Agriculture, Forestry and Other Land Uses (AFOLU) is unique due to its capacity to mitigate climate change through greenhouse gas (GHG) emission reductions, as well as enhance removals (IPCC 2019). However, despite the attention on AFOLU since early 1990s it was reported in the IPCC Special Report on Climate Change and Land (SRCCL) as accounting for almost a quarter of anthropogenic emission (IPCC, 2019), with three main GHGs associated with AFOLU; carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Overall emission levels had remained similar since the publication of AR4 (Nabuurs et al. 2007). The diverse nature of the sector, its linkage with wider societal, ecological and environmental aspects and the required coordination of related policy, was suggested to make implementation of known and available supply- and demand-side mitigation measures particularly challenging (IPCC 2019). Despite such implementation barriers, the considerable mitigation potential of AFOLU as a sector on its own and its capacity to contribute to mitigation within other sectors was emphasised, with land-related measures, including bioenergy, estimated as capable of contributing between 20% and 60% of the total cumulative abatement to 2030 identified within transformation pathways (IPCC 2018). However, the vast mitigation potential from AFOLU initially portrayed in literature and in Integrated Assessment Models (IAMs), as explored in the IPCC Special Report on Climate Change of 1.5°C (SR1.5), is being questioned in terms of feasibility (Roe et al. 2021) and a more balanced perspective on the role of land in mitigation is developing, while at the same time, interest by private investors in land-based mitigation is increasing fast.

The SRCCL (IPCC 2019) outlined with medium evidence and medium agreement that supply-side agriculture and forestry measures had an economic (at USD100 tCO2-eq –1) mitigation potential of 7.2–10.6 GtCO2-eq –1 in 2030 (using GWP100 and multiple IPCC values for CH4 and N2O) of which about a third was estimated as achievable at <USD20 tCO2-eq –1. Agricultural measures were reported as sensitive to carbon price, with cropland and grazing land soil organic carbon management having the greatest potential at USD20 tCO2-eq –1 and restoration of organic soils at USD100 tCO2-eq –1. Forestry measures were less sensitive to carbon price, but varied regionally, with reduced deforestation, forest management and afforestation having the greatest potential depending on region. Although demand-side measures related to food could in theory make a large contribution to mitigation, in reality the contribution has been very small. Overall, the dependency of mitigation within AFOLU on a complex range of factors, from population growth, economic and technological developments, to the sustainability of mitigation measures and impacts of climate change, was suggested to make realisation highly challenging (IPCC 2019).

Land can only be part of the solution alongside rapid emission reduction in other sectors (IPCC 2019). It was recognised that land supports many ecosystem services on which human existence, well-being and livelihoods ultimately depend. Yet over-exploitation of land resources was reported as driving considerable and unprecedented rate of biodiversity loss, and wider environmental degradation (IPBES 2019b; IPCC 2019). Urgent action to reverse this trend was deemed crucial in helping to accommodate the increasing demands on land and enhance climate change adaptation capacity. There was high confidence that global warming was already causing an increase in the frequency and intensity of extreme weather and climate events, impacting ecosystems, food security, disturbances and production processes, with existing (and new) carbon stocks in soils and biomass at serious risk. The impact of land cover on regional climate (through biophysical effects) was also highlighted, although there was no confidence regarding impacts on global climate.

Since the IPCC Fifth Assessment Report (AR5), the share of AFOLU to anthropogenic GHG emissions had remained largely unchanged at 13–21% of total GHG emissions (medium confidence), though uncertainty in estimates of both sources and sinks of CO2, exacerbated by difficulties in separating natural and anthropogenic fluxes, was emphasised. Models indicated land (including the natural sink) to have very likely provided a net removal of CO2 between 2007 and 2016. As in AR5, land cover change, notably deforestation, was identified as a major driver of anthropogenic CO2 emissions while agriculture was a major driver of the increasing anthropogenic CH4 and N2O emissions.

In terms of mitigation, without reductions in overall anthropogenic emissions, increased reliance on large-scale land-based mitigation was predicted, which would add to the many already competing demands on land. However, some mitigation measures were suggested to not compete with other land uses, while also having multiple co-benefits, including adaptation capacity and potential synergies with some Sustainable Development Goals (SDGs). As in AR5, there was large uncertainty surrounding mitigation within AFOLU, in part because current carbon stocks and fluxes are unclear and subject to temporal variability. Additionally, the non-additive nature of individual measures that are often inter-linked and the highly context specific applicability of measures, causes further uncertainty. Many AFOLU measures were considered well-established and some achievable at low to moderate cost, yet contrasting economic drivers, insufficient policy, lack of incentivisation and institutional support to stimulate implementation among the many stakeholders involved, in regionally diverse contexts, was recognised as hampering realisation of potential.

None the less, the importance of mitigation within AFOLU was highlighted in all IPCC reports, with modelled scenarios demonstrating the considerable potential role and land-based mitigation forming an important component of pledged mitigation in Nationally Determined Contributions (NDCs) under the Paris Agreement. The sector was identified as the only one in which large-scale carbon dioxide removal (CDR) may currently and at short term be possible (e.g., through afforestation/reforestation or soil organic carbon management). This CDR component was deemed crucial to limit climate change and its impacts, which would otherwise lead to enhanced release of carbon from land. However, the SRCCL emphasised that mitigation cannot be pursued in isolation. The need for integrated response options, that mitigate and adapt to climate change, but also deal with land degradation and desertification, while enhancing food and fibre security, biodiversity and contributing to other SDGs has been made clear (IPCC 2019; IPBES 2019a; IPBES-IPCC 2021).

This chapter assesses GHG fluxes between land and the atmosphere due to AFOLU, the associated drivers behind these fluxes, mitigation response options and related policy, at time scales of 2030 and 2050. Land and its management has important links with other sectors and therefore associated chapters within this report, notably concerning the provision of food, feed, fuel or fibre for human consumption and societal well-being (Chapter 5), for bioenergy (Chapter 6), the built environment (Chapter 9), transport (Chapter 10) and industry (Chapter 11). Mitigation within these sectors may in part, be dependent on contributions from land and the AFOLU sector, with interactions between all sectors discussed in Chapter 12. This chapter also has important links with IPCC AR6 WGII regarding climate change impacts and adaptation. Linkages are illustrated in Figure 7.1.

As highlighted in both AR5 and the SRCCL, there is a complex interplay between land management and GHG fluxes as illustrated in Figure 7.2, with considerable variation in management regionally, as a result of geophysical, climatic, ecological, economic, technological, institutional and socio-cultural diversity. The capacity for land-based mitigation varies accordingly. The principal focus of this chapter is therefore, on evaluating regional land-based mitigation potential, identifying applicable AFOLU mitigation measures, estimating associated costs and exploring policy options that could enable implementation.

Mitigation measures are broadly categorised as those relating to (i) forests and other ecosystems (ii) agriculture (iii) biomass production for products and bioenergy and (iv) demand-side levers. Assessment is made in the context that land-mitigation is expected to contribute roughly 25% of the 2030 mitigation pledged in Nationally Determined Contributions (NDCs) under the Paris Agreement (Grassi et al. 2017), yet very few countries have provided details on how this will be achieved. In light of AR5 and the SRCCL findings, that indicate large land-based mitigation potential, considerable challenges to its realisation, but also a clear nexus at which humankind finds itself, whereby current land management, driven by population growth and consumption patterns, is undermining the very capacity of land, a finite resource, to support wider critical functions and services on which humankind depends. Mitigation within AFOLU is occasionally and wrongly perceived as an opportunity for in-action within other sectors. AFOLU simply cannot compensate for mitigation shortfalls in other sectors. As the outcomes of many critical challenges (UNEP 2019), including biodiversity loss (IPBES 2019a) and soil degradation (FAO and ITPS 2015), are inextricably linked with how we manage land, the evaluation and assessment of AFOLU is crucial. This chapter aims to address three core topics:

1. What is the latest estimated (economic) mitigation potential of AFOLU measures according to both sectoral studies and integrated assessment models, and how much of this may be realistic within each global region?

