Demand-side mitigation and new ways of providing services can helpavoid, shift , andimprovefinal service demand. Rapid and deep changes in demand make it easier for every sector to reduce greenhouse gas (GHG) emissions in the short and medium term (high confidence). {5.2, 5.3}

The indicative potential of demand-side strategies to reduce emissions of direct and indirect CO2 and non-CO2 GHG emissions in three end-use sectors (buildings, land transport, and food) is 40–70% globally by 2050 (high confidence). Technical mitigation potentials compared to the 2050 emissions projection of two scenarios consistent with policies announced by national governments until 2020 amount to 6.8 GtCO2 for building use and construction, 4.6 GtCO2 for land transport and 8.0 GtCO2-eq for food demand, and amount to 4.4 GtCO2 for industry. Mitigation strategies can be classified as Avoid-Shift-Improve (ASI) options, that reflect opportunities for socio-cultural, infrastructural, and technological change. The greatest ‘Avoid’ potential comes from reducing long-haul aviation and providing short-distance low-carbon urban infrastructures. The greatest ‘Shift’ potential would come from switching to plant-based diets. The greatest ‘Improve’ potential comes from within the building sector, and in particular increased use of energy-efficient end-use technologies and passive housing. {5.3.1, 5.3.2, Figure 5.7, Figure 5.8, Table 5.1, <a class='section-link' data-title='Demand, services and social aspects of mitigation' href='/chapters/chapter-5'>Chapter 5</a> Supplementary Material II, Table 5.SM.2}

Socio-cultural and lifestyle changes can accelerate climate change mitigation (medium confidence). Among 60 identified actions that could change individual consumption, individual mobility choices have the largest potential to reduce carbon footprints. Prioritising car-free mobility by walking and cycling and adoption of electric mobility could save 2 tCO2-eq cap –1 yr –1. Other options with high mitigation potential include reducing air travel, heating and cooling set-point adjustments, reduced appliance use, shifts to public transit, and shifting consumption towards plant-based diets. {5.3.1, 5.3.1.2, Figure 5.8}

Leveraging improvements in end-use service delivery through behavioural and technological innovations, and innovations in market organisation, leads to large reductions in upstream resource use (high confidence). Analysis of indicative potentials range from a factor 10- to 20-fold improvement in the case of available energy (exergy) analysis, with the highest improvement potentials at the end-user and service-provisioning levels. Realisable service-level efficiency improvements could reduce upstream energy demand by 45% in 2050. {5.3.2, Figure 5.10}

Alternative service provision systems, for example those enabled through digitalisation, sharing economy initiatives and circular economy initiatives, have to date made a limited contribution to climate change mitigation (medium confidence). While digitalisation through specific new products and applications holds potential for improvement in service-level efficiencies, without public policies and regulations, it also has the potential to increase consumption and energy use. Reducing the energy use of data centres, networks, and connected devices is possible in managing low-carbon digitalisation. Claims on the benefits of the circular economy for sustainability and climate change mitigation have limited evidence. {5.3.4, 5.3.4.1, 5.3.4.2, Figure 5.12, Figure 5.13}

Decent living standards and well-beingfor all are achievable through the implementation of high-efficiency low demand mitigation pathways (medium confidence). Decent living standards (DLS) – a benchmark of minimum material conditions for human well-being – overlaps with many Sustainable Development Goals (SDGs). Minimum requirements of energy use consistent with enabling well-being for all is between 20 and 50 GJ per person per year (cap –1 yr –1) depending on the context. {5.2.2.1, 5.2.2.2, Box 5.3}

Providing better services with less energy and resource input has high technical potential and is consistent with providing well-being for all (medium confidence). Assessment of 19 demand-side mitigation options and 18 different constituents of well-being show that positive impacts on well-being outweigh negative ones by a factor of 11. {5.2, 5.2.3, Figure 5.6}

Demand-side mitigation options bring multiple interacting benefits (high confidence). Energy services to meet human needs for nutrition, shelter, health, and so on are met in many different ways, with different emissions implications that depend on local contexts, cultures, geography, available technologies, and social preferences. In the near term, many less-developed countries and poor people everywhere require better access to safe and low-emissions energy sources to ensure decent living standards and increase energy savings from service improvements by about 20–25%. {5.2, 5.4.5, Figure 5.3, Figure 5.4, Figure 5.5, Figure 5.6, Box 5.2, Box 5.3}

Granular technologies and decentralised energy end use, characterised by modularity, small unit sizes and small unit costs, diffuse faster into markets and are associated with faster technological learning benefits, greater efficiency, more opportunities to escape technological lock-in, and greater employment (high confidence) . Examples include solar photovoltaic systems, batteries, and thermal heat pumps. {5.3, 5.5, 5.5.3}

Wealthy individuals contribute disproportionately to higher emissions and have a high potential for emissions reductions while maintaining decent living standards and well-being (high confidence) . Individuals with high socio-economic status are capable of reducing their GHG emissions by becoming role models of low-carbon lifestyles, investing in low-carbon businesses, and advocating for stringent climate policies. {5.4.1, 5.4.3, 5.4.4, Figure 5.14}

Demand-side solutions require both motivation and capacity for change (high confidence). Motivation by individuals or households worldwide to change energy consumption behaviour is generally low. Individual behavioural change is insufficient for climate change mitigation unless embedded in structural and cultural change. Different factors influence individual motivation and capacity for change in different demographics and geographies. These factors go beyond traditional socio-demographic and economic predictors and include psychological variables such as awareness, perceived risk, subjective and social norms, values, and perceived behavioural control. Behavioural nudges promote easy behaviour change, for example ‘Improve ’ actions such as making investments in energy efficiency, but fail to motivate harder lifestyle changes ( high confidence). {5.4}

Meta-analyses demonstrate that behavioural interventions, including the way choices are presented to consumers, 1 work synergistically with price signals, making the combination more effective (medium confidence). Behavioural interventions through nudges, and alternative ways of redesigning and motivating decisions, alone provide small to medium contributions to reduce energy consumption and GHG emissions. Green defaults, such as automatic enrolment in ‘green energy’ provision, are highly effective. Judicious labelling, framing, and communication of social norms can also increase the effect of mandates, subsidies, or taxes. {5.4, 5.4.1, Table 5.3a, Table 5.3b}

Coordinated change in several domains leads to the emergence of new low-carbon configurations with cascading mitigation effects (high confidence) . Demand-side transitions involve interacting and sometimes antagonistic processes on the behavioural, socio-cultural, institutional, business, and technological dimensions. Individual- or sectoral-level change may be stymied by reinforcing social, infrastructural, and cultural lock-ins. Coordinating the way choices are presented to end users and planners, physical infrastructures, new technologies and related business models can rapidly realise system-level change. {5.4.2, 5.4.3, 5.4.4, 5.4.5, 5.5}

Cultural change, in combination with new or adapted infrastructure, is necessary to enable and realise many ‘Avoid’ and ‘Shift’ options (medium confidence). By drawing support from diverse actors, narratives of change can enable coalitions to form, providing the basis for social movements to campaign in favour of (or against) societal transformations. People act and contribute to climate change mitigation in their diverse capacities as consumers, citizens, professionals, role models, investors, and policymakers. {5.4, 5.5, 5.6}

Collective action as part of social or lifestyle movements underpins system change (high confidence). Collective action and social organising are crucial to shift the possibility space of public policy on climate change mitigation. For example, climate strikes have given voice to youth in more than 180 countries. In other instances, mitigation policies allow the active participation of all stakeholders, resulting in building social trust, new coalitions, legitimising change, and thus initiate a positive cycle in climate governance capacity and policies. {5.4.2, Figure 5.14}

Transition pathways and changes in social norms often start with pilot experiments led by dedicated individuals and niche groups (high confidence). Collectively, such initiatives can find entry points to prompt policy, infrastructure, and policy reconfigurations, supporting the further uptake of technological and lifestyle innovations. Individuals’ agency is central as social change agents and narrators of meaning. These bottom-up socio-cultural forces catalyse a supportive policy environment, which enables changes. {5.5.2}

The current effects of climate change, as well as some mitigation strategies, are threatening the viability of existing business practices, while some corporate efforts also delay mitigation action (medium confidence). Policy packages that include job creation programmes help to preserve social trust, livelihoods, respect, and dignity of all workers and employees involved. Business models that protect rent-extracting behaviour may sometimes delay political action. Corporate advertisement and marketing strategies may also attempt to deflect corporate responsibility to individuals or aim to appropriate climate care sentiments in their own brand building. {5.4.3, 5.6.4}

Middle actors – professionals, experts, and regulators – play a crucial, albeit underestimated and underutilised, role in establishing low-carbon standards and practices (medium confidence). Building managers, landlords, energy efficiency advisers, technology installers, and car dealers influence patterns of mobility and energy consumption by acting as middle actors or intermediaries in the provision of building or mobility services and need greater capacity and motivation to play this role. {5.4.3}

Social influencers and thought leaders can increase the adoption of low-carbon technologies, behaviours, and lifestyles (high confidence). Preferences are malleable and can align with a cultural shift. The modelling of such shifts by salient and respected community members can help bring about changes in different service provisioning systems. Between 10% and 30% of committed individuals are required to set new social norms. {5.2.1, 5.4}

