Editing IPCC:AR6/WGIII/Chapter-7 (section)

== 7.4 Assessment of AFOLU Mitigation Measures Including Trade-offs and Synergies ==

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AFOLU mitigation or land-based climate change mitigation (used in this chapter interchangeably) are a variety of land management or demand management practices that reduce GHG emissions and/or enhance carbon sequestration within the land system (i.e., in forests, wetlands, grasslands, croplands and pasturelands). If implemented with benefits to human well-being and biodiversity, land-based mitigation measures are often referred to as nature-based solutions and/or natural climate solutions (Glossary). Measures that result in a net removal of GHGs from the atmosphere and storage in either living or dead organic material, or in geological stores, are known as CDR, and in previous IPCC reports were sometimes referred to as greenhouse gas removal (GGR) or negative emissions technologies (NETs) ( [[#Rogelj--2018a|Rogelj et al. 2018a]] ; [[#Jia--2019|Jia et al. 2019]] ). This section evaluates current knowledge and latest scientific literature on AFOLU mitigation measures and potentials, including land-based CDR measures. [[#7.4.1|Section 7.4.1]] provides an overview of the approaches for estimating mitigation potential, the co-benefits and risks from land-based mitigation measures, estimated global and regional mitigation potential and associated costs according to literature published over the last decade. Subsequent subsections assess literature on 20 key AFOLU mitigation measures specifically providing:

• A description of activities, co-benefits, risks and implementation opportunities and barriers.

'''•''' A summary of conclusions in the IPCC Fifth Assessment Report (AR5) and IPCC Special Reports (Special Report on Climate Change of 1.5°C (SR1.5), Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) and Special Report on Climate Change and Land (SRCCL)).

'''•''' An overview of literature and developments since the AR5 and IPCC Special Reports.

• An assessment and conclusion based on current evidence.

Measures are categorised as supply-side activities in: (i) forests and other ecosystems ( [[#7.4.2|Section 7.4.2]] ); (ii) agriculture ( [[#7.4.3|Section 7.4.3]] ); (iii) bioenergy and other land-based energy technologies ( [[#7.4.4|Section 7.4.4]] ); as well as (iv) demand-side activities ( [[#7.4.5|Section 7.4.5]] and Figure 7.11). Several information boxes are dispersed within the section and provide supporting material, including case studies exploring a range of topics from climate-smart forestry in Europe (Box 7.2), agroforestry in Brazil (Box 7.3), climate-smart village approaches (Box 7.4), farm systems approaches (Box 7.5), mitigation within Indian agriculture (Box 7.6), and bioenergy and BECCS mitigation calculations (Box 7.7). Novel measures, including enhanced weathering and novel foods are covered in Chapter 12, this report. In addition, as mitigation within AFOLU concerns land management and use of land resources, AFOLU measures impact other sectors. Accordingly, AFOLU measures are also discussed in other sectoral chapters within this report, notably demand-side solutions (Chapter 5), bioenergy and bioenergy with carbon capture and storage (BECCS) (Chapter 6), the use of wood products and biomass in buildings (Chapter 9), and CDR measures, food systems and land related impacts, risks and opportunities of mitigation measures (Chapter 12).

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=== 7.4.1 Introduction and Overview of Mitigation Potential ===

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==== 7.4.1.1 Estimating Mitigation Potentials ====

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Mitigation potentials for AFOLU measures are estimated by calculating the scale of emissions reductions or carbon sequestration against a counterfactual scenario without mitigation activities. The types of mitigation potential estimates in recent literature include: (i) technical potential (the biophysical potential or amount possible with current technologies); (ii) economic potential (constrained by costs, usually by a given carbon price (Table 7.3); (iii) sustainable potential (constrained by environmental safeguards and/or natural resources, e.g., limiting natural forest conversion), and (iv) feasible potential (constrained by environmental, socio-cultural, and/or institutional barriers), however, there are no set definitions used in literature. In addition to types of mitigation estimates, there are two AFOLU mitigation categories often calculated: supply-side measures (land management interventions) and demand-side measures (interventions that require a change in consumer behaviour).

'''Table 7.3 | Estimated annual mitigation potential (GtCO''' 2 '''-eq y''' '''r''' –1 ''') in''' '''2020–2050''' '''of AFOLU mitigation options by carbon price.''' Estimates reflect sectoral studies based on a comprehensive literature review updating data from ( [[#Roe--2019|Roe et al. 2019]] ) and integrated assessment models using the IPCC AR6 database ( [[#7.5|Section 7.5]] ). Values represent the mean, and full range of potential. Sectoral mitigation estimates are averaged for the years 2020–2050 to capture a wider range of literature, and the IAM estimates are given for 2050 as many model assumptions delay most land-based mitigation to mid-century. The sectoral potentials are the sum of global estimates for the individual measures listed for each option. IAM potentials are given for mitigation options with available data; for example, net land-use CO 2 for total forests and other ecosystems, and land sequestration from A/R, but not reduced deforestation (protect). Sectoral estimates predominantly use GWP100 IPCC AR5 values (CH 4 = 28, N 2 O = 265), although some use GWP100 IPCC AR4 values (CH 4 = 25, N 2 O = 298); and the IAMs use GWP100 IPCC AR6 values (CH 4 = 27, N 2 O = 273). The sectoral and IAM estimates reflected here do not account for the substitution effects of avoiding fossil fuel emissions nor emissions from other more energy intensive resources/materials. For example, BECCS estimates only consider the carbon dioxide removal (CDR) via geological storage component and not potential mitigation derived from the displacement of fossil fuel use in the energy sector. Mitigation potential from substitution effects are included in the other sectoral chapters like energy, transport, buildings and industry. The total AFOLU sectoral estimate aggregates potential from agriculture, forests and other ecosystems, and diverted agricultural production from avoided food waste and diet shifts (excluding land-use impacts to avoid double counting). Because of potential overlaps between measures, sectoral values from BECCS and the full value chain potential from demand-side measures are not summed with AFOLU. IAMs account for land competition and resource optimisation and can therefore sum across all available categories to derive the total AFOLU potential. Key: ND = no data; Sectoral = as assessed by sectoral literature review; IAM = as assessed by integrated assessment models; EJ = exajoule primary energy.

{| class="wikitable"
|-
! '''Mi''' '''tigation option'''

! '''Estimate type'''

! '''&lt;USD20 tCO''' 2 '''-eq''' –1

! '''&lt;USD50 tCO''' 2 '''-eq''' –1

! '''&lt;USD100''' '''tCO''' 2 '''-eq''' –1

! '''Technical'''

|-
| rowspan="2"| '''Agriculture total'''

| Sectoral

| 0.9 (0.5–1.4)

| 1.6 (1–2.4)

| 4.1 (1.7–6.7)

| 11.2 (1.6–28.5)

|-
| IAM

| 0.9 (0–3.1)

| 1.3 (0–3.2)

| 1.8 (0.7–3.3)

| ND

|-
| rowspan="2"| '''Agriculture – Carbon sequestration''' (Soil carbon management in croplands and grasslands, agroforestry, and biochar)

| Sectoral

| 0.5 (0.4–0.6)

| 1.2 (0.9–1.6)

| 3.4 (1.4–5.5)

| 9.5 (1.1–25.3)

|-
| IAM

| ND

| ND

| ND

| ND

|-
| rowspan="2"| '''Agriculture – Reduce CH''' 4 '''and N''' 2 '''O emissions''' (Improve enteric fermentation, manure management, nutrient management, and rice cultivation)

| Sectoral

| 0.4 (0.1–0.8)

| 0.4 (0.1–0.8)

| 0.6 (0.3–1.3)

| 1.7 (0.5–3.2)

|-
| IAM

| 0.9 (0–3.1)

| 1.3 (0–3.2)

| 1.8 (0.7–3.3)

| ND

|-
| rowspan="2"| '''Forests and other ecosystems total'''

| Sectoral

| 2.9 (2.2–3.5)

| 3.1 (1.4–5.1)

| 7.3 (3.9–13.1)

| 13 (5–29.5)

|-
| IAM

| 2.4 (0–10.5)

| 3.3 (0–9.9)

| 4.2 (0–12.1)

| ND

|-
| rowspan="2"| '''Forests and other ecosystems – Protect''' (Reduce deforestation, loss and degradation of peatlands, coastal wetlands, and grasslands)

| Sectoral

| 2.3 (1.7–2.9)

| 2.4 (1.2–3.6)

| 4.0 (2.5–7.4)

| 6.2 (2.8–14.4)

|-
| IAM

| ND

| ND

| ND

| ND

|-
| rowspan="2"| '''Forests and other ecosystems – Restore''' (Afforestation, reforestation, peatland restoration, coastal wetland restoration)

| Sectoral

| 0.15

| 0.7 (0.2–1.5)

| 2.1 (0.8–3.8)

| 5 (1.1–12.3)

|-
| IAM (A/R)

| 0.6 (0.2–6.5)

| 0.6 (0.01–8.3)

| 0.7 (0.07–6.8)

| ND

|-
| rowspan="2"| '''Forests and other ecosystems – Manage''' (Improve forest management, fire management)

| Sectoral

| 0.4 (0.3–0.4)

| ND

| 1.2 (0.6–1.9)

| 1.8 (1.1–2.8)

|-
| IAM

| ND

| ND

| ND

| ND

|-
| rowspan="2"| '''Demand-side measures''' (Shift to sustainable healthy diets, reduce food waste, and enhanced and improved use of wood products) ''* For all three only the direct avoided emissions; land-use effects are in measures above''

| Sectoral

| ND

| ND

| ''2.2 (1.1–3.6)*''

| ''4.2 (2.2–7.1)*''

|-
| IAM

| ND

| ND

| ND

| ND

|-
| rowspan="2"| '''BECCS''' (Only the CDR component, for example, the geological storage. Substitution effects are accounted in other sectoral chapters e.g: Energy (ch 6), Transport (ch 10))

| Sectoral

| ND

| ND

| 1.6 (0.5–3.5)

| 5.9 (0.5–11.3)

|-
| IAM

| 0.08 (0–0.7)

| 0.5 (0–6)

| 1.8 (0.2–9.9)

| ND

|-
| '''Bioenergy from residues'''

| Sectoral

| ND

| ND

| ND

| Up to 57 EJ yr –1

|-
| '''TOTAL AFOLU''' (Agriculture, forests and other ecosystems, diverted agricultural production from demand-side)

| Sectoral

| 3.8 (2.7–4.9)

| 4.3 (2.3–6.7)

| 13.6 (6.7–23.4)

| 28.4 (8.8–65.1)

|-
| '''TOTAL AFOLU''' (Agriculture, forests and other ecosystems, BECCS)

| IAM

| 3.4 (0–14.6)

| 5.3 (0.6–19.4)

| 7.9 (4.1–17.3)

| ND

|}

Two main approaches to estimating mitigation potentials include: (i) studies on individual measures and/or sectors – henceforth referred to as sectoral assessments, and (ii) integrated assessment models (IAM). Sectoral assessments include studies focusing on one activity (e.g., agroforestry) based on spatial and biophysical data, as well as econometric and optimisation models for a sector, for example, the forest or agriculture sector, and therefore cover a large suite of practices and activities while representing a broad body of literature. Sectoral assessments, however, rarely capture cross-sector interactions or impacts, making it difficult to completely account for land competition, trade-offs, and double counting when aggregating sectoral estimates across different studies and methods (Smith et al. 2014; [[#Jia--2019|Jia et al. 2019]] ). On the other hand, IAMs assess the climate impact of multiple and interlinked practices across sectors and therefore, can account for interactions and trade-offs (including land competition, use of other resources and international trade) between them. However, the number of land-based measures used in IAMs are limited compared with the sectoral portfolio (Figure 7.11). The resolution of land-based measures in IAMs are also generally coarser compared to some sectoral estimates, and as such, may be less robust for individual measures ( [[#Roe--2021|Roe et al. 2021]] ). Given the differences between and strengths and weaknesses of the two approaches, it is helpful to compare the estimates from both. We combine estimates from both approaches to establish an updated range of global land-based mitigation potential.

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[[File:2ad406a6ef1ac592127f53b23020298a IPCC_AR6_WGIII_Figure_7_11.png]]

'''Figure 7.11 | Global and regional mitigation potential (GtCO''' 2 '''-eq y''' '''r''' –1 ''')''' '''in''' '''2020–2050''' '''for 20 land-based measures.''' 2 '''-eq y''' '''r''' –1 ''')''' '''in''' '''2020–2050''' '''for 20 land-based measures.''' '''(a)''' Global estimates represent the mean (bar) and full range (error bars) of the economic potential (up to USD100 tCO 2 -eq –1 ) based on a comprehensive literature review of sectoral studies (references are outlined in the sub-section for each measure in Sections 7.4.2–7.4.5). Potential co-benefits and trade-offs for each of the 20 measures are summarised in icons. '''(b)''' Regional estimates illustrate the mean technical (T) and economic (E) (up to USD100 tCO 2 -eq –1 ) sectoral potential based on data from ( [[#Roe--2021|Roe et al. 2021]] ). IAM economic potential (M) (USD100 tCO 2 -eq –1 ) data is from the IPCC AR6 database.

For the 20 land-based mitigation measures outlined in this section, the mitigation potential estimates are largely derived from sectoral approaches, and where data is available, are compared to IAM estimates. Integrated assessment models and the emissions trajectories, cost-effectiveness and trade-offs of various mitigation pathways are detailed in [[#7.5|Section 7.5]] . It should be noted that the underlying literature for sectoral as well as IAM mitigation estimates consider GWP100 IPCC AR5 values (CH 4 = 28, N 2 O = 265) as well as GWP100 IPCC AR4 values (CH 4 = 25, N 2 O = 298) to convert CH 4 and N 2 O to CO 2 -eq. Where possible, we note the various GWP100 values (in IAM estimates, and the wetlands and agriculture sections), however in some instances, the varying GWP100 values used across studies prevents description of non-CO 2 gases in native units as well as conversion to AR6 GWP100 (CH 4 = 27, N 2 O = 273) CO 2 -eq values to aggregate sectoral assessment estimates.

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==== 7.4.1.2 Co-benefits and Risks ====

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Land interventions have interlinked implications for climate mitigation, adaptation, food security, biodiversity, ecosystem services, and other environmental and societal challenges ( [[#7.6.5|Section 7.6.5]] ). Therefore, it is important to consider the net effect of mitigation measures for achieving both climate and non-climate goals ( [[#7.1|Section 7.1]] ).

While it is helpful to assess the general benefits, risks and opportunities possible for land-based mitigation measures (L.G. [[#Smith--2019|Smith et al. 2019]] ), their efficacy and scale of benefit or risk largely depends on the type of activity undertaken, deployment strategy (e.g., scale, method), and context (e.g., soil, biome, climate, food system, land ownership) that vary geographically and over time ( ''robust evidence'' , ''high agreement'' ) (L.G. [[#Smith--2019|Smith et al. 2019]] ; P. [[#Smith--2019|Smith et al. 2019]] a; [[#Hurlbert--2019|Hurlbert et al. 2019]] ) ( [[IPCC:Wg3:Chapter:Chapter-12#12.5|Section 12.5]] ) ''.'' Impacts of land-based mitigation measures are therefore highly context specific and conclusions from specific studies may not be universally applicable. If implemented at appropriate scales and in a sustainable manner, land-based mitigation practices have the capacity to reduce emissions and sequester billions of tonnes of carbon from the atmosphere over coming decades, while also preserving or enhancing biodiversity, water quality and supply, air quality, soil fertility, food and wood security, livelihoods, resilience to droughts, floods and other natural disasters, and positively contributing to ecosystem health and human well-being ( ''high confidence'' ) ( [[#Toensmeier--2016|Toensmeier 2016]] ; [[#Karlsson--2020|Karlsson et al. 2020]] ).

Overall, measures in the AFOLU sector are uniquely positioned to deliver substantial co-benefits. However, the negative consequences of inappropriate or misguided design and implementation of measures may be considerable, potentially impacting for example, mitigation permanence, longevity, and leakage, biodiversity, wider ecosystem functioning, livelihoods, food security and human well-being ( [[#7.6|Section 7.6]] ) (AR6 WGII, Box 2.2). Land-based mitigation may also face limitations and trade-offs in achieving sustained emission reductions and/or removals due to other land challenges including climate change impacts. It is widely recognised that land-use planning that is context-specific, considers other sustainable development goals, and is adaptable over time can help achieve land-based mitigation that maximises co-benefits, avoids or limits trade-offs, and delivers on international policy goals including the SDGs, Land Degradation Neutrality, and Convention on Biological Diversity ( [[#7.6|Section 7.6]] ; Chapter 12).

Potential co-benefits and trade-offs are outlined for each of the 20 land-based mitigation measures in the proceeding sub-sections and summarised in Figure 7.12. [[#7.6.5|Section 7.6.5]] . discusses general links with ecosystem services, human well-being and adaptation, while [[IPCC:Wg3:Chapter:Chapter-12|Chapter 12]] ( [[IPCC:Wg3:Chapter:Chapter-12#12.5|Section 12.5]] ) provides an in-depth assessment of the land related impacts, risks and opportunities associated with mitigation options across sectors, including positive and negative effects on land resources, water, biodiversity, climate, and food security.

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[[File:2dff89cb98cd260cf67f4be8552cb4ad IPCC_AR6_WGIII_Figure_7_12.png]]

'''Figure 7.12 | Historic land sector GHG flux estimates and illustrative AFOLU mitigation pathways to 2050, based on data presented in Sections 7.''' '''2, 7.4 and 7.5.''' Historic trends consider both '''A''' anthropogenic (AFOLU) GHG fluxes (GtCO 2 -eq yr –1 ) according to FAOSTAT ( [[#FAO--2021a|FAO 2021a]] ; 2021b) and '''B''' the estimated natural land CO 2 sink according to ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). Note that for the anthropogenic net land CO 2 flux component, several approaches and methods are described within the literature ( [[#7.2.2|Section 7.2.2]] ) with a wide range in estimates. For clarity, only one dataset (FAOSTAT) is illustrated here. It is not intended to indicate preference for one particular method over others. Historic flux trends are illustrated to 2019, the latest year for which data is available. Projected economic mitigation potential (at costs of up to USD100 tCO 2 -eq –1 ) includes estimates from IAMs and sectoral studies (Table 7.3). The ‘sectoral studies’ are disaggregated into several cumulative parts: first ‘sectoral studies’ involves measures in agriculture, forests and other ecosystems, then an additional BECCS share (‘+ BECCS’), then the additional effect of demand-side measures and BECCS (‘+BECCS and demand-side measures’). The latter only accounting for diverted agricultural production to avoid double counting. Projected mitigation assumes adoption of measures to achieve increasing, linear mitigation, reaching average annual potential in 2050, although this does not reflect deployment rates for most measures. For illustrative purposes, a pathway to projected emissions in 2050 according to a scenario of current policy (C7 – above 3.0°C – Model: GCAM 5.3) is additionally included for reference.

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==== 7.4.1.3 Overview of Global and Regional Technical and Economic Potentials in AFOLU ====

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'''IPCC AR5 (2014).''' In the AR5, the economic mitigation potential of supply-side measures in the AFOLU sector was estimated at 7.18–10.60 GtCO 2 -eq yr –1 in 2030 with carbon prices up to USD100 tCO 2 -eq –1 , about a third of which could be achieved at &lt;USD20 tCO 2 -eq –1 ( ''medium evidence'' , ''medium agreement'' ) (Smith et al. 2014). The AR5 provided a summary table of individual AFOLU mitigation measures, but did not conduct a detailed assessment for each.

'''IPCC SRCCL (2019).''' The SRCCL assessed the full range of technical, economic and sustainability mitigation potentials in AFOLU for the period 2030–2050 and identified reduced deforestation and forest degradation to have greatest potential for reducing supply-side emissions (0.4 to 5.8 GtCO 2 -eq yr –1 ) ( ''high confidence'' ) followed by combined agriculture measures, 0.3 to 3.4 GtCO 2 -eq yr –1 ( ''medium confidence'' ) ( [[#Jia--2019|Jia et al. 2019]] ). For the demand-side estimates, shifting towards healthy, sustainable diets (0.7 to 8.0 GtCO 2 -eq yr –1 ) ( ''high confidence'' ) had the highest potential, followed by reduced food loss and waste (0.8 to 4.5 GtCO 2 -eq yr –1 ) ( ''high confidence'' ). Measures with greatest potential for CDR were afforestation/reforestation (0.5 to 10.1 GtCO 2 -eq yr –1 ) ( ''medium confidence'' ), soil carbon sequestration in croplands and grasslands (0.4 to 8.6 GtCO 2 -eq yr –1 ) ( ''medium confidence'' ) and BECCS (0.4 to 11.3 GtCO 2 -eq yr –1 ) ( ''medium confidence'' ). The SRCCL did not explore regional potential, associated feasibility nor provide detailed analysis of costs.

'''IPCC AR6.''' This assessment concludes the likely range of global land-based mitigation potential is approximately 8–14 GtCO 2 -eq yr –1 between 2020–2050 with carbon prices up to USD100 tCO 2 -eq –1 , about half of the technical potential ( ''medium evidence'' , ''medium agreement'' ). About 30–50% could be achieved &lt;USD20 tCO 2 -eq –1 (Table 7.3). The global economic potential estimates in this assessment are slightly higher than the AR5 range. Since AR5, there have been numerous new global assessments of sectoral land-based mitigation potential ( [[#Fuss--2018|Fuss et al. 2018]] ; [[#Griscom--2017|Griscom et al. 2017]] , 2020; [[#Roe--2019|Roe et al. 2019]] ; [[#Jia--2019|Jia et al. 2019]] ; [[#Griscom--2020|Griscom et al. 2020]] ; [[#Roe--2021|Roe et al. 2021]] ) as well as IAM estimates of mitigation potential ( [[#Riahi--2017|Riahi et al. 2017]] ; [[#Popp--2017|Popp et al. 2017]] ; [[#Rogelj--2018a|Rogelj et al. 2018a]] ; [[#Frank--2019|Frank et al. 2019]] ; [[#Johnston--2019|Johnston and Radeloff 2019]] ; [[#Baker--2019|Baker et al. 2019]] ), expanding the scope of AFOLU mitigation measures included and substantially improving the robustness and spatial resolution of mitigation estimates. A recent development is an assessment of country-level technical and economic (USD100 tCO 2 -eq –1 ) mitigation potential for 20 AFOLU measures, including for demand-side and soil organic carbon sequestration in croplands and grasslands, not estimated before ( [[#Roe--2021|Roe et al. 2021]] ). Estimates on costs, feasibility, sustainability, benefits, and risks have also been developed for some mitigation measures, and they continue to be active areas of research. Developing more refined sustainable potentials at a country-level will be an important next step. Although most mitigation estimates still do not consider the impact of future climate change, there are some emerging studies that do ( [[#Sonntag--2016|Sonntag et al. 2016]] ; Doelman et al. 2019). Given the IPCC WG1 finding that the land sink is continuing to increase although its efficiency is decreasing with climate change, it will be critical to better understand how future climate will affect mitigation potentials, particularly from CDR measures.

Across global sectoral studies, the economic mitigation potential (up to USD100 tCO 2 -eq –1 ) of supply-side measures in AFOLU for the period 2020–2050 is 11.4 mean (5.6–19.8 full range) GtCO 2 -eq yr –1 , about 50% of the technical potential of 24.2 (4.9–58) GtCO 2 -eq yr –1 (Table 7.3). Adding 2.1 GtCO 2 -eq yr –1 from demand-side measures (accounting only for diverted agricultural production to avoid double counting with land-use change effects), total land-based mitigation potential up to USD100 tCO 2 -eq –1 is 13.6 (6.7–23.4) GtCO 2 -eq yr –1 . This estimate aligns with the most recent regional assessment ( [[#Roe--2021|Roe et al. 2021]] ), which found the aggregate global mitigation potential of supply and demand-side measures to be 13.8 ± 3.1 GtCO 2 -eq yr –1 up to USD100 tCO 2 -eq –1 for the period 2020–2050. Across integrated assessment models (IAMs), the economic potential for land-based mitigation (Agriculture, LULUCF and BECCS) for USD100 tCO 2 -eq –1 is 7.9 mean (4.1–17.3 range) GtCO 2 -eq yr –1 in 2050 (Table 7.3). We add the estimate for BECCS here to provide the full land-based potential, as IAMs optimise land allocation based on costs, which displaces land-based CDR activities for BECCS. Combining both IAM and sectoral approaches, the likely range is therefore 7.9–13.6 (rounded to 8–14) GtCO 2 -eq yr –1 up to USD100 tCO 2 -eq –1 between 2020–2050. Considering both IAM and sectoral economic potential estimates, land-based mitigation could have the capacity to make the AFOLU sector net negative in GHG emissions from 2036 (Figure 7.12), although there are highly variable mitigation strategies for how AFOLU potential can be deployed for achieving climate targets (Illustrative Mitigation Pathways in [[#7.5.5|Section 7.5.5]] ). Economic potential estimates, which reflect a public willingness to pay, may be more relevant for policy making compared with technical potentials which reflect a theoretical maximum that may not be feasible or sustainable.

