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

== 5.4 Transition Toward High Well-being and Low-carbon-demand Societies ==

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Demand-side mitigation involves individuals (e.g., consumption choices), culture (e.g., social norms, values), corporate (e.g., investments), institutions (e.g., political agency), and infrastructure change ( ''high evidence, high agreement'' ). These five drivers of human behaviour either contribute to the status quo of a global high-carbon, consumption- and GDP growth-oriented economy or help generate the desired change to a low-carbon energy-services, well-being, and equity-oriented economy ( [[#Jackson--2016|Jackson 2016]] ; [[#Cassiers--2018|Cassiers et al. 2018]] ; [[#Yuana--2020|Yuana et al. 2020]] ; Nielsen et al. 2021) (Figure 5.14). Each driver has novel implications for the design and implementation of demand-side mitigation policies. They show important synergies, making energy demand mitigation a dynamic problem where the packaging and/or sequencing of different policies play a role in their effectiveness, demonstrated in Sections 5.5 and 5.6. The Social Science Primer ( [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I) describes theory and empirical insights about the interplay between individual agency, the social and physical context of demand-side decisions in the form of social roles and norms, infrastructure and technological constraints and affordances, and other formal and informal institutions. Incremental interventions on all five fronts change social practices, affecting simultaneously energy and well-being ( [[#Schot--2018|Schot and Kanger 2018]] ). Transformative change will require coordinated use of all five drivers, as described in Figure 5.14 and, using novel insights about behaviour change for policy design and implementation ( ''high evidence'' , ''high agreement'' ). In particular, socio-economic factors, such as equity, public service quality, electricity access and democracy are found to be highly significant in enabling need satisfaction at low energy use, whereas economic growth beyond moderate incomes and extractive economic activities are observed to be prohibiting factors ( [[#Vogel--2021|Vogel et al. 2021]] ).

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[[File:7d4315f4ed1254796a1386247ec09446 IPCC_AR6_WGIII_Figure_5_14.png]]

'''Figure 5.14 | Role of people, demand-side action and consumption in reversing a planetary trajectory to a warming Earth towards effective climate change mitigation and dignified living standards for all.'''

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<span id="behavioural-drivers"></span>
=== 5.4.1 Behavioural Drivers ===

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Behaviour change by individuals and households requires both ''motivation'' to change and ''capacity'' for change (option availability/knowledge; material/cognitive resources to initiate and maintain change) ( [[#Moser--2010|Moser and Ekstrom 2010]] ; [[#Michie--2011|Michie et al. 2011]] ) and is best seen as part of more encompassing collective action. Motivation for change for collective good comes from economic, legal, and social incentives, and regard for deeper intrinsic value of concern for others over extrinsic values. Capacity for change varies; people in informal settlements or rural areas are incapacitated by socio-political realities and have limited access to new energy-service options.

Motivation and effort required for behaviour change increase from ‘Improve’ to ‘Shift’ to ‘Avoid’ decisions. ‘Improve’ requires changes in personal purchase decisions, ‘Shift’ involves changes in behavioural routines, ‘Avoid’ also involves changes in deeper values or mindsets. People set easy goals for themselves and more difficult ones for others ( [[#Attari--2016|Attari et al. 2016]] ) and underestimate the energy savings of behaviour changes that make a large difference ( [[#Attari--2010|Attari et al. 2010]] ). Most personal actions taken so far have small mitigation potential (recycling, ecodriving), and people refrain from options advocated more recently with high impact (less flying, living car free) ( [[#Dubois--2019|Dubois et al. 2019]] ).

As individuals pursue a broad set of goals and use calculation-, emotion-, and rule-based processes when they make energy decisions, demand-side policies can use a broad range of behavioural tools that complement subsidies, taxes, and regulations ( [[#Chakravarty--2016|Chakravarty and Roy 2016]] ; [[#Mattauch--2016|Mattauch et al. 2016]] ; [[#Niamir--2019|Niamir 2019]] ) ( ''high evidence, high agreement'' ). The provision of targeted information, social advertisements, and influence of trusted in-group members and/role models or admired role models like celebrities can be used to create better climate change knowledge and awareness ( [[#Niamir--2019|Niamir 2019]] ; [[#Niamir--2020b|Niamir et al. 2020b]] ; [[#Niamir--2020c|Niamir et al. 2020c]] ). Behavioural interventions like communicating changes in social norms can accelerate behaviour change by creating tipping points ( [[#Nyborg--2016|Nyborg et al. 2016]] ). When changes in energy-demand decisions (such as switching to a plant-based diet, (Box 5.5)) are motivated by the creation and activation of a social identity consistent with this and other behaviours, positive spillover can accelerate behaviour change ( [[#Truelove--2014|Truelove et al. 2014]] ), both within a domain or across settings, for example from work to home ( [[#Maki--2017|Maki and Rothman 2017]] ).

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<span id="box-5.5-dietary-shifts-in-uk-society-towards-lower-emission-foods"></span>
=== Box 5.5 | Dietary Shifts in UK Society Towards Lower-emission Foods ===

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Meat eating is declining in the UK, alongside a shift from carbon-intensive red meat towards poultry. This is due to the interaction of behavioural, socio-cultural and organisational drivers ( [[#Vinnari--2014|Vinnari and Vinnari 2014]] ). Reduced meat consumption is primarily driven by issues of personal health and animal welfare, instead of climate or environment concerns ( [[#Latvala--2012|Latvala et al. 2012]] ; [[#Dibb--2014|Dibb and Fitzpatrick 2014]] ; [[#Hartmann--2017|Hartmann and Siegrist 2017]] ; [[#Graça--2019|Graça et al. 2019]] ). Social movements have promoted shifts to a vegan diet ( [[#Morris--2014|Morris et al. 2014]] ; [[#Laestadius--2016|Laestadius et al. 2016]] ) yet their impact on actual behaviour is the subject of debate ( [[#Taufik--2019|Taufik et al. 2019]] ; [[#Harguess--2020|Harguess et al. 2020]] ; [[#Sahakian--2020|Sahakian et al. 2020]] ). Companies have expanded new markets in non-meat products ( [[#MINTEL--2019|MINTEL 2019]] ). Both corporate food actors and new entrants offering more innovative ‘meat alternatives’ view consumer preferences as an economic opportunity, and are responding by increasing the availability of meat replacement products. No significant policy change has taken place in the UK to enable dietary shift ( [[#Wellesley--2015|Wellesley and Froggatt 2015]] ); however the Climate Change Committee has recommended dietary shift in the Sixth Carbon Budget ( [[#Climate%20Change%20Committee--2020|Climate Change Committee 2020]] ), involving reduced consumption of high-carbon meat and dairy products by 20% by 2030, with further reductions in later years in order to reach net zero GHG emissions by 2050. Agricultural policies serve to support meat production with large subsidies that lower production cost and effectively increase the meat intensity of diets at a population level ( [[#Simon--2003|Simon 2003]] ; [[#Godfray--2018|Godfray et al. 2018]] ). Deeper, population-wide reductions in meat consumption are hampered by these lock-in mechanisms which continue to stabilise the existing meat production-consumption system. The extent to which policymakers are willing to actively stimulate reduced meat consumption thus remains an open question ( [[#Godfray--2018|Godfray et al. 2018]] ). See more in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I, Section 5.SM.6.4.

People’s general perceptions of climate risks, first covered in AR5, motivate behaviour change; more proximate and personal feelings of being at risk triggered by extreme weather and climate-linked natural disasters will increase concern and willingness to act ( [[#Bergquist--2019|Bergquist et al. 2019]] ), though the window of increased support is short ( [[#Sisco--2017|Sisco et al. 2017]] ). 67% of individuals in 26 countries see climate change as a major threat to their country, an increase from 53% in 2013, though 29% also consider it a minor or no threat ( [[#Fagan--2019|Fagan and Huang 2019]] ). Concern that the COVID-19 crisis may derail this momentum due to a finite pool of worry ( [[#Weber--2006|Weber 2006]] ) appears to be unwarranted: Americans’ positions on climate change in 2020 matched high levels of concern measured in 2019 ( [[#Leiserowitz--2020|Leiserowitz et al. 2020]] ). Younger, female, and more educated individuals perceive climate risks to be larger ( [[#Weber--2016|Weber 2016]] ; [[#Fagan--2019|Fagan and Huang 2019]] ). Moral values and political ideology influence climate risk perception and beliefs about the outcomes and effectiveness of climate action ( [[#Maibach--2011|Maibach et al. 2011]] ). Motivation for demand-side solutions can be increased by focusing on personal health or financial risks and benefits that clearly matter to people ( [[#Petrovic--2014|Petrovic et al. 2014]] ). Consistent with climate change as a normally distant, non-threatening, statistical issue ( [[#Gifford--2011|Gifford 2011]] ; [[#Fox-Glassman--2016|Fox-Glassman and]] [[#Weber--2016|Weber 2016]] ), personal experience with climate-linked flooding or other extreme weather events increases perceptions of risk and willingness to act ( [[#Weber--2013|Weber 2013]] ; [[#Atreya--2015|Atreya and Ferreira 2015]] ; [[#Sisco--2017|Sisco et al. 2017]] ) when plausible mediators and moderators are considered [[#Brügger--2021|Brügger et al. (2021)]] , confirmed in all 24 countries studied by [[#Broomell--2015|Broomell et al. (2015)]] . Discounting the future matters ( [[#Hershfield--2014|Hershfield et al. 2014]] ): across multiple countries, individuals more focused on future outcomes are more likely to engage in environmental actions ( [[#Milfont--2012|Milfont et al. 2012]] ).

