Editing IPCC:AR6/WGIII/Chapter-16 (section)

=== Cross-Chapter Box 12 | Transition Dynamics ===

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'''Authors:''' Anthony Patt (Switzerland), Heleen de Coninck (the Netherlands), Xuemei Bai (Australia), Paolo Bertoldi (Italy), Sarah Burch (Canada), Clara Caiafa (Brazil/the Netherlands), Felix Creutzig (Germany), Renée van Diemen (the Netherlands/United Kingdom), Frank Geels (United Kingdom/the Netherlands), Michael Grubb (United Kingdom), María Josefina Figueroa Meza (Venezuela/Denmark), Şiir Kilkiş (Turkey), Jonathan Köhler (Germany), Catherine Mitchell (United Kingdom), Lars J. Nilsson (Sweden), Patricia Perkins (Canada), Yamina Saheb (France/Algeria), Harald Winkler (South Africa)

'''Introduction'''

Numerous studies suggest that transformational changes would be required in many areas of society if climate change is to be limited to 2°C warming or less. Many of these involve shifts to low-carbon technologies, such as renewable energy, which typically involve changes in associated regulatory and social systems; others more explicitly concern behavioural shifts, such as towards plant-based diets or cleaner cooking fuels, or, at the broadest level, a shift in development pathways. [[IPCC:Wg3:Chapter:Chapter-1|Chapter 1]] establishes an analytic framework focusing on transitions, which chapters 5, 13, 14, 15 and 16 further develop. In this Cross-Chapter Box, we provide a complementary overview of the dynamics of different kinds of transformational changes for climate mitigation and sustainable development. We first focus on insights from socio-technical transitions approaches, and then expand to broader system transitions.

'''Dynamics of socio-techni''' '''cal transitions'''

A large volume of literature documents the processes associated with transformational changes in technology and the social systems associated with their production and use ( [[#Geels--2019|Geels 2019]] ; [[#Köhler--2019|Köhler et al. 2019]] ). Transformational technological change typically goes hand in hand with shifts in knowledge, behaviour, institutions, and markets ( [[#Geels--2010|Geels and Schot 2010]] ; [[#Markard--2012|Markard et al. 2012]] ); stickiness in these factors often keeps society ‘locked in’ to those technologies already in widespread use, rather than allowing a shift to new ones – even those that offer benefits ( [[#David--1985|David 1985]] ; [[#Arthur--1994|Arthur 1994]] ). Exceptions often follow consistent patterns ( [[#Geels--2002|Geels 2002]] ; [[#Unruh--2002|Unruh 2002]] ); since AR5 a growing number of scholars have suggested using these insights to design more effective climate policies and actions ( [[#Geels--2017|Geels et al. 2017]] ). [[IPCC:Wg3:Chapter:Chapter-1|Chapter 1]] ( [[IPCC:Wg3:Chapter:Chapter-1#1.7|Section 1.7]] and Figure 1.6) represents technology diffusion and a corresponding shift in policy emphasis as a continuous process; it is also useful to identify a sequence of distinct stages that typically occur, associating each stage with a distinct set of processes, challenges, and effective policies ( [[#Patt--2018|Patt and Lilliestam 2018]] ; [[#Victor--2019|Victor et al. 2019]] ). Consistent with elsewhere in this report ( [[IPCC:Wg3:Chapter:Chapter-5#5.5.2|Section 5.5.2]] and Supplementary Material 5.5.3 in Chapter 5, and [[#16.3|Section 16.3]] in Chapter 16), Cross-Chapter Box 12 Figure 1 elaborates on four distinct stages: it portrays these as occurring in a cycle, recognising that even transformative technologies will eventually be replaced with newer ones.

The ''emergence'' stage is marked by experimentation, innovation in the laboratory, and demonstration in the field, to produce technologies and system architectures ( [[#Geels--2005|Geels 2005]] ). By its very nature, experimentation includes both successes and failures, and implies high risks. Because of these risks, especially in the case of fundamentally new technologies, government funding for research, development and demonstration (RD&amp;D) projects is crucial to sustaining development ( [[#Mazzucato--2015b|Mazzucato 2015b]] ).

