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

== 3.8 Socio/Techno/Economic Transitions ==

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The objective of this section is to discuss concepts of feasibility in the context of the low-carbon transition and pathways. We aim to identify drivers of low-carbon scenarios feasibility and to highlight enabling conditions which can ameliorate feasibility concerns.

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=== 3.8.1 Frameworks for the Low-carbon Transition and Scenarios ===

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Effectively responding to climate change and achieving sustainable development requires overcoming a series of challenges to transition away from fossil-based economies. Feasibility can be defined in many ways (Chapter 1). The political science literature ( [[#Majone--1975a|Majone 1975a]] ,b; [[#Gilabert--2012|Gilabert and Lawford-Smith 2012]] ) distinguishes the feasibility of ‘what’ (i.e., emission reduction strategies), ‘when and where’ (i.e., in the year 2050, globally) and ‘whom’ (i.e., cities). It distinguishes desirability from political feasibility ( [[#von%20Stechow--2015|von Stechow et al. 2015]] ): the former represents a normative assessment of the compatibility with societal goals (i.e., SDGs), while the latter evaluates the plausibility of what can be attained given the prevailing context of transformation ( [[#Nielsen--2020|Nielsen et al. 2020]] ). Feasibility concerns are context and time dependent and malleable: enabling conditions can help overcome them. For example, public support for carbon taxes has been hard to secure but appropriate policy design and household rebates can help dissipate opposition ( [[#Murray--2015|Murray and Rivers 2015]] ; [[#Carattini--2019|Carattini et al. 2019]] ).

Regarding scenarios, the feasibility ‘what’ question is the one most commonly dealt with in the literature, though most of the studies have focused on expanding low-carbon system, and yet political constraints might arise mostly from phasing out fossil fuel-based ones ( [[#Spencer--2018|Spencer et al. 2018]] ; [[#Fattouh--2019|Fattouh et al. 2019]] ). The ‘when and where’ dimension can also be related to the scenario assessment, but only insofar that the models generating them can differentiate time and geographical contextual factors. Distinguishing mitigation potential by regional institutional capacity has a significant influence on the costs of stabilising climate ( [[#Iyer--2015c|Iyer et al. 2015c]] ). The ‘whom’ question is the most difficult to capture by scenarios, given the multitude of actors involved as well as their complex interactions. The focus of socio-technical transition sciences on the co-evolutionary processes can shed light on the dynamics of feasibility ( [[#Nielsen--2020|Nielsen et al. 2020]] ).

The when-where-whom distinction allows depicting a feasibility frontier beyond which implementation challenges prevent mitigation action ( [[#Jewell--2020|Jewell and Cherp 2020]] ). Even if the current feasibility frontier appears restraining in some jurisdictions, it is context-dependent and dynamic as innovation proceeds and institutional capacity builds up ( [[#Nielsen--2020|Nielsen et al. 2020]] ). The question is whether the feasibility frontier can move faster than the pace at which the carbon budget is being exhausted. [[#Jewell--2019|Jewell et al. (2019)]] show that the emission savings from the pledges of premature retirement of coal plants is 150 times less than globally committed emissions from existing coal power plants. The pledges come from countries with high institutional capacity and relatively low shares of coal in electricity. Other factors currently limiting the capacity to steer transitions at the necessary speed include the electoral-market orientation of politicians ( [[#Willis--2017|Willis 2017]] ), the status-quo orientation of senior public officials ( [[#Geden--2016|Geden 2016]] ), path dependencies created by ‘instrument constituencies’ ( [[#Béland--2016|Béland and Howlett 2016]] ), or the impacts of deliberate inconsistencies between talk, decisions and actions in climate policy ( [[#Rickards--2014|Rickards et al. 2014]] ). All in all, a number of different delay mechanisms in both science and policy have been identified to potentially impede climate goal achievement ( [[#Karlsson--2020|Karlsson and Gilek 2020]] ) (Chapter 13).

In addition to its contextual and dynamic nature, feasibility is a multi-dimensional concept. The IPCC SR1.5 distinguishes six dimensions of feasibility: geophysical, environmental-ecological, technological, economic, socio-cultural and institutional. At the individual option level, different mitigation strategies face various barriers as well as enablers (see [[IPCC:Wg3:Chapter:Chapter-6|Chapter 6]] for the option-level assessment). However, a systemic transformation involves interconnections of a wide range of indicators. Model-based assessments are meant to capture the integrative elements of the transition and of associated feasibility challenges. However, the translation of model-generated pathways into feasibility concerns ( [[#Rogelj--2018|Rogelj et al. 2018]] b) has developed only recently. Furthermore, multiple forms of knowledge can be mobilised to support strategic decision-making and complement scenario analysis ( [[#Turnheim--2019|Turnheim and Nykvist 2019]] ). We discuss both approaches next.

