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

== 5.5 An Integrative View on Transitioning ==

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=== 5.5.1 Demand-side Transitions as Multi-dimensional Processes ===

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Several integrative frameworks including social practice theory ( [[#Røpke--2009|Røpke 2009]] ; [[#Shove--2014|Shove and Walker 2014]] ), the energy cultures framework ( [[#Stephenson--2015|Stephenson et al. 2015]] ; [[#Jürisoo--2019|Jürisoo et al. 2019]] ) and socio-technical transitions theory ( [[#McMeekin--2012|McMeekin and Southerton 2012]] ; [[#Geels--2017|Geels et al. 2017]] ) conceptualise demand-side transitions as multi-dimensional and interacting processes ( ''high evidence, high agreement'' ). Social practice theory emphasises interactions between artefacts, competences, and cultural meanings ( [[#Røpke--2009|Røpke 2009]] ; [[#Shove--2014|Shove and Walker 2014]] ). The energy cultures framework highlights feedbacks between materials, norms, and behavioural practices ( [[#Stephenson--2015|Stephenson et al. 2015]] ; [[#Jürisoo--2019|Jürisoo et al. 2019]] ). Socio-technical transitions theory addresses interactions between technologies, user practices, cultural meanings, business, infrastructures, and public policies ( [[#McMeekin--2012|McMeekin and Southerton 2012]] ; [[#Geels--2017|Geels et al. 2017]] ) and can thus accommodate the five drivers of change and stability discussed in [[#5.4|Section 5.4]] .

[[#5.4|Section 5.4]] shows with ''high evidence'' and ''high agreement'' that the relative influence of different drivers varies between demand-side solutions. The deployment of ‘Improve’ options like LEDs and clean cookstoves mostly involves technological change, adoption by consumers who integrate new technologies in their daily life practices ( [[#Smith--1993|Smith et al. 1993]] ; [[#Sanderson--2014|Sanderson and Simons 2014]] ; [[#Franceschini--2016|Franceschini and Alkemade 2016]] ), and some policy change. Changes in meanings are less pertinent for those ‘Improve’ options that are primarily about technological substitution. Other ‘Improve’ options, like clean cookstoves, involve both technological substitution and changes in cultural meanings and traditions.

Deployment of ‘Shift’ options like enhanced public transport involves substantial behavioural change and transitions to new or expanded provisioning systems, which may include new technologies (buses, trams), infrastructures (light rail, dedicated bus lanes), institutions (operational licences, performance contracts), financial arrangements, and new organisations (with particular responsibilities and oversight) ( ''high evidence, high agreement'' ) ( [[#Deng--2011|Deng and Nelson 2011]] ; [[#Turnheim--2019|Turnheim and Geels 2019]] ). Changes in cultural meanings can facilitate ‘Shift’ options. Shifts towards low-meat diets, for instance, are motivated by costs and by beliefs about the undesirability of meat that relate more to issues like health, nutrition and animal welfare than climate change ( [[#De%20Boer--2014|De Boer et al. 2014]] ; [[#Mylan--2018|Mylan 2018]] ).

‘Avoid’ options that reduce service levels (e.g., sufficiency or downshifting) imply very substantial behavioural and cultural changes that may not resonate with mainstream consumers ( [[#Dubois--2019|Dubois et al. 2019]] ). Other ‘Avoid’ options like teleworking also require changes in cultural meanings and beliefs (about the importance of supervision, coaching, social contacts, or office politics), as well as changes in behaviour, institutions, business, and technology (including good internet connections and office space at home). Because these interconnected changes were not widespread, teleworking remained stuck in small niches and did not diffuse widely before the COVID-19 crisis ( [[#Hynes--2014|Hynes 2014]] ; [[#Hynes--2016|Hynes 2016]] ; [[#Belzunegui-Eraso--2020|Belzunegui-Eraso and Erro-Garcés 2020]] ; [[#Stiles--2020|Stiles 2020]] ). As preferences change, new infrastructures and social settings can also elicit new desires associated with emerging low-energy demand service provisioning systems ( [[#5.4.5|Section 5.4.5]] ).