The second stage is ''early adoption'' , during which successful technologies jump from the laboratory to limited commercial application ( [[#Pearson--2012|Pearson and Foxon 2012]] ). Reaching this stage is often described as crossing the ‘Valley of Death’, because the cost/performance ratio for these new market entrants is too low for them to appear viable to investors ( [[#Murphy--2003|Murphy and Edwards 2003]] ). A key process in the early adoption phase is induced innovation, a result of incremental improvements in both design and production processes, and of mass-production of a growing share of key components ( [[#Nemet--2006|Nemet 2006]] ; [[#Grubb--2021|Grubb et al. 2021]] ). There is diversity across classes of technologies, and learning tends to occur faster for technologies that are modular ( [[#Wilson--2020|Wilson et al. 2020]] ) – such as photovoltaics – and slower for those that require site- or context-specific engineering, such as in the shift to low-carbon materials production ( [[#Malhotra--2020|Malhotra and Schmidt 2020]] ). Public policies that create a secure return on investment for project developers can lead to learning associated with industry expansion (Chapter 16, Figure 16.1); typically these are economically and politically viable when they promote growth within a market niche, causing little disruption to the mainstream market ( [[#Roberts--2018|Roberts et al. 2018]] ). Direct support mechanisms are effective, including cross-subsidies (such as feed-in tariffs) and market quotas (such as renewable portfolio standards) ( [[#Geels--2017|Geels et al. 2017]] b; [[#Patt--2018|Patt and Lilliestam 2018]] ; and [[IPCC:Wg3:Chapter:Chapter-9|Chapter 9]] for assessment of early adoption policies in the building sector). The value of these policies is less in their immediate emissions reductions, but more in generating the conditions for self-sustaining transformational change to take place as technologies later move from niche to mainstream ( [[#Hanna--2021|Hanna and Victor 2021]] ).

The third stage, ''diffusion'' , is where niche technologies become mainstream, with accelerating diffusion rates (Sections 1.7 and 16.4), and is marked by changes to the socio-technical ‘regime’, including infrastructure networks, value chains, user practices, and institutions. This stage is often the most visible and turbulent, because more widespread adoption of a new technology gives rise to structural changes in institutions and actors’ behaviour (e.g., increased adoption of smartphones to new payment systems and social media), and because when incumbent market actors become threatened, they often contest policies promoting the new technologies ( [[#Köhler--2019|Köhler et al. 2019]] ). In the diffusion stage, policy emphasis is shifted from financial support during the early adoption stage, towards supporting regime-level factors needed to sustain, or cope with, rapid and widespread diffusion ( [[#Markard--2018|Markard 2018]] ). These factors and policies are context specific. For example, [[#Patt--2019|Patt et al. (2019)]] document that the policies needed to expand residential charging networks for electric vehicles depend on the local structure of the housing market.

The fourth stage is ''stabilisation'' , in which the new technologies, systems, and behaviours are both standardised and insulated from rebound effects and backsliding ( [[#Andersen--2020|Andersen and Gulbrandsen 2020]] ). Sectoral bans on further investment in high-carbon technologies may become politically feasible at this point ( [[#Breetz--2018|Breetz et al. 2018]] ; [[#Economidou--2020|Economidou et al. 2020]] ). The decline of previously dominant products or industries can lead to calls for policymakers to help those negatively affected, enabling a just transition ( [[#McCauley--2018|McCauley and Heffron 2018]] ; [[#Newell--2020|Newell and Simms 2020]] ). Political opposition to the system reconfiguration that comes with integration and stabilisation can also be overcome by offering incumbent actors an attractive exit strategy ( [[#de%20Gooyert--2016|de Gooyert et al. 2016]] ).

Because different sectors are at different stages of low-carbon transitions, and because the barriers that policies need to address are stage- and often context-specific, effective policies stimulating socio-technical transitions operate primarily at the sectoral level ( [[#Victor--2019|Victor et al. 2019]] ). This is particularly the case during early adoption, where economic barriers predominate; during diffusion, policies that address regime-level factors often need to deal with cross-sectoral linkages and coupling, such as those between power generation, transportation, and heating ( [[#Patt--2015|Patt 2015]] ; [[#Bloess--2019|Bloess 2019]] ; [[#Fridgen--2020|Fridgen et al. 2020]] ). The entire cycle can take multiple decades. However, later stages can go faster by building on the earlier stages that have taken place elsewhere. For example, early RD&amp;D into wind energy took place primarily in Denmark, was followed by early adoption in Denmark, Germany, and Spain, before other countries, including the USA, India, and China, leapfrogged directly to the diffusion stage ( [[#Chaudhary--2015|Chaudhary et al. 2015]] ; [[#Dai--2015|Dai and Xue 2015]] ; [[#Lacal-Arántegui--2019|Lacal-Arántegui 2019]] ). A similar pattern played out for solar power ( [[#Nemet--2019|Nemet 2019]] ). International cooperation, geared towards technology transfer, capacity and institution-building, and finance, can help ensure that developing countries leapfrog to low-carbon technologies that have undergone commercialisation elsewhere ( [[#Adenle--2015|Adenle et al. 2015]] ; [[#Fankhauser--2018|Fankhauser and Jotzo 2018]] ) (see also Chapter 5, Box 5.9, Chapter 15, [[IPCC:Wg3:Chapter:Chapter-15#15.5|Section 15.5]] , and [[#16.5|Section 16.5]] in this chapter).