There is ''medium evidence'' and ''high agreement'' that demographics, values, goals, personal and social norms differentially determine ASI behaviours, in the Netherlands and Spain ( [[#Abrahamse--2009|Abrahamse and Steg 2009]] ; [[#Niamir--2019|Niamir 2019]] ; [[#Niamir--2020b|Niamir et al. 2020b]] ), the OECD ( [[#Ameli--2015|Ameli and Brandt 2015]] ), and 11 European countries ( [[#Mills--2012|Mills and Schleich 2012]] ; [[#Roy--2012|Roy et al. 2012]] ). Education and income increase ‘Shift’ and ‘Improve’ behaviour, whereas personal norms help to increase the more difficult ‘Avoid’ behaviours ( [[#Mills--2012|Mills and Schleich 2012]] ). Socio-demographic variables (household size and income) predict energy use, but psychological variables (perceived behavioural control, perceived responsibility) predict ''changes'' in energy use; younger households are more likely to adopt ‘Improve’ decisions, whereas education increases ‘Avoid’ decisions ( [[#Ahmad--2015|Ahmad et al. 2015]] ). In India and developing countries, ‘Avoid’ decisions are made by individuals championing a cause, while ‘Improve’ and ‘Shift’ behaviour are increased by awareness programmes and promotional materials highlighting environmental and financial benefits ( [[#Chakravarty--2016|Chakravarty and Roy 2016]] ; [[#Roy--2018a|Roy et al. 2018a]] ). Cleaner cookstove adoption Box 5.6), a widely studied ‘Improve’ solution in developing countries ( [[#Nepal--2010|Nepal et al. 2010]] ; [[#Pant--2014|Pant et al. 2014]] ), goes up with income, education, and urban location. Female education and investments in reproductive health are evident measures to reduce world population growth ( [[#Abel--2016|Abel et al. 2016]] ).

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=== Box 5.6 | Socio-behavioural Aspects of Deploying Cookstoves ===

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Universal access to clean and modern cooking energy could cut premature deaths from household air pollution by two-thirds, while reducing forest degradation and deforestation and contributinh to the reduction of up to 50% of CO 2 emissions from cooking (relative to baseline by 2030) ( [[#IEA--2017c|IEA 2017c]] ; [[#Dagnachew--2019|Dagnachew et al. 2019]] ). However, in the absence of policy reform and substantial energy investments, 2.3 billion people will have no access to clean cooking fuels such as biogas, LPG, natural gas or electricity in 2030 ( [[#IEA--2017c|IEA 2017c]] ). Studies reveal that a combination of drivers influence adoption of new cookstove appliances, including affordability, behavioural and cultural aspects (lifestyles, social norms around cooking and dietary practices), information provision, availability, aesthetic qualities of the technology, perceived health benefits, and infrastructure (spatial design of households and cooking areas). The increasing efficiency improvements in electric cooking technologies could enable households to shift to electrical cooking at mass scale. The use of pressure cookers and rice cookers is now widespread in South Asia and beginning to penetrate the African market as consumer attitudes are changing towards household appliances with higher energy efficiencies ( [[#Batchelor--2019|Batchelor et al. 2019]] ). There are shifts towards electric and LPG stoves in Bhutan ( [[#Dendup--2019|Dendup and Arimura 2019]] ), India ( [[#Pattanayak--2019|Pattanayak et al. 2019]] ), Ecuador ( [[#Martínez--2017|Martínez et al. 2017]] ; [[#Gould--2018|Gould et al. 2018]] ) and Ethiopia ( [[#Tesfamichael--2021|Tesfamichael et al. 2021]] ); and improved biomass stoves in China ( [[#Smith--1993|Smith et al. 1993]] ). Significant subsidy, information ( [[#Dendup--2019|Dendup and Arimura 2019]] ), social marketing and availability of technology in the local markets are some of the key policy instruments helping to adopt improved cookstoves ( [[#Pattanayak--2019|Pattanayak et al. 2019]] ). There is no one-size-fits-all solution to household air pollution – different levels of shift and improvement occur in different cultural contexts, indicating the importance of socio-cultural and behavioural aspects in shifts in cooking practices. See more in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I, Section 5.SM.6.2.

There is ''high agreement'' in the literature that the updating of educational systems from a commercialised, individualised, entrepreneurial training model to an education cognisant of planetary health and human well-being can accelerate climate change awareness and action ( [[#Mendoza--2014|Mendoza and Roa 2014]] ; [[#Dombrowski--2016|Dombrowski et al. 2016]] ) (Supplementary Material I Chapter 5).

There is ''high evidence'' and ''high agreement'' that people’s core values affect climate-related decisions and climate policy support by shaping beliefs and identities ( [[#Dietz--2014|Dietz 2014]] ; [[#Steg--2016|Steg 2016]] ; [[#Hayward--2019|Hayward and Roy 2019]] ). People with altruistic and biospheric values are more likely to act on climate change and support climate policies than those with hedonic or egoistic values ( [[#Taylor--2014|Taylor et al. 2014]] ), because these values are associated with higher awareness and concern about climate change, stronger belief that personal actions can help mitigate climate change, and stronger feelings of responsibility for taking climate action ( [[#Dietz--2014|Dietz 2014]] ; [[#Steg--2016|Steg 2016]] ). Research also suggest that egalitarian, individualistic, and hierarchical worldviews ( [[#Wildavsky--1990|Wildavsky and Dake 1990]] ) have their role, and that successful solutions require policy-makers of all three worldviews to come together and communicate with each other ( [[#Chuang--2020|Chuang et al. 2020]] ).

Core values also influence which costs and benefits are considered ( [[#Hahnel--2015|Hahnel et al. 2015]] ; [[#Gölz--2016|Gölz and Hahnel 2016]] ; [[#Steg--2016|Steg 2016]] ). Information provision and appeals are thus more effective when tailored to those values ( [[#Bolderdijk--2013|Bolderdijk et al. 2013]] ; [[#Boomsma--2014|Boomsma and Steg 2014]] ), as implemented by the energy cultures framework ( [[#Stephenson--2015|Stephenson et al. 2015]] ; [[#Klaniecki--2020|Klaniecki et al. 2020]] ). Awareness, personal norms, and perceived behavioural control predict willingness to change energy-related behaviour above and beyond traditional socio-demographic and economic predictors ( [[#Schwartz--1977|Schwartz 1977]] ; [[#Ajzen--1985|Ajzen 1985]] ; [[#Stern--2000|Stern 2000]] ), as do perceptions of self-efficacy ( [[#Bostrom--2019|Bostrom et al. 2019]] ). However, such motivation for change is often not enough, as actors also need capacity for change and help to overcome individual, institutional and market barriers ( [[#Young--2010|Young et al. 2010]] ; [[#Bray--2011|Bray et al. 2011]] ; [[#Carrington--2014|Carrington et al. 2014]] ).

Table 5.4 describes common obstacles to demand-side energy behaviour change, from loss aversion to present bias (for more detail see [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I). Choice architecture refers to interventions (‘nudges’) that shape the choice context and how choices are presented, with seemingly-irrelevant details (e.g., option order or labels) often more important than option price ( [[#Thaler--2009|Thaler and Sunstein 2009]] ). There is ''high evidence'' and ''high agreement'' that choice architecture nudges shape energy decisions by capturing deciders’ attention; engaging their desire to contribute to the social good; facilitating accurate assessment of risks, costs, and benefits; and making complex information more accessible ( [[#Yoeli--2017|Yoeli et al. 2017]] ; Zangheri et al. 2019). Climate-friendly choice architecture includes the setting of proper defaults, the salient positioning of green options (in stores and online), forms of framing, and communication of social norms ( [[#Johnson--2012|Johnson et al. 2012]] ). Simplifying access to greener options (and hence lowering effort) can promote ASI changes ( [[#Mani--2013|Mani et al. 2013]] ). Setting effective ‘green’ defaults may be the most effective policy to mainstream low-carbon energy choices ( [[#Sunstein--2014|Sunstein and Reisch 2014]] ), adopted in many contexts ( [[#Jachimowicz--2019|Jachimowicz et al. 2019]] ) and deemed acceptable in many countries ( [[#Sunstein--2019|Sunstein et al. 2019]] ). Table 5.3a lists how often different choice-architecture tools were used in many countries over the past 10 years to change ASI behaviours, and how often each tool was used to enhance an economic incentive. These tools have been tested mostly in developed countries. Reduction in energy use (typically electricity consumption) is the most widely studied behaviour (because metering is easily observable). All but one tool was applied to increase this ‘Avoid’ behaviour, with demand-side reductions from 0% to up to 20%, with most values below 3% (see also meta-analyses by [[#Hummel--2019|Hummel and Maedche (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#van%20der%20Linden--2020|van der Linden and Goldberg (2020)]] ; [[#Stankuniene--2020|Stankuniene et al. (2020)]] ; and [[#Khanna--2021|Khanna et al. (2021)]] . Behavioural, economic, and legal instruments are most effective when applied as an internally consistent ensemble where they can reinforce each other, a concept referred to as ‘policy packaging’ in transport policy research ( [[#Givoni--2014|Givoni 2014]] ). A meta-analysis, combining evidence of psychological and economic studies, demonstrates that feedback, monetary incentives and social comparison operate synergistically and are together more effective than the sum of individual interventions ( [[#Khanna--2021|Khanna et al. 2021]] ). The same meta-analysis also shows that combined with monetary incentives, nudges and choice architecture can reduce global GHG emissions from household energy use by 5–6% ( [[#Khanna--2021|Khanna et al. 2021]] ).