Among the mitigation options, the protection, improved management, and restoration of forests and other ecosystems (wetlands, savannas and grasslands) have the largest potential to reduce emissions and/or sequester carbon at 7.3 (3.9–13.1) GtCO 2 -eq yr –1 (up to USD100 tCO 2 -eq –1 ), with measures that ‘protect’ having the single highest total mitigation and mitigation densities (mitigation per area) in AFOLU (Table 7.3 and Figure 7.11). Agriculture provides the second largest share of mitigation, with 4.1 (1.7–6.7) GtCO 2 -eq yr –1 potential (up to USD100 tCO 2 -eq –1 ), from soil carbon management in croplands and grasslands, agroforestry, biochar, rice cultivation, and livestock and nutrient management (Table 7.3 and Figure 7.11). Demand-side measures including shifting to sustainable healthy diets, reducing food waste, and improving wood products can mitigate 2.2 (1.1–3.6) GtCO 2 -eq yr –1 when accounting only for diverted agricultural production from diets and food waste to avoid double counting with measures in forests and other ecosystems (Table 7.3 and Figure 7.11). The potential of demand-side measures increases three-fold, to 6.5 (4–9.5) GtCO 2 -eq yr –1 when accounting for the entire value chain including land-use effects, but would overlap with other measures and is therefore not additive.

Most mitigation options are available and ready to deploy. Emissions reductions can be unlocked relatively quickly, whereas CDR need upfront investment to generate sequestration over time. The protection of natural ecosystems, carbon sequestration in agriculture, sustainable healthy diets and reduced food waste have especially high co-benefits and cost efficiency. Avoiding the conversion of carbon-rich primary peatlands, coastal wetlands and forests is particularly important as most carbon lost from those ecosystems are irrecoverable through restoration by the 2050 timeline of achieving net zero carbon emissions ( [[#Goldstein--2020|Goldstein et al. 2020]] ). Sustainable intensification, shifting diets, reducing food waste could enhance efficiencies and reduce agricultural land needs, and are therefore critical for enabling supply-side measures such as reduced deforestation, restoration, as well as reducing N 2 O and CH 4 emissions from agricultural production – as seen in the Illustrative Mitigation Pathway (IMP-SP) ( [[#7.5|Section 7.5]] .6). Although agriculture measures that reduce non-CO 2 , particularly of CH 4 , are important for near-term emissions reductions, they have less economic potential due to costs. Demand-side measures may be able to deliver non-CO 2 emissions reductions more cost efficiently.

Regionally, economic mitigation potential up to USD100 tCO 2 -eq –1 is estimated to be greatest in tropical countries in Asia and Pacific (34%), Latin America and the Caribbean (24%), and Africa and the Middle East (18%) because of the large potential from reducing deforestation and sequestering carbon in forests and agriculture (Figure 7.11). However, there is also considerable potential in Developed Countries (18%) and more modest potential in Eastern Europe and West-Central Asia (5%). These results are in line with the IAM regional mitigation potentials (Figure 7.11). The protection of forests and other ecosystems is the dominant source of mitigation potential in tropical regions, whereas carbon sequestration in agricultural land and demand-side measures are important in Developed Countries and Asia and Pacific. The restoration and management of forests and other ecosystems is more geographically distributed, with all regions having significant potential. Regions with large livestock herds (Developed Countries, Latin America) and rice paddy fields (Asia and Pacific) have potential to reduce CH 4 . As expected, the highest total potential is associated with countries and regions with large land areas, however when considering mitigation density (total potential per hectare), many smaller countries, particularly those with wetlands have disproportionately high levels of mitigation for their size ( [[#Roe--2021|Roe et al. 2021]] ). As global commodity markets connect regions, AFOLU measures may create synergies and trade-offs across the world, which could make national demand-side measures for example, important in mitigating supply-side emissions elsewhere (Kallio et al. 2018).

Although economic potentials provide more realistic, near-term climate mitigation compared to technical potentials, they still do not account for feasibility barriers and enabling conditions that vary by region and country. For example, according to most models, including IAMs, avoided deforestation is the cheapest land-based mitigation option (Table 7.3, Sections 7.5.3 and 7.5.4), however implementing interventions aimed at reducing deforestation (including REDD+) often have higher transaction and implementation costs than expected due to various barriers and enabling conditions ( [[#Luttrell--2018|Luttrell et al. 2018]] ) ( [[#7.6|Section 7.6]] ). The feasibility of implementing AFOLU mitigation measures, including those with multiple co-benefits, depends on varying economic, technological, institutional, socio-cultural, environmental and geophysical barriers ( ''high confidence'' ) (L.G. [[#Smith--2019|Smith et al. 2019]] ). The section for each individual mitigation measure provides an overview of co-benefits and risks associated with the measure and [[#7.6|Section 7.6]] .6 outlines key enabling factors and barriers for implementation.

<div id="7.4.2" class="h2-container"></div>

<span id="forests-and-other-ecosystems"></span>
=== 7.4.2 Forests and Other Ecosystems ===

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<div id="7.4.2.1" class="h3-container"></div>

<span id="reduce-deforestation-and-degradation"></span>
==== 7.4.2.1 Reduce Deforestation and Degradation ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Reducing deforestation and forest degradation conserves existing carbon pools in forest vegetation and soil by avoiding tree cover loss and disturbance. Protecting forests involves controlling the drivers of deforestation (such as commercial and subsistence agriculture, mining, urban expansion) and forest degradation (such as overharvesting including fuelwood collection, poor harvesting practices, overgrazing, pest outbreaks, and extreme wildfires), as well as by establishing well designed, managed and funded protected areas ( [[#Barber--2014|Barber et al. 2014]] ), improving law enforcement, forest governance and land tenure, supporting community forest management and introducing forest certification (P. [[#Smith--2019|Smith et al. 2019]] a). Reducing deforestation provides numerous and substantial co-benefits, preserving biodiversity and ecosystem services (e.g., air and water filtration, water cycling, nutrient cycling) more effectively and at lower costs than afforestation/reforestation ( [[#Jia--2019|Jia et al. 2019]] ). Potential adverse side effects of these conservation measures include reducing the potential for agriculture land expansion, restricting the rights and access of local people to forest resources, or increasing the dependence of local people to insecure external funding. Barriers to implementation include unclear land tenure, weak environmental governance, insufficient funds, and increasing pressures associated to agriculture conversion, resource exploitation and infrastructure development (Sections 7.3 and 7.6).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC''' '''and SRCCL); mitigation potential, costs, and pathways.''' Reducing deforestation and forest degradation represents one of the most effective options for climate change mitigation, with technical potential estimated at 0.4–5.8 GtCO 2 yr –1 by 2050 ( ''high confidence'' ) (SRCCL, Chapters 2 and 4, and Table 6.14). The higher technical estimate represents a complete halting of land-use conversion in forests and peatland forests (i.e., assuming recent rates of carbon loss are saved each year) and includes vegetation and soil carbon pools. Ranges of economic potentials for forestry ranged in AR5 from 0.01–1.45 GtCO 2 yr –1 for USD20 tCO 2 –1 to 0.2–13.8 GtCO 2 yr –1 for USD100 tCO 2 –1 by 2030 with reduced deforestation dominating the forestry mitigation potential LAM and MAF, but very little potential in OECD-1990 and EIT (IPCC AR5).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Since the SRCCL, several studies have provided updated and convergent estimates of economic mitigation potentials by region ( [[#Busch--2019|Busch et al. 2019]] ; [[#Griscom--2020|Griscom et al. 2020]] ; [[#Austin--2020|Austin et al. 2020]] ; [[#Roe--2021|Roe et al. 2021]] ). Tropical forests and savannas in Latin America provide the largest share of mitigation potential (3.9 GtCO 2 yr –1 technical, 2.5 GtCO 2 yr –1 at USD100 tCO 2 –1 ) followed by South-East Asia (2.2 GtCO 2 yr –1 technical, 1.5 GtCO 2 yr –1 at USD100 tCO 2 –1 ) and Africa (2.2 GtCO 2 yr –1 technical, 1.2 GtCO 2 yr –1 at USD100 tCO 2 –1 ) ( [[#Roe--2021|Roe et al. 2021]] ). Tropical forests continue to account for the highest rates of deforestation and associated GHG emissions. While deforestation shows signs of decreasing in several countries, in others, it continues at a high rate or is increasing ( [[#Turubanova--2018|Turubanova et al. 2018]] ). Between 2010–2020, the rate of net forest loss was 4.7 Mha yr –1 with Africa and South America presenting the largest shares (3.9 Mha and 2.6 Mha, respectively) ( [[#FAO--2020a|FAO 2020a]] ).

A major uncertainty in all studies on avoided deforestation potential is their reliance on future reference levels that vary across studies and approaches. If food demand increases in the future, for example, the area of land deforested will likely increase, suggesting more technical potential for avoiding deforestation. Transboundary leakage due to market adjustments could also increase costs or reduce effectiveness of avoiding deforestation (e.g., [[#Ingalls--2018|Ingalls et al. 2018]] ; [[#Gingrich--2019|Gingrich et al. 2019]] ). Regarding forest regrowth, there are uncertainties about the time for the secondary forest carbon saturation ( [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Zhu--2018|Zhu et al. 2018]] ). Permanence of avoided deforestation may also be a concern due to the impacts of climate change and disturbance of other biogeochemical cycles on the world’s forests that can result in future potential changes in terrestrial ecosystem productivity, climate-driven vegetation migration, wildfires, forest regrowth and carbon dynamics ( [[#Ballantyne--2012|Ballantyne et al. 2012]] ; [[#Kim--2017b|Kim et al. 2017b]] ; [[#Lovejoy--2018|Lovejoy and Nobre 2018]] ; [[#Aragão--2018|Aragão et al. 2018]] ).

'''Critical assessment and conclusion.''' Based on studies since AR5, the technical mitigation potential for reducing deforestation and degradation is significant, providing 4.5 (2.3–7) GtCO 2 yr –1 globally by 2050, of which 3.4 (2.3–6.4) GtCO 2 yr –1 is available at below USD100 tCO 2 –1 ( ''medium confidence'' ) (Figure 7.11). Over the last decade, hundreds of subnational initiatives that aim to reduce deforestation related emissions have been implemented across the tropics ( [[#7.6|Section 7.6]] ). Reduced deforestation is a significant piece of the NDCs in the Paris Agreement ( [[#Seddon--2020|Seddon et al. 2020]] ) and keeping the temperature below 1.5°C ( [[#Crusius--2020|Crusius 2020]] ). Conservation of forests provides multiple co-benefits linked to ecosystem services, biodiversity and sustainable development ( [[#7.6|Section 7.6]] .). Still, ensuring good governance, accountability (e.g., enhanced monitoring and verification capacity; [[#Bos--2020|Bos 2020]] ), and the rule of law are crucial for implementing forest-based mitigation options. In many countries with the highest deforestation rates, insecure land rights often are significant barriers for forest-based mitigation options (Gren and Zeleke 2016; [[#Essl--2018|Essl et al. 2018]] ).

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<span id="afforestation-reforestation-and-forest-ecosystem-restoration"></span>
==== 7.4.2.2 Afforestation, Reforestation and Forest Ecosystem Restoration ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Afforestation and reforestation (A/R) are activities that convert land to forest, where reforestation is on land that has previously contained forests, while afforestation is on land that historically has not been forested (Box 7.2). Forest restoration refers to a form of reforestation that gives more priority to ecological integrity as well, even though it can still be a managed forest. Depending on the location, scale, and choice and management of tree species, A/R activities have a wide variety of co-benefits and trade-offs. Well-planned, sustainable reforestation and forest restoration can enhance climate resilience and biodiversity, and provide a variety of ecosystem services including water regulation, microclimatic regulation, soil erosion protection, as well as renewable resources, income and livelihoods ( [[#Locatelli--2015|Locatelli et al. 2015]] ; [[#Stanturf--2015|Stanturf et al. 2015]] ; [[#Ellison--2017|Ellison et al. 2017]] ; [[#Verkerk--2020|Verkerk et al. 2020]] ). Afforestation, when well planned, can help address land degradation and desertification by reducing runoff and erosion and lead to cloud formation however, when not well planned, there are localised trade-offs such as reduced water yield or biodiversity ( [[#Teuling--2017|Teuling et al. 2017]] ; [[#Ellison--2017|Ellison et al. 2017]] ). The use of non-native species and monocultures may have adverse impacts on ecosystem structure and function, and water availability, particularly in dry regions ( [[#Ellison--2017|Ellison et al. 2017]] ). A/R activities may change the surface albedo and evapotranspiration regimes, producing net cooling in the tropical and subtropical latitudes for local and global climate and net warming at high latitudes ( [[#7.4.2|Section 7.4.2]] ). Very large-scale implementation of A/R may negatively affect food security since an increase in global forest area can increase food prices through land competition ( [[#Kreidenweis--2016|Kreidenweis et al. 2016]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The AR5 did not provide a new specification of A/R potential, but referred to IPCC AR4 mostly for forestry measures ( [[#Nabuurs--2007|Nabuurs et al. 2007]] ). The AR5 did view the feasible A/R potential from a diets change scenario that released land for reforestation and bioenergy crops. The AR5 provided top-down estimates of costs and potentials for forestry mitigation options – including reduced deforestation, forest management, afforestation, and agroforestry, estimated to contribute between 1.27 and 4.23 GtCO 2 yr –1 of economically viable abatement in 2030 at carbon prices up to USD100 tCO 2 -eq –1 (Smith et al. 2014).

The SRCCL remained with a reported wide range of mitigation potential for A/R of 0.5–10.1 GtCO 2 yr –1 by 2050 ( ''medium confidence'' ) ( [[#Kreidenweis--2016|Kreidenweis et al. 2016]] ; [[#Griscom--2017|Griscom et al. 2017]] ; [[#Hawken--2017|Hawken 2017]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Roe--2019|Roe et al. 2019]] ) (SRCCL Chapters 2 and 6). The higher estimate represents a technical potential of reforesting all areas where forests are the native cover type (reforestation), constrained by food security and biodiversity considerations, considering above and below-ground carbon pools and implementation on a rather theoretical maximum of 678 Mha of land ( [[#Griscom--2017|Griscom et al. 2017]] ; [[#Roe--2019|Roe et al. 2019]] ). The lower estimates represent the minimum range from an Earth System Model and a sustainable global CDR potential ( [[#Fuss--2018|Fuss et al. 2018]] ). Climate change will affect the mitigation potential of reforestation due to impacts in forest growth and composition, as well as changes in disturbances including fire. However, none of the mitigation estimates included in the SRCCL account for climate impacts.

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Since SRCCL, additional studies have been published on A/R mitigation potential by [[#Bastin--2019|Bastin et al. (2019)]] , [[#Lewis--2019|Lewis et al. (2019)]] , Doelman et al. (2019), [[#Favero--2020|Favero et al. (2020)]] and [[#Austin--2020|Austin et al. (2020)]] . These studies are within the range reported in the SRCCL stretching the potentials at the higher range. The rising public interest in nature-based solutions, along with high profile initiatives being launched (UN Decade on Restoration announced in 2019, the Bonn challenge on 150 million ha of restored forest in 2020 and the one trillion trees campaign launched by the World Economic Forum in 2020), has prompted intense discussions on the scale, effectiveness, and pitfalls of A/R and tree planting for climate mitigation ( [[#Luyssaert--2018|Luyssaert et al. 2018]] ; [[#Bond--2019|Bond et al. 2019]] ; [[#Anderegg--2020|Anderegg et al. 2020]] ; [[#Heilmayr--2020|Heilmayr et al. 2020]] ; [[#Holl--2020|Holl and Brancalion 2020]] ). The sometimes sole attention on afforestation and reforestation '''–''' suggesting it may solve the climate problem to large extent, in combination with the very high estimates of potentials '''–''' have led to polarisation in the debate, resulting in criticism to these measures or an emphasis on nature restoration only ( [[#Lewis--2019|Lewis et al. 2019]] ). Our assessment based on most recent literature produced regional economic mitigation potential at USD100 tCO 2 –1 estimate of 100–400 MtCO 2 yr –1 in Africa, 210–266 MtCO 2 yr –1 in Asia and Pacific, 291 MtCO 2 -eq yr –1 in Developed Countries (87% in North America), 30 MtCO 2 -eq yr –1 in Eastern Europe and West-Central Asia, and 345–898 MtCO 2 -eq yr –1 in Latin America and Caribbean ( [[#Roe--2021|Roe et al. 2021]] ), which totals to about 1200 MtCO 2 yr –1 , leaning to the lower range of the potentials in earlier IPCC reports. A recent global assessment of the aggregate costs for afforestation and reforestation suggests that at USD100 tCO 2 –1 , 1.6 GtCO 2 yr –1 could be sequestered globally for an annual cost of USD130 billion ( [[#Austin--2020|Austin et al. 2020]] ). Sectoral studies that are able to deal with local circumstances and limits estimate A/R potentials at 20 MtCO 2 yr –1 in Russia (Eastern Europe and West-Central Asia) ( [[#Romanovskaya--2020|Romanovskaya et al. 2020]] ) and 64 MtCO 2 yr –1 in Europe ( [[#Nabuurs--2017|Nabuurs et al. 2017]] ). ( [[#Domke--2020|Domke et al. 2020]] ) estimated for the USA an additional 20% sequestration rate from tree planting to achieve full stocking capacity of all understocked productive forestland, in total reaching 187 MtCO 2 yr –1 sequestration. A new study on costs in the USA estimates 72–91 MtCO 2 yr –1 could be sequestered between now and 2050 for USD100 tCO 2 –1 (Wade et al. 2019). The tropical and subtropical latitudes are the most effective for forest restoration in terms of carbon sequestration because of the rapid growth and lower albedo of the land surface compared with high latitudes ( [[#Lewis--2019|Lewis et al. 2019]] ). Costs may be higher if albedo is considered in North America, Russia, and Africa ( [[#Favero--2017|Favero et al. 2017]] ). In addition, a wide variety of sequestration rates have been collected and published in the IPCC Good Practice Guidance for the AFOLU sector ( [[#IPCC--2006|IPCC 2006]] ).

'''Critical assessment and conclusion.''' There is ''medium confidence'' that the global technical mitigation potential of afforestation and reforestation activities by 2050 is 3.9 (0.5–10.1) GtCO 2 yr –1 , and the economic mitigation potential (&lt;USD100 tCO 2 –1 ) is 1.6 (0.5–3.0) GtCO 2 yr –1 (requiring about 200 Mha). Per hectare a long (about 100 year) sustained effect of 5–10 t CO 2 ha –1 yr –1 is realistic with ranges between 1–20 t(CO 2 ) ha –1 yr –1 . Not all sectoral studies rely on economic models that account for leakage ( [[#Murray--2004|Murray et al. 2004]] ; Sohngen and Brown 2004), suggesting that technical potential may be overestimated.

<div id="7.4.2.3" class="h3-container"></div>

<span id="improved-forest-management"></span>
==== 7.4.2.3 Improved Forest Management ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Improved sustainable forest management of already managed forests can lead to higher forest carbon stocks, better quality of produced wood, continuously produced wood, while maintaining and enhancing the forest carbon stock, and can also partially prevent and counteract the impacts of disturbances ( [[#Kurz--2008|Kurz et al. 2008]] ; [[#Marlon--2012|Marlon et al. 2012]] ; [[#Abatzoglou--2016|Abatzoglou and Williams 2016]] ; [[#Seidl--2017|Seidl et al. 2017]] ; [[#Nabuurs--2017|Nabuurs et al. 2017]] ; [[#Tian--2018|Tian et al. 2018]] ; [[#Ekholm--2020|Ekholm 2020]] ). Furthermore, it can provide benefits for climate change adaptation, biodiversity conservation, microclimatic regulation, soil erosion protection and water and flood regulation with reduced lateral carbon fluxes ( [[#Ashton--2012|Ashton et al. 2012]] ; [[#Martínez-Mena--2019|Martínez-Mena et al. 2019]] ; [[#Verkerk--2020|Verkerk et al. 2020]] ). Often, in existing (managed) forests with existing carbon stocks, large changes per hectare cannot be expected, although many forest owners may respond to carbon price incentives ( [[#Favero--2020|Favero et al. 2020]] ; [[#Ekholm--2020|Ekholm 2020]] ). The full mitigation effects can be assessed in conjunction with the overall forest and wood use system i.e., carbon stock changes in standing trees, soil, harvested wood products (HWPs) and its bioenergy component with the avoided emissions through substitution. Forest management strategies aimed at increasing the biomass stock may have adverse side effects, such as decreasing the stand-level structural complexity, large emphasis on pure fast-growing stands, risks for biodiversity and resilience to natural disasters.

Generally, measures can consist of one or combination of longer rotations, less intensive harvests, continuous-cover forestry, mixed stands, more adapted species, selected provenances, high quality wood assortments, and so on. Further, there is a trade-off between management in various parts of the forest product value chain, resulting in a wide range of results on the role of managed forests in mitigation (Agostini et al. 2013; [[#Braun--2016|Braun et al. 2016]] ; [[#Soimakallio--2016|Soimakallio et al. 2016]] ; [[#Gustavsson--2017|Gustavsson et al. 2017]] ; [[#Erb--2017|Erb et al. 2017]] ; [[#Favero--2020|Favero et al. 2020]] ; [[#Hurmekoski--2020|Hurmekoski et al. 2020]] ). Some studies conclude that reduction in forest carbon stocks due to harvest exceeds for decades the joint sequestration of carbon in harvested wood product stocks and emissions avoided through wood use ( [[#Soimakallio--2016|Soimakallio et al. 2016]] ; [[#Seppälä--2019|Seppälä et al. 2019]] ), whereas others emphasise country level examples where investments in forest management have led to higher growing stocks while producing more wood ( [[#Schulze--2020|Schulze et al. 2020]] ; [[#Ouden--2020|Ouden et al. 2020]] ; [[#Cowie--2021|Cowie et al. 2021]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' In the SRCCL, forest management activities have the potential to mitigate 0.4–2.1 GtCO 2 -eq yr –1 by 2050 ( ''medium confidence'' ) (SRCCL: [[#Griscom--2017|Griscom et al. 2017]] ; [[#Roe--2019|Roe et al. 2019]] ). The higher estimate stems from assumptions of applications on roughly 1.9 billion ha of already managed forest which can be seen as very optimistic. It combines both natural forest management as well as improved plantations, on average with a small net additional effect per hectare, not including substitution effects in the energy sector nor the buildings sector.

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' The area of forest under management plans has increased in all regions since 2000 by 233 Mha ( [[#FAO--2020e|FAO 2020e]] ). The roughly 1 billion ha of secondary and degraded forests would be ideal to invest in and develop a sustainable sector that pays attention to biodiversity, wood provision and climate mitigation at the same time. This all depends on the effort made, the development of expertise, know-how in the field, nurseries with adapted provenances, etc as was also found for Russian climate-smart forestry options ( [[#Leskinen--2020|Leskinen et al. 2020]] ). Regionally, recently updated economic mitigation potential at USD100 tCO 2 –1 have 179–186 MtCO 2 -eq yr –1 in Africa, 193–313 MtCO 2 -eq yr –1 in Asia and Pacific, 215–220 MtCO 2 -eq yr –1 in Developed Countries , 82–152 MtCO 2 -eq yr –1 in Eastern Europe and West-Central Asia, and 62–204 MtCO 2 -eq yr –1 in Latin America and Caribbean ( [[#Roe--2021|Roe et al. 2021]] ).

Regional studies can take into account the local situation better: Russia [[#Romanovskaya--2020|Romanovskaya et al. (2020)]] estimate the potential of forest fires management at 220–420 MtCO 2 yr –1 , gentle logging technology at 15–59, reduction of wood losses at 61–76 MtCO 2 yr –1 . In North America, ( [[#Austin--2020|Austin et al. 2020]] ) estimate that in the next 30 years, forest management could contribute 154 MtCO 2 yr –1 in the USA and Canada with 81 MtCO 2 yr –1 available at less than USD100 tCO 2 –1 . In one production region (British Columbia) a cost-effective portfolio of scenarios was simulated that directed more of the harvested wood to longer-lived wood products, stopped burning of harvest residues and instead produced bioenergy to displace fossil fuel burning, and reduced harvest levels in regions with low disturbance rates. Net GHG emissions were reduced by an average of –9 MtCO 2 -eq yr –1 ( [[#Smyth--2020|Smyth et al. 2020]] ). In Europe, climate-smart forestry could mitigate an additional 0.19 GtCO 2 yr –1 by 2050 ( [[#Nabuurs--2017|Nabuurs et al. 2017]] ), in line with the regional estimates in ( [[#Roe--2021|Roe et al. 2021]] ).

In the tropics, estimates of the pantropical climate mitigation potential of natural forest management (a light intensity management in secondary forests), across three tropical regions (Latin America, Africa, Asia), is around 0.66 GtCO 2 -eq yr –1 with Asia responding for the largest share followed by Africa and Latin America ( [[#Roe--2021|Roe et al. 2021]] ). Selective logging occurs in at least 20% of the world’s tropical forests and causes at least half of the emissions from tropical forest degradation ( [[#Asner--2005|Asner et al. 2005]] ; [[#Blaser--2011|Blaser and Küchli 2011]] ; [[#Pearson--2017|Pearson et al. 2017]] ). Reduced-impact logging for climate (RIL-C; promotion of reduced wood waste, narrower haul roads, and lower impact skidding equipment) has the potential to reduce logging emissions by 44% ( [[#Ellis--2019|Ellis et al. 2019]] ), while also providing timber production.