Social equity reinforces capacity and motivation for mitigating climate change (medium confidence). Impartial governance such as fair treatment by law and order institutions, fair treatment by gender, and income equity, increases social trust, thus enabling demand-side climate policies. High status (often high carbon) item consumption may be reduced by taxing absolute wealth without compromising well-being. {5.2, 5.4.2, 5.6}

Policies that increase the political access and participation of women, racialised, and marginalised groups increase the democratic impetus for climate action (high confidence). Including more differently situated knowledge and diverse perspectives makes climate mitigation policies more effective. {5.2, 5.6}

Carbon pricing is most effective if revenues are redistributed or used impartially (high confidence). A carbon levy earmarked for green infrastructures or saliently returned to taxpayers corresponding to widely accepted notions of fairness increases the political acceptability of carbon pricing. {5.6, Box 5.11}

Greater contextualisation and granularity in policy approaches better addresses the challenges of rapid transitions towards zero-carbon systems (high confidence). Larger systems take more time to evolve, grow, and change compared to smaller ones . Creating and scaling up entirely new systems takes longer than replacing existing technologies and practices. Late adopters tend to adopt faster than early pioneers. Obstacles and feasibility barriers are high in the early transition phases. Barriers decrease as a result of technical and social learning processes, network building, scale economies, cultural debates, and institutional adjustments. {5.5, 5.6}

The lockdowns implemented in many countries in response to the COVID-19 pandemic demonstrated that behavioural change at a massive scale and in a short time is possible (high confidence). COVID-19 accelerated some specific trends, such as increased uptake of urban cycling. However, the acceptability of collective social change over a longer term towards less resource-intensive lifestyles depends on social mandate building through public participation, discussion and debate over information provided by experts, to produce recommendations that inform policymaking. {Box 5.2}

Mitigation policies that integrate and communicate with the values people hold are more successful (high confidence). Values differ between cultures. Measures that support autonomy, energy security and safety, equity and environmental protection, and fairness resonate well in many communities and social groups. Changing 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. {5.4.1, 5.4.2}

Changes in consumption choices that are supported by structural changes and political action enable the uptake of low-carbon choices (high confidence). Policy instruments applied in coordination can help to accelerate change in a consistent desired direction. Targeted technological change, regulation, and public policy can help in steering digitalisation, the sharing economy, and circular economy towards climate change mitigation. {5.3, 5.6}

Complementarity in policies helps in the design of an optimal demand-side policy mix (medium confidence). In the case of energy efficiency, for example, this may involve CO2 pricing, standards and norms, and information feedback. {5.3, 5.4, 5.6}

There is high evidence and high agreement in the literature that human well-being and related metrics provide a societal perspective which is inclusive, compatible with sustainable development, and generates multiple ways to mitigate emissions. Development targeted to basic needs and well-being for all entails less carbon intensity than GDP-focused growth (Rao et al. 2014; Lamb and Rao 2015).

Current socioeconomic systems are based on high-carbon economic growth and resource use (Steffen et al. 2018b). Several systematic reviews confirm that economic growth is tightly coupled with increasing CO2 emissions (Ayres and Warr 2005; Tiba and Omri 2017; Mardani et al. 2019; Wiedenhofer et al. 2020) although the level of emissions depends on inequality (Baležentis et al. 2020; Liu et al. 2020b), and on geographic and infrastructural constraints that force consumers to use fossil fuels (Pottier et al. 2021). Different patterns emerge in the causality of the energy–growth nexus: (i) energy consumption causes economic growth; (ii) growth causes energy consumption; (iii) bidirectional causality; and (iv) no significant causality (Ozturk 2010). In a systematic review, Mardani et al. (2019) found that in most cases, energy use and economic growth have a bidirectional causal effect, indicating that as economic growth increases, further CO2 emissions are stimulated at higher levels; in turn, measures designed to lower GHG emissions may reduce economic growth. However, energy substitution and efficiency gains may offer opportunities to break the bidirectional dependency (Komiyama 2014; Brockway et al. 2017; Shuai et al. 2019). Worldwide trends reveal that at best only relative decoupling (resource use grows at a slower pace than GDP) was the norm during the twentieth century (Jackson 2009; Krausmann et al. 2009; Ward et al. 2016; Jackson 2016), while absolute decoupling (when material use declines as GDP grows) is rare, observed only during recessions or periods of low or no economic growth (Heun and Brockway 2019; Hickel and Kallis 2019; Vadén et al. 2020; Wiedenhofer et al. 2020). Recent trends in OECD countries demonstrate the potential for absolute decoupling of economic growth not only from territorial but also from consumption-based emissions (Le Quéré et al. 2019), albeit at scales insufficient for mitigation pathways (Vadén et al. 2020) (Chapter 2).

Energy demand and demand for GHG-intensive products increased from 2010 until 2020 across all sectors and categories. 2019 witnessed a reduction in energy demand growth rate to below 1% and 2020 an overall decline in energy demand, with repercussions for energy supply disproportionally affecting coal via merit order effects (Bertram et al. 2021) (Cross-Chapter Box 1 in Chapter 1). There was a slight but significant shift from high-carbon beef consumption to medium-carbon intensive poultry consumption. Final energy use in buildings grew from 118 EJ in 2010 to around 128 EJ in 2019 (increased about 8%). The highest increase was observed in non-residential buildings, with a 13% increase against 8% in residential energy demand (IEA 2019a). While electricity accounted for one-third of building energy use in 2019, fossil fuel use also increased at a marginal annual average growth rate of 0.7% since 2010 (IEA 2020a). Energy-related CO2 emissions from buildings have risen in recent years after flattening between 2013 and 2016. Direct and indirect emissions from electricity and commercial heat used in buildings rose to 10 GtCO2 in 2019, the highest level ever recorded. Several factors have contributed to this rise, including growing energy demand for heating and cooling with rising air conditioner ownership and extreme weather events. A critical issue remains how comfortable people feel with temperatures they will be exposed to in the future and this depends on physical, psychological and behavioural factors (Singh et al. 2018; Jacobs et al. 2019). Literature now shows high evidence and high agreement around the observation that policies and infrastructure interventions that lead to change in human preferences are more valuable for climate change mitigation. In economics, welfare evaluations are predominantly based on the preference approach. Preferences are typically assumed to be fixed, so that only changes in relative prices will reduce emissions. However, as decarbonisation is a societal transition, individuals’ preferences do shift and this can contribute to climate change mitigation (Gough 2015). Even if preferences are assumed to change in response to policy, it is nevertheless possible to evaluate policy, and demand-side solutions, by approaches to well-being and welfare that are based on deeper concepts of preferences across disciplines (Roy and Pal 2009; Fleurbaey and Tadenuma 2014; Komiyama 2014; Dietrich and List 2016; Mattauch and Hepburn 2016). In cases of past societal transitions, such as smoking reduction, there is evidence that societies guided the processes of shifting preferences, and values changed along with changing relative prices (Nyborg and Rege 2003; Stuber et al. 2008; Brownell and Warner 2009). Further evidence on changing preferences in consumption choices pertinent to decarbonisation includes Grinblatt et al. (2008) and Weinberger and Goetzke (2010) for mobility; Erb et al. (2016), Muller et al. (2017), and Costa and Johnson (2019) for diets; and Baranzini et al. (2017) for solar panel uptake. If individuals’ preferences and values change during a transition to the low-carbon economy, then this overturns conclusions on what count as adequate or even optimal policy responses to climate change mitigation in economics (Jacobsen et al. 2012; Schumacher 2015; Dasgupta et al. 2016; Daube and Ulph 2016; Ulph and Ulph 2021). In particular, if policy instruments, such as awareness campaigns, infrastructure development or education, can change people’s preferences, then policies or infrastructure provision – socially constrained by deliberative decision making – which change both relative prices and preferences, are more valuable for mitigation than previously thought (Creutzig et al. 2016b; Mattauch et al. 2016; Mattauch et al. 2018). The provisioning context of human needs is participatory, so transformative mitigation potential arises from social as well as technological change (Lamb and Steinberger 2017). Many dimensions of well-being and ‘basic needs’ are social, not individual, in character (Schneider 2016), so extending well-being and DLS analysis to emissions also involves understanding individual situations in social contexts. This includes building supports for collective strategies to reduce emissions (Chan et al. 2019), going beyond individual consumer choice. Climate policies that affect collective behaviour fairly are the most acceptable policies across political ideologies (Clayton 2018); thus collective preferences for mitigation are synergistic with evolving policies and norms in governance contexts that reduce risk, ensure social justice and build trust (Atkinson et al. 2017; Cramton et al. 2017; Milkoreit 2017; Tvinnereim et al. 2017; Smith and Reid 2018; Carattini et al. 2019).

Because of data limitations, which can make cross-country comparisons difficult, health-based indicators and in particular life expectancy (Lamb et al. 2014) have sometimes been proposed as quick and practical ways to compare local or national situations, climate impacts, and policy effects (Decancq et al. 2009; Sager 2017; Burstein et al. 2019). A number of different well-being metrics are valuable in emphasising the constituents of what is needed for a decent life in different dimensions (Lamb and Steinberger 2017; Porter et al. 2017; Smith and Reid 2018). The SDGs overlap in many ways with such indicators, and the data needed to assess progress in meeting the SDGs is also useful for quantifying well-being (Gough 2017). For the purposes of this chapter, indicators directly relating GHG emissions to well-being for all are particularly relevant.

