climate change

Observed

               temperature change relative to 1850–1900

Since the

               pre-industrial period (1850–1900) the observed mean land surface air temperature has risen considerably
               more than the global mean surface (land and ocean) temperature (GMST).

CHANGE in

               TEMPERATURE rel. to 1850–1900 (°C)

2

[Image: image]

Change in

               surface air

GHG emissions

An estimated

               23% of total anthropogenic greenhouse gas emissions (2007–2016) derive from Agriculture, Forestry and
               Other Land Use (AFOLU).

CHANGE in

               EMISSIONS since 1961

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TS

[Image: image]

1

2

3

Net CO2

               emissions from FOLU (GtCO2 yr–1) CH4 emissions from Agriculture (GtCO2eq
               yr–1)

1.5

1

0.5

0

-0.5

temperature

over land

               (°C)

Change in

               global (land-ocean) mean surface temperature (GMST) (°C)

[Image: image]

1850 1880 1900

               1920 1940 1960 1980 2000 2018

N2O

               emissions from Agriculture (GtCO2eq yr–1) GtCO2eq yr–1

[Image: image]

1

2

3

[Image: image]

6

4

2

0[Image: image]

1961 1980 2000

Global ice-free land surface100%(130

               Mkm2)

[Image: image]

Extensive pasture 19%

Forests managed for

           timber and other uses 20%

Other land (barren, rock) 12%

Forests (intact or

           primary) with minimal human use 9%

Used savannahs and shrublands 16%

Non-irrigated cropland 10%

Unforested ecosystems with minimal human use 7%

Plantation forests

2%

Intensive pasture

2%

Irrigated cropland

2%

Infrastructure 1%

28% (24 – 31%)

22% (16 – 23%)

37% (30 – 47%)

12% (12 – 14%)

1% (1 – 1%)

0

Global land

               use in circa 2015

The barchart

               depicts shares of different uses

of the global,

               ice-free10

land area.

               Bars are

ordered along

               a gradient of decreasing land-use intensity from left to right.

Agricultural production

Land use

               change and rapid land use intensification have supported the increasing production of food, feed and
               fibre. Since 1961, the total production of food (cereal crops) has increased by 240% (until 2017)
               because of land area expansion and increasing yields. Fibre production (cotton) increased by 162% (until
               2013).

[Image: image]CHANGE in % rel. to 1961

[Image: image]

1 Inorganic N

               fertiliser use

Food demand

20

30

Desertification and land degradation

2 Cereal[Image: image]yields

[Image: image]

3 Irrigation[Image: image]water volume

4 Total number of ruminant livestock

[Image: image]

%8001

700

[Image: image]

2

3

4

300

250

200

150

100

50

0

-50[Image: image]

Increases in production are linked to consumption changes.

CHANGE in % rel. to

       1961 and 1975

[Image: image]

Population

Prevalence of

               overweight + obese

Total calories

               per capita

Prevalence of

               underweight

%

250[Image: image]

200[Image: image]

[Image: image]

1

2

3

4

150

100

50

0

-50

Land-use

               change, land-use intensification and climate change have contributed to desertification and land
               degradation.

CHANGE in %

               rel. to 1961 and 1970

[Image: image]

1 Population in

               areas experiencing desertification

2 Dryland areas

               in drought annually

3 Inland

               wetland extent

[Image: image]

%[Image: image]

250[Image: image]

[Image: image]

1

2

3

200

150

100

50

0

-50

[Image: image][Image: image][Image: image][Image: image]

1961[Image: image]1980

               2000 2017

[Image: image][Image: image][Image: image][Image: image][Image: image]

1961 1980 2000 2017

1961 1980 2000

[Image: image][Image: image][Image: image][Image: image][Image: image]

Figure TS.2 |

               Land use and observed climate change: A representation of the principal land challenges and land–climate
               system processes covered in this assessment report.

Figure TS.2

                   (continued):Panels A-F show the status and trends in selected land use and climate variables
               that represent many of the core topics covered in this report. The annual time series in B and D–F are
               based on the most comprehensive, available data from national statistics, in most cases from FAOSTAT
               which starts in 1961. Y-axes in panels D–F are expressed relative to the starting year of the time
               series (rebased to zero). Data sources and notes:A:The warming curves are
               averages of four datasets {2.1; Figure 2.2; Table 2.1}B:N2O and CH4from agriculture are from FAOSTAT; Net
               CO2emissions from FOLU using the mean of two bookkeeping models (including
               emissions from peatland fires since 1997). All values expressed in units of CO2-eq are based on AR5 100-year Global Warming Potential values without
               climate-carbon feedbacks (N2O = 265; CH4= 28). {see
               Table SPM.1, 1.1, 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

[Image: image]

TS

% 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. {1.2, Table 1.1, Figure 1.3}D:Note that fertiliser use is shown on a split axis. 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; underweight is defined as BMI <18.5 kg m-2. {5.1,

               5.2}F:Dryland areas were estimated using TerraClimate precipitation and
               potential evapotranspiration (1980–2015) to identify areas where the Aridity Index is below 0.65.
               Population data are from the HYDE3.2 database. Areas in drought are based on the 12-month accumulation
               Global Precipitation Climatology Centre Drought Index. 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. {3.1, 4.2, 4.6}

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 CO2, 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 (SDG) 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 CO2emissions from
               deforestation, CH4emissions from rice and ruminant livestock and N2O 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

                   gender-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, cultural
               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}

[Image: image]

TS

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}

TS.2

               Land–climate interactions

Implications

               of climate change, variability and extremes for land systems

[Image: image]

TS

It is

               certain that globally averaged land surface air temperature (LSAT) has risen faster than the global mean
               surface temperature (i.e., combined LSAT and sea surface temperature) from the preindustrial period
               (1850–1900) to the present day (1999–2018). According to the single longest and most extensive dataset,
               from 1850–1900 to 2006–2015 mean land surface air temperature has increased by 1.53°C (very likely
                   rangefrom 1.38°C to 1.68°C) while global mean surface temperature has increased by 0.87°C
               (likely rangefrom 0.75°C to 0.99°C). For the 1881–2018 period, when four independently produced
               datasets exist, the LSAT increase was 1.41°C (1.31–1.51°C), where the range represents the spread in the
               datasets’ median estimates.Analyses of paleo records, historical observations, model
                   simulations and underlying physical principles are all in agreement that LSATs are increasing at a
                   higher rate than SST as a result of differences in evaporation, land–climate feedbacks and changes
                   in the aerosol forcing over land (very high confidence). For the 2000–2016 period, the land-to-ocean warming ratio (about 1.6) is in close
                   agreement between different observational records and the CMIP5 climate model simulations (thelikely rangeof 1.54–1.81). {2.2.1}

Anthropogenic warming has resulted in shifts of climate zones, primarily as an increase in dry climates

               and decrease of polar climates (high confidence). Ongoing warming is projected to result in new,
               hot climates in tropical regions and to shift climate zones poleward in the mid- to high latitude and
               upward in regions of higher elevation (high confidence).Ecosystems in
                   these regions will become increasingly exposed to temperature and rainfall extremes beyond the
                   climate regimes they are currently adapted to (high confidence), which can alter their structure, composition and functioning. Additionally,
                   high-latitude warming is projected to accelerate permafrost thawing and increase disturbance in
                   boreal forests through abiotic (e.g., drought, fire) and biotic (e.g., pests, disease) agents
                   (high confidence). {2.2.1, 2.2.2, 2.5.3}

Globally, greening trends (trends of increased photosynthetic activity in vegetation)

                   have increased over the last 2–3 decades by 22–33%, particularly over China, India, many parts of
                   Europe, central North America, southeast Brazil and southeast Australia (high confidence).This results from a combination of direct (i.e., land use and
               management, forest conservation and expansion) and indirect factors (i.e., CO2fertilisation, extended growing season, global warming, nitrogen deposition, increase of diffuse
               radiation) linked to human activities (high confidence). Browning trends (trends of decreasing
               photosynthetic activity) are projected in many regions where increases in drought and heatwaves are
               projected in a warmer climate. There islow confidencein the projections of global greening and
               browning trends. {2.2.4, Cross-Chapter Box 4 in Chapter 2}

[Image: image]

CO2

solar radiation

condensation evaporation

sensible heat

precipitation

long wave

               radiation

CO2

carbonaceous

N deposition & emission

CH4albedo

BVOCs

aerosols

CO2

mineral aerosols

CH4,N2O

BVOCs

fertilizer

windroughness

emissivity

unmanaged lands

agriculture

forestry

soil carbon

& nutrients

soil carbon

& nutrients

l

i

z

e

r r

f

e

r

t

i

soil carbon

& nutrients

l

i

z

f

e

r

t

i

Figure TS.3 | The structure

               and functioning of managed and unmanaged ecosystems that affect local, regional and global climate.Land surface characteristics such as albedo and emissivity determine the amount of
                   solar and long-wave radiation absorbed by land and reﬂected or emitted to the atmosphere. Surface
                   roughness inﬂuences turbulent exchanges of momentum, energy, water and biogeochemical tracers. Land
                   ecosystems modulate the atmospheric composition through emissions and removals of many GHGs and
                   precursors of SLCFs, including biogenic volatile organic compounds (BVOCs) and mineral dust.
                   Atmospheric aerosols formed from these precursors affect regional climate by altering the amounts of
                   precipitation and radiation reaching land surfaces through their role in clouds physics.

The frequency and intensity of some extreme weather and climate events have increased as

                   a consequence of global warming and will continue to increase under medium and high emission
                   scenarios (high confidence).Recent
               heat-related events, for example, heatwaves, have been made more frequent or intense due to
               anthropogenic GHG emissions in most land regions and the frequency and intensity of drought has
               increased in Amazonia, north-eastern Brazil, the Mediterranean, Patagonia, most of Africa and
               north-eastern China (medium confidence). Heatwaves are projected to increase in frequency,
               intensity and duration in most parts of the world (high confidence) and drought frequency and
               intensity is projected to increase in some regions that are already drought prone, predominantly in the
               Mediterranean, central Europe, the southern Amazon and southern Africa (medium confidence). These
               changes will impact ecosystems, food security and land processes including GHG fluxes (high
                   confidence). {2.2.5}

Climate change is playing an increasing role in determining wildfire regimes alongside

                   human activity (medium confidence), with future
                   climate variability expected to enhance the risk and severity of wildfires in many biomes such as
                   tropical rainforests (high confidence).Fire weather seasons have lengthened globally between 1979 and 2013 (low confidence).
               Global land area burned has declined in recent decades, mainly due to less burning in grasslands and
               savannahs (high confidence). While drought remains the dominant driver of fire emissions, there
               has recently been increased fire activity in some tropical and temperate regions during normal to wetter
               than average years due to warmer temperatures that increase vegetation flammability (medium
                   confidence). The boreal zone is also experiencing larger and more frequent fires, and this may
               increase under a warmer climate (medium confidence). {Cross-Chapter Box 4 in Chapter 2}

Terrestrial greenhouse gas fluxes on unmanaged and managed lands

Agriculture, forestry and other land use (AFOLU) is a significant net source of GHG emissions (high

                   confidence), contributing to about 23% of anthropogenic emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) combined as CO2equivalents in 2007–2016 (medium
                   confidence).AFOLU results in both emissions and removals of CO2, CH4and
                   N2O to and from the atmosphere (high confidence). These fluxes are affected simultaneously by
                   natural and human drivers, making it difficult to separate natural from anthropogenic fluxes
                   (very high confidence). (Figure TS.3) {2.3}

The

                   total net land-atmosphere flux of CO2on both
                   managed and unmanaged landsvery likelyprovided a
                   global net removal from 2007 to 2016 according to models (-6.0 ± 3.7 GtCO2yr–1,likely range).This net removal is comprised of
               two major components: (i) modelled net anthropogenic emissions from AFOLU are 5.2 ± 2.6 GtCO2yr–1(likely range) driven by land cover
               change, including deforestation and afforestation/reforestation, and wood harvesting (accounting for
               about 13% of total net anthropogenic emissions of CO2) (medium
                   confidence), and (ii) modelled net removals due to non-anthropogenic processes are 11.2 ± 2.6
               GtCO2yr–1(likely

range) on managed and unmanaged lands, driven by environmental changes such as increasing CO2, nitrogen deposition and changes in climate (accounting for a removal of 29% of

               the CO2emitted from all anthropogenic activities (fossil fuel, industry and
               AFOLU) (medium confidence). {2.3.1}

[Image: image]

TS

Global

               models and national GHG inventories use different methods to estimate anthropogenic CO2emissions and removals for the land sector. Consideration of differences in
               methods can enhance understanding of land sector net emission such as under the Paris Agreement’s global
               stocktake (medium confidence).Both models and inventories produce estimates that
                   are in close agreement for land-use change involving forest (e.g., deforestation, afforestation),
                   and differ for managed forest. Global models consider as managed forest those lands that were
                   subject to harvest whereas, consistent with IPCC guidelines, national GHG inventories define managed
                   forest more broadly. On this larger area, inventories can also consider the natural response of land
                   to human-induced environmental changes as anthropogenic, while the global model approach treats this
                   response as part of the non-anthropogenic sink. For illustration, from 2005 to 2014, the sum of the
                   national GHG inventories net emission estimates is

0.1 ± 1.0 GtCO2yr–1, while the mean of two global bookkeeping models

               is 5.1 ± 2.6 GtCO2yr–1(likely range). {Table
               SPM.1}

The gross

               emissions from AFOLU (one-third of total global emissions) are more indicative of mitigation potential
               of reduced deforestation than the global net emissions (13% of total global emissions), which include
               compensating deforestation and afforestation fluxes (high confidence).The net
                   flux of CO2from AFOLU is composed of two opposing
                   gross fluxes:

(i) gross emissions (20

               GtCO2yr–1) from deforestation, cultivation of soils
               and oxidation of wood products, and (ii) gross removals (–14 GtCO2yr–1), largely from forest growth following wood harvest and agricultural
               abandonment (medium confidence). (Figure TS.4) {2.3.1}

Land

                   is a net source of CH4, accounting for 44% of
                   anthropogenic CH4emissions for the 2006–2017
                   period (medium confidence).The pause in
               the rise of atmospheric CH4concentrations between 2000 and 2006 and the
               subsequent renewed increase appear to be partially associated with land use and land use change. The
               recent depletion trend of the 13C isotope in the atmosphere indicates that higher biogenic sources
               explain part of the current CH4increase and that biogenic sources make up a
               larger proportion of the source mix than they did before 2000 (high confidence). In agreement
               with the findings of AR5, tropical wetlands and peatlands continue to be important drivers of
               inter-annual variability and current CH4concentration increases (medium
                   evidence, high agreement). Ruminants and the expansion of rice cultivation are also important
               contributors to the current trend (medium evidence, high agreement). There is significant and
               ongoing accumulation of CH4in the atmosphere (very high confidence).
               {2.3.2}

[Image: image]Net land

               flux[Image: image]Gross AFOLU
               emissions

[Image: image]Net AFOLU

               flux[Image: image]Gross AFOLU
               removals

[Image: image]Gross

               indirect emissions on land[Image: image]Gross
               indirect removals on land

[Image: image]Net indirect flux on land

[Image: image]

Source

30

indirect

               effects due to environmental changes on managed and unmanaged lands

[Image: image]

?

20

[Image: image]

TS

10

Gt CO2yr–1

0

–10

Sink

–20

–30[Image: image]

Total land

               AFOLU Indirect* land

Figure TS.4 | Net and gross

               fluxes of CO2from land (annual averages for 2008–2017).Left:
                   The total net ﬂux of CO2between land and
                   atmosphere (grey) is shown with its two component ﬂuxes, (i) net AFOLU emissions (blue), and (ii)
                   the net land sink (brown), due to indirect environmental effects and natural effects on managed and
                   unmanaged lands. Middle: The gross emissions and removals contributing to the net AFOLU ﬂux. Right:
                   The gross emissions and removals contributing to the land sink.