'''Critical assessment and conclusion.''' There is ''medium confidence'' that the global technical mitigation potential for improved forest management by 2050 is 1.7 (1–2.1) GtCO 2 yr –1 , and the economic mitigation potential (&lt;USD100 tCO 2 –1 ) is 1.1 (0.6–1.9) GtCO 2 yr –1 . Efforts to change forest management do not only require, for example, a carbon price incentive, but especially require knowledge, institutions, skilled labour, good access and so on. These requirements outline that although the potential is of medium size, we estimate a feasible potential towards the lower end. The net effect is also difficult to assess, as management changes impact not only the forest biomass, but also the wood chain and substitution effects. Further, leakage can arise from efforts to change management for carbon sequestration. Efforts, for example to set aside large areas of forest, may be partly counteracted by higher harvesting pressures elsewhere (Kallio et al. 2018). Studies such as ( [[#Austin--2020|Austin et al. 2020]] ) implicitly account for leakage and thus suggest higher costs than other studies. We therefore judge the mitigation potential at medium potential with medium agreement ''.''

<div id="box-7.2" class="h2-container box-container"></div>

<span id="box-7.2-climate-smart-for-estry-in-europe"></span>
=== Box 7.2 | Climate-smart Forestry in Europe ===

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'''Summary'''

European forests have been regarded as prospering and increasing for the last five decades. However, these views also changed recently. Climate change is putting a large pressure on mono species and high stocked areas of Norway spruce in Central Europe ( [[#Hlásny--2021|Hlásny et al. 2021]] ; [[#Senf--2021|Senf and Seidl 2021]] ) with estimates of mortality reaching 200 million m 3 , biodiversity under pressure, the Mediterranean area showing a weak sector and harvesting pressure in the Baltics and north reaching maxima achievable. A European strategy for unlocking the EU’s forests and forest sector potential was needed at the time of developing the LULUCF regulation and was based on the concept of ‘climate-smart forestry’ (CSF) ( [[#Nabuurs--2017|Nabuurs et al. 2017]] ; [[#Verkerk--2020|Verkerk et al. 2020]] ).

'''Background'''

The idea behind CSF is that it considers the whole value chain from forest to wood products and energy, illustrating that a wide range of measures can be applied to provide positive incentives for more firmly integrating climate objectives into the forest and forest sector framework. CSF is more than just storing carbon in forest ecosystems; it builds upon three main objectives; (i) reducing and/or removing GHG emissions; (ii) adapting and building diverse forests for forest resilience to climate change; and (iii) sustainably increasing forest productivity and incomes. These three CSF objectives can be achieved by tailoring policy measures and actions to regional circumstances in member states’ forest sectors.

Box 7.2

'''Case description'''

The 2015 annual mitigation effect of EU-28 forests via contributions to the forest sink, material substitution and energy substitution is estimated at 569 MtCO 2 yr –1 , or 13% of total current EU emissions. With the right set of incentives in place at EU and member states levels, it was found that the EU-28 has the potential to achieve an additional combined mitigation impact through the implementation of CSF of 441 MtCO 2 yr –1 by 2050. Also, with the Green Deal and its biodiversity and forest strategy, more emphasis will be placed on forests, forest management and the provision of renewables. It is the diversity of measures (from strict reserves to more intensively managed systems while adapting the resource) that will determine the success. Only with co-benefits in, for example, nature conservation, soil protection, and provision of renewables, wood for buildings and income, the mitigation and adaptation measures will be successful.

'''Interactions, limitati''' '''ons and lessons'''

Climate-smart forestry is now taking shape across Europe with various research and implementation projects ( [[#Climate%20Smart%20Forest%20and%20Nature%20Management--2021|Climate Smart Forest and Nature Management, 2021]] ). Pilots and projects are being implemented by a variety of forest owners, some with more attention on biodiversity and adaptation, some with more attention on production functions. They establish examples and in longer term the outreach to the 16 million private owners in Europe. However, the right triggers and incentives are often still lacking. For example, adapting the spruce forest areas in Central Europe to climate change requires knowledge about different species, biodiversity and different management options and eventually use in industry. It requires alternative species to be available from the nurseries, as well as improved monitoring to assess the success and steer activities.

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<span id="fire-management-forest-and-grasslandsavanna-fires"></span>
==== 7.4.2.4 Fire Management (Forest and Grassland/Savanna Fires) ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Fire management objectives include safeguarding life, property, and resources through the prevention, detection, control, restriction, and management of fire for diverse purposes in natural ecosystems (SRCCL, Chapter 6). Controlled burning is an effective economic method of reducing fire danger and stimulating natural regeneration. Co-benefits of fire management include reduced air pollution compared to much larger, uncontrolled fires, prevention of soil erosion and land degradation, biodiversity conservation in rangelands, and improvement of forage quality ( [[#Hurteau--2011|Hurteau and Brooks 2011]] ; [[#Falk--2017|Falk 2017]] ; [[#Hurteau--2019|Hurteau et al. 2019]] ). Fire management is still challenging because it is not only fire suppression at times of fire, but especially proper natural resource management in between fire events. Furthermore, it is challenging because of legal and policy issues, equity and rights concerns, governance, capacity, and research needs ( [[#Wiedinmyer--2010|Wiedinmyer and Hurteau 2010]] ; [[#Goldammer--2016|Goldammer 2016]] ; [[#Russell-Smith--2017|Russell-Smith et al. 2017]] ). It will increasingly be needed under future enhanced climate change.

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' In the SRCCL, fire management is among the nine options that can deliver medium-to-large benefits across multiple land challenges (climate change mitigation, adaptation, desertification, land degradation, and food security) ( ''high confidence'' ). Total emissions from fires have been on the order of 8.1 GtCO 2 -eq yr –1 in terms of gross biomass loss for the period 1997–2016 (SRCCL, Chapter 2, and Cross-Chapter Box 3 in Chapter 2). Reduction in fire CO 2 emissions was calculated to enhance land carbon sink by 0.48 GtCO 2 -eq yr –1 for the 1960–2009 period ( [[#Arora--2018|Arora and Melton 2018]] ) (SRCCL, Table 6.16).

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<span id="developments-since-ar5-and-ipcc-special-reports-sr1.5-srocc-and-srccl"></span>
===== Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL) =====

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'''Savannas.''' Savannas constitute one of the most fire-prone vegetation types on Earth and are a significant source of GHG emissions. Savanna fires contributed 62% (4.92 PgCO 2 -eq yr –1) ) of gross global mean fire emissions between 1997 and 2016. Regrowth from vegetation postfire sequesters the CO 2 released into the atmosphere, but not the CH 4 and N 2 O emissions which contributed an approximate net of 2.1 PgCO 2 -eq yr −1 ( [[#Lipsett-Moore--2018|Lipsett-Moore et al. 2018]] ). Therefore, implementing prescribed burning with low intensity fires, principally in the early dry season, to effectively manage the risk of wildfires occurring in the late dry season is associated with reducing emissions ( [[#Whitehead--2014|Whitehead et al. 2014]] ). Considering this fire management practice, estimates of global opportunities for emissions reductions were estimated at 69.1 MtCO 2 -eq yr −1 in Africa (29 countries, with 20 least developed African countries accounting for 74% of the mitigation potential), 13.3 MtCO 2 -eq yr −1 in South America (six countries), and 6.9 MtCO 2 -eq yr –1 in Australia and Papua New Guinea ( [[#Lipsett-Moore--2018|Lipsett-Moore et al. 2018]] ). In Australia, savanna burning emissions abatement methodologies have been available since 2012, and abatement has exceeded 9.3 MtCO 2 -eq mainly through the management of low intensity early dry season fire. Until September 2021, 78 projects were registered (Australian Government, Clean Energy Regulator, 2021).

'''Forests.''' Fire is also a prevalent forest disturbance ( [[#Falk--2011|Falk et al. 2011]] ; [[#Scott--2014|Scott et al. 2014]] ; [[#Andela--2019|Andela et al. 2019]] ). About 98 Mha of forest were affected by fire in 2015, affecting about 4% of the tropical (dry) forests, 2% of the subtropical forests, and 1% of temperate and boreal forests ( [[#FAO--2020a|FAO 2020a]] ). Between 2001–2018, remote sensing data showed that tree-covered areas correspond to about 29% of the total area burned by wildfires, most in Africa. Prescribed fires are also applied routinely in forests worldwide for fuel reduction and ecological reasons ( [[#Kalies--2016|Kalies and Yocom Kent 2016]] ). Fire resilience is increasingly managed in Southwestern USA forest landscapes, which have experienced droughts and widespread, high-severity wildfires ( [[#Keeley--2019|Keeley et al. 2019]] ). In these forests, fire exclusion management, coupled with a warming climate, has led to increasingly severe wildfires ( [[#Hurteau--2014|Hurteau et al. 2014]] ). However, the impacts of prescribed fires in forests in reducing carbon emissions are still inconclusive. Some positive impacts of prescribed fires are associated with other fuel reduction techniques ( [[#Loudermilk--2017|Loudermilk et al. 2017]] ; [[#Flanagan--2019|Flanagan et al. 2019]] ; [[#Stephens--2020|Stephens et al. 2020]] ), leading to maintaining carbon stocks and reducing carbon emissions in the future where extreme fire weather events are more frequent ( [[#Krofcheck--2018|Krofcheck et al. 2018]] , 2019; [[#Hurteau--2019|Hurteau et al. 2019]] ; [[#Bowman--2020a|Bowman et al. 2020a]] ,b; [[#Goodwin--2020|Goodwin et al. 2020]] ). Land management approaches will certainly need to consider the new climatic conditions (e.g., the proportion of days in fire seasons with the potential for unmanageable fires more than doubling in some regions in northern and eastern boreal forest) ( [[#Wotton--2017|Wotton et al. 2017]] ).

'''Critical assessment and conclusion.''' There is ''low confidence'' that the global technical mitigation potential for grassland and savanna fire management by 2050 is 0.1 (0.09–0.1) GtCO 2 yr –1 , and the economic mitigation potential (&lt;USD100 tCO 2 –1 ) is 0.05 (0.03–0.07) GtCO 2 yr –1 . Savanna fires produce significant emissions globally, but prescribed fires in the early dry season could mitigate emissions in different regions, particularly Africa. Evidence is less clear for fire management of forests, with the contribution of GHG mitigation depending on many factors that affect the carbon balance (e.g., [[#Simmonds--2021|Simmonds et al. 2021]] ). Although prescribed burning is promoted to reduce uncontrolled wildfires in forests, the benefits for the management of carbon stocks are unclear, with different studies reporting varying results especially concerning its long-term effectiveness ( [[#Wotton--2017|Wotton et al. 2017]] ; [[#Bowman--2020b|Bowman et al. 2020b]] ). Under increasing climate change however, an increased attention on fire management will be necessary.

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<span id="reduce-degradation-and-conversion-of-grasslands-and-savannas"></span>
==== 7.4.2.5 Reduce Degradation and Conversion of Grasslands and Savannas ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Grasslands cover approximately 40.5% of the terrestrial area (i.e., 52.5 million km 2 ) divided as 13.8% woody savanna and savanna; 12.7% open and closed shrub; 8.3% non-woody grassland; and 5.7% is tundra ( [[#White--2000|White et al. 2000]] ). Sub-Saharan Africa and Asia have the most extensive total area, 14.5 and 8.9 million km 2 , respectively. A review by [[#Conant--2017|Conant et al. (2017)]] reported based on data on grassland area ( [[#FAO--2013|FAO 2013]] ) and grassland soil carbon stocks ( [[#Sombroek--1993|Sombroek et al. 1993]] ) a global estimate of about 343 PgC (in the top 1 m), nearly 50% more than is stored in forests worldwide ( [[#FAO--2007|FAO 2007]] ). Reducing the conversion of grasslands and savannas to croplands prevents soil carbon losses by oxidation, and to a smaller extent, biomass carbon loss due to vegetation clearing (SRCCL, Chapter 6). Restoration of grasslands through enhanced soil carbon sequestration, including (i) management of vegetation, (ii) animal management, and (iii) fire management, was also included in the SRCCL and is covered in [[#7.4.3.1|Section 7.4.3.1]] . Similar to other measures that reduce conversion, conserving carbon stocks in grasslands and savannas can be achieved by controlling conversion drivers (e.g., commercial and subsistence agriculture, see [[#7.3|Section 7.3]] ) and improving policies and management. In addition to mitigation, conserving grasslands provide various socio-economic, biodiversity, water cycle and other environmental benefits ( [[#Claassen--2010|Claassen et al. 2010]] ; [[#Ryals--2015|Ryals et al. 2015]] ; [[#Bengtsson--2019|Bengtsson et al. 2019]] ). Annual operating costs, and opportunity costs of income foregone by undertaking the activities needed for avoiding conversion of grasslands making costs one of the key barriers for implementation ( [[#Lipper--2010|Lipper et al. 2010]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The SRCCL reported a mitigation potential for reduced conversion of grasslands and savannas of 0.03–0.12 GtCO 2 -eq yr –1 ( [[#Griscom--2017|Griscom et al. 2017]] ; [[#IPCC--2019|IPCC 2019]] ) considering the higher loss of soil organic carbon in croplands ( [[#Sanderman--2017|Sanderman et al. 2017]] ). Assuming an average starting soil organic carbon stock of temperate grasslands ( [[#Poeplau--2011|Poeplau et al. 2011]] ), and the mean annual global cropland conversion rates (1961–2003) ( [[#Krause--2017|Krause et al. 2017]] ), the equivalent loss of soil organic carbon over 20 years would be 14 GtCO 2 -eq, for example, 0.7 GtCO 2 yr –1 (SRCCL, Chapter 6). IPCC AR5 and AR4 did not explicitly consider the mitigation potential of avoided conversion of grasslands-savannas but the management of grazing land is accounted for considering plant, animal, and fire management with a mean mitigation potential of 0.11–0.80 tCO 2 -eq ha –1 yr –1 depending on the climate region. This resulted in 0.25 GtCO 2 -eq yr –1 at USD20 tCO 2 –1 to 1.25 GtCO 2 -eq yr –1 at USD100 tCO 2 –1 by 2030.

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Unlike most of the measures covered in [[#7.4|Section 7.4]] , there are currently no global, spatially explicit mitigation potential estimates for reduced grassland conversion to generate technical and economic potentials by region. Literature developments since AR5 and SRCCL are studies that provide mitigation estimates in one or a few countries or regions. Modelling experiments comparing Californian forests and grasslands found that grasslands resulted in a more resilient carbon sink than forests to future climate change ( [[#Dass--2018|Dass et al. 2018]] ). However, previous studies indicated that precipitation is a key controller of the carbon storage in these grasslands, with the grassland became a carbon sink in 2005, when the region received relatively high spring precipitation ( [[#Ma--2007|Ma et al. 2007]] ). In North America, grassland conversion was the source for 77% of all new croplands from 2008–2012 ( [[#Lark--2015|Lark et al. 2015]] ). Avoided conversion of North American grasslands to croplands presents an economic mitigation potential of 0.024 GtCO 2 -eq yr –1 and technical potential of 0.107 GtCO 2 -eq yr –1 ( [[#Fargione--2018|Fargione et al. 2018]] ). This potential is related mainly to root biomass and soils (81% of emissions from soils). Estimates of GHG emissions from any future deforestation in Australian savannas also point to the potential mitigation of around 0.024 GtCO 2 -eq yr –1 ( [[#Bristow--2016|Bristow et al. 2016]] ). The expansion of the Soy Moratorium (SoyM) from the Brazilian Amazon to the Cerrado (Brazilian savannas) would prevent the direct conversion of 3.6 Mha of native vegetation to soybeans by 2050 and avoid the emission of 0.02 GtCO 2 -eq yr –1 ( [[#Soterroni--2019|Soterroni et al. 2019]] ).

'''Critical assessment and conclusion.''' There is ''low confidence'' that the global technical mitigation potential for reduced grassland and savanna conversion by 2050 is 0.2 (0.1–0.4) GtCO 2 yr –1 , and the economic mitigation potential (&lt;USD100 tCO 2 –1 ) is 0.04 GtCO 2 yr –1 . Most of the carbon sequestration potential is in below-ground biomass and soil organic matter. However, estimates of potential are still based on few studies and vary according to the levels of soil carbon, and ecosystem productivity (e.g., in response to rainfall distribution). Conservation of grasslands presents significant benefits for desertification control, especially in arid areas (SRCCL, Chapter 3). Policies supporting avoided conversion can help protect at-risk grasslands, reduce GHG emissions, and produce positive outcomes for biodiversity and landowners ( [[#Ahlering--2016|Ahlering et al. 2016]] ). In comparison to tropical rainforest regions that have been the primary target for mitigation policies associated to natural ecosystems (e.g., REDD+), conversion grasslands and savannas has received less national and international attention, despite growing evidence of concentrated cropland expansion into these areas with impacts of carbon losses.

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<span id="reduce-degradation-and-conversion-of-peatlands-activities-co-benefits-risks-and-implementation-barriers"></span>
==== 7.4.2.6 Reduce Degradation and Conversion of Peatlands Activities, Co-benefits, Risks and Implementation Barriers ====

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Peatlands are carbon-rich wetland ecosystems with organic soil horizons in which soil organic matter concentration exceeds 30% (dry weight) and soil carbon concentrations can exceed 50% ( [[#Page--2016|Page and Baird 2016]] , [[#Boone%20Kauffman--2017|Boone Kauffman et al. 2017]] ). Reducing the conversion of peatlands avoids emissions of above- and below-ground biomass and soil carbon due to vegetation clearing, fires, and peat decomposition from drainage. Similar to deforestation, peatland carbon stocks can be conserved by controlling the drivers of conversion and degradation (e.g., commercial and subsistence agriculture, mining, urban expansion) and improving governance and management. Reducing conversion is urgent because peatland carbon stocks accumulate slowly and persist over millennia; loss of existing stocks cannot be easily reversed over the decadal time scales needed to meet the Paris Agreement ( [[#Goldstein--2020|Goldstein et al. 2020]] ). The main co-benefits of reducing conversion of peatlands include conservation of a unique biodiversity including many critically endangered species, provision of water quality and regulation, and improved public health through decreased fire-caused pollutants ( [[#Griscom--2017|Griscom et al. 2017]] ). Although reducing peatland conversion will reduce land availability for alternative uses including agriculture or other land-based mitigation, drained peatlands constitute a small share of agricultural land globally while contributing significant emissions ( [[#Joosten--2009|Joosten 2009]] ). Mitigation through reduced conversion of peatlands therefore has a high potential of avoided emissions per hectare ( [[#Roe--2019|Roe et al. 2019]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' In the SRCCL (Chapters 2 and 6), it was estimated that avoided peat impacts could deliver 0.45–1.22 GtCO 2 -eq yr –1 technical potential by 2030–2050 ( ''medium confidence'' ) ( [[#Hooijer--2010|Hooijer et al. 2010]] ; [[#Griscom--2017|Griscom et al. 2017]] ; [[#Hawken--2017|Hawken 2017]] ). The mitigation potential estimates cover tropical peatlands and include CO 2 , N 2 O and CH 4 emissions. The mitigation potential is derived from quantification of losses of carbon stocks due to land conversion, shifts in GHG fluxes, alterations in net ecosystem productivity, input factors such as fertilisation needs, and biophysical climate impacts (e.g., shifts in albedo, water cycles, etc.). Tropical peatlands account for only about 10% of peatland area and about 20% of peatland carbon stock but about 80% of peatland carbon emissions, primarily from peatland conversion in Indonesia (about 60%) and Malaysia (about 10%) ( [[#Hooijer--2010|Hooijer et al. 2010]] ; [[#Page--2011|Page et al. 2011]] ; [[#Leifeld--2018|Leifeld and Menichetti 2018]] ). While the total mitigation potential of peatland conservation is considered moderate, the per hectare mitigation potential is the highest among land-based mitigation measures ( [[#Roe--2019|Roe et al. 2019]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Recent studies continue to report high carbon stocks in peatlands and emphasise the vulnerability of peatland carbon after conversion. The carbon stocks of tropical peatlands are among the highest of any forest, 1,211–4,257 tCO 2 -eq ha –1 in the Peruvian Amazon ( [[#Bhomia--2019|Bhomia et al. 2019]] ) and 1,956–14,757 tCO 2 -eq ha –1 in Indonesia ( [[#Novita--2021|Novita et al. 2021]] ). Ninety percent of tropical peatland carbon stocks are vulnerable to emission during conversion and may not be recoverable through restoration; in contrast, boreal and temperate peatlands hold similar carbon stocks (1,439–5,619 tCO 2 -eq ha –1 ) but only 30% of northern carbon stocks are vulnerable to emission during conversion and irrecoverable through restoration ( [[#Goldstein--2020|Goldstein et al. 2020]] ). A recent study shows global mitigation potential of about 0.2 GtCO 2 -eq yr –1 at costs up to USD100 tCO 2 –1 ( [[#Roe--2021|Roe et al. 2021]] ). Another study estimated that 72% of mitigation is achieved through avoided soil carbon impacts, with the remainder through avoided impacts to vegetation ( [[#Bossio--2020|Bossio et al. 2020]] ). Recent model projections show that both peatland protection and peatland restoration ( [[#7.4.2.7|Section 7.4.2.7]] ) are needed to achieve a 2°C mitigation pathway and that peatland protection and restoration policies will have minimal impacts on regional food security ( [[#Leifeld--2019|Leifeld et al. 2019]] , [[#Humpenöder--2020|Humpenöder et al. 2020]] ). Global studies have not accounted for extensive peatlands recently reported in the Congo Basin, estimated to cover 145,500 km 2 and contain 30.6 PgC, as much as 29% of total tropical peat carbon stock ( [[#Dargie--2017|Dargie et al. 2017]] ). These Congo peatlands are relatively intact; continued preservation is needed to prevent major emissions ( [[#Dargie--2019|Dargie et al. 2019]] ). In northern peatlands that are underlain by permafrost roughly 50% of the total peatlands north of 23° latitude, ( [[#Hugelius--2020|Hugelius et al. 2020]] ), climate change (i.e., warming) is the major driver of peatland degradation (e.g., through permafrost thaw) ( [[#Schuur--2015|Schuur et al. 2015]] , [[#Goldstein--2020|Goldstein et al. 2020]] ). However, in non-permafrost boreal and temperate peatlands, reduction of peatland conversion is also a cost-effective mitigation strategy. Peatlands are sensitive to climate change and there is ''low confidence'' about the future peatland sink globally (SRCCL, Chapter 2). Permafrost thaw may shift northern peatlands from a net carbon sink to net source ( [[#Hugelius--2020|Hugelius et al. 2020]] ). Uncertainties in peatland extent and the magnitude of existing carbon stocks, in both northern ( [[#Loisel--2014|Loisel et al. 2014]] ) and tropical ( [[#Dargie--2017|Dargie et al. 2017]] ) latitudes limit understanding of current and future peatland carbon dynamics ( [[#Minasny--2019|Minasny et al. 2019]] ).

'''Critical assessment and conclusion.''' Based on studies to date, there is ''medium confidence'' that peatland conservation has a technical potential of 0.86 (0.43–2.02) GtCO 2 -eq yr –1 of which 0.48 (0.2–0.68) GtCO 2 -eq yr –1 is available at USD100 tCO 2 –1 (Figure 7.11). High per hectare mitigation potential and high rate of co-benefits particularly in tropical countries, support the effectiveness of this mitigation strategy ( [[#Roe--2019|Roe et al. 2019]] ). Feasibility of reducing peatland conversion may depend on countries’ governance, financial capacity and political will.

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<span id="peatland-restoration"></span>
==== 7.4.2.7 Peatland Restoration ====

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'''Activities, co-benefits, risks and implementation barriers.''' Peatland restoration involves restoring degraded and damaged peatlands, for example through rewetting and revegetation, which both increases carbon accumulation in vegetation and soils and avoids ongoing CO 2 emissions. Peatlands only account for about 3% of the terrestrial surface, predominantly occurring in boreal ecosystems (78%), with a smaller proportion in tropical regions (13%), but may store about 600 GtC or 21% of the global total soil organic carbon stock of about 3000 Gt ( [[#Page--2011|Page et al. 2011]] ; [[#Leifeld--2018|Leifeld and Menichetti 2018]] ). Peatland restoration delivers co-benefits for biodiversity, as well as regulating water flow and preventing downstream flooding, while still allowing for extensive management such as paludiculture ( [[#Tan--2021|Tan et al. 2021]] ). Rewetting of peatlands also reduces the risk of fire, but may also mobilise salts and contaminants in soils ( [[#van%20Diggelen--2020|van Diggelen et al. 2020]] ) and in severely degraded peatlands, restoration of peatland hydrology and vegetation may not be feasible ( [[#Andersen--2017|Andersen et al. 2017]] ). At a local level, restoration of peatlands drained for agriculture could displace food production and damage local food supply, although impacts to regional and global food security would be minimal ( [[#Humpenöder--2020|Humpenöder et al. 2020]] ). Collaborative and transparent planning processes are needed to reduce conflict between competing land uses ( [[#Tanneberger--2020b|Tanneberger et al. 2020b]] ). Adequate resources for implementing restoration policies are key to engage local communities and maintain livelihoods ( [[#Resosudarmo--2019|Resosudarmo et al. 2019]] ; [[#Ward--2021|Ward et al. 2021]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' Large areas (0.51 Mkm 2 ) of global peatlands are degraded of which 0.2 Mkm 2 are tropical peatlands ( [[#Griscom--2017|Griscom et al. 2017]] ; [[#Leifeld--2018|Leifeld and Menichetti 2018]] ). According the SRCCL, peatland restoration could deliver technical mitigation potentials of 0.15 – 0.81GtCO 2 -eq yr –1 by 2030–2050 ( ''low confidence'' ) ( [[#Couwenberg--2010|Couwenberg et al. 2010]] ; [[#Griscom--2017|Griscom et al. 2017]] ) '''(''' Chapters 2 and 6 of the SRCCL), though there could be an increase in methane emissions after restoration ( [[#Jauhiainen--2008|Jauhiainen et al. 2008]] ). The mitigation potential estimates cover global peatlands and include CO 2 , N 2 O and CH 4 emissions. Peatlands are highly sensitive to climate change ( ''high confidence'' ), however there are currently no studies that estimate future climate effects on mitigation potential from peatland restoration.