Well-being can be categorised either as ‘hedonic’ or ‘eudaimonic’. Hedonic well-being is related to a subjective state of human motivation, balancing pleasure over pain, and has gained influence in psychology assessing ‘subjective well-being’, assuming that the individual is motivated to enhance personal freedom, self-preservation and enhancement (Sirgy 2012; Brand-Correa and Steinberger 2017; Lamb and Steinberger 2017; Ganglmair-Wooliscroft and Wooliscroft 2019). Eudaimonic well-being focuses on the individual in the broader context, associating happiness with virtue (Sirgy 2012), allowing for the creation of social institutions and political systems and considering their ability to enable individuals to flourish. Eudaimonic analysis supports numerous development approaches (Fanning and O’Neill 2019) such as the capabilities (Sen 1985), human needs (Doyal and Gough 1991; Max-Neef et al. 1991) and models of psychosocial well-being (Ryan and Deci 2001). Measures of well-being differ somewhat in developed and developing countries (Sulemana et al. 2016; Ng and Diener 2019); for example, food insecurity, associated everywhere with lower subjective well-being, is more strongly associated with poor subjective well-being in more-developed countries (Frongillo et al. 2019); in wealthier countries, the relationship between living in rural areas is less strongly associated with negative well-being than in less-developed countries (Requena 2016); and income inequality is negatively associated with subjective well-being in developed countries, but positively so in less-developed countries (Ngamaba et al. 2018). This chapter connects demand-side climate mitigation options to multiple dimensions of well-being, going beyond the single dimensional metric of GDP which is at the core of IAMs. Many demand side-mitigation solutions generate positive and negative impacts on wider dimensions of human well-being which are not always quantifiable (medium evidence, medium agreement ).

There is high evidence and high agreement in literature that socio-economic equity builds not only well-beingfor all, but also trust and effective participatory governance,which in turn strengthen demand-side climate mitigation. Equity, participation, social trust, well-being, governance and mitigation are parts of a continuous interactive and self-reinforcing process (Figure 5.5). Chapter 5 Supplementary Material I (Section 5.SM.1) contains more detail on these links, drawing from social science literature.

Economic growth in equitable societies is associated with lower emissions than in inequitable societies (McGee and Greiner 2018), and income inequality is associated with higher global emissions (Ravallion et al. 1997; McGee and Greiner 2018; Rao and Min 2018c; Diffenbaugh and Burke 2019; Fremstad and Paul 2019; Liu and Hao 2020). Relatively slight increases in energy consumption and carbon emissions produce great increases in human development and well-being in less-developed countries, and the amount of energy needed for a high global level of human development is dropping (Steinberger and Roberts 2010). Equitable and democratic societies which provide high quality public services to their population have high well-being outcomes at lower energy use than those which do not, whereas those which prioritise economic growth beyond moderate incomes and extractive sectors display a reversed effect (Vogel et al. 2021).

Well-designed climate mitigation policies ameliorate constituents of well-being (Creutzig et al. 2021b). The study shows that of all demand-side option effects on well-being, 79% are positive, 18% are neutral (or not relevant or specified), and only 3% are negative ( high confidence) (Creutzig et al. 2021b) (Figure 5.6). Figure 5.6 illustrates that active mobility (cycling and walking), efficient buildings and prosumer choices of renewable technologies have the most encompassing beneficial effects on well-being, with no negative outcomes detected. Urban and industry strategies are highly positive overall for well-being, but they will also reshape supply-side businesses with transient intermediate negative effects. Shared mobility, like all the others, has overall highly beneficial effects on well-being, but also displays a few negative consequences, depending on implementation, such as a minor decrease in personal security for patrons of ride-sourcing.

Well-being improvements are most notable in health, air, and energy ( high confidence). These categories are also most substantiated in the literature, often under the framing of co-benefits. In many cases, co-benefits outweigh the mitigation benefits of specific GHG emission reduction strategies. Food (medium confidence), mobility ( high confidence), and water (medium confidence) are further categories where well-being is improved. Mobility has entries with highest well-being rankings for teleworking, compact cities, and urban system approaches. Effects on well-being in water and sanitation mostly come from buildings and urban solutions. Social dimensions, such as personal security, social cohesion, and especially political stability, are less predominantly represented. An exception is economic stability, suggesting that demand-side options generate stable opportunities to participate in economic activities ( high confidence). Although the relation between demand-side mitigation strategies and the social aspects of human well-being is important, this has been less reflected in the literature so far, and hence the assessment finds more neutral/unknown interactions (Figure 5.6).

Policies designed to foster higher well-being for all via climate mitigation include reducing emissions through wider participation in climate action, building more effective governance for improved mitigation, and including social trust, greater equity, and informal-sector support as integral parts of climate policies. Public participation facilitates social learning and people’s support of and engagement with climate change priorities; improved governance is closely tied to effective climate policies (Phuong et al. 2017). Better education, health care, valuing of social diversity, and reduced poverty – characteristics of more equal societies – all lead to resilience, innovation, and readiness to adopt progressive and locally-appropriate mitigation policies, whether high-tech or low-tech, centralised or decentralised (Tanner et al. 2009; Lorenz 2013; Chu 2015; Cloutier et al. 2015; Mitchell 2015; Martin and Shaheen 2016; Vandeweerdt et al. 2016; Turnheim et al. 2018). Moreover, these factors are the ones identified as enablers of high need satisfaction at lower energy use (Vogel et al. 2021).

There is less policy lock-in in more equitable societies (Seto et al. 2016). International communication, networking, and global connections among citizens are more prevalent in more equitable societies, and these help spread promising mitigation approaches (Scheffran et al. 2012). Climate-related injustices are addressed where equity is prioritised (Klinsky and Winkler 2014). Thus, there is high confidence in the literature that addressing inequities in income, wealth, and DLS not only raises overall well-being and furthers the SDGs but also improves the effectiveness of climate change mitigation policies. For example, job creation, retraining for new jobs, local production of livelihood necessities, social provisioning, and other positive steps toward climate mitigation and adaptation are all associated with more equitable and resilient societies (Okvat and Zautra 2011; Bentley 2014; Klinsky et al. 2016; Roy et al. 2018a). At all scales of governance, the popularity and sustainability of climate policies requires attention to the fairness of their health and economic implications for all, and participatory engagement across social groups – a responsible development framing (Cazorla and Toman 2001; Dulal et al. 2009; Chuku 2010; Shonkoff et al. 2011; Navroz 2019; Hofstad and Vedeld 2020; Muttitt and Kartha 2020; Roy and Schaffartzik 2020; Temper et al. 2020; Waller et al. 2020). Far from being secondary or even a distraction from climate mitigation priorities, an equity focus is intertwined with mitigation goals (Klinsky et al. 2016). Demand-side climate mitigation options have pervasive ancillary, equity-enhancing benefits, for example for health, local livelihoods, and community forest resources (Chhatre and Agrawal 2009; Garg 2011; Shaw et al. 2014; Serrao-Neumann et al. 2015; Klausbruckner et al. 2016; Salas and Jha 2019) (Figure 5.6). Limiting climate change risks is fundamental to collective well-being (Max-Neef et al. 1989; Yamin et al. 2005; Nelson et al. 2013; Gough 2015; Gough 2017; Pecl et al. 2017; Tschakert et al. 2017). Section 5.6 discusses well-designed climate policies more fully, with examples. Rapid changes in social norms which are underway and which underlie socially-acceptable climate policy initiatives are discussed in section 5.4.

The distinction between necessities and luxuries helps to frame a growing stream of social sciences literature with climate policy relevance (Arrow et al. 2004; Ramakrishnan and Creutzig 2021). Given growing public support worldwide for strong sustainability, sufficiency, and sustainable consumption, changing demand patterns and reduced demand are accompanying environmental and social benefits (Jackson 2008; Fedrigo et al. 2010; Schroeder 2013; Figge et al. 2014; Spangenberg and Germany 2016; Spengler 2016; Burke 2020; Mont et al. 2020). Beyond a threshold, increased material consumption is not closely correlated with improvements in human progress (Frank 1999; Kahneman and Deaton 2010; Steinberger and Roberts 2010; Roy et al. 2012; Oishi et al. 2018; Xie et al. 2018; Vita et al. 2019b; Wang et al. 2019; Vita et al. 2020). Policies focusing on the ‘super-rich’, also called the ‘polluter elite’, are gaining attention for moral or norms-based as well as emissions-control reasons (Kenner 2019; Otto et al. 2019; Pascale et al. 2020; Stratford 2020) (Section 5.2.2.3). Conspicuous consumption by the wealthy is the cause of a large proportion of emissions in all countries, related to expenditures on such things as air travel, tourism, large private vehicles and large homes (Brand and Boardman 2008; Roy and Pal 2009; Roy et al. 2012; Brand and Preston 2010; Gore 2015; Hubacek et al. 2017; Jorgenson et al. 2017; Sahakian 2018; Gössling 2019; Kenner 2019; Lynch et al. 2019; Osuoka and Haruna 2019).

Since no country now meets its citizens’ basic needs at a level of resource use that is globally sustainable, while high levels of life satisfaction for those just escaping extreme poverty require even more resources, the need for transformative shifts in governance and policies is large (O’Neill et al. 2018; Vogel et al. 2021).