This report contains numerous examples of the positive feedbacks in the centre of Cross-Chapter Box 12, Figure 1, predominantly arising during the early adoption and diffusion stages, and leading to rapid or unexpected acceleration of change. For example, public acceptance of meat alternatives leads to firms improving the products, increasing political and economic feedbacks ( [[IPCC:Wg3:Chapter:Chapter-5#5.4|Section 5.4]] and Box 5.5). Declining costs in solar and wind cause new investment in the power-generation sector being dominated by those technologies, leading to increased political support and further cost reductions (Chapter 6). In buildings (Chapter 9) and personal mobility (Chapter 10), low-carbon heating systems and electric vehicles are gaining public acceptance, leading to improved infrastructure and human resources, more employment in those sectors, and behavioural contagion. Some have argued that technologies cross societal tipping points on account of these feedbacks ( [[#Obama--2017|Obama 2017]] ; [[#Sharpe--2021|Sharpe and Lenton 2021]] ).

'''Dynamics between enabling conditions for syst''' '''em transitions'''

[[#Abson--2017|Abson et al. (2017)]] argue that it is possible to make use of ‘leverage points’ inherent in system dynamics in order to accelerate sustainability transitions. [[#Otto--2020|Otto et al. (2020)]] argue that interventions geared towards the social factors driving change can ‘activate contagious processes’ leading to the transformative changes required for climate mitigation. These self-reinforcing dynamics involve the interaction of enabling conditions, including public policy and governance, institutional and technological innovation capacity, behaviour change, and finance. For example, [[#Mercure--2018|Mercure et al. (2018)]] simulated financial flows into fossil-fuel extraction, and showed how investors taking into account transition risk in combination with technological innovation would lead to the enhancement of investments in low-carbon assets and further enhanced innovation. As another example, behaviour, lifestyle, and policy can also initiate demand-side transitions ( [[#Tziva--2020|Tziva et al. 2020]] ) (Chapter 5), such as with food systems ( [[#Rust--2020|Rust et al. 2020]] ) ( [[IPCC:Wg3:Chapter:Chapter-7#7.4.5|Section 7.4.5]] ), and can contribute to both resilience and carbon storage ( [[#Sendzimir--2011|Sendzimir et al. 2011]] ) (Box 16.5).

In the urban context, the concept of sustainability experiments has been used to examine innovative policies and practices adopted by cities that have significant impact on transition towards low-carbon and sustainable futures ( [[#Bai--2010|Bai et al. 2010]] ; [[#Castán%20Broto--2013|Castán Broto and Bulkeley 2013]] ). Individual innovative practices can potentially be upscaled to achieve low-carbon transition in cities ( [[#Peng--2018|Peng and Bai 2018]] ), leading to a process of broadening and scaling innovative practices in other cities ( [[#Peng--2019|Peng et al. 2019]] ). Such sustainability experiments give rise to new actor networks, which in some cases may accelerate change, and in others may lead to conflict ( [[#Bulkeley--2014|Bulkeley et al. 2014]] ). As in the diffusion phase in Cross-Chapter Box 12, Figure 1, contextual factors play a strong role. Examining historical transitions to cycling across European cities, [[#Oldenziel--2016|Oldenziel et al. (2016)]] found that contextual factors, including specific configurations of actors, can lead to very different outcomes. [[#Kraus--2021|Kraus and Koch (2021)]] found a short-term social shock – such as the COVID-19 crisis – to lead to differential increases in cycling behaviour, contingent on other enabling conditions.