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' The most recent literature and reviews indicate with ''high confidence'' that restoration would decrease CO 2 emissions and with ''medium confidence'' that restoration would decrease net GHG emissions from degraded peatlands ( [[#Wilson--2016|Wilson et al. 2016]] ; [[#Ojanen--2020|Ojanen and Minkkinen 2020]] ; [[#van%20Diggelen--2020|van Diggelen et al. 2020]] ). Although rewetting of drained peatlands increases CH 4 emissions, this effect is often outweighed by decreases in CO 2 and N 2 O emissions but depends very much on local circumstances ( [[#Günther--2020|Günther et al. 2020]] ). Restoration and rewetting of almost all drained peatlands is needed by 2050 to meet 1.5°C–2°C pathways which is unlikely to happen ( [[#Leifeld--2019|Leifeld et al. 2019]] ); immediate rewetting and restoration minimises the warming from cumulative CO 2 emissions ( [[#Nugent--2019|Nugent et al. 2019]] ).

According to recent data, the technical mitigation potential for global peatland restoration is estimated at 0.5–1.3 GtCO 2 -eq yr –1 ( [[#Leifeld--2018|Leifeld and Menichetti 2018]] ; [[#Griscom--2020|Griscom et al. 2020]] ; [[#Bossio--2020|Bossio et al. 2020]] ; [[#Roe--2021|Roe et al. 2021]] ) (Figure 7.11), with 80% of the mitigation potential derived from improvements to soil carbon ( [[#Bossio--2020|Bossio et al. 2020]] ). The regional mitigation potentials of all peatlands outlined in [[#Roe--2021|Roe et al. (2021)]] reflect the country-level estimates from ( [[#Humpenöder--2020|Humpenöder et al. 2020]] ).

Climate mitigation effects of peatland rewetting depend on the climate zone and land use. Recent analysis shows the strongest mitigation gains from rewetting drained temperate and boreal peatlands used for agriculture and drained tropical peatlands ( [[#Ojanen--2020|Ojanen and Minkkinen 2020]] ). However, estimates of emission factors from rewetting drained tropical peatlands remain uncertain ( [[#Wilson--2016|Wilson et al. 2016]] ; [[#Murdiyarso--2019|Murdiyarso et al. 2019]] ). Topsoil removal, in combination with rewetting, may improve restoration success and limit CH 4 emissions during restoration of highly degraded temperate peatlands ( [[#Zak--2018|Zak et al. 2018]] ). In temperate and boreal regions, co-benefits mentioned above are major motivations for peatland restoration ( [[#Chimner--2017|Chimner et al. 2017]] ; [[#Tanneberger--2020a|Tanneberger et al. 2020a]] ).

'''Critical assessment and conclusion.''' Based on studies to date, there is ''medium confidence'' that peatland restoration has a technical potential of 0.79 (0.49–1.3) GtCO 2 -eq yr –1 (median) of which 0.4 (0.2–0.6) GtCO 2 -eq yr –1 is available up to USD100 tCO 2 –1 . The large land area of degraded peatlands suggests that significant emissions reductions could occur through large-scale restoration especially in tropical peatlands. There is ''medium confidence'' in the large carbon stocks of tropical peat forests (1956–14,757 tCO 2 -eq ha –1 ) and large rates of carbon loss associated with land cover change (640–1650 tCO 2 -eq ha –1 ) ( [[#Goldstein--2020|Goldstein et al. 2020]] ; [[#Novita--2021|Novita et al. 2021]] ). However, large-scale implementation of tropical peatland restoration will likely be limited by costs and other demands for these tropical lands.

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<span id="reduce-conversion-of-coastal-wetlands"></span>
==== 7.4.2.8 Reduce Conversion of Coastal Wetlands ====

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'''Activities, co-benefits, risks and implementation barriers.''' Reducing conversion of coastal wetlands, including mangroves, marshes and seagrass ecosystems, avoids emissions from above and below ground biomass and soil carbon through avoided degradation and/or loss. Coastal wetlands occur mainly in estuaries and deltas, areas that are often densely settled, with livelihoods closely linked to coastal ecosystems and resources ( [[#Moser--2012|Moser et al. 2012]] ). The carbon stocks of these highly productive ecosystems are sometimes referred to as ‘blue carbon’. Loss of existing stocks cannot be easily reversed over decadal time scales ( [[#Goldstein--2020|Goldstein et al. 2020]] ). The main drivers of conversion include intensive aquaculture, agriculture, salt ponds, urbanisation and infrastructure development, the extensive use of fertilisers, and extraction of water resources ( [[#Lovelock--2018|Lovelock et al. 2018]] ). Reduced conversion of coastal wetlands has many co-benefits, including biodiversity conservation, fisheries production, soil stabilisation, water flow and water quality regulation, flooding and storm surge prevention, and increased resilience to cyclones ( [[#Windham-Myers--2018|Windham-Myers et al. 2018]] a; [[#UNEP--2020|UNEP 2020]] ). Risks associated with the mitigation potential of coastal wetland conservation include uncertain permanence under future climate scenarios, including the effects of coastal squeeze, where coastal wetland area may be lost if upland area is not available for migration as sea levels rise ( [[#Lovelock--2020|Lovelock and Reef 2020]] ) (AR6 WGII, [[IPCC:Wg3:Chapter:Chapter-3#3.4.2|Section 3.4.2]] .5). Preservation of coastal wetlands also conflicts with other land use in the coastal zone, including aquaculture, agriculture, and human development; economic incentives are needed to prioritise wetland preservation over more profitable short-term land use. Integration of policies and efforts aimed at coastal climate mitigation, adaptation, biodiversity conservation, and fisheries, for example through integrated coastal zone management and marine spatial planning, will bundle climate mitigation with co-benefits and optimise outcomes ( [[#Herr--2017|Herr et al. 2017]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' Coastal wetlands contain high, yet variable, organic carbon stocks, leading to a range of estimates of the global mitigation potential of reduced conversion. The SRCCL (Chapter 2) and SROCC (Chapter 5), report a technical mitigation potential of 0.15–5.35 GtCO 2 -eq yr –1 by 2050 ( [[#Pendleton--2012|Pendleton et al. 2012]] ; [[#Lovelock--2017|Lovelock et al. 2017]] ; [[#Howard--2017|Howard et al. 2017]] ; [[#Griscom--2017|Griscom et al. 2017]] ) '''.''' The mitigation potential is derived from quantification of losses of carbon stocks in vegetation and soil due to land conversion, shifts in GHG fluxes associated with land use, and alterations in net ecosystem productivity. The wide range in estimates mostly relate to the scope (all coastal ecosystems vs mangroves only) and different assumptions on decomposition rates. Loss rates of coastal wetlands have been estimated at 0.2–3% yr –1 , depending on the vegetation type and location ( [[#Atwood--2017|Atwood et al. 2017]] ; [[#Howard--2017|Howard et al. 2017]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Global technical mitigation potential for conservation of coastal wetlands from recent literature have focused on protection of mangroves; estimates range from 0.06–2.25 GtCO 2 -eq yr –1 ( [[#Griscom--2020|Griscom et al. 2020]] ; [[#Bossio--2020|Bossio et al. 2020]] ) with 80% of the mitigation potential derived from improvements to soil carbon ( [[#Bossio--2020|Bossio et al. 2020]] ). Regional potentials ( [[#Roe--2021|Roe et al. 2021]] ) reflect mangrove protection; marsh and seagrass protection were not included due to lack of country-level data on marsh and seagrass distribution and conversion.

Global estimates show mangroves have the largest per hectare carbon stocks (see IPCC AR6 WGII Box 3.4 for estimates of carbon stocks, burial rates and ecosystem extent for coastal wetland ecosystems). Mean ecosystem carbon stock in mangroves is 3131 tCO 2 -eq ha –1 among the largest carbon stocks on Earth. Recent studies emphasise the variability in total ecosystem carbon stocks for each wetland type, based on species and climatic and edaphic conditions ( [[#Kauffman--2020|Kauffman et al. 2020]] ; [[#Bedulli--2020|Bedulli et al. 2020]] ; [[#Ricart--2020|Ricart et al. 2020]] ; Alongi et al. 2020; F. [[#Wang--2021|Wang et al. 2021]] ), and highlight the vulnerability of soil carbon below 1 m depth ( [[#Arifanti--2019|Arifanti et al. 2019]] ). Sea level strongly influences coastal wetland distribution, productivity, and sediment accretion; therefore, sea level rise will impact carbon accumulation and persistence of existing carbon stocks ( [[#Macreadie--2019|Macreadie et al. 2019]] ) (IPCC AR6 WGII Box 3.4).

Recent loss rates of mangroves are 0.16–0.39% yr –1 and are highest in South-East Asia ( [[#Hamilton--2016|Hamilton and Casey 2016]] ; [[#Friess--2019|Friess et al. 2019]] ; [[#Hamilton--2016|Hamilton and Casey 2016]] ). Assuming loss of soil carbon to 1 m depth after deforestation, avoiding mangrove conversion has the technical potential to mitigate approximately 23.5–38.7 MtCO 2 -eq yr –1 ( [[#Ouyang--2020|Ouyang and Lee 2020]] ); note, this potential is additional to reduced conversion of forests ( [[#Griscom--2020|Griscom et al. 2020]] ) ( [[#7.4.2.1|Section 7.4.2.1]] ). Regional estimates show that about 85% of mitigation potential for avoided mangrove conversion is in South-East Asia and Pacific (32 MtCO 2 -eq yr –1 at USD100 tCO 2 –1 ), 10% is in Latin American and the Caribbean (4 MtCO 2 -eq yr –1 ), and approximately 5% in other regions ( [[#Griscom--2020|Griscom et al. 2020]] ; [[#Roe--2021|Roe et al. 2021]] ).

Key uncertainties remain in mapping extent and conversion rates for salt marshes and seagrasses ( [[#McKenzie--2020|McKenzie et al. 2020]] ). Seagrass loss rates were estimated at 1–2% yr –1 ( [[#Dunic--2021|Dunic et al. 2021]] ) with stabilisation in some regions ( [[#de%20los%20Santos--2019|de los]] [[#Santos--2019|Santos et al. 2019]] ) (AR6 WGII, [[IPCC:Wg3:Chapter:Chapter-3#3.4.2|Section 3.4.2]] .5); however, loss occurs non-linearly and depends on site-specific context. Tidal marsh extent and conversion rates remains poorly estimated, outside of the USA, Europe, South Africa, and Australia ( [[#Mcowen--2017|Mcowen et al. 2017]] ; [[#Macreadie--2019|Macreadie et al. 2019]] ).

'''Critical assessment and conclusion.''' There is ''medium confidence'' that coastal wetland protection has a technical potential of 0.8 (0.06–5.4) GtCO 2 -eq yr –1 of which 0.17 (0.06–0.27) GtCO 2 -eq yr –1 is available up to USD100 tCO 2 –1 . There is a ''high certainty'' ( ''robust evidence'' , ''high agreement'' ) that coastal ecosystems have among the largest carbon stocks of any ecosystem. As these ecosystems provide many important services, reduced conversion of coastal wetlands is a valuable mitigation strategy with numerous co-benefits. However, the vulnerability of coastal wetlands to climatic and other anthropogenic stressors may limit the permanence of climate mitigation.

<div id="7.4.2.9" class="h3-container"></div>

<span id="coastal-wetland-restoration"></span>
==== 7.4.2.9 Coastal Wetland Restoration ====

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'''Activities, co-benefits, risks and implementation barriers.''' Coastal wetland restoration involves restoring degraded or damaged coastal wetlands including mangroves, salt marshes, and seagrass ecosystems, leading to sequestration of ‘blue carbon’ in wetland vegetation and soil (SRCCL, Chapter 6; SROCC, Chapter 5). Successful approaches to wetland restoration include: (i) passive restoration, the removal of anthropogenic activities that are causing degradation or preventing recovery; and (ii) active restoration, purposeful manipulations to the environment in order to achieve recovery to a naturally functioning system ( [[#Elliott--2016|Elliott et al. 2016]] ) (IPCC AR6 WGII Chapter 3). Restoration of coastal wetlands delivers many valuable co-benefits, including enhanced water quality, biodiversity, aesthetic values, fisheries production (food security), and protection from rising sea levels and storm impacts ( [[#Barbier--2011|Barbier et al. 2011]] ; [[#Hochard--2019|Hochard et al. 2019]] ; [[#Sun--2020|Sun and Carson 2020]] ; [[#Duarte--2020|Duarte et al. 2020]] ). Of the 0.3 Mkm 2 coastal wetlands globally, 0.11 Mkm 2 of mangroves are considered feasible for restoration ( [[#Griscom--2017|Griscom et al. 2017]] ). Risks associated with coastal wetland restoration include uncertain permanence under future climate scenarios (IPCC AR6 WGII, Box 3.4), partial offsets of mitigation through enhanced methane and nitrous oxide release and carbonate formation, and competition with other land uses, including aquaculture and human settlement and development in the coastal zone (SROCC, Chapter 5). To date, many coastal wetland restoration efforts do not succeed due to failure to address the drivers of degradation (van [[#Katwijk--2016|Katwijk et al. 2016]] ). However, improved frameworks for implementing and assessing coastal wetland restoration are emerging that emphasise the recovery of ecosystem functions ( [[#Zhao--2016|Zhao et al. 2016]] ; [[#Cadier--2020|Cadier et al. 2020]] ). Restoration projects that involve local communities at all stages and consider both biophysical and socio-political context are more likely to succeed ( [[#Brown--2014|Brown et al. 2014]] ; [[#Wylie--2016|Wylie et al. 2016]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The SRCCL reported that mangrove restoration has the technical potential to mitigate 0.07 GtCO 2 yr –1 through rewetting ( [[#Crooks--2011|Crooks et al. 2011]] ) and take up 0.02–0.84 GtCO 2 yr –1 from vegetation biomass and soil enhancement through 2030 ( ''medium confidence'' ) ( [[#Griscom--2017|Griscom et al. 2017]] ). The SROCC concluded that cost-effective coastal blue carbon restoration had a potential of about 0.15–0.18 GtCO 2 -eq yr –1 , a low global potential compared to other ocean-based solutions but with extensive co-benefits and limited adverse side effects ( [[#Gattuso--2018|Gattuso et al. 2018]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Recent studies emphasise the time frame needed to achieve the full mitigation potential ( [[#Duarte--2020|Duarte et al. 2020]] ; [[#Taillardat--2020|Taillardat et al. 2020]] ). The first project-derived estimate of the net GHG benefit from seagrass restoration found 1.54 tCO 2 -eq (0.42 MgC) ha –1 yr –1 10 years after restoration began ( [[#Oreska--2020|Oreska et al. 2020]] ); comparable to the default emission factor in the Wetlands Supplement ( [[#Kennedy--2014|Kennedy et al. 2014]] ). Recent studies of rehabilitated mangroves also indicate that annual carbon sequestration rates in biomass and soils can return to natural levels within decades of restoration ( [[#Cameron--2019|Cameron et al. 2019]] ; [[#Sidik--2019|Sidik et al. 2019]] ). A meta-analysis shows increasing carbon sequestration rates over the first 15 years of mangrove restoration with rates stabilising at 25.7 ± 7.7 tCO 2 -eq (7.0 ± 2.1 MgC) ha –1 yr –1 through forty years, although success depends on climate, sediment type, and restoration methods ( [[#Sasmito--2019|Sasmito et al. 2019]] ). Overall, 30% of mangrove soil carbon stocks and 50–70% of marsh and seagrass carbon stocks are unlikely to recover within 30 years of restoration, underscoring the importance of preventing conversion of coastal wetlands ( [[#Goldstein--2020|Goldstein et al. 2020]] ) ( [[#7.4.2.8|Section 7.4.2.8]] ).

According to recent data, the technical mitigation potential for global coastal wetland restoration is 0.04–0.84 GtCO 2 -eq yr –1 ( [[#Griscom--2020|Griscom et al. 2020]] ; [[#Bossio--2020|Bossio et al. 2020]] ; [[#Roe--2021|Roe et al. 2021]] ) with 60% of the mitigation potential derived from improvements to soil carbon ( [[#Bossio--2020|Bossio et al. 2020]] ). Regional potentials based on country-level estimates from [[#Griscom--2020|Griscom et al. (2020)]] show the technical and economic (up to USD100 tCO 2 –1 ) potential of mangrove restoration; seagrass and marsh restoration was not included due to lack of country-level data on distribution and conversion (but see [[#McKenzie--2020|McKenzie et al. 2020]] for updates on global seagrass distribution). Although global potential is relatively moderate, mitigation can be quite significant for countries with extensive coastlines (e.g., Indonesia, Brazil) and for small island states where coastal wetlands have been shown to comprise 24–34% of their total national carbon stock ( [[#Donato--2012|Donato et al. 2012]] ). Furthermore, non-climatic co-benefits can strongly motivate coastal wetland restoration worldwide ( [[#UNEP--2021a|UNEP 2021a]] ). Major successes in both active and passive restoration of seagrasses have been documented in North America and Europe ( [[#Lefcheck--2018|Lefcheck et al. 2018]] ; [[#de%20los%20Santos--2019|de los]] [[#Santos--2019|Santos et al. 2019]] ; [[#Orth--2020|Orth et al. 2020]] ); passive restoration may also be feasible for mangroves ( [[#Cameron--2019|Cameron et al. 2019]] ).

There is high site-specific variation in carbon sequestration rates and uncertainties regarding the response to future climate change ( [[#Jennerjahn--2017|Jennerjahn et al. 2017]] ; [[#Nowicki--2017|Nowicki et al. 2017]] ) (IPCC AR6 WGII Box 3.4). Changes in distributions ( [[#Kelleway--2017|Kelleway et al. 2017]] ; [[#Wilson--2019|Wilson and Lotze 2019]] ) ''',''' methane release (Al-Haj and Fulweiler 2020), carbonate formation ( [[#Saderne--2019|Saderne et al. 2019]] ), and ecosystem responses to interactive climate stressors are not well-understood ( [[#Short--2016|Short et al. 2016]] ; Fitzgerald and Hughes 2019; [[#Lovelock--2020|Lovelock and Reef 2020]] ).

'''Critical assessment and conclusion.''' There is ''medium confidence'' that coastal wetland restoration has a technical potential of 0.3 (0.04–0.84) GtCO 2 -eq yr –1 of which 0.1 (0.05–0.2) GtCO 2 -eq yr –1 is available up to USD100 tCO 2 –1 . There is ''high confidence'' that coastal wetlands, especially mangroves, contain large carbon stocks relative to other ecosystems and ''medium confidence'' that restoration will reinstate pre-disturbance carbon sequestration rates. There is ''low confidence'' on the response of coastal wetlands to climate change; however, there is ''high confidence'' that coastal wetland restoration will provide a suite of valuable co-benefits.

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<span id="agriculture"></span>

=== 7.4.3 Agriculture ===

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<div id="7.4.3.1" class="h3-container"></div>

<span id="soil-carbon-management-in-croplands-and-grasslands"></span>
==== 7.4.3.1 Soil Carbon Management in Croplands and Grasslands ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Increasing soil organic matter in croplands are agricultural management practices that include (i) crop management: for example, high input carbon practices such as improved crop varieties, crop rotation, use of cover crops, perennial cropping systems (including agroforestry; see [[#7.4.3.3|Section 7.4.3.3]] ), integrated production systems, crop diversification, agricultural biotechnology; (ii) nutrient management including fertilisation with organic amendments/green manures ( [[#7.4.3.6|Section 7.4.3.6]] ); (iii) reduced tillage intensity and residue retention, (iv) improved water management: including drainage of waterlogged mineral soils and irrigation of crops in arid/semi-arid conditions, (v) improved rice management ( [[#7.4.3.5|Section 7.4.3.5]] ) and (vi) biochar application (P. [[#Smith--2019|Smith et al. 2019]] a) ( [[#7.4.3.2|Section 7.4.3.2]] ). For increased soil organic matter in grasslands, practices include (i) ''management of vegetation'' : including improved grass varieties/sward composition, deep rooting grasses, increased productivity, and nutrient management, (ii) ''livestock management'' : including appropriate stocking densities fit to carrying capacity, fodder banks, and fodder diversification, and (iii) ''fire management'' : improved use of fire for sustainable grassland management, including fire prevention and improved prescribed burning (Smith et al. 2014, 2019b). All these measures are recognised as Sustainable Soil Management Practices by FAO ( [[#Baritz--2018|Baritz et al. 2018]] ). While there are co-benefits for livelihoods, biodiversity, water provision and food security (P. [[#Smith--2019|Smith et al. 2019]] a), and impacts on leakage, indirect land-use change and foregone sequestration do not apply (since production in not displaced), the climate benefits of soil carbon sequestration in croplands can be negated if achieved through additional fertiliser inputs (potentially causing increased N 2 O emissions; ( [[#Guenet--2021|Guenet et al. 2021]] ), and both saturation and permanence are relevant concerns. When considering implementation barriers, soil carbon management in croplands and grasslands is a low-cost option at a high level of technology readiness (it is already widely deployed globally) with low socio-cultural and institutional barriers, but with difficulty in monitoring and verification proving a barrier to implementation ( [[#Smith--2020a|Smith et al. 2020a]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' Building on AR5, the SRCCL reported the global mitigation potential for soil carbon management in croplands to be 1.4–2.3 GtCO 2 -eq yr –1 (Smith et al. 2014), though the full literature range was 0.3–6.8 GtCO 2 -eq yr –1 ( [[#Sommer--2014|Sommer and Bossio 2014]] ; [[#Powlson--2014|Powlson et al. 2014]] ; [[#Dickie--2014b|Dickie et al. 2014b]] ; [[#Henderson--2015|Henderson et al. 2015]] ; [[#Herrero--2016|Herrero et al. 2016]] ; [[#Paustian--2016|Paustian et al. 2016]] ; [[#Zomer--2016|Zomer et al. 2016]] ; [[#Frank--2017|Frank et al. 2017]] ; [[#Conant--2017|Conant et al. 2017]] ; [[#Griscom--2017|Griscom et al. 2017]] ; [[#Hawken--2017|Hawken 2017]] ; [[#Sanderman--2017|Sanderman et al. 2017]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Roe--2019|Roe et al. 2019]] ). The global mitigation potential for soil organic carbon management in grasslands was assessed to be 1.4–1.8 GtCO 2 -eq yr –1 , with the full literature range being 0.1–2.6 GtCO 2 -eq yr –1 ( [[#Herrero--2013|Herrero et al. 2013]] ; 2016; [[#Conant--2017|Conant et al. 2017]] ; [[#Roe--2019|Roe et al. 2019]] ). Lower values in the range represented economic potentials, while higher values represented technical potentials – and uncertainty was expressed by reporting the whole range of estimates. The SR1.5 outlined associated costs reported in literature to range from USD –45 to 100 tCO 2 –1 , describing enhanced soil carbon sequestration as a cost-effective measure ( [[#IPCC--2018|IPCC 2018]] ). Despite significant mitigation potential, there is limited inclusion of soil carbon sequestration as a response option within IAM mitigation pathways ( [[#Rogelj--2018a|Rogelj et al. 2018a]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' No recent literature has been published which conflict with the mitigation potentials reported in the SRCCL. Relevant papers include [[#Lal--2018|Lal et al. (2018)]] which estimated soil carbon sequestration potential to be 0.7–4.1 GtCO 2 -eq yr –1 for croplands and 1.1–2.9 GtCO 2 -eq yr –1 for grasslands. [[#Bossio--2020|Bossio et al. (2020)]] assessed the contribution of soil carbon sequestration to natural climate solutions and found the potential to be 5.5 GtCO 2 yr –1 across all ecosystems, with only small portions of this (0.41 GtCO 2 -eq yr –1 for cover cropping in croplands; 0.23, 0.15, 0.15 GtCO 2 -eq yr –1 for avoided grassland conversion, optimal grazing intensity and legumes in pastures, respectively) arising from croplands and grasslands. Regionally, soil carbon management in croplands is feasible anywhere, but effectiveness can be limited in very dry regions ( [[#Sanderman--2017|Sanderman et al. 2017]] ). For soil carbon management in grasslands the feasibility is greatest in areas where grasslands have been degraded (e.g., by overgrazing) and soil organic carbon is depleted. For well managed grasslands, soil carbon stocks are already high and the potential for additional carbon storage is low. [[#Roe--2021|Roe et al. (2021)]] estimate the greatest economic (up to USD100 tCO 2 –1 ) potential between 2020 and 2050 for croplands to be in Asia and the Pacific (339.7 MtCO 2 yr –1 ) and for grasslands, in Developed Countries (253.6 MtCO 2 yr –1 ).

'''Critical assessment and conclusion.''' In conclusion, there is ''medium confidence'' that enhanced soil carbon management in croplands has a global technical mitigation potential of 1.9 (0.4–6.8) GtCO 2 yr –1 , and in grasslands of 1.0 (0.2–2.6) GtCO 2 yr –1 , of which, 0.6 (04–0.9) and 0.9 (0.3–1.6) GtCO 2 yr –1 is estimated to be available at up to USD100 tCO 2 –1 respectively. Regionally, soil carbon management in croplands and grasslands is feasible anywhere, but effectiveness can be limited in very dry regions, and for grasslands it is greatest in areas where degradation has occurred (e.g., by overgrazing) and soil organic carbon is depleted. Barriers to implementation include regional capacity for monitoring and verification (especially in developing countries), and more widely through concerns over saturation and permanence.