Inequitable societies use energy and resources less efficiently. Higher income inequality is associated with higher carbon emissions, at least in developed countries (Grunewald et al. 2011; Golley and Meng 2012; Chancel et al. 2015; Grunewald et al. 2017; Jorgenson et al. 2017; Sager 2017; Klasen 2018; Liu et al. 2019); reducing inequality in high-income countries helps to reduce emissions (Klasen 2018). There is high agreement in the literature that alienation or distrust weakens collective governance and fragments political approaches towards climate action (Smit and Pilifosova 2001; Adger et al. 2003; Hammar and Jagers 2007; Van Vossole 2012; Bulkeley and Newell 2015; Smith and Howe 2015; ISSC et al. 2016; Alvaredo et al. 2018; Smith and Mayer 2018; Fairbrother et al. 2019; Hayward and Roy 2019; Kulin and Johansson Sevä 2019; Liao et al. 2019).

Populism and politics of fear are less prevalent under conditions of more income equality (Chevigny 2003; Bryson and Rauwolf 2016; O’Connor 2017; Fraune and Knodt 2018; Myrick and Evans Comfort 2019). Ideology and other social factors also play a role in populist climate scepticism, but many of these also relate to resentment of elites and desire for engagement (Swyngedouw 2011; Lockwood 2018; Huber et al. 2020). ‘Climate populism’ movements are driven by an impetus for justice (Beeson 2019; Hilson 2019). When people feel powerless and/or that climate change is too big a problem to solve because others are not acting, they may take less action themselves (Williams and Jaftha 2020). However, systems for benefit-sharing can build trust and address large-scale ‘commons dilemmas’, in the context of strong civil society (Barnett 2003; Mearns and Norton 2009; Inderberg et al. 2015; Sovacool et al. 2015; Hunsberger et al. 2017; Soliev and Theesfeld 2020). Leadership is also important in fostering environmentally-responsible group behaviours (Liu and Hao 2020).

In some less-developed countries, higher income inequality may in fact be associated with lower per capita emissions, but this is because people who are excluded by poverty from access to fossil fuels must rely on biomass (Klasen 2018). Such energy poverty – the fact that millions of people do not have access to energy sources to help meet human needs – implies the opposite of development (Guruswamy 2010; Guruswamy 2020). In developing countries, livelihood improvements do not necessarily cause increases in emissions (Peters et al. 2012; Reusser et al. 2013; Creutzig et al. 2015a; Chhatre and Agrawal 2009; Baltruszewicz et al. 2021) and poverty alleviation causes negligible emissions (Chakravarty et al. 2009). Greater equity is an important step towards sustainable service provisioning (Godfray et al. 2018; Dorling 2019; Timko 2019).

As discussed in Section 5.6, policies to assist the low-carbon energy transition can be designed to include additional benefits for income equality, besides contributing to greater energy access for the poor (Burke and Stephens 2017; Frank 2017; Healy and Barry 2017; Sen 2017; Chapman et al. 2018; La Viña et al. 2018; Chapman and Fraser 2019; Piggot et al. 2019; Sunderland et al. 2020). Global and intergenerational climate inequities impact people’s well-being, which affects their consumption patterns and political actions (Albrecht et al. 2007; Fritze et al. 2008; Gori-Maia 2013; Clayton et al. 2015; Pizzigati 2018) (Box 5.4).

Consumption reductions, both voluntary and policy-induced, can have positive and double-dividend effects on efficiency as well as reductions in energy and materials use (Mulder et al. 2006; Harriss and Shui 2010; Figge et al. 2014; Grinde et al. 2018; Spangenberg and Lorek 2019; Vita et al. 2020). Less waste, better emissions control and more effective carbon policies lead to better governance and stronger democracies. Systems-dynamics models linking strong emissions-reducing policies and strong social equity policies show that a low-carbon transition in conjunction with social sustainability is possible, even without economic growth (Kallis et al. 2012; Jackson and Victor 2016; Stuart et al. 2017; Chapman and Fraser 2019; D’Alessandro et al. 2019; Gabriel and Bond 2019; Huang et al. 2019; Victor 2019). Such degrowth pathways may be crucial in combining technical feasibility of mitigation with social development goals (Hickel et al. 2021; Keyßer and Lenzen 2021).

Multi-level or polycentric governance can enhance well-being and improve climate governance and social resilience, due to varying adaptive, flexible policy interventions at different times and scales (Kern and Bulkeley 2009; Lidskog and Elander 2009; Amundsen et al. 2010; Keskitalo 2010; Lee and Koski 2015; Jokinen et al. 2016; Lepeley 2017; Marquardt 2017; Di Gregorio et al. 2019). Institutional transformation may also result from socio-ecological stresses that accompany climate change, leading to more effective governance structures (David Tàbara et al. 2018; Patterson and Huitema 2019; Barnes et al. 2020). An appropriate, context-specific mix of options facilitated by policies can deliver both higher well-being and reduced disparity in access to basic needs for services concurrently with climate mitigation (Thomas and Twyman 2005; Mearns and Norton 2009; Klinsky and Winkler 2014; Lamb et al. 2014; Lamb and Steinberger 2017). Hence, nurturing equitable human well-being through provision of decent living standards for all goes hand in hand with climate change mitigation (ISSC et al. 2016; OECD 2019a). There is high confidence in the literature that addressing inequities in income, wealth, and DLS not only raises overall well-being and furthers the SDGs but also improves the effectiveness of climate change mitigation policies.

Participatory governance involves understanding and engagement with policies, including climate policies. Greater public participation in climate policy processes and governance, by increasing the diversity of ideas and stakeholders, builds resilience and allows broader societal transformation towards systemic change, even in complex, dynamic and contested contexts (Dombrowski 2010; Wise et al. 2014; Haque et al. 2015; Jodoin et al. 2015; Mitchell 2015; Kaiser 2020; Alegria 2021). This sometimes involves complex policy discussions that can lead to governance innovations, also influencing social norms (Martinez 2020). A specific example are citizen assemblies, deliberating public policy challenges, such as climate change (Devaney et al. 2020). Activist climate movements are changing policies as well as normative values (Section 5.4 and the Social Science Primer, Chapter 5 Supplementary Material I). Environmental justice and climate justice activists worldwide have called attention to the links between economic and environmental inequities, collected and publicised data about them, and demanded stronger mitigation (Goodman 2009; Schlosberg and Collins 2014; Jafry 2019; Cheon 2020). Youth climate activists, and Indigenous leaders, are also exerting growing political influence towards mitigation (Helferty and Clarke 2009; White 2011; Powless 2012; Petheram et al. 2015; UN 2015; Curnow and Gross 2016; Grady-Benson and Sarathy 2016; Claeys and Delgado Pugley 2017; O’Brien et al. 2018; Rowlands and Gomez Peña 2019; Bergmann and Ossewaarde 2020; Han and Ahn 2020; Nkrumah 2021). Indigenous resurgence (activism fuelled by ongoing colonial social and environmental injustices, land claims, and deep spiritual and cultural commitment to environmental protection) not only strengthens climate leadership in many countries, but also changes broad social norms by raising knowledge of Indigenous governance systems which supported sustainable lifeways over thousands of years (Wildcat 2014; Chanza and De Wit 2016; Whyte 2017; Whyte 2018, Temper et al. 2020). Related trends include recognition of the value of traditional ecological knowledge, Indigenous governance principles, decentralisation, and appropriate technologies (Lange et al. 2007; Goldthau 2014; Whyte 2017).

Social trust aids policy implementation. More equal societies display higher trust, which is a key requirement for successful implementation of climate policies (Rothstein and Teorell 2008; Carattini et al. 2015; Klenert et al. 2018; Patterson et al. 2018). Inter-personal trust among citizens often promotes pro-environment behaviour by influencing perceptions (Harring and Jagers 2013), enhancing cooperation, and reducing free-riding and opportunistic behaviour (Gür 2020). Individual support for carbon taxes and energy innovations falls when collective community support is lacking (Bolsen et al. 2014; Smith and Mayer 2018; Simon 2020). Social trust has a positive influence on civic engagement among local communities, NGOs, and self-help groups for local clean cooking fuel installation (Nayak et al. 2015).

Section 5.6 includes examples of climate mitigation policies and policy packages which address the interrelationships shown in Figure 5.5. Improving well-being for all through climate mitigation includes emissions-reduction goals in policy packages that ensure equitable outcomes, prioritise social trust-building, support wide public participation in climate action including within the informal sector, and facilitate institutional change for effective multi-level governance, as integral components of climate strategies. This strategic approach, and its feasibility of success, rely on complex contextual factors that may differ widely, especially between the Global North and Global South (Atteridge et al. 2012; Patterson et al. 2018; Jewell and Cherp 2020; Singh et al. 2020; Singh et al. 2021).

Thissection organises demand reductions under the ASI framework. It presents service-oriented demand-side solutions consistent with decent living standards (Creutzig et al. 2018) (Table 5.1). The sharing economy, digitalisation, and the circular economy can all contribute to ASI strategies, with the circular economy tentatively more on the supply side, and the sharing economy and digitalisation tentatively more on the demand side (Section 5.3.4). These new service delivery models go beyond sectoral boundaries (IPCC sector chapter boundaries are explained in Chapter 12) and take advantage of technological innovations, design concepts, and innovative forms of cooperation, cutting across sectors to contribute to systemic changes worldwide. Some of these changes can be realised in the short term, such as energy access, while others may take a longer period, such as radical and systemic eco-innovations like shared electric autonomous vehicles. It is important to understand benefits and distributional impacts of these systemic changes.

