Editing IPCC:AR6/WGIII/Chapter-5 (section)

=== 5.5.3 Feasible Rate of Change ===

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Transitional change is usually slow in the first and second transition phases, because experimentation, social and technological learning, and stabilisation processes take a long time, often decades, and remain restricted to small niches ( ''high confidence'' ) ( [[#Wilson--2012|Wilson 2012]] ; [[#Bento--2013|Bento 2013]] ; [[#Bento--2018b|Bento et al. 2018b]] ). Transitional change accelerates in the third phase, as radical innovations diffuse from initial niches into mainstream markets, propelled by the self-reinforcing mechanisms discussed above. The rate of adoption (diffusion) of new practices, processes, artefacts, and behaviours is determined by a wide range of factors at the macro- and micro-scales, which have been identified by several decades of diffusion research in multiple disciplines ( [[#Mansfield--1968|Mansfield 1968]] ; Martino et al.1978; [[#Davis--1979|Davis 1979]] ; [[#Mahajan--1990|Mahajan et al. 1990]] ; [[#Ausubel--1991|Ausubel 1991]] ; [[#Grubler--1991|Grubler 1991]] ; [[#Feder--1993|Feder and Umali 1993]] ; [[#Bayus--1994|Bayus 1994]] ; [[#Comin--2003|Comin and Hobijn 2003]] ; [[#Rogers--2003|Rogers 2003]] ; [[#Van%20den%20Bulte--2004|Van den Bulte and Stremersch 2004]] ; [[#Meade--2006|Meade and Islam 2006]] ; [[#Peres--2010|Peres et al. 2010]] ).

Diffusion rates are determined by two broad categories of variables: those intrinsic to the technology, product or practice under consideration (typically performance, costs, benefits), and those intrinsic to the adoption environment (e.g., socio-economic and market characteristics).

Despite differences, the literature offers three robust conclusions on acceleration ( ''high evidence, high agreement'' ): First, size matters. Acceleration of transitions is more difficult for social, economic, or technological systems of larger size (in terms of number of users, financial investments, infrastructure, powerful industries) ( [[#Wilson--2009|Wilson 2009]] ; [[#Wilson--2012|Wilson 2012]] ). Size also matters at the level of the systems component involved in a transition. Components with smaller unit-scale (‘granular’ and thus relatively cheap), such as light bulbs or household appliances, turn over much faster (often within a decade) than large-scale, capital-intensive lumpy technologies and infrastructures (such as transport systems) where rates of change typically involve several decades, even up to a century ( [[#Grubler--1991|Grubler 1991]] ; [[#Leibowicz--2018|Leibowicz 2018]] ). Also, the creation of entirely new systems (diffusion) takes longer time than replacements of existing technologies or practices (substitution) (Grübler et al. 1999); and late adopters tend to adopt faster than early pioneers ( [[#Wilson--2012|Wilson 2012]] ; [[#Grubler--1996|Grubler 1996]] ).

Arguments about scale in the energy system date back at least to the 1970s when Schumacher, Lovins and others argued the case for smaller-scale, distributed technologies ( [[#Schumacher--1974|Schumacher 1974]] ; [[#Lovins--1976|Lovins 1976]] ; [[#Lovins--1979|Lovins 1979]] ). In ''Small is Profitable'' Lovins and colleagues evidenced over 200 reasons why decentralised energy resources, from distributed generation to end-use efficiency, made good business sense in addition to their social, human-centred benefits ( [[#Lovins--2003|Lovins et al. 2003]] ). More recent advances in digital, solar and energy storage technologies have renewed technical and economic arguments in favour of adopting decentralised approaches to decarbonisation ( [[#Cook--2016|Cook et al. 2016]] ; [[#Jain--2017|Jain et al. 2017]] ; [[#Lovins--2018|Lovins et al. 2018]] ). Smaller-scale technologies from microprocessors to solar panels show dramatically faster cost and performance improvement trajectories than large-scale energy supply facilities ( [[#Trancik--2014|Trancik 2014]] ; [[#Sweerts--2020|Sweerts et al. 2020]] , Creutzig et al. 2021) (Figure 5.15). Analysing the performance of over 80 energy technologies historically, [[#Wilson--2020a|Wilson et al. (2020a)]] found that smaller scale, more ‘granular’ technologies are empirically associated with faster diffusion, lower investment risk, faster learning, more opportunities to escape lock-in, more equitable access, more job creation, and higher social returns on innovation investment. These advantages of more granular technologies are consistent with accelerated low-carbon transformation ( [[#Wilson--2020a|Wilson et al. 2020a]] ).

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[[File:9f3bc7bf1c4624740befea2f727f26de IPCC_AR6_WGIII_Figure_5_15.png]]

'''Figure 5.15 | Demand technologies show high learning rates.''' Learning from small-scale granular technologies outperforms learning from larger supply-side technologies. Line is linear fit of log unit size to learning rate for all 41 technologies plotted. Source: Creutzig et al. (2021); based on [[#Sweerts--2020|Sweerts et al. (2020)]] .

Second, complexity matters, which is often related to unit scale ( [[#Ma--2008|Ma et al. 2008]] ). Acceleration is more difficult for options with higher degrees of complexity (e.g., carbon capture, transport and storage, or a hydrogen economy) representing higher technological and investment risks that can slow down change. Options with lower complexity are easier to accelerate because they involve less experimentation and debugging and require less adoption efforts and risk.

Third, agency, structure and meaning can accelerate transitions. The creation and mobilisation of actor coalitions is widely seen as important for acceleration, especially if these involve actors with technical skills, financial resources and political capital ( [[#Kern--2016|Kern and Rogge 2016]] ; [[#Hess--2019b|Hess 2019b]] ; [[#Roberts--2019|Roberts and Geels 2019]] ). Changes in policies and institutions can also accelerate transitions, especially if these create stable and attractive financial incentives or introduce technology-forcing standards or regulations ( [[#Brand--2013|Brand et al. 2013]] ; [[#Kester--2018|Kester et al. 2018]] ; [[#Roberts--2018|Roberts et al. 2018]] ). Changes in meanings and cultural norms can also accelerate transitions, especially when they affect consumer practices, enhance social acceptance, and create legitimacy for stronger policy support ( [[#Lounsbury--2001|Lounsbury and Glynn 2001]] ; [[#Rogers--2003|Rogers 2003]] ; [[#Buschmann--2019|Buschmann and Oels 2019]] ). Adoption of most advanced practices can support leapfrogging of polluting technologies (Box 5.9).

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