Choice architecture has been depicted as an anti-democratic attempt at manipulating the behaviour of actors without their awareness or approval ( [[#Gumbert--2019|Gumbert 2019]] ). Such critiques ignore the fact that there is no neutral way to present energy-use-related decisions, as every presentation format and choice environment influences choice, whether intentionally or not. Educating households and policy makers about the effectiveness of choice architecture and adding these behavioural tools to existing market- and regulation-based tools in a transparent and consultative way can provide desired outcomes with increased effectiveness, while avoiding charges of manipulation or deception. People consent to choice-architecture tools if their use is welfare-enhancing, policymakers are transparent about their goals and processes, public deliberation and participation are encouraged, and the choice architect is trusted ( [[#Sunstein--2019|Sunstein et al. 2019]] ).

'''Table 5.3a | Inventory of behavioural interventions experimentally tested to change energy behaviours.'''

{| class="wikitable"
|-
! '''Behavioural tool'''

! '''# of papers'''

! '''# in developed''' '''countries'''

! '''# in other countries'''

! '''Energy demand behaviour'''

! '''Avoid'''

! '''Shift'''

! '''Improve'''

! '''Economic incentive'''

|-
| '''Set the proper defaults'''

| 27

| 26

| 1

| '''Carbon Offset Programme (3)''' [[#Löfgren--2012|Löfgren et al. (2012)]] ; [[#Araña--2013|Araña and León (2013)]]

'''Energy Source (4)''' [[#Kaiser--2020|Kaiser et al. (2020)]] ; [[#Wolske--2020|Wolske et al. (2020)]] *

'''Energy Use (16)''' [[#Jachimowicz--2019|Jachimowicz et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Investment in Energy Efficiency (7)''' [[#Theotokis--2015|Theotokis and Manganari (2015)]] ; [[#Ohler--2020|Ohler et al. (2020)]]

'''Mode of Transportation (1)''' [[#Goodman--2013|Goodman et al. (2013)]]

| 11

| 12

| 9

| 6

|-
| '''Reach out during transitions'''

| 10

| 9

| 1

| '''Energy Use (4)''' [[#Verplanken--2006|Verplanken (2006)]] ; [[#Jack--2016|Jack and Smith (2016)]] ; [[#Iweka--2019|Iweka et al. (2019)]] *

'''Investment in Energy Efficiency (4)''' [[#Gimpel--2020|Gimpel et al. (2020)]]

'''Mode of Transportation (2)''' [[#Verplanken--2008|Verplanken et al. (2008)]]

| 1

| 3

| 7

| 1

|-
| '''Provide timely feedback and reminders'''

| 256

| 246

| 10

| '''Energy Use (252)''' [[#Darby--2006|Darby (2006)]] ; [[#Buckley--2019|Buckley (2019)]] * [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Fischer--2008|Fischer (2008)]] ; [[#Steg--2008|Steg (2008)]] ; [[#Faruqui--2010|Faruqui et al. (2010)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#McKerracher--2013|McKerracher and Torriti (2013)]] ; [[#Karlin--2015|Karlin et al. (2015)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; Zangheri et al. (2019); [[#Ahir--2021|Ahir and Chakraborty (2021)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Mode of Transportation (3)''' [[#Steg--2008|Steg (2008)]] ; [[#Sanguinetti--2020|Sanguinetti et al. (2020)]] *

| 244

| 6

| 7

| 33

|-
| '''Make information intuitive and easy to access'''

| 247

| 235

| 12

| '''Energy Source (3)''' [[#Havas--2015|Havas et al. (2015)]] ; [[#Jagger--2019|Jagger et al. (2019)]]

'''Energy Use (202)''' [[#Henryson--2000|Henryson et al. (2000)]] ; [[#Darby--2006|Darby (2006)]] ; [[#Carlsson-Kanyama--2007|Carlsson-Kanyama and Lindén (2007)]] ; [[#Chen--2017|Chen et al. (2017)]] ; [[#Iwafune--2017|Iwafune et al. (2017)]] ; [[#Burkhardt--2019|Burkhardt et al. (2019)]] ; [[#Henry--2019|Henry et al. (2019)]] ; [[#Wong-Parodi--2019|Wong-Parodi et al. (2019)]] ; [[#Mi--2020|Mi et al. (2020)]] ; [[#Stojanovski--2020|Stojanovski et al. (2020)]] [ [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; Ehrhardt-Martinez and Donnelly (2010); [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Buckley--2019|Buckley (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; Zangheri et al. (2019); [[#Wolske--2020|Wolske et al. (2020)]] ; [[#Ahir--2021|Ahir and Chakraborty (2021)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] ]*

'''Investment in Energy Efficiency (30)''' [[#Larrick--2008|Larrick and Soll (2008)]] ; [[#Steg--2008|Steg (2008)]] ; [[#Andor--2018|Andor and Fels (2018)]] *

'''Mode of Transportation (19)''' [[#Steg--2008|Steg (2008)]] ; [[#Pettifor--2017|Pettifor et al. (2017)]] *

| 197

| 38

| 24

| 33

|-
| '''Make behaviour observable and provide recognition'''

| 58

| 53

| 5

| '''Energy Use (24)''' [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Investment in Energy Efficiency (30)''' [[#Pettifor--2017|Pettifor et al. (2017)]] *

'''Mode of Transportation (4)''' [[#Pettifor--2017|Pettifor et al. (2017)]] *

| 27

| 28

| 5

| 6

|-
| '''Communicate a norm'''

| 138

| 131

| 7

| '''Energy Source (1)''' [[#Hafner--2019|Hafner et al. (2019)]]

'''Energy Use (116)''' [[#Nolan--2008|Nolan et al. (2008)]] ; [[#Ayers--2009|Ayers and Forsyth (2009)]] ; [[#Allcott--2011|Allcott (2011)]] ; [[#Costa--2013|Costa and Kahn (2013)]] ; [[#Allcott--2014|Allcott and Rogers (2014)]] [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Abrahamse--2013|Abrahamse and Steg (2013)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Buckley--2019|Buckley (2019)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Ahir--2021|Ahir and Chakraborty (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Investment in Energy Efficiency (15)''' [[#Pettifor--2017|Pettifor et al. (2017)]] ; [[#Niamir--2020b|Niamir et al. (2020b)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Mode of Transportation (7)''' [[#Bamberg--2007|Bamberg et al. (2007)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] *

| 106

| 21

| 16

| 15

|-
| '''Reframe consequences in terms people care about'''

| 74

| 68

| 6

| '''Energy Source (5)''' [[#Wolske--2018|Wolske et al. (2018)]] ; [[#Hafner--2019|Hafner et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] *

'''Energy Use (47)''' [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Darby--2006|Darby (2006)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Chen--2017|Chen et al. (2017)]] ; [[#Eguiguren-Cosmelli--2018|Eguiguren-Cosmelli (2018)]] ; [[#Bergquist--2019|Bergquist et al. (2019)]] ; [[#Ghesla--2020|Ghesla et al. (2020)]] ; [[#Mi--2020|Mi et al. (2020)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Investment in Energy Efficiency (22)''' [[#Andor--2018|Andor and Fels (2018)]] ;* [[#Forster--2021|Forster et al. (2021)]]

'''Mode of Transportation (2)''' [[#Nepal--2010|Nepal et al. (2010)]] ; [[#Mattauch--2016|Mattauch et al. (2016)]]

| 41

| 18

| 19

| 18

|-
| '''Obtain a commitment'''

| 52

| 47

| 5

| '''Energy Source (1)''' [[#Jagger--2019|Jagger et al. (2019)]]

'''Energy Use (47)''' [[#Ghesla--2020|Ghesla et al. (2020)]] ; [[#Abrahamse--2005|Abrahamse et al. (2005)]] ; [[#Steg--2008|Steg (2008)]] ; [[#Delmas--2013|Delmas et al. (2013)]] ; [[#Andor--2018|Andor and Fels (2018)]] ; [[#Iweka--2019|Iweka et al. (2019)]] ; [[#Nisa--2019|Nisa et al. (2019)]] ; [[#Grilli--2021|Grilli and Curtis (2021)]] ; [[#Khanna--2021|Khanna et al. (2021)]] *

'''Investment in Energy Efficiency (1)''' [[#Steg--2008|Steg (2008)]] *

'''Mode of Transportation (5)''' [[#Matthies--2006|Matthies et al. (2006)]] ; [[#Steg--2008|Steg (2008)]] *

| 45

| 4

| 4

| 10

|}

Note: Papers in this review of behavioural interventions to reduce household energy demand were collected through a systemic literature search up to August 2021. Studies are included in the reported counts if they are (i) experimental, (ii) peer-reviewed or highly cited reports, (iii) the intervention is behavioural, and (iv) the targeted behaviour is household energy demand. 559 papers are included in the review. Each paper was coded for: type of behavioural intervention, country of study, energy demand behaviour targeted, whether the target is an ‘Avoid’, ‘Shift’, or ‘Improve’ behaviour, and whether the intervention includes an economic incentive. Some papers do not report all elements. The energy demand behaviour column provides the count of papers that focus on each behaviour type (in parentheses after the behaviour). The citations that follow are not exhaustive but exemplify papers in the category, selected for impact, range, and recency. The asterisk (*) indicates references that are meta-analyses or systematic reviews. Papers within meta-analyses and systematic reviews that meet the inclusion criteria are counted individually in the total counts. The full reference list is available at https://osf.io/9463u/ .

'''Table 5.3b | Summary of effects of behavioural interventions in Table 5.''' '''3a.'''