AFOLU is the main anthropogenic source of N2O primarily due to nitrogen application to soils (high

                   confidence).In croplands, the main driver of N2O emissions is a lack of synchronisation between crop nitrogen demand and soil
               nitrogen supply, with approximately 50% of the nitrogen applied to agricultural land not taken up by the
               crop. Cropland soils emit over 3 MtN2O-N yr–1(medium confidence). Because the response of N2O emissions to
               fertiliser application rates is non-linear, in regions of the world where low nitrogen application rates
               dominate, such as sub-Saharan Africa and parts of Eastern Europe, increases in nitrogen fertiliser use
               would generate relatively small increases in agricultural N2O emissions.
               Decreases in application rates in regions where application rates are high and exceed crop demand for
               parts of the growing season will have very large effects on emissions reductions (medium evidence,
                   high agreement). {2.3.3}

While managed pastures make up only one-quarter of grazing lands, they contributed more

                   than three-quarters of N2O emissions from grazing
                   lands between 1961 and 2014 with rapid recent increases of nitrogen inputs resulting in
                   disproportionate growth in emissions from these lands (medium
                   confidence).Grazing lands (pastures and rangelands) are responsible
               for more than one-third of total anthropogenic N2O emissions or more than
               one-half of agricultural emissions (high confidence). Emissions are largely from North America,
               Europe, East Asia, and South Asia, but hotspots are shifting from Europe to southern Asia (medium
                   confidence). {2.3.3}

Increased emissions from vegetation and soils due to climate change in the future are

                   expected to counteract potential sinks due to CO2fertilisation (low confidence).Responses
               of vegetation and soil organic carbon (SOC) to rising atmospheric CO2concentration and climate change are not well constrained by observations (medium confidence).
               Nutrient (e.g., nitrogen, phosphorus) availability can limit future plant growth and carbon storage
               under rising CO2(high confidence). However, new evidence suggests that
               ecosystem adaptation through plant-microbe symbioses could alleviate some nitrogen limitation (medium
                   evidence, high agreement). Warming of soils and increased litter inputs will accelerate carbon
               losses through microbial respiration (high confidence). Thawing of high latitude/ altitude
               permafrost will increase rates of SOC loss and change the balance between CO2and CH4emissions (medium confidence). The balance between increased
               respiration in warmer climates and carbon uptake from enhanced plant growth is a key uncertainty for the
               size of the future land carbon sink (medium confidence). {2.3.1, 2.7.2, Box 2.3}

Biophysical and biogeochemical land forcing and feedbacks to the climate system

Changes in land conditions from human use or climate change in turn affect regional and

                   global climate (high confidence).On the
               global scale, this is driven by changes in emissions or removals of CO2,
               CH4and N2O by land (biogeochemical effects) and by
               changes in the surface albedo (very high confidence). Any local land changes

that

               redistribute energy and water vapour between the land and the atmosphere influence regional climate
               (biophysical effects;high confidence). However, there isno confidencein whether such
               biophysical effects influence global climate. {2.1, 2.3, 2.5.1, 2.5.2}

Changes in

               land conditions modulate the likelihood, intensity and duration of many extreme events including
               heatwaves (high confidence) and heavy precipitation events (medium confidence).Dry soil conditions favour or strengthen summer heatwave conditions through reduced
                   evapotranspiration and increased sensible heat. By contrast wet soil conditions, for example from
                   irrigation or crop management practices that maintain a cover crop all year round, can dampen
                   extreme warm events through increased evapotranspiration and reduced sensible heat. Droughts can be
                   intensified by poor land management. Urbanisation increases extreme rainfall events over or downwind
                   of cities (medium confidence). {2.5.1, 2.5.2,
                   2.5.3}

Historical

               changes in anthropogenic land cover have resulted in a mean annual global warming of surface air from
               biogeochemical effects (very high confidence), dampened by a cooling from biophysical effects
               (medium confidence).Biogeochemical warming results from increased emissions of
                   GHGs by land, with model-based estimates of +0.20 ± 0.05°C (global climate models) and +0.24 ±
                   0.12°C – dynamic global vegetation models (DGVMs) as well as an observation-based estimate of
                   +0.25

± 0.10°C. A net

               biophysical cooling of –0.10 ± 0.14°C has been derived from global climate models in response to the
               increased surface albedo and decreased turbulent heat fluxes, but it is smaller than the warming effect
               from land-based emissions. However, when both biogeochemical and biophysical effects are accounted for
               within the same global climate model, the models do not agree on the sign of the net change in mean
               annual surface air temperature. {2.3, 2.5.1, Box 2.1}

The

                   future projected changes in anthropogenic land cover that have been examined for AR5 would result in
                   a biogeochemical warming and a biophysical cooling whose magnitudes depend on the scenario
                   (high confidence).Biogeochemical warming
               has been projected for RCP8.5 by both global climate models (+0.20 ± 0.15°C) and DGVMs (+0.28 ± 0.11°C)
               (high confidence). A global biophysical cooling of 0.10 ± 0.14°C is estimated from global climate
               models and is projected to dampen the land-based warming (low confidence). For RCP4.5, the
               biogeochemical warming estimated from global climate models (+0.12 ± 0.17°C) is stronger than the
               warming estimated by DGVMs (+0.01 ± 0.04°C) but based on limited evidence, as is the biophysical cooling
               (–0.10 ± 0.21°C). {2.5.2}

Regional

               climate change can be dampened or enhanced by changes in local land cover and land use (high
                   confidence) but this depends on the location and the season (high confidence).In boreal regions, for example, where projected climate change will migrate the treeline
                   northward, increase the growing season length and thaw permafrost, regional winter warming will be
                   enhanced by decreased surface albedo and snow, whereas warming will be dampened during the growing
                   season due to larger evapotranspiration (high confidence). In the tropics, wherever climate

change

               will increase rainfall, vegetation growth and associated increase in evapotranspiration will result in a
               dampening effect on regional warming (medium confidence). {2.5.2, 2.5.3}

[Image: image]

TS

According

               to model-based studies, changes in local land cover or available water from irrigation will affect
               climate in regions as far as few hundreds of kilometres downwind (high confidence).The local redistribution of water and energy following the changes on land affect the
                   horizontal and vertical gradients of temperature, pressure and moisture, thus altering regional
                   winds and consequently moisture and temperature advection and convection and subsequently,
                   precipitation. {2.5.2, 2.5.4, Cross-Chapter Box 4

in Chapter 2}

Future increases in both climate change and urbanisation will enhance warming in cities

                   and their surroundings (urban heat island), especially during heatwaves (high confidence).Urban and peri-urban agriculture, and
               more generally urban greening, can contribute to mitigation (medium confidence) as well as to
               adaptation (high confidence), with co-benefits for food security and reduced soil-water-air
               pollution. {Cross-Chapter Box 4 in Chapter 2}

Regional

               climate is strongly affected by natural land aerosols (medium confidence) (e.g., mineral dust,
               black, brown and organic carbon), but there islow confidencein historical trends, inter-annual
               and decadal variability and future changes.Forest cover affects climate through
                   emissions of biogenic volatile organic compounds (BVOC) and aerosols (low
                   confidence). The decrease in the emissions of BVOC resulting from the
                   historical conversion of forests to cropland has resulted in a positive radiative forcing through
                   direct and indirect aerosol effects, a negative radiative forcing through the reduction in the
                   atmospheric lifetime of methane and it has contributed to increased ozone concentrations in
                   different regions (low confidence). {2.4,
                   2.5}

Consequences for the climate system of land-based adaptation and mitigation options, including carbon

               dioxide removal(negative emissions)

About

                   one-quarter of the 2030 mitigation pledged by countries in their initial Nationally Determined
                   Contributions (NDCs) under the Paris Agreement is expected to come from land-based mitigation
                   options (medium confidence).Most of the
               NDCs submitted by countries include land-based mitigation, although many lack details. Several refer
               explicitly to reduced deforestation and forest sinks, while a few include soil carbon sequestration,
               agricultural management and bioenergy. Full implementation of NDCs (submitted by February 2016) is
               expected to result in net removals of 0.4–1.3 GtCO2y–1in 2030 compared to the net flux in 2010, where the range represents low to high mitigation
               ambition in pledges, not uncertainty in estimates (medium confidence). {2.6.3}

LAND MANAGEMENTReferences

Reduce emissions from

               Agriculture

[Image: image]

TS

Cropland nutrient management N2O Reduced N2O from

               manure on pasture Manure management N2O and CH4Improved rice cultivation CH4

Reduced

               enteric fermentation CH4Improved synthetic fertilizer production

0 03 0 7

0 0

0 0 0 26

0 08 0 87

0 12 18

0 05 0 36

1–5

[Image: image]

TECHNICAL POTENTIAL ECONOMIC POTENTIAL SUSTAINABLE POTENTIAL MEDIAN

INTERMODEL RANGE 1.5ºC INTERMODEL RANGE 2ºC

6

5, 7

1–5, 8

1, 5, 7, 9

5, 10

Reduce emissions from

       Forests and other Ecosystems

Reduce deforestation

0 4 5.80

1, 2, 11, 18

Reduce forest

       degradation

Reduce conversion,

       draining, burning of peatlands

Reduce conversion of

       coastal wetlands (mangroves, seagrass and marshes)

Reduce conversion of

       savannas and natural grasslands

2 18

0.45 22

0.1 2 25

0.03 0 12

13, 16, 19

1, 2, 20

1, 2, 21, 22

1

Carbon Dioxide Removal

Afforestation/Reforestation (A/R) Forest management Agroforestry

Peatland restoration

Coastal wetland

       restoration

Soil

       carbon sequestration in croplands Soil carbon sequestration in grazing lands Biochar application

BECCS deployment

0 44 2 10

0 15 0 8

0 20 0 84

0 3 2.56

0 1 5 68

0 25 6.78

0 03 6.60

0 50 012

15.57

0 40 1 30

13.50

1, 2, 29, 30, 11, 15, 23–28

1, 31, 32

1, 2, 5, 33

1, 34

1

1, 2, 40, 3, 5, 7, 35–39

1, 2, 43, 44, 3, 29, 36, 37, 39–42

1, 2, 47, 48, 3, 5, 23, 28, 30, 42, 45,

23, 28–30, 45, 49, 50

0 2 4 6 8 10

Mitigation potential (GtCO2-eq yr–1)

DEMAND MANAGEMENT

[Image: image]

0 76 4.5

0 25

0 10 0 8

Waste

       and Losses

Reduce food and

       agricultural waste

Diets

Shift to plant-based

       diets

Wood

       Products

Increase substitution

       of cement/steel

Wood

       Fuel

Increase cleaner

       cookstoves

0

2 4 6 8

Mitigation potential

       (GtCO2-eq yr–1)

0 70 8

[Image: image]

10

2, 5, 51

[Image: image]

2, 5, 7, 18, 51–54

[Image: image]

29, 55

[Image: image]

1, 2, 56

Figure TS.5 | Mitigation potential of

       response options in 2020–2050, measured in GtCO2-eq yr–1, adapted from Roe et al.
       (2017).

Figure TS.5

           (continued):Mitigation potentials reflect the full range of low to high estimates from studies
       published after 2010, differentiated according to technical (possible with current technologies), economic
       (possible given economic constraints) and sustainable potential (technical or economic potential constrained by
       sustainability considerations). Medians are calculated across all potentials in categories with more than four
       data points. We only include references that explicitly provide mitigation potential estimates in CO2-eq yr–1(or a similar derivative) by 2050. Not all options for
       land management potentials are additive, as some may compete for land. Estimates reflect a range of
       methodologies (including definitions, global warming potentials and time horizons) that may not be directly
       comparable or additive. Results from IAMs are shown to compare with single option ‘bottom-up’ estimates, in
       available categories from the 2°C and 1.5°C scenarios in the SSP Database (version 2.0). The models reflect land
       management changes, yet in some instances, can also reflect demand-side effects from carbon prices, so may not
       be defined exclusively as ‘supply-side’.

Several mitigation response options have technical potential for >3 GtCO2-eq yr–1by 2050

           through reduced emissions and Carbon Dioxide Removal (CDR) (high
           confidence), some of which compete for land and other resources, while others may
           reduce the demand for land (high confidence).Estimates of the technical potential of individual response options are not necessarily additive. The
       largest potential for reducing AFOLU emissions are through reduced deforestation and forest degradation (0.4–5.8
       GtCO2-eq yr–1) (high confidence), a shift towards
       plant-based diets (0.7–8.0 GtCO2-eq yr–1) (high
           confidence) and reduced food and agricultural waste (0.8–4.5 CO2-eq yr–1) (high confidence). Agriculture measures combined could mitigate 0.3–3.4
       GtCO2-eq yr–1(medium confidence). The options with
       largest potential for CDR are afforestation/reforestation (0.5–10.1 CO2-eq yr–1) (medium confidence), soil carbon sequestration in croplands and grasslands
       (0.4–8.6 CO2-eq yr–1) (high confidence) and Bioenergy
       with Carbon Capture and Storage (BECCS) (0.4–11.3 CO2-eq yr–1)
       (medium confidence). While some estimates include sustainability and cost considerations, most do not
       include socio-economic barriers, the impacts of future climate change or non-GHG climate forcings. {2.6.1}

Response

           options intended to mitigate global warming will also affect the climate locally and regionally through
           biophysical effects (high confidence).Expansion of
       forest area, for example, typically removes CO2from the atmosphere and thus dampens
       global warming (biogeochemical effect,high confidence), but the biophysical effects can dampen or
       enhance regional warming depending on location, season and time of day. During the growing season, afforestation
       generally brings cooler days from increased evapotranspiration, and warmer nights (high confidence).
       During the dormant season, forests are warmer than any other land cover, especially in snow-covered areas where
       forest cover reduces albedo (high confidence). At the global level, the temperature effects of boreal
       afforestation/reforestation run counter to GHG effects, while in the tropics they enhance GHG effects. In
       addition, trees locally dampen the amplitude of heat extremes (medium confidence). {2.5.2, 2.5.4, 2.7,
       Cross-Chapter Box 4 in Chapter 2}

Mitigation

           response options related to land use are a key element of most modelled scenarios that provide strong
           mitigation, alongside emissions reduction in other sectors (high
           confidence). More stringent climate targets rely more heavily on land-based
           mitigation options, in particular, CDR (high confidence).Across a range of scenarios in 2100, CDR is delivered by both afforestation (median values of –1.3, –1.7
       and –2.4 GtCO2yr–1for scenarios RCP4.5, RCP2.6 and RCP1.9
       respectively) and BECCS (–6.5, –11 and –14.9 GtCO2yr–1respectively). Emissions of

CH4and N2O are reduced through improved agricultural and

       livestock management as well as dietary shifts away from emission-intensive livestock products by 133.2, 108.4
       and 73.5 MtCH4yr–1; and 7.4,

[Image: image]

TS

6.1 and 4.5 MtN2O yr–1for the same set of scenarios in 2100 (high

           confidence). High levels of bioenergy crop production can result in increased N2O emissions due to fertiliser use. The Integrated Assessment Models that produce these
       scenarios mostly neglect the biophysical effects of land-use on global and regional warming.

