Editing IPCC:AR6/SR15/Chapter-2 (section)

=== 2.1.4 Utility of Integrated Assessment Models (IAMs) in the Context of this Report ===

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IAMs lie at the basis of the assessment of mitigation pathways in this chapter, as much of the quantitative global scenario literature is derived with such models. IAMs combine insights from various disciplines in a single framework, resulting in a dynamic description of the coupled energy–economy–land-climate system that cover the largest sources of anthropogenic greenhouse gas (GHG) emissions from different sectors. Many of the IAMs that contributed mitigation scenarios to this assessment include a process-based description of the land system in addition to the energy system (e.g., Popp et al., 2017) <sup>[[#fn:r15|15]]</sup> , and several have been extended to cover air pollutants (Rao et al., 2017) <sup>[[#fn:r16|16]]</sup> and water use (Hejazi et al., 2014; Fricko et al., 2016; Mouratiadou et al., 2016) <sup>[[#fn:r17|17]]</sup> . Such integrated pathways hence allow the exploration of the whole-system transformation, as well as the interactions, synergies, and trade-offs between sectors, and, increasingly, questions beyond climate mitigation (von Stechow et al., 2015) <sup>[[#fn:r18|18]]</sup> . The models do not, however, fully account for all constraints that could affect realization of pathways (see Chapter 4).

Section 2.3 assesses the overall characteristics of 1.5°C pathways based on fully integrated pathways, while Sections 2.4 and 2.5 describe underlying sectoral transformations, including insights from sector-specific assessment models and pathways that are not derived from IAMs. Such models provide detail in their domain of application and make exogenous assumptions about cross-sectoral or global factors. They often focus on a specific sector, such as the energy (Bruckner et al., 2014; IEA, 2017a; Jacobson, 2017; OECD/IEA and IRENA, 2017) <sup>[[#fn:r19|19]]</sup> , buildings (Lucon et al., 2014) <sup>[[#fn:r20|20]]</sup> or transport (Sims et al., 2014) <sup>[[#fn:r21|21]]</sup> sector, or a specific country or region (Giannakidis et al., 2018) <sup>[[#fn:r22|22]]</sup> . Sector-specific pathways are assessed in relation to integrated pathways because they cannot be directly linked to 1.5°C by themselves if they do not extend to 2100 or do not include all GHGs or aerosols from all sectors.

AR5 found sectoral 2°C decarbonization strategies from IAMs to be consistent with sector-specific studies (Clarke et al., 2014) <sup>[[#fn:r23|23]]</sup> . A growing body of literature on 100%-renewable energy scenarios has emerged (e.g., see Creutzig et al., 2017; Jacobson et al., 2017) <sup>[[#fn:r24|24]]</sup> , which goes beyond the wide range of IAM projections of renewable energy shares in 1.5°C and 2°C pathways. While the representation of renewable energy resource potentials, technology costs and system integration in IAMs has been updated since AR5, leading to higher renewable energy deployments in many cases (Luderer et al., 2017; Pietzcker et al., 2017) <sup>[[#fn:r25|25]]</sup> , none of the IAM projections identify 100% renewable energy solutions for the global energy system as part of cost-effective mitigation pathways (Section 2.4.2). Bottom-up studies find higher mitigation potentials in the industry, buildings, and transport sectors in 2030 than realized in selected 2°C pathways from IAMs (UNEP 2017), indicating the possibility to strengthen sectoral decarbonization strategies until 2030 beyond the integrated 1.5°C pathways assessed in this chapter (Luderer et al., 2018) <sup>[[#fn:r26|26]]</sup> .

Detailed, process-based IAMs are a diverse set of models ranging from partial equilibrium energy–land models to computable general equilibrium models of the global economy, from myopic to perfect foresight models, and from models with to models without endogenous technological change (Supplementary Material 2.SM.1.2). The IAMs used in this chapter have limited to no coverage of climate impacts. They typically use GHG pricing mechanisms to induce emissions reductions and associated changes in energy and land uses consistent with the imposed climate goal. The scenarios generated by these models are defined by the choice of climate goals and assumptions about near-term climate policy developments. They are also shaped by assumptions about mitigation potentials and technologies as well as baseline developments such as, for example, those represented by different Shared Socio-Economic Pathways (SSPs), especially those pertaining to energy and food demand (Riahi et al., 2017) <sup>[[#fn:r27|27]]</sup> . See Section 2.3.1 for discussion of these assumptions. Since the AR5, the scenario literature has greatly expanded the exploration of these dimensions. This includes low-demand scenarios (Grubler et al., 2018; van Vuuren et al., 2018) <sup>[[#fn:r28|28]]</sup> , scenarios taking into account a larger set of sustainable development goals (Bertram et al., 2018) <sup>[[#fn:r29|29]]</sup> , scenarios with restricted availability of CDR technologies (Bauer et al., 2018; Grubler et al., 2018; Holz et al., 2018b; Kriegler et al., 2018a; Strefler et al., 2018b; van Vuuren et al., 2018) <sup>[[#fn:r30|30]]</sup> , scenarios with near-term action dominated by regulatory policies (Kriegler et al., 2018a) <sup>[[#fn:r31|31]]</sup> and scenario variations across the SSPs (Riahi et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r32|32]]</sup> . IAM results depend upon multiple underlying assumptions, for example, the extent to which global markets and economies are assumed to operate frictionless and policies are cost-optimized, assumptions about technological progress and availability and costs of mitigation and CDR measures, assumptions about underlying socio-economic developments and future energy, food and materials demand, and assumptions about the geographic and temporal pattern of future regulatory and carbon pricing policies (see Supplementary Material  2.SM.1.2 for additional discussion on IAMs and their limitations).

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