'''Linking system dynamics to development pathways and broader''' '''societal goals'''

Transition dynamics insights can be broadened to shifting development pathways. Development paths are characterised by particular sets of interlinking regime rules and behaviours, including inertia and cascading effects over time, and are reinforced at multiple levels, with varied capacities and constraints on local agency occurring at each level ( [[#Burch--2014|Burch et al. 2014]] ) (Cross-Chapter Box 5 in Chapter 4). This is also observed by [[#Schot--2018|Schot and Kanger (2018)]] , who identify a needed change in a ‘meta-regime’, crossing sectoral lines in linking value chains or infrastructure and overall development objectives. In the context of the UN climate change regime, international cooperation can bring together such best practices and lessons learnt ( [[#Adenle--2015|Adenle et al. 2015]] ; [[#Pandey--2021|Pandey et al. 2021]] ). This is especially relevant for developing countries, which often depend on technologies and financial resources from abroad, witnessing their pace and direction influenced by transnational actors ( [[#Marquardt--2016|Marquardt et al. 2016]] ; [[#Bhamidipati--2019|Bhamidipati et al. 2019]] ), and benefitting little in terms of participating in high value-added activities ( [[#Whittaker--2020|Whittaker et al. 2020]] ).

System transitions differ according to context, such as across industrialised and developing countries ( [[#Ramos-Mejía--2018|Ramos-Mejía et al. 2018]] ), and within countries. Lower levels of social capital and trust negatively impact niche commercialisation ( [[#Lepoutre--2018|Lepoutre and Oguntoye 2018]] ). In contexts of poverty and inequality, stakeholders’ – including users’ – capabilities for meaningful participation are limited, and transition outcomes can end up marginalising or further excluding social groups ( [[#Osongo--2017|Osongo and Schot 2017]] ; [[#Hansen--2018|Hansen et al. 2018]] ). Many studies of transitions in developing countries make note of the importance of innovation in the informal sector ( [[#Charmes--2016|Charmes 2016]] ) (Box 5.10 in Chapter 5). Facilitating informal sector access to renewable energy sources, safe and sustainable buildings, and finance can advance low-carbon transitions ( [[#McCauley--2019|McCauley et al. 2019]] ; [[#Masuku--2021|Masuku and Nzewi 2021]] ). On the contrary, disregarding its importance can result in misleading or ineffective innovation and climate strategies ( [[#Maharajh--2010|Maharajh and Kraemer-Mbula 2010]] ; Mazhar and Ummad 2014; [[#de%20Beer--2016|de Beer et al. 2016]] ; [[#Masuku--2021|Masuku and Nzewi 2021]] ).

Policies shifting innovation in climate-compatible directions can also reinforce other development benefits, for instance better health, increased energy access, poverty alleviation and economic competitiveness ( [[#Deng--2018|Deng et al. 2018]] ; [[#IPCC--2018a|IPCC 2018a]] ; [[#Karlsson--2020|Karlsson et al. 2020]] ). Development benefits, in turn, can create feedback effects that sustain public support for subsequent policies, and hence help to secure effective long-term climate mitigation ( [[#Geels--2014|Geels 2014]] ; [[#Meckling--2015|Meckling et al. 2015]] ; [[#Schmidt--2017|Schmidt and Sewerin 2017]] ; [[#Breetz--2018|Breetz et al. 2018]] ), increasing legitimacy of environmental sustainability actions ( [[#Hansen--2018|Hansen et al. 2018]] ; [[#Herslund--2018|Herslund et al. 2018]] ; [[#van%20Welie--2018|van Welie and Romijn 2018]] ) and addressing negative socio-economic impacts ( [[#Deng--2018|Deng et al. 2018]] ; [[#McCauley--2018|McCauley and Heffron 2018]] ; [[#Eisenberg--2019|Eisenberg 2019]] ; [[#Henry--2020|Henry et al. 2020]] ).

'''Summary and ga''' '''ps in knowledge'''

Strategies to accelerate climate mitigation can be most effective at accelerating and achieving transformative change when they are synchronised with transition processes in systems. They address technological stage characteristics, take advantage of high-leverage intervention points, and respond to societal dynamics ( [[#Abson--2017|Abson et al. 2017]] ; [[#Geels--2017|Geels et al. 2017]] ; [[#Köhler--2019|Köhler et al. 2019]] ). Gaps in knowledge remain on how to tailor policy mixes, the interaction of enabling conditions, the generalisability of socio-technical transition insights to other types of systems, and how to harness these insights to better shift development pathways.

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