{2.5, 2.6.2}

Large-scale

           implementation of mitigation response options that limit warming to 1.5 or 2°C would require conversion of
           large areas of land for afforestation/reforestation and bioenergy crops, which could lead to short-term
           carbon losses (high confidence).The change of
       global forest area in mitigation pathways ranges from about –0.2 to +7.2 Mkm2between
       2010 and 2100 (median values across a range of models and scenarios: RCP4.5, RCP2.6, RCP1.9), and the land
       demand for bioenergy crops ranges from about 3.2 to 6.6 Mkm2in 2100 (high
           confidence). Large-scale land-based CDR is associated with multiple feasibility and sustainability
       constraints. In high carbon lands such as forests and peatlands, the carbon benefits of land protection are
       greater in the short-term than converting land to bioenergy crops for BECCS, which can take several harvest
       cycles to ‘pay-back’ the carbon emitted during conversion (carbon-debt), from decades to over a century
       (medium confidence). (Figure TS.5) {2.6.2, Chapters 6, 7}

It is

           possible to achieve climate change targets with low need for land-demanding CDR such as BECCS, but such
           scenarios rely more on rapidly reduced emissions or CDR from forests, agriculture and other sectors.Terrestrial CDR has the technical potential to balance emissions that are difficult to eliminate with
       current technologies (including food production). Scenarios that achieve climate change targets with less need
       for terrestrial CDR rely on agricultural demand-side changes (diet change, waste reduction), and changes in
       agricultural production such as agricultural intensification. Such pathways that minimise land use for bioenergy
       and BECCS are characterised by rapid and early reduction of GHG emissions in all sectors, as well as earlier CDR
       in through afforestation. In contrast, delayed mitigation action would increase reliance on land-based CDR
       (high confidence). {2.6.2}

TS.3

       Desertification

[Image: image]

TS

Desertification is land degradation in arid, semi-arid, and dry sub-humid areas, collectively

known as drylands, resulting from many factors, including human activities and climatic variations. The
range and intensity of desertification have increased in some dryland areas over the past several decades
(high confidence).Drylands currently cover about
46.2% (±0.8%) of the global land area and are home to 3 billion people. The multiplicity and complexity of the
processes of desertification make its quantification difficult. Desertification hotspots, as identified by a
decline in vegetation productivity between the 1980s and 2000s, extended to about 9.2% of drylands (±0.5%),
affecting about 500 (±120) million people in 2015. The highest numbers of people affected are in South and East
Asia, the circum Sahara region including North Africa and the Middle East including the Arabian Peninsula
(low confidence). Other dryland regions have also experienced desertification. Desertification has
already reduced agricultural productivity and incomes (high confidence) and contributed to the loss of
biodiversity in some dryland regions (medium confidence). In many dryland areas, spread of invasive
plants has led to losses in ecosystem services (high confidence), while over-extraction is leading to
groundwater depletion (high confidence). Unsustainable land management, particularly when coupled with
droughts, has contributed to higher dust-storm activity, reducing human well-being in drylands and beyond
(high confidence). Dust storms were associated with global cardiopulmonary mortality of about 402,000
people in 2005. Higher intensity of sand storms and sand dune movements are causing disruption and damage to
transportation and solar and wind energy harvesting infrastructures (high confidence). (Figure TS.6)
{3.1.1, 3.1.4, 3.2.1, 3.3.1, 3.4.1, 3.4.2, 3.4.2, 3.7.3, 3.7.4}

Attribution of

       desertification to climate variability and change, and to human activities, varies in space and time (high
           confidence).Climate variability and anthropogenic climate change, particularly through
           increases in both land surface air temperature and evapotranspiration, and decreases in precipitation, are
           likely to have played a role, in interaction with human activities, in causing desertification in some
           dryland areas. The major human drivers of desertification interacting with climate change are expansion of
           croplands, unsustainable land management practices and increased pressure on land from population and income
           growth. Poverty is limiting both capacities to adapt to climate change and availability of financial
           resources to invest in sustainable land management (SLM) (high
           confidence). {3.1.4, 3.2.2, 3.4.2}

Climate

           change will exacerbate several desertification processes (medium
           confidence).Although CO2fertilisation effect is
       enhancing vegetation productivity in drylands (high confidence), decreases in water availability have a
       larger effect than CO2fertilisation in many dryland areas. There ishigh confidencethat aridity will increase in some places, but no evidence for a projected global trend in dryland aridity
       (medium confidence). The area at risk of salinisation is projected to increase in the future (limited
           evidence,high agreement). Future climate change is projected to increase the potential for water
       driven soil erosion in many dryland areas (medium

confidence), leading to soil organic carbon decline in some dryland areas. {3.1.1, 3.2.2, 3.5.1,

           3.5.2, 3.7.1, 3.7.3}

Risks from

       desertification are projected to increase due to climate change (high confidence).Under
           shared socio-economic pathway SSP2 (‘Middle of the Road’) at 1.5°C, 2°C and 3°C of global warming, the
           number of dryland population exposed (vulnerable) to various impacts related to water, energy and land
           sectors (e.g. water stress, drought intensity, habitat degradation) is projected to reach 951 (178) million,
           1152 (220) million and 1285 (277) million, respectively. While at global warming of 2°C, under SSP1
           (‘Sustainability’), the exposed (vulnerable) dryland population is 974

(35) million, and under SSP3

       (‘Fragmented World’) it is 1267 (522) million. Around half of the vulnerable population is in South Asia,
       followed by Central Asia, West Africa and East Asia. {2.2, 3.1.1, 3.2.2, 3.5.1, 3.5.2, 7.2.2}

Desertification and climate change, both individually and in combination, will reduce the

           provision of dryland ecosystem services and lower ecosystem health, including losses in biodiversity
           (high confidence).Desertification and changing
       climate are projected to cause reductions in crop and livestock productivity (high confidence), modify
       the composition of plant species and reduce biological diversity across drylands (medium confidence).
       Rising CO2levels will favour more rapid expansion of some invasive plant species in
       some regions. A reduction in the quality and quantity of resources available to herbivores can have knock-on
       consequences for predators, which can potentially lead to disruptive ecological cascades (limited evidence,
           low agreement). Projected increases in temperature and the severity of drought events across some
       dryland areas can increase chances of wildfire occurrence (medium confidence). {3.1.4, 3.4.1, 3.5.2,
       3.7.3}

Increasing

           human pressures on land, combined with climate change, will reduce the resilience of dryland populations and
           constrain their adaptive capacities (medium confidence).The combination of pressures coming from climate variability, anthropogenic climate change and
       desertification will contribute to poverty, food insecurity, and increased disease burden (high
           confidence), as well as potentially to conflicts (low confidence). Although strong impacts of
       climate change on migration in dryland areas are disputed (medium evidence, low agreement), in some
       places, desertification under changing climate can provide an added incentive to migrate (medium
           confidence). Women will be impacted more than men by environmental degradation, particularly in those
       areas with higher dependence on agricultural livelihoods (medium evidence, high agreement). {3.4.2,
       3.6.2}

Desertification

       exacerbates climate change through several mechanisms such as changes in vegetation cover, sand and dust
       aerosols and greenhouse gas fluxes (high confidence). The extent of areas in which dryness (rather than
       temperature) controls CO2exchange has increased by 6% between 1948 and 2012, and is
       projected to increase by at least another 8% by 2050 if the expansion continues at the same rate. In these
       areas, net carbon uptake is about 27% lower than in other areas (low confidence).Desertification also tends to increase

[Image: image]

TS

Figure TS.6 |

       Geographical distribution of drylands, delimited based on the aridity index (AI).The
           classification of AI is: Humid AI > 0.65, Dry sub-humid

0.50 < AI ≤ 0.65, Semi-arid 0.20 <

       AI ≤ 0.50, Arid 0.05 < AI ≤ 0.20, Hyper-arid AI < 0.05. Data: TerraClimate precipitation and potential
       evapotranspiration (1980–2015) (Abatzoglou et al. 2018).

albedo,

       decreasing the energy available at the surface and associated surface temperatures, producing a negative
       feedback on climate change (high confidence). Through its effect on vegetation and soils, desertification
       changes the absorption and release of associated greenhouse gases (GHGs). Vegetation loss and drying of surface
       cover due to desertification increases the frequency of dust storms (high confidence). Arid ecosystems
       could be an important global carbon sink, depending on soil water availability (medium evidence, high
           agreement). {3.3.3, 3.4.1, 3.5.2}

Site and

           regionally-specific technological solutions, based both on new scientific innovations and indigenous and
           local knowledge (ILK), are available to avoid, reduce and reverse desertification, simultaneously
           contributing to climate change mitigation and adaptation (high
           confidence).SLM practices in drylands increase agricultural productivity
       and contribute to climate change adaptation with mitigation co-benefits (high confidence). Integrated
       crop, soil and water management measures can be employed to reduce soil degradation and increase the resilience
       of agricultural production systems to the impacts of climate change (high confidence). These measures
       include crop diversification and adoption of drought-resilient econogically appropriate plants, reduced tillage,
       adoption of improved irrigation techniques (e.g. drip irrigation) and moisture conservation methods (e.g.
       rainwater harvesting using indigenous and local practices), and maintaining vegetation and mulch cover.
       Conservation agriculture increases the capacity of agricultural households to adapt to climate change (high
           confidence) and can lead to increases in soil organic carbon over time, with quantitative estimates of
       the rates of carbon sequestration in drylands following changes in agricultural practices ranging between

0.04 and 0.4 t ha–1(medium confidence). Rangeland management systems based on sustainable grazing

       and re-vegetation increase rangeland productivity and the flow of ecosystem services (high confidence).
       The combined use of salt-tolerant crops, improved irrigation practices, chemical remediation measures and
       appropriate

mulch and

       compost is effective in reducing the impact of secondary salinisation (medium confidence). Application of
       sand dune stabilisation techniques contributes to reducing sand and dust storms (high confidence).
       Agroforestry practices and shelterbelts help reduce soil erosion and sequester carbon. Afforestation programmes
       aimed at creating windbreaks in the form of ‘green walls’ and ‘green dams’ can help stabilise and reduce dust
       storms, avert wind erosion, and serve as carbon sinks, particularly when done with locally adapted native and
       other climate resilient tree species (high confidence).

{3.4.2, 3.6.1, 3.7.2}

Investments

           into SLM, land restoration and rehabilitation in dryland areas have positive economic returns (high confidence).Each USD invested into land restoration can
       have social returns of about 3–6 USD over a 30-year period. Most SLM practices can become financially profitable
       within 3 to 10 years (medium evidence, high agreement). Despite their benefits in addressing
       desertification, mitigating and adapting to climate change, and increasing food and economic security, many SLM
       practices are not widely adopted due to insecure land tenure, lack of access to credit and agricultural advisory
       services, and insufficient incentives for private land-users (robust evidence, high agreement). {3.6.3}

Indigenous

           and local knowledge often contributes to enhancing resilience against climate change and combating
           desertification (medium confidence).Dryland
       populations have developed traditional agroecological practices which are well adapted to resource-sparse
       dryland environments. However, there isrobust evidencedocumenting losses of traditional agroecological
       knowledge. Traditional agroecological practices are also increasingly unable to cope with growing demand for
       food. Combined use of ILK and new SLM technologies can contribute to raising the resilience to the challenges of
       climate change and desertification (high confidence). {3.1.3, 3.6.1, 3.6.2}

[Image: image]

TS

Policy frameworks promoting the adoption of SLM solutions contribute to addressing

           desertification as well as mitigating and adapting to climate change, with co-benefits for poverty
           eradication and food security among dryland populations (high
           confidence). Implementation of Land Degradation Neutrality (LDN) policies allows
           populations to avoid, reduce and reverse desertification, thus contributing to climate change adaptation
           with mitigation co-benefits (high confidence).Strengthening land tenure security is a major factor contributing to the adoption of soil conservation
       measures in croplands (high confidence). On-farm and off-farm livelihood diversification strategies
       increase the resilience of rural households against desertification and extreme weather events, such as droughts
       (high confidence). Strengthening collective action is important for addressing causes and impacts of
       desertification, and for adapting to climate change (medium confidence). A greater emphasis on
       understanding gender-specific differences over land use and land management practices can help make land
       restoration projects more successful (medium confidence). Improved access to markets raises agricultural
       profitability and motivates investment into climate change adaptation and SLM (medium confidence).
       Payments for ecosystem services give additional incentives to land users to adopt SLM practices (medium
           confidence). Expanding access to rural advisory services increases the knowledge on SLM and facilitates
       their wider adoption (medium confidence). Developing, enabling and promoting access to cleaner energy
       sources and technologies can contribute to reducing desertification and mitigating climate change through
       decreasing the use of fuelwood and crop residues for energy (medium confidence). Policy responses to
       droughts based on proactive drought preparedness and drought risk mitigation are more efficient in limiting
       drought-caused damages than reactive drought relief efforts (high confidence). {3.4.2, 3.6.2, 3.6.3,
       Cross-

Chapter Box 5 in Chapter 3}

The knowledge on limits of adaptation to the combined effects of climate change and

           desertification is insufficient. However, the potential for residual risks and maladaptive outcomes is high
           (high confidence).Empirical evidence on the limits
       to adaptation in dryland areas is limited. Potential limits to adaptation include losses of land productivity
       due to irreversible forms of desertification. Residual risks can emerge from the inability of SLM measures to
       fully compensate for yield losses due to climate change impacts. They also arise from foregone reductions in
       ecosystem services due to soil fertility loss even when applying SLM measures could revert land to initial
       productivity after some time. Some activities favouring agricultural intensification in dryland areas can become
       maladaptive due to their negative impacts on the environment (medium confidence) Even when solutions are
       available, social, economic and institutional constraints could pose barriers to their implementation (medium
           confidence) {3.6.4}.

Improving

           capacities, providing higher access to climate services, including local-level early warning systems, and
           expanding the use of remote sensing technologies are high-return investments for enabling effective
           adaptation and mitigation responses that help address desertification (high
           confidence).Reliable and timely climate services, relevant to
       desertification, can aid the development of appropriate adaptation and mitigation options reducing, the impact
       of desertification on human and natural systems (high confidence), with quantitative estimates showing
       that every USD invested in strengthening hydro-meteorological and early warning services in developing countries
       can yield between 4 and 35 USD (low confidence). Knowledge and flow of knowledge on desertification is
       currently fragmented. Improved knowledge and data exchange and sharing will increase the effectiveness of
       efforts to achieve LDN (high confidence). Expanded use of remotely sensed information for data collection
       helps in measuring progress towards achieving LDN (low evidence, high agreement). {3.2.1, 3.6.2, 3.6.3,
       Cross-Chapter Box 5 in Chapter 3}

TS.4 Land

       degradation

Land degradation affects people and ecosystems throughout the planet and is both affected by

           climate change and contributes to it.In this report, land degradation is defined as anegative
           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.Forest degradation is land degradation that
       occurs in forest land. Deforestation is the conversion of forest to non-forest land and can result in land
       degradation. {4.1.3}

Land degradation

       adversely affects people’s livelihoods (very high confidence) and occurs over a quarter of the Earth’s
       ice-free land area (medium confidence). The majority of the

1.3 to 3.2

           billion affected people (low confidence) are living in
           poverty in developing countries (medium confidence).Land-use changes and unsustainable land management are direct human causes of land degradation (very
           high confidence), with agriculture being a dominant sector driving degradation (very high
           confidence). Soil loss from conventionally tilled land exceeds the rate of soil formation by >2
       orders of magnitude (medium confidence). Land degradation affects humans in multiple ways, interacting
       with social, political, cultural and economic aspects, including markets, technology, inequality and demographic
       change (very high confidence). Land degradation impacts extend beyond the land surface itself, affecting
       marine and freshwater systems, as well as people and ecosystems far away from the local sites of degradation
       (very high confidence). {4.1.6, 4.2.1, 4.2.3, 4.3, 4.6.1, 4.7, Table 4.1}

Climate

           change exacerbates the rate and magnitude of several ongoing land degradation processes and introduces new
           degradation patterns (high confidence).Human-induced global warming has already caused observed changes in two drivers of land degradation:
       increased frequency, intensity and/or amount of heavy precipitation (medium confidence); and increased
       heat stress (high confidence). In some areas sea level rise has exacerbated coastal erosion (medium
           confidence). Global warming beyond present day will further exacerbate ongoing land degradation
       processes through increasing floods (medium confidence), drought frequency and severity (medium
           confidence), intensified cyclones (medium confidence), and sea level rise (very high
           confidence), with outcomes being modulated by land management (very high confidence). Permafrost
       thawing due to warming (high confidence), and coastal erosion due to sea level rise and impacts of
       changing storm paths (low confidence), are examples of land degradation affecting places where it has not
       typically been a problem. Erosion of coastal areas because of sea level rise will increase worldwide (high
           confidence). In cyclone prone areas, the combination of sea level rise and more intense cyclones will
       cause land degradation with serious consequences for people and livelihoods (very high confidence).

{4.2.1, 4.2.2, 4.2.3, 4.4.1,

       4.4.2, 4.9.6, Table 4.1}

Land degradation

       and climate change, both individually and in combination, have profound implications for natural resource-based
       livelihood systems and societal groups (high

confidence).The number of people whose livelihood depends on

       degraded lands has been estimated to be about 1.5 billion worldwide (very low confidence). People in
       degraded areas who directly depend on natural resources for subsistence, food security and income, including
       women and youth with limited adaptation options, are especially vulnerable to land degradation and climate
       change (high confidence). Land degradation reduces land productivity and increases the workload of
       managing the land, affecting women disproportionally in some regions. Land degradation and climate change act as
       threat multipliers for already precarious livelihoods (very high confidence), leaving them highly
       sensitive to extreme climatic events, with consequences such as poverty and food insecurity (high
           confidence) and, in some cases, migration, conflict and loss of cultural heritage (low
           confidence). Changes in vegetation cover and distribution due to climate change increase the risk of
       land degradation in some areas (medium confidence). Climate change will have detrimental effects on
       livelihoods, habitats and infrastructure through increased rates of land degradation (high confidence)
       and from new degradation patterns (low evidence, high agreement).