Demand-side transitions involve interactions between radical social or technical innovations (such as the Avoid-Shift-Improve options discussed in [[#5.3|Section 5.3]] ) and existing socio-technical systems, energy cultures, and social practices ( ''high evidence'' , ''high agreement'' ) ( [[#Stephenson--2015|Stephenson et al. 2015]] ; [[#Geels--2017|Geels et al. 2017]] ). Radical innovations such as teleworking, plant-based burgers, car sharing, vegetarianism, or electric vehicles initially emerge in small, peripheral niches ( [[#Kemp--1998|Kemp et al. 1998]] ; [[#Schot--2008|Schot and Geels 2008]] ), constituted by R&amp;D projects, technological demonstration projects ( [[#Borghei--2016|Borghei and Magnusson 2016]] ; [[#Rosenbloom--2018b|Rosenbloom et al. 2018b]] ), local community initiatives or grassroots projects by environmental activists (Hargreaves et al. 2013a; [[#Hossain--2016|Hossain 2016]] ). Such niches offer protection from mainstream selection pressures and nurture the development of radical innovations ( [[#Smith--2012|Smith and Raven 2012]] ). Many low-carbon niche innovations, such as those described in [[#5.3|Section 5.3]] , face uphill struggles against existing socio-technical systems, energy cultures, and social practices that are stabilised by multiple lock-in mechanisms ( ''high evidence'' , ''high agreement'' ) ( [[#Klitkou--2015|Klitkou et al. 2015]] ; [[#Seto--2016|Seto et al. 2016]] ; [[#Clausen--2017|Clausen et al. 2017]] ; [[#Ivanova--2018|Ivanova et al. 2018]] ). Demand-side transitions therefore do not happen easily and involve interacting processes and struggles on the behavioural, socio-cultural, institutional, business and technological dimensions ( [[#Nikas--2020|Nikas et al. 2020]] ) ( [[#5.4|Section 5.4]] ).

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=== 5.5.2 Phases in Transitions ===

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Transitions often take several decades, unfolding through several phases. Although there is variability across innovations, sectors, and countries, the transitions literature distinguishes four phases, characterised by generic core processes and challenges: (i) emergence, (ii) early adaptation, (i) diffusion, (iv) stabilisation ( ''high confidence'' ) ( [[#Rotmans--2001|Rotmans et al. 2001]] ; [[#Markard--2012|Markard et al. 2012]] ; [[#Geels--2017|Geels et al. 2017]] ) (Cross-Chapter Box 12 in Chapter 16). These four phases do not imply that transitions are linear, teleological processes, because set-backs or reversals may occur as a result of learning processes, conflicts, or changing coalitions ( ''very high confidence'' ) ( [[#Geels--2006|Geels and Raven 2006]] ; [[#Messner--2015|Messner 2015]] ; [[#Davidescu--2018|Davidescu et al. 2018]] ). There is also no guarantee that technological, social, or business model innovations progress beyond the first phase.

In the first phase, radical innovations emerge in peripheral niches, where researchers, inventors, social movement organisations or community activists dedicate time and effort to their development ( ''high confidence'' ) ( [[#Kemp--1998|Kemp et al. 1998]] ; [[#Schot--2008|Schot and Geels 2008]] ). Radical social, technical and business model innovations are initially characterised by many uncertainties about technical performance, consumer interest, institutions and cultural meanings. Learning processes are therefore essential and can be stimulated through R&amp;D, demonstration projects, local community initiatives or grassroots projects ( [[#Borghei--2016|Borghei and Magnusson 2016]] ; [[#Hossain--2016|Hossain 2016]] ; [[#Rosenbloom--2018b|Rosenbloom et al. 2018b]] ; [[#van%20Mierlo--2020|van Mierlo and Beers 2020]] ). Typical challenges are fragmentation and high rates of project failure ( [[#den%20Hartog--2018|den Hartog et al. 2018]] ; [[#Dana--2021|Dana et al. 2021]] ), limited funding ( [[#Auerswald--2003|Auerswald and Branscomb 2003]] ), limited consumer interest, and socio-cultural acceptance problems due to being perceived as strange or unfamiliar ( [[#Lounsbury--2001|Lounsbury and Glynn 2001]] ).