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<span id="biochar"></span>
==== 7.4.3.2 Biochar ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Biochar is produced by heating organic matter in oxygen-limited environments (pyrolysis and gasification) ( [[#Lehmann--2012|Lehmann and Joseph 2012]] ). Feedstocks include forestry and sawmill residues, straw, manure and biosolids. When applied to soils, biochar is estimated to persist from decades to thousands of years, depending on feedstock and production conditions (J. [[#Wang--2016|Wang et al. 2016]] ; [[#Singh--2015|Singh et al. 2015]] ). Biochar systems producing biochar for soil application plus bioenergy, generally give greater mitigation than bioenergy alone and other uses of biochar, and are recognised as a CDR strategy. Biochar persistence is increased through interaction with clay minerals and soil organic matter ( [[#Fang--2015|Fang et al. 2015]] ). Additional CDR benefits arise through ‘negative priming’ whereby biochar stabilises soil carbon and rhizodeposits ( [[#Weng--2015|Weng et al. 2015]] ; J. [[#Wang--2016|Wang et al. 2016]] ; [[#Archanjo--2017|Archanjo et al. 2017]] ; [[#Hagemann--2017|Hagemann et al. 2017]] ; [[#Han%20Weng--2017|Han Weng et al. 2017]] ; [[#Weng--2018|Weng et al. 2018]] ). Besides CDR, additional mitigation can arise from displacing fossil fuels with pyrolysis gases, lower soil N 2 O emissions ( [[#Cayuela--2014|Cayuela et al. 2014]] , 2015; [[#Song--2016|Song et al. 2016]] ; [[#He--2017|He et al. 2017]] ; [[#Verhoeven--2017|Verhoeven et al. 2017]] ; [[#Borchard--2019|Borchard et al. 2019]] ), reduced nitrogen fertiliser requirements due to reduced nitrogen leaching and volatilisation from soils ( [[#Liu--2019|Liu et al. 2019]] ; [[#Borchard--2019|Borchard et al. 2019]] ), and reduced GHG emissions from compost when biochar is added ( [[#Agyarko-Mintah--2017|Agyarko-Mintah et al. 2017]] ; [[#Wu--2017|Wu et al. 2017]] ). Biochar application to paddy rice has resulted in substantial reductions (20–40% on average) in N 2 O ( [[#Song--2016|Song et al. 2016]] ; [[#Awad--2018|Awad et al. 2018]] ; [[#Liu--2018|Liu et al. 2018]] ) ( [[#7.4.3.5|Section 7.4.3.5]] ) and smaller reduction in CH 4 emissions ( [[#Song--2016|Song et al. 2016]] ; [[#Kammann--2017|Kammann et al. 2017]] ; [[#Kim--2017a|Kim et al. 2017a]] ; [[#He--2017|He et al. 2017]] ; [[#Awad--2018|Awad et al. 2018]] ). Potential co-benefits include yield increases particularly in sandy and acidic soils with low cation exchange capacity ( [[#Woolf--2016|Woolf et al. 2016]] ; [[#Jeffery--2017|Jeffery et al. 2017]] ); increased soil water-holding capacity ( [[#Omondi--2016|Omondi et al. 2016]] ), nitrogen use efficiency ( [[#Liu--2019|Liu et al. 2019]] ; [[#Borchard--2019|Borchard et al. 2019]] ), biological nitrogen fixation ( [[#Van%20Zwieten--2015|Van Zwieten et al. 2015]] ); adsorption of organic pollutants and heavy metals (e.g., [[#Silvani--2019|Silvani et al. 2019]] ); odour reduction from manure handling (e.g., [[#Hwang--2018|Hwang et al. 2018]] ) and managing forest fuel loads ( [[#Puettmann--2020|Puettmann et al. 2020]] ). Due to its dark colour, biochar could decrease soil albedo ( [[#Meyer--2012|Meyer et al. 2012]] ), though this is insignificant under recommended rates and application methods. Biochar could reduce enteric CH 4 emissions when fed to ruminants ( [[#7.4.3.4|Section 7.4.3.4]] ). Barriers to upscaling include insufficient investment, limited large-scale production facilities, high production costs at small scale, lack of agreed approach to monitoring, reporting and verification, and limited knowledge, standardisation and quality control, restricting user confidence ( [[#Gwenzi--2015|Gwenzi et al. 2015]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' Biochar is discussed as a mitigation option in AR5 and CDR strategy in the SR1.5. Consideration of potential was limited as biochar is not included in IAMs. The SRCCL estimated mitigation potential of 0.03–6.6 GtCO 2 -eq yr –1 by 2050 based on studies with widely varying assumptions, definitions of potential, and scope of mitigation processes included (SRCCL, Chapters 2 and 4: ( [[#Roberts--2010|Roberts et al. 2010]] ; [[#Pratt--2010|Pratt and Moran 2010]] ; [[#Hristov--2013|Hristov et al. 2013]] ; [[#Lee--2013|Lee and Day 2013]] ; [[#Dickie--2014a|Dickie et al. 2014a]] ; [[#Hawken--2017|Hawken 2017]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Powell--2012|Powell and Lenton 2012]] ; [[#Woolf--2010|Woolf et al. 2010]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Developments include mechanistic understanding of ‘negative priming’ and biochar-soil-microbes-plant interactions ( [[#DeCiucies--2018|DeCiucies et al. 2018]] ; [[#Fang--2019|Fang et al. 2019]] ). Indirect climate benefits are associated with persistent yield response to biochar ( [[#Kätterer--2019|Kätterer et al. 2019]] ; [[#Ye--2020|Ye et al. 2020]] ), improved crop water use efficiency ( [[#Du--2018|Du et al. 2018]] ; [[#Gao--2020|Gao et al. 2020]] ) and reduced GHG and ammonia emissions from compost and manure ( [[#Sanchez-Monedero--2018|Sanchez-Monedero et al. 2018]] ; [[#Bora--2020a|Bora et al. 2020a]] ,b; [[#Zhao--2020|Zhao et al. 2020]] ). A quantification method based on biochar properties is included in the IPCC guidelines for NGHGIs ( [[#Domke--2019|Domke et al. 2019]] ). Studies report a range of biochar responses, from positive to occasionally adverse impacts, including on GHG emissions, and identify risks ( [[#Tisserant--2019|Tisserant and Cherubini 2019]] ). This illustrates the expected variability ( [[#Lehmann--2014|Lehmann and Rillig 2014]] ) of responses, which depend on the biochar type and climatic and edaphic characteristics of the site ( [[#Zygourakis--2017|Zygourakis 2017]] ). Biochar properties vary with feedstock, production conditions and post-production treatments, so mitigation and agronomic benefits are maximised when biochars are chosen to suit the application context ( [[#Mašek--2018|Mašek et al. 2018]] ). A recent assessment finds greatest economic potential (up to USD100 tCO 2 –1 ) between 2020 and 2050 to be in Asia and the Pacific (793 MtCO 2 yr –1 ) followed by Developed Countries (447 MtCO 2 yr –1 ) ( [[#Roe--2021|Roe et al. 2021]] ). Mitigation through biochar will be greatest where biochar is applied to responsive soils (acidic, low fertility), where soil N 2 O emissions are high (intensive horticulture, irrigated crops), and where the syngas co-product displaces fossil fuels. Due to the early stage of commercialisation, mitigation estimates are based pilot-scale facilities, leading to uncertainty. However, the long-term persistence of biochar carbon in soils has been widely studied ( [[#Singh--2012|Singh et al. 2012]] ; [[#Fang--2019|Fang et al. 2019]] ; [[#Zimmerman--2019|Zimmerman and Ouyang 2019]] ). The greatest uncertainty is the availability of sustainably-sourced biomass for biochar production.

'''Critical assessment and conclusion.''' Biochar has significant mitigation potential through CDR and emissions reduction, and can also improve soil properties, enhancing productivity and resilience to climate change ( ''medium agreement'' , ''robust evidence'' ). There is ''medium evidence'' that biochar has a technical potential of 2.6 (0.2–6.6) GtCO 2 -eq yr –1 , of which 1.1 (0.3–1.8) GtCO 2 -eq yr –1 is available up to USD100 tCO 2 –1 . However, mitigation and agronomic co-benefits depend strongly on biochar properties and the soil to which biochar is applied ( ''strong agreement'' , ''robust evidence'' ). While biochar could provide moderate to large mitigation potential, it is not yet included in IAMs, which has restricted comparison and integration with other CDR strategies.

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<span id="agroforestry"></span>
==== 7.4.3.3 Agroforestry ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Agroforestry is a set of diverse land management systems that integrate trees and shrubs with crops and/or livestock in space and/or time. Agroforestry accumulates carbon in woody vegetation and soil ( [[#Ramachandran%20Nair--2010|Ramachandran Nair et al. 2010]] ) and offers multiple co-benefits such as increased land productivity, diversified livelihoods, reduced soil erosion, improved water quality, and more hospitable regional climates ( [[#Ellison--2017|Ellison et al. 2017]] ; [[#Kuyah--2019|Kuyah et al. 2019]] ; [[#Mbow--2020|Mbow et al. 2020]] ; [[#Zhu--2020|Zhu et al. 2020]] ). Incorporation of trees and shrubs in agricultural systems, however, can affect food production, biodiversity, local hydrology and contribute to social inequality ( [[#Amadu--2020|Amadu et al. 2020]] ; [[#Fleischman--2020|Fleischman et al. 2020]] ; [[#Holl--2020|Holl and Brancalion 2020]] ). To minimise risks and maximise co-benefits, agroforestry should be implemented as part of support systems that deliver tools, and information to increase farmers’ agency. This may include reforming policies, strengthening extension systems and creating market opportunities that enable adoption ( [[#Jamnadass--2020|Jamnadass et al. 2020]] ; [[#Sendzimir--2011|Sendzimir et al. 2011]] ; P. [[#Smith--2019|Smith et al. 2019]] a). Consideration of carbon sequestration in the context of food and fuel production, as well as environmental co-benefits at the farm, local, and regional scales can further help support decisions to plant, regenerate and maintain agroforestry systems ( [[#Kumar--2011|Kumar and Nair 2011]] ; [[#Miller--2020|Miller et al. 2020]] ). In spite of the advantages, biophysical and socio-economic factors can limit the adoption ( [[#Pattanayak--2003|Pattanayak et al. 2003]] ). Contextual factors may include, but are not limited to; water availability, soil fertility, seed and germplasm access, land policies and tenure systems affecting farmer agency, access to credit, and to information regarding the optimum species for a given location.

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The SRCCL estimated the global technical mitigation potential of agroforestry, with medium confidence, to be between 0.08 and 5.6 GtCO 2 -eq yr –1 by 2050 ( [[#Griscom--2017|Griscom et al. 2017]] ; [[#Dickie--2014a|Dickie et al. 2014a]] ; [[#Zomer--2016|Zomer et al. 2016]] ; [[#Hawken--2017|Hawken 2017]] ). Estimates are derived from syntheses of potential area available for various agroforestry systems, for example, windbreaks, farmer managed natural regeneration, and alley cropping and average annual rates of carbon accumulation. The cost-effective economic potential, also with medium confidence, is more limited at 0.3–2.4 GtCO 2 -eq yr –1 ( [[#Zomer--2016|Zomer et al. 2016]] ; [[#Griscom--2017|Griscom et al. 2017]] ; [[#Roe--2019|Roe et al. 2019]] ). Despite this potential, agroforestry is currently not considered in integrated assessment models used for mitigation pathways ( [[#7.5|Section 7.5]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Updated estimates of agroforestry’s technical mitigation potential and synthesised estimates of carbon sequestration across agroforestry systems have since been published. The most recent global analysis estimates technical potential of 9.4 GtCO 2 -eq yr –1 ( [[#Chapman--2020|Chapman et al. 2020]] ) of agroforestry on 1.87 and 1.89 billion ha of crop and pasture lands below median carbon content, respectively. This estimate is at least 68% greater than the largest estimate reported in the SRCCL ( [[#Hawken--2017|Hawken 2017]] ) and represents a new conservative upper bound as [[#Chapman--2020|Chapman et al. (2020)]] only accounted for above-ground carbon. Considering both above- and below-ground carbon of windbreaks, alley cropping and silvopastoral systems at a more limited areal extent ( [[#Griscom--2020|Griscom et al. 2020]] ), the economic potential of agroforestry was estimated to be only about 0.8 GtCO 2 -eq yr –1 . Variation in estimates primarily result from assumptions on the agroforestry systems including, extent of implementation and estimated carbon sequestration potential when converting to agroforestry.

Regional estimates of mitigation potential are scant with agroforestry options differing significantly by geography ( [[#Feliciano--2018|Feliciano et al. 2018]] ). For example, multi-strata shaded coffee and cacao are successful in the humid tropics ( [[#Somarriba--2013|Somarriba et al. 2013]] ; [[#Blaser--2018|Blaser et al. 2018]] ), silvopastoral systems are prevalent in Latin American ( [[#Peters--2013|Peters et al. 2013]] ; [[#Landholm--2019|Landholm et al. 2019]] ) while agrosilvopastoral systems, shelterbelts, hedgerows, and windbreaks are common in Europe ( [[#Joffre--1988|Joffre et al. 1988]] ; Rigueiro-Rodriguez 2009). At the field scale, agroforestry accumulates between 0.59 and 6.24 t ha –1 yr –1 of carbon above-ground. Below-ground carbon often constitutes 25% or more of the potential carbon gains in agroforestry systems (De Stefano and Jacobson 2018; [[#Cardinael--2018|Cardinael et al. 2018]] ). [[#Roe--2021|Roe et al. (2021)]] estimate greatest regional economic (up to USD100 tCO 2 –1 ) mitigation potential for the period 2020–2050 to be in Asia and the Pacific (368.4 MtCO 2 -eq yr –1 ) and Developed Countries (264.7 MtCO 2 -eq yr –1 ).

Recent research has also highlighted co-benefits and more precisely identified implementation barriers. In addition to aforementioned co-benefits, evidence now shows that agroforestry can improve soil health, regarding infiltration and structural stability ( [[#Muchane--2020|Muchane et al. 2020]] ); reduces ambient temperatures and crop heat stress ( [[#Arenas-Corraliza--2018|Arenas-Corraliza et al. 2018]] ; [[#Sida--2018|Sida et al. 2018]] ); increases groundwater recharge in drylands when managed at moderate density ( [[#Ilstedt--2016|Ilstedt et al. 2016]] ; Bargués-Tobella et al. 2020); positively influences human health ( [[#Rosenstock--2019|Rosenstock et al. 2019]] ); and can improve dietary diversity ( [[#McMullin--2019|McMullin et al. 2019]] ). Along with previously mentioned barriers, low social capital, assets, and labour availability have been identified as pertinent to adoption. Practically all barriers are interdependent and subject to the context of implementation.

'''Critical assessment and conclusion.''' There is medium confidence that agroforestry has a technical potential of 4.1 (0.3–9.4) GtCO 2 -eq yr –1 for the period 2020–2050, of which 0.8 (0.4–1.1) GtCO 2 -eq yr –1 is available at USD100 tCO 2 –1 . Despite uncertainty around global estimates due to regional preferences for management systems, suitable land availability, and growing conditions, there is high confidence in agroforestry’s mitigation potential at the field scale. With countless options for farmers and land managers to implement agroforestry, there is medium confidence in the feasibility of achieving estimated regional mitigation potential. Appropriately matching agroforestry options, to local biophysical and social contexts is important in maximising mitigation and co-benefits, while avoiding risks ( [[#Sinclair--2019|Sinclair and Coe 2019]] ).

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<span id="box-7.3-case-study-agroforestry-in-br-azil-canopies"></span>
=== Box 7.3 | Case Study: Agroforestry in Brazil – CANOPIES ===

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'''Summary'''

Brazilian farmers are integrating trees into their croplands in various ways, ranging from simple to highly complex agroforestry systems. While complex systems are more effective in the mitigation of climate change, trade-offs with scalability need to be resolved for agroforestry systems to deliver on their potential. The Brazilian-Dutch CANOPIES project ( [[#Janssen--2020|Janssen 2020]] ) is exploring transition pathways to agroforestry systems optimised for local ecological and socio-economic conditions.

'''Background'''

The climate change mitigation potential of agroforestry systems is widely recognised ( [[#Zomer--2016|Zomer et al. 2016]] ; [[#FAO--2017b|FAO 2017b]] ) and Brazilian farmers and researchers are pioneering diverse ways of integrating trees into croplands, from planting rows of eucalyptus trees in pastures up to highly complex agroforests consisting of &gt;30 crop and tree species. The degree of complexity influences the multiple functions that farmers and societies can attain from agroforestry: the more complex it is, the more it resembles a natural forest with associated benefits for its carbon storage capacity and its habitat quality for biodiversity ( [[#Santos--2019|Santos et al. 2019]] ). However, trade-offs exist between the complexity and scalability of agroforestry as complex systems rely on intensive manual labour to achieve high productivity ( [[#Tscharntke--2011|Tscharntke et al. 2011]] ). To date, mechanisation of structurally diverse agroforests is scarce and hence, efficiencies of scale are difficult to achieve.

'''Case description'''

These synergies and trade-offs between complexity, multifunctionality and scalability are studied in the CANOPIES (Co-existence of Agriculture and Nature: Optimisation and Planning of Integrated Ecosystem Services) project, a collaboration between Wageningen University (NL), the University of São Paulo and EMBRAPA (both Brazil). Soil and management data are collected on farms of varying complexity to evaluate carbon sequestration and other ecosystem services, economic performance and labour demands.

'''Interactions''' '''and limitations'''

The trade-off between complexity and labour demand is less pronounced in EMBRAPA’s integrated crop-livestock-forestry (ICLF) systems, where grains and pasture are planted between widely spaced tree rows. Here, barriers for implementation relate mostly to livestock and grain farmers’ lack of knowledge on forestry management and financing mechanisms 5 ( [[#Gil--2015|Gil et al. 2015]] ). Additionally, linking these financing mechanisms to carbon sequestration remains a Monitoring, Reporting and Verification challenge ( [[#Smith--2020b|Smith et al. 2020b]] ).

Box 7.3

'''Lessons'''

Successful examples of how more complex agroforestry can be upscaled do exist in Brazil. For example, on farm trials and consistent investments over several years have enabled Rizoma Agro to develop a citrus production system that integrates commercial and native trees in a large-scale multi-layered agroforestry system. The success of their transition resulted in part from their corporate structure that allowed them to tap into the certified Green Bonds market ( [[#CBI--2020|CBI 2020]] ). However, different transition strategies need to be developed for family farmers and their distinct socio-economic conditions.

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<span id="enteric-fermentation"></span>
==== 7.4.3.4 Enteric Fermentation ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Mitigating CH 4 emissions from enteric fermentation can be direct (i.e., targeting ruminal methanogenesis and emissions per animal or unit of feed consumed) or indirect, by increasing production efficiency (i.e., reducing emission intensity per unit of product). Measures can be classified as those relating to (i) feeding, (ii) supplements, additives and vaccines, and (iii) livestock breeding and wider husbandry ( [[#Jia--2019|Jia et al. 2019]] ). Co-benefits include enhanced climate change adaptation and increased food security associated with improved livestock breeding (Smith et al. 2014). Risks include mitigation persistence, ecological impacts associated with improving feed quality and supply, or potential toxicity and animal welfare issues concerning feed additives. Implementation barriers include feeding/administration constraints, the stage of development of measures, legal restrictions on emerging technologies and negative impacts, such as the previously described risks (Smith et al. 2014; [[#Jia--2019|Jia et al. 2019]] ; P. [[#Smith--2019|Smith et al. 2019]] a).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The AR5 indicated medium (5–15%) technical mitigation potential from both feeding and breeding related measures (Smith et al. 2014). More recently, the SRCCL estimated with ''medium confidence'' , a global potential of 0.12–1.18 GtCO 2 -eq yr –1 between 2020 and 2050, with the range reflecting technical, economic and sustainability constraints (SRCCL, Chapter 2: [[#Hristov--2013|Hristov et al. 2013]] ; [[#Dickie--2014a|Dickie et al. 2014a]] ; [[#Herrero--2016|Herrero et al. 2016]] ; [[#Griscom--2017|Griscom et al. 2017]] ). The underlying literature used a mixture of IPCC GWP100 values for CH 4 , preventing conversion of CO 2 -eq to CH 4 . Improved livestock feeding and breeding were included in IAM emission pathway scenarios within the SRCCL and SR1.5, although it was suggested that the full mitigation potential of enteric CH 4 measures is not captured in current models ( [[#Rogelj--2018b|Rogelj et al. 2018b]] ; [[#IPCC--2018|IPCC 2018]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Recent reviews generally identify the same measures as those outlined in the SRCCL, with the addition of early life manipulation of the ruminal biome ( [[#Grossi--2019|Grossi et al. 2019]] ; [[#Eckard--2020|Eckard and Clark 2020]] ; [[#Thompson--2020|Thompson and Rowntree 2020]] ; [[#Beauchemin--2020|Beauchemin et al. 2020]] ; [[#Ku-Vera--2020|Ku-Vera et al. 2020]] ; [[#Honan--2021|Honan et al. 2021]] ). There is ''robust evidence'' and ''high agreement'' that chemically synthesised inhibitors are promising emerging near-term measures ( [[#Patra--2016|Patra 2016]] ; [[#Jayanegara--2018|Jayanegara et al. 2018]] ; [[#Van%20Wesemael--2019|Van Wesemael et al. 2019]] ; [[#Beauchemin--2020|Beauchemin et al. 2020]] ) with high (e.g., 16–70% depending on study) mitigation potential reported (e.g., [[#Hristov--2015|Hristov et al. 2015]] ; [[#McGinn--2019|McGinn et al. 2019]] ; [[#Melgar--2020|Melgar et al. 2020]] ) and commercial availability expected within two years in some countries ( [[#Reisinger--2021|Reisinger et al. 2021]] ). However, their mitigation persistence ( [[#McGinn--2019|McGinn et al. 2019]] ), cost ( [[#Carroll--2019|Carroll and Daigneault 2019]] ; [[#Alvarez-Hess--2019|Alvarez-Hess et al. 2019]] ) and public acceptance ( [[#Jayasundara--2016|Jayasundara et al. 2016]] ) or regulatory approval is currently unclear while administration in pasture-based systems is likely to be challenging ( [[#Patra--2017|Patra et al. 2017]] ; [[#Leahy--2019|Leahy et al. 2019]] ). Research into other inhibitors/feeds containing inhibitory compounds, such as macroalga or seaweed ( [[#Chagas--2019|Chagas et al. 2019]] ; [[#Kinley--2020|Kinley et al. 2020]] ; [[#Roque--2019|Roque et al. 2019]] ), shows promise, although concerns have been raised regarding palatability, toxicity, environmental impacts and the development of industrial-scale supply chains ( [[#Abbott--2020|Abbott et al. 2020]] ; [[#Vijn--2020|Vijn et al. 2020]] ). In the absence of CH 4 vaccines, which are still under development ( [[#Reisinger--2021|Reisinger et al. 2021]] ) pasture-based and non-intensive systems remain reliant on increasing production efficiency ( [[#Beauchemin--2020|Beauchemin et al. 2020]] ). Breeding of low emitting animals may play an important role and is a subject under ongoing research ( [[#Pickering--2015|Pickering et al. 2015]] ; [[#Jonker--2018|Jonker et al. 2018]] ; [[#López-Paredes--2020|López-Paredes et al. 2020]] ).

Approaches differ regionally, with more focus on direct, technical options in Developed Countries, and improved efficiency in developing countries ( [[#Caro%20Torres--2016|Caro Torres et al. 2016]] ; [[#Mottet--2017b|Mottet et al. 2017b]] ; [[#MacLeod--2018|MacLeod et al. 2018]] ; [[#Frank--2018|Frank et al. 2018]] ). A recent assessment finds greatest economic (up to USD100 tCO 2 -eq –1 ) potential (using the IPCC AR4 GWP100 value for CH 4 ) for 2020–2050 in Asia and the Pacific (32.9 MtCO 2 -eq yr –1 ) followed by Developed Countries (25.5 MtCO 2 -eq yr –1 ) ( [[#Roe--2021|Roe et al. 2021]] ). Despite numerous country and sub-sector specific studies, most of which include cost analysis ( [[#Hasegawa--2012|Hasegawa and Matsuoka 2012]] ; [[#Hoa--2014|Hoa et al. 2014]] ; [[#Jilani--2015|Jilani et al. 2015]] ; [[#Eory--2015|Eory et al. 2015]] ; [[#Pradhan--2017|Pradhan et al. 2017]] ; [[#Pellerin--2017|Pellerin et al. 2017]] ; [[#Ericksen--2018|Ericksen and Crane 2018]] ; [[#Habib--2018|Habib and Khan 2018]] ; [[#Kashangaki--2018|Kashangaki and Ericksen 2018]] ; [[#Salmon--2018|Salmon et al. 2018]] ; [[#Brandt--2019b|Brandt et al. 2019b]] ; [[#Kiggundu--2019|Kiggundu et al. 2019]] ; [[#Kavanagh--2019|Kavanagh et al. 2019]] ; [[#Mosnier--2019|Mosnier et al. 2019]] ; [[#Pradhan--2019|Pradhan et al. 2019]] ; [[#Sapkota--2019|Sapkota et al. 2019]] ; [[#Carroll--2019|Carroll and Daigneault 2019]] ; [[#Leahy--2019|Leahy et al. 2019]] ; [[#Dioha--2020|Dioha and Kumar 2020]] ), sectoral assessment of regional technical and notably economic ( [[#Beach--2015|Beach et al. 2015]] ; [[#USEPA--2019|USEPA 2019]] ) potential is restricted by lack comprehensive and comparable data. Therefore, verification of regional estimates indicated by global assessments is challenging. Feed quality improvement, which may have considerable potential in developing countries ( [[#Caro--2016|Caro et al. 2016]] ; [[#Mottet--2017a|Mottet et al. 2017a]] ), may have negative wider impacts. For example, potential land-use change and greater emissions associated with production of concentrates ( [[#Brandt--2019b|Brandt et al. 2019b]] ).

