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

=== 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.

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