2. How do we realise the mitigation potential, while minimising trade-offs and risks and maximising co-benefits that can enhance food and fibre security, conserve biodiversity and address other land challenges?

3. How effective have policies been so far and what additional policies or incentives might enable realisation of mitigation potential and at what costs?

This chapter first outlines the latest trends in AFOLU fluxes and the methodology supporting their estimation (Section 7.2). Direct and indirect drivers behind emission trends are discussed in Section 7.3. Mitigation measures, their costs, co-benefits, trade-offs, estimated regional potential and contribution within integrated global mitigation scenarios, is presented in Sections 7.4 and 7.5 respectively. Assessment of associated policy responses and links with SDGs are explored in Section 7.6. The chapter concludes with gaps in knowledge (Section 7.7) and frequently asked questions.

National greenhouse gas inventory (NGHGI) reporting following the IPCC 1996 guidelines (IPCC 1996), separates the total anthropogenic AFOLU flux into: (i) net anthropogenic flux from Land Use, Land-Use Change, and Forestry (LULUCF) due to both change in land cover and land management; and (ii) the net flux from Agriculture. While fluxes of CO2 (Section 7.2.2) are predominantly from LULUCF and fluxes of CH4 and N2O (Section 7.2.3) are predominantly from agriculture, fluxes of all three gases are associated with both sub-sectors. However, not all methods separate them consistently according to these sub-sectors, thus here we use the term AFOLU, separate by gas and implicitly include CO2 emissions that stem from the agriculture part of AFOLU, though these account for a relatively small portion.

Total global net anthropogenic GHG emissions from AFOLU were 11.9 ± 4.4 GtCO2-eq yr –1 on average over the period 2010–2019, around 21% of total global net anthropogenic GHG emissions (Table 7.1 and Figure 7.3, using the sum of bookkeeping models for the CO2 component). When using FAOSTAT/NGHGIs CO2 flux data, then the contribution of AFOLU to total emissions amounts to 13% of global emissions.

Table 7.1 | Net anthropogenic emissions (annual averages for2010–2019a) from Agriculture, Forestry and Other Land Use (AFOLU). For context, the net flux due to the natural response of land to climate and environmental change is also shown for CO2 in column E. Positive values represent emissions, negative values represent removals.

aEstimates are given until 2019 as this is the latest date when data are available for all gases, consistent with Chapter 2, this report. Positive fluxes are emission from land to the atmosphere. Negative fluxes are removals.

bNet anthropogenic flux of CO2 are due to land-use change such as deforestation and afforestation and land management, including wood harvest and regrowth, peatland drainage and fires, cropland and grassland management. Average of three bookkeeping models (Hansis et al. 2015; Houghton and Nassikas 2017; Gasser et al. 2020), complemented by data on peatland drainage and fires from FAOSTAT (Prosperi et al. 2020) and GFED4s (van der Werf et al. 2017). Bookkeeping based CO2-LULUCF emissions (5.7±4.0) are consistent with AR6 WGI and Chapter 2 of this report. The value of 5.9(±4.1) includes CO2 emissions from urea application to managed soils and pasture. Comparisons with other estimates are discussed in 7.2.2. Based on NGHGIs and FAOSTAT, the range is 0 to 0.8 GtCO2 yr –1.

cCH4 and N2O emission estimates and assessed uncertainty of 30 and 60% respectively, are based on Emissions Database for Global Atmospheric Research (EDGAR) data (Crippa et al. 2021) in accordance with Chapter 2, this report (Sections 2.2.1.3 and 2.2.1.4). Both FAOSTAT (Tubiello 2019; USEPA 2019; FAO 2021a) and the USA EPA (USEPA 2019) also provide data on agricultural non-CO2 emissions, however, mean global CH4 and N2O values considering the three databases are within the uncertainty bounds of EDGAR. EDGAR only considers agricultural and not overall AFOLU non-CO2 emissions. Agriculture is estimated to account for approximately 89 and 96% of total AFOLU CH4 and N2O emissions respectively. See Section 7.2.3 for further discussion.

dTotal non-AFOLU emissions are the sum of total CO2-eq emissions values for energy, industrial sources, waste and other emissions with data from the Global Carbon Project for CO2, including international aviation and shipping, and from the PRIMAP database for CH4 and N2O averaged over 2007–2014, as that was the period for which data were available.

eThe modelled CO2 estimates include natural processes in vegetation and soils and how they respond to both natural climate variability and to human-induced environmental changes, for example, the response of vegetation and soils to environmental changes such as increasing atmospheric CO2 concentration, nitrogen deposition, and climate change (indirect anthropogenic effects) on both managed and unmanaged lands. The estimate shown represents the average from 17 Dynamic Global Vegetation Models with 1SD uncertainty (Friedlingstein et al. 2020).

f The NGHGIs take a different approach to calculating ‘anthropogenic’ CO2 fluxes than the models (Section 7.2.2). In particular the sinks due to environmental change (indirect anthropogenic fluxes) on managed lands are generally treated as anthropogenic in NGHGIs and non-anthropogenic in models such as bookkeeping and IAMs. A reconciliation of the results between IAMs and NGHGIs is presented in Cross-Chapter Box 6 in this chapter. If applied to this table, it would transfer approximately –5.5 GtCO2 yr –1 (a sink) from Column E (which would become –7.0 GtCO2 yr –1) to Column A (which would then be 0.4 GtCO2 yr –1).

gAll values expressed in units of CO2-eq are based on IPCC AR6 100-year Global Warming Potential (GWP100) values with climate-carbon feedbacks (CH4= 27, N2O = 273) (Chapter 2, Supplementary Material 2.SM.3; IPCC AR6 WGI Section 7.6).

hFor assessment of cross-sector fluxes related to the food sector, see Chapter 12.

iWhile it is acknowledged that soils are a natural CH4 sink (Jackson et al. 2020) with soil microbial removals estimated to be 30 ± 19 MtCH4 yr –1 for the period 2008–2017 (according to bottom-up estimates), natural CH4 sources are considerably greater (371 (245–488) MtCH4 yr –1) resulting in natural processes being a net CH4 source (IPCC AR6 WGI Section 5.2.2). The soil CH4 sink is therefore omitted from Column E.

jTotal GHG emissions concerning non-AFOLU sectors and all sectors combined (Columns B and C) include fluorinated gases in addition to CO2, CH4 and N2O. Therefore, total values do not equal the sum of estimates for CO2, CH4 and N2O.

This AFOLU flux is the net of anthropogenic emissions of CO2, CH4 and N2O, and anthropogenic removals of CO2. The contribution of AFOLU to total emissions varies regionally with highest in Latin America and Caribbean with 58% and lowest in Europe and North America with each 7% (Chapter 2, Section 2.2.3). There is a discrepancy in the reported CO2AFOLU emissions magnitude because alternative methodological approaches that incorporate different assumptions are used (Section 7.2.2.2). While there is low agreement in the trend of global AFOLU CO2 emissions over the past few decades (Section 7.2.2), they have remained relatively constant (medium confidence) (Chapter 2). Average non-CO2 emission (aggregated using GWP100 IPCC AR6 values) from agriculture have risen from 5.2 ± 1.4 GtCO2-eq yr –1 for the period 1990 to 1999, to 6.0 ± 1.7 GtCO2-eq yr –1 for the period 2010 to 2019 (Crippa et al. 2021) (Section 7.2.3).

