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

== 7.5 AFOLU Integrated Models and Scenarios ==

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This section assesses the literature and data available on potential future GHG dynamics in the AFOLU sector, the cost-effectiveness of different mitigation measures, and consequences of climate change mitigation pathways on land-use dynamics as well as relevant sustainable development indicators at the regional and global level based on global integrated models.

Land-based mitigation options interact and create various trade-offs, and thus need to be assessed together as well as with mitigation options in other sectors, and in combination with other sustainability goals ( [[#Popp--2014|Popp et al. 2014]] ; [[#Obersteiner--2016|Obersteiner et al. 2016]] ; [[#Roe--2019|Roe et al. 2019]] ; [[#Van%20Vuuren--2019|Van Vuuren et al. 2019]] ; [[#Prudhomme--2020|Prudhomme et al. 2020]] ; [[#Strefler--2021|Strefler et al. 2021]] ). The assessments of individual mitigation measures or sectoral estimates used to estimate mitigation potential in [[#7.4|Section 7.4]] , when aggregated together, do not account for interactions and trade-offs. Integrative land-use models (ILMs) combine different land-based mitigation options and are partially included in Integrated Assessment Models (IAMs) which combine insights from various disciplines in a single framework and cover the largest sources of anthropogenic GHG emissions from different sectors. Over time, ILMs and IAMs have extended their system coverage ( [[#Johnson--2019|Johnson et al. 2019]] ). However, the explicit modelling and analysis of integrated land-use systems is relatively new compared to other sectoral assessments such as the energy system ( [[#Jia--2019|Jia et al. 2019]] ). Consequently, ILMs as well as IAMs differ in their portfolio and representation of land-based mitigation options, the representation of sustainability goals other than climate action as well as the interplay with mitigation in other sectors ( [[#van%20Soest--2019|van Soest et al. 2019]] ; [[#Johnson--2019|Johnson et al. 2019]] ). These structural differences have implications for the regional and global deployment of different mitigation options as well as their sustainability impacts.

As a consequence of the relative novelty of land-based mitigation assessment in ILMs and IAMs, the portfolio of land-based mitigation options does not cover the full option space as outlined in [[#7.4|Section 7.4]] . The inclusion and detail of a specific mitigation measure differs across models. Land-based mitigation options are only partially included in ILM and IAM analyses, which mostly rely on afforestation/reforestation and bioenergy with CCS (BECCS). Most ILM and IAM scenarios are based on the Shared Socio-economic Pathways (SSPs) ( [[#Riahi--2017|Riahi et al. 2017]] ), which is a set of contrasting future scenarios widely used in the research community such as in the CMIP6 exercise, the SRCCL and the IPBES global assessment. However, the coverage of land-based mitigation options in these scenarios is mostly limited to dietary changes, higher efficiency in food processing (especially in livestock production systems), reduction of food waste, increasing agricultural productivity, methane reductions in rice paddies, livestock and grazing management for reduced methane emissions from enteric fermentation, manure management, improvement of N-efficiency, international trade, first generation of biofuels, avoided deforestation, afforestation, bioenergy and BECCS ( [[#Popp--2017|Popp et al. 2017]] ; Van Meijl et al. 2018; [[#Frank--2019|Frank et al. 2019]] ). Hence, there are mitigation options not being broadly included in integrated pathway modelling as soil carbon, forest management, agroforestry or wetland management ( [[#Humpenöder--2020|Humpenöder et al. 2020]] ) which have the potential to alter the contribution of land-based mitigation in terms of timing, potential and sustainability consequences ( [[#Frank--2017|Frank et al. 2017]] ).