'''Critical review and conclusion.''' Based on studies to date, using a range of IPCC GWP100 values for CH 4 , there is ''medium confidence'' that activities to reduce enteric CH 4 emissions have a global technical potential of 0.8 (0.2–1.2) GtCO 2 -eq yr –1 , of which 0.2 (0.1–0.3) GtCO 2 -eq yr –1 is available up to USD100 tCO 2 -eq –1 (Figure 7.11). The CO 2 -eq value may also slightly differ if the GWP100 IPCC AR6 CH 4 value was uniformly applied within calculations. Lack of comparable country and sub-sector studies to assess the context applicability of measures, associated costs and realistic adoption likelihood, prevents verification of estimates.

<div id="7.4.3.5" class="h3-container"></div>

<span id="improve-rice-management"></span>
==== 7.4.3.5 Improve Rice Management ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Emissions from rice cultivation mainly concern CH 4 associated with anaerobic conditions, although N 2 O emission also occur via nitrification and denitrification processes. Measures to reduce CH 4 and N 2 O emissions include (i) improved water management (e.g., single drainage and multiple drainage practices), (ii) improved residue management, (iii) improved fertiliser application (e.g., using slow release fertiliser and nutrient specific application), and (iv) soil amendments (including biochar and organic amendments) ( [[#Pandey--2014|Pandey et al. 2014]] ; [[#Kim--2017b|Kim et al. 2017b]] ; [[#Yagi--2020|Yagi et al. 2020]] ; [[#Sriphirom--2020|Sriphirom et al. 2020]] ). These measures not only have mitigation potential but can improve water use efficiency, reduce overall water use, enhance drought adaptation and overall system resilience, improve yield, reduce production costs from seed, pesticide, pumping and labour, increase farm income, and promote sustainable development ( [[#Quynh--2015|Quynh and Sander 2015]] ; [[#Yamaguchi--2017|Yamaguchi et al. 2017]] ; [[#Tran--2018|Tran et al. 2018]] ; [[#Sriphirom--2019|Sriphirom et al. 2019]] ). However, in terms of mitigation of CH 4 and N 2 O, antagonistic effects can occur, whereby water management can enhance N 2 O emissions due to creation of alternate wet and dry conditions ( [[#Sriphirom--2019|Sriphirom et al. 2019]] ), with trade-offs between CH 4 and N 2 O during the drying period potentially offsetting some mitigation benefits. Barriers to adoption may include site-specific limitations regarding soil type, percolation and seepage rates or fluctuations in precipitation, water canal or irrigation infrastructure, paddy surface level and rice field size, and social factors including farmer perceptions, pump ownership, and challenges in synchronising water management between neighbours and pumping stations ( [[#Quynh--2015|Quynh and Sander 2015]] ; [[#Yamaguchi--2017|Yamaguchi et al. 2017]] ; [[#Yamaguchi--2019|Yamaguchi et al. 2019]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The AR5 outlined emissions from rice cultivation of 0.49–0.723 GtCO 2 -eq yr –1 in 2010 with an average annual growth of 0.4% yr –1 . The SRCCL estimated a global mitigation potential from improved rice cultivation of 0.08–0.87 GtCO 2 -eq yr –1 between 2020 and 2050, with the range representing the difference between technical and economic constraints, types of activities included (e.g., improved water management and straw residue management) and GHGs considered ( [[#Dickie--2014a|Dickie et al. 2014a]] ; [[#Beach--2015|Beach et al. 2015]] ; [[#Paustian--2016|Paustian et al. 2016]] ; [[#Griscom--2017|Griscom et al. 2017]] ; [[#Hawken--2017|Hawken 2017]] ) (SRCCL, Chapter 2).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Since AR5 and the SRCCL, studies on mitigation have principally focused on water and nutrient management practices with the aim of improving overall sustainability as well as measurements of site-specific emissions to help improve the resolution of regional estimates. Intensity of emissions show considerable spatial and temporal variation, dependent on site specific factors including degradation of soil organic matter, management of water levels in the field, the types and amount of fertilisers applied, rice variety and local cultivation practices. Variation in CH 4 emissions have been found to range from 0.5–41.8 mg m 2 hr –1 in South-East Asia ( [[#Sander--2014|Sander et al. 2014]] ; [[#Chidthaisong--2018|Chidthaisong et al. 2018]] ; [[#Setyanto--2018|Setyanto et al. 2018]] ; [[#Sibayan--2018|Sibayan et al. 2018]] ; J. [[#Wang--2018|Wang et al. 2018]] ; [[#Maneepitak--2019|Maneepitak et al. 2019]] ), 0.5–37.0 mg m 2 hr –1 in Southern and Eastern Asia ( [[#Zhang--2010|Zhang et al. 2010]] ; [[#Wang--2012|Wang et al. 2012]] ; [[#Oo--2018|Oo et al. 2018]] ; J. [[#Wang--2018|Wang et al. 2018]] ; [[#Takakai--2020|Takakai et al. 2020]] ) ''',''' and 0.5–10.4 mg m 2 hr –1 in North America (J. [[#Wang--2018|Wang et al. 2018]] ). Current studies on emissions of N 2 O also showed high variation in the range of 0.13–654 ug/m 2 /hr ( [[#Akiyama--2005|Akiyama et al. 2005]] ; [[#Islam--2018|Islam et al. 2018]] ; [[#Kritee--2018|Kritee et al. 2018]] ; [[#Zschornack--2018|Zschornack et al. 2018]] ; [[#Oo--2018|Oo et al. 2018]] ).

Recent studies on water management have highlighted the potential to mitigate GHG emissions, while also enhancing water use efficiency ( [[#Tran--2018|Tran et al. 2018]] ). A meta-analysis on multiple drainage systems found that Alternative Wetting and Drying (AWD) with irrigation management, can reduce CH 4 emissions by 20–30% and water use by 25.7%, though this resulted in a slight yield reduction (5.4%) ( [[#Carrijo--2017|Carrijo et al. 2017]] ). Other studies have described improved yields associated with AWD ( [[#Tran--2018|Tran et al. 2018]] ). Water management for both single and multiple drainage can (most likely ) reduce methane emissions by about 35% but increase N 2 O emissions by about 20% ( [[#Yagi--2020|Yagi et al. 2020]] ). However, N 2 O emissions occur only under dry conditions, therefore total reduction in terms of net GWP is approximately 30%. Emissions of N 2 O are higher during dry seasons ( [[#Yagi--2020|Yagi et al. 2020]] ) and depend on site specific factors as well as the quantity of fertiliser and organic matter inputs into the paddy rice system. Variability of N 2 O emissions from single and multiple drainage can range from 0.06–33 kg/ha ( [[#Hussain--2015|Hussain et al. 2015]] ; [[#Kritee--2018|Kritee et al. 2018]] ). AWD in Vietnam was found to reduce both CH 4 and N 2 O emissions by 29–30 and 26–27% respectively with the combination of net GWP about 30% as compared to continuous flooding ( [[#Tran--2018|Tran et al. 2018]] ). Overall, greatest average economic mitigation potential (up to USD100 tCO 2 -eq –1 ) between 2020 and 2050 is estimated to be in Asia and the Pacific (147.2 MtCO 2 -eq yr –1 ) followed by Latin America and the Caribbean (8.9 MtCO 2 -eq yr –1 ) using the IPCC AR4 GWP100 value for CH 4 ( [[#Roe--2021|Roe et al. 2021]] ).

'''Critical assessment and conclusion.''' There is ''medium confidence'' that improved rice management has a technical potential of 0.3 (0.1–0.8) GtCO 2 -eq yr –1 between 2020 and 2050, of which 0.2 (0.05–0.3) GtCO 2 -eq yr –1 is available up to USD100 tCO 2 -eq –1 (Figure 7.11). Improving rice cultivation practices will not only reduce GHG emissions, but also improve production sustainability in terms of resource utilisation including water consumption and fertiliser application. However, emission reductions show high variability and are dependent on site specific conditions and cultivation practices.

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<span id="crop-nutrient-management"></span>
==== 7.4.3.6 Crop Nutrient Management ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Improved crop nutrient management can reduce N 2 O emissions from cropland soils. Practices include optimising fertiliser application delivery, rates and timing, utilising different fertiliser types (i.e., organic manures, composts and synthetic forms), and using slow or controlled-released fertilisers or nitrification inhibitors (Smith et al. 2014; [[#Griscom--2017|Griscom et al. 2017]] ; P. [[#Smith--2019|Smith et al. 2019]] a). In addition to individual practices, integrated nutrient management that combines crop rotations including intercropping, nitrogen biological fixation, reduced tillage, use of cover crops, manure and bio-fertiliser application, soil testing and comprehensive nitrogen management plans, is suggested as central for optimising fertiliser use, enhancing nutrient uptake and potentially reducing N 2 O emissions ( [[#Bationo--2012|Bationo et al. 2012]] ; [[#Lal--2018|Lal et al. 2018]] ; [[#Bolinder--2020|Bolinder et al. 2020]] ; [[#Jensen--2020|Jensen et al. 2020]] ; [[#Namatsheve--2020|Namatsheve et al. 2020]] ). Such practices may generate additional mitigation by indirectly reducing synthetic fertiliser manufacturing requirements and associated emissions, though such mitigation is accounted for in the Industry Sector and not considered in this chapter. Tailored nutrient management approaches, such as 4R nutrient stewardship, are implemented in contrasting farming systems and contexts and supported by best management practices to balance and match nutrient supply with crop requirements, provide greater stability in fertiliser performance and to minimise N 2 O emissions and nutrient losses from fields and farms ( [[#Fixen--2020|Fixen 2020]] ; [[#Maaz--2021|Maaz et al. 2021]] ). Co-benefits of improved nutrient management can include enhanced soil quality (notably when manure, crop residues or compost is utilised), carbon sequestration in soils and biomass, soil water holding capacity, adaptation capacity, crop yields, farm incomes, water quality (from reduced nitrate leaching and eutrophication), air quality (from reduced ammonia emissions) and in certain cases, it may facilitate land sparing ( [[#Sapkota--2014|Sapkota et al. 2014]] ; [[#Johnston--2014|Johnston and Bruulsema 2014]] ; [[#Zhang--2017|Zhang et al. 2017]] ; P. [[#Smith--2019|Smith et al. 2019]] a; [[#Mbow--2019|Mbow et al. 2019]] ).

A potential risk under certain circumstances, is yield reduction, while implementation of practices should consider current soil nutrient status. There are significant regional imbalances, with some regions experiencing nutrient surpluses from over fertilisation and others, nutrient shortages and chronic deficiencies ( [[#FAO--2021e|FAO 2021e]] ). Additionally, depending on context, practices may be inaccessible, expensive or require expertise to implement ( [[#Hedley--2015|Hedley 2015]] ; [[#Benson--2018|Benson and Mogues 2018]] ) while impacts of climate change may influence nutrient use efficiency ( [[#Amouzou--2019|Amouzou et al. 2019]] ) and therefore, mitigation potential.

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The SRCCL broadly identified the same practices as outlined in AR5 and estimated that improved cropland nutrient management could mitigate between 0.03 and 0.71 GtCO 2 -eq yr –1 between 2020 and 2050 (SRCCL Chapter 2) ( [[#Dickie--2014a|Dickie et al. 2014a]] ; [[#Beach--2015|Beach et al. 2015]] ; [[#Paustian--2016|Paustian et al. 2016]] ; [[#Griscom--2017|Griscom et al. 2017]] ; [[#Hawken--2017|Hawken 2017]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Research since the SRCCL highlights the mitigation potential and co-benefits of adopting improved nutrient management strategies, notably precision fertiliser application methods and nutrient expert systems, and applicability in both large-scale mechanised and small-scale systems ( [[#USEPA--2019|USEPA 2019]] ; [[#Hijbeek--2019|Hijbeek et al. 2019]] ; [[#Griscom--2020|Griscom et al. 2020]] ; [[#Tian--2020|Tian et al. 2020]] ; [[#Aryal--2020|Aryal et al. 2020]] ; [[#Sapkota--2021|Sapkota et al. 2021]] ). Improved crop nutrient management is feasible in all regions, but effectiveness is context dependent. Sub-Saharan Africa has one of the lowest global fertiliser consumption rates, with increased fertiliser use suggested as necessary to meet projected future food requirements ( [[#Mueller--2012|Mueller et al. 2012]] ; [[#ten%20Berge--2019|ten Berge et al. 2019]] ; [[#Adam--2020|Adam et al. 2020]] ; [[#Falconnier--2020|Falconnier et al. 2020]] ). Fertiliser use in Developed Countries is already high (Figure 7.10) with increased nutrient use efficiency among the most promising mitigation measures ( [[#Roe--2019|Roe et al. 2019]] ; [[#Hijbeek--2019|Hijbeek et al. 2019]] ). Considering that Asia and Pacific, and Developed Countries accounted for the greatest share of global nitrogen fertiliser use, it is not surprising that these regions are estimated to have greatest economic mitigation potential (up to USD100 tCO 2 -eq –1 ) between 2020 and 2050, at 161.8 and 37.1 MtCO 2 -eq yr –1 respectively (using the IPCC AR4 GWP100 value for N 2 O) ( [[#Roe--2021|Roe et al. 2021]] ).

'''Critical assessment and conclusion.''' There is ''medium confidence'' that crop nutrient management has a technical potential of 0.3 (0.06–0.7) GtCO 2 -eq yr –1 of which 0.2 (0.05–0.6) GtCO 2 -eq yr –1 is available up to USD100 tCO 2 -eq –1 . This value is based on GWP100 using a mixture of IPCC values for N 2 O and may slightly differ if calculated using AR6 values. The development of national roadmaps for sustainable fertiliser (nutrient) management can help in scaling-up related practices and in realising this potential. Crop nutrient management measures can contribute not only to mitigation, but food and nutrition security and wider environmental sustainability goals.

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<span id="box-7.4-case-study-the-climate-smart-v-illage-approach"></span>

=== Box 7.4 | Case Study: The Climate-smart Village Approach ===

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'''Summary'''

The climate-smart villages (CSV) approach aims to generate local knowledge, with the involvement of farmers, researchers, practitioners, and governments, on climate change adaptation and mitigation while improving productivity, food security, and farmers’ livelihoods ( [[#Aggarwal--2018|Aggarwal et al. 2018]] ). This knowledge feeds a global network that includes 36 climate-smart villages in South and South-East Asia, West and East Africa, and Latin America.

'''Background'''

It is expected that agricultural production systems across the world will change in response to climate change, posing significant challenges to the livelihoods and food security of millions of people ( [[#Kennedy--2014|Kennedy et al. 2014]] ). Maintaining agricultural growth while minimising climate shocks is crucial to building a resilient food production system and meeting sustainable development goals in vulnerable countries.

'''Case description'''

The CSV approach seeks an integrated vision so that sustainable rural development is the final goal for rural communities. At the same time, it fosters the understanding of climate change with the implementation of adaptation and mitigation actions, as much as possible. Rural communities and local stakeholders are the leaders of this process, where scientists facilitate their knowledge to be useful for the communities and learn at the same time about challenges but also the capacity those communities have built through time. The portfolio includes weather-smart activities, water-smart practices, seed/breed smart, carbon-/nutrient-smart practices, and institutional-/market-smart activities.

'''Interactions''' '''and limitations'''

The integration of technologies and services that are suitable for the local conditions resulted in many gains for food security and adaptation and for mitigation where appropriate. It was also shown that, in all regions, there is considerable yield advantage when a portfolio of technologies is used, rather than the isolated use of technologies ( [[#Govaerts--2005|Govaerts et al. 2005]] ; [[#Zougmoré--2014|Zougmoré et al. 2014]] ). Moreover, farmers are using research results to promote their products as climate-smart leading to increases in their income ( [[#Acosta-Alba--2019|Acosta-Alba et al. 2019]] ). However, climatic risk sites and socio-economic conditions together with a lack of resource availability are key issues constraining agriculture across all five regions.

'''Lessons'''

i. Understanding the priorities, context, challenges, capacity, and characteristics of the territory and the communities regarding climate, as well as the environmental and socio-economic dimensions, is the first step. Then, understanding climate vulnerability in their agricultural systems based on scientific data but also listening to their experience will set the pathway to identify climate-smart agriculture (CSA) options (practices and technologies) to reduce such vulnerability.

ii. Building capacity is also a critical element of the CSV approach, rural families learn about the practices and technologies in a neighbour’s house, and as part of the process, families commit to sharing their knowledge with other families, to start a scaling-out process within the communities. Understanding the relationship between climate and their crop is key, as well as the use of weather forecasts to plan their agricultural activities.

The assessment of the implementation of the CSA options should be done together with community leaders to understand changes in livelihoods and climate vulnerability. Also, knowledge appropriation by community leaders has led to farmer-to-farmer knowledge exchange within and outside the community (Ortega Fernandez and Martínez-Barón 2018).

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<span id="manure-management"></span>
==== 7.4.3.7 Manure Management ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Manure management measures aim to mitigate CH 4 and N 2 O emissions from manure storage and deposition. Mitigation of N 2 O considers both direct and indirect (i.e., conversion of ammonia and nitrate to N 2 O) sources. According to the SRCCL, measures may include (i) anaerobic digestion, (ii) applying nitrification or urease inhibitors to stored manure or urine patches, (iii) composting, (iv) improved storage and application practices, (v) grazing practices and (vi) alteration of livestock diets to reduce nitrogen excretion ( [[#Mbow--2019|Mbow et al. 2019]] ; [[#Jia--2019|Jia et al. 2019]] ). Implementation of manure management with other livestock and soil management measures can enhance system resilience, sustainability, food security and help prevent land degradation (Smith et al. 2014; [[#Mbow--2019|Mbow et al. 2019]] ; P. [[#Smith--2019|Smith et al. 2019]] a), while potentially benefiting the localised environment, for example, regarding water quality ( [[#Di--2016|Di and Cameron 2016]] ). Risks include increased N 2 O emission from the application of manure to poorly drained or wet soils, trade-offs between N 2 O and ammonia emissions and potential eco-toxicity associated with some measures.

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' The AR5 reported manure measures to have high (&gt;10%) mitigation potential. The SRCCL estimated a technical global mitigation potential between 2020 and 2050 of 0.01–0.26 GtCO 2 -eq yr –1 , with the range depending on economic and sustainable capacity ( [[#Dickie--2014a|Dickie et al. 2014a]] ; [[#Herrero--2016|Herrero et al. 2016]] ) (SRCCL, Chapter 2). Conversion of estimates to native units is restricted as a mixture of GWP100 values was used in underlying studies. Measures considered were typically more suited to confined production systems ( [[#Jia--2019|Jia et al. 2019]] ; [[#Mbow--2019|Mbow et al. 2019]] ), while improved manure management is included within IAM emission pathways ( [[#Rogelj--2018b|Rogelj et al. 2018b]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Research published since SRCCL broadly focuses on measures relevant to intensive or confined systems (e.g., ( [[#Hunt--2019|Hunt et al. 2019]] ; [[#Kavanagh--2019|Kavanagh et al. 2019]] ; Sokolov et al. 2020; [[#Im--2020|Im et al. 2020]] ; [[#Adghim--2020|Adghim et al. 2020]] ; [[#Mostafa--2020|Mostafa et al. 2020]] ), highlighting co-benefits and risks. For example, measures may enhance nutrient recovery, fertiliser value ( [[#Sefeedpari--2019|Sefeedpari et al. 2019]] ; [[#Ba--2020|Ba et al. 2020]] ; [[#Yao--2020|Yao et al. 2020]] ) and secondary processes such as biogas production ( [[#Shin--2019|Shin et al. 2019]] ). However, the potential antagonistic relationship between GHG and ammonia mitigation and need for appropriate management is emphasised ( [[#Aguirre-Villegas--2019|Aguirre-Villegas et al. 2019]] ; [[#Grossi--2019|Grossi et al. 2019]] ; [[#Kupper--2020|Kupper et al. 2020]] ; [[#Ba--2020|Ba et al. 2020]] ). In some circumstances, fugitive emissions may reduce the potential mitigation benefits of biogas production ( [[#Scheutz--2019|Scheutz and Fredenslund 2019]] ; [[#Bakkaloglu--2021|Bakkaloglu et al. 2021]] ), while high implementation cost is identified as an adoption barrier, notably of anaerobic digestion ( [[#Liu--2018|Liu and Liu 2018]] ; [[#Niles--2019|Niles and Wiltshire 2019]] ; [[#Ndambi--2019|Ndambi et al. 2019]] ; [[#Ackrill--2020|Ackrill and Abdo 2020]] ; [[#Adghim--2020|Adghim et al. 2020]] ). Nitrification inhibitors have been found to be effective at reducing N 2 O emissions from pasture deposited urine (López-Aizpún et al. 2020), although the use of nitrification inhibitors is restricted in some jurisdictions due to concerns regarding residues in food products ( [[#Di--2016|Di and Cameron 2016]] ; [[#Eckard--2020|Eckard and Clark 2020]] ) while ''limited evidence'' suggests eco-toxicity risk under certain circumstances ( [[#Kösler--2019|Kösler et al. 2019]] ). Some forage crops may naturally contain inhibitory substances ( [[#Simon--2019|Simon et al. 2019]] , 2020; [[#de%20Klein--2020|de Klein et al. 2020]] ), though this warrants further research ( [[#Podolyan--2020|Podolyan et al. 2020]] ; [[#Gardiner--2020|Gardiner et al. 2020]] ).

Country specific studies provide insight into regionally applicable measures, with emphasis on small-scale anaerobic digestion (e.g., dome digesters), solid manure coverage and daily manure spreading in Asia and the Pacific, and Africa ( [[#Hasegawa--2012|Hasegawa and Matsuoka 2012]] ; [[#Hoa--2014|Hoa et al. 2014]] ; [[#Jilani--2015|Jilani et al. 2015]] ; [[#Hasegawa--2016|Hasegawa et al. 2016]] ; [[#Pradhan--2017|Pradhan et al. 2017]] ; [[#Ericksen--2018|Ericksen and Crane 2018]] ; [[#Pradhan--2019|Pradhan et al. 2019]] ; [[#Kiggundu--2019|Kiggundu et al. 2019]] ; [[#Dioha--2020|Dioha and Kumar 2020]] ). Tank/lagoon covers, large-scale anaerobic digestion, improved application timing, nitrogen inhibitor application to urine patches, soil-liquid separation, reduced livestock nitrogen intake, trailing shoe, band or injection slurry spreading and acidification are emphasised in Developed Countries ( [[#Kaparaju--2011|Kaparaju and Rintala 2011]] ; [[#Eory--2015|Eory et al. 2015]] ; Pape et al. 2016; [[#Jayasundara--2016|Jayasundara et al. 2016]] ; [[#Pellerin--2017|Pellerin et al. 2017]] ; [[#Liu--2018|Liu and Liu 2018]] ; [[#Lanigan--2018|Lanigan et al. 2018]] ; [[#Carroll--2019|Carroll and Daigneault 2019]] ; [[#Eckard--2020|Eckard and Clark 2020]] ). Using IPCC AR4 GWP100 values for CH 4 and N 2 O, a recent assessment finds 69% (63.4 MtCO 2 -eq yr –1 ) of economic potential (up to USD100 tCO 2 -eq –1 ) between 2020–2050, to be in Developed Countries ( [[#Roe--2021|Roe et al. 2021]] ).

'''Critical assessment and conclusion.''' There is ''medium confidence'' that manure management measures have a global technical potential of 0.3 (0.1–0.5) GtCO 2 -eq yr –1 , (using a range of IPCC GWP100 values for CH 4 and N 2 O), of which 0.1 (0.09–0.1) GtCO 2 -eq yr –1 is available at up to USD100 tCO 2 -eq –1 (Figure 7.11). As with other non-CO 2 GHG mitigation estimates, values may slightly differ depending upon which IPCC GWP100 values were used. There is ''robust evidence'' and ''high agreement'' that there are measures that can be applied in all regions, but greatest mitigation potential is estimated in Developed Countries in more intensive and confined production systems.

<div id="box-7.5" class="h2-container box-container"></div>

<span id="box-7.5-farming-system-approaches-and-mitigation"></span>

=== Box 7.5 | Farming System Approaches and Mitigation ===

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'''Introduction'''

There is ''robust evidence'' and ''high agreement'' that agriculture needs to change to facilitate environment conservation while maintaining and where appropriate, increase overall production. The SRCCL identified several farming system approaches, deemed alternative to conventional systems ( [[#Olsson--2019|Olsson et al. 2019]] ; [[#Mbow--2019|Mbow et al. 2019]] ; L.G. [[#Smith--2019|Smith et al. 2019]] ). These may incorporate several of the mitigation measures described in [[#7.4.3|Section 7.4.3]] , while potentially also delivering environmental co-benefits. This Box assesses evidence specifically on the mitigation capacity of some such system approaches. The approaches are not mutually exclusive, may share similar principles or practices and can be complimentary. In all cases, mitigation may result from either (i) emission reductions or (ii) enhanced carbon sequestration, via combinations of management practices as outlined in Figure 1 within this Box. The approaches will have pros and cons concerning multiple factors, including mitigation, yield and co-benefits, with trade-offs subject to the diverse contexts and ways in which they are implemented.