[Image: image]

TS

{4.1.6, 4.2.1, 4.7}

Land

           degradation is a driver of climate change through emission of greenhouse gases (GHGs) and reduced rates of
           carbon uptake (very high confidence).Since 1990,
       globally the forest area has decreased by 3% (low confidence) with net decreases in the tropics and net
       increases outside the tropics (high confidence). Lower carbon density in re-growing forests compared, to
       carbon stocks before deforestation, results in net emissions from land-use change (very high confidence).
       Forest management that reduces carbon stocks of forest land also leads to emissions, but global estimates of
       these emissions are uncertain. Cropland soils have lost 20–60% of their organic carbon content prior to
       cultivation, and soils under conventional agriculture continue to be a source of GHGs (medium
           confidence). Of the land degradation processes, deforestation, increasing wildfires, degradation of peat
       soils, and permafrost thawing contribute most to climate change through the release of GHGs and the reduction in
       land carbon sinks following deforestation (high confidence). Agricultural practices also emit non-CO2GHGs from soils and these emissions are exacerbated by climate change (medium
           confidence). Conversion of primary to managed forests, illegal logging and unsustainable forest
       management result in GHG emissions (very high confidence) and can have additional physical effects on the
       regional climate including those arising from albedo shifts (medium confidence). These interactions call
       for more integrative climate impact assessments. {4.2.2, 4.3, 4.5.4, 4.6}

Large-scale

       implementation of dedicated biomass production for bioenergy increases competition for land with potentially
       serious consequences for food security and land degradation (high confidence). Increasing
           the extent and intensity of biomass production, for example, through fertiliser additions, irrigation or
           monoculture energy plantations, can result in local land degradation. Poorly implemented intensification of
           land management contributes to land degradation (e.g., salinisation from irrigation) and disrupted
           livelihoods (high confidence). In areas where afforestation
           and reforestation occur on previously degraded lands, opportunities exist to restore and rehabilitate lands
           with potentially significant

[Image: image]

TS

co-benefits

       (high confidence) that depend on whether restoration involves natural or plantation forests. The total
       area of degraded lands has been estimated at 10–60 Mkm2(very low confidence).
       The extent of degraded and marginal lands suitable for dedicated biomass production is highly uncertain and
       cannot be established without due consideration of current land use and land tenure. Increasing the area of
       dedicated energy crops can lead to land degradation elsewhere through indirect land-use change (medium
           confidence). Impacts of energy crops can be reduced through strategic integration with agricultural and
       forestry systems (high confidence) but the total quantity of biomass that can be produced through
       synergistic production systems is unknown. {4.1.6, 4.4.2, 4.5, 4.7.1, 4.8.1, 4.8.3,

4.8.4, 4.9.3}

Reducing unsustainable use of traditional biomass reduces land degradation and emissions of CO2while providing social and economic co-benefits (very high confidence).Traditional biomass in the form of fuelwood, charcoal and agricultural residues remains a primary

           source of energy for more than one-third of the global population, leading to unsustainable use of biomass
           resources and forest degradation and contributing around 2% of global GHG emissions (low confidence). Enhanced forest protection, improved forest and
           agricultural management, fuel-switching and adoption of efficient cooking and heating appliances can promote
           more sustainable biomass use and reduce land degradation, with co-benefits of reduced GHG emissions,
           improved human health, and reduced workload especially for women and youth (very
           high confidence). {4.1.6, 4.5.4}

[Image: image]

Land management options

Unsustainable land managementSustainable land management

[Image: image]

Restoration and rehabilitation

[Image: image]

Degraded land

Carbon stock

Climate changeDegradation

Sustainably managed

       land

[Image: image]

Forest Agriculture

Sink

Source

Net carbon uptake

+

–

[Image: image]

More degraded Less

       degraded

Forest Agriculture

[Image: image]

Figure TS.7 | Conceptual figure

       illustrating that climate change impacts interact with land management to determine sustainable or degraded
       outcome.Climate change can exacerbate many degradation processes (Table 4.1) and introduce
           novel ones (e.g., permafrost thawing or biome shifts), hence management needs to respond to climate impacts
           in order to avoid, reduce or reverse degradation. The types and intensity of human land-use and climate
           change impacts on lands affect their carbon stocks and their ability to operate as carbon sinks. In managed
           agricultural lands, degradation typically results in reductions of soil organic carbon stocks, which also
           adversely affects land productivity and carbon sinks. In forest land, reduction in biomass carbon stocks
           alone is not necessarily an indication of a reduction in carbon sinks. Sustainably managed forest landscapes
           can have a lower biomass carbon density but the younger forests can have a higher growth rate, and therefore
           contribute stronger carbon sinks, than older forests. Ranges of carbon sinks in forest and agricultural
           lands are overlapping. In some cases, climate change impacts may result in increased productivity and carbon
           stocks, at least in the short term.

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TS

Figure TS.8 | Interaction of human

       and climate drivers can exacerbate desertification and land degradation. Figure shows key desertification and
       land degradation issues, how they impact climate change, and the key drivers, with potential solutions.Climate change exacerbates the rate and magnitude of several ongoing land degradation and
           desertification processes. Human drivers of land degradation and desertification include expanding
           agriculture, agricultural practices and forest management. In turn, land degradation and desertification are
           also drivers of climate change through GHG emissions, reduced rates of carbon uptake, and reduced capacity
           of ecosystems to act as carbon sinks into the future. Impacts on climate change are either warming (in red)
           or cooling (in blue).

Land degradation can be avoided, reduced or reversed by implementing sustainable land

           management, restoration and rehabilitation practices that simultaneously provide many co-benefits, including
           adaptation to and mitigation of climate change (high confidence).Sustainable land management involves a comprehensive array of technologies and
       enabling conditions, which have proven to address land degradation at multiple landscape scales, from local
       farms (very high confidence) to entire watersheds (medium confidence). Sustainable forest
       management can prevent deforestation, maintain and enhance carbon sinks and can contribute towards GHG
       emissions-reduction goals. Sustainable forest management generates socio-economic benefits, and provides fibre,
       timber and biomass to meet society’s growing needs. While sustainable forest management sustains high carbon
       sinks, the conversion from primary forests to sustainably managed forests can result in carbon emission during
       the transition and loss of biodiversity (high confidence). Conversely, in areas of

degraded

       forests, sustainable forest management can increase carbon stocks and biodiversity (medium confidence).
       Carbon storage in long-lived wood products and reductions of emissions from use of wood products to substitute
       for emissions-intensive materials also contribute to mitigation objectives. (Figure TS.8) {4.8, 4.9, Table 4.2}

Lack of

           action to address land degradation will increase emissions and reduce carbon sinks and is inconsistent with
           the emissions reductions required to limit global warming to 1.5°C or 2°C. (high
           confidence).Better management of soils can offset 5–20% of current global
       anthropogenic GHG emissions (medium confidence). Measures to avoid, reduce and reverse land degradation
       are available but economic, political, institutional, legal and socio-cultural barriers, including lack of
       access to resources and knowledge, restrict their uptake (very high confidence). Proven measures that
       facilitate implementation of practices that avoid, reduce, or reverse land degradation include tenure reform,
       tax

[Image: image]

TS

incentives,

       payments for ecosystem services, participatory integrated land-use planning, farmer networks and rural advisory
       services. Delayed action increases the costs of addressing land degradation, and can lead to irreversible
       biophysical and human outcomes (high confidence). Early actions can generate both site-specific and
       immediate benefits to communities affected by land degradation, and contribute to long-term global benefits
       through climate change mitigation (high confidence). (Figure TS.7) {4.1.5, 4.1.6, 4.7.1, 4.8,

Table 4.2}

Even with adequate

       implementation of measures to avoid, reduce and reverse land degradation, there will be residual degradation in
       some situations (high confidence).Limits to adaptation are dynamic, site specific and
           determined through the interaction of biophysical changes with social and institutional conditions.
           Exceeding the limits of adaptation will trigger escalating losses or result in undesirable changes, such as
           forced migration, conflicts, or poverty. Examples of potential limits to adaptation due to
           climate-change-induced land degradation are coastal erosion (where land disappears, collapsing
           infrastructure and livelihoods due to thawing of permafrost), and extreme forms of soil erosion. {4.7,
           4.8.5, 4.8.6, 4.9.6, 4.9.7, 4.9.8}

Land

degradation is a serious and widespread problem, yet key uncertainties remain concerning its extent,
severity, and linkages to climate change (very high confidence). Despite the difficulties of objectively measuring the extent and severity of land
degradation, given its complex and value-based characteristics, land degradation represents – along with climate
change – one of the biggest and most urgent challenges for humanity (very high confidence). The current
global extent, severity and rates of land degradation are not well quantified. There is no single method by
which land degradation can be measured objectively and consistently over large areas because it is such a
complex and value-laden concept (very high confidence). However, many existing scientific and locally
based approaches, including the use of ILK, can assess different aspects of land degradation or provide proxies.
Remote sensing, corroborated by other data, can generate geographically explicit and globally consistent data
that can be used as proxies over relevant time scales (several decades). Few studies have specifically addressed
the impacts of proposed land-based negative emission technologies on land degradation. Much research has tried
to understand how livelihoods and ecosystems are affected by a particular stressor – for example, drought, heat
stress, or waterlogging. Important knowledge gaps remain in understanding how plants, habitats and ecosystems
are affected by the cumulative and interacting impacts of several stressors, including potential new stressors
resulting from large-scale implementation of negative emission technologies. {4.10}

TS.5 Food security

The current food system (production, transport, processing, packaging, storage, retail, consumption, loss and

       waste) feeds the great majority of world population and supports the livelihoods of over 1 billion people.Since 1961, food supply per capita has increased more than 30%, accompanied by greater use of
           nitrogen fertilisers (increase of about 800%) and water resources for irrigation (increase of more than
           100%). However, an estimated 821 million people are currently undernourished, 151 million children under
           five are stunted, 613 million women and girls aged 15 to 49 suffer from iron deficiency, and 2 billion
           adults are overweight or obese. The food system is under pressure from non-climate stressors (e.g.,
           population and income growth, demand for animal-sourced products), and from climate change. These climate
           and non-climate stresses are impacting the four pillars of food security (availability, access, utilisation,
           and stability). (Figure TS.9) {5.1.1, 5.1.2}

Observed climate

       change is already affecting food security through increasing temperatures, changing precipitation patterns, and
       greater frequency of some extreme events (high confidence).Studies that separate out climate change from other factors affecting crop yields have shown that
           yields of some crops (e.g., maize and wheat) in many lower-latitude regions have been affected negatively by
           observed climate changes, while in many higher-latitude regions, yields of some crops (e.g., maize, wheat,
           and sugar beets) have been affected positively over recent decades. Warming compounded by drying has caused
           large negative effects on yields in parts of the Mediterranean. Based on ILK, climate change is affecting
           food security in drylands, particularly those in Africa, and high mountain regions of Asia and South
           America. (Figure TS.10) {5.2.2}

Food

           security will be increasingly affected by projected future climate change (high
           confidence).Across SSPs 1, 2, and 3, global crop and economic models
       projected a 1–29% cereal price increase in 2050 due to climate change (RCP 6.0), which would impact consumers
       globally through higher food prices; regional effects will vary (high confidence). Low-income consumers
       are particularly at risk, with models projecting increases of 1–183 million additional people at risk of hunger
       across the SSPs compared to a no climate change scenario (high confidence). While increased CO2is projected to be beneficial for crop productivity at lower temperature increases, it
       is projected to lower nutritional quality (high confidence) (e.g., wheat grown at 546–586 ppm CO2has 5.9–12.7% less protein,

3.7–6.5% less zinc, and

       5.2–7.5% less iron). Distributions of pests and diseases will change, affecting production negatively in many
       regions (high confidence). Given increasing extreme events and interconnectedness, risks of food system
       disruptions are growing (high confidence). {5.2.3, 5.2.4}

Vulnerability of

       pastoral systems to climate change is very high (high confidence).Pastoralism is
           practiced in more than 75% of countries by between 200 and 500 million people, including nomadic
           communities, transhumant herders, and agropastoralists. Impacts in pastoral systems in Africa include lower
           pasture and animal productivity, damaged reproductive function, and biodiversity loss. Pastoral system
           vulnerability is exacerbated by non-climate factors

[Image: image]

(a)

Maize yield

Rice yield

Wheat yield

[Image: image]

(b)

Crop production

Feed use

Animal production

Calories (1015kcal/yr)

5 15

10

4

Yield (t/ha)

35

2

1

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TS

0

1960 1970 1980 1990

       2000 2010

Year

1960 1970 1980 1990

       2000 2010

Year

[Image: image]

(c)

Captured fisheries

Aquaculture

[Image: image]

(d)

ermane t me dows and past res

le lan ermane t c o

5

Fisheries (107tonnes)

Land use (in billion

ha)

10

4

3

5

2

1

0

3.5

1950 1960 1970 1980

       1990 2000 2010

Year

[Image: image]

(e)

Total

Crop products

Animal products

3000

1960 1970 1980 1990 2000 2010

Year

Food Trade (1015kcal/yr)

3

2.5

2

1.5

1

0.5

0

2500

[Image: image]

(f)

er ls

n al Product Starchy Root

u ses

Required f o

Sugar & products Oilcrops & product Fruits &

       vegetables Other

Food supply

       (kcal/cap/day)

2000

1500

1000

500

0

1960 1970 1980 1990

       2000 2010

Year

[Image: image]

(g)

Male

Overweight

Female

Underweight

Obesity

30

Prevalence (%)

25

20

15

10

1960 1970 1980 1990

       2000 2010

Year

[Image: image]

(h)

tericmentai

anure left on pasture anure a plied

       to s ils anure management

S thetic fertilizers

Paddy rice cultivation

Crop resi ues

Burning − rop residues Burning − vanna Cultivati n of

       organic soils

Emissions (Gt CO2eq/yr)

5

4

3

2

5

0

1975 1985 1995 2005 2015

Year

1

0

1960 1970 1980 1990

       2000 2010

Year

Figure TS.9 | Global

       trends in (a)yields of maize, rice, and wheat (FAOSTAT 2018) – the top three crops grown in
           the world;(b)production of crop and animal calories and use of crop calories as
           livestock feed (FAOSTAT 2018);(c)production from marine and aquaculture fisheries
           (FishStat 2019);(d)land used for agriculture (FAOSTAT 2018);(e)food trade in calories (FAOSTAT 2018);(f)food supply and required food
           (i.e., based on human energy requirements for medium physical activities) from 1961–2012 (FAOSTAT 2018; Hiç
           et al. 2016);(g)prevalence of overweight, obesity and underweight from 1975–2015
           (Abarca-Gómez et al. 2017); and(h)GHG emissions for the agriculture sector,
           excluding land-use change (FAOSTAT 2018). For figures (b) and (e), data provided in mass units were
           converted into calories using nutritive factors (FAO 2001b). Data on emissions due to burning of savanna and
           cultivation of organic soils is provided only after 1990 (FAOSTAT 2018).

(land tenure,

       sedentarisation, changes in traditional institutions, invasive species, lack of markets, and conflicts). {5.2.2}

[Image: image]

TS

Fruit and

       vegetable production, a key component of healthy diets, is also vulnerable to climate change (medium
           evidence, high agreement).Declines in yields and crop suitability are projected
           under higher temperatures, especially in tropical and semi-tropical regions. Heat stress reduces fruit set
           and speeds up development of annual vegetables, resulting in yield losses, impaired product quality, and
           increasing food loss and waste. Longer growing seasons enable a greater number of plantings to be cultivated
           and can contribute to greater annual yields. However, some fruits and vegetables need a period of cold
           accumulation to produce a viable harvest, and warmer winters may constitute a risk. {5.2.2}

Food security and

       climate change have strong gender and equity dimensions (high confidence).Worldwide,
           women play a key role in food security, although regional differences exist. Climate change impacts vary
           among diverse social groups depending on age, ethnicity, gender, wealth, and class. Climate extremes have
           immediate and long-term impacts on livelihoods of poor and vulnerable communities, contributing to greater
           risks of food insecurity that can be a stress multiplier for internal and external migration (medium confidence). Empowering women and rights-based approaches to
           decision-making can create synergies among household food security, adaptation, and mitigation. {5.2.6,
           5.6.4}

Many

           practices can be optimised and scaled up to advance adaptation throughout the food system (high confidence).Supply-side options include increased soil
       organic matter and erosion control, improved cropland, livestock, grazing land management, and genetic
       improvements for tolerance to heat and drought. Diversification in the food system (e.g., implementation of
       integrated production systems, broad-based genetic resources, and heterogeneous diets) is a key strategy to
       reduce risks (medium confidence). Demand-side adaptation, such as adoption of healthy and sustainable
       diets, in conjunction with reduction in food loss and waste, can contribute to adaptation through reduction in
       additional land area needed for food production and associated food system vulnerabilities. ILK can contribute
       to enhancing food system resilience (high confidence). {5.3, 5.6.3 Cross-Chapter Box 6 in Chapter 5}.