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=== 7.5.1 Regional GHG Emissions and Land Dynamics ===

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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 CO 2 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-CO 2 emissions from agricultural production and (iv) interplays with sustainability dimensions other than climate action ( [[#Popp--2017|Popp et al. 2017]] ; [[#Rogelj--2017|Rogelj et al. 2017]] ; Van Vuuren et al. 2018; [[#Frank--2018|Frank et al. 2018]] ; [[#Hasegawa--2018|Hasegawa et al. 2018]] ; [[#van%20Soest--2019|van Soest et al. 2019]] ). Regional AFOLU GHG emissions in scenarios with &lt;4°C warming in 2100 (scenario category C7), as shown in Figure 7.13, are shaped by considerable CH 4 and N 2 O emissions throughout 2050 and 2100, mainly from ASIA and AFRICA. CH 4 emissions from enteric fermentation are largely caused by ASIA, followed by AFRICA, while CH 4 emissions from paddy rice production are almost exclusively caused by ASIA. N 2 O emissions from animal waste management and soils are more equally distributed across region.

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'''Figure 7.13 | Land-based regional GHG emissions and removals in 2050 (top) and 2100 (bottom) for scenarios from the AR6 Database with &lt;1.''' '''5°C (C1, C2), &lt;2°C (C3, C4), &lt;3°C (C5, C6) and &lt;4°C (C7) global warming in 2100 (scenario type is indicated by colour).''' The categories shown include CH 4 emissions from enteric fermentation (EntF) and rice production (Rice), N 2 O emissions from animal waste management (AWM) and fertilisation (Soil). The category CO 2 Land includes CO 2 emissions from land-use change as well as removals due to afforestation/reforestation. BECCS reflects the CO 2 emissions captured from bioenergy use and stored in geological deposits. The annual GHG emission data from various models and scenarios is converted to CO 2 equivalents using GWP factors of 27 for CH 4 and 273 for N 2 O. The data is summarised in boxplots (Tukey style), which show the median (vertical line), the interquartile range (IQR box) and the range of values within 1.5 × interquartile range at either end of the box (horizontal lines) across all models and scenarios. The number of data points available for each emission category, scenario type, region and year is shown at the edge of each panel. Regional definitions: AFRICA = sub-Saharan Africa, ASIA = Asia, LAM = Latin America and Caribbean, MID_EAST = Middle East, OECD90+EU = OECD 90 and EU, REF = Reforming Economies of Eastern Europe and the Former Soviet Union.

In most regions, CH 4 and N 2 O emission are both lower in mitigation pathways that limit warming to 3°C (&gt;50%) or lower (C1–C6) compared to scenarios with &lt;4°C ( [[#Popp--2017|Popp et al. 2017]] ; [[#Rogelj--2018a|Rogelj et al. 2018a]] ). In particular, the reduction of CH 4 emissions from enteric fermentation in ASIA and AFRICA is profound. Land-related CO 2 emissions, which include emissions from deforestation as well as removals from afforestation, are slightly negative (i.e., AFOLU systems turn into a sink) in &lt;1.5°C, &lt;2°C and &lt;3°C mitigation pathways compared to &lt;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 &lt;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.

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'''Figure 7.14 | Regional change of major land cover types by 2050 (top) and 2100 (bottom) relative to 2020 for scenarios from the AR6 Database with &lt;1.''' '''5°C (C1, C2), &lt;2°C (C3, C4), &lt;3°C (C5, C6) and &lt;4°C (C7) global warming in 2100 (scenario type is indicated by colour).''' The data is summarised in boxplots (Tukey style), which show the median (vertical line), the interquartile range (IQR box) and the range of values within 1.5 × IQR at either end of the box (horizontal lines) across all models and scenarios. The number of data points available for each land cover type, scenario type, region and year is shown at the right edge of each panel. Regional definitions: AFRICA = sub-Saharan Africa, ASIA = Asia, LAM = Latin America and Caribbean, MID_EAST = Middle East, OECD90+EU = OECD 90 and EU, REF = Reforming Economies of Eastern Europe and the Former Soviet Union.

The overall land dynamics in &lt;1.5°C, &lt;2°C and &lt;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 &lt;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.

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=== 7.5.2 Marginal Abatement Costs According to Integrated Assessments ===

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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 (N 2 O, CH 4 , CO 2 ) 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 [[#7.4|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 CO 2 emissions from AFOLU. Scenarios with contradicting assumptions (for example, fixing some of the emissions to baseline levels) are excluded. Furthermore, only scenarios with consistent [[#footnote-000|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--2018|Huppmann et al. 2018]] ).

