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

=== 3.8.2 Feasibility Appraisal of Low-carbon Scenarios ===

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Evaluating the feasibility of low-carbon pathways can take different forms. In the narrowest sense, there is feasibility pertaining the reporting of model-generated scenarios: here an infeasible scenario is one which cannot meet the constraints embedded implicitly or explicitly in the models which attempted to generate it. Second, there is a feasibility that relates to specific elements or overall structure characterising the low-carbon transition compared to some specified benchmark.

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==== 3.8.2.1 Model Solvability ====

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In order to be generated, scenarios must be coherent with the constraints and assumptions embedded in the models (i.e., deployment potential of given technologies, physical and geological limits) and in the scenario design (i.e., carbon budget). Sometimes, models cannot solve specific scenarios. This provides a first, coarse indication of feasibility concerns. Specific vetting criteria can be imposed, such as carbon-price values above which scenarios should not be reported, as in [[#Clarke--2009|Clarke et al. (2009)]] . However, model solvability raises issues of aggregation in model ensembles. Since model solving is not a random process, but a function of the characteristics of the models, analysing only reported outcomes leads to statistical biases ( [[#Tavoni--2010|Tavoni and Tol 2010]] ).

Although model-feasibility differs distinctly from feasibility in the real world, it can indicate the relative challenges of low-carbon scenarios – primarily when performed in a model ensemble of sufficient size. [[#Riahi--2015|Riahi et al. (2015)]] interpreted infeasibility across a large number of models as an indication of increased risk that the transformation may not be attainable due to technical or economic concerns. All models involved in a model comparison of 1.5°C targets ( [[#Rogelj--2018|Rogelj et al. 2018]] b) (Table S1) were able to solve under favourable underlying socio-economic assumptions (SSP1), but none for the more challenging SSP3. This interpretation of feasibility was used to highlight the importance of socio-economic drivers for attaining climate stabilisation. [[#Gambhir--2017|Gambhir et al. (2017)]] constrained the models to historically observed rates of change and found that it would no longer allow to solve for 2°C, highlighting the need for rapid technological change.

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==== 3.8.2.2 Scenario Feasibility ====

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Evaluating the feasibility of scenarios involves several steps (Figure 3.41). First, one needs to identify which dimensions of feasibility to focus on. Then, for each dimension, one needs to select relevant indicators for which sufficient empirical basis exists and which are an output of models (or at least of a sufficient number of them). Then, thresholds marking different levels of feasibility concerns are defined based on available literature, expert elicitations and empirical analysis based on appropriately chosen historical precedents. Finally, scenario feasibility scores are obtained for each indicator, and where needed aggregated up in time or dimensions, as a way to provide an overall appraisal of feasibility trade-offs, depending on the timing, disruptiveness and scale of transformation.

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[[File:a0349df7d6b153d578960537c581f18d IPCC_AR6_WGIII_Figure_3_41.png]]

'''Figure 3.41 | Steps involved in evaluating the feasibility of scenarios.''' '''Source: adapted with permission from Brutschin''' '''et al.''' '''2021.'''

Most of the existing literature has focused on the technological dimensions, given the technology focus of models and the ease of comparison. The literature points to varied findings. Some suggest that scenarios envision technological progress consistent with historical benchmarks ( [[#Wilson--2013|Wilson et al. 2013]] ; [[#Loftus--2015|Loftus et al. 2015]] ). Others that scenarios exceed historically observed rates of low-carbon technology deployment and of energy demand transformation globally ( [[#van%20der%20Zwaan--2013|van der Zwaan et al. 2013]] ; [[#Napp--2017|Napp et al. 2017]] ; [[#Cherp--2021|Cherp et al. 2021]] ; [[#Semieniuk--2021|Semieniuk et al. 2021]] ), but not for all countries ( [[#Cherp--2021|Cherp et al. 2021]] ). The reason for these discrepancies depends on the unit of analysis and the indicators used. Comparing different kinds of historical indicators, ( [[#van%20Sluisveld--2015|van Sluisveld et al. 2015]] ) find that indicators that look into the absolute change of energy systems remain within the range of historical growth frontiers for the next decade, but increase to unprecedented levels before mid-century. Expert assessments provide another way of benchmarking scenarios, though they have shown to be systematically biased ( [[#Wiser--2021|Wiser et al. 2021]] ) and to underperform empirical methods ( [[#Meng--2021|Meng et al. 2021]] ). [[#van%20Sluisveld--2018a|van Sluisveld et al. (2018a)]] find that scenarios and experts align for baseline scenarios but differ for low-carbon ones. Scenarios rely more on conventional technologies based on existing infrastructure (such as nuclear and CCS) than what is forecasted by experts. Overall, the technology assessment of the feasibility space highlights that Paris-compliant transformations would have few precedents, but not zero ( [[#Cherp--2021|Cherp et al. 2021]] ).