{| class="wikitable"
|-
! '''Behavioural tool'''

! '''Results'''

'''(expressed in household energy savings, unless otherwise stated)'''

! '''Results summary'''

|-
| '''Set proper default'''

| Meta-analyses find a medium to strong effect of defaults on environmental behaviour. [[#Jachimowicz--2019|Jachimowicz et al. (2019)]] report a strong average effect of defaults on environmental behaviour (Cohen’s d = 0.75, confidence interval 0.39–1.12), though not as high as for consumer decisions. They find that defaults, across domains, are more effective when they reflect an endorsement (recommendation by a trusted source) or endowment (reflecting the status quo). [[#Nisa--2019|Nisa et al. (2019)]] * report a medium average effect size (Cohen’s d = 0.35; range 0.04–0.55).

| [[File:f2e683c2a4b5cee31d5ee09199e92681 IPCC_AR6_WGIII_Table_5_3b_1.png]]

|-
| '''Reach out during transitions'''

| The few interventions that focus on transitions and measure behaviour change (rather than energy savings) report mixed, moderate effect sizes. People were unwilling to change their behaviour if they were satisfied with current options ( [[#Mahapatra--2008|Mahapatra and Gustavsson 2008]] ). [[#Iweka--2019|Iweka et al. (2019)]] find that effective messages can prompt habit disruption.

| [[File:6fb964ee39ad3f109525b9e62f28209f IPCC_AR6_WGIII_Table_5_3b_2.png]]

|-
| '''Timely feedback and reminders'''

| The average effects of meta-analyses of feedback interventions on household energy use reductions range from 1.8% to 7.7%, with large variations ( [[#Delmas--2013|Delmas et al. 2013]] ; [[#Buckley--2019|Buckley 2019]] ; [[#Nisa--2019|Nisa et al. 2019]] ; [[#Buckley--2020|Buckley 2020]] ; [[#Ahir--2021|Ahir and Chakraborty 2021]] ; [[#Khanna--2021|Khanna et al. 2021]] ). The same is true for two literature reviews ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ; [[#Bergquist--2019|Bergquist et al. 2019]] ). Most studies find a 4–10% average reduction during the intervention; some studies find a non-significant result ( [[#Dünnhoff--2008|Dünnhoff and Duscha 2008]] ) or a negative reduction ( [[#Winett--1978|Winett et al. 1978]] ).

Real-time feedback is most effective, followed by personalised feedback ( [[#Buckley--2019|Buckley 2019]] ; [[#Buckley--2020|Buckley 2020]] ). A review by Darby et al. (2006) finds direct feedback (from the meter or display monitor) is more effective than indirect feedback (via billing) (5–15% savings vs 0–10% savings). Feedback effects (Cohen’s d = 0.241) are increased when combined with a monetary incentive (Cohen’s d = 0.96) and with a social comparison and a monetary incentive (Cohen’s d = 0.714) ( [[#Khanna--2021|Khanna et al. 2021]] ).

[[#Sanguinetti--2020|Sanguinetti et al. (2020)]] find that onboard feedback results in a 6.6% improvement in the fuel economy of cars (Cohen’s d: 0.07, [range 0.05–0.08]).

| [[File:a64229224e5e7113ceb1bd6d0516a482 IPCC_AR6_WGIII_Table_5_3b_3.png]]

|-
| '''Timely feedback and reminders'''

| The effectiveness of feedback from in home displays is highly studied. Two reviews find them to have result in a 2–14% energy saving (Ehrhardt-Martinez and Donnelly 2010; [[#Faruqui--2010|Faruqui et al. 2010]] ). A meta-analysis by [[#McKerracher--2013|McKerracher and Torriti (2013)]] finds a smaller range of results, with 3–5% energy savings.

| [[File:d734f7c84f07204e952ae1e4d9d5785c IPCC_AR6_WGIII_Table_5_3_x1.png]]

|-
| '''Make information intuitive and easy to access'''

| Meta-analyses of information interventions on household energy use find average energy savings between 1.8–7.4% and Cohen’s d effect sizes between 0.05 and 0.30 ( [[#Delmas--2013|Delmas et al. 2013]] ; [[#Buckley--2019|Buckley 2019]] ; [[#Nisa--2019|Nisa et al. 2019]] );* [[#Buckley--2020|Buckley 2020]] ; [[#Nemati--2020|Nemati and Penn 2020]] ; [[#Ahir--2021|Ahir and Chakraborty 2021]] ; [[#Khanna--2021|Khanna et al. 2021]] ). Study quality affects the measured effect – small sample sizes, shorter measurement windows, and self-selection are correlated with larger effects ( [[#Nisa--2019|Nisa et al. 2019]] ; [[#Nemati--2020|Nemati and Penn 2020]] ). RCTs have a smaller effect size, 5.2% savings (95% confidence interval [range 0.5% –9.5%]) ( [[#Nemati--2020|Nemati and Penn 2020]] ).

Information combined with comparative feedback is more effective than information alone (d = .34 vs. 30 ( [[#Khanna--2021|Khanna et al. 2021]] ); 8.5% vs 7.4% ( [[#Delmas--2013|Delmas et al. 2013]] ). Monetary incentives make information interventions more effective ( [[#Khanna--2021|Khanna et al. 2021]] ).

Energy efficiency labeling has a heterogenous effect on investment in energy efficiency ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ; [[#Andor--2018|Andor and Fels 2018]] ). Efficiency labels on houses lead to higher price mark ups (Jensen et al. 2016) and house prices ( [[#Brounen--2011|Brounen and Kok 2011]] ). Energy star labels lead to significantly higher willingness to pay for refrigerators ( [[#Houde--2013|Houde et al. 2013]] ), but energy and water conservation varies by appliance from 0–23% ( [[#Kurz--2005|Kurz et al. 2005]] ).

A meta-analysis of interventions to increase alternative fuel vehicle adoption find a small effect (d = .20–.28) ( [[#Pettifor--2017|Pettifor et al. 2017]] ).

| [[File:6280ed3534b3b06c1f235aa938b270f4 IPCC_AR6_WGIII_Table_5_3_x2.png]]

|-
| '''Make behaviour observable and provide recognition'''

| Making behaviour observable and providing recognition lead to 6–7% energy savings ( [[#Winett--1978|Winett et al. 1978]] ; [[#Handgraaf--2013|Handgraaf et al. 2013]] ; [[#Nemati--2020|Nemati and Penn 2020]] ) and a large effects size (Cohen’s d = 0.79-1.06); ( [[#Nisa--2019|Nisa et al. 2019]] *). Community-wide interventions result in 1–27% energy savings ( [[#Iweka--2019|Iweka et al. 2019]] ).

Neighbourhood social influence has a small (d = .28) effect on alternative fuel vehicle adoption ( [[#Pettifor--2017|Pettifor et al. 2017]] ).

| [[File:81d63e600e53d365b02ad2b7fa27aa18 IPCC_AR6_WGIII_Table_5_3b_8.png]]

|-
| '''Communicate a norm'''

| The effect of social norm information on household energy savings ranges from 1.7–11.5% ( [[#Delmas--2013|Delmas et al. 2013]] ; [[#Buckley--2020|Buckley 2020]] ) and Cohen’s d from 0.08–0.32, ( [[#Abrahamse--2013|Abrahamse and Steg 2013]] ; [[#Bergquist--2019|Bergquist et al. 2019]] ; [[#Khanna--2021|Khanna et al. 2021]] ); ( [[#Nisa--2019|Nisa et al. 2019]] )* with similar effects on choice of mode of transportation. [[#Pettifor--2017|Pettifor et al. (2017)]] report a small effect (d = .20–.28) on selecting a more energy efficient car.

The OPOWER study ( [[#Allcott--2011|Allcott 2011]] ), prototypical for the impact of social norms on household energy consumption, finds 2% reduction in long-term energy use and 11–20% energy reduction in the short run ( [[#Allcott--2011|Allcott 2011]] ; [[#Ayres--2013|Ayres et al. 2013]] ; [[#Costa--2013|Costa and Kahn 2013]] ; [[#Allcott--2014|Allcott and Rogers 2014]] ). Impact decays over time ( [[#Allcott--2012|Allcott and Rogers 2012]] ). Norm interventions are less effective for low energy users ( [[#Schultz--2007|Schultz et al. 2007]] ; [[#Andor--2020|Andor et al. 2020]] ). Moral licensing and negative spillover can reduce the overall positive feedback of normative feedback ( [[#Tiefenbeck--2013|Tiefenbeck et al. 2013]] ).

Interventions are more effective when the norm is implicitly inducted, in individual countries, and when people care about the norm ( [[#Nolan--2008|Nolan et al. 2008]] ; [[#Bergquist--2019|Bergquist et al. 2019]] ; [[#Khanna--2021|Khanna et al. 2021]] ). Descriptive norm interventions (social comparisons) are more effective when communicated online,by email or through in-home displays compared to billing letters ( [[#Andor--2018|Andor and Fels 2018]] ), when the reference group is more specific (Shen et al. 2015). [[#Dolan--2013|Dolan and Metcalfe (2013)]] find conservation increased from 4% to 11% when energy savings tips are added.

| [[File:41b504f354221e87aff336b2c347fc3d IPCC_AR6_WGIII_Table_5_3_x3.png]]

|-
| '''Reframe consequences in terms people care about'''

| A meta-analysis by Khanna et al. ( 2021) finds a small and variable effect of motivational interventions that reframe consequences (Cohen’s d = [0–0.423]). Effects are larger when reframing is combined with monetary incentives and feedback (d = .96). Darby et al. (2006) report 10–20% savings for US pay-as-you-go systems. Providing lifecycle cost information increases likelihood of purchasing eco-innovative products ( [[#Kaenzig--2010|Kaenzig and Wüstenhagen 2010]] ). Long term (10-year) operating cost information leads to higher willingness to pay for energy efficiency compared to short-term (1-year) cost information ( [[#Heinzle--2012|Heinzle and Wüstenhagen 2012]] ). Monetary information increases the success of energy reduction interventions ( [[#Newell--2014|Newell and Siikamäki 2014]] ; [[#Andor--2018|Andor and Fels 2018]] ). Reframing interventions are more effective when combined with feedback (d = .24–.96) and with social comparisons and feedback (d = .42) ( [[#Khanna--2021|Khanna et al. 2021]] ).