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'''Figure 7.15 | Mitigation of CO''' 2 ''', CH''' 4 '''and N''' 2 '''O emissions (in CO''' 2 '''-eq y''' '''r''' –1 '''using IPCC AR6 GWP100 values) from the AFOLU sector for increasing carbon price levels for 2030 and 2050.''' In the left-hand panels, single data points are generated by comparing emissions between a policy scenario and a related benchmark scenario, and mapping these differences with the respective carbon price difference. Plots only show the price range of up to 250 USD2010 tCO 2 -eq –1 and the mitigation range between –2000 and 6000 MtCO 2 -eq yr –1 for better visibility. At the right-hand side, based on the same data as left-hand side panels, boxplots show medians (vertical line within the boxes), means (dots), 33%–66% intervals (box) and 10%–90% intervals (horizontal lines). Numbers on the very right indicate the number of observations falling into the respective price range per variable. A wide range of carbon price induced mitigation options (such as technical, structural and behavioural options in the agricultural sector, afforestation, reforestation, natural re-growth or avoided deforestation in the LULUCF sector, excluding carbon capture and sequestration from BECCS) are reflected in different scenarios.

This approach is close to integrated assessment marginal abatement cost curves (MACCs) as described in the literature ( [[#Fujimori--2016|Fujimori et al. 2016]] ; [[#Frank--2018|Frank et al. 2018]] , 2019; [[#Harmsen--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--2019|Daioglou et al. 2019]] ; [[#Kalt--2020|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--2020|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--2019|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 N 2 O and CH 4 emissions from AFOLU (which mainly can be related to agricultural activities) and CO 2 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.

N 2 O 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 GtCO 2 -eq yr –1 mitigation in 2030 and 0.9 GtCO 2 -eq yr –1 in 2050, respectively, while afterwards with higher prices the expansion is very limited. Mitigation of CH 4 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 GtCO 2 -eq yr –1 in 2030 and around 2.4 GtCO 2 -eq yr –1 in 2050, respectively. The highest mitigation potentials are observed for CO 2, but also the highest ranges of observations among the three gases. In 2030, a median of 4 GtCO 2 -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 GtCO 2 -eq yr –1 can be observed.

When compared with the sectoral estimates from [[#Harmsen--2019|Harmsen et al. (2019)]] , the integrated assessment median potentials are broadly comparable for the N 2 O mitigation potential; Harmsen et al. 2050 mitigation potential at USD125 is 0.6 GtCO 2 -eq yr –1 while the integrated assessment estimate for the same price range is 0.7 GtCO 2 -eq yr –1 . The difference is substantially larger for the CH 4 mitigation potential; 0.9 GtCO 2 -eq yr –1 in Harmsen et al. while 2 GtCO 2 -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-CO 2 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 CH 4 emissions ( [[#Frank--2019|Frank et al. 2019]] ).

Economic CO 2 mitigation potentials from land-use change and forestry are larger compared to potentials from non-CO 2 gases, and at the same time reveal high levels of variation in absolute terms. The 66th percentile in 2050 goes up to 5.2 GtCO 2 -eq yr –1 mitigation, while the lowest observations are even negative, indicating higher CO 2 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 CO 2 mitigation/sequestration potentials in the land sector. Biomass demand may lead to an increase in CO 2 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--2015|Kriegler et al. 2015]] ).

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=== 7.5.3 Interaction Between Mitigation in the AFOLU Sector and Other SDGs in the Context of Integrated Assessments ===

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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--2020|Camia et al. 2020]] ) (Sections 7.6.5 and 17.3.3.7, Figure 17.1 and Supplementary Material Table 17.SM.1).