In the second phase, social or technical innovations are appropriated or purchased by early adopters, which increases visibility and may provide a small but steady flow of financial resources ( ''high evidence'' , ''high agreement'' ) ( [[#Zimmerman--2002|Zimmerman and Zeitz 2002]] ; [[#Dewald--2011|Dewald and Truffer 2011]] ). Learning processes, knowledge sharing and codification activities help stabilise the innovation, leading to best practice guidelines, standards, and formalised knowledge ( ''high evidence'' , ''high agreement'' ) ( [[#Raven--2008|Raven et al. 2008]] ; [[#Borghei--2018|Borghei and Magnusson 2018]] ). User innovation may lead to the articulation of new routines and social practices, often in tandem with the integration of new technologies into people’s daily lives ( [[#Nielsen--2016|Nielsen et al. 2016]] ; [[#Schot--2016|Schot et al. 2016]] ). Radical innovations remain confined to niches in the second phase because adoption is limited to small, dedicated groups ( [[#Schot--2016|Schot et al. 2016]] ), innovations are expensive or do not appeal to wider groups, or because complementary infrastructure are missing ( [[#Markard--2016|Markard and Hoffmann 2016]] ).

In the third phase, radical innovations diffuse into wider communities and mainstream markets. Typical drivers are performance improvements, cost reductions, widespread consumer interest, investments in infrastructure and complementary technologies, institutional support and strong cultural appeal ( ''high evidence'' , ''high agreement'' ) ( [[#Wilson--2012|Wilson 2012]] ; [[#Markard--2016|Markard and Hoffmann 2016]] ; [[#Malone--2017|Malone et al. 2017]] ; [[#Raven--2017|Raven et al. 2017]] ; [[#Kanger--2019|Kanger et al. 2019]] ). The latter may be related to wider cultural shifts such as increased public attention to climate change and new framings like ‘climate emergency’ which gained traction before the Covid-19 pandemic ( [[#Bouman--2020b|Bouman et al. 2020b]] ). These concerns may not last, however, since public attention typically follows cycles ( [[#Downs--1972|Downs 1972]] ; [[#Djerf-Pierre--2012|Djerf-Pierre 2012]] ).

This phase often involves multiple struggles: economic competition between low-carbon innovations and existing technologies and practices, business struggles between incumbents and new entrants ( [[#Hockerts--2010|Hockerts and Wüstenhagen 2010]] ), cultural and framing struggles in public opinion arenas ( [[#Kammermann--2018|Kammermann and Dermont 2018]] ; [[#Rosenbloom--2018|Rosenbloom 2018]] ; [[#Hess--2019a|Hess 2019a]] ), and political struggles over adjustments in policies and institutions, which shape markets and innovations ( [[#Meadowcroft--2011|Meadowcroft 2011]] ; [[#Roberts--2019|Roberts and Geels 2019]] ). The lock-in mechanisms of existing practices and systems tend to weaken in the third phase, either because competing innovations erode their economic viability, cultural legitimacy or institutional support ( [[#Turnheim--2012|Turnheim and Geels 2012]] ; [[#Roberts--2017|Roberts 2017]] ; [[#Kuokkanen--2018|Kuokkanen et al. 2018]] ; [[#Leipprand--2018|Leipprand and Flachsland 2018]] ) or because exogenous shocks and pressures disrupt the status quo ( [[#Kungl--2018|Kungl and Geels 2018]] ; [[#Simpson--2019|Simpson 2019]] ).