| [[File:bfddadfaf277578adfed593b4e5be37c Table_5.3b_11.png]]

|-
| '''Obtain a commitment'''

| Commitment and goal interventions result in significant energy reduction in half of studies ( [[#Abrahamse--2005|Abrahamse et al. 2005]] ; [[#Andor--2018|Andor and Fels 2018]] ; [[#Nisa--2019|Nisa et al. 2019]] *). [[#Nisa--2019|Nisa et al. (2019)]] report a moderate average effect (Cohen’s d = 0.34, [0.11–0.66]). When results are significant, the energy savings are around 10% ( [[#Andor--2018|Andor and Fels 2018]] ). Self-set goals perform better than assigned goals ( [[#van%20Houwelingen--1989|van Houwelingen and van Raaij 1989]] ; [[#McCalley--2002|McCalley and Midden 2002]] ; [[#Andor--2018|Andor and Fels 2018]] ) and reasonable goals perform better than unreasonably high or low goals ( [[#van%20Houwelingen--1989|van Houwelingen and van Raaij 1989]] ; [[#Abrahamse--2007|Abrahamse et al. 2007]] ; [[#Harding--2014|Harding and Hsiaw 2014]] ). Interventions are more effective when the commitment is public ( [[#Pallak--1976|Pallak and Cummings 1976]] ) and when combined with information and rewards ( [[#Slavin--1981|Slavin et al. 1981]] ; [[#Völlink--1999|Völlink and Meertens 1999]] ).

| [[File:4a4a0053fc73736359ee1494fdaf6a98 IPCC_AR6_WGIII_Table_5_3b_12.png]]

|}

Note: The second column describes the effects of each of the eight behavioural tools. The third column plots the results of meta-analyses and reviews that focus on each tool. Effects are reported as described in the referenced paper, either as percentage of energy saved (dotted box) or by the effect size, measured as Cohen’s d (dashed box).

\*Two responses to [[#Nisa--2019|Nisa et al. (2019)]] challenge their conclusion that behavioural interventions have a small impact on household energy use (Stern 2020; [[#van%20der%20Linden--2020|van der Linden and Goldberg, 2020]] ). We report the raw data collected and used in [[#Nisa--2019|Nisa et al. (2019)]] . Our data summary supports the arguments by Stern (2020) and [[#van%20der%20Linden--2020|van der Linden and Goldberg (2020)]] that interventions should be evaluated in combination, as well as individually, and that the results are highly sensitive to the chosen estimator.

a Range reported as 95% confidence interval of results used in the meta-analysis or review.

b Range reported as all results included in the meta-analysis or review.

c No range reported.

d Range indicates the reported results within a meta-analysis; this applies when multiple intervention types in a meta-analysis are classified as a single behavioural tool.

<div id="footnote-001" class="_idFootnote"></div>

[[#footnote-001-backlink|1]] The way choices are presented to consumers is known as ‘choice architecture’ in the field of behavioural economics.

<div id="footnote-000" class="_idFootnote"></div>

[[#footnote-000-backlink|2]] The countries and areas classification in this figure deviate from the standard classification scheme adopted by WGIII as set out in Annex II, section 1.

<div id="5.4.2" class="h2-container"></div>

<span id="socio-cultural-drivers-of-climate-mitigation"></span>

=== 5.4.2 Socio-cultural Drivers of Climate Mitigation ===

<div id="h2-18-siblings" class="h2-siblings"></div>

Collective behaviours and social organisation are part of everyday life, and feeling part of active collective action renders mitigation measures efficient and pervasive ( [[#Climact--2018|Climact 2018]] ). Social and cultural processes play an important role in shaping what actions people take on climate mitigation, interacting with individual, structural, institutional and economic drivers ( [[#Barr--2014|Barr and Prillwitz 2014]] ). Just like infrastructure, social and cultural processes can ‘lock in’ societies to carbon-intensive patterns of service delivery. They also offer potential levers to change normative ideas and social practices in order to achieve extensive emissions cuts ( ''high confidence'' ) (Table 5.4).

In terms of cultural processes, we can distinguish two levels of analysis: specific meanings associated with particular technologies or practices, and general narratives about climate change mitigation. Specific '''meanings''' (e.g., comfort, status, identity and agency) are associated with many technologies and everyday social practices that deliver energy services, from driving a car to using a cookstove ( ''high evidence'' , ''high agreement'' ) ( [[#5.5|Section 5.5]] ). Meanings are symbolic and influence the willingness of individuals to use existing technologies or shift to new ones ( [[#Wilhite--1995|Wilhite and Ling 1995]] ; [[#Wilhite--2009|Wilhite 2009]] ; [[#Sorrell--2015|Sorrell 2015]] ). Symbolic motives are more important predictors of technology adoption than instrumental motives ( [[#Steg--2005|Steg 2005]] ; [[#Noppers--2014|Noppers et al. 2014]] ; [[#Noppers--2015|Noppers et al. 2015]] ; [[#Noppers--2016|Noppers et al. 2016]] ) (see case study on app cabs in Kolkata, India (Box 5.8)). If an individual’s pro-environmental behaviour is associated with personal meaning than it also increases subjective well-being ( [[#Zawadzki--2020|Zawadzki et al. 2020]] ). Status consciousness is highly relevant in GHG emission-intensive consumption choices (cars, houses). However, inversely framing energy-saving behaviour as high status is a promising strategy for emission reduction ( [[#Ramakrishnan--2021|Ramakrishnan and Creutzig 2021]] ).

At a broader level, '''narratives''' about climate mitigation circulate within and across societies, as recognised in SR1.5, and are broader than the meanings associated with specific technologies ( ''high evidence, high agreement'' ). Narratives enable people to imagine and make sense of the future through processes of interpretation, understanding, communication and social interaction ( [[#Smith--2017|Smith et al. 2017]] ). Stories about climate change are relevant for mitigation in numerous ways. They can be utopian or dystopian (e.g., ''The great derangement'' by Amitav Ghosh) ( [[#Ghosh--2016|Ghosh 2016]] ), for example presenting apocalyptic stories and imagery to capture people’s attention and evoke emotional and behavioural response ( [[#O’Neill--2014|O’Neill and Smith 2014]] ). Reading climate stories has been shown to cause short-term influences on attitudes towards climate change, increasing the belief that climate change is human caused and increasing its issue priority ( [[#Schneider-Mayerson--2020|Schneider-Mayerson et al. 2020]] ). Climate narratives can also be used to justify scepticism of science, drawing together coalitions of diverse actors into social movements that aim to prevent climate action ( [[#Lejano--2020|Lejano and Nero 2020]] ). Narratives are also used in integrated assessment and energy system models that construct climate stabilisation scenarios, for example in the choice of parameters, their interpretation and model structure ( [[#Ellenbeck--2019|Ellenbeck and Lilliestam 2019]] ). One important narrative choice of many models involves framing climate change as market failure (which leads to the result that carbon pricing is required). While such a choice can be justified, other model framings can be equally justified ( [[#Ellenbeck--2019|Ellenbeck and Lilliestam 2019]] ).

Power and agency shape which climate narratives are told and how prevalent they are ( [[#O’Neill--2014|O’Neill and Smith 2014]] ; [[#Schneider-Mayerson--2020|Schneider-Mayerson et al. 2020]] ). For example, narratives have been used by indigenous communities to imagine climate futures divergent from top-down, government-led narratives ( [[#Streeby--2018|Streeby 2018]] ). The uptake of new climate narratives is influenced by political beliefs and trust. Policymakers can enable emissions reduction by employing narratives that have broad societal appeal, encourage behavioural change and complement regulatory and fiscal measures ( [[#Terzi--2020|Terzi 2020]] ). Justice narratives may not have universal appeal: in a UK study, justice narratives polarised individuals along ideological lines, with lower support amongst individuals with right-wing beliefs; by contrast, narratives centred on saving energy, avoiding waste and patriotic values were more widely supported across society ( [[#Whitmarsh--2017|Whitmarsh and Corner 2017]] ). More research is needed to assess if these findings are prevalent in diverse socio-cultural contexts, as well as the role played by social media platforms to influence emerging narratives of climate change ( [[#Pearce--2019|Pearce et al. 2019]] ).

Trust in organisations is a key predictor of the take-up of novel energy services ( [[#Lutzenhiser--1993|Lutzenhiser 1993]] ), particularly when financial incentives are high ( [[#Stern--1985|Stern et al. 1985]] ; [[#Joskow--1995|Joskow 1995]] ). Research has shown that if there is low public trust in utility companies, service delivery by community-based non-profit organisations in the US ( [[#Stern--1985|Stern et al. 1985]] ) or public/private partnerships in Mexico ( [[#Friedmann--1998|Friedmann and Sheinbaum 1998]] ), offer more effective solutions, yet only if public trust is higher in these types of organisations. UK research shows that acceptance of shifts to less resource-intensive service provision (e.g., more resource-efficient products, extending product lifetimes, community schemes for sharing products) varies depending on factors including trust in suppliers and manufacturers, affordability, quality and hygiene of shared products, and fair allocation of responsibilities ( [[#Cherry--2018|Cherry et al. 2018]] ). Trust in other people plays an important role in the sharing economy ( [[#Li--2020|Li and Wang 2020]] ), for example predicting shifts in transport mode, specifically car sharing involving rides with strangers ( [[#Acheampong--2019|Acheampong and Siiba 2019]] ) ( [[#5.3.4.2|Section 5.3.4.2]] ).