To present a fuller understanding of land–atmosphere interactions, Table 7.1 includes an estimate of the natural sink of land to atmospheric CO2 (Jia et al. 2019) (IPCC AR6 WGI Chapter 5). Land fluxes respond naturally to human-induced environmental change (e.g., climate change, and the fertilising effects of increased atmospheric CO2 concentration and nitrogen deposition), known as ‘indirect anthropogenic effects’, and also to ‘natural effects’ such as climate variability (IPCC 2010) (Table 7.1 and Section 7.2.2). This showed a removal of –12.5 ± 3.2 GtCO2 yr –1 (medium confidence) from the atmosphere during 2010–2019 according to global dynamic global vegetation model (DGVM) models (Friedlingstein et al. 2020) 31% of total anthropogenic net emissions of CO2 from all sectors. It is likely that the NGHIs and FAOSTAT implicitly cover some part of this sink and thus provide a net CO2AFOLU balance with some 5 GtCO2 lower net emissions than according to bookkeeping models, with the overall net CO2 value close to being neutral. Model results and atmospheric observations concur that, when combining both anthropogenic (AFOLU) and natural processes on the entire land surface (the total ‘land–atmosphere flux’), the land was a global net sink for CO2 of –6.6 ± 4.6 GtCO2 yr –1 with a range for 2010 to 2019 from –4.4 to –8.4 GtCO2 yr –1. (Rödenbeck et al. 2003, 2018; Chevallier et al. 2005; Feng et al. 2016; van der Laan-Luijkx et al. 2017; Niwa et al. 2017; Patra et al. 2018). The natural land sink is highly likely to be affected by both future AFOLU activity and climate change (IPCC AR6 WGI Box 5.1 and Figure SPM. 7), whereby under more severe climate change, the amount of carbon stored on land would still increase although the relative share of the emissions that land takes up, declines.

Trends in atmospheric CH4 and N2O concentrations and the associated sources, including land and land use are discussed in Sections 5.2.2 and 5.2.3 of the IPCC AR6 WGI. Regarding AFOLU, the SRCCL and AR5 (Jia et al. 2019; Smith et al. 2014) identified three global non-CO2 emissions data sources: EDGAR (Crippa et al. 2021), FAOSTAT (FAO 2021a; Tubiello, 2019) and the USA EPA (USEPA 2019). Methodological differences have been previously discussed (Jia et al. 2019). In accordance with Chapter 2, this report, EDGAR data are used in Table 7.1 and Figure 7.3. It is important to note that in terms of AFOLU sectoral CH4 and N2O emissions, only FAOSTAT provides data on AFOLU emissions, while EDGAR and USEPA data consider just the agricultural component. However, the mean of values across the three databases for both CH4 and N2O, fall within the assessed uncertainty bounds (30 and 60% for CH4 and N2O respectively, Section 2.2.1, in this report) of EDGAR data. NGHGIs annually submitted to the UNFCCC (Section 7.2.2.3) provide national AFOLU CH4 and N2O data, as included in the SRCCL (Jia et al. 2019). Aggregation of NGHGIs to indicate global emissions must be considered with caution, as not all countries compile inventories, nor submit annually. Additionally, NGHGIs may incorporate a range of methodologies for CH4 and N2O accounting (e.g., van der Weerden et al. 2016; Ndung’u et al. 2019; Thakuri et al. 2020), making comparison difficult. The analysis of complete AFOLU emissions presented here, is based on FAOSTAT data. For agricultural specific discussion, analysis considers EDGAR, FAOSTAT and USEPA data.

Despite new literature, general conclusions from the SRCCL and WGI-AR6 on biophysical effects and short-lived climate forcers remain the same. Changes in land conditions from land cover change or land management jointly affect water, energy, and aerosol fluxes (biophysical fluxes) as well as GHG fluxes (biogeochemical fluxes) exchanged between the land and atmosphere ( high agreement , robust evidence) (Anderson et al. 2011; O’Halloran et al. 2012; Alkama and Cescatti 2016; Naudts et al. 2016; Erb et al. 2017). There is high confidence that changes in land condition do not just have local impacts but also have non-local impacts in adjacent and more distant areas (Pielke et al. 2011; Mahmood et al. 2014) which may contribute to surpassing climate tipping points (Nepstad et al. 2008; Brando et al. 2014). Non-local impacts may occur through: GHG fluxes and subsequent changes in radiative transfer, changes in atmospheric chemistry, thermal, moisture and surface pressure gradients creating horizontal transport (advection) (de Vrese et al. 2016; Davin and de Noblet-Ducoudré 2010) and vertical transport (convection and subsidence) (Devaraju et al. 2018). Although regional and global biophysical impacts emerge from model simulations (Davin and de Noblet-Ducoudré 2010; de Vrese et al. 2016; Devaraju et al. 2018), especially if the land condition has changed over large areas, there is very low agreement on the location, extent and characteristics of the non-local effects across models. Recent methodological advances, empirically confirmed changes in temperature and precipitation owing to distant changes in forest cover (Cohn et al. 2019; Meier et al. 2021).

Following changes in land conditions, CO2, CH4 and N2O fluxes are quickly mixed into the atmosphere and dispersed, resulting in the biogeochemical effects being dominated by the biophysical effects at local scales ( high confidence) (Y. Li et al. 2015; Alkama and Cescatti 2016). Afforestation/reforestation (Lejeune et al. 2018; Strandberg and Kjellström 2019), urbanisation (Li and Bou-Zeid 2013) and irrigation (Mueller et al. 2016 and Thiery et al. 2017) modulate the likelihood, intensity, and duration of many extreme events including heatwaves ( high confidence) and heavy precipitation events (medium confidence) (Haberlie et al. 2015). There is high confidence and high agreement that afforestation in the tropics (Perugini et al. 2017), irrigation (Alter et al. 2015; Mueller et al. 2016) and urban greening result in local cooling, high agreement and medium confidence on the impact of tree growth form (deciduous vs evergreen) (Naudts et al. 2016; Luyssaert et al. 2018 and Schwaab et al. 2020), and low agreement on the impact of wood harvest, fertilisation, tillage, crop harvest, residue management, grazing, mowing, and fire management on the local climate.

Studies of biophysical effects have increased since AR5 reaching high agreement for the effects of changes in land condition on surface albedo (Leonardi et al. 2015). Low confidence remains in proposing specific changes in land conditions to achieve desired impacts on local, regional and global climates due to: a poor relationship between changes in surface albedo and changes in surface temperature (Davin and de Noblet-Ducoudré 2010), compensation and feedbacks among biophysical processes (Bonan 2016; Kalliokoski et al. 2020), climate and seasonal dependency of the biophysical effects (Bonan 2016), omittance of short-lived chemical forcers (Unger 2014; Kalliokoski et al. 2020), and study domains often being too small to document possible conflicts between local and non-local effects (Swann et al. 2012; Hirsch et al. 2018).

The global forest area in 2020 is estimated at 4.1 billion ha, representing 31% of the total land area (FAO 2020a). Most forests are situated in the tropics (45%), followed by boreal (27%), temperate (16%) and subtropical (11%) domains. Considering regional distribution of global forest area, Europe and the Russian Federation accounts for 25%, followed by South America (21%), North and Central America (19%), Africa (16%), Asia (15%) and Oceania (5%). However, a significant share (54%) of the world’s forest area concerns five countries – The Russian Federation, Brazil, Canada, the USA and China (FAO 2020a). Forest loss rates differ among regions though the global trend is towards a net forest loss (UNEP 2019). The global forest area declined by about 178 Mha in the 30 years from 1990 to 2020 (FAO 2020a). The rate of net forest loss has decreased since 1990, a result of reduced deforestation in some countries and forest gains in others. The annual net loss of forest area declined from 7.8 Mha in 1990–2000, to 5.2 Mha in 2000–2010, to 4.7 Mha in 2010–2020, while the total growing stock in global forests increased (FAO 2020a). The rate of decline in net forest loss during the last decade was due mainly to an increase in the rate of forest gain (i.e., afforestation and the natural expansion of forests).

Globally, the area of the more open, other wooded land is also of significant importance, with almost 1 billion hectares (FAO 2020a). The area of other wooded land decreased by 30.6 Mha between 1990 and 2020 with larger declines between 1990–2000 (FAO 2020a). There are still significant challenges in monitoring the area of other wooded land, largely associated with difficulties in measuring tree-canopy cover in the range of 5–10%.The global area of mangroves, one of the most productive terrestrial ecosystems (Neogi 2020a), has also experienced a significant decline (Thomas et al. 2017; Neogi 2020b), with a decrease of 1.0 Mha between 1990 and 2020 (FAO 2020a) due to agriculture and aquaculture (Bhattarai 2011; Ajonina et al. 2014; Webb et al. 2014; Giri et al. 2015; Thomas et al. 2017; Fauzi et al. 2019). Some relevant direct drivers affecting emissions and removal in forests and other ecosystems are discussed in proceeding sections.