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[[File:14e9c7f60d5b50ca0980c4b9c61fd49f IPCC_AR6_WGIII_Box_7_5_Figure_1.png]]

'''Box 7.5, Figure 1 | Potential mitigation mechanisms and associated management practices.''' N = nitrogen, SOM = soil organic matter, LUC = land-use change. a The farming system approaches outlined are not necessarily mutually exclusive. ''b'' Only agricultural emissions are considered. Mitigation may also result from reduced production of fertilisers and agrochemicals. ''c'' Reduced emissions intensity per unit of milk/meat will only result in a reduction in absolute emissions where increased productivity facilitates a reduction in animal numbers. 1 = [[#Altieri--2015|Altieri et al. 2015]] ; 2 = [[#Altieri--2017|Altieri and Nicholls 2017]] ; 3 = [[#Powlson--2016|Powlson et al. 2016]] ; 4 = [[#Corbeels--2019|Corbeels et al. 2019]] ; 5 = [[#Lal--2015|Lal 2015]] ; 6 = [[#Gonzalez-Sanchez--2019|Gonzalez-Sanchez et al. 2019]] ; 7 = [[#Thierfelder--2017|Thierfelder et al. 2017]] ; 8 = [[#Hendrickson--2008|Hendrickson et al. 2008]] ; 9 = [[#Weindl--2015|Weindl et al. 2015]] ; 10 = [[#Thornton--2015|Thornton and Herrero 2015]] ; 11 = Lal al. 2020; 12 = Scialabba and Müller–Lindenlauf 2010; 13 = [[#Goh--2011|Goh 2011]] ; 14 = [[#IFOAM--2016|IFOAM 2016]] .

'''Is there evidence that these approaches deliver mitigation?'''

Agroecology (AE) including Regenerative Agriculture (RA)

There is limited discussion on the mitigation potential of AE ( [[#Gliessman--2013|Gliessman 2013]] ; [[#Altieri--2017|Altieri and Nicholls 2017]] ), but ''robust evidence'' that AE can improve system resilience and bring multiple co-benefits ( [[#Altieri--2015|Altieri et al. 2015]] ; [[#Mbow--2019|Mbow et al. 2019]] ; [[#Aguilera--2020|Aguilera et al. 2020]] ; [[#Tittonell--2020|Tittonell 2020]] ; [[#Wanger--2020|Wanger et al. 2020]] ) (AR6 WGII Box 5.10). ''Limited evidence'' concerning the mitigation capacity of AE at a system level ( [[#Saj--2017|Saj et al. 2017]] ; [[#Snapp--2021|Snapp et al. 2021]] ) makes conclusions difficult, yet studies into specific practices that may be incorporated, suggest AE may have mitigation potential ( ''medium confidence'' ) ( [[#7.4.3|Section 7.4.3]] ). However, AE, that incorporates management practices used in organic farming (see below), may result in reduced yields, driving compensatory agricultural production elsewhere. Research into GHG mitigation by AE as a system and impacts of wide-scale implementation is required. Despite absence of a universally accepted definition (see Annex I), RA is gaining increasing attention and shares principles of AE. Some descriptions include carbon sequestration as a specific aim ( [[#Elevitch--2018|Elevitch et al. 2018]] ). Few studies have assessed mitigation potential of RA at a system level (e.g., [[#Colley--2020|Colley et al. 2020]] ). Like AE, it is ''likely'' that RA can contribute to mitigation, the extent to which is currently unclear and by its case-specific design, will vary ( ''medium confidence'' ).

Conservation agriculture (CA)

The SRCCL noted both positive and inconclusive results regarding CA and soil carbon, with sustained sequestration dependent on productivity and residue returns ( [[#Jia--2019|Jia et al. 2019]] ; [[#Mirzabaev--2019|Mirzabaev et al. 2019]] ; [[#Mbow--2019|Mbow et al. 2019]] ). Recent research is in broad agreement ( [[#Ogle--2019|Ogle et al. 2019]] ; [[#Corbeels--2020|Corbeels et al. 2020]] , 2019; [[#Gonzalez-Sanchez--2019|Gonzalez-Sanchez et al. 2019]] ; [[#Munkholm--2020|Munkholm et al. 2020]] ) with greatest mitigation potential suggested in dry regions ( [[#Sun--2020|Sun et al. 2020]] ). Theoretically, CA may facilitate improved nitrogen use efficiency ( ''limited evidence'' ) ( [[#Lal--2015|Lal 2015]] ; [[#Powlson--2016|Powlson et al. 2016]] ), though CA appears to have mixed effects on soil N 2 O emission ( [[#Six--2004|Six et al. 2004]] ; [[#Mei--2018|Mei et al. 2018]] ). CA is noted for its adaptation benefits, with ''wide agreement'' that CA can enhance system resilience to climate related stress, notably in dry regions. There is evidence that CA can contribute to mitigation, but its contribution is depended on multiple factors including climate and residue returns ( ''hi'' ''gh confidence'' ).

Integrated production systems (IPS)

The integration of different enterprises in space and time (e.g., diversified cropping, crop and livestock production, agroforestry), therefore facilitating interaction and transfer of recourses between systems, is suggested to enhance sustainability and adaptive capacity ( [[#Hendrickson--2008|Hendrickson et al. 2008]] ; [[#Franzluebbers--2014|Franzluebbers et al. 2014]] ; [[#Lemaire--2014|Lemaire et al. 2014]] ; [[#Weindl--2015|Weindl et al. 2015]] ; [[#Gil--2017|Gil et al. 2017]] ; [[#Olsson--2019|Olsson et al. 2019]] ; [[#Peterson--2020|Peterson et al. 2020]] ; [[#Walkup--2020|Walkup et al. 2020]] ; [[#Garrett--2020|Garrett et al. 2020]] ). Research indicates some mitigation potential, including by facilitating sustainable intensification (Box 7.11), though benefits are likely to be highly context specific ( [[#Herrero--2013|Herrero et al. 2013]] ; [[#Carvalho--2014|Carvalho et al. 2014]] ; [[#Piva--2014|Piva et al. 2014]] ; [[#de%20Figueiredo--2017|de Figueiredo et al. 2017]] ; [[#Rosenstock--2014|Rosenstock et al. 2014]] ; [[#Weindl--2015|Weindl et al. 2015]] ; [[#Thornton--2015|Thornton and Herrero 2015]] ; [[#Descheemaeker--2016|Descheemaeker et al. 2016]] ; [[#Lal--2020|Lal 2020]] ; [[#Guenet--2021|Guenet et al. 2021]] ). The other systems outlined within this Box may form or facilitate IPS.

Organic farming (OF)

OF can be considered a form of AE ( [[#Lampkin--2017|Lampkin et al. 2017]] ) though it is discussed separately here as it is guided by specific principles and associated regulations (Annex I). OF is perhaps noted more for potential co-benefits, such as enhanced system resilience and biodiversity promotion, than mitigation. Several studies have reviewed the emissions footprint of organic compared to conventional systems ( [[#Mondelaers--2009|Mondelaers et al. 2009]] ; [[#Tuomisto--2012|Tuomisto et al. 2012]] ; [[#Skinner--2014|Skinner et al. 2014]] ; [[#Meier--2015|Meier et al. 2015]] ; [[#Seufert--2017|Seufert and Ramankutty 2017]] ; [[#Clark--2017|Clark and Tilman 2017]] ; [[#Meemken--2018|Meemken and Qaim 2018]] ; [[#Bellassen--2021|Bellassen et al. 2021]] ). Acknowledging potential assessment limitations ( [[#Meier--2015|Meier et al. 2015]] ; [[#van%20der%20Werf--2020|van der Werf et al. 2020]] ), evidence suggests organic production to typically generate lower emissions per unit of area, while emissions per unit of product vary and depend on the product ( ''high agreement'' , ''medium evidence'' ). OF has been suggested to increase soil carbon sequestration ( [[#Gattinger--2012|Gattinger et al. 2012]] ), though definitive conclusions are challenging ( [[#Leifeld--2013|Leifeld et al. 2013]] ). Fewer studies consider impacts of large-scale conversion from conventional to organic production globally. Though context specific ( [[#Seufert--2017|Seufert and Ramankutty 2017]] ), OF is reported to typically generate lower yields ( [[#Seufert--2012|Seufert et al. 2012]] ; De Ponti et al. 2012; [[#Kirchmann--2019|Kirchmann 2019]] ; [[#Biernat--2020|Biernat et al. 2020]] ). Large-scale conversion, without fundamental changes in food systems and diets ( [[#Muller--2017|Muller et al. 2017]] ; [[#Theurl--2020|Theurl et al. 2020]] ), may lead to increases in absolute emissions from land-use change, driven by greater land requirements to maintain production (L.G. [[#Smith--2019|Smith et al. 2019]] ; Leifeld 2016; [[#Meemken--2018|Meemken and Qaim 2018]] ).

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<span id="box-7.6-case-study-mitigation-options-and-costs-in-the-indian-agri-cultural-sector"></span>

=== Box 7.6 | Case Study: Mitigation Options and Costs in the Indian Agricultural Sector ===

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'''Objective'''

To assess the technical mitigation potentials of Indian agriculture and costs under a business as usual scenario (BAU) and Mitigation scenario up to 2030 ( [[#Sapkota--2019|Sapkota et al. 2019]] ).

'''Results'''

The study shows that by 2030 under BAU scenario GHG emissions from the agricultural sector in India would be 515 MtCO 2 -eq yr –1 (using GWP100 and IPCC AR4 values) with a technical mitigation potential of 85.5 MtCO 2 -eq yr –1 through the adoption of various mitigation practices. About 80% of the technical mitigation potential could be achieved by adopting cost-saving measures. Three mitigation options, for example, efficient use of fertiliser, zero-tillage, and rice-water management, could deliver more than 50% of the total technical abatement potential. Under the BAU scenario the projected GHG emissions from major crop and livestock species is estimated at 489 MtCO 2 -eq in 2030, whereas under mitigation scenario GHG emissions are estimated at 410 MtCO 2 -eq implying a technical mitigation option of about 78.67 MtCO 2 -eq yr –1 (Box 7.6, Figure 1). Major sources of projected emissions under the BAU scenario, in order of importance, were cattle, rice, buffalo, and small ruminants. Although livestock production and rice cultivation account for a major share of agricultural emissions, the highest mitigation potential was observed in rice (about 36 MtCO 2 -eq yr –1 ) followed by buffalo (about 14 MtCO 2 -eq yr –1 ), wheat (about 11 MtCO 2 -eq yr –1 ) and cattle (about 7 MtCO 2 -eq yr –1 ). Crops such as cotton and sugarcane each offered mitigation potential of about 5 MtCO 2 -eq yr –1 while the mitigation potential from small ruminants (goat/sheep) was about 2 MtCO 2 -eq yr –1 .

[[#Sapkota--2019|Sapkota et al. (2019)]] also estimated the magnitude of GHG savings per year through adoption of various mitigation measures, together with the total cost and net cost per unit of CO 2 -eq abated. When the additional benefits of increased yield due to adoption of the mitigation measures were considered, about 80% of the technical mitigation potential (67.5 out of 85.5 MtCO 2 -eq) could be achieved by cost-saving measures. When yield benefits were considered, green fodder supplements to ruminant diets were the most cost-effective mitigation measure, followed by vermicomposting and improved diet management of small ruminants. Mitigation measures such as fertigation and micro-irrigation, various methods of restoring degraded land and feed additives in livestock appear to be cost-prohibitive, even when considering yield benefits, if any. The study accounted for GHG emissions at the farm level and excluded emissions arising due to processing, marketing or consumption post farm-gate. It also did not include emissions from feed production, since livestock in India mostly rely on crop by-products and concentrates. Further the potential of laser land levelling seems exaggerated which may also be redundant with already accounted potential from ‘improved water management in rice’. The mitigation potential of agroecological approaches/technologies such as natural farming which is picking up in India in recent years has also been overlooked.

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'''Box 7.6, Figure 1''' '''|''' '''Contribution of various crops and livestock species to total agricultural emission in 2012 (baseline) and by 2030 under business as usual (BAU) and mitigation scenarios for Indian agricultural sector.''' Source: [[#Sapkota--2019|Sapkota et al. (2019)]] . Reprinted from Science of The Total Environment, 655, Sapkota T.B. et al., Cost-effective opportunities for climate change mitigation in Indian agriculture., 2019, with permission from Elsevier.

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<span id="bioenergy-and-beccs"></span>

=== 7.4.4 Bioenergy and BECCS ===

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'''Activities, co-benefits, risks and implementation opportunities and barriers''' '''.''' Bioenergy refers to energy products (solid, liquid and gaseous fuels, electricity, heat) derived from multiple biomass sources including organic waste, harvest residues and by-flows in the agriculture and forestry sectors, and biomass from tree plantations, agroforestry systems, lignocellulosic crops, and conventional food/feed crops. It may reduce net GHG emissions by displacing the use of coal, oil and natural gas with renewable biomass in the production of heat, electricity, and fuels. When combined with carbon capture and storage (BECCS) and biochar production, bioenergy systems may provide CDR by durably storing biogenic carbon in geological, terrestrial, or ocean reservoirs, or in products, further contributing to mitigation ( [[#Chum--2011|Chum et al. 2011]] ; [[#Cabral--2019|Cabral et al. 2019]] ; [[#Hammar--2020|Hammar and Levihn 2020]] ; [[#Emenike--2020|Emenike et al. 2020]] ; [[#Moreira--2020b|Moreira et al. 2020b]] ; Y. [[#Wang--2020|Wang et al. 2020]] : [[#Johnsson--2020|Johnsson et al. 2020]] ) ( [[#7.4.3.2|Section 7.4.3.2]] , Chapters 3, 4, 6 and 12).

This section addresses especially aspects related to land use and biomass supply for bioenergy and BECCS. The mitigation potential presented here and in Table 7.3, includes only the CDR component of BECCS. The additional mitigation achieved from displacing fossil fuels is covered elsewhere (Chapters 6, 8, 9, 10, 11 and 12).

Modern bioenergy systems (as opposed to traditional use of fuelwood and other low-quality cooking and heating fuels) currently provide approximately 30 EJ yr –1 of primary energy, making up 53% of total renewable primary energy supply ( [[#IEA--2019|IEA 2019]] ). Bioenergy systems are commonly integrated within forest and agriculture systems that produce food, feed, lumber, paper and other bio-based products. They can also be combined with other AFOLU mitigation options: deployment of energy crops, agroforestry and A/R can provide biomass while increasing land carbon stocks (Sections 7.4.2.2 and 7.4.3.3) and anaerobic digestion of manure and wastewater, to reduce methane emissions, can produce biogas and CO 2 for storage ( [[#7.4.3.7|Section 7.4.3.7]] ). But ill-deployment of energy crops can also cause land carbon losses ( [[#Hanssen--2020|Hanssen et al. 2020]] ) and increased biomass demand for energy could hamper other mitigation measures such as reduced deforestation and degradation (Sections 7.4.2.1).

Bioenergy and BECCS can be associated with a range of co-benefits and adverse side effects ( [[#Smith--2016|Smith et al. 2016]] ; [[#Jia--2019|Jia et al. 2019]] ; [[#Calvin--2021|Calvin et al. 2021]] ) ( [[IPCC:Wg3:Chapter:Chapter-12#12.5|Section 12.5]] ). It is difficult to disentangle bioenergy development from the overall development in the AFOLU sector given its multiple interactions with food, land, and energy systems. It is therefore not possible to precisely determine the scale of bioenergy and BECCS deployment at which negative impacts outweigh benefits. Important uncertainties include governance systems, future food and biomaterials demand, land-use practices, energy systems development, climate impacts, and time scale considered when weighing negative impacts against benefits ( [[#Robledo-Abad--2017|Robledo-Abad et al. 2017]] ; [[#Turner--2018b|Turner et al. 2018b]] ; [[#Daioglou--2019|Daioglou et al. 2019]] ; [[#Wu--2019|Wu et al. 2019]] ; [[#Kalt--2020|Kalt et al. 2020]] ; [[#Hanssen--2020|Hanssen et al. 2020]] ; [[#Calvin--2021|Calvin et al. 2021]] ; [[#Cowie--2021|Cowie et al. 2021]] ) (SRCCL, Cross-Chapter Box 7; Box 7.7). The use of municipal organic waste, harvest residues, and biomass processing by-products as feedstock is commonly considered to have relatively lower risk, provided that associated land-use practices are sustainable ( [[#Cowie--2021|Cowie et al. 2021]] ). Deployment of dedicated biomass production systems can have positive and negative implications on mitigation and other sustainability criteria, depending on location and previous land use, feedstock, management practice, deployment strategy and scale ( [[#Rulli--2016|Rulli et al. 2016]] ; [[#Popp--2017|Popp et al. 2017]] ; [[#Daioglou--2017|Daioglou et al. 2017]] ; [[#Staples--2017|Staples et al. 2017]] ; [[#Carvalho--2017|Carvalho et al. 2017]] ; [[#Humpenöder--2018|Humpenöder et al. 2018]] ; [[#Fujimori--2019|Fujimori et al. 2019]] ; [[#Hasegawa--2020|Hasegawa et al. 2020]] ; [[#Drews--2020|Drews et al. 2020]] ; [[#Schulze--2020|Schulze et al. 2020]] ; [[#Stenzel--2020|Stenzel et al. 2020]] ; [[#Mouratiadou--2020|Mouratiadou et al. 2020]] ; [[#Buchspies--2020|Buchspies et al. 2020]] ; [[#Hanssen--2020|Hanssen et al. 2020]] , [[#IPBES--2019b|IPBES 2019b]] ) (Sections 12.5 and 17.3.3.1).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' Many more stringent mitigation scenarios in AR5 relied heavily on bioenergy and BECCS. The SR1.5 reported a range for the CDR potential of BECCS (2100) at 0.5 to 5 GtCO 2 -eq yr –1 when applying constraints reflecting sustainability concerns, at a cost of 100–200 USD tCO 2 –1 ( [[#Fuss--2018|Fuss et al. 2018]] ). The SRCCL reported a technical CDR potential for BECCS at 0.4–11.3 GtCO 2 yr –1 ( ''medium confidence'' ), noting that most estimates do not include socio-economic barriers, the impacts of future climate change, or non-GHG climate forcing (IPCC. 2019). The SR1.5 and SRCCL highlighted that bioenergy and BECCS can be associated with multiple co-benefits and adverse side effects that are context specific.

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' The role of bioenergy and BECCS in mitigation pathways has been reduced as IAM-based studies have incorporated broader mitigation portfolios and have explored non-CO 2 emissions reduction and a wider variation of underlying assumptions about socio-economic drivers and associated energy and food demand, as well as deployment limits such as land availability for A/R and for cultivation of crops used for bioenergy and BECCS ( [[#Grubler--2018|Grubler et al. 2018]] ; Van Vuuren et al. 2018).

Increased availability of spatially explicit data and advances in the modelling of crop productivity and land use, land carbon stocks, hydrology, and ecosystem properties, have enabled more comprehensive analyses of factors that influence the contribution of bioenergy and BECCS in IAM-based mitigation scenarios, and also associated co-benefits and adverse side effects (Turner et al.2018a; [[#Wu--2019|Wu et al. 2019]] , [[#Li--2020|Li et al. 2020]] , [[#Hanssen--2020|Hanssen et al. 2020]] ; [[#Drews--2020|Drews et al. 2020]] ; [[#Ai--2021|Ai et al. 2021]] ; [[#Hasegawa--2021|Hasegawa et al. 2021]] ). Yet, IAMs are still coarse in local land-use practices. ( [[#Daioglou--2019|Daioglou et al. 2019]] ; [[#Wu--2019|Wu et al. 2019]] ; [[#Moreira--2020b|Moreira et al. 2020b]] ). Literature complementary to IAM studies indicate opportunities for integration of biomass production systems into agricultural landscapes (e.g., agroforestry, double cropping) to produce biomass while achieving co-benefits ( [[IPCC:Wg3:Chapter:Chapter-12#12.5|Section 12.5]] ). Similarly, climate-smart forestry puts forward measures (Box 7.3) adapted to regional circumstances in forest sectors, enabling co-benefits in nature conservation, soil protection, employment and income generation, and provision of wood for buildings, bioenergy and other bio-based products ( [[#Nabuurs--2017|Nabuurs et al. 2017]] ).

Studies have also investigated the extent and possible use of marginal, abandoned, and degraded lands, and approaches to help restore the productive value of these lands ( [[#Awasthi--2017|Awasthi et al. 2017]] ; [[#Fritsche--2017|Fritsche et al. 2017]] ; Chiaramonti and Panoutsou, 2018; [[#Fernando--2018|Fernando et al. 2018]] ; Elbersen et al. 2019; [[#Rahman--2019|Rahman et al. 2019]] ; [[#Næss--2021|Næss et al. 2021]] ). In the SRCCL, the presented range for degraded or abandoned land was 32–1400 Mha ( [[#Jia--2019|Jia et al. 2019]] ). Recent regional assessments not included in the SRCCL found up to 69 Mha in EU-28, 185 Mha in China, 9.5 Mha in Canada, and 127 Mha in the USA ( [[#Emery--2017|Emery et al. 2017]] ; [[#Liu--2017|Liu et al. 2017]] ; Elbersen et al. 2019; Zhang et al.2020; [[#Vera--2021|Vera et al. 2021]] ). The definitions of marginal/abandoned/degraded land, and the methods used to assess such lands remain inconsistent across studies ( [[#Jiang--2019|Jiang et al. 2019]] ), causing large variation amongst them ( [[#Jiang--2021|Jiang et al. 2021]] ). Furthermore, the availability of such lands has been contested since they may serve other functions, such as: subsistence, biodiversity protection, and so on ( [[#Baka--2014|Baka 2014]] ).

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<span id="box-7.7-climate-change-mitigation-value-of-bioe-nergy-and-beccs"></span>
=== Box 7.7 | Climate Change Mitigation Value of Bioenergy and BECCS ===

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Besides emissions, and possible avoided emissions, related to the supply chain, the GHG effects of using bioenergy depend on: (i) change in GHG emissions when bioenergy substitutes another energy source; and (ii) how the associated land use and possible land-use change influence the amount of carbon that is stored in vegetation and ( [[#Calvin--2021|Calvin et al. 2021]] ) soils over time. Studies arrive at varying mitigation potentials for bioenergy and BECCS due to the large diversity of bioenergy systems, and varying conditions concerning where and how they are deployed (Elshout 2015; Harper et al.2018; [[#Muri--2018|Muri 2018]] ; Kalt et al.2019; [[#Brandão--2019|Brandão et al. 2019]] ; [[#Buchspies--2020|Buchspies et al. 2020]] ; [[#Cowie--2021|Cowie et al. 2021]] ; [[#Calvin--2021|Calvin et al. 2021]] ). Important factors include feedstock type, land management practice, energy conversion efficiency, type of bioenergy product (and possible co-products), emissions intensity of the products being displaced, and the land use/cover prior to bioenergy deployment ( [[#Zhu--2017|Zhu et al. 2017]] ; [[#Staples--2017|Staples et al. 2017]] ; [[#Daioglou--2017|Daioglou et al. 2017]] ; [[#Carvalho--2017|Carvalho et al. 2017]] ; [[#Hanssen--2020|Hanssen et al. 2020]] ; [[#Mouratiadou--2020|Mouratiadou et al. 2020]] ). Studies arrive at contrasting conclusions also when similar bioenergy systems and conditions are analysed, due to different methodologies, assumptions, and parametrization (Harper et al.2018; Kalt et al.2019; [[#Brandão--2019|Brandão et al. 2019]] ; Albers et al. 2019; [[#Buchspies--2020|Buchspies et al. 2020]] ; [[#Bessou--2020|Bessou et al. 2020]] ; [[#Rolls--2020|Rolls and Forster 2020]] ; [[#Cowie--2021|Cowie et al. 2021]] ).

Box 7.7, Figure 1 shows emissions associated with biomass supply (residues and crops grown on cropland not needed for food) in 2050, here designated emission-supply curves. The curves are constructed assuming that additional biomass supply consistently comes from the available land/biomass resource that has the lowest GHG emissions, for example, the marginal GHG emissions increase with increasing biomass use for bioenergy. Net negative emissions indicate cases where biomass production increases land carbon stocks.

One curve ( ''EMF-33'' ) is determined from stylised scenarios using IAMs ( [[#Rose--2020|Rose et al. 2020]] ). One of the two curves determined from sectoral models, ''Constant Land Cover'' , reflects supply chain emissions and changes in land carbon storage caused by the biomass supply system itself. These two curves are obtained with modelling approaches compatible with the modelling protocol used for the scenarios in the AR6 database, which accounts for the land-use change and all other GHG emissions along a given transformation trajectory, enabling assessments of the warming level incurred.