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=== 7.5.4 Regional AFOLU Abatement for Different Carbon Prices ===

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At the regional level (Figure 7.16), the highest potential from non-CO 2 emissions abatement, and mostly from CH 4 , is reported for ASIA with the median of mitigation potential observations from CH 4 increasing up to a price of USD200 in the year 2050, reaching a median of 1.2 GtCO 2 -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 CO 2 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, CO 2 mitigation potentials show a wide range of results in comparison to non-CO 2 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.

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'''Figure 7.16 | Regional mitigation efforts for CO''' 2 ''', CH''' 4 '''and N''' 2 '''O emissions (in CO''' 2 '''-eq y''' '''r''' –1 '''using IPCC AR6 GWP100 values) from the AFOLU sector for increasing carbon price levels for 2030 and 2050.''' Underlying datapoints are generated by comparing emissions between a policy scenario and a related benchmark scenario, mapping these differences with the respective carbon price differences. Boxplots show Medians (vertical line within the boxes), Means (dots), 33%–66% intervals (box) and 10%–90% intervals (horizontal lines) for respective scenarios of carbon prices implemented in intervals of USD50 from a price of USD0 to USD250. Regions: Asia (ASIA), Latin America and Caribbean (LAM), Middle East (MIDDLE_EAST), Africa (AFRICA), Developed Countries (OECD 90 and EU) (OECD+EU) and Reforming Economies of Eastern Europe and the Former Soviet Union (REF).

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=== 7.5.5 Illustrative Mitigation Pathways ===

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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.

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'''Figure 7.17 | Evolution and breakdown of (a) global land-based GHG emissions and removals and (b) global land-use dynamics under four Illustrative Mitigation Pathways, which illustrate the differences in timing and magnitude of land-based mitigation approaches including afforestation and BECCS.''' All pathways are based on different IAM realisations: ModAct scenario (below 3.0°C, C6) from IMAGE 3.0; IMP Neg-2.0 (limit warming to 2°C (&gt;67%), C3) from AIM/CGE 2.2; IMP Ren (1.5°C with no or low overshoot, C1) from REMIND-MAgPIE 2.1–4.3; IMP SP (1.5°C with no or low overshoot, C1) from REMIND-MAgPIE 2.1–4.2. In panel A the categories CO 2 AFOLU, CH 4 AFOLU and N 2 O AFOLU include GHG emissions from land-use change and agricultural land use (including emissions related to bioenergy production). In addition, the category CO 2 Land includes removals due to afforestation/reforestation. BECCS reflects the CO 2 emissions captured from bioenergy use and stored in geological deposits. CH 4 and N 2 O emissions are converted to CO 2 -eq using GWP100 factors of 27 and 273 respectively.

The scenario ''ModAct'' (limit warming to 3°C (&gt;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--2018|Grassi et al. 2018]] ). This pathway shows a strong decrease of CO 2 emissions from land-use change in 2030, mainly due to reduced deforestation, as well as moderately decreasing N 2 O and CH 4 emissions from agricultural production due to improved agricultural management and dietary shifts away from emissions-intensive livestock products. However, in contrast to CO 2 emissions, which turn net-negative around 2050 due to afforestation/reforestation, CH 4 and N 2 O emissions persist throughout the century due to difficulties of eliminating these residual emissions based on existing agricultural management methods ( [[#Frank--2017|Frank et al. 2017]] ; [[#Stevanović--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 (&gt;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 CO 2 emissions, which turn net-negative around 2030 due to afforestation/reforestation, CH 4 and N 2 O emissions persist throughout the century, similar to ''ModAct'' , due to ongoing increasing demand for total calories and animal-based commodities ( [[#Bodirsky--2020|Bodirsky et al. 2020]] ) and difficulties of eliminating these residual emissions based on existing agricultural management methods ( [[#Stevanović--2017|Stevanović et al. 2017]] ; [[#Frank--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 GtCO 2 for BECCS and 121 GtCO 2 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--2018|Hof et al. 2018]] ) and food security ( [[#Fujimori--2019|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, CH 4 and N 2 O 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--2021|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 CO 2 emissions from land-use change, mainly due to reduced deforestation, and in difference to all other IMPs described in this chapter strongly decreasing N 2 O and CH 4 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.

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