About 21–37%

           of total greenhouse gas (GHG) emissions are attributable to the food system. These are from agriculture and
           land use, storage, transport, packaging, processing, retail, and consumption (medium confidence).This estimate includes emissions of 9–14%
       from crop and livestock activities within the farm gate and 5–14% from land use and land-use change including
       deforestation and peatland degradation (high confidence); 5–10% is from supply chain activities
       (medium confidence). This estimate includes GHG emissions from food loss and waste. Within the food
       system, during the period 2007–2016, the major sources of emissions from the supply side were agricultural
       production, with crop and livestock activities within the farm gate generating respectively 142 ± 42 TgCH4yr–1(high confidence) and 8.0 ± 2.5 TgN2O yr–1(high confidence), and CO2emissions linked to relevant land-use change dynamics such as deforestation and peatland degradation, generating
       4.9 ± 2.5 GtCO2yr–1. Using 100-year GWP values (no

climate

       feedback) from the IPCC AR5, this implies that total GHG emissions from agriculture were 6.2 ± 1.4 GtCO2-eq yr–1, increasing to 11.1 ± 2.9 GtCO2-eq yr–1including relevant land use. Without intervention,
       these are likely to increase by about 30–40% by 2050, due to increasing demand based on population and income
       growth and dietary change (high confidence). {5.4}

Supply-side

       practices can contribute to climate change mitigation by reducing crop and livestock emissions, sequestering
       carbon in soils and biomass, and by decreasing emissions intensity within sustainable production systems
       (high confidence).Total technical mitigation potential from crop and livestock
           activities and agroforestry is estimated as

2.3–9.6 GtCO2-eq yr–1by 2050 (medium confidence). Options with large

       potential for GHG mitigation in cropping systems include soil carbon sequestration (at decreasing rates over
       time), reductions in N2O emissions from fertilisers, reductions in CH4emissions from paddy rice, and bridging of yield gaps. Options with large potential for
       mitigation in livestock systems include better grazing land management, with increased net primary production
       and soil carbon stocks, improved manure management, and higher-quality feed. Reductions in GHG emissions
       intensity (emissions per unit product) from livestock can support reductions in absolute emissions, provided
       appropriate governance to limit total production is implemented at the same time (medium confidence).
       {5.5.1}

Consumption

           of healthy and sustainable diets presents major opportunities for reducing GHG emissions from food systems
           and improving health outcomes (high confidence).
       Examples of healthy and sustainable diets are high in coarse grains, pulses, fruits and vegetables, and nuts and
       seeds; low in energy-intensive animal-sourced and discretionary foods (such as sugary beverages); and with a
       carbohydrate threshold. Total technical mitigation potential of dietary changes is estimated as 0.7–8.0
       GtCO2-eq yr–1by 2050 (medium confidence). This
       estimate includes reductions in emissions from livestock and soil carbon sequestration on spared land, but
       co-benefits with health are not taken into account. Mitigation potential of dietary change may be higher, but
       achievement of this potential at broad scales depends on consumer choices and dietary preferences that are
       guided by social, cultural, environmental, and traditional factors, as well as income growth. Meat analogues
       such as imitation meat (from plant products), cultured meat, and insects may help in the transition to more
       healthy and sustainable diets, although their carbon footprints and acceptability are uncertain. {5.5.2, 5.6.5}

Reduction of

           food loss and waste could lower GHG emissions and improve food security (medium
           confidence).Combined food loss and waste amount to 25–30% of total food
       produced (medium confidence). During 2010–2016, global food loss and waste equalled 8–10% of total
       anthropogenic GHG emissions (medium confidence); and cost about 1 trillion USD2012 per year (low
           confidence). Technical options for reduction of food loss and waste include improved harvesting
       techniques, on-farm storage, infrastructure, and packaging. Causes of food loss (e.g., lack of refrigeration)
       and waste (e.g., behaviour) differ substantially in developed and developing countries, as well as across
       regions (robust evidence, medium agreement). {5.5.2}

[Image: image]

TS

GGCMs with explicit N stress

[Image: image]

%

<-50[Image: image]>50

0

χ

[Image: image]

Figure TS.10 | AgMIP

           median yield changes (%) for RCP8.5(2070–2099 in comparison to 1980–2010 baseline) with CO2effects and explicit nitrogen stress over five GCMs[Image: image]four Global Gridded Crop Models
       (GGCMs) for rainfed maize, wheat, rice, and soy (20 ensemble members from EPIC, GEPIC, pDSSAT, and PEGASUS;
       except for rice which has 15). Grey areas indicate historical areas with little to no yield capacity. All models
       use a 0.5° grid, but there are differences in grid cells simulated to represent agricultural land. While some
       models simulated all land areas, others simulated only potential suitable cropland area according to evolving
       climatic conditions. Others utilised historical harvested areas in 2000 according to various data sources
       (Rosenzweig et al. 2014).

Agriculture and the food system are key to global climate change responses. Combining supply-side actions such

       as efficient production, transport, and processing with demand-side interventions such as modification of food
       choices, and reduction of food loss and waste, reduces GHG emissions and enhances food system resilience
       (high confidence).Such combined measures can enable the implementation of large-scale
           land-based adaptation and mitigation strategies without threatening food security from increased competition
           for land for food production and higher food prices. Without combined food system measures in farm
           management, supply chains, and demand, adverse effects would include increased numbers of malnourished
           people and impacts on smallholder farmers (medium evidence, high
           agreement). Just transitions are needed to address these effects. (Figure TS.11)
           {5.5, 5.6, 5.7}

For adaptation and mitigation throughout the food system, enabling conditions need to be created

           through policies, markets, institutions, and governance (high
           confidence).For adaptation, resilience to increasing extreme events can be
       accomplished through risk sharing and transfer mechanisms such as insurance markets and index-based weather
       insurance (high confidence). Public health policies to improve nutrition – such as school procurement,
       health insurance incentives, and awareness-raising campaigns – can potentially change demand, reduce healthcare
       costs, and contribute to lower GHG emissions (limited evidence, high agreement). Without inclusion of
       comprehensive food system responses in broader climate change policies, the mitigation and adaptation potentials
       assessed in Chapter 5 will not be realised and food security will be jeopardised (high confidence).
       {5.7.5}

Food system response

       options

Mitigation and

       adaptation potential

[Image: image]Very high[Image: image]High

[Image: image]

Limited None

Response options

Increased soil organic matter content

Change in crop variety Improved water management Adjustment of planting dates Precision fertiliser management

       Integrated pest management Counter season crop production

Biochar application

[Image: image]

TS

Improved crop

       management

[Image: image]

Agroforestry Changing monoculture to crop diversification

Mitigation Adaptation

Changes in cropping area, land rehabilitation

       (enclosures, afforestation) perennial farming

Improved livestock

       managment

Tillage and

       crop establishment Residue management Crop–livestock systems Silvopastural system New livestock breed Livestock
       fattening

Shifting to small ruminants or

       drought-resistant livestock or fish farming

[Image: image]

Feed and fodder banks Methane inhibitors Thermal stress control

[Image: image]

Seasonal feed

       supplementation Improved animal health and parasites control

Climate services

Early warning

       systems Planning and prediction at seasonal to intra-seasonal climate risk

Improved supply chain

Crop and

       livestock insurance Food storage infrastructures Shortening supply chains

Improved food

       transport and distribution Improved efficiency and sustainability of food processing, retail and agrifood
       industries

Improved energy efficiencies of

       agriculture

[Image: image]

Reduce food loss Urban and peri-urban agriculture Bioeconomy (e.g. energy from waste)

Demand management

Dietary changes

       Reduce food waste Packaging reductions

[Image: image]

New ways of

       selling (e.g. direct sales) Transparency of food chains and external costs

Figure TS.11 | Response options related

       to food system and their potential impacts on mitigation and adaptation.Many response options
           offer significant potential for both mitigation and adaptation.

TS.6 Interlinkages between desertification, land degradation, food security and GHG fluxes: Synergies,

       trade-offs and integrated response options

The land challenges, in the context of this report, are climate change mitigation, adaptation, desertification,

       land degradation, and food security.The chapter also discusses implications for Nature’s
           Contributions to People (NCP), including biodiversity and water, and sustainable development, by assessing
           intersections with the Sustainable Development Goals (SDGs). The chapter assesses response options that
           could be used to address these challenges. These response options were derived from the previous chapters
           and fall into three broad categories: land management, value chain, and risk management.

The land

           challenges faced today vary across regions; climate change will increase challenges in the future, while
           socio-economic development could either increase or decrease challenges (high
           confidence).Increases in biophysical impacts from climate change can worsen
       desertification, land degradation, and food insecurity (high confidence). Additional pressures from
       socio-economic development could further exacerbate these challenges; however, the effects are scenario
       dependent. Scenarios with increases in income and reduced pressures on land can lead to reductions in food
       insecurity; however, all assessed scenarios result in increases in water demand and water scarcity (medium
           confidence). {6.1}

The

           applicability and efficacy of response options are region and context specific; while many value chain and
           risk management options are potentially broadly applicable, many land management options are applicable on
           less than 50% of the ice-free land surface (high confidence).Response options are limited by land type, bioclimatic region, or local food system
       context (high confidence). Some response options produce adverse side effects only in certain regions or
       contexts; for example, response options that use freshwater may have no adverse side effects in regions where
       water is plentiful, but large adverse side effects in regions where water is scarce (high confidence).
       Response options with biophysical climate effects (e.g., afforestation, reforestation) may have different
       effects on local climate, depending on where they are implemented (medium confidence). Regions with more
       challenges have fewer response options available for implementation (medium confidence). {6.1, 6.2, 6.3,
       6.4}

Nine options

       deliver medium-to-large benefits for all five land challenges (high confidence).The
           options with medium-to-large benefits for all challenges are increased food productivity, improved cropland
           management, improved grazing land management, improved livestock management, agroforestry, forest
           management, increased soil organic carbon content, fire management and reduced post-harvest losses. A
           further two options, dietary change and reduced food waste, have no global estimates for adaptation but have
           medium-to-large benefits for all other challenges (high confidence). {6.3, 6.4}

Five options have large mitigation potential (>3 GtCO2e yr–1) without adverse impacts on the other challenges (high confidence).These are: increased food productivity; reduced deforestation and forest degradation; increased

           soil organic carbon content; fire management; and reduced post-harvest losses. Two further options with
           large mitigation potential, dietary change and reduced food waste, have no global estimates for adaptation
           but show no negative impacts across the other challenges. Five options: improved cropland management;
           improved grazing land managements; agroforestry; integrated water management; and forest management, have
           moderate mitigation potential, with no adverse impacts on the other challenges (high confidence). {6.3.6}

[Image: image]

TS

Sixteen

           response options have large adaptation potential (more than 25 million people benefit), without adverse side
           effects on other land challenges (high confidence).These are increased food productivity, improved cropland management, agroforestry, agricultural
       diversification, forest management, increased soil organic carbon content, reduced landslides and natural
       hazards, restoration and reduced conversion of coastal wetlands, reduced post-harvest losses, sustainable
       sourcing, management of supply chains, improved food processing and retailing, improved energy use in food
       systems, livelihood diversification, use of local seeds, and disaster risk management (high confidence).
       Some options (such as enhanced urban food systems or management of urban sprawl) may not provide large global
       benefits but may have significant positive local effects without adverse effects (high confidence).
       (Figure TS.13)

{6.3, 6.4}

Seventeen of 40

       options deliver co-benefits or no adverse side effects for the full range of NCPs and SDGs; only three options
       (afforestation, BECCS), and some types of risk sharing instruments, such as insurance) have potentially adverse
       side effects for five or more NCPs or SDGs (medium confidence).The 17 options with
           co-benefits and no adverse side effects include most agriculture- and soil-based land management options,
           many ecosystem-based land management options, forest management, reduced post-harvest losses, sustainable
           sourcing, improved energy use in food systems, and livelihood diversification (medium confidence). Some of the synergies between response options and
           SDGs include positive poverty eradication impacts from activities like improved water management or improved
           management of supply chains. Examples of synergies between response options and NCPs include positive
           impacts on habitat maintenance from activities like invasive species management and agricultural
           diversification. However, many of these synergies are not automatic, and are dependent on well-implemented
           activities requiring institutional and enabling conditions for success. {6.4}

Most response

       options can be applied without competing for available land; however, seven options result in competition for
       land (medium confidence).A large number of response options do not require dedicated
           land, including several land management options, all value chain options, and all risk management options.
           Four options could greatly increase competition for land if applied at scale: afforestation, reforestation,
           and land used to provide feedstock for BECCS or biochar, with three further options: reduced
           grassland

[Image: image]

TS

conversion to

       croplands, restoration and reduced conversion of peatlands and restoration, and reduced conversion of coastal
       wetlands having smaller or variable impacts on competition for land. Other options such as reduced deforestation
       and forest degradation, restrict land conversion for other options and uses. Expansion of the current area of
       managed land into natural ecosystems could have negative consequences for other land challenges, lead to the
       loss of biodiversity, and adversely affect a range of NCPs (high confidence).

{6.3.6, 6.4}

Some options, such

       as bioenergy and BECCS, are scale dependent. The climate change mitigation potential for bioenergy and BECCS is
       large (up to 11 GtCO2yr–1); however, the effects of bioenergy
       production on land degradation, food insecurity, water scarcity, GHG emissions, and other environmental goals
       are scale- and context-specific (high confidence).These effects depend on the scale of
           deployment, initial land use, land type, bioenergy feedstock, initial carbon stocks, climatic region and
           management regime (high confidence). Large areas of
           monoculture bioenergy crops that displace other land uses can result in land competition, with adverse
           effects for food production, food consumption, and thus food security, as well as adverse effects for land
           degradation, biodiversity, and water scarcity (medium confidence). However, integration of bioenergy into sustainably managed agricultural landscapes can
           ameliorate these challenges (medium confidence). {6.2, 6.3,
           6.4, Cross-Chapter Box 7

in Chapter 6}

Response

           options are interlinked; some options (e.g., land sparing and sustainable land management options) can
           enhance the co-benefits or increase the potential for other options (medium
           confidence). Some response options can be more effective when applied
       together (medium confidence); for example, dietary change and waste reduction expand the potential to
       apply other options by freeing as much as 5.8 Mkm2(0.8–2.4 Mkm2for dietary change; about 2 Mkm2for reduced post-harvest losses, and 1.4
       Mkm2for reduced food waste) of land (low confidence). Integrated water
       management and increased soil organic carbon can increase food productivity in some circumstances. {6.4}

Other

           response options (e.g., options that require land) may conflict; as a result, the potentials for response
           options are not all additive, and a total potential from the land is currently unknown (high confidence).Combining some sets of options (e.g., those
       that compete for land) may mean that maximum potentials cannot be realised, for example, reforestation,
       afforestation, and bioenergy and BECCS, all compete for the same finite land resource so the combined potential
       is much lower than the sum of potentials of each individual option, calculated in the absence of alternative
       uses of the land (high confidence). Given the interlinkages among response options and that mitigation
       potentials for individual options assume that they are applied to all suitable land, the total mitigation
       potential is much lower than the sum of the mitigation potential of the individual response options (high
           confidence). (Figure TS.12) {6.4}

The feasibility of

       response options, including those with multiple co-benefits, is limited due to economic, technological,

institutional, socio-cultural, environmental and geophysical barriers (high confidence).A number of response options (e.g., most

       agriculture-based land management options, forest management, reforestation and restoration) have already been
       implemented widely to date (high confidence). There isrobust evidencethat many other response
       options can deliver co-benefits across the range of land challenges, yet these are not being implemented. This
       limited application is evidence that multiple barriers to implementation of response options exist (high
           confidence). {6.3, 6.4}

Coordinated

           action is required across a range of actors, including business, producers, consumers, land managers,
           indigenous peoples and local communities and policymakers to create enabling conditions for adoption of
           response options (high confidence). The response
       options assessed face a variety of barriers to implementation (economic, technological, institutional,
       socio-cultural, environmental and geophysical) that require action across multiple actors to overcome (high
           confidence). There are a variety of response options available at different scales that could form
       portfolios of measures applied by different stakeholders – from farm to international scales. For example,
       agricultural diversification and use of local seeds by smallholders can be particularly useful poverty
       eradication and biodiversity conservation measures, but are only successful when higher scales, such as national
       and international markets and supply chains, also value these goods in trade regimes, and consumers see the
       benefits of purchasing these goods. However, the land and food sectors face particular challenges of
       institutional fragmentation, and often suffer from a lack of engagement between stakeholders at different scales
       (medium confidence). {6.3, 6.4}

Delayed

           action will result in an increased need for response to land challenges and a decreased potential for
           land-based response options due to climate change and other pressures (high
           confidence).For example, failure to mitigate climate change will increase
       requirements for adaptation and may reduce the efficacy of future land-based mitigation options (high
           confidence). The potential for some land management options decreases as climate change increases; for
       example, climate alters the sink capacity for soil and vegetation carbon sequestration, reducing the potential
       for increased soil organic carbon (high confidence). Other options (e.g., reduced deforestation and
       forest degradation) prevent further detrimental effects to the land surface; delaying these options could lead
       to increased deforestation, conversion, or degradation, serving as increased sources of GHGs and having
       concomitant negative impacts on NCPs (medium confidence). Carbon dioxide removal (CDR) options – such as
       reforestation, afforestation, bioenergy and BECCS – are used to compensate for unavoidable emissions in other
       sectors; delayed action will result in larger and more rapid deployment later (high confidence). Some
       response options will not be possible if action is delayed too long; for example, peatland restoration might not
       be possible after certain thresholds of degradation have been exceeded, meaning that peatlands could not be
       restored in certain locations (medium confidence) {6.2, 6.3, 6.4}.