Table 5.1 | Avoid-Shift-Improve options in selected sectors and services. Many options, such as urban form and infrastructures, are systemic, and influence several sectors simultaneously. Linkages to concepts presented in sectoral chapters are indicated in parentheses in the first column. Source: adapted from Creutzig at al. (2018).

Service delivery systems to satisfy a variety of service needs (e.g., mobility, nutrition, thermal comfort, etc.) comprise a series of interlinked processes to convert primary resources (e.g., coal, minerals) into useable products (e.g., electricity, copper wires, lamps, light bulbs). It is useful to differentiate between conversion and processing steps ‘upstream’ of end users (mines, power plants, manufacturing facilities) and ‘downstream’, that is, those associated with end-users, including service levels, and direct well-being benefits for people (Kalt et al. 2019). Illustrative examples of such resource processing systems and associated conversion losses drawn from the literature are shown in Figure 5.9, in the form of resource processing cascades for energy (direct energy conversion efficiencies (Nakićenović et al. 1993; De Stercke 2014)), water use in food production systems (water use efficiency and embodied water losses in food delivery and consumption (Lundqvist et al. 2008; Sadras et al. 2011)), and materials (Ayres and Simonis 1994; Fischer-Kowalski et al. 2011), using the example of steel manufacturing, use and recycling at the global level (Allwood and Cullen 2012). Invariably, conversion losses along the entire service delivery systems are substantial, ranging from 83% (water) to 86% (energy) and 87% (steel) of primary resource inputs (TWI2050 2018). In other words, only between 14 to 17% of the harnessed primary resources remain at the level of ultimate service delivery.

Examples of conversion losses on the supply side of resource processing systems include, for instance: for energy, electricity generation (global output/input conversion efficiency of electric plants of 45% as shown in energy balance statistics (IEA 2020b)); for water embodied in food, irrigation water use efficiency (some 40% (Sadras et al. 2011)) and calorific conversion efficiency (food calories in to food calories out) in meat production of 60% (Lundqvist et al. 2008), or for materials, globally only 47% of primary iron ore extracted and recovered steel scrap end up as steel in purchased products, (i.e., a loss of 57%) (Allwood and Cullen 2012).

A substantial part of losses happens at the end-use point and in final service delivery (where losses account for 47% to 60% of aggregate systems losses for steel and energy respectively, and 23% in the case of water embodied in food). The efficiency of service delivery (Brand-Correa and Steinberger 2017) has usually both a technological component (efficiency of end-use devices such as cars, light bulbs) and a behavioural component (i.e., how efficiently end-use devices are used, e.g., load factors) (Dietz et al. 2009; Laitner et al. 2009; Norton 2012; Kane and Srinivas 2014; Ehrhardt-Martinez 2015; Thaler 2015; Lopes et al. 2017). Using the example of mobility, where service levels are usually expressed by passenger-km, service delivery efficiency is thus a function of the fuel efficiency of the vehicle and its drivetrain (typically only about 20%–25% for internal combustion engines, but close to 100% for electric motors) plus how many passengers the vehicle actually transports (load factor, typically as low as 20–25%, i.e. one passenger per vehicle that could seat four to five), that is, an aggregate end-use efficiency of between 4–6% only. Aggregated energy end-use efficiencies at the global level are estimated as low as 20% (De Stercke 2014), 13% for steel (recovered post-use scrap) (Allwood and Cullen 2012), and some 70% for food (including distribution losses and food waste of some 30%) (Lundqvist et al. 2008).

To harness additional gains in efficiency by shifting the focus in service delivery systems to the end user can translate into large upstream resource reductions. For each unit of improvement at the end-use point of the service delivery system (examples shown in Figure 5.9), primary resource inputs are reduced between a factor of 6 to 7 units (water, steel, energy) (TWI2050 2018). For example, reducing energy needs for final service delivery equivalent to 1 EJ, reduces primary energy needs by some 7 EJ. There is thus high evidence and high agreement in the literature that the leverage effect for improvements in end-use service delivery efficiency through behavioural, technological, and market organisational innovations is very large, ranging from a factor 6 to 7 (resource cascades) to up to a factor 10 to 20 (exergy analysis), with the highest improvement potentials at the end-user and service provisioning levels (for systemic reviews see Nakićenović et al. (1996a), Grubler et al. (2012b), and Sousa et al. (2017)). Also, the literature shows high agreement that current conversion efficiencies are invariably low, particularly for those components at the end-use and service-delivery back end of service provisioning systems. It also suggests that efficiencies might actually be even lower than those revealed by direct input-output resource accounting, as discussed above (Figure 5.9). Illustrative exergy efficiencies of entire national or global service delivery systems range from 2.5% (USA (Ayres 1989)) to 5% (OECD average (Grubler et al. 2012b)) and 10% (global (Nakićenović et al., 1996)). Studies that adopt more restricted systems boundaries, either leaving out upstream resource processing/conversion or conversely end-use and service provision, show typical exergetic efficiencies between 15% (city of Geneva (Grubler et al. 2012a)) to below 25% (Japan, Italy, and Brazil, albeit with incomplete systems coverage that miss important conversion losses (Nakićenović et al. 1996b)). These findings are confirmed by more recent exergy efficiency studies that also include longitudinal time trend analysis (Cullen and Allwood 2010; Brockway et al. 2014; Serrenho et al. 2014; Brockway et al. 2015; Guevara et al. 2016). Figure 5.10 illustrates how energy demand reductions can be realised by improving the resource efficiency cascades shown in Figure 5.9.

Long-term mitigation scenarios play a crucial role in climate policy design in the near term, by illuminating transition pathways, interactions between supply-side and demand-side interventions, their timing, and the scales of required investments needed to achieve mitigation goals (Chapter 3). Historically, most long-term mitigation scenarios have taken technology-centric approaches with heavy reliance on supply-side solutions and the use of carbon dioxide removal, particularly in 1.5°C scenarios (Rogelj et al. 2018). Comparatively less attention has been paid to deep demand-side reductions incorporating socio-cultural change and the cascade effects (Section 5.3.2) associated with ASI strategies, primarily due to limited past representation of such service-oriented interventions in long-term integrated assessment models (IAMs) and energy systems models (ESMs) (Grubler et al. 2018; van de Ven et al. 2018; Napp et al. 2019). There is ample evidence of savings from sector- or issue-specific bottom-up studies (Section 5.3.1.2). However, these savings typically get lost in the dominant narrative provided by IAMs and ESMs and in their aggregate-level evaluations of combinations of ASI and efficiency strategies. As a result, their interaction effects do not typically get equal focus alongside supply-side and carbon dioxide removal options (Samadi et al. 2017; Van Vuuren et al. 2018; Van den Berg et al. 2019).

In response to 1.5°C ambitions, and a growing desire to identify participatory pathways with less reliance on carbon dioxide removal which has high uncertainty, some recent IAM and ESM mitigation scenarios have explored the role of deep demand-side energy and resource use reduction potentials at global and regional levels. Table 5.2 summarises long-term scenarios that aimed to: minimise service-level energy and resource demand as a central mitigation tenet; specifically evaluate the role of behavioural change and ASI strategies; and/or achieve a carbon budget with limited or no carbon dioxide removal. From assessment of this emerging body of literature, several general observations arise and are presented below.

First, socio-cultural changes within transition pathways can offer gigatonne-scale CO2 savings potential at the global level, and therefore represent a substantial overlooked strategy in traditional mitigation scenarios. Two lifestyle change scenarios conducted with the IMAGE IAM suggested that behaviour and cultural changes such as heating and cooling set-point adjustments, shorter showers, reduced appliance use, shifts to public transit, less meat-intensive diets, and improved recycling can deliver an additional 1.7 Gt and 3 GtCO2 savings in 2050, beyond the savings achieved in traditional technology-centric mitigation scenarios for the 2°C and 1.5°C ambitions, respectively (van Sluisveld et al. 2016; Van Vuuren et al. 2018). In its Sustainable Development Scenario, the IEA’s behavioural change and resource efficiency wedges deliver around 3 GtCO2-eq reduction in 2050, combined savings, roughly equivalent to those of solar PV that same year (IEA 2019a). In Europe, a Global Change Assessment Model (GCAM) scenario evaluating combined lifestyle changes such as teleworking, travel avoidance, dietary shifts, food waste reductions, and recycling reduced cumulative EU 27 CO2 emissions 2011–2050 by up to 16% compared to an SSP2 baseline (van de Ven et al. 2018). Also in Europe, a multi-regional input-output analysis suggested that adoption of low-carbon consumption practices could reduce carbon footprints by 25%, or 1.4 Gt (Moran et al. 2020). A global transport scenario suggests that transport sector emissions can decline from business-as-usual 18 GtCO2-eq to 2 GtCO2-eq if ASI strategies are deployed (Gota et al. 2019), a value considerably below the estimates provided in IAM scenarios that have limited or no resolution in ASI strategies (Chapter 10).

The IEA’s Net-Zero Emissions by 2050 (NZE) scenario, in which behavioural changes lead to 1.7 GtCO2 savings in 2030, expresses the substantial mitigation opportunity in terms of low-carbon technology equivalencies: to achieve the same emissions reductions, the global share of EVs in the NZE would have to increase from 20% to 45% by 2030 or the number of installed heat pumps in homes would have to increase from 440 to 660 million by 2030 (IEA 2021).