Action on climate mitigation is influenced by our perception of what other people commonly do, think or expect, known as social norms ( ''high evidence, high agreement'' ) ( [[#Cialdini--2006|Cialdini 2006]] ) (Table 5.3), even though people often do not acknowledge this ( [[#Nolan--2008|Nolan et al. 2008]] ; [[#Noppers--2014|Noppers et al. 2014]] ). Changing social norms can encourage societal transformation and social tipping points to address climate mitigation ( [[#Nyborg--2016|Nyborg et al. 2016]] ; [[#Otto--2020|Otto et al. 2020]] ). Providing feedback to people about how their own actions compare to others’ can encourage mitigation ( [[#Delmas--2013|Delmas et al. 2013]] ), although the overall effect size is not strong ( [[#Abrahamse--2013|Abrahamse and Steg 2013]] ). Trending norms are behaviours that are becoming more popular, even if currently practised by a minority. Communicating messages that the number of people engaging in a mitigation behaviour (e.g., giving a financial donation to an environmental conservation organisation) is increasing – a simple low-cost policy intervention – can encourage shifts to the targeted behaviour, even if the effect size is relatively small ( [[#Mortensen--2019|Mortensen et al. 2019]] ).

Socially comparative feedback seems to be more effective when people strongly identify with the reference group ( [[#De%20Dominicis--2019|De Dominicis et al. 2019]] ). Descriptive norms (perceptions of behaviours common in others) are more strongly related to mitigation actions when injunctive norms (perceptions of whether certain behaviours are commonly approved or disapproved) are also strong, when people are not strongly personally involved with mitigation topics ( [[#Göckeritz--2010|Göckeritz et al. 2010]] ), when people are currently acting inconsistently with their preferences, when norm-based interventions are supported by other interventions and when the context supports norm-congruent actions ( [[#Miller--2016|Miller and Prentice 2016]] ). A descriptive norm prime (‘most other people try to reduce energy consumption’) together with injunctive norm feedback (‘you are very good at saving energy’) is a very effective combination to motivate further energy savings ( [[#Bonan--2020|Bonan et al. 2020]] ). Second-order beliefs (perceptions of what others in the community believe) are particularly important for leveraging descriptive norms ( [[#Jachimowicz--2018|Jachimowicz et al. 2018]] ).

Behavioural contagion, which describes how ideas and behaviours often spread like infectious diseases, is a major contributor to the climate crisis ( [[#Sunstein--2019|Sunstein 2019]] ). But harnessing contagion can also mitigate warming. Carbon-heavy consumption patterns have become the norm only in part because we’re not charged for environmental damage we cause ( [[#Pigou--1920|Pigou 1920]] ). The deeper source of these patterns has been peer influence ( [[#Frank--1999|Frank 1999]] ), because what we do influences others. A rooftop solar installation early in the adoption cycle, for example, spawns a copycat installation in the same neighbourhood within four months, on average. With such installations thus doubling every four months, a single new order results in 32 additional installations in just two years. And contagion doesn’t stop there, since each family also influences friends and relatives in distant locations.

Harnessing contagion can also underwrite the investment necessary for climate stability. If taxed more heavily, top earners would spend less, shifting the frames of reference that shape spending of those just below, and so on – each step simultaneously reducing emissions and liberating resources for additional green investment ( [[#Frank--2020|Frank 2020]] ). Many resist, believing that higher taxes would make it harder to buy life’s special extras. But that belief is a cognitive illusion ( [[#Frank--2020|Frank 2020]] ). Acquiring special things, which are inherently in short supply, requires outbidding others who also want them. When top tax rates rise in tandem, relative bidding power is completely unchanged, so the same penthouse apartments would end up in the same hands as before. More generally, behavioural contagion is important to leverage all relevant social tipping points for stabilising Earth’s climate ( [[#Otto--2020|Otto et al. 2020]] ).

For new climate policies and mitigation technologies to be rapidly and extensively implemented, they must be socially acceptable to those who are directly impacted by those policies and technologies ( ''medium evidence, high agreement'' ). Policies that run counter to social norms or cultural meanings are less likely to be effective in reducing emissions ( [[#Demski--2015|Demski et al. 2015]] ; [[#Perlaviciute--2018|Perlaviciute et al. 2018]] ; [[#Roy--2018b|Roy et al. 2018b]] ). More just and acceptable implementation of renewable energy technologies requires taking account of the cultural meanings, emotional attachments and identities linked to particular landscapes and places where those technologies are proposed ( [[#Devine-Wright--2009|Devine-Wright 2009]] ) and enabling fairness in how decisions are taken and costs and benefits distributed ( [[#Wolsink--2007|Wolsink 2007]] ). This is important for achieving the goal of SDG 7 (increased use of renewable energy resources) in developing countries while achieving energy justice ( [[#Calzadilla--2017|Calzadilla and Mauger 2017]] ). ‘Top-down’ imposition of climate policies by governments can translate into local opposition when perceived to be unjust and lacking transparency ( ''high evidence, high agreement'' ). Policymakers can build trust and increase the legitimacy of new policies by implementing early and extensive public and stakeholder participation, avoiding ‘Nimby’ (Not In My Back Yard) assumptions about objectors and adopting ‘Just Transition’ principles ( [[#Owens--2000|Owens 2000]] ; [[#Wolsink--2007|Wolsink 2007]] ; [[#Wüstenhagen--2007|Wüstenhagen et al. 2007]] ; [[#Dietz--2008|Dietz and Stern 2008]] ; [[#Devine-Wright--2011|Devine-Wright 2011]] ; [[#Heffron--2018|Heffron and McCauley 2018]] ). Participatory mechanisms that enable deliberation by a representative sample of the public ( [[#Climate%20Assembly%20UK--2020|Climate Assembly UK 2020]] ) can inform policymaking and increase the legitimacy of new and difficult policy actions ( [[#Dryzek--2019|Dryzek et al. 2019]] ).

Collective action by civil society groups and social movements can work to enable or constrain climate mitigation. Civil society groups can advocate policy change, provide policy research and open up opportunities for new political reforms ( ''high evidence'' , ''high agreement'' ) as recognised in previous IPCC reports ( [[#IPCC--2007|IPCC 2007]] ). Grassroots environmental initiatives, including community energy groups, are collective responses to, and critiques of, normative ways that everyday material needs (e.g., food, energy, making) are produced, supplied and circulated ( [[#Schlosberg--2016|Schlosberg and Coles 2016]] ). Such initiatives can reconcile lower carbon footprints with higher life satisfaction and higher incomes ( [[#Vita--2020|Vita et al. 2020]] ). Local initiatives such as Transition Towns and community energy projects can lead to improvements in energy efficiency, ensure a decent standard of living and increase renewable energy uptake, while building on existing social trust, and, in turn, building social trust and initiating engagement, capacity building, and social capital formation ( [[#Hicks--2018|Hicks and Ison 2018]] ). Another example are grassroot initiatives that aim to reduce food loss and waste, even as overall evidence on their effectiveness remains limited ( [[#Mariam--2020|Mariam et al. 2020]] ). However, community energy initiatives are not always inclusive and require policy support for widespread implementation across all socio-economic groups ( [[#Aiken--2017|Aiken et al. 2017]] ). In addition, more evidence is required of the impacts of community energy initiatives ( [[#Creamer--2018|Creamer et al. 2018]] ; [[#Bardsley--2019|Bardsley et al. 2019]] ).

Civil society social movements are a primary driver of social and institutional change ( ''high evidence'' , ''high agreement'' ) and can be differently positioned as, on the one hand, ‘insider’ social movements (e.g., World Wildlife Fund) that seek to influence existing state institutions through lobbying, advice and research and, on the other hand, ‘outsider’ social movements (e.g., Rising Tide, Extinction Rebellion) that advocate radical reform through protests and demonstrations ( [[#Newell--2005|Newell 2005]] ; [[#Caniglia--2015|Caniglia et al. 2015]] ). Civil society social movements frame grievances that resonate with society, mobilise resources to coordinate and sustain mass collective action, and operate within – and seek to influence – external conditions that enable or constrain political change ( [[#Caniglia--2015|Caniglia et al. 2015]] ). When successful, social movements open up windows of opportunity (so called ‘Overton Windows’) to unlock structural change ( ''high evidence'' , ''high agreement'' ) ( [[#Szałek--2013|Szałek 2013]] ; [[#Piggot--2018|Piggot 2018]] ).

Climate social movements advocate new narratives or framings for climate mitigation (e.g., ‘climate emergency’) ( [[#della%20Porta--2014|della Porta and Parks 2014]] ); criticise positive meanings associated with high emission technologies or practices (see case studies on diet and solar PV, (Boxes 5.5 and 5.7)); show disapproval for high-emission behaviours (e.g., through ‘flight shaming’); model behaviour change (e.g., shifting to veganism or public transport – see case study on mobility in Kolkata, India (Box 5.8)); demonstrate against extraction and use of fossil fuels ( [[#Cheon--2018|Cheon and Urpelainen 2018]] ); and aim to increase a sense of agency amongst certain social groups (e.g., young people or indigenous communities) that structural change is possible. Climate strikes have become internationally prevalent, for example the September 2019 strikes involved participants in more than 180 countries ( [[#Rosane--2019|Rosane 2019]] ; [[#Fisher--2020|Fisher and Nasrin 2020]] ; [[#Martiskainen--2020|Martiskainen et al. 2020]] ). Enabled by digitalisation, these have given voice to youth on climate ( [[#Lee--2020|Lee et al. 2020]] ) and created a new cohort of active citizens engaged in climate demonstrations ( [[#Fisher--2019|Fisher 2019]] ). Research on bystanders shows that marches increase positive beliefs about marchers and collective efficacy ( [[#Swim--2019|Swim et al. 2019]] ).