In the fourth phase, the diffusing innovations replace or substantially reconfigure existing practices and systems, which may lead to the downfall or reorientation of incumbent firms ( [[#Bergek--2013|Bergek et al. 2013]] ; [[#McMeekin--2019|McMeekin et al. 2019]] ). The new system becomes institutionalised and anchored in professional standards, technical capabilities, infrastructures, educational programmes, regulations and institutional logics, user habits, and views of normality, which create new lock-ins ( [[#Galaskiewicz--1985|Galaskiewicz 1985]] ; [[#Shove--2000|Shove and Southerton 2000]] ; [[#Barnes--2018|Barnes et al. 2018]] ).

‘Avoid’, ‘Shift’ and ‘Improve’ options vary with regard to the four transition phases. Incremental ‘Improve’ options, such as energy-efficient appliances or stand-alone insulation measures, are not transitions but upgrades of existing technologies. They have progressed furthest since they build on existing knowledge and do not require wider changes ( [[#Geels--2018|Geels et al. 2018]] ). Some radical ‘Improve’ options, which have a different technological knowledge base, are beginning to diffuse, moving from phase two to three in multiple countries. Examples are electric vehicles, light-emitting diodes (LED), or passive house designs ( [[#Franceschini--2016|Franceschini and Alkemade 2016]] ; [[#Berkeley--2017|Berkeley et al. 2017]] ). Many ‘Shift’ and ‘Avoid/Reduce’ options like heat pumps, district heating, passive house designs, compact cities, less meat initiatives, flight and car use reduction have low momentum in most countries, and are mostly in the first phase of isolated initiatives and projects ( [[#Bergman--2013|Bergman 2013]] ; [[#Morris--2014|Morris et al. 2014]] ; [[#Bows-Larkin--2015|Bows-Larkin 2015]] ; [[#Bush--2016|Bush et al. 2016]] ; [[#Kivimaa--2018|Kivimaa and Martiskainen 2018]] ; [[#Hoolohan--2018|Hoolohan et al. 2018]] ). Structural transitions in Dutch cities, Copenhagen, and more recently Paris, however, demonstrate that transitions towards low-carbon lifestyles, developed around cycling, are possible ( [[#Colville-Andersen--2018|Colville-Andersen 2018]] ). Low-carbon demand-side transitions are often still in early phases ( ''high evidence, high agreement'' ).

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=== 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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=== Box 5.9 | Is Leapfrogging Possible? ===

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The concept of leapfrogging emerged in development economics ( [[#Soete--1985|Soete 1985]] ), energy policy ( [[#Goldemberg--1991|Goldemberg 1991]] ) and environmental regulation ( [[#Perkins--2003|Perkins 2003]] , which provides a first critical review of the concept), and refers to a development strategy that skips traditional and polluting development in favour of the most advanced concepts. For instance, in rural areas without telephone landlines or electricity access (cables), a direct shift to mobile telephony or distributed, locally-sourced energy systems is promoted, or economic development policies for pre-industrial economies forego the traditional initial emphasis on heavy industry industrialisation, instead focusing on services like finance or tourism. Often leapfrogging is enabled by learning and innovation externalities where improved knowledge and technologies become available for late adopters at low costs. The literature highlights many cases of successful leapfrogging but also highlights limitations ( [[#Watson--2011|Watson and Sauter 2011]] ); with example case studies for China ( [[#Gallagher--2006|Gallagher 2006]] ; [[#Chen--2011|Chen and Li-Hua 2011]] ); Mexico ( [[#Gallagher--2007|Gallagher and Zarsky 2007]] ); and Japan and Korea ( [[#Cho--1998|Cho et al. 1998]] ). Increasingly the concept is being integrated into the literature of low-carbon development, including innovation and technology transfer policies ( [[#Pigato--2020|Pigato et al. 2020]] ), highlighting in particular the importance of contextual factors of successful technology transfer and leapfrogging including: domestic absorptive capacity and technological capabilities ( [[#Cirera--2017|Cirera and Maloney 2017]] ); human capital, skills, and relevant technical know-how ( [[#Nelson--1966|Nelson and Phelps 1966]] ); the size of the market ( [[#Keller--2004|Keller 2004]] ); greater openness to trade ( [[#Sachs--1995|Sachs and Warner 1995]] ; [[#Keller--2004|Keller 2004]] ); geographical proximity to investors and financing ( [[#Comin--2012|Comin et al. 2012]] ); environmental regulatory proximity ( [[#Dechezleprêtre--2015|Dechezleprêtre et al. 2015]] ); and stronger protection of intellectual property rights ( [[#Dechezleprêtre--2013|Dechezleprêtre et al. 2013]] ; [[#Dussaux--2017|Dussaux et al. 2017]] ). The existence of a technological potential for leapfrogging therefore needs to be considered within a wider context of social, institutional, and economic factors that influence whether leapfrogging potentials can be realised ( ''high evidence, high agreement'' ).