In light of the limited number of mitigation scenarios that represent socio-behavioural changes explicitly, there is medium evidence in the literature that such changes can reduce emissions at regional and global levels, but high agreement within that literature that such changes hold up to gigatonne-scale CO2 emissions reduction potentials.

Second, pursuant to the ASI principle, deep demand reductions require parallel pursuit of behavioural change and advanced energy-efficient technology deployment; neither is sufficient on its own. The LED scenario (Figure 5.10) combines behavioural and technological change consistent with numerous ASI strategies that leverage digitalisation, sharing, and circular economy megatrends to deliver decent living standards while reducing global final energy demand in 2050 to 245 EJ (Grubler et al. 2018). This value is 40% lower than final energy demand in 2018 (IEA 2019a), and a lower 2050 outcome than other IAM/ESM scenarios with primarily technology-centric mitigation approaches (Teske et al. 2015; IEA 2017b). In the IEA’s B2DS scenario, Avoid/Shift in the transport sector accounts for around 2 GtCO2-eq yr –1 in 2060, whereas parallel vehicle efficiency improvements increase the overall mitigation wedge to 5.5 GtCO2-eq yr –1 in 2060 (IEA 2017b). Through a combination of behavioural change and energy-efficient technology adoption, the IEA’s NZE requires only 340 EJ of global final energy demand with universal energy access in 2050, which is among the lowest of IPCC net zero SR1.5 scenarios (IEA 2021).

Third, low demand scenarios can reduce both supply-side capacity additions and the need for carbon capture and removal technologies to reach emissions targets. Of the scenarios listed in Table 5.2, one (LED-MESSAGE) reaches 2050 emissions targets with no carbon capture or removal technologies (Grubler et al. 2018), whereas others report significant reductions in reliance on bioenergy with carbon capture and storage (BECCS) compared to traditional technology-centric mitigation pathways (Liu et al. 2018; Van Vuuren et al. 2018; Napp et al. 2019), with the IEA’s NZE notably requiring the least carbon dioxide removal (1.8 Gt in 2050) and primary bioenergy (100 EJ in 2050) compared to IPCC net zero SR1.5 scenarios (IEA 2021).

Fourth, the costs of reaching mitigation targets may be lower when incorporating ASI strategies for deep energy and resource demand reductions. The TIAM-Grantham low demand scenarios displayed reduction in mitigation costs (0.87–2.4% of GDP), while achieving even lower cumulative emissions to 2100 (228 to ~475 GtCO2) than its central demand scenario (741 to 1066 GtCO2), which had a cost range of (2.4–4.1% of GDP) (Napp et al. 2019). The GCAM behavioural change scenario concluded that domestic emission savings would contribute to reducing the costs of achieving the internationally agreed climate goal of the EU by 13.5% to 30% (van de Ven et al. 2018). The AIMS lifestyle case indicated that mitigation costs, expressed as global GDP loss, would be 14% lower than the SSP2 reference scenario in 2100, for both 2°C and 1.5°C mitigation targets (Liu et al. 2018). These findings mirror earlier AIM results, which indicated lower overall mitigation costs for scenarios focused on energy service demand reductions (Fujimori et al. 2014). In the IEA’s NZE, behavioural changes that avoid energy and resource demand save USD4 trillion (cumulatively 2021–2050) compared to if those emissions reductions were achieved through low‐carbon electricity and hydrogen deployment (IEA 2021).

Based on the limited number of long-term mitigation scenarios that explicitly represent demand reductions enabled by ASI strategies, there is medium evidence but with high agreement within the literature that such scenarios can reduce dependence on supply-side capacity additions and carbon capture and removal technologies, with opportunites for lower overall mitigation costs.

If the limitations within most IAMs and ESMs regarding non-inclusion of granular ASI strategy analysis can be addressed, it will expand and improve long-term mitigation scenarios (Van den Berg et al. 2019). These include broader inclusion of mitigation costs for behavioural interventions (van Sluisveld et al. 2016), much greater incorporation of rebound effects (Krey et al. 2019), including from improved efficiencies (Brockway et al. 2021) and avoided spending (van de Ven et al. 2018), improved representation of materials cycles to assess resource cascades (Pauliuk et al. 2017), broader coverage of behavioural change (Samadi et al. 2017; Saujot et al. 2020), improved consideration of how economic development affects service demand (Semieniuk et al. 2021), explicit representation of intersectoral linkages related to digitalisation, sharing economy, and circular economy strategies (Section 5.3.4), and institutional, political, social, entrepreneurial, and cultural factors (van Sluisveld et al. 2018). Addressing the current significant modelling limitations will require increased investments in data generation and collection, model development, and inter-model comparisons, with a particular focus on socio-behavioural research, which has been underrepresented in mitigation research funding to date (Overland and Sovacool 2020).

COVID-19 interacts with demand-side scenarios (Box 5.2). Energy demand will mostly likely be reduced between 2020 and 2030 compared to the default pathway, and if recovery is steered towards low energy demand, carbon prices for a 1.5°C-consistent pathway will be reduced by 19%, energy supply investments until 2030 will be reduced by USD1.8 trillion, and the pressure to rapidly upscale renewable energy technologies will be softened (Kikstra et al. 2021a).

Table 5.2 | Summary of long-term scenarios with elements that aimed to minimise service-level energy and resource demand.

Sources: a van Sluisveld et al. (2016); b IEA (2019a); c IEA (2017b); d Van Vuuren et al. (2018); e Grubler et al. (2018); f Teske et al. (2015); g Esmeijer et al. (2018): h Liu et al. (2018); i Gota et al. (2019); j IEA (2020a); k Millward-Hopkins et al. (2020); l IEA (2021); m Creutzig et al. (2015b); n Millot et al. (2018); o van de Ven et al. (2018); p van Sluisveld et al. (2018); q Moran et al. (2020); r négawatt Association (2018); s Niamir et al. (2020c); t, u Niamir et al. (2020a); v Kuhnhenn et al. (2020).

aR = residential (Chapters 8, 9); C = commercial (Chapters 8, 9), T = transport (Chapters 8, 10), I = industry (Chapter 11), F = food (Chapters 6, 12).

bA= Avoid; S = Shift, I = Improve, BAU = business as usual.

cRelative to indicated baseline scenario value in stated year.

The sharing economy, the circular economy, and digitalisation have all received much attention from the research, advocacy, business models and policy communities as potentially transformative trends for climate change mitigation (IEA 2017a; Material Economics 2018; TWI2050 2019). All are essentially emerging and contested concepts (Gallie 1955) that have the common goal of increasing convenience for users and rendering economic systems more resource efficient, but which exhibit variability in the literature on their definitions and system boundaries. Historically, both sharing and circular economies have been commonplace in developing countries, where reuse, repair, and waste scavenging and recycling comprise the core of informal economies facilitated by human interventions (Wilson et al. 2006; Asim et al. 2012; Pacheco et al. 2012). Digitalisation is now propelling sharing and circular economy concepts in developed and developing countries alike (Roy et al. 2021), and the three megatrends are highly interrelated, as seen in Figure 5.11. For example, many sharing economy concepts rely on corporate or, to lesser degree, non-profit digital platforms that enable efficient information and opportunity sharing, thus making it part of the digitalisation trend. Parts of the sharing economy are also included in some circular economy approaches, as shared resource use renders utilisation of material more efficient. Digital approaches to material management also support the circular economy, such as through waste exchanges and industrial symbiosis. Digitalisation aims more broadly to deliver services in more efficient, timely, intelligent, and less resource-intensive ways (i.e., by moving bits and not atoms), through the use of increasingly interconnected physical and digital systems in many facets of economies. With rising digitalisation also comes the risk of increased electricity use to power billions of devices and the internet infrastructure that connects them, as well as growing quantities of e-waste, presenting an important policy agenda for monitoring and balancing the carbon and resource costs and benefits of digitalisation (Malmodin and Lundén 2018; TWI2050 2019). Rebound effects and instigated consumption of digitalisation are risking to lead to a net increase in GHG emissions (Belkhir and Elmeligi 2018). The determinants and possible scales of mitigation potentials associated with each megatrend are discussed below.

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 and Ekstrom 2010; 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 et al. 2016) and underestimate the energy savings of behaviour changes that make a large difference (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 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 and Roy 2016; Mattauch et al. 2016; 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 et al. 2020b; Niamir et al. 2020c). Behavioural interventions like communicating changes in social norms can accelerate behaviour change by creating tipping points (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 et al. 2014), both within a domain or across settings, for example from work to home (Maki and Rothman 2017).

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). 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 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 ) (Section 5.5). Meanings are symbolic and influence the willingness of individuals to use existing technologies or shift to new ones (Wilhite and Ling 1995; Wilhite 2009; Sorrell 2015). Symbolic motives are more important predictors of technology adoption than instrumental motives (Steg 2005; Noppers et al. 2014; Noppers et al. 2015; 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 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 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 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), for example presenting apocalyptic stories and imagery to capture people’s attention and evoke emotional and behavioural response (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 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 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 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 and Lilliestam 2019).

Power and agency shape which climate narratives are told and how prevalent they are (O’Neill and Smith 2014; 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). 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). 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 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 et al. 2019).