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=== 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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=== 3.8.3 Feasibility in Light of Socio-technical Transitions ===

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The limitations associated with quantitative low-carbon transition pathways stem from a predominant reliance on techno-economic considerations with a simplified or non-existent representation of the socio-political and institutional agreement. Accompanying the required deployment of low-carbon technologies will be the formation of new socio-technical systems ( [[#Bergek--2008|Bergek et al. 2008]] ). With a socio-technical system being defined as a cluster of elements comprising of technology, regulation, user practices and markets, cultural meaning, infrastructure, maintenance networks, and supply networks ( [[#Hofman--2004|Hofman et al. 2004]] ; [[#Geels--2005|Geels and Geels 2005]] ); the inter-relationship between technological systems and social systems must be comprehensively understood. It is of vital importance that the process of technical change must be considered in its institutional and social context so as to ascertain potential transition barriers which in turn provide an indication of pathway feasibility. In order to address the multitudinous challenges associated with low-carbon transition feasibility and governance, it has been opined that the robustness of evaluating pathways may be improved by the bridging of differing quantitative-qualitative analytical approaches ( [[#Haxeltine--2008|Haxeltine et al. 2008]] ; [[#Foxon--2010|Foxon et al. 2010]] ; [[#Hughes--2013|Hughes 2013]] ; [[#Wangel--2013|Wangel et al. 2013]] ; [[#Li--2015|Li et al. 2015]] ; [[#Turnheim--2015|Turnheim et al. 2015]] ; [[#Geels--2016a|Geels et al. 2016a]] ,b, 2020; [[#Moallemi--2017|Moallemi et al. 2017]] ; [[#De%20Cian--2020|De Cian et al. 2020]] ; [[#Li--2019|Li and Strachan 2019]] ). The rationale for such analytical bridging is to rectify the issue that in isolation each disciplinary approach can only generate a fragmented comprehension of the transition pathway with the consequence being an incomplete identification of associated challenges in terms of feasibility. Concerning low-carbon transition pathways generated by IAMs, it has been argued that a comprehensive analysis should include social scientific enquiry ( [[#Geels--2016a|Geels et al. 2016a]] , 2020; [[#van%20Sluisveld--2018b|van Sluisveld et al. 2018b]] ). The normative analysis of IAM pathways assists in the generation of a vision or the formulation of a general plan with this being complemented by socio-technical transition theory ( [[#Geels--2016a|Geels et al. 2016a]] ). Such an approach thereby allowing for the socio-political feasibility and the social acceptance and legitimacy of low-carbon options to be considered. Combining computer models and the multi-level perspective can help identify ‘transition bottlenecks’ ( [[#Geels--2020|Geels et al. 2020]] ). Similarly, increased resolution of integrated assessment models’ actors has led to more realistic narratives of transition in terms of granularity and behaviour ( [[#McCollum--2017|McCollum et al. 2017]] ; [[#van%20Sluisveld--2018b|van Sluisveld et al. 2018b]] ). Increased data availability of actual behaviour from smart technology lowers the barriers to representing behavioural change in computer simulations, and thus better represents crucial demand-side transformations ( [[#Creutzig--2018|Creutzig et al. 2018]] ). Increasing the model resolution is a meaningful way forward. However, integrating a much broader combination of real-life aspects and dynamics into models could lead to an increased complexity that could restrict them to smaller fields of applications ( [[#De%20Cian--2020|De Cian et al. 2020]] ).

Other elements of feasibility relate to social justice, which could be essential to enhance the political and public acceptability of the low-carbon transition. Reviewing the literature, one study finds that employing social justice as an orienting principle can increase the political feasibility of low-carbon policies ( [[#Patterson--2018|Patterson et al. 2018]] ). Three elements are identified as key: (i) protecting vulnerable people from climate change impacts, (ii) protecting people from disruptions of transformation, (iii) enhancing the process of envisioning and implementing an equitable post-carbon society.