Early action,

       however, has challenges including technological readiness, upscaling, and institutional barriers (high
           confidence).Some of the response options have technological

barriers that

       may limit their wide-scale application in the near term (high confidence). Some response options, for
       example, BECCS, have only been implemented at small-scale demonstration facilities; challenges exist with
       upscaling these options to the levels discussed in Chapter 6 (medium confidence). Economic and
       institutional barriers, including governance, financial incentives and financial resources, limit the near-term
       adoption of many response options, and ‘policy lags’, by which implementation is delayed by the slowness of the
       policy implementation cycle, are significant across many options (medium confidence). Even some actions
       that initially seemed like ‘easy wins’ have been challenging to implement, with stalled policies for reducing
       emissions from deforestation and forest degradation and fostering conservation (REDD+) providing clear examples
       of how response options need sufficient funding, institutional support, local buy-in, and clear metrics for
       success, among other necessary enabling conditions. {6.2, 6.4}

Some response

       options reduce the consequences of land challenges, but do not address underlying drivers (high
           confidence).For example, management of urban sprawl can help reduce the environmental
           impact of urban systems; however, such

management

       does not address the socio-economic and demographic changes driving the expansion of urban areas. By failing to
       address the underlying drivers, there is a potential for the challenge to re-emerge in the future (high
           confidence). {6.4}

[Image: image]

TS

Many

           response options have been practised in many regions for many years; however, there is limited knowledge of
           the efficacy and broader implications of other response options (high
           confidence).For the response options with a large
       evidence base and ample experience, further implementation and upscaling would carry little risk of adverse side
       effects (high confidence). However, for other options, the risks are larger as the knowledge gaps are
       greater; for example, uncertainty in the economic and social aspects of many land response options hampers the
       ability to predict their effects (medium confidence). Furthermore, Integrated Assessment Models, like
       those used to develop the pathways in the IPCC Special Report on Global Warming of 1.5°C (SR15), omit many of
       these response options and do not assess implications for all land challenges (high confidence). {6.4}

[Image: image]Croplands[Image: image]Semi-natural forests

[Image: image]Rangelands[Image: image]Wild forests and sparse

           trees[Image: image]Villages

[Image: image]Wetlands and organic soils[Image: image]Dense settlements

Fire management Increased soil organic carbon

Agroforestry

       Improved grazing management Livestock management

Reduced deforestation and degradation

Increased food productivity Forest management and restoration

Reforestation

       Cropland management

Reduced grassland conversion to cropland

Bioenergy and BECCS Restoration and reduced conversion of peatland Restoration and reduced conversion of coastal

       wetland

[Image: image][Image: image]

0 10 20 30 40 50 60 70 80

Potential deployment

       (% global ice-free land area)

Figure TS.12 | Potential deployment

       area of land management responses (see Table 6.1) across land-use types (or anthromes, see Section 6.3), when
       selecting responses having only co-benefits for local challenges and for climate change mitigation and no large
       adverse side effects on global food security.See Figure 6.2 for the criteria used to map
           challenges considered (desertification, land degradation, climate change adaptation, chronic
           undernourishment, biodiversity, groundwater stress and water quality). No response option was identified for
           barren lands.

Potential global contribution of

       response options to mitigation, adaptation, combating desertification and land degradation, and enhancing food
       security

Panel Ashows response options that can be implemented without or with limited competition for

           land, including some that have the potential to reduce the demand for land. Co-benefits and adverse side
           effects are shown quantitatively based on the high end of the range of potentials assessed. Magnitudes of
           contributions are categorised using thresholds for positive or negative impacts. Letters within the cells
           indicate confidence in the magnitude of the impact relative to the thresholds used (see legend). Confidence
           in the direction of change is generally higher.

[Image: image]

TS

[Image: image]

Forests

Agriculture

Response options based

       on land management

Mitigation

Adaptation

Desertification

Land Degradation

Food Security Cost

[Image: image]

Demand

Other ecosystems

Soils

Resp

[Image: image]

L

Risk

Supply

Resp

[Image: image]

Risk sharing instruments

[Image: image]

Confidence level

Indicates confidence in

       the estimate of magnitude category.

HHigh confidence

MMedium

           confidence

LLow

           confidence

[Image: image]

Key for criteria used to define magnitude of impact of

       each integrated response option

Mitigation Adaptation

       Desertification Land Degradation Food Security

Gt CO2-eq yr–1Million people Million km2 Million km2 Million people

Large Moderate Small Negligible Small Moderate

Large

Variable: Can be positive or

           negative

no data

not applicable

na

Positive

Options shown are

       those for which data are available to assess global potential for three or more land challenges. The magnitudes
       are assessed independently for each option and are not additive.

[Image: image]

Cost range

See technical caption

       for cost ranges in US$ tCO2e-1 or US$ ha-1.

High

       cost Medium cost Low cost

no data

Negative

[Image: image]

Figure TS.13 | Potential global

       contribution of response options to mitigation, adaptation, combating desertification and land degradation, and
       enhancing food security (Panel A).

Potential global contribution of

       response options to mitigation, adaptation, combating desertification and land degradation, and enhancing food
       security

[Image: image]

TS

Panel Bshows response options that rely on additional land-use change and could have implications across

       three or more land challenges under different implementation contexts. For each option, the first row (high
       level implementation) shows a quantitative assessment (as in Panel A) of implications for global implementation
       at scales delivering CO2 removals of more than 3 GtCO2 yr–1using the magnitude
       thresholds shown in Panel A. The red hatched cells indicate an increasing pressure but unquantified impact. For
       each option, the second row (best practice implementation) shows qualitative estimates of impact if implemented
       using best practices in appropriately managed landscape systems that allow for efficient and sustainable
       resource use and supported by appropriate governance mechanisms. In these qualitative assessments, green
       indicates a positive impact, grey indicates a neutral interaction.

Bioenergy and BECCS

Mitigation

       Adaptation Desertification Land degradation Food security Cost

[Image: image]

H

[Image: image]

L[Image: image]L[Image: image]

High level:Impacts on adaptation, desertification, land degradation and food security are maximum potential impacts,

       assuming carbon dioxide removal by BECCS at a scale of 11.3 GtCO2 yr–1in 2050, and
       noting that bioenergy without CCS can also achieve emissions reductions of up to several GtCO2 yr–1when it is a low carbon energy source {2.6.1; 6.3.1}. Studies linking bioenergy to
       food security estimate an increase in the population at risk of hunger to up to 150 million people at this level
       of implementation {6.3.5}. The red hatched cells for desertification and land degradation indicate that while up
       to 15 million km2of additional land is required in 2100 in 2°C scenarios which will
       increase pressure for desertification and land degradation, the actual area affected by this additional pressure
       is not easily quantified

{6.3.3; 6.3.4}.

Mitigation Adaptation Desertification Land degradation Food security

[Image: image]

Best practice:The sign and magnitude of the effects of bioenergy and BECCS depends on the scale of deployment,

           the type of bioenergy feedstock, which other response options are included, and where bioenergy is grown
           (including prior land use and indirect land use change emissions). For example, limiting bioenergy
           production to marginal lands or abandoned cropland would have negligible effects on biodiversity, food
           security, and potentially co-benefits for land degradation; however, the benefits for mitigation could also
           be smaller. {Table 6.58}

Reforestation and forest restoration

[Image: image]

M

[Image: image]

M

[Image: image]

Mitigation

       Adaptation Desertification Land degradation Food security Cost

MM

High level:Impacts on adaptation, desertification, land degradation and food security are maximum potential impacts

       assuming implementation of reforestation and forest restoration (partly overlapping with afforestation) at a
       scale of 10.1 GtCO2 yr–1removal {6.3.1}. Large-scale afforestation could cause
       increases in food prices of 80% by 2050, and more general mitigation measures in the AFOLU sector can translate
       into a rise in undernourishment of 80–300 million people; the impact of reforestation is lower {6.3.5}.

Mitigation Adaptation Desertification Land degradation Food security

[Image: image]

Best practice:There are co-benefits of reforestation and forest restoration in previously forested areas,

           assuming small scale deployment using native species and involving local stakeholders to provide a safety
           net for food security. Examples of sustainable implementation include, but are not limited to, reducing
           illegal logging and halting illegal forest loss in protected areas, reforesting and restoring forests in
           degraded and desertified lands {Box6.1C; Table 6.6}.

Afforestation

Mitigation

       Adaptation Desertification Land degradation Food securityCost

[Image: image]

L

M

MMM[Image: image]

High level:Impacts on adaptation, desertification, land degradation and food security are maximum potential impacts

       assuming implementation of afforestation (partly overlapping with reforestation and forest restoration) at a
       scale of 8.9 GtCO2 yr–1removal {6.3.1}. Large-scale afforestation could cause
       increases in food prices of 80% by 2050, and more general mitigation measures in the AFOLU sector can translate
       into a rise in undernourishment of 80–300 million people {6.3.5}.

Mitigation Adaptation Desertification Land degradation Food security

[Image: image]

Best practice:Afforestation is used to prevent desertification and to tackle land degradation. Forested land

           also offers benefits in terms of food supply, especially when forest is established on degraded land,
           mangroves, and other land that cannot be used for agriculture. For example, food from forests represents a
           safety-net during times of food and income insecurity {6.3.5}.

Biochar addition to soil

Mitigation

       Adaptation Desertification Land degradation Food security Cost

[Image: image]

L

[Image: image]

ML[Image: image]

High level:Impacts on adaptation, desertification, land degradation and food security are maximum potential impacts

       assuming implementation of biochar at a scale of 6.6 GtCO2 yr–1removal {6.3.1}.
       Dedicated biomass crops required for feedstock production could occupy 0.4–2.6 Mkm2 of land, equivalent to
       around 20% of the global cropland area, which could potentially have a large effect on food security for up to
       100 million people {6.3.5}.

Mitigation Adaptation Desertification Land degradation Food security

[Image: image]

Best practice:When applied to land, biochar could provide moderate benefits for food security by improving

           yields by 25% in the tropics, but with more limited impacts in temperate regions, or through improved water
           holding capacity and nutrient use efficiency. Abandoned cropland could be used to supply biomass for
           biochar, thus avoiding competition with food production; 5–9 Mkm2 of land is estimated to be available for
           biomass production without compromising food security and biodiversity, considering marginal and degraded
           land and land released by pasture intensification {6.3.5}.

Figure TS.13 | Potential global

       contribution of response options to mitigation, adaptation, combating desertification and land degradation, and
       enhancing food security (Panel B).

[Image: image]

TS

Figure TS.13

           (continued):This Figure is based on an aggregation of information from studies with a wide variety
       of assumptions about how response options are implemented and the contexts in which they occur. Response options
       implemented differently at local to global scales could lead to different outcomes.Magnitude
           of potential:For panel A, magnitudes are for the technical potential of response options globally.
       For each land challenge, magnitudes are set relative to a marker level as follows. For mitigation, potentials
       are set relative to the approximate potentials for the response options with the largest individual impacts (~3
       GtCO2-eq yr–1). The threshold for the ‘large’ magnitude
       category is set at this level. For adaptation, magnitudes are set relative to the 100 million lives estimated to
       be affected by climate change and a carbon-based economy between 2010 and 2030. The threshold for the ‘large’
       magnitude category represents 25% of this total. For desertification and land degradation, magnitudes are set
       relative to the lower end of current estimates of degraded land, 10–60 million km2. The
       threshold for the ‘large’ magnitude category represents 30% of the lower estimate. For food security, magnitudes
       are set relative to the approximately 800 million people who are currently undernourished. The threshold for the
       ‘large’ magnitude category represents 12.5% of this total. For panel B, for the first row (high level
       implementation) for each response option, the magnitude and thresholds are as defined for panel A. In the second
       row (best practice implementation) for each response option, the qualitative assessments that are green denote
       potential positive impacts, and those shown in grey indicate neutral interactions. Increased food production is
       assumed to be achieved through sustainable intensification rather than through injudicious application of
       additional external inputs such as agrochemicals.Levels of confidence:Confidence in
       the magnitude category (high, medium or low) into which each option falls for mitigation, adaptation, combating
       desertification and land degradation, and enhancing food security.High confidencemeans that there is a
       high level of agreement and evidence in the literature to support the categorisation as high, medium or low
       magnitude.Low confidencedenotes that the categorisation of magnitude is based on few studies.Medium
           confidencereflects medium evidence and agreement in the magnitude of response.Cost
           ranges:Cost estimates are based on aggregation of often regional studies and vary in the components
       of costs that are included. In panel B, cost estimates are not provided for best practice implementation. One
       coin indicates low cost (<USD10 tCO2-eq–1or <USD20
       ha–1), two coins indicate medium cost (USD10–USD100 tCO2-eq–1or USD20–USD200 ha–1), and three
       coins indicate high cost (>USD100 tCO2-eq–1or USD200
       ha–1). Thresholds in USD ha–1are chosen to be comparable, but
       precise conversions will depend on the response option.Supporting evidence:Supporting
       evidence for the magnitude of the quantitative potential for land management-based response options can be found
       as follows: for mitigation Tables 6.13 to 6.20, with further evidence in Section 2.7.1; for adaptation Tables
       6.21 to 6.28; for combating desertification Tables 6.29 to 6.36, with further evidence in Chapter 3; for
       combating degradation tables 6.37 to 6.44, with further evidence in Chapter 4; for enhancing food security
       Table’s 6.45 to 6.52, with further evidence in Chapter 5. Other synergies and trade-offs not shown here are
       discussed in Chapter 6. Additional supporting evidence for the qualitative assessments in the second row for
       each option in panel B can be found in the Table’s 6.6, 6.55, 6.56 and 6.58, Section 6.3.5.1.3, and Box 6.1c.