Trust in organisations is a key predictor of the take-up of novel energy services (Lutzenhiser 1993), particularly when financial incentives are high (Stern et al. 1985; 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 et al. 1985) or public/private partnerships in Mexico (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 et al. 2018). Trust in other people plays an important role in the sharing economy (Li and Wang 2020), for example predicting shifts in transport mode, specifically car sharing involving rides with strangers (Acheampong and Siiba 2019) (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) (Table 5.3), even though people often do not acknowledge this (Nolan et al. 2008; Noppers et al. 2014). Changing social norms can encourage societal transformation and social tipping points to address climate mitigation (Nyborg et al. 2016; Otto et al. 2020). Providing feedback to people about how their own actions compare to others’ can encourage mitigation (Delmas et al. 2013), although the overall effect size is not strong (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 et al. 2019).

Socially comparative feedback seems to be more effective when people strongly identify with the reference group (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 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 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 et al. 2020). Second-order beliefs (perceptions of what others in the community believe) are particularly important for leveraging descriptive norms (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). 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). The deeper source of these patterns has been peer influence (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). 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). 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 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 et al. 2015; Perlaviciute et al. 2018; 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) and enabling fairness in how decisions are taken and costs and benefits distributed (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 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; Wolsink 2007; Wüstenhagen et al. 2007; Dietz and Stern 2008; Devine-Wright 2011; Heffron and McCauley 2018). Participatory mechanisms that enable deliberation by a representative sample of the public (Climate Assembly UK 2020) can inform policymaking and increase the legitimacy of new and difficult policy actions (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). 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 and Coles 2016). Such initiatives can reconcile lower carbon footprints with higher life satisfaction and higher incomes (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 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 et al. 2020). However, community energy initiatives are not always inclusive and require policy support for widespread implementation across all socio-economic groups (Aiken et al. 2017). In addition, more evidence is required of the impacts of community energy initiatives (Creamer et al. 2018; 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; 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 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; Piggot 2018).

Climate social movements advocate new narratives or framings for climate mitigation (e.g., ‘climate emergency’) (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 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; Fisher and Nasrin 2020; Martiskainen et al. 2020). Enabled by digitalisation, these have given voice to youth on climate (Lee et al. 2020) and created a new cohort of active citizens engaged in climate demonstrations (Fisher 2019). Research on bystanders shows that marches increase positive beliefs about marchers and collective efficacy (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). There is evidence that US opposition to climate action by carbon-connected industries is broad-based, highly organised, and matched with extensive lobbying (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).

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 et al. 2012; Haluza-DeLay 2014; Caniglia et al. 2015; Hulme 2015). Religion can be an important cultural resource towards sustainability at individual, community and institutional levels (Ives and Kidwell 2019), providing leverage points for inner transformation towards sustainability (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 et al. 2018). This suggests the value for policymakers of involving religious constituencies as significant civil society organisations in devising and delivering climate responses.

Businesses and corporate organisations play a key role in the mitigation of global warming, through their own commitments to zero-carbon footprints (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 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). A good number of corporate agents have attempted to derail climate change mitigation by targeted lobbying and doubt-inducing media strategies (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 and McCright 2015; Brulle 2019; 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 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). 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). 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 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). The divestment movement adds additional pressure on fossil fuel related investments (Braungardt et al. 2019), even though fossil fuel financing remains resilient (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).

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 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 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) by acting as ‘middle actors’ (Janda and Parag 2013; Parag and Janda 2014) or intermediaries in the provision of building or mobility services (Grandclément et al. 2015; 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), 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).

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 et al. 2021). They shape and interact with technological systems (Unruh 2000; Foxon et al. 2004; Seto et al. 2014) and represent rules, norms and conventions that organise and structure actions (Vatn 2015) and help create new path dependency or strengthen existing path dependency (Mattioli et al. 2020) (see case studies in Boxes 5.5 to 5.8 and 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 and 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 et al. 2020). The behaviour of actors, their processes and implications on policy options and decisions are discussed further in Section 5.6.

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 et al. 2018; Haberl et al. 2021), and their intersection explains their relevance (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 et al. 2017; 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 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 and Annegarn 2016) (Box 5.5 and 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 et al. 2016a; Mattauch et al. 2016; Mattauch et al. 2018). Changes in infrastructure provision for active travel may contribute to uptake of more walking and cycling (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 et al. 2013; Goodman et al. 2014; Song et al. 2017; Javaid et al. 2020; 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 CO2-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 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 et al. 2003). Similarly, an exogenous change to route choice in public transport makes commuters change their habitual routes (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.

Several integrative frameworks including social practice theory (Røpke 2009; Shove and Walker 2014), the energy cultures framework (Stephenson et al. 2015; Jürisoo et al. 2019) and socio-technical transitions theory (McMeekin and Southerton 2012; 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; Shove and Walker 2014). The energy cultures framework highlights feedbacks between materials, norms, and behavioural practices (Stephenson et al. 2015; Jürisoo et al. 2019). Socio-technical transitions theory addresses interactions between technologies, user practices, cultural meanings, business, infrastructures, and public policies (McMeekin and Southerton 2012; Geels et al. 2017) and can thus accommodate the five drivers of change and stability discussed in Section 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 et al. 1993; Sanderson and Simons 2014; 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 and Nelson 2011; 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 Boer et al. 2014; 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 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 2016; Belzunegui-Eraso and Erro-Garcés 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 (Section 5.4.5).

Demand-side transitions involve interactions between radical social or technical innovations (such as the Avoid-Shift-Improve options discussed in Section 5.3) and existing socio-technical systems, energy cultures, and social practices ( high evidence, high agreement ) (Stephenson et al. 2015; 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 et al. 1998; Schot and Geels 2008), constituted by R&D projects, technological demonstration projects (Borghei and Magnusson 2016; Rosenbloom et al. 2018b), local community initiatives or grassroots projects by environmental activists (Hargreaves et al. 2013a; Hossain 2016). Such niches offer protection from mainstream selection pressures and nurture the development of radical innovations (Smith and Raven 2012). Many low-carbon niche innovations, such as those described in 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 et al. 2015; Seto et al. 2016; Clausen et al. 2017; 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 et al. 2020) (Section 5.4).

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 et al. 2001; Markard et al. 2012; 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 and Raven 2006; Messner 2015; 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 et al. 1998; 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&D, demonstration projects, local community initiatives or grassroots projects (Borghei and Magnusson 2016; Hossain 2016; Rosenbloom et al. 2018b; van Mierlo and Beers 2020). Typical challenges are fragmentation and high rates of project failure (den Hartog et al. 2018; Dana et al. 2021), limited funding (Auerswald and Branscomb 2003), limited consumer interest, and socio-cultural acceptance problems due to being perceived as strange or unfamiliar (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 and Zeitz 2002; 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 et al. 2008; 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 et al. 2016; Schot et al. 2016). Radical innovations remain confined to niches in the second phase because adoption is limited to small, dedicated groups (Schot et al. 2016), innovations are expensive or do not appeal to wider groups, or because complementary infrastructure are missing (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; Markard and Hoffmann 2016; Malone et al. 2017; Raven et al. 2017; 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 et al. 2020b). These concerns may not last, however, since public attention typically follows cycles (Downs 1972; 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 and Wüstenhagen 2010), cultural and framing struggles in public opinion arenas (Kammermann and Dermont 2018; Rosenbloom 2018; Hess 2019a), and political struggles over adjustments in policies and institutions, which shape markets and innovations (Meadowcroft 2011; 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 and Geels 2012; Roberts 2017; Kuokkanen et al. 2018; Leipprand and Flachsland 2018) or because exogenous shocks and pressures disrupt the status quo (Kungl and Geels 2018; 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 et al. 2013; 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; Shove and Southerton 2000; 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 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 and Alkemade 2016; 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; Morris et al. 2014; Bows-Larkin 2015; Bush et al. 2016; Kivimaa and Martiskainen 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). Low-carbon demand-side transitions are often still in early phases ( high evidence, high agreement ).

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; Bento 2013; 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; Martino et al.1978; Davis 1979; Mahajan et al. 1990; Ausubel 1991; Grubler 1991; Feder and Umali 1993; Bayus 1994; Comin and Hobijn 2003; Rogers 2003; Van den Bulte and Stremersch 2004; Meade and Islam 2006; 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 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; 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; 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; Lovins 1976; Lovins 1979). In Small is ProfitableLovins 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 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 et al. 2016; Jain et al. 2017; 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; Sweerts et al. 2020, Creutzig et al. 2021) (Figure 5.15). Analysing the performance of over 80 energy technologies historically, 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 et al. 2020a).

Second, complexity matters, which is often related to unit scale (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 and Rogge 2016; Hess 2019b; 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 et al. 2013; Kester et al. 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 and Glynn 2001; Rogers 2003; Buschmann and Oels 2019). Adoption of most advanced practices can support leapfrogging of polluting technologies (Box 5.9).