Countermovement coalitions work to oppose climate mitigation ( ''high confidence'' ). Examples include efforts in the US to oppose mandatory limits on carbon emissions supported by organisations from the coal and electrical utility sectors ( [[#Brulle--2019|Brulle 2019]] ). There is evidence that US opposition to climate action by carbon-connected industries is broad-based, highly organised, and matched with extensive lobbying ( [[#Cory--2021|Cory et al., 2021]] ). Social movements can also work to prevent policy changes, for example in France the Gilet Jaunes objected to increases in fuel costs on the grounds that they unfairly distributed the costs and benefits of price rises across social groups, for example between urban, peri-urban and rural areas ( [[#Copland--2019|Copland 2019]] ).

Religion could play an important role in enabling collective action on climate mitigation by providing cultural interpretations of change and institutional responses that provide resources and infrastructure to sustain collective actions ( [[#Roy--2012|Roy et al. 2012]] ; [[#Haluza-DeLay--2014|Haluza-DeLay 2014]] ; [[#Caniglia--2015|Caniglia et al. 2015]] ; [[#Hulme--2015|Hulme 2015]] ). Religion can be an important cultural resource towards sustainability at individual, community and institutional levels ( [[#Ives--2019|Ives and Kidwell 2019]] ), providing leverage points for inner transformation towards sustainability ( [[#Woiwode--2021|Woiwode et al. 2021]] ). Normative interpretations of climate change for and from religious communities are found in nearly every geography, and often observe popular movements for climate action drawing on religious symbols or metaphors ( [[#Jenkins--2018|Jenkins et al. 2018]] ). This suggests the value for policymakers of involving religious constituencies as significant civil society organisations in devising and delivering climate responses.

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=== Box 5.7 | Solar PV and the Agency of Consumers ===

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As an innovative technology, solar PV was strongly taken up by consumers (Nemet 2019). Several key factors explain its success. First, modular design made it applicable to different scales of deployment in different geographical contexts (e.g., large-scale grid-connected projects and smaller-scale off-grid projects) and allowed its application by companies taking advantage of emerging markets (Shum and Watanabe 2009). Second, culturally, solar PV symbolised an environmentally progressive technology that was valued by users (Morris and Jungjohann 2016). Large-scale adoption led to policy change (i.e., the introduction of feed-in tariffs that guaranteed a financial return) that in turn enabled improvements to the technology by companies. Over time, this has driven large-scale reductions in cost and increase in deployment worldwide. The relative importance of drivers varied across contexts. In Japan, state subsidies were lower yet did not hinder take-up because consumer behaviour was motivated by non-cost symbolic aspects. In Germany, policy change arose from social movements that campaigned for environmental conservation and opposed nuclear power, making solar PV policies politically acceptable. In summary, the seven-decade evolution of solar PV shows an evolution in which the agency of consumers has consistently played a key role in multiple countries, such that deriving 30–50% of global electricity supply from solar is now a realistic possibility (Creutzig et al. 2017). See more in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I, 5.SM.6.1.

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=== 5.4.3 Business and Corporate Drivers ===

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Businesses and corporate organisations play a key role in the mitigation of global warming, through their own commitments to zero-carbon footprints ( [[#Mendiluce--2021|Mendiluce 2021]] ), decisions to invest in researching and implementing new energy technologies and energy-efficient measures, and the supply-side interaction with changing consumer preferences and behaviours, such as via marketing. Business models and strategies work both as a barrier to and an accelerator of decarbonisation. Still existing locked-in infrastructures and business models advantages fossil fuel industry over renewable and energy efficient end use industry ( [[#Klitkou--2015|Klitkou et al. 2015]] ). The fossil fuel energy generation and delivery system therefore epitomises a barrier to the acceptance and implementation of new and cleaner renewable energy technologies ( [[#Kariuki--2018|Kariuki 2018]] ). A good number of corporate agents have attempted to derail climate change mitigation by targeted lobbying and doubt-inducing media strategies ( [[#Oreskes--2011|Oreskes and Conway 2011]] ). A number of corporations that are involved in both upstream and downstream supply chains of fossil fuel companies make up the majority of organisations opposed to climate action ( [[#Dunlap--2015|Dunlap and McCright 2015]] ; [[#Brulle--2019|Brulle 2019]] ; [[#Cory--2021|Cory et al. 2021]] ). Corporate advertisement and brand-building strategies also attempt to deflect corporate responsibility to individuals, and/or to appropriate climate care sentiments in their own brand building; climate change mitigation is uniquely framed through choice of products and consumption, avoiding the notion of the political collective action sphere ( [[#Doyle--2011|Doyle 2011]] ; [[#Doyle--2019|Doyle et al. 2019]] ).

Business and corporations are also agents of change towards decarbonisation, as demonstrated in the case of PV and battery electric cars ( [[#Teece--2018|Teece 2018]] ). Beyond new low-carbon technologies, strong sustainability business models are characterised by identifying nature as the primary stakeholder, strong local anchorage, the creation of diversified income sources, and deliberate limitations on economic growth ( [[#Brozovic--2019|Brozovic 2019]] ). However, such business models are difficult to maintain if generally traditional business models, which require short-term accounting, prevail.

Liability of fossil fuel business models and insurance against climate damages are key concerns of corporations and business. Limitations and regulation on GHG emissions will compel reductions in demand for fossil fuel companies’ products ( [[#Porter--2006|Porter and Kramer 2006]] ). According to a report by the Advisory Scientific Committee of the European Systemic Risk Board, insurance industries are very likely to incur losses due to liability risks ( [[#ESRB--2016|ESRB 2016]] ). The divestment movement adds additional pressure on fossil fuel related investments ( [[#Braungardt--2019|Braungardt et al. 2019]] ), even though fossil fuel financing remains resilient ( [[#Curran--2020|Curran 2020]] ). Companies, businesses and organisations, especially those in the carbon-intensive energy sector, might face liability claims for their contribution to climate change. A late transition to a low-carbon economy would exacerbate the physical costs of climate change on governments, businesses and corporations ( [[#ESRB--2016|ESRB 2016]] ).

Despite the seemingly positive roles that businesses and corporate organisations tend to play towards sustainable transitions, there is a need to highlight the dynamic relationship between sustainable and unsustainable trends ( [[#Antal--2020|Antal et al. 2020]] ), or example, the production of sport utility vehicles (SUVs) in the automobile market at the same time that car manufacturers are producing electric vehicles. An analysis of the role of consumers as drivers of unsustainability for businesses and corporate organisations is very important here as this trend will offset the sustainability progress being made by these businesses and organisations ( [[#Antal--2020|Antal et al. 2020]] ).

Professional actors, such as building managers, landlords, energy efficiency advisers, technology installers and car dealers, influence patterns of mobility and energy consumption ( [[#Shove--2003|Shove 2003]] ) by acting as ‘middle actors’ ( [[#Janda--2013|Janda and Parag 2013]] ; [[#Parag--2014|Parag and Janda 2014]] ) or intermediaries in the provision of building or mobility services ( [[#Grandclément--2015|Grandclément et al. 2015]] ; [[#De%20Rubens--2018|De Rubens et al. 2018]] ). Middle actors can bring about change in several different directions, be it, upstream, downstream or sideways. They can redefine professional ethics around sustainability issues, and, as influencers on the process of diffusion of innovations ( [[#Rogers--2003|Rogers 2003]] ), professionals can enable or obstruct improvements in efficient service provision or shifts towards low-carbon technologies (e.g., air and ground source heat pumps, solar hot water, underfloor heating, programmable thermostats, and mechanical ventilation with heat recovery) and mobility technologies (e.g., electric vehicles).

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=== 5.4.4 Institutional Drivers ===

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The allocationof political power to incumbent actors and coalitions has contributed to lock-in of particular institutions, stabilising the interests of incumbents through networks that include policymakers, bureaucracies, advocacy groups and knowledge institutions ( ''high agreement, high evidence'' ). There is ''high evidence'' and ''high agreement'' that institutions are central in addressing climate change mitigation. Indeed, social provisioning contexts, including equity, democracy, public services and high quality infrastructure, are found to facilitate high levels of need satisfaction at lower energy use, whereas economic growth beyond moderate incomes and dependence on extractive industries inhibit it ( [[#Vogel--2021|Vogel et al. 2021]] ). They shape and interact with technological systems ( [[#Unruh--2000|Unruh 2000]] ; [[#Foxon--2004|Foxon et al. 2004]] ; [[#Seto--2014|Seto et al. 2014]] ) and represent rules, norms and conventions that organise and structure actions ( [[#Vatn--2015|Vatn 2015]] ) and help create new path dependency or strengthen existing path dependency ( [[#Mattioli--2020|Mattioli et al. 2020]] ) (see case studies in Boxes 5.5 to 5.8 and [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I). These drive behaviour of actors through formal (e.g., laws, regulations, and standards) or informal (e.g., norms, habits, and customs) processes, and can create constraints on policy options ( [[#Breukers--2007|Breukers and]] [[#Wolsink--2007|Wolsink 2007]] ). For example, the car-dependent transport system is maintained by interlocking elements and institutions, consisting of (i) the automotive industry; (ii) the provision of car infrastructure; (iii) the political economy of urban sprawl; (iv) the provision of public transport; (v) cultures of car consumption ( [[#Mattioli--2020|Mattioli et al. 2020]] ). The behaviour of actors, their processes and implications on policy options and decisions are discussed further in [[#5.6|Section 5.6]] .