TS.7 Risk management and decision making in relation to sustainable development

Increases in global mean surface temperature are projected to result in continued permafrost degradation and

       coastal degradation (high confidence), increased wildfire, decreased crop yields in low latitudes,
       decreased food stability, decreased water availability, vegetation loss (medium confidence), decreased
       access to food and increased soil erosion (low confidence). There ishigh agreementandhigh
           evidencethat increases in global mean temperature will result in continued increase in global
       vegetation loss, coastal degradation, as well as decreased crop yields in low latitudes, decreased food
       stability, decreased access to food and nutrition, andmedium confidencein continued permafrost
       degradation and water scarcity in drylands.Impacts are already observed across all components
           (high confidence). Some processes may experience
           irreversible impacts at lower levels of warming than others. There are high risks from permafrost
           degradation, and wildfire, coastal degradation, stability of food systems at 1.5°C while high risks from
           soil erosion, vegetation loss and changes in nutrition only occur at higher temperature thresholds due to
           increased possibility for adaptation (medium confidence).
           {7.2.2.1, 7.2.2.2, 7.2.2.3; 7.2.2.4;

7.2.2.5; 7.2.2.6; 7.2.2.7;

       Figure 7.1}

These

           changes result in compound risks to food systems, human and ecosystem health, livelihoods, the viability of
           infrastructure, and the value of land (high confidence). The experience and dynamics of risk change over time as a result of both human and
       natural processes (high confidence). There ishigh confidencethat climate and land changes pose
       increased risks at certain periods of life (i.e. to the very young and ageing populations) as well as sustained
       risk to those living in poverty. Response options may also increase risks. For example, domestic efforts to
       insulate populations from food price spikes associated with climatic stressors in the mid-2000s inadequately
       prevented food insecurity and poverty, and worsened poverty globally. (Figure TS.14) {7.2.1, 7.2.2, 7.3, Table
       7.1}

There is

           significant regional heterogeneity in risks: tropical regions, including Sub-Saharan Africa, Southeast Asia
           and Central and South America are particularly vulnerable to decreases in crop yield (high confidence).Yield of crops in higher latitudes may
       initially benefit from warming as well as from higher carbon dioxide (CO2)
       concentrations. But temperate zones, including the Mediterranean, North Africa, the Gobi desert, Korea and
       western United States are susceptible to disruptions from increased drought frequency and intensity, dust storms
       and fires (high confidence). {7.2.2}

Risks related to

       land degradation, desertification and food security increase with temperature and can reverse development gains
       in some socio-economic development pathways (high confidence). SSP1 reduces the vulnerability and
       exposure of human and natural systems and thus limits risks resulting from desertification, land degradation and
       food insecurity compared to SSP3 (high confidence). SSP1

is

       characterized by low population growth, reduced inequalities, land-use regulation, low meat consumption,
       increased trade and few barriers to adaptation or mitigation. SSP3 has the opposite characteristics. Under SSP1,
       only a small fraction of the dryland population (around 3% at 3°C for the year 2050) will be exposed and
       vulnerable to water stress. However under SSP3, around 20% of dryland populations (for the year 2050) will be
       exposed and vulnerable to water stress by 1.5°C and 24% by 3°C. Similarly under SSP1, at 1.5°C, 2 million people
       are expected to be exposed and vulnerable to crop yield change. Over 20 million are exposed and vulnerable to
       crop yield change in SSP3, increasing to 854 million people at 3°C (low confidence). Livelihoods
       deteriorate as a result of these impacts, livelihood migration is accelerated, and strife and conflict is
       worsened (medium confidence). {Cross-Chapter Box 9 in Chapter 6, 7.2.2, 7.3.2, Table 7.1, Figure 7.2}

[Image: image]

TS

Land-based

       adaptation and mitigation responses pose risks associated with the effectiveness and potential adverse
       side-effects of measures chosen (medium confidence).Adverse side-effects on food
           security, ecosystem services and water security increase with the scale of BECCS deployment. In a SSP1
           future, bioenergy and BECCS deployment up to 4 million km2is compatible with sustainability constraints, whereas risks are already high in a SSP3 future for
           this scale of deployment. {7.2.3}

There ishigh

           confidencethat policies addressing vicious cycles of poverty, land degradation and greenhouse gas (GHG)
       emissions implemented in a holistic manner can achieve climate-resilient sustainable development. Choice and
       implementation of policy instruments determine future climate and land pathways (medium confidence).Sustainable development pathways (described in SSP1) supported by effective regulation of land
           use to reduce environmental trade-offs, reduced reliance on traditional biomass, low growth in consumption
           and limited meat diets, moderate international trade with connected regional markets, and effective GHG
           mitigation instruments can result in lower food prices, fewer people affected by floods and other climatic
           disruptions, and increases in forested land (high agreement, limited
           evidence) (SSP1). A policy pathway with limited regulation of land use, low
           technology development, resource intensive consumption, constrained trade, and ineffective GHG mitigation
           instruments can result in food price increases, and significant loss of forest (high agreement, limited evidence) (SSP3). {3.7.5, 7.2.2, 7.3.4, 7.5.5,
           7.5.6, Table 7.1, Cross-Chapter Box 9 in Chapter 6,

Cross-Chapter Box 12 in

       Chapter 7}

Delaying deep

       mitigation in other sectors and shifting the burden to the land sector, increases the risk associated with
       adverse effects on food security and ecosystem services (high confidence).The
           consequences are an increased pressure on land with higher risk of mitigation failure and of temperature
           overshoot and a transfer of the burden of mitigation and unabated climate change to future generations.
           Prioritising early decarbonisation with minimal reliance on CDR decreases the risk of mitigation failure
           (high confidence). {2.5, 6.2, 6.4, 7.2.1, 7.2.2, 7.2.3,
           7.5.6, 7.5.7,

Cross-Chapter Box 9 in Chapter

6}

[Image: image]

TS

Trade-offs can occur between using land for climate mitigation or Sustainable Development Goal

           (SDG) 7 (affordable clean energy) with biodiversity, food, groundwater and riverine ecosystem services
           (medium confidence).There ismedium confidencethat trade-offs currently do not figure into climate policies and decision making. Small hydro power
       installations (especially in clusters) can impact downstream river ecological connectivity for fish (high
           agreement, medium evidence). Large scale solar farms and wind turbine installations can impact
       endangered species and disrupt habitat connectivity (medium agreement, medium evidence). Conversion of
       rivers for transportation can disrupt fisheries and endangered species (through dredging and traffic) (medium
           agreement, low evidence). {7.5.6}

The full

           mitigation potential assessed in this report will only be realised if agricultural emissions are included in
           mainstream climate policy (high agreement, high evidence).Carbon markets are theoretically more cost-effective than taxation but challenging to implement in the
       land-sector (high confidence) Carbon pricing (through carbon markets or carbon taxes) has the potential
       to be an effective mechanism to reduce GHG emissions, although it remains relatively untested in agriculture and
       food systems. Equity considerations can be balanced by a mix of both market and non-market mechanisms (medium
           evidence, medium agreement). Emissions leakage could be reduced by multi-lateral action (high
           agreement, medium evidence). {7.4.6, 7.5.5, 7.5.6, Cross

Chapter Box 9 in Chapter 6}

A suite of

       coherent climate and land policies advances the goal of the Paris Agreement and the land-related SDG targets on
       poverty, hunger, health, sustainable cities and communities, responsible consumption and production, and life on
       land. There ishigh confidencethat acting early will avert or minimise risks, reduce losses and generate
       returns on investment.The economic costs of action on sustainable land management (SLM),
           mitigation, and adaptation are less than the consequences of inaction for humans and ecosystems
           (medium confidence). Policy portfolios that make ecological
           restoration more attractive, people more resilient – expanding financial inclusion, flexible carbon credits,
           disaster risk and health insurance, social protection and adaptive safety nets, contingent finance and
           reserve funds, and universal access to early warning systems – could save 100 billion USD a year, if
           implemented globally. {7.3.1, 7.4.7, 7.4.8,

7.5.6, Cross-Chapter Box 10 in

       Chapter 7}

Coordination of

       policy instruments across scales, levels, and sectors advances co-benefits, manages land and climate risks,
       advances food security, and addresses equity concerns (medium confidence).Flood
           resilience policies are mutually reinforcing and include flood zone mapping, financial incentives to move,
           and building restrictions, and insurance. Sustainability certification, technology transfer, land-use
           standards and secure land tenure schemes, integrated with early action and preparedness, advance response
           options. SLM improves with investment in agricultural research, environmental farm practices,
           agri-environmental payments, financial support for sustainable agricultural water infrastructure (including
           dugouts), agriculture emission trading, and elimination

of

       agricultural subsidies (medium confidence). Drought resilience policies (including drought preparedness
       planning, early warning and monitoring, improving water use efficiency), synergistically improve agricultural
       producer livelihoods and foster SLM. (Figure TS.15)

{3.7.5, Cross-Chapter Box 5 in

       Chapter 3, 7.4.3, 7.4.6, 7.5.6, 7.4.8,

7.5.6, 7.6.3}

Technology transfer

       in land use sectors offers new opportunities for adaptation, mitigation, international cooperation, R&D
       collaboration, and local engagement (medium confidence).International cooperation to
           modernise the traditional biomass sector will free up both land and labour for more productive uses.
           Technology transfer can assist the measurement and accounting of emission reductions by developing
           countries. {7.4.4, 7.4.6, Cross-Chapter Box 12 in Chapter 7}

Measuring progress

       towards goals is important in decision-making and adaptive governance to create common understanding and advance
       policy effectiveness (high agreement, medium evidence).Measurable indicators, selected
           with the participation of people and supporting data collection, are useful for climate policy development
           and decision-making. Indicators include the SDGs, nationally determined contributions (NDCs), land
           degradation neutrality (LDN) core indicators, carbon stock measurement, measurement and monitoring for
           REDD+, metrics for measuring biodiversity and ecosystem services, and governance capacity. {7.5.5, 7.5.7,
           7.6.4, 7.6.6}

The complex

       spatial, cultural and temporal dynamics of risk and uncertainty in relation to land and climate interactions and
       food security, require a flexible, adaptive, iterative approach to assessing risks, revising decisions and
       policy instruments (high confidence).Adaptive, iterative decision-making moves beyond
           standard economic appraisal techniques to new methods such as dynamic adaptation pathways with risks
           identified by trigger points through indicators. Scenarios can provide valuable information at all planning
           stages in relation to land, climate and food; adaptive management addresses uncertainty in scenario planning
           with pathway choices made and reassessed to respond to new information and data as it becomes available.
           {3.7.5, 7.4.4, 7.5.2, 7.5.3, 7.5.4, 7.5.7, 7.6.1, 7.6.3}

ILK can play a key

       role in understanding climate processes and impacts, adaptation to climate change, SLM across different
       ecosystems, and enhancement of food security (high confidence).ILK is context-specific,
           collective, informally transmitted, and multi-functional, and can encompass factual information about the
           environment and guidance on management of resources and related rights and social behaviour. ILK can be used
           in decision-making at various scales and levels, and exchange of experiences with adaptation and mitigation
           that include ILK is both a requirement and an entry strategy for participatory climate communication and
           action. Opportunities exist for integration of ILK with scientific knowledge. {7.4.1, 7.4.5, 7.4.6, 7.6.4,
           Cross-Chapter

Box 13 in Chapter 7}

Risks to humans and ecosystems

               from changes in land-based processes as a result of climate change

[Image: image]

Increases in global mean surface temperature (GMST), relative to pre-industrial levels, affect processes

               involved indesertification(water scarcity),land degradation(soil erosion, vegetation
               loss, wildfire, permafrost thaw) andfood security(crop yield and food supply instabilities).
               Changes in these processes drive risks to food systems, livelihoods, infrastructure, the value of land,
               and human and ecosystem health. Changes in one process (e.g. wildfire or water scarcity) may result in
               compound risks. Risks are location-specific and differ by region.

[Image: image]

TS

GMST

               change

relative to

               levels in pre-industrial time (°C)

5°

4°

3°

2°

1.5°

1°

M

Dryland

L

H

M

L

Soil

H

Vegetation

Wildfire

M

H

M

Permafrost

Tropical crop

M

H

M

M H

Food

M

2006–2015

water scarcity

Systems

               at risk:

Food[Image: image]Livelihoods[Image: image]Value of

               land[Image: image]Human
               health[Image: image]Ecosystem
               health[Image: image]Infrastructure[Image: image]

erosion

[Image: image]

loss

[Image: image]

damage

[Image: image]

degradation

[Image: image]

yield decline

[Image: image]

supply instabilities

[Image: image]

5°

4°

3°

2°

1°

[Image: image]

Indicative

               example of transitions

Over 50%

               increase in area burned in Mediterranean region

Over 100M

               people additionally exposed

M

5°

4°

3°

2°

1°

Indicative

               example of transitions

Sustained food supply disruptions globally

Infrequent price spikes

affect

               individual countries

Periodic food

Increase

               in fire weather season

M

H

M

M H

shocks

               across regions

GMST

               change

relative to

               levels in pre-industrial time (°C)

Wildfire damage Food supply

               instabilities

Different socioeconomic

               pathways affect levels of climate related risks

GMST

       change

relative to levels in

       pre-industrial time (°C)

3°

2°

1.5°

1°

Desertification Land

       degradation Food insecurity

(water scarcity in drylands) (habitat degr., wildfire,

       floods) (availability, access)

[Image: image][Image: image][Image: image]

[Image: image]

M

SSP1 SSP3

H

M

L

M

2006–2015

Socio-economic choices can reduce or exacerbate climate related risks as well as influence the rate of

       temperature increase. TheSSP1pathway illustrates a world with low population growth, high income and
       reduced inequalities, food produced in low GHG emission systems, effective land use regulation and high adaptive
       capacity. TheSSP3pathway has the opposite trends. Risks are lower in SSP1 compared with SSP3 given the
       same level of

GMST increase.

[Image: image]

Legend:Level of

           impact/risk

Very high

High

Purple: Very high probability of severe impacts/ risks and the presence of significant irreversibility

           or the persistence of climate-related hazards, combined with limited ability to adapt due to the nature of
           the hazard

Legend:Confidence

level for transition

High

HHigh

Example

M Medium L Low

Risks

Impacts

Moderate

Undetectable

or impacts/risks.

Red: Significant and widespread impacts/risks.Yellow: Impacts/risks are

           detectable and attributable to climate change with at least medium confidence.White: Impacts/risks are undetectable.

HExample

Figure TS.14 | Risks to land-related

       human systems and ecosystems from global climate change, socio-economic development and mitigation choices.

Figure TS.14

       (continued):As in previous IPCC reports the literature was used to make expert judgements to
           assess the levels of global warming at which levels of risk are undetectable, moderate, high or very high,
           as described further in Chapter 7 and other parts of the underlying report. The figure indicates assessed
           risks at approximate warming levels which may be influenced by a variety of factors, including adaptation
           responses. The assessment considers adaptive capacity consistent with the SSP pathways as described below.Panel A:Risks to selected elements of the land system as a function of global mean
           surface temperature {2.1; Box 2.1; 3.5;

3.7.1.1; 4.4.1.1; 4.4.1.2; 4.4.1.3;

       5.2.2; 5.2.3; 5.2.4; 5.2.5; 7.2;7.3, Table SM7.1}. Links to broader systems are illustrative and not intended to
       be comprehensive. Risk levels are estimated assuming medium exposure and vulnerability driven by moderate trends
       in socioeconomic conditions broadly consistent with an SSP2 pathway.

[Image: image]

TS

{Table SM7.4}.Panel

B:Risks associated with desertification, land degradation and food security due to climate change
and patterns of socio-economic development. Increasing risks associated with desertification include population
exposed and vulnerable to water scarcity in drylands. Risks related to land degradation include increased
habitat degradation, population exposed to wildfire and floods and costs of floods. Risks to food security
include availability and access to food, including population at risk of hunger, food price increases and
increases in disability adjusted life years attributable due to childhood underweight. Risks are assessed for
two contrasted socio-economic pathways (SSP1 and SSP3 {SPM Box 1}) excluding the effects of targeted mitigation
policies {3.5; 4.2.1.2; 5.2.2; 5.2.3; 5.2.4; 5.2.5; 6.1.4; 7.2, Table SM7.5}. Risks are not indicated beyond 3°C
because SSP1 does not exceed this level of temperature change. All panels: As part of the assessment, literature
was compiled and data extracted into a summary table. A formal expert elicitation protocol (based on
modified-Delphi technique and the Sheffield Elicitation Framework), was followed to identify risk transition
thresholds. This included a multi-round elicitation process with two rounds of independent anonymous threshold
judgement, and a final consensus discussion. Further information on methods and underlying literature can be
found in Chapter 7 Supplementary Material.