The indirect drivers behind how humans both use and impact natural resources are outlined in Table 7.2. Specifically; demographic, economic and cultural, scientific and technological, and institutional and governance drivers. These indirect drivers not only interact with each other at different temporal and spatial scales but are also subject to impacts and feedbacks from the direct drivers (Barger et al. 2018).

Table 7.2| Indirect drivers of anthropogenic land and natural resource use patterns.

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 et al. 2011; Cabral et al. 2019; Hammar and Levihn 2020; Emenike et al. 2020; Moreira et al. 2020b; Y. Wang et al. 2020: Johnsson et al. 2020) (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). 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 CO2 for storage (Section 7.4.3.7). But ill-deployment of energy crops can also cause land carbon losses (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 et al. 2016; Jia et al. 2019; Calvin et al. 2021) (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 et al. 2017; Turner et al. 2018b; Daioglou et al. 2019; Wu et al. 2019; Kalt et al. 2020; Hanssen et al. 2020; Calvin et al. 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 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 et al. 2016; Popp et al. 2017; Daioglou et al. 2017; Staples et al. 2017; Carvalho et al. 2017; Humpenöder et al. 2018; Fujimori et al. 2019; Hasegawa et al. 2020; Drews et al. 2020; Schulze et al. 2020; Stenzel et al. 2020; Mouratiadou et al. 2020; Buchspies et al. 2020; Hanssen et al. 2020, 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 GtCO2-eq yr –1 when applying constraints reflecting sustainability concerns, at a cost of 100–200 USD tCO2–1 (Fuss et al. 2018). The SRCCL reported a technical CDR potential for BECCS at 0.4–11.3 GtCO2 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-CO2 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 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 et al. 2019, Li et al. 2020, Hanssen et al. 2020; Drews et al. 2020; Ai et al. 2021; Hasegawa et al. 2021). Yet, IAMs are still coarse in local land-use practices. (Daioglou et al. 2019; Wu et al. 2019; 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 (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 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 et al. 2017; Fritsche et al. 2017; Chiaramonti and Panoutsou, 2018; Fernando et al. 2018; Elbersen et al. 2019; Rahman et al. 2019; Næss et al. 2021). In the SRCCL, the presented range for degraded or abandoned land was 32–1400 Mha (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 et al. 2017; Liu et al. 2017; Elbersen et al. 2019; Zhang et al.2020; Vera et al. 2021). The definitions of marginal/abandoned/degraded land, and the methods used to assess such lands remain inconsistent across studies (Jiang et al. 2019), causing large variation amongst them (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).

In most of the assessed mitigation pathways, the AFOLU sector is of great importance for climate change mitigation as it (i) turns from a source into a sink of atmospheric CO2 due to large-scale afforestation and reforestation, (ii) provides high amounts of biomass for bioenergy with or without CCS and (iii), even under improved agricultural management, still causes residual non-CO2 emissions from agricultural production and (iv) interplays with sustainability dimensions other than climate action (Popp et al. 2017; Rogelj et al. 2017; Van Vuuren et al. 2018; Frank et al. 2018; Hasegawa et al. 2018; van Soest et al. 2019). Regional AFOLU GHG emissions in scenarios with <4°C warming in 2100 (scenario category C7), as shown in Figure 7.13, are shaped by considerable CH4 and N2O emissions throughout 2050 and 2100, mainly from ASIA and AFRICA. CH4 emissions from enteric fermentation are largely caused by ASIA, followed by AFRICA, while CH4 emissions from paddy rice production are almost exclusively caused by ASIA. N2O emissions from animal waste management and soils are more equally distributed across region.

In most regions, CH4 and N2O emission are both lower in mitigation pathways that limit warming to 3°C (>50%) or lower (C1–C6) compared to scenarios with <4°C (Popp et al. 2017; Rogelj et al. 2018a). In particular, the reduction of CH4 emissions from enteric fermentation in ASIA and AFRICA is profound. Land-related CO2 emissions, which include emissions from deforestation as well as removals from afforestation, are slightly negative (i.e., AFOLU systems turn into a sink) in <1.5°C, <2°C and <3°C mitigation pathways compared to <4°C scenarios. Carbon sequestration via BECCS is most prominent in ASIA, LAM, AFRICA and OECD90+EU, which are also the regions with the highest bioenergy area.

Figure 7.14 indicates that regional land-use dynamics in scenarios with <4°C warming in 2100 are characterised by rather static agricultural land (i.e., cropland and pasture) in ASIA, LAM, OECD90+EU and REF, and increasing agricultural land in AFRICA. Bioenergy area is relatively small in all regions. Agricultural land in AFRICA expands at the cost of forests and other natural land.

The overall land dynamics in <1.5°C, <2°C and <3°C mitigation pathways are shaped by land-demanding mitigation options such as bioenergy and afforestation, in addition to the demand for other agricultural and forest commodities. Bioenergy production and afforestation take place largely in the (partly) tropical regions ASIA, LAM and AFRICA, but also in OECD90+EU. Land for dedicated second generation bioenergy crops and afforestation displace agricultural land for food production (cropland and pasture) and other natural land. For instance, in the <1.5°C mitigation pathway in ASIA, bioenergy and forest area together increased by about 2.1 million km 2 between 2020 and 2100, mostly at the cost of cropland and pasture (median values). Such large-scale transformations of land use have repercussions on biogeochemical cycles (e.g., fertiliser and water) but also on the economy (e.g., food prices) and potential socio-political conditions.

In this section, Integrated Assessment Model (IAM) results from the AR6 database are used to derive marginal abatement costs which indicate the economic mitigation potential for the different gases (N2O, CH4, CO2) related to the AFOLU sector, at the global level and at the level of five world regions. This review provides a complementary view on the economic mitigation potentials estimated in Section 7.4 by implicitly taking into account the interlinkages between the land-based mitigation options themselves as well as the interlinkages with mitigation options in the other sectors such as BECCS. The review systematically evaluates a range of possible economic potential estimates across gases, time, and carbon prices.

For different models and scenarios from the AR6 database, the amount of mitigated emissions is presented together with the respective carbon price (Figure 7.15). To determine mitigation potentials, scenarios are compared to a benchmark scenario which usually assumes business-as-usual trends and no explicit additional mitigation efforts. Scenarios have been excluded, if they do not have an associated benchmark scenario or fail the vetting according to the AR6 scenario database, or if they do not report carbon prices and CO2 emissions from AFOLU. Scenarios with contradicting assumptions (for example, fixing some of the emissions to baseline levels) are excluded. Furthermore, only scenarios with consistent 3 regional and global level results are considered. Mitigation potentials are computed by subtracting scenario specific emissions and sequestration amounts from their respective benchmark scenario values. This difference accounts for the mitigation that can be credited to the carbon price which is applied in a scenario. A few benchmark scenarios, however, apply already low carbon prices. For consistency reasons, a carbon price that is applied in a benchmark scenario is subtracted from the respective scenario specific carbon price. This may generate a bias because low carbon prices tend to have a stronger marginal impact on mitigation than high carbon prices. Scenarios with carbon prices which become negative due to the correction are not considered. The analysis considers all scenarios from the AR6 database which pass the criteria and should be considered as an ensemble of opportunity (Huppmann et al. 2018).

