Editing IPCC:AR6/WGIII/Chapter-3 (section)

== 3.9 Methods of Assessment and Gaps in Knowledge and Data ==

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=== 3.9.1 AR6 Mitigation Pathways ===

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The analysis in this chapter relies on the available literature as well as an assessment of the scenarios contained in the AR6 scenarios database. Scenarios were submitted by research and other institutions following an open call (Annex III.II.3.1). The scenarios included in the AR6 scenarios database are an unstructured ensemble, as they are from multiple underlying studies and depend on which institutions chose to submit scenarios to the database. As noted in [[#3.2|Section 3.2]] , they do not represent the full scenario literature or the complete set of possible scenarios. For example, scenarios that include climate change impacts or economic degrowth are not fully represented, as these scenarios, with a few exceptions, were not submitted to the database. Additionally, sensitivity studies, which could help elucidate model behaviour and drivers of change, are mostly absent from the database – though examples exist in the literature ( [[#Marangoni--2017|Marangoni et al. 2017]] ).

The AR6 scenarios database contains 3131 scenarios of which 2425 with global scope were considered by this chapter, generated by almost 100 different model versions, from more than 50 model families. Of the 1686 vetted scenarios, 1202 provided sufficient information for a climate categorisation. Around 46% of the pathways are consistent with an end-of-century temperature of at least ''likely'' limiting warming to below 2°C (&gt;67%). There are many ways of constructing scenarios that limit warming to a particular level and the choice of scenario construction has implications for the timing of both net zero CO 2 and GHG emissions and the deployment of CDR ( [[#Emmerling--2019|Emmerling et al. 2019]] ; [[#Rogelj--2019b|Rogelj et al. 2019b]] ; [[#Johansson--2020|Johansson et al. 2020]] ). The AR6 scenarios database includes scenarios where temperature is temporarily exceeded (40% of all scenarios in the database have median temperature in 2100 that is 0.1°C lower than median peak temperature). Climate stabilisation scenarios are typically implemented by assuming a carbon price rising at a particular rate per year, though that rate varies across model, scenario, and time period. Standard scenarios assume a global single carbon price to minimise policy costs. Cost-minimising pathways can be reconciled with equity considerations through posterior international transfers. Many scenarios extrapolate current policies and include non-market, regulatory instruments such as technology mandates.

Scenarios are not independent of each other and not representative of all possible outcomes, nor of the underlying scenario generation process; thus, the statistical power of the database is limited. Dependencies in the data-generation process originate from various sources. Certain model groups, and types, are over-represented. For example, eight model teams contributed 90% of scenarios. Second, not all models can generate all scenarios, and these differences are not random, thereby creating selection bias ( [[#Tavoni--2010|Tavoni and Tol 2010]] ). Third, there are strong model dependencies: the modelling scientific community shares code and data, and several IAMs are open-source.

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=== 3.9.2 Models Assessed in This Chapter ===

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The models assessed in this chapter differ in their sectoral coverage and the level of complexity in each sector. Models tend to have more detail in their representation of energy supply and transportation, than they do for industry ( [[#3.4|Section 3.4]] and Annex III.I). Some models include detailed land-use models, while others exclude land models entirely and use supply curves to represent bioenergy potential ( [[#Bauer--2018a|Bauer et al. 2018a]] ). IAMs do not include all mitigation options available in the literature ( [[#Rogelj--2018|Rogelj et al. 2018]] b; [[#Smith--2019|Smith et al. 2019]] ). For example, most IAM pathways exclude many granular demand-side mitigation options and land-based mitigation options found in more detailed sectoral models; additionally, only a few pathways include CDR options beyond afforestation/reforestation and BECCS. [[#3.4|Section 3.4]] and [[IPCC:Wg3:Chapter:Chapter-12|Chapter 12]] include some results and comparisons to non-IAM models (e.g., bottom-up studies and detailed sectoral models). These sectoral studies often include a more complete set of mitigation options but exclude feedbacks and linkages across sectors which may alter the mitigation potential of a given sector. There is an increasing focus in IAM studies on SDGs ( [[#3.7|Section 3.7]] ), with some studies reporting the implications of mitigation pathways on SDGs (e.g., [[#Bennich--2020|Bennich et al. 2020]] ) and others using achieving SDGs as a constraint on the scenario itself ( [[#van%20Vuuren--2015|van Vuuren et al. 2015]] ; [[#Soergel--2021a|Soergel et al. 2021a]] ). However, IAMs are still limited in the SDGs they represent, often focusing on energy, water, air pollution and land. On the economic side, the majority of the models report information on marginal costs (i.e., carbon price). Only a subset provides full economic implications measured by either economic activity or welfare. Also often missing, is detail about economic inequality within countries or large aggregate regions.

For further details about the models and scenarios, see Annex III.

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