Participation of people in land and climate decision making and policy formation allows for transparent

       effective solutions and the implementation of response options that advance synergies, reduce trade-offs in
       sustainable land management (high confidence), and overcomes barriers to adaptation and mitigation
       (high confidence).Improvements to sustainable land management are achieved by: (1)
           engaging people in citizen science by mediating and facilitating landscape conservation planning, policy
           choice, and early warning systems (medium confidence); (2)
           involving people in identifying problems (including species decline, habitat loss, land use change in
           agriculture, food production and forestry), selection of indicators, collection of climate data, land
           modelling, agricultural innovation opportunities. When social learning is combined with collective action,
           transformative change can occur addressing tenure issues and changing land use practices (medium confidence). Meaningful participation overcomes barriers by
           opening up policy and science surrounding climate and land decisions to inclusive discussion that promotes
           alternatives. {3.8.5, 7.5.1, 7.5.9;

7.6.1, 7.6.4, 7.6.5, 7.6.7,

       7.7.4, 7.7.6}

Empowering

           women can bolster synergies among household food security and sustainable land management (high confidence).This can be achieved with policy instruments
       that account for gender differences. The overwhelming presence of women in many land-based activities including
       agriculture provides opportunities to mainstream gender policies, overcome gender barriers, enhance gender
       equality, and increase sustainable land management and food security (high confidence). Policies that
       address barriers include gender qualifying criteria and gender appropriate delivery, including access to
       financing, information, technology, government transfers, training, and extension may be built into existing
       women’s programs, structures (civil society groups) including collective micro enterprise (medium
           confidence). {Cross-Chapter Box 11 in Chapter 7}

The significant

       social and political changes required for sustainable land use, reductions in demand and land-based mitigation
       efforts associated with climate stabilisation require a wide range of governance mechanisms.The
           expansion and diversification of land use and biomass systems and markets requires

hybrid

       governance: public-private partnerships, transnational, polycentric, and state governance to insure
       opportunities are maximised, trade-offs are managed equitably, and negative impacts are minimised (medium
           confidence). {7.5.6, 7.7.2, 7.7.3, Cross-

Chapter Box 7 in Chapter 6}

Land tenure systems

       have implications for both adaptation and mitigation, which need to be understood within specific socio-economic
       and legal contexts, and may themselves be impacted by climate change and climate action (limited evidence,
           high agreement).Land policy (in a diversity of forms beyond focus on freehold title)
           can provide routes to land security and facilitate or constrain climate action, across cropping, rangeland,
           forest, fresh-water ecosystems and other systems. Large-scale land acquisitions are an important context for
           the relations between tenure security and climate change, but their scale, nature and implications are
           imperfectly understood. There ismedium confidencethat land
           titling and recognition programs, particularly those that authorise and respect indigenous and communal
           tenure, can lead to improved management of forests, including for carbon storage. Strong public coordination
           (government and public administration) can integrate land policy with national policies on adaptation and
           reduce sensitivities to climate change. {7.7.2; 7.7.3; 7.7.4, 7.7.5}

Significant gaps in

       knowledge exist when it comes to understanding the effectiveness of policy instruments and institutions related
       to land use management, forestry, agriculture and bioenergy.Interdisciplinary research is
           needed on the impacts of policies and measures in land sectors. Knowledge gaps are due in part to the highly
           contextual and local nature of land and climate measures and the long time periods needed to evaluate land
           use change in its socio-economic frame, as compared to technological investments in energy or industry that
           are somewhat more comparable. Significant investment is needed in monitoring, evaluation and assessment of
           policy impacts across different sectors and levels. {7.8}

[Image: image]

TS

Table TS.1 | Selection of

       Policies/Programmes/Instruments that support response options.

(Content)

Pathways linking socioeconomic

               development, mitigation responses and land

Socioeconomic development and land management influence the evolution of the land system including the

               relative amount of land allocated toCROPLAND,PASTURE,BIOENERGY CROPLAND,FOREST, andNATURAL LAND. The lines show the median
               across Integrated Assessment Models (IAMs) for three alternative shared socioeconomic pathways
               (SSP1,SSP2andSSP5atRCP1.9); shaded areas show the range across models.
               Note that pathways illustrate the effects of climate change mitigation but not those of climate change
               impacts or adaptation.

[Image: image]

TS

Sustainability-focused (SSP1)Sustainability in land management, agricultural

                           intensification, production and consumption patterns result in reduced need for agricultural
                           land, despite increases in per capita food consumption. This land can instead be used for
                           reforestation, afforestation, and bioenergy.

Middle of the road (SSP2 )

Societal as well as technological development follows historical patterns. Increased demand for

                       land mitigation options such as bioenergy, reduced deforestation or afforestation decreases
                       availability of agricultural land for food, feed and fibre.

Resource intensive (SSP5)Resource-intensive production and consumption

                           patterns, results in high baseline emissions. Mitigation focuses on technological solutions
                           including substantial bioenergy and BECCS .

Intensification and competing land uses contribute to declines in agricultural land.

SSP1

               Sustainability-focused

[Image: image]

Change in

               Land from 2010 (Mkm2) 10

7.5

5

2.5

0

-2.5

-5

-7.5

-10

SSP2 Middle

               of the road

[Image: image]

Change in

               Land from 2010 (Mkm2) 10

F7.5

5

BC

2.5

NL0

-2.5

C-5

P

-7.5

-10

SSP5 Resource

               intensive

[Image: image]

Change in

               Land from 2010 (Mkm2) 10

BC7.5BC

F

2.5

0NL

NL-2.5

CC

-5

P-7.5

P

-10

2010

2025 2050 2075 2100

2010

2025 2050 2075 2100

2010

2025 2050 2075 2100

[Image: image]CROPLAND[Image: image]PASTURE[Image: image]BIOENERGY CROPLAND[Image: image]FOREST[Image: image]NATURAL LAND

Figure TS.15 | Pathways linking

               socioeconomic development, mitigation responses and land (Panel A).

(Content)

Land use and land cover change in the

               SSPs

SSP1

SSP2

SSP3

SSP4

SSP5

Quantitative indicators

for the SSPs

[Image: image]

RCP2.6in

           2050 5/5 -0.9( -2.2 , 1.5 )1.3( 0.4
               , 1.9 )-1( -4.7 , 1 )2.6( -0.1 , 8.4 )-3( -4 , -2.4 )

RCP1.9in

           2050 5/5 0.5( -4.9 , 1 )2.1( 0.9 , 5
               )-1.2( -4.6 , -0.3 )3.4( -0.1 , 9.4 )-4.1( -5.6 , -2.5 )

[Image: image]2100

RCP4.5in

           2050 5/5 0.5( -1 , 1.7 )0.8( 0.5 ,
               1.3 )0.1( -3.2 , 1.5 )0.6( -0.7 , 4.2 )-2.4( -3.3 , -0.9 )

[Image: image]2100

Baselinein

           2050 5/5 0.3( -1.1 , 1.8 )0.5( 0.2 ,
               1.4 )0.2( -1.6 , 1.9 )-0.1( -0.8 , 1.1 )-1.5( -2.9 , -0.2 )

[Image: image]2100

[Image: image]

RCP2.6in

           2050 5/5 -3.2( -4.2 , 0.1 )2.2( 1.7
               , 4.7 )0.6( -1.9 , 1.9 )1.6( -0.9 , 4.2 )-1.4( -3.7 , 0.4 )

RCP1.9in

           2050 4/5 -2.2( -7 , 0.6 )4.5( 2.1 ,
               7 )-1.2( -2 , 0.3 )3.4( -0.9 , 7 )-4.8( -6.2 , -0.4 )

[Image: image]2100

RCP4.5in

           2050 5/5 -2.2( -2.2 , 0.7 )1.5( 0.1
               , 2.1 )1.2( -0.9 , 2.7 )-0.9( -2.5 , 2.9 )-0.1( -2.5 , 1.6 )

[Image: image]2100

Baselinein

           2050 5/5 -1.5( -2.6 , -0.2 )0.7( 0 ,
               1.5 )1.3( 1 , 2.7 )-1.3( -2.5 , -0.4 )-0.1( -1.2 , 1.6 )

[Image: image]2100

[Image: image]

RCP2.6in 2050Infeasible in all assessed models- ---

RCP1.9in 2050Infeasible in all assessed models- ---

[Image: image]2100

RCP4.5in

           2050 3/3 -3.4( -4.4 , -2 )1.3( 1.3 ,
               2 )2.3( 1.2 , 3 )-2.4( -4 , -1 )2.1( -0.1 , 3.8 )

[Image: image]2100

Baselinein

           2050 4/4 -3( -4.6 , -1.7 )1( 0.2 ,
               1.5 )2.5( 1.5 , 3 )-2.5( -4 , -1.5 )2.4( 0.6 , 3.8 )

[Image: image]2100

[Image: image]

RCP2.6in

           2050 3/3 -4.5( -6 , -2.1 )3.3( 1.5 ,
               4.5 )0.5( -0.1 , 0.9 )0.7( -0.3 , 2.2 )-0.6( -0.7 , 0.1 )

RCP1.9in 2050Infeasible in all assessed models**- ---

[Image: image]2100

RCP4.5in

           2050 3/3 -2.7( -4.4 , -0.4 )1.7( 1 ,
               1.9 )1.1( -0.1 , 1.7 )-1.8( -2.3 , 2.1 )0.8( -0.5 , 1.5 )

[Image: image]2100

Baselinein

           2050 3/3 -2.8( -2.9 , -0.2 )1.1( 0.7
               , 2 )1.1( 0.7 , 1.8 )-1.8( -2.3 , -1 )1.5( -0.5 , 2.1 )

[Image: image]2100

[Image: image]

RCP2.6in

           2050 4/4 -3.4( -6.9 , 0.3 )4.8( 3.8
               , 5.1 )-2.1( -4 , 1 )3.9( -0.1 , 6.7 )-4.4( -5 , 0.2 )

RCP1.9in

           2050 2/4 -1.5( -3.9 , 0.9 )6.7( 6.2
               , 7.2 )-1.9( -3.5 , -0.4 )3.1( -0.1 , 6.3 )-6.4( -7.7 , -5.1 )

[Image: image]2100

RCP4.5in

           2050 4/4 -2.5( -3.7 , 0.2 )1.7( 0.6
               , 2.9 )0.6( -3.3 , 1.9 )-0.1( -1.7 , 6 )-1.2( -2.6 , 2.3 )

[Image: image]2100

Baselinein

           2050 4/4 -0.6( -3.8 , 0.4 )0.8( 0 ,
               2.1 )1.5( -0.7 , 3.3 )-1.9( -3.4 , 0.5 )-0.1( -1.5 , 2.9 )

[Image: image]2100

Count of models

       included*

Change in

       Natural Land from 2010 Mkm

[Image: image]

0(

           -7.3 , 7.1 )

0.2(

           -3.5 , 1.1 )

1.8(

           -1.7 , 6 )

3.3(

           -0.3 , 5.9 )

-2.3( -9.6 , 2.7 )

-5.2( -7.2 , 0.5 )

-3.4( -4.7 , 1.5 )

-2.1( -5.9 , 0.3 )

-6.2( -6.8 , -5.4 )

-5(

           -7.1 , -4.2 )

-5.8( -10.2 , -4.7 )

-2.8( -7.8 , -2 )

-2.4( -5 , -1 )

-0.5( -4.2 , 3.2 )

-4.3( -8.4 , 0.5 )

-4.1( -4.6 , 0.7 )

-0.2( -2.4 , 1.8 )

Change in Bioenergy Cropland from 2010 Mkm

[Image: image]

4.3(

           1.5 , 7.2 )

5.1(

           1.6 , 6.3 )

1.9(

           1.4 , 3.7 )

1.8(

           1.4 , 2.4 )

6.6(

           3.6 , 11 )

6.9(

           2.3 , 10.8 )

4.1(

           0.4 , 6.3 )

1.2(

           0.1 , 2.4 )

-

4.6(

           1.5 , 7.1 )

1.1(

           0.9 , 2.5 )

-

2.5(

           2.3 , 15.2 )

2.7(

           2.3 , 4.7 )

1.7(

           1.4 , 2.6 )

7.6(

           7.2 , 8 )

9.1(

           7.7 , 9.2 )

4.8( 2

           , 8 )

1(

           0.2 , 2.3 )

Change in Cropland

       from 2010

Mkm

[Image: image]

-5.2(

           -7.6 , -1.8 )

-3.2(

           -7.7 , -1.8 )

-2.3(

           -6.4 , -1.6 )

-1.5(

           -5.7 , -0.9 )

-2.9(

           -4 , 0.1 )

-1.4(

           -4 , 0.8 )

0.7(

           -2.6 , 3.1 )

1.9(

           0.8 , 2.8 )

-

3.4(

           1.9 , 4.5 )

5.1(

           3.8 , 6.1 )

-

-0.8(

           -0.8 , 1.8 )

1.1(

           0.2 , 1.2 )

1.2(

           1.2 , 1.9 )

-3.4(

           -6.2 , -0.5 )

-3.3(

           -6.5 , -0.5 )

-1(

           -5.5 , 1 )

1( -2

           , 2.5 )

Change in Forest

       from 2010

Mkm

[Image: image]

7.5(

           0.4 , 15.8 )

6.6(

           -0.1 , 10.5 )

3.9(

           0.2 , 8.8 )

0.9(

           0.3 , 3 )

6.4(

           -0.8 , 9.5 )

5.6(

           -0.9 , 5.9 )

-0.5(

           -3.1 , 5.9 )

-1.3(

           -2.7 , -0.2 )

-

-3.1(

           -5.5 , -0.3 )

-5.3(

           -6 , -2.6 )

-

1.4(

           -1.7 , 4.1 )

-0.7(

           -2.6 , 1 )

-2.4(

           -2.5 , -2 )

4.7(

           0.1 , 9.4 )

3.9(

           -0.1 , 9.3 )

-0.2(

           -1.4 , 9.1 )

-2.1(

           -3.4 , 1.1 )

Change in Pasture

       from 2010

Mkm

[Image: image]

TS

-6.5( -12.2 , -4.8 )

-5.5( -9.9 , -4.2 )

-4.6( -7.3 , -2.7 )

-2.1( -7 , 0 )

-7.6( -11.7 , -1.3 )

-7.2( -8 , 0.5 )

-2.8( -5.3 , 1.9 )

-0.2( -1.9 , 2.1 )

-

2(

           -2.5 , 4.4 )

3.4(

           0.9 , 6.4 )

-

-1.2( -2.5 , -0.2 )

1.4(

           -1 , 1.8 )

1.3(

           -1 , 4.4 )

-8.5( -10.7 , -6.2 )

-6.3( -9.1 , -1.4 )

-3(

           -5.2 , 2.1 )

-0.4( -2.4 , 2.8 )

Count of models included / Count of

       models attempted. One model did not provide land data and is excluded from all entries.

- One model could

       reach RCP1.9 with SSP4, but did not provide land data.

Figure TS.15 | Pathways linking

       socioeconomic development, mitigation responses and land (Panel B).

Figure TS.15

(continued):Future scenarios provide a framework for understanding the implications of mitigation
and socioeconomics on land. The SSPs span a range of different socioeconomic assumptions (Box SPM.1). They are
combined with Representative Concentration Pathways (RCPs)2which imply different
levels of mitigation. The changes in cropland, pasture, bioenergy cropland, forest, and natural land from 2010
are shown. For this Figure, Cropland includes all land in food, feed, and fodder crops, as well as other arable
land (cultivated area). This category includes first generation non-forest bioenergy crops (e.g., corn for
ethanol, sugar cane for ethanol, soybeans for biodiesel), but excludes second generation bioenergy crops.
Pasture includes categories of pasture land, not only high-quality rangeland, and is based on FAO definition of
‘permanent meadows and pastures’. Bioenergy cropland includes land dedicated to second generation energy crops
(e.g., switchgrass, miscanthus, fast-growing wood species). Forest includes managed and unmanaged forest.
Natural land includes other grassland, savannah, and shrubland.Panel A:This panel
shows integrated assessment model (IAM)3results for SSP1, SSP2 and SSP5 at
RCP1.9.4For each pathway, the shaded areas show the range across all IAMs; the line
indicates the median across models. For RCP1.9, SSP1, SSP2 and SSP5 results are from five, four and two IAMs
respectively.Panel B: Land use and land cover change are indicated for various SSP-RCP
combinations, showing multi-model median and range (min, max). (Box SPM.1) {1.3.2, 2.7.2, 6.1, 6.4.4, 7.4.2,
7.4.4, 7.4.5, 7.4.6, 7.4.7, 7.4.8, 7.5.3,

[Image: image]

TS

7.5.6, Cross-Chapter Box 1 in Chapter

       1, Cross-Chapter Box 9 in Chapter 6}

[Image: image]

Representative Concentration Pathways (RCPs) are scenarios that include timeseries of emissions and

               concentrations of the full suite of GHGs and aerosols and chemically active gases, as well as land
               use/land cover.

Integrated Assessment Models (IAMs) integrate knowledge from two or more domains into a single

               framework. In this figure, IAMs are used to assess linkages between economic, social and technological
               development and the evolution of the climate system.

The RCP1.9

               pathways assessed in this report have a 66% chance of limiting warming to 1.5°C in 2100, but some of
               these pathways overshoot 1.5°C of warming during the 21st century by >0.1°C.

IPCC:AR6/SRCCL/TS: Difference between revisions

Revision as of 03:27, 4 May 2026

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