Recent approaches have addressed multiple dimensions of feasibility, an important advancement since social and institutional aspects are as, if not more, important than technology ones ( [[#Jewell--2020|Jewell and Cherp 2020]] ). Feasibility corridors of scenarios based on their scale, rate of change and disruptiveness have been identified ( [[#Kriegler--2018b|Kriegler et al. 2018b]] ; [[#Warszawski--2021|Warszawski et al. 2021]] ). The reality check shows that many 1.5°C-compatible scenarios violate the feasibility corridors. The ones that didn’t are associated with a greater coverage of the available mitigation levers ( [[#Warszawski--2021|Warszawski et al. 2021]] ).

[[#Brutschin--2021|Brutschin et al. (2021)]] proposed an operational framework covering all six dimensions of feasibility. They developed a set of multi-dimensional metrics capturing the timing, disruptiveness and the scale of the transformative change within each dimension (as in [[#Kriegler--2018b|Kriegler et al. 2018b]] ). Thresholds of feasibility risks of different intensity are obtained through the review of the relevant literature and empirical analysis of historical data. Novel indicators include governance levels ( [[#Andrijevic--2020a|Andrijevic et al. 2020a]] ). The 17 bottom-up indicators are then aggregated up across time and dimension, as a way to highlight feasibility trade-offs. Aggregation is done via compensatory approaches such as the geometric mean. This is employed, for instance, for the Human Development Index. A conceptual example of this approach as applied to the IPCC AR6 scenarios database is shown in Figure 3.42 and further described in the Annex III.II.2.3.

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[[File:978da0dbb7ba40db559c85730da58a98 IPCC_AR6_WGIII_Figure_3_42.png]]

'''Figure 3.42 | Example of multi-dimensional feasibility analysis and indicators used in the IPCC AR6 scenarios.''' The approach defines relevant indicators characterising the key dimensions of feasibility. Indicators capture the timing, scale and disruptiveness challenges. Low-, medium- and high-feasibility concerns are defined based on historical trends and available literature. Details about indicator and threshold values can be found in Annex III.II.2.3.

In Figure 3.43, we show the results of applying the methodology of [[#Brutschin--2021|Brutschin et al. (2021)]] to the AR6 scenarios database. The charts highlight the dynamic nature of feasibility risks, which are mostly concentrated in the decades before mid-century except for geophysical risks driven by CO 2 removals later in the century. Different dimensions pose differentiated challenges: for example, institutional feasibility challenges appear to be the most relevant, in line with the qualitative literature. Thus, feasibility concerns might be particularly relevant in countries with weaker institutional capacity. Figure 3.43 also highlights the key roles of policy and technology as enabling factors. In particular (panel b), internationally coordinated and immediate emission reductions allow to smooth out feasibility concerns and reduce long-term challenges compared to delayed policy action, as a result of a more gradual transition and lower requirements of CO 2 removals. For the same climate objective, different Illustrative Mitigation Pathways entail somewhat different degrees and distributions of implementation challenges (panel c).

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[[File:cc4feb2bcbbe75370b0cbd2e2a8d11cf IPCC_AR6_WGIII_Figure_3_43.png]]

'''Figure 3.43 | Feasibility characteristics of the Paris-consistent scenarios in the AR6 scenarios database''' ''': Feasibility corridors for the AR6 scenarios database, applying the methodology by (Brutschin''' '''et al.''' '''2021).''' '''(a)''' The fraction of scenarios falling within three categories of feasibility concerns (plausible, best case, unprecedented), for different times (2030, 2050, 2100), different climate categories consistent with the Paris Agreement and five dimensions. '''(b)''' Composite feasibility score (obtained by geometric mean of underlying indicators) over time for scenarios with immediate and delayed global mitigation efforts, for different climate categories (C1, C2, C3. Note: no C1 scenario has delayed participation). '''(c)''' The fraction of scenarios which in any point in time over the century exceed the feasibility concerns, for C1 and C3 climate categories. Overlayed are the Illustrative Mitigation Pathways ( ''IMP-LP'' , ''IMP-SP'' , ''IMP-Ren'' : C1 category; ''IMP-Neg'' , ''IMP-GS'' : C3 category).

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