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=== Box 5.8 | Shifts from Private to Public Transport in an Indian Megacity ===

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In densely populated, fast-growing megacities, policymakers face the difficult challenge of preventing widespread adoption of petrol or diesel fuelled private cars as a mode of transport. The megacity of Kolkata in India provides a useful case study. As many as twelve different modes of public transportation, each with its own system structure, actors and meanings, co-exist and offer means of mobility to its 14 million citizens. Most of the public transport modes are shared mobility options, ranging from sharing between two people in a rickshaw or a few hundred in metro or sub-urban trains. Sharing also happens informally as daily commuters avail shared taxis and neighbours borrow each other’s car or bicycle for urgent or day trips.

Box 5.8

A key role is played by the state government, in collaboration with other stakeholders, to improve the system as whole and formalise certain semi-formal modes of transport. An important policy consideration has been to make Kolkata’s mobility system more efficient (in terms of speed, reliability and avoidance of congestion) and sustainable through strengthening coordination between different mode-based regimes ( [[#Ghosh--2019|Ghosh 2019]] ) and more comfortable with air conditioned space in a hot and humid climate ( [[#Roy--2018b|Roy et al. 2018b]] ). Policymakers have introduced multiple technological, behavioural and socio-cultural measures to tackle this challenge. New buses have been purchased by public authorities ( [[#Ghosh--2019|Ghosh and Schot 2019]] ). These have been promoted to middle class workers in terms of modernity, efficiency and comfort, and implemented using premium fares. Digitalisation and the sharing economy have encouraged take-up of shared taxi rides (‘app cabs’), being low cost and fast, but also influenced by levels of social trust involved in rides with strangers ( [[#Acheampong--2019|Acheampong and Siiba 2019]] ; [[#Ghosh--2019|Ghosh and Schot 2019]] ). Rickshaws have been improved through use of LNG and cycling has been banned from busy roads. These measures contributed positively to halving greenhouse gas emissions per unit of GDP tin one decade within the Kolkata metropolitan area, with potential for further reduction ( [[#Colenbrander--2016|Colenbrander et al. 2016]] ). However, social movements have opposed some changes due to concerns about social equity, since many of the new policies cater to middle class aspirations and preferences, at the cost of low-income and less privileged communities.

To conclude, urban mobility transitions in Kolkata show interconnected policy, institutional and socio-cultural drivers for socio-technical change. Change has unfolded in complex interactions between multiple actors, sustainability values and megatrends, where direct causalities are hard to identify. However, the prominence of policy actors as change agents is clear as they are changing multiple regimes from within. The state government initiated infrastructural change in public bus systems, coordinated with private and non-governmental actors such as auto-rickshaw operators and app cab owners, who hold crucial agency in offering public transport services in the city. The latter can directly be attributed to the global momentum of mobility-as-a-service platforms, at the intersection of digitalisation and sharing economy trends. More thoughtful action at a policy level is required to sustain and coordinate the diversity of public transport modes through infrastructure design and reflect on the overall direction of change ( [[#Roy--2018b|Roy et al. 2018b]] ; [[#Schot--2018|Schot and Steinmueller 2018]] ). See more in [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I, Section 5.SM.6.3.

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=== 5.4.5 Technological and Infrastructural Drivers ===

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Technologies and infrastructures shape social practices and their design matters for effective mitigation measures ( ''high evidence'' , ''high agreement'' ). There are systemic interconnections between infrastructures and practices ( [[#Cass--2018|Cass et al. 2018]] ; [[#Haberl--2021|Haberl et al. 2021]] ), and their intersection explains their relevance ( [[#Thacker--2019|Thacker et al. 2019]] ). The design of a new electricity system to meet new emerging demand based on intermittent renewable sources can lead to a change in consumption habits and the adaption of lifestyles compliant with more power supply interruption ( [[#Maïzi--2017|Maïzi et al. 2017]] ; [[#Maïzi--2019|Maïzi and Mazauric 2019]] ). The quality of the service delivery impacts directly the potential user uptake of low-carbon technologies among rural households. In the state of Himachal Pradesh in India, a shift from LPG to electricity among rural households, with induction stoves, has been successful due to the availability of stable and continuous electricity, which has been difficult to achieve in any other Indian state ( [[#Banerjee--2016|Banerjee et al. 2016]] ). In contrast, in South Africa, people who were using electricity earlier are now adopting LPG to diversify the energy source for cooking due to high electricity tariffs and frequent blackouts ( [[#Kimemia--2016|Kimemia and Annegarn 2016]] ) (Box 5.5 and [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I).

From a welfare point of view, infrastructure investments are not constrained by revealed or stated preferences ( ''high evidence, high agreement'' ). Preferences change with social and physical environment, and infrastructure interventions can be justified by objective measures, such as public health and climate change mitigation, not only given preferences ( ''high agreement'' , ''high evidence'' ). Specifically, there is a case for more investment in low-carbon transport infrastructure than assumed in environmental economics as it induces low-carbon preferences ( [[#Creutzig--2016a|Creutzig et al. 2016a]] ; [[#Mattauch--2016|Mattauch et al. 2016]] ; [[#Mattauch--2018|Mattauch et al. 2018]] ). Changes in infrastructure provision for active travel may contribute to uptake of more walking and cycling ( [[#Frank--2019|Frank et al. 2019]] ). These effects contribute to higher uptake of low-carbon travel options, albeit the magnitude of effects depends on design choices and context ( [[#Goodman--2013|Goodman et al. 2013]] ; [[#Goodman--2014|Goodman et al. 2014]] ; [[#Song--2017|Song et al. 2017]] ; [[#Javaid--2020|Javaid et al. 2020]] ; [[#Abraham--2021|Abraham et al. 2021]] ). Infrastructure is thus not only required to make low-carbon travel possible but can also be a pre-condition for the formation of low-carbon mobility preferences (see case study in Box 5.8).

The dynamic interaction of habits and infrastructures also predict CO 2 -intensive choices. When people move from a city with good public transport to a car-dependent city, they are more likely to own fewer vehicles due to learned preferences for lower levels of car ownership ( [[#Weinberger--2010|Weinberger and Goetzke 2010]] ). When individuals moving to a new city with extensive public transport were given targeted material about public transport options, the modal share of public transport increased significantly ( [[#Bamberg--2003|Bamberg et al. 2003]] ). Similarly, an exogenous change to route choice in public transport makes commuters change their habitual routes ( [[#Larcom--2017|Larcom et al. 2017]] ).

'''Table 5.4 | Main features, insights, and policyimplications of five drivers of decision and action.''' Entries in each column are independent lists, not intended to line up with each other.

{| class="wikitable"
|-
| '''Driver'''

| '''How does driver contribute to status quo bias?'''

| '''What needs to change?'''

| '''Driver’s policy implications'''

| '''Examples'''

|-
| '''Behavioural'''

| – Habits and routines formed under different circumstances do not get updated

– Present bias penalises upfront costs and discourages energy efficiency investments

– Loss aversion magnifies the costs of change

– When climate change is seen as distant, it is not feared

– Nuclear power and accident potential score high on psychological dread

| – New goals (sustainable lifestyle)

– New capabilities (online real-time communication)

– New resources (increased education)

– Use of full range of incentives and mechanisms to change demand-side behaviour

| – Policies need to be context specific and coordinate economic, legal, social, and infrastructural tools and nudges

– Relate climate action to salient local risks and issues

| – India’s new LPG scale up policy uses insights about multiple behavioural drivers of adoption and use

– Rooftop solar adoption expanded in Germany, when feed-in tariffs removed risk from upfront-cost recovery

– Nuclear power policies in Germany post Fukushima affected by emotional factors

|-
| '''Socio-cultural'''

| – Cultural norms (e.g., status, comfort, convenience) support existing behaviour

– Lack of social trust reduces willingness to shift behaviour (e.g., adopt car sharing)

– Fear of social disapproval decreases willingness to adopt new behaviours

– Lack of opportunities to participate in policy create reactance against ‘top-down’ imposition

– Unclear or dystopian narratives of climate response reduce willingness to change and to accept new policies and technologies

| – Create positive meanings and norms around low-emission service delivery (e.g., mass transit)

– Community initiatives to build social trust and engagement, capacity building, and social capital formation

– Climate movements that call out the insufficient, highly problematic state of delayed climate action

– Public participation in policymaking and technology implementation that increases trust, builds capacity and increases social acceptance

– Positive narratives about possible futures that avoid emissions (e.g., emphasis upon health and slow/active travel)

| – Embed policies in supportive social norms

– Support collective action on climate mitigation to create social trust and inclusion

– Involve arts and humanities to create narratives for policy process

| – Communicate descriptive norms to electricity end users

– Community energy initiative

– REScoop

– Fridays For Future

|-
| '''Business and corporate'''

| – Lock-in mechanisms that make incumbent firms reluctant to change: core capabilities, sunk investments in staff and factories, stranded assets

| – New companies (like car-sharing companies, renewable energy start-ups) that pioneer new business models or energy service provisions

| – Influence consumer behaviour via product innovation

– Provide capital for clean energy innovation

| – Electrification of transport opens up new markets for more than a hundred million new vehicles

|-
| '''Institutional'''

| – Lock-in mechanisms related to power struggles, lobbying, political economy

| – New policy instruments, policy discussions, policy platforms, implementation agencies, including capacity

| – Feed-in tariffs and other regulations that turn energy consumers into prosumers

| – Mobility case study, India’s LPG policy sequence

|-
| '''Infrastructural'''

| – Various lock-in mechanisms such as sunk investments, capabilities, embedding in routines/lifestyles

| – Many emerging technologies, which are initially often more expensive, but may benefit from learning curves and scale economies that drive costs down

| – Systemic governance to avoid rebound effects

| – Urban walking and bike paths

– Stable and continuous electricity supply fostering induction stoves

|}

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