This approach is close to integrated assessment marginal abatement cost curves (MACCs) as described in the literature (Fujimori et al. 2016; Frank et al. 2018, 2019; Harmsen et al. 2019) in the sense that it incorporates besides the technical mitigation options also structural options, as well as behavioural changes and market feedbacks. Furthermore, indirect emission changes and interactions with other sectors can be highly relevant (Daioglou et al. 2019; Kalt et al. 2020) and are also accounted for in the presented potentials. Hereby, some sequestration efforts can occur in other sectors, while leading to less mitigation in the AFOLU sector. For instance, as an integral part of many scenarios, BECCS deployment will lead to overall emissions reductions, and even provision of CDR as a result of the interplay between three direct components (i) LULUCF emissions/sinks, (ii) reduction of fossil fuel use/emissions, (iii) carbon capture and sequestration. Since the latter two effects can compensate for the LULUCF effect, BECCS deployment in ambitious stabilisation scenarios may lead to reduced sink/increased emissions in LULUCF (Kalt et al. 2020). The same holds for trade-offs between carbon sequestration in forests versus harvested wood products both for enhancing the HWP pool and for material substitution. The strengths of the competition between biomass use and carbon sequestration will depend on the biomass feedstocks considered (Lauri et al. 2019).

In the individual cases, the accounting of all these effects is dependent on the respective underlying model and its coverage of inter-relations of different sectors and sub-sectors. The presented potentials cover a wide range of models, and additionally, a wide range of background assumptions on macro-economic, technical, and behavioural developments as well as policies, which the models have been fed with. Subsequently, the range of the resulting marginal abatement costs is relatively wide, showing the full range of expected contributions from land-use sector mitigation and sequestration in applied mitigation pathways.

At the global level, the analysis of the economic mitigation potentials from N2O and CH4 emissions from AFOLU (which mainly can be related to agricultural activities) and CO2 emissions (which mainly can be related to LULUCF emissions) revealsa relatively good agreement of models and scenarios in terms of ranking between the gases. On the right-hand side panels of Figure 7.15, only small overlaps between the ranges (showing the 10–90% intervals of observations) and mainly for lower price levels, can be observed, despite all differences in underlying model structure and scenario assumptions.

N2O emissions show the smallest economic potential of the three different gases in 2030 as well as in 2050. The mitigation potential increases until a price range of USD150–200 and to a median value of around 0.6 GtCO2-eq yr –1 mitigation in 2030 and 0.9 GtCO2-eq yr –1 in 2050, respectively, while afterwards with higher prices the expansion is very limited. Mitigation of CH4 emissions has a higher potential, also with increasing mitigation potentials until a price range of USD150–200 in both years, with median mitigation of around 1.3 GtCO2-eq yr –1 in 2030 and around 2.4 GtCO2-eq yr –1 in 2050, respectively. The highest mitigation potentials are observed for CO2, but also the highest ranges of observations among the three gases. In 2030, a median of 4 GtCO2-eq yr –1 mitigation potential is reported for the price range of USD200–250. In 2050, for the carbon price range of between USD100 and USD200, a median of around 4.8 GtCO2-eq yr –1 can be observed.

When compared with the sectoral estimates from Harmsen et al. (2019), the integrated assessment median potentials are broadly comparable for the N2O mitigation potential; Harmsen et al. 2050 mitigation potential at USD125 is 0.6 GtCO2-eq yr –1 while the integrated assessment estimate for the same price range is 0.7 GtCO2-eq yr –1. The difference is substantially larger for the CH4 mitigation potential; 0.9 GtCO2-eq yr –1 in Harmsen et al. while 2 GtCO2-eq yr –1 the median integrated assessment estimate. While the Harmsen et al. MACCs consider only technological mitigation options, integrated assessments typically include also demand side response to the carbon price and GHG efficiency improvements through structural change and international trade. These additional mitigation options can represent more than 60% of the total non-CO2 mitigation potential in the agricultural sector, where they are more important in the livestock sector, and thus the difference between sectoral and integrated assessments is more pronounced for the CH4 emissions (Frank et al. 2019).

Economic CO2 mitigation potentials from land-use change and forestry are larger compared to potentials from non-CO2 gases, and at the same time reveal high levels of variation in absolute terms. The 66th percentile in 2050 goes up to 5.2 GtCO2-eq yr –1 mitigation, while the lowest observations are even negative, indicating higher CO2 emissions from land use in scenarios with carbon price compared to scenarios without (counterintuitive dynamics explained below).

Land use is at the centre of the interdependencies with other sectors, including energy. Some models see a strong competition between BECCS deployment with its respective demand for biomass, and CO2 mitigation/sequestration potentials in the land sector. Biomass demand may lead to an increase in CO2 emissions from land use despite the application of a carbon price when land-use expansion for dedicated biomass production, such as energy plantations, comes from carbon rich land use/land cover alternatives, or when increased extraction of biomass from existing land uses, such as forest management, leads to reduction of the carbon sink (Daioglou 2019; Luderer et al. 2018) and can explain the high variety of observations in some cases. Overall, the large variety of observations shows a large variety of plausible results, which can go back to different model structures and assumptions, showing a robust range of plausible outcomes (Kriegler et al. 2015).

Besides the level of biomass supply for bioenergy, the adoption of SDGs may also significantly impact AFOLU emissions and the land-use sector’s ability for GHG abatement (Frank et al. 2021). Selected SDGs are found to have positive synergies for AFOLU GHG abatement and to consistently decrease GHG emissions for both agriculture and forestry, thereby allowing for even more rapid and deeper emissions cuts. In particular, the decreased consumption of animal products and less food waste (SDG 12), and the protection of high biodiversity ecosystems such as primary forests (SDG 15) deliver high synergies with GHG abatement. On the other hand, protection of highly biodiverse ecosystems from conversion (SDG 15) limits the global biomass potentials for bioenergy (Frank et al. 2021), and while several forestry measures enhancing woody biomass supply for bioenergy may have synergies with improving ecosystems conditions, many represent a threat to biodiversity (Camia et al. 2020) (Sections 7.6.5 and 17.3.3.7, Figure 17.1 and Supplementary Material Table 17.SM.1).

At the regional level (Figure 7.16), the highest potential from non-CO2 emissions abatement, and mostly from CH4, is reported for ASIA with the median of mitigation potential observations from CH4 increasing up to a price of USD200 in the year 2050, reaching a median of 1.2 GtCO2-eq yr –1. In terms of economic potential, ASIA is followed by LAM, AFRICA, and OECD+EU, where emission reduction mainly is achieved in the livestock sector.

The highest potentials from land-related CO2 emissions, including avoided deforestation as well as afforestation, can be observed in LAM and AFRICA with strong responses of mitigation (indicated by the median value) to carbon prices mainly in the lower range of displayed carbon prices. In general, CO2 mitigation potentials show a wide range of results in comparison to non-CO2 mitigation potentials, but mostly also a higher median value. The most extreme ranges are reported for the regions LAM and AFRICA. A medium potential is reported for ASIA and OECD+EU, while REF has the smallest potential according to model submissions. These estimates reflect techno-economic potentials and do not necessarily include feasibility constraints which are discussed in Chapter 7.6.

Different mitigation strategies can achieve the net emission reductions that would be required to follow a pathway limiting global warming, with very different consequences for the land system. Figure 7.17 shows Illustrative Mitigation Pathways (IMPs) for achieving different climate targets highlighting AFOLU mitigation strategies, resulting GHG and land-use dynamics as well as the interaction with other sectors. For consistency this chapter discusses IMPs as described in detail in chapters 1 and 3 of this report but focusing on the land-use sector. All pathways are assessed by different IAM realisations and do not only reduce GHG emissions but also use CDR options, whereas the amount and timing varies across pathways, as do the relative contributions of different land-based CDR options.

The scenario ModAct (limit warming to 3°C (>50%), C6) is based on the prolongation of current trends (SSP2) but contains measures to strengthen policies for the implementation of National Determined Contributions (NDCs) in all sectors including AFOLU (Grassi et al. 2018). This pathway shows a strong decrease of CO2 emissions from land-use change in 2030, mainly due to reduced deforestation, as well as moderately decreasing N2O and CH4 emissions from agricultural production due to improved agricultural management and dietary shifts away from emissions-intensive livestock products. However, in contrast to CO2 emissions, which turn net-negative around 2050 due to afforestation/reforestation, CH4 and N2O emissions persist throughout the century due to difficulties of eliminating these residual emissions based on existing agricultural management methods (Frank et al. 2017; Stevanović et al. 2017). Comparably small amounts of BECCS are applied by the end of the century. Forest area increases at the cost of other natural vegetation.

