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

=== 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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[[File:987c582aef3adb5cc01b1a623c6cfb88 IPCC_AR6_WGIII_Figure_7_15.png]]

'''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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