There are also some contentious topics in the debate on accelerated low-carbon transitions. First, while acceleration is desirable to mitigate climate change, there is a risk that accelerating change too much may short-cut crucial experimentation and social and technological learning in ‘formative phases’ ( [[#Bento--2013|Bento 2013]] ; [[#Bento--2018b|Bento et al. 2018b]] ) and potentially lead to a pre-mature lock-in of solutions that later turn out to have negative impacts ( [[#Cowan--1990|Cowan 1990]] ; [[#Cowan--1991|Cowan 1991]] ) ( ''high evidence, medium agreement'' ).

Second, there is an ongoing debate about the most powerful leverage points and policies for speeding up change in social and technological systems. [[#Farmer--2019|Farmer et al. (2019)]] suggested ‘sensitive intervention points’ for low-carbon transitions, but do not quantify the impacts on transformations. [[#Grubler--2018|Grubler et al. (2018)]] proposed an end-user and efficiency-focused strategy to achieve rapid emission reductions and quantified their scenario with a leading IAM. However, discussion of the policy implications of such a strategy have only just started ( [[#Wilson--2019a|Wilson et al. 2019a]] ), suggesting an important area for future research.

The last contentious issue is if policies can or should substitute for lack of economic or social appeal of change or for technological risks. Many large-scale supply-side climate mitigation options, such as CCS or nuclear power, involve high technological risks, critically depend on a stable carbon price, and are controversial in terms of social and environmental impacts ( [[#Sovacool--2014|Sovacool et al. 2014]] ; [[#Smith--2016|Smith et al. 2016]] ; [[#Wilson--2020a|Wilson et al. 2020a]] ) ( ''high evidence'' , ''medium agreement'' ). There is continuing debate if and how policies could counterbalance these impacts in order to accelerate transitions ( [[#Nordhaus--2019|Nordhaus 2019]] ; [[#Lovins--2015|Lovins 2015]] ). Some demand-side options like large-scale public transport infrastructures such as ‘Hyperloop’ ( [[#Decker--2017|Decker et al. 2017]] ) or concepts such as the Asian Super Grid (maglev fast train coupled with superconducting electricity transmission networks) ( [[#AIGC--2017|AIGC 2017]] ) may face similar challenges, which adds weight and robustness to those demand-side options that are more decentralised, granular in scale, and provide potential tangible consumer benefits besides being low-carbon (like more efficient buildings and appliances, ‘soft’ urban mobility options (walking and cycling), digitalisation, among others ( [[#Grubler--2018|Grubler et al. 2018]] )).

A robust conclusion from this review is that there are no generic acceleration policies that are independent from the nature of what changes, by whom and how. Greater contextualisation and granularity in policy approaches is therefore important to address the challenges of rapid transitions towards zero-carbon systems ( ''high evidence'' , ''high agreement'' ).

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