IMP-Neg is similar to ModAct scenario in terms of socio-economic setting (SSP2) but differs strongly in terms of the mitigation target (limit warming to 2°C (>67%), C3) and its strong focus on the supply side of mitigation measures with strong reliance on net-negative emissions. Consequently, all GHG emission reductions as well as afforestation/reforestation and BECCS-based CDR start earlier in time at a higher rate of deployment. However, in contrast to CO2 emissions, which turn net-negative around 2030 due to afforestation/reforestation, CH4 and N2O emissions persist throughout the century, similar to ModAct , due to ongoing increasing demand for total calories and animal-based commodities (Bodirsky et al. 2020) and difficulties of eliminating these residual emissions based on existing agricultural management methods (Stevanović et al. 2017; Frank et al. 2017). In addition to abating land-related GHG emissions as well as increasing the terrestrial sink, this example also shows the potential importance of the land sector in providing biomass for BECCS and hence CDR in the energy sector. Cumulative CDR (2020–2100) amounts to 502 GtCO2 for BECCS and 121 GtCO2 for land-use change (including afforestation and reduced deforestation). In consequence, compared to ModAct scenario , competition for land is increasing and much more other natural land as well as agricultural land (cropland and pasture land) is converted to forest or bioenergy cropland with potentially severe consequences for various sustainability dimensions such as biodiversity (Hof et al. 2018) and food security (Fujimori et al. 2019).

IMP-Ren is similar to IMP Neg-2.0 in terms of socio-economic setting (SSP2) but differs substantially in terms of mitigation target and mitigation efforts in the energy sector. Even under the more ambitious climate change mitigation target (1.5°C with no or low overshoot (OS), C1), the high share of renewable energy in IMP Ren strongly reduces the need for large-scale land-based CDR, which is reflected in smaller bioenergy and afforestation areas compared to IMP Neg-2.0. However, CH4 and N2O emissions from AFOLU persist throughout the century, similar to ModAct scenario and IMP Neg-2.0.

In contrast to IMPs Neg-2.0 and Ren, IMP SP (Soergel et al. 2021; 1.5°C with no or low OS, C1) displays a future of generally low resource and energy consumption (including healthy diets with low animal-calorie shares and low food waste) as well as significant but sustainable agricultural intensification in combination with high levels of nature protection. This pathway shows a strong near-term decrease of CO2 emissions from land-use change, mainly due to reduced deforestation, and in difference to all other IMPs described in this chapter strongly decreasing N2O and CH4 emissions from agricultural production due to improved agricultural management but also based on dietary shifts away from emissions-intensive livestock products as well as lower shares of food waste. In consequence, comparably small amounts of land are needed for land demanding mitigation activities such as BECCS and afforestation. In particular, the amount of agricultural land converted to bioenergy cropland is smaller compared to other mitigation pathways. Forest area increases either by regrowth of secondary vegetation following the abandonment of agricultural land or by afforestation/reforestation at the cost of agricultural land.

Since the establishment of the UNFCCC, international agencies, countries, sub-national units and NGO’s have developed policies to facilitate and encourage GHG mitigation within AFOLU (Figure 7.18). Early guidance and policies focused on developing GHG inventory methodology with some emphasis on afforestation and reforestation projects, but the Clean Development Mechanism (CDM) in the Kyoto Protocol focused attention on emission reduction projects, mostly outside of AFOLU. As successive IPCC AR6 WGIII reports illustrated large potential for AFOLU mitigation, methods to quantify and verify carbon emission reductions emerged within several projects in the early 2000s, through both voluntary (e.g., the Chicago Climate Exchange (CCX)) and regulated (e.g., New South Wales and California) markets. The CDM dedicated large attention to LULUCF, including dedicated methodologies and bodies. The reasons for limited uptake of CDM afforestation/reforestation projects were multiple and not limited to the regulatory constraints, but also due to the low abatement potential (poor cost/performance ratio) compared to other mitigation opportunities.

Following COP 13 in Bali, effort shifted to advancing policies to reduce deforestation and forest degradation (REDD+) in developing countries. According to Simonet et al. (2019), nearly 65 Mha have been enrolled in REDD+ type programmes or projects funded through a variety of sources, including United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation (UN-REDD), the World Bank Forest Carbon Partnership Facility, and bi-lateral agreements between countries with Norway being the largest donor. While there has been considerable focus on forest and agricultural project-based mitigation actions, national governments were encouraged to incorporate project-based approaches with other sectoral strategies in their Nationally Appropriate Mitigation Actions (NAMAs) after 2012. NAMAs reflect the country’s proposed strategy to reduce net emissions across various sectors within their economy (e.g., forests or agriculture). More recently, Nationally Determined Contributions (NDCs) indicate whether individual countries plan to use forestry and agricultural policies or related projects amongst a set of measures in other sectors, to reduce their net emissions as part of the Paris Agreement (e.g., Forsell et al. 2016; Fyson and Jeffery 2019).

The many protocols now available can be used to quantify the potential mitigation to date resulting from various projects or programs. For instance, carbon registries issue credits using protocols that typically account for additionality, permanence and leakage, thus providing evidence that the projects are a net carbon benefit to the atmosphere. Protocol development engages the scientific community, project developers, and the public over a multi-year period. Some protocols have been revised multiple times, such as the USA State of California’s forest carbon protocol, which is in its fifth revision, with the latest in 2019 (see http://www.climateactionreserve.org/how/protocols/forest/). Credits from carbon registries feed into regulatory programs, such as the cap and trade programme in California, or voluntary offset markets (Hamrick and Gallant 2017a). Although AFOLU measures have been deployed across a range of projects and programmes globally to reduce net carbon emissions, debate about the net carbon benefits of some projects continues (e.g., Krug 2018).

A new assessment of projects over the last two decades finds emission reductions or offsets of at least 7.9 GtCO2-eq (using GWP100 and a mix of IPCC values for CH4 and N2O) over the last 12 years due to agricultural and forestry activities (Table 7.4). More than 80% of these emission reductions or offsets have been generated by forest-based activities. The total amounts to 0.66 GtCO2 yr –1 for the period 2010–2019, which is 1.2% of total global, and 5.5% of AFOLU emissions reported in Table 7.1, over the same time period (high confidence).

The array of activities in Table 7.4 includes the Clean Development Mechanism, REDD+ activities reported in technical annexes of country biennial update reports to the UNFCCC, voluntary market transactions, and carbon stored as a result of carbon markets in Australia, New Zealand and California in the USA. Although other countries and sub-national units have developed programmes and policies, these three regions are presented due to their focus on forest and agricultural carbon mitigation, their use of generally accepted protocols or measures and the availability of data to quantify outcomes.

The largest share of emission reductions or carbon offsets in Table 7.4 has been from slowing deforestation and REDD+, specifically from efforts in Brazil (86% of total), which substantially reduced deforestation rates between 2004 and 2012 (Nepstad et al. 2014), as well as other countries in Latin America. With the exception of Roopsind et al. (2019), estimated reductions in carbon emissions from REDD+ in Table 7.4 are measured relative to a historical baseline. As noted in Brazil’s Third Biennial Update Report (Ministry of Foreign Affairs 2019), estimates are made in accordance with established methodologies to determine the benefits of results-based REDD+ payments to Brazil. REDD+ estimates from other countries also have been derived from biennial update reports.

Regulatory markets provide the next largest share of carbon removal to date. Data from the Australia Emissions Reduction Fund are carbon credits issued in for agricultural, and vegetation and savanna burning projects. In the case of California, offset credits from forest and agricultural activities, using methods approved by a third-party certification authority (Climate Action Reserve), have been allowed as part of their state-wide cap and trade system. Transaction prices for forest and agricultural credits in California were around USD13 tCO2–1 in 2018, and represented 7.4% of total market compliance. By the end of 2018, 80 MtCO2 had been used for compliance purposes.