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=== 3.8.4 Enabling Factors ===

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There is strong agreement that the climate policy institutional framework as well as technological progress have a profound impact on the attainability of low-carbon pathways. Delaying international cooperation reduces the available carbon budget and locks into carbon-intensive infrastructure exacerbating implementation challenges ( [[#Keppo--2007|Keppo and Rao 2007]] ; [[#Bosetti--2009|Bosetti et al. 2009]] ; [[#Boucher--2009|Boucher et al. 2009]] ; [[#Clarke--2009|Clarke et al. 2009]] ; [[#Krey--2009|Krey and Riahi 2009]] ; [[#van%20Vliet--2009|van Vliet et al. 2009]] ; [[#Knopf--2011|Knopf et al. 2011]] ; [[#Jakob--2012|Jakob et al. 2012]] ; [[#Luderer--2013|Luderer et al. 2013]] ; [[#Rogelj--2013a|Rogelj et al. 2013a]] ; Aboumahboub et al. 2014; [[#Kriegler--2014a|Kriegler et al. 2014a]] ; [[#Popp--2014|Popp et al. 2014]] ; [[#Riahi--2015|Riahi et al. 2015]] ; [[#Gambhir--2017|Gambhir et al. 2017]] ; [[#Bertram--2021|Bertram et al. 2021]] ). Similarly, technological availability influences the feasibility of climate stabilisation, though differently for different technologies ( [[#Kriegler--2014a|Kriegler et al. 2014a]] ; [[#Iyer--2015a|Iyer et al. 2015a]] ; [[#Riahi--2015|Riahi et al. 2015]] ).

One of the most relevant factors affecting mitigation pathways and their feasibility is the rate and kind of socio-economic development. For example, certain socio-economic trends and assumptions about policy effectiveness preclude achieving stringent mitigation futures ( [[#Rogelj--2018|Rogelj et al. 2018]] b). The risk of failure increases markedly in high-growth, unequal and/or energy-intensive worlds such as those characterised by the shared socio-economic pathways SSP3, SSP4 and SSP5. On the other hand, socio-economic development conducive to mitigation relieves the energy sector transformation from relying on large-scale technology development: for example, the amount of biomass with CCS in SSP1 is one third of that in SSP5. The reason why socio-economic trends matter so much is that they both affect the CO 2 emissions in counterfactual scenarios as well as the mitigation capacity ( [[#Riahi--2017|Riahi et al. 2017]] ; [[#Rogelj--2018|Rogelj et al. 2018]] b). Economic growth assumptions are the most important determinant of scenario emissions ( [[#Marangoni--2017|Marangoni et al. 2017]] ). Degrowth and post-growth scenarios have been suggested as valuable alternatives to be considered ( [[#Hickel--2021|Hickel et al. 2021]] ; [[#Keyßer--2021|Keyßer and Lenzen 2021]] ), though substantial challenges remain regarding political feasibility ( [[#Keyßer--2021|Keyßer and Lenzen 2021]] ).

The type of policy instrument assumed to drive the decarbonisation process also plays a vital role for determining feasibility. The majority of scenarios exploring climate stabilisation pathways in the past have focused on uniform carbon pricing as the most efficient instrument to regulate emissions. However, carbon taxation raises political challenges ( [[#Beiser-McGrath--2019|Beiser-McGrath and Bernauer 2019]] ) (Chapters 13 and 14). Carbon pricing will transfer economic surplus from consumers and producers to the government. Losses for producers will be highly concentrated in those industries possessing fixed or durable assets with ‘high asset specificity’ ( [[#Murphy--2002|Murphy 2002]] ; [[#Dolphin--2020|Dolphin et al. 2020]] ). These sectors have opposed climate jurisdictions ( [[#Jenkins--2014|Jenkins 2014]] ). Citizens are sensitive to rising energy prices, though revenue recycling can be used to increase support ( [[#Carattini--2019|Carattini et al. 2019]] ). A recent model comparison project confirms findings from the extant literature: using revenues to reduce pre-existing capital or, to a lesser extent, labour taxes, reduces policy costs and eases distributional concerns ( [[#Barron--2018|Barron et al. 2018]] ; [[#Mcfarland--2018|Mcfarland et al. 2018]] ).

Nonetheless, winning support will require a mix of policies which go beyond carbon pricing, and include subsidies, mandates and feebates ( [[#Jenkins--2014|Jenkins 2014]] ; [[#Rozenberg--2018|Rozenberg et al. 2018]] ). More recent scenarios take into account a more comprehensive range of policies and regional heterogeneity in the near to medium term ( [[#Roelfsema--2020|Roelfsema et al. 2020]] ). Regulatory policies complementing carbon prices could reduce the implementation challenges by increasing short-term emission reduction, though they could eventually reduce economic efficiency ( [[#Bertram--2015b|Bertram et al. 2015b]] ; [[#Kriegler--2018a|Kriegler et al. 2018a]] ). Innovation policies such as subsidies to R&amp;D have been shown to be desirable due to innovation market failures, and also address the dynamic nature of political feasibility ( [[#Bosetti--2011|Bosetti et al. 2011]] ).

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