The ''Natural Regrowth'' curve attribute additional ‘counterfactual emissions’ to the bioenergy system, corresponding to estimated uptake of CO 2 in a counterfactual scenario where land is not used for bioenergy but instead subject to natural vegetation regrowth. This curve does not show actual emissions from the bioenergy system, but it provides insights in the mitigation value of the bioenergy option compared to alternative land-use strategies. To illustrate, if biomass is used instead of a primary energy source with emission factor 75 kgCO 2 GJ –1 , and the median values in the ''Natural Regrowth'' curve are adopted, then the curve indicates that up to about 150 EJ of biomass can be produced and used for energy while achieving higher net GHG savings than the alternative to set aside the same land for natural vegetation regrowth (assuming same conversion factor).

The large ranges in the bars signify the importance of uncertainties and how the biomass is deployed. Variation in energy conversion efficiencies and uncertainty about magnitude, timing, and permanence of land carbon storage further complicate the comparison. Finally, not shown in Box 7.7, Figure 1, the emission-supply curves would be adjusted downwards if displacement of emission intensive energy was included or if the bioenergy is combined with CCS to provide CDR.

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[[File:aca3a6d32b1ece26214b2545cb95dc9a IPCC_AR6_WGIII_Box_7_6_Figure_1.png]]
[[File:3e30f559ddbc39ff17217954b9448502 IPCC_AR6_WGIII_Box_7_7_Figure_1.png]]

'''Box 7.7, Figure 1 |''' '''Emissions associated with primary biomass supply in 2050 (residues and crops grown on cropland not needed for food), as determined from sectoral models (Daioglou et al. 2017; Kalt et al. 2020), and stylised scenarios from the EMF-33 project using Integrated Assessment Models (Rose et al. 2020).''' All methods include LUC (direct and indirect) emissions. Emissions associated with Natural Regrowth include counterfactual carbon fluxes (see text). The sectoral models include a more detailed representation of the emissions, including lifecycle emissions from fertiliser production. IAM models may include economic feedbacks such as intensification as a result of increasing prices. As an indication: for natural gas the emission factor is around 56, for coal around 95 kgCO 2 GJ –1 .

'''Critical assessment and conclusion''' ''.'' Recent estimates of technical biomass potentials constrained by food security and environmental considerations fall within previous ranges corresponding to ''medium agreement'' , (e.g., [[#Turner--2018b|Turner et al. 2018b]] ; [[#Daioglou--2019|Daioglou et al. 2019]] ; [[#Wu--2019|Wu et al. 2019]] , Hansen et al.2020; [[#Kalt--2020|Kalt et al. 2020]] ) arriving at 4–57 and 46–245 EJ yr –1 by 2050 for residues and dedicated biomass crops, respectively. Based on studies to date, the technical net CDR potential of BECCS (including LUC and other supply chain emissions, but excluding energy carrier substitution) by 2050 is 5.9 (0.5–11.3) GtCO 2 yr –1 globally, of which 1.6 (0.5–3.5) GtCO 2 yr –1 is available at below USD100 tCO 2 –1 ( ''medium confidence)'' ( [[#Lenton--2010|Lenton 2010]] ; [[#Koornneef--2012|Koornneef et al. 2012]] ; [[#McLaren--2012|McLaren 2012]] ; [[#Powell--2012|Powell and Lenton 2012]] ; [[#Fuss--2018|Fuss et al. 2018]] ; [[#Turner--2018a|Turner et al. 2018a]] ; [[#Hanssen--2020|Hanssen et al. 2020]] ; [[#Roe--2021|Roe et al. 2021]] ) (Figure 7.11). The equivalent economic potential as derived from IAMs is 1.8 (0.2–9.9) GtCO 2 yr –1 (Table 7.3).

Technical land availability does not imply that dedicated biomass production for bioenergy and BECCS is the most effective use of this land for mitigation. Further, implications of deployment for climate change mitigation and other sustainability criteria are context dependent and influenced by many factors, including rate and total scale. While governance has a critical influence on outcome, larger scale and higher expansion rate generally translates into higher risk for negative outcomes for GHG emissions, biodiversity, food security and a range of other sustainability criteria (Searchinger 2017; [[#Vaughan--2018|Vaughan et al. 2018]] ; [[#Rochedo--2018|Rochedo et al. 2018]] ; [[#de%20Oliveira%20Garcia--2018|de Oliveira Garcia et al. 2018]] ; [[#Daioglou--2019|Daioglou et al. 2019]] ; [[#Junginger--2019|Junginger et al. 2019]] ; Galik et al. 2020; [[#Stenzel--2020|Stenzel et al. 2020]] ).

However, literature has also highlighted how the agriculture and forestry sectors may respond to increasing demand by devising management approaches that enable biomass production for energy in conjunction with supply of food, construction timber, and other bio-based products, providing climate change mitigation while enabling multiple co-benefits including for nature conservation ( [[#Nabuurs--2017|Nabuurs et al. 2017]] ; [[#Parodi--2018|Parodi et al. 2018]] ; [[#Springmann--2018|Springmann et al. 2018]] ; [[#Rosenzweig--2020|Rosenzweig et al. 2020]] ; [[#Clark--2020|Clark et al. 2020]] ; [[#Favero--2020|Favero et al. 2020]] ; [[#Hanssen--2020|Hanssen et al. 2020]] ) ( [[#7.4|Section 7.4]] and Cross-Working Group Box 3 in Chapter 12).

Strategies to enhance the benefits of bioenergy and BECCS include (i) management practices that protect carbon stocks and the productive and adaptive capacity of lands, as well as their environmental and social functions ( [[#van%20Ittersum--2013|van Ittersum et al. 2013]] , [[#Gerssen-Gondelach--2015|Gerssen-Gondelach et al. 2015]] ; [[#Moreira--2020b|Moreira et al. 2020b]] ) (ii) supply chains from primary production to final consumption that are well managed and deployed at appropriate levels ( [[#Fajardy--2018|Fajardy et al. 2018]] ; [[#Donnison--2020|Donnison et al. 2020]] ); and (iii) development of a cross-sectoral agenda for bio-based production within a circular economy, and international cooperation and governance of global trade in products to maximise synergies while limiting trade-offs concerning environmental, economic and social outcomes ( ''very high confidence'' ). Finally, the technical feasibility of BECCS depends on investments in and the roll-out of advanced bioenergy technologies currently not widely available ( [[#Baker--2015|Baker et al. 2015]] ; Daioglou et al. 2020b).

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<span id="demand-side-measures"></span>

=== 7.4.5 Demand-side Measures ===

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<span id="shift-to-sustainable-healthy-diets"></span>
==== 7.4.5.1 Shift to Sustainable Healthy Diets ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' The term ‘sustainable healthy diets’ refers to dietary patterns that ‘promote all dimensions of individuals’ health and well-being; have low environmental pressure and impact; are accessible, affordable, safe and equitable; and are culturally acceptable’ ( [[#FAO%20and%20WHO--2019|FAO and WHO 2019]] ). In addition to climate mitigation gains, a transition towards more plant-based consumption and reduced consumption of animal-based foods, particularly from ruminant animals, could reduce pressure on forests and land used for feed, support the preservation of biodiversity and planetary health ( [[#FAO--2018c|FAO 2018c]] ; [[#Theurl--2020|Theurl et al. 2020]] ), and contribute to preventing forms of malnutrition (i.e., undernutrition, micronutrient deficiency, and obesity) in developing countries ( [[IPCC:Wg3:Chapter:Chapter-12#12.4|Section 12.4]] ). Other co-benefits include lowering the risk of cardiovascular disease, type 2 diabetes, and reducing mortality from diet-related non-communicable diseases ( [[#Toumpanakis--2018|Toumpanakis et al. 2018]] ; [[#Satija--2018|Satija and Hu 2018]] ; [[#Faber--2020|Faber et al. 2020]] ; [[#Magkos--2020|Magkos et al. 2020]] ). However, transition towards sustainable healthy diets could have adverse impacts on the economic stability of the agricultural sector (MacDiarmid 2013; [[#Aschemann-Witzel--2015|Aschemann-Witzel 2015]] ; [[#Van%20Loo--2017|Van Loo et al. 2017]] ). Therefore, shifting toward sustainable and healthy diets requires effective food-system oriented reform policies that integrate agriculture, health, and environment policies to comprehensively address synergies and conflicts in co-lateral sectors (agriculture, trade, health, environment protection etc.) and capture spill-over effects, for example, climate change, biodiversity loss, food poverty ( [[#FAO%20and%20WHO--2019|FAO and WHO 2019]] ; [[#Galli--2020|Galli et al. 2020]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' According to the AR5, changes in human diets and consumption patterns can reduce emissions 5.3 to 20.2 GtCO 2 -eq yr –1 by 2050 from diverted agricultural production and avoided land-use change (Smith et al. 2014). In the SRCCL, a ‘contract and converge’ model of transition to sustainable healthy diets was suggested as an effective approach, reducing food consumption in over-consuming populations and increasing consumption of some food groups in populations where minimum nutritional needs are not met (P. [[#Smith--2019|Smith et al. 2019]] a). The total technical mitigation potential of changes in human diets was estimated as 0.7–8 GtCO 2 -eq yr –1 by 2050 ( [[#Tilman--2014|Tilman and Clark 2014]] ; [[#Springmann--2016|Springmann et al. 2016]] ; [[#Hawken--2017|Hawken 2017]] ) (SRCCL, [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] and 6), ranging from a 50% adoption of healthy diets (&lt;60g of animal-based protein) and only accounting for diverted agricultural production, to the global adoption of a vegetarian diet.

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Since the SRCCL, global studies continue to find high mitigation potential from reducing animal-source foods and increasing proportions of plant-rich foods in diets. [[#Springmann--2018|Springmann et al. (2018)]] estimated that diet changes in line with global dietary guidelines for total energy intake and consumption of red meat, sugar, fruits, and vegetables, could reduce GHG emissions by 29% and other environmental impacts by 5–9% compared with the baseline in 2050. [[#Poore--2018|Poore and Nemecek (2018)]] revealed that shifting towards diets that exclude animal-source food could reduce land use by 3.1 billion ha, decrease food-related GHG emissions by 6.5 GtCO 2 -eq yr –1 , acidification by 50%, eutrophication by 49%, and freshwater withdrawals by 19% for a 2010 reference year. [[#Frank--2019|Frank et al. (2019)]] estimated non-CO 2 emissions reductions of 0.4 GtCO 2 -eq yr –1 at a carbon price of USD100 tCO 2 –1 and 0.6 GtCO 2 -eq yr –1 at USD20 tCO 2 –1 in 2050 from shifting to lower animal-source diets (430 kcal of livestock calorie intake) in developed and emerging countries. From a systematic literature review, [[#Ivanova--2020|Ivanova et al. (2020)]] found mitigation potentials of 0.4–2.1 tCO 2 -eq capita –1 for a vegan diet, of 0.01–1.5 for a vegetarian diet, and of 0.1–2.0 for Mediterranean or similar healthy diet.

Regionally, mitigation potentials for shifting towards sustainable healthy diets (50% convergence to &lt;60g of meat-based protein, only accounting for diverted production) vary across regions. A recent assessment finds greatest economic (up to USD100 tCO 2 –1 ) potential for 2020–2050 in Asia and the Pacific (609 MtCO 2 -eq yr –1 ) followed by Developed Countries (322 MtCO 2 -eq yr –1 ) based on IPCC AR4 GWP100 values for CH 4 and N 2 O) ( [[#Roe--2021|Roe et al. 2021]] ). In the EU, ( [[#Latka--2021|Latka et al. 2021]] ) found that moving to healthy diets through price incentives could bring about annual reductions of non-CO 2 emissions from agriculture of 12–111 MtCO 2 -eq yr –1 . At the country level, recent studies show that following National Dietary Guidelines (NDG) would reduce food system GHG emissions by 4–42%, confer large health gains (1.0–1.5 million quality-adjusted life-years) and lower health care system costs (NZD 14–20 billion) in New Zealand [[#Drew--2020|Drew et al. (2020)]] ; reduce 28% of GHG emissions in Argentina [[#Arrieta--2018|Arrieta and González (2018)]] ; about 25% in Portugal [[#Esteve-Llorens--2020|Esteve-Llorens et al. (2020)]] and reduce GHG emissions, land use and blue water footprint by 15–60% in Spain ( [[#Batlle-Bayer--2020|Batlle-Bayer et al. 2020]] ). In contrast, [[#Aleksandrowicz--2019|Aleksandrowicz et al. (2019)]] found that meeting healthy dietary guidelines in India required increased dietary energy intake overall, which slightly increased environmental footprints by about 3–5% across GHG emissions, blue and green water footprints and land use.

'''Critical assessment and conclusion''' ''.'' Shifting to sustainable healthy diets has large potential to achieve global GHG mitigation targets as well as public health and environmental benefits ( ''high confidence'' ). Based on studies to date, there is ''medium confidence'' that shifting toward sustainable healthy diets has a technical potential including savings in the full value chain of 3.6 (0.3–8.0) GtCO 2 -eq yr –1 of which 2.5 (1.5–3.9) GtCO 2 -eq yr –1 is considered plausible based on a range of GWP100 values for CH 4 and N 2 O. When accounting for diverted agricultural production only, the feasible potential is 1.7 (1–2.7) GtCO 2 -eq yr –1 . A shift to more sustainable and healthy diets is generally feasible in many regions ( ''medium confidence'' ). However, potential varies across regions as diets are location- and community- specific, and thus may be influenced by local production practices, technical and financial barriers and associated livelihoods, everyday life and behavioural and cultural norms around food consumption ( [[#Meybeck--2017|Meybeck and Gitz 2017]] ; [[#Creutzig--2018|Creutzig et al. 2018]] ; [[#FAO--2018b|FAO 2018b]] ). Therefore, a transition towards low-GHG emission diets and achieving their mitigation potential requires a combination of appropriate policies, financial and non-financial incentives and awareness-raising campaigns to induce changes in consumer behaviour with potential synergies between climate objectives, health and equity ( [[#Rust--2020|Rust et al. 2020]] ).

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<span id="reduce-food-loss-and-waste"></span>
==== 7.4.5.2 Reduce Food Loss and Waste ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' Food loss and waste (FLW) refer to the edible parts of plants and animals produced for human consumption that are not ultimately consumed ( [[#UNEP--2021b|UNEP 2021b]] ). Food loss occurs through spoilage, spilling or other unintended consequences due to limitations in agricultural infrastructure, storage and packaging ( [[#Parfitt--2010|Parfitt et al. 2010]] ). Food waste typically takes place at the distribution (retail and food service) and consumption stages in the food supply chain and refers to food appropriate for human consumption that is discarded or left to spoil ( [[#HLPE--2014|HLPE 2014]] ). Options that could reduce FLW include: investing in harvesting and post-harvesting technologies in developing countries, taxing and other incentives to reduce business and consumer-level waste in developed countries, mandatory FLW reporting and reduction targets for large food businesses, regulation of unfair trading practices, and active marketing of cosmetically imperfect products ( [[#van%20Giesen--2019|van Giesen and de Hooge 2019]] ; Sinclair [[#Taylor--2019|Taylor et al. 2019]] ). Other studies suggested providing options of longer-lasting products and behavioural changes (e.g., through information provision) that cause dietary and consumption changes and motivate consumers to actively make decisions that reduce FLW. Reductions of FLW along the food chain bring a range of benefits beyond GHG mitigation, including reducing environmental stress (e.g., water and land competition, land degradation, desertification), safeguarding food security, and reducing poverty ( [[#Galford--2020|Galford et al. 2020]] ; [[#Venkatramanan--2020|Venkatramanan et al. 2020]] ). Additionally, FLW reduction is crucial for achieving SDG 12 which calls for ensuring ‘sustainable consumption and production patterns’ through lowering per capita global food waste by 50% at the retail and consumer level and reducing food losses along food supply chains by 2030. In line with these SDG targets, it is estimated that reducing FLW can free up several million km 2 of land ( ''high confidence'' ). The interlinkages between reducing FLW and food system sustainability are discussed in Chapter 12. Recent literature identifies a range of barriers to climate change mitigation through FLW reduction, which are linked to technological, biophysical, socio-economic, financial and cultural contexts at regional and local levels ( [[#Vogel--2018|Vogel and Meyer 2018]] ; [[#Gromko--2019|Gromko and Abdurasalova 2019]] ; [[#Rogissart--2019|Rogissart et al. 2019]] ; [[#Blok--2020|Blok et al. 2020]] ). Examples of these barriers include infrastructural and capacity limitations, institutional regulations, financial resources, constraining resources (e.g., energy), information gaps (e.g., with retailers), and consumers’ behaviour ( [[#Gromko--2019|Gromko and Abdurasalova 2019]] ; [[#Blok--2020|Blok et al. 2020]] ).

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' In AR5, reduced FLW was considered as a mitigation measure that could substantially lower emissions, with estimated mitigation potential of 0.6–6.0 GtCO 2 -eq yr –1 in the food supply chain (Smith et al. 2014). In the SRCCL, the technical mitigation potential of reducing food and agricultural waste was estimated at 0.76–4.5 GtCO 2 -eq yr –1 ( [[#Bajželj--2014|Bajželj et al. 2014]] ; [[#Dickie--2014b|Dickie et al. 2014b]] ; [[#Hawken--2017|Hawken 2017]] ) (SRCCL, [[IPCC:Wg3:Chapter:Chapter-2|Chapter 2]] and 6).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Since the SRCCL, there have been very few quantitative estimates of the mitigation potential of FLW reductions. Evidence suggests that reducing FLW together with overall food intake could have substantial mitigation potential, equating to an average of 0.3 tCO 2 -eq capita –1 ( [[#Ivanova--2020|Ivanova et al. 2020]] ). Some regional sectoral studies indicate that reducing FLW in the EU can reduce emissions by 186 MtCO 2 -eq yr –1 , the equivalent of around 15% of the environmental impacts (climate, acidification, and eutrophication) of the entire food value chain ( [[#Scherhaufer--2018|Scherhaufer et al. 2018]] ). In the UK, disruptive low-carbon innovations relating to FLW reduction were found to be associated with potential emissions reductions ranging between 2.6 and 3.6 MtCO 2 -eq ( [[#Wilson--2019|Wilson et al. 2019]] ). Other studies investigated the effect of tax mechanisms, such as ‘pay as you throw’ for household waste, on the mitigation potential of reducing FLW. Generally, these mechanisms are recognised as particularly effective in reducing the amount of waste and increasing the recycling rate of households ( [[#Carattini--2018|Carattini et al. 2018]] ; [[#Rogissart--2019|Rogissart et al. 2019]] ). Technological FWL mitigation opportunities exist throughout the food supply chain; post-harvest opportunities for FLW reductions are discussed in Chapter 12. Based on IPCC AR4 GWP100 values for CH 4 and N 2 O, greatest economic mitigation potential (up to USD100 tCO 2 –1 ) for the period 2020–2050 from FLW reduction is estimated to be in Asia and Pacific (192.3 GtCO 2 -eq yr –1 ) followed by Developed Countries (101.6 GtCO 2 -eq yr –1 ) ( [[#Roe--2021|Roe et al. 2021]] ). These estimates reflect diverted agricultural production and do not capture potential from avoided land-use changes.

'''Critical assessment and conclusion.''' There is ''medium confidence'' that reduced FLW has large global technical mitigation potential of 2.1 (0.1–5.8) GtCO 2 -eq yr –1 including savings in the full value chain and using GWP100 and a range of IPCC values for CH 4 and N 2 O. Potentials at 3.7 (2.2–5.1) GtCO 2 -eq yr –1 are considered plausible. When accounting for diverted agricultural production only, the feasible potential is 0.5 (0.0–0.9) GtCO 2 -eq yr –1 . See the section above for the joint land-use effects of food related demand-side measures which increases three-fold when accounting for the land-use effects as well. But this would overlap with other measures and is therefore not additive. Regionally, FLW reduction is feasible anywhere but its potential needs to be understood in a wider and changing socio-cultural context that determines nutrition ( ''hig'' ''h confidence'' ).

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==== 7.4.5.3 Improved and Enhanced Use of Wood Products ====

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'''Activities, co-benefits, risks and implementation opportunities and barriers.''' The use of wood products refers to the fate of harvested wood for material uses and includes two distinctly different components affecting the carbon cycle, including carbon storage in wood products and material substitution. When harvested wood is used for the manufacture of wood products, carbon remains stored in these products depending on their end use and lifetime. Carbon storage in wood products can be increased through enhancing the inflow of products in use, or effectively reducing the outflow of the products after use. This can be achieved through additional harvest under sustainable management ( [[#Pilli--2015|Pilli et al. 2015]] ; [[#Johnston--2019|Johnston and Radeloff 2019]] ), changing the allocation of harvested wood to long-lived wood products or by increasing products’ lifetime and increasing recycling ( [[#Brunet-Navarro--2017|Brunet-Navarro et al. 2017]] ; [[#Jasinevičius--2017|Jasinevičius et al. 2017]] ; [[#Xu--2018|Xu et al. 2018]] ; [[#Xie--2021|Xie et al. 2021]] ). Material substitution involves the use of wood for building, textiles, or other applications instead of other materials (e.g., concrete or steel, which consume more energy to produce) to avoid or reduce emissions associated with the production, use and disposal of those products it replaces.

The benefits and risks of improved and enhanced improved use of wood products are closely linked to forest management. First of all, the enhanced use of wood products could potentially activate or lead to improved sustainable forest management that can mitigate and adapt ( [[#Verkerk--2020|Verkerk et al. 2020]] ). Secondly, carbon storage in wood products and the potential for substitution effects can be increased by additional harvest, but with the risk of decreasing carbon storage in forest biomass when not done sustainably (P. [[#Smith--2019|Smith et al. 2019]] a). Conversely, reduced harvest may lead to gains in carbon storage in forest ecosystems locally, but these gains may be offset through international trade of forest products causing increased harvesting pressure or even degradation elsewhere ( [[#Kastner--2011|Kastner et al. 2011]] ; Kallio et al. 2018; [[#Pendrill--2019b|Pendrill et al. 2019b]] ). There are also environmental impacts associated with the processing, manufacturing, use and disposal of wood products ( [[#Adhikari--2018|Adhikari and Ozarska 2018]] ; [[#Baumgartner--2019|Baumgartner 2019]] ). See [[IPCC:Wg3:Chapter:Chapter-9#9.6.4|Section 9.6.4]] of this report for additional discussion on benefits and risks.

'''Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways.''' There is strong evidence at the product level that wood products from sustainably managed forests are associated with less greenhouse emissions in their production, use and disposal over their life-time compared to products made from emission-intensive and non-renewable materials. However, there is still limited understanding of the substitution effects at the level of markets, countries ( [[#Leskinen--2018|Leskinen et al. 2018]] ). The AR5 did not report on the mitigation potential of wood products. The SRCCL (Chapters 2 and 6) finds that some studies indicate significant mitigation potentials for material substitution, but concludes that the global, technical mitigation potential for material substitution for construction applications ranges from 0.25–1 GtCO 2 -eq yr –1 ( ''medium confidence'' ) ( [[#Miner--2010|Miner 2010]] ; [[#McLaren--2012|McLaren 2012]] ; [[#Roe--2019|Roe et al. 2019]] ).

'''Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL).''' Since the SRCCL, several studies have examined the mitigation potential of the enhanced and improved use of wood products. A global forest sector modelling study ( [[#Johnston--2019|Johnston and Radeloff 2019]] ) estimated that carbon storage in wood products represented a net carbon stock increase of 0.34 GtCO 2 -eq yr –1 globally in 2015 and which could provide an average mitigation potential (by increasing the HWP pool) of 0.33–0.41 GtCO 2 -eq yr –1 for the period 2020–2050, based on the future socio-economic development (SSP scenarios) and its effect on the production and consumption of wood products. Traded feedstock provided another 0.071 GtCO 2 yr –1 of carbon storage in 2015 and 0.12 GtCO 2 yr –1 by 2065. These potentials exclude the effect of material substitution. Another recent study estimated the global mitigation potential of mid-rise urban buildings designed with engineered wood products at 0.04–3.7 GtCO 2 yr –1 ( [[#Churkina--2020|Churkina et al. 2020]] ). Another study ( [[#Oliver--2014|Oliver et al. 2014]] ) estimated that using wood to substitute for concrete and steel as building materials could provide a technical mitigation potential of 0.78–1.73 GtCO 2 yr –1 achieved through carbon storage in wood products and through material and energy substitution.

The limited availability or absence of estimates of the future mitigation potential of improved use of wood products for many world regions represents an important knowledge gap, especially with regards to material substitution effects. At the product level, wood products are often associated with lower fossil-based emissions from production, use and disposal, compared to products made from emission-intensive and non-renewable materials ( [[#Sathre--2010|Sathre and O’Connor 2010]] ; [[#Geng--2017|Geng et al. 2017]] ; [[#Leskinen--2018|Leskinen et al. 2018]] ).

'''Critical assessment and conclusion.''' Based on studies to date, there is ''strong evidence'' and ''medium agreement'' that the improved use of wood products has a technical potential of 1.0 (0.04–3.7) GtCO 2 -eq yr –1 and economic potential of 0.4 (0.3–0.5) GtCO 2 -eq yr –1 . There is ''strong evidence'' and ''high agreement'' at the product level that material substitution provides on average benefits for climate change mitigation as wood products are associated with less fossil-based GHG emissions over their lifetime compared to products made from emission-intensive and non-renewable materials. However, the evidence at the level of markets or countries is uncertain and fairly limited for many parts of the world. There is ''medium confidence'' that material substitution and carbon storage in wood products contribute to climate change mitigation when also the carbon balances of forest ecosystems are considered of sustainably managed large areas of forests in medium term. The total future mitigation potential will depend on the forest system considered, the type of wood products that are produced and substituted and the assumed production technologies and conversion efficiencies of these products.

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