For New Zealand, the carbon reduction in Table 7.4 represents forest removals that were surrendered from post-1989 forests between 2008 and the 2020. Unlike offsets in voluntary markets or in California, where permanence involves long-term contracts or insurance pools, forests in the New Zealand market liable for emissions when harvested or following land-use change. This means sellers account for future emission risks related to harvesting when they enter forests into carbon contracts. Offset prices were around USD13 tCO2–1 in 2016 but have risen to more than USD20 tCO2–1 in 2020.

The voluntary market data in Table 7.4 are offsets developed under the major standard-setting organisations, and issued from 2008–2018 (e.g., Hamrick and Gallant 2018). Note that there is some potential for double counting of voluntary offsets that may have been transacted in the California compliance market, however this would only have applied to transactions of US-issued offsets, and the largest share of annual transactions of voluntary AFOLU credits occurs with credits generated in Latin America, followed by Africa, Asia and North America. Europe and Oceania have few voluntary carbon market transactions. Within forestry and agriculture, most of the voluntary offsets were generated by forestry projects. Using historical transaction data from various Forest Trends reports, the offsets generated were valued at USD46.9 million yr –1. Prices for voluntary offset transactions in the period 2014–2016 ranged from USD4.90 to USD5.40 tCO2–1 (Hamrick and Gallant 2017a).

Voluntary finance has amounted to USD0.5 billion over a 10-year period for development of forest and agricultural credits. The three regulatory markets quantified amount to USD2.7 billion in funding from 2010 to 2019. For the most part, this funding has focused on forest projects and programs, with agricultural projects accounting for 5–10% of the total. In total, reported funding for AFOLU projects and programmes has been USD4.4 billion over the past decade, or about USD569 million yr –1 (low confidence). The largest share of the total carbon includes efforts in the Amazon by Brazil. Government expenditures on regulatory programmes and business expenditures on voluntary programmes in Brazil (e.g., the soy or cattle moratoriums) were not included in financing estimates due to difficulties obtaining that data. If Brazil and CDM (for which we have no cost estimates) are left out of the calculation, average cost per ton has been USD3.20 tCO2–1.

The large number of policy approaches described in Table 7.4 combined with efforts by other international actors, such as the Global Environmental Facility (GEF), as well as non-state actors (e.g., eco-labelling programmes and corporate social responsibility initiatives) illustrate significant policy experimentation over the last several decades. Despite widespread effort, AFOLU measures have thus far failed to achieve the large potential for climate mitigation described in earlier IPCC WG III reports ( high confidence). The limited gains from AFOLU to date appear largely to result from lack of investment and other institutional and social barriers, rather than methodological concerns (high confidence).

Table 7.4 | Estimates of achieved emission offsets or reductions in AFOLU through 2018. Data include CDM, voluntary carbon standards, compliance markets, and reduced deforestation from official UNFCCC reports. Carbon sequestration due to other government policies not included.

aClean Development Mechanism Registry: (accessed 22 June 2021).

bRoopsind et al. 2019.

cUNFCCC REDD+ Web Platform (https://redd.unfccc.int/submissions.html) and UNFCCC Biennial Update Report database (https://unfccc.int/BURs).

d (Hamrick and Gallant 2017a). State of Forest Carbon Finance. Forest Trends Ecosystem Marketplace. Washington, DC, USA.

eData for Australia carbon credit units (ACCUs) from Australia Emissions Reduction Fund Registry for agricultural and vegetation and savanna burning projects through FY2018/19 (downloaded on 24/10/2019): (http://www.cleanenergyregulator.gov.au/ERF/project-and-contracts-registers/project-register) and from Emissions Reduction Fund auction results to December 2018: (http://www.cleanenergyregulator.gov.au/ERF/auctions-results/december-2018).

f Data from the California Air Resources Board Offset Issuance registry (https://ww2.arb.ca.gov/our-work/programs/compliance-offset-program) for forestry and agricultural early action and compliance credits.

g Surrendered forest carbon credits from post-1989 forests in New Zealand. Obtained from New Zealand Environmental Protection Authority. ETS Unit Movement interactive report (Excel based). https://www.epa.govt.nz/industry-areas/emissions-trading-scheme/ets-reports/unit-movement/.

hObtained 13/08/2020. All non-CO2 gases are converted to CO2-eq using IPCC GWP100 values recommended at the time the project achieved approval by the relevant organisation or agency.

The Paris Agreement encourages a wide range of policy approaches, including REDD+, sustainable management of forests, joint mitigation and adaptation, and emphasises the importance of non-carbon benefits and equity for sustainable development (Martius et al. 2016). Around USD0.7 billion yr –1 has been invested in land-based carbon offsets (Table 7.4), but as noted in Streck (2012), there is a large funding gap between these efforts and the scale of efforts necessary to meet 1.5 or 2.0°C targets outlined in SR1.5. As Box 7.12 discusses, forestry actions could achieve up to 5.8 GtCO2 yr –1 with costs rising from USD178 billion yr –1 to USD400 billion yr –1 by 2050. Over half of this investment is expected to occur in Latin America, with 13% in SE Asia and 17% in sub-Saharan Africa (Austin et al. 2020). Other studies have suggested that similar sized programmes are possible, although they do not quantify total costs (e.g., Griscom et al. 2017 ; Busch et al. 2019). The currently quantified efforts to reduce net emissions with forests and agricultural actions are helpful, but society will need to quickly ramp up investments to achieve carbon sequestration levels consistent with high levels of mitigation. Only 2.5% of climate mitigation funding goes to land-based mitigation options, an order of magnitude below the potential proportional contribution (Buchner et al. 2015).

To date, there has been significantly less investment in agricultural projects than forestry projects to reduce net carbon emissions (Table 7.4). For example, the economic potential (available up to USD100 tCO2–1) for soil carbon sequestration in croplands is 1.9 (0.4–6.8) GtCO2 yr –1 (Section 7.4.3.1), however, less than 2% of the carbon in Table 7.4 is derived from soil carbon sequestration projects. While reductions in CH4 emissions due to enteric fermentation constitute a large share of potential agricultural mitigation reported in Section 7.4, agricultural CH4 emission reductions so far have been relatively modest compared to forestry sequestration. The protocols to quantify emission reductions in the agricultural sector are available and have been tested, and the main limitation appears to be the lack of available financing or the unwillingness to re-direct current subsidies (medium confidence).

Although quantified emission reductions in agricultural projects are limited to date, a number of OECD and economy in transition parties have reduced their net emissions through carbon storage in cropland soils since 2000. These reductions in emissions have typically resulted from policy innovations outside of the climate space, or market trends. For example, in the USA, there has been widespread adoption of conservation tillage in the last 30 years as a labour-saving crop management technique. In Europe, agricultural N2O and CH4 emissions have declined due to reductions in nutrient inputs and cattle numbers (Henderson et al. 2020). These reductions may be attributed to mechanism within the Common Agricultural Policy (Section 7.6.2.1), but could also be linked to higher nutrient prices in the 2000–2014 period. Other environmental policies could play a role, for example, efforts to reduce water pollution from phosphorus in The Netherlands, may ultimately reduce cattle numbers, also lowering CH4 emissions.

Numerous developing countries have established policy efforts to abate agricultural emissions or increase carbon storage. Brazil, for instance, developed a subsidy programme in 2010 to promote sustainable development in agriculture, and practices that would reduce GHG emissions. Henderson et al. (2020) report that this programme reduced GHG emission in agricultural by up to 170 MtCO2 between 2010 and 2018. However, the investments in low-carbon agriculture in Brazil amounted only 2% of the total funds for conventional agriculture in 2019. Programmes on deforestation in Brazil had successes and failures, as described in Box 7.9. Indonesia has engaged in a wide range of programmes in the REDD+ space, including a moratorium implemented in 2011 to prevent the conversion of primary forests and peatlands to oil palm and logging concessions (Wijaya et al. 2017; Tacconi and Muttaqin 2019; Henderson et al. 2020). Efforts to restore peatlands and forests have also been undertaken. Indonesia reports that results-based REDD+ programmes have been successful and have led to lower rates of deforestation (Table 7.4).