In demand-side mitigation, governance is key to drive the multidimensional changes needed to meet service needs within a society that provide people with a decent living while increasingly reducing resource and energy input levels (Rojas-Rueda et al. 2012; Batchelor et al. 2018; OECD 2019a). Impartial governance, understood as equal treatment of everyone by the rule of law, creates social trust and is thus a key enabler of inclusive and participatory demand-side climate policies (Rothstein 2011). Inclusive and broad-based participation itself also leads to greater social trust and thus is also a key enabler of demand-side climate mitigation (Section 5.2). Higher social trust and inclusive participatory processes also reduce inequality, restrain opportunistic behaviour and enhance cooperation (Drews and van den Bergh 2016; Gür 2020) (Section 5.2). Altogether, broad-based participatory processes are central to the successful implementation of climate policies (Rothstein and Teorell 2008; Klenert et al. 2018) ( high evidence, medium agreement ). A culture of cooperation feeds back to increase social trust and enables action that reduce GHG emissions (Carattini et al. 2015; Jo and Carattini 2021), and requires including explicit consideration of the informal sector (Box 5.10). More equitable societies also have the institutional flexibility to allow for mitigation to advance faster, given their readiness to adopt locally-appropriate mitigation policies; they also suffer less from policy lock-in (Tanner et al. 2009; Lorenz 2013; Chu 2015; Cloutier et al. 2015; Martin 2016; Seto et al. 2016; Vandeweerdt et al. 2016; Turnheim et al. 2018).

There is high untapped potential of demand-side mitigation options if considered holistically within the domains of Avoid-Shift-Improve (Sections 5.3 and 5.4, Tables 5.1, 5.2, and 5.3a,b). Within the demand-side mitigation options opportunity space, policies currently focus more on efficiency and ‘Improve’ options and relatively less on ‘Shift’ and ‘Avoid’ options (Dubois et al. 2019; Moberg et al. 2019). Current demand-side policies are fragmented, piecemeal and too weak to drive demand-side transitions commensurate with 1.5°C or 2°C climate goals (Wilson et al. 2012; Fawcett et al. 2019; Mundaca et al. 2019; Moberg et al. 2019) ( high evidence, highagreement ). However, increasingly policy mix in a number of countries has seen a rise in prohibitions on fossil fuel use as a way to weaken lock-ins, for example, on fossil fuel heating in favour of low-carbon alternatives (Rosenbloom et al. 2020). Policies that are aimed at behaviour and lifestyle changes carry a perception of political risks for policymakers, which may explain why policy instruments focus more on information provision and adoption of incentives than on regulation and investment (Rosenow et al. 2017; Moberg et al. 2019). Acceleration of demand-side transitions would thus require both a broadening of demand-side options and the creation of comprehensive and targeted policy mixes (Kern et al. 2017; Rosenow et al. 2017; IPCC 2018) that strengthen the five drivers of decision and action identified in Section 5.4, Table 5.4 and in Tables 5.5–5.7 ( high evidence, high agreement ). Demand-side transitions in developing and emerging economies would also require stronger administrative capacity as well as technical and financial support (UN-Habitat 2013; Creutzig et al. 2016b).

Systematic categorisation of demand-side policy options in different sectors and services through the Avoid-Shift-Improve framework enables identification of major entry points and possible associated social struggles to overcome for the policy instruments/interventions as discussed below.

Demand-side policies tend to vary for different transition phases ( high evidence, high agreement ) (Roberts and Geels 2019; Sandin et al. 2019). In the first phase, which is characterised by the emergence or introduction of radical innovations in small niches, policies focus on: (i) supporting R&D and demonstration projects to enable learning and capability developments, (ii) nurturing the building of networks and multi-stakeholder interactions, and (iii) providing future orientation through visions or targets (Brown et al. 2003; López-García et al. 2019; Roesler and Hassler 2019). In the second phase, the policy emphasis shifts towards upscaling of experiments, standardisation, cost reduction, and the creation of early market niches (Borghei and Magnusson 2018; Ruggiero et al. 2018). In the third and later phases, comprehensive policy mixes are used to stimulate mass adoption, infrastructure creation, social acceptance and business investment (Fichter and Clausen 2016; Geels et al. 2018; Strauch 2020). In the fourth phase, transitions can also be stimulated through policies that weaken or phase out existing regimes, such as removing inefficient subsidies (for cheap petrol or fuel oil) that encourage wasteful consumption, increasing taxes on carbon-intensive products and practices (Box 5.11), or substantially tightening regulations and standards (Kivimaa and Kern 2016; David 2017; Rogge and Johnstone 2017).

Policy coordination is critical to manage infrastructure interdependence across sectors, and to avoid trade-off effects (Raven and Verbong 2007; Hiteva and Watson 2019), specifically requiring the consideration of interactions among supply-side and demand-side measures ( high evidence, high agreement ) (Kivimaa and Virkamäki 2014; Rogge and Reichardt 2016; de Coninck et al. 2018; Edmondson et al. 2019). For example, the amount of electricity required for cooking can overwhelm the grid which can lead to failure, causing end-users to shift back to traditional biomass or fossil fuels (Ateba et al. 2018; Israel-Akinbo et al. 2018); thus grid stability policies need to be undertaken in conjunction.

Policymakers operate in a politically dynamic national and international environment, and their policies often reflect their contextual situations and constraints with regards to climate-related reforms (Levin et al. 2012; Copland 2019), including differentiation between developed and developing countries ( high evidence, high agreement ) (Beer and Beer 2014; Roy et al. 2018c). Variables such as internal political stability, equity, informality (Box 5.10), macro-economic conditions, public debt, governance of policies, global oil prices, quality of public services, and the maturity of green technologies play important roles in determining policy directions.

Sequencing policies appropriately is a success factor for climate policy regimes ( high evidence, high agreement ). In most situations policy measures require a preparatory phase that prepares the ground by lowering the costs of policies, communicating the costs and benefits to citizens, and building coalitions for policies, thus reducing political resistance (Meckling et al. 2017). This policy sequencing aims to incrementally relax or remove barriers over time to enable significant cumulative increases in policy stringency and create coalitions that support future policy development (Pahle et al. 2018). German policies on renewables began with funding for research, design and development (RD&D), then subsidies for demonstration projects during the 1970s and 1980s, and continued to larger-scale projects such as ‘Solar Roofs’ programmes in the 1990s, including scaled-up feed-in tariffs for solar power (Jacobsson and Lauber 2006). These policies led to industrial expansion in wind and solar energy systems, giving rise to powerful renewables interest coalitions that defend existing measures and lend political support for further action. Policy sequencing has also been deployed to introduce technology bans and strict performance standards with a view to eliminating emissions as the end goal, and may involve simultaneous support for low-carbon options while deliberately phasing out established technological regimes (Rogge and Johnstone 2017).

As a key contending policy instrument, carbon pricing also requires embedding into policy packages ( high evidence, medium agreement ). Pricing may be regressive and perceived as additional costs by households and industry, making investments in green infrastructure politically unfeasible, as examples from France and Australia show (Copland 2019; Douenne and Fabre 2020). Reforms that would push up household energy expenses are often left aside for fear of how citizens, especially the poor, would react or cope with higher bills ( high evidence, medium agreement ) (Martinez and Viegas 2017; Tesfamichael et al. 2021). This makes it important to precede carbon pricing with investments in renewable energy and low-carbon transport modes (Biber et al. 2017; Tvinnereim and Mehling 2018), and especially support for developing countries by building up low-carbon energy and mobility infrastructures and technologies, thus reducing resistance to carbon pricing (Creutzig 2019). Additionally, carbon pricing receives higher acceptance if fairness and distributive considerations are made explicit in revenue distribution (Box 5.11).

The effectiveness of a policy package is determined by design decisions as well as the wider governance context that include the political environment, institutions for coordination across scales, bureaucratic traditions, and judicial functioning ( high evidence, high agreement ) (Howlett and Rayner 2013; Rogge and Reichardt 2013; Rosenow et al. 2016). Policy packages often emerge through interactions between different policy instruments as they operate in either complementary or contradictory ways, resulting from conflicting policy goals (Cunningham et al. 2013; Givoni et al. 2013). An example includes the acceleration in shift from traditional biomass to the adoption of modern cooking fuel for 80 million households in rural India over a very short period of four years (2016–2020), which employed a comprehensive policy package including financial incentives, infrastructural support and strengthening of the supply chain to induce households to shift towards a clean cooking fuel from the use of biomass (Kumar 2019). This was operationalised by creating a LPG supply chain by linking oil and gas companies with distributors to assure availability, and create infrastructure for local storage along with an improvement of the rural road network, especially in the rural context (Sankhyayan and Dasgupta 2019). State governments initiated separate policies to increase the distributorship of LPG in their states (Kumar et al. 2016). Similarly, policy actions for scaling up electric vehicles need to be well designed and coordinated where EV policy, transport policy and climate policy are used together, working on different decision points and different aspects of human behaviour (Barton and Schütte 2017). The coordination of the multiple policy actions enables co-evolution of multiple outcomes that involve shifting towards renewable energy production, improving access to charging infrastructure, carbon pricing and other GHG measures (Wolbertus et al. 2018).

Design of policy packages should consider not only policies that support low-carbon transitions but also those that challenge existing carbon-intensive regimes, generating not just policy ‘winners’ but also ‘losers’ ( high evidence, high agreement ) (Carley and Konisky 2020). The winners include low-carbon innovators and entrepreneurs, while the potential losers include incumbents with vested interests in sustaining the status quo (Mundaca et al. 2018; Monasterolo and Raberto 2019). Low-carbon policy packages would benefit from looking beyond climate benefits to include non-climate benefits such as health benefits, fuel poverty reductions and environmental co-benefits (Ürge-Vorsatz et al. 2014; Sovacool et al. 2020b). The uptake of decentralised energy services using solar PV in rural areas in developing countries is one such