Existing policies focused on GHG management in agriculture and forestry is less advanced in Africa than in Latin American and Asia, however, Henderson et al. (2020) report on 10 countries in sub-Saharan Africa that have included explicit policy proposals for reducing AFOLU GHG emissions through their NDCs. These include efforts to reduce N2O emission, increase implementation of conservation agriculture, improve livestock management, and implement forestry and grassland practices, including agroforestry (Box 7.10). Within several of the NDCs, countries have explicitly suggested intensification as an approach to reduce emission in the livestock sector. However, it is important to note caveats associated with pursuing mitigation via intensification (Box 7.11).

The agricultural sector throughout the world is influenced by many policies that affect production practices, crop choices and land use. It is difficult to quantify the effect of these policies on reference level GHG emissions from the sector, as well as the cost estimates presented in Sections 7.4 and 7.5. The presence of significant subsidy programmes intended to improve farmer welfare and rural livelihoods makes it more difficult to implement regulatory programmes aimed at reducing net emissions in agriculture, however, it may increase the potential to implement new subsidy programmes that encourage practices aimed at reducing net emissions (medium confidence). For instance, in the USA, crop insurance can influence both crop choices and land use (Miao et al. 2016; Claassen et al. 2017), both of which will affect emission trends. Regulations to limit nutrient applications have not been widely considered, however, federal subsidy programmes have been implemented to encourage farmers to conduct nutrient management planning.

A factor that will influence future carbon storage in so-called land-based reservoirs involves considering short- and long-term climate benefits, as well as interactions among various natural climate solution options. The benefits of various natural climate solutions depend on a variety of spatially dependent issues as well as institutional factors, including their management status (managed or unmanaged systems), their productivity, opportunity costs, technical difficulty of implementation, local willingness to consider, property rights and institutions, among other factors. Biomass energy, as described elsewhere in this chapter and in (Cross-Working Group Box 3 in Chapter 12), is a potential example of an option with trade-offs that emerge when policies favour one type of mitigation strategy over another. Bioenergy production needs safeguards to limit negative impacts on carbon stocks on the land base as is already in place in the EU Renewable Energy Directive and several national schemes in Netherlands, UK and Denmark (Buchholz et al. 2016; Khanna et al. 2017; DeCicco and Schlesinger 2018; Favero et al. 2020). It is argued that a carbon tax on only fossil fuel derived emissions, may lead to massive deployment of bioenergy, although the effects of such a policy can be mitigated when combined with policies that encourage sustainable forest management and protection of forest carbon stocks as well as forest management certification ( high confidence) (Nabuurs et al. 2017, Baker et al. 2019 and Favero et al. 2020).

If biomass energy production expands and shifts to carbon capture and storage (e.g., BECCS) during the century, there could be a significant increase in the area of crop and forestland used for biomass energy production (Sections 7.4 and 7.5). BECCS is not projected to be widely implemented for several decades, but in the meantime, policy efforts to advance land-based measures including reforestation and restoration activities (Strassburg et al. 2020) combined with sustainable management and provision of agricultural and wood products are widely expected to increase the terrestrial pool of carbon (Cross-Working Group Box 3 in Chapter 12). Carbon sequestration policies, sustainable land management (forest and agriculture), and biomass energy policies can be complementary (Favero et al. 2017; Baker et al. 2019). However, if private markets emerge for biomass and BECCS on the scale suggested in the SR1.5, policy efforts must ramp up to substantially value, encourage, and protect terrestrial carbon stocks and ecosystems to avoid outcomes inconsistent with many SDGs ( high confidence).

The AR5 and other assessments have acknowledged many barriers and opportunities to effective implementation of AFOLU measures. Many of these barriers and opportunities focus on the context in developing countries, where a significant portion of the world’s cost-effective mitigation exists, but where domestic financing for implementation is likely to be limited. The SSPs capture some of this context, and as a result, IAMs (Section 7.5) exhibit a wide range of land-use outcomes, as well as mitigation potential. Potential mitigation, however, will be influenced by barriers and opportunities that are not considered by IAMs or by bottom-up studies reviewed here. For example, more efficient food production systems, or sustainable intensification within agriculture, and globalised trade could enhance the extent of natural ecosystems leading to lower GHG emissions from the land system and lower food prices (Popp et al. 2017), but this (or any) pathway will create new barriers to implementation and encourage new opportunities, negating potential benefits (Box 7.11). It is critically important to consider the current context in any country.

Closing knowledge gaps and narrowing uncertainties are crucial to advance AFOLU mitigation. Knowledge gaps exist across a range of areas, from emissions accounting and mitigation measure development to integration of scientific and traditional knowledge and development and sustainable implementation strategies. The following are identified as priorities:

•Uncertainty in contemporary emissions and sinks within AFOLU is still high. There is ongoing need to develop and refine emission factors, improve activity data and facilitate knowledge exchange, concerning inventories and accounting. For example, insufficient knowledge on CO2 emissions relating to forest management and burning or draining of organic soils (wetlands and peatlands), limits certainty on CO2 and CH4 fluxes.

•Improved monitoring of the land CO2 balance is urgently needed, including impacts of land degradation and restoration efforts (e.g., in tropical and boreal regions), making use of combined remote sensing, artificial intelligence, ground-based and modelling tools (Grassi et al. 2021). Improved estimates would provide more reliable projections of nationally determined contributions to emissions reduction and enhancement of sinks, and reconciliation of national accounting and modelling results (Nabuurs et al. 2019).

•The future impacts of climate change on land systems are highly uncertain, for example, the role of permafrost thaw, tipping points, increased disturbances and enhanced CO2 fertilisation (Friedlingstein et al. 2020). Further research into these mechanisms is critical to better understand the permanence of mitigation measures in land sector.

•There is need to understand the role of forest management, carbon and nitrogen fertilisation and associated interactions in the current forest carbon sink that has emerged in the last 50 to 70 years. These aspects are likely to explain much of the difference between bookkeeping models and other methodologies.

•Continued research into novel and emerging mitigation measures and associated cost efficiency (e.g., CH4 inhibitors or vaccines for ruminants) is required. In addition to developing specific measures, research is also needed into best practice regarding implementation and optimal agricultural land and livestock management at regional and country level. Further research into the feasible mitigation potential of sustainable intensification in terms of absolute GHG emissions and appropriate policy mechanisms, is required to implement and advance this strategy.

•Research into accounting systems and policy options that will enable agricultural soil and forest carbon to be utilised as offsets (voluntary or regulatory) is needed to increase financing for land-based CDR. Design of incentives that consider local institutions and novel frameworks for cooperation between private finance and public governance can encourage investment. Equally, research to adjust or remove regulations and subsidy schemes that may hamper land-based mitigation efforts, is urgently required.

•Improving mitigation potential estimates, whether derived from sectoral studies or IAMs to account for biophysical climate effects, and impacts of future climate change (e.g., mitigation permanence), biodiversity loss and corresponding feedbacks is needed. IAM ‘usability’ can be enhanced by integrating a wider set of measures and incorporating sustainability considerations.

•Research into the feasibility of improving and enhancing sustainable agricultural and forestry value chains, provision of renewable products (building with wood) and the sustainability of bioenergy is critically important. Modelled scenarios do not examine many poverty, employment and development trade-offs, which are highly context specific and vary enormously by region. Trade-off analysis and cost-benefit analysis can assist decision-making and policy.

•In-depth understanding of mitigation-SDG interactions is critical for identifying mitigation options that maximise synergies and minimise trade-offs. Mitigation measures have important synergies, trade-offs and co-benefits, impacting biodiversity and resource-use, human well-being, ecosystem services, adaptation capacity and many SDGs. In addition to exploring localised economic implementation costs, studies are needed to understand how measures will impact and interact with wider environmental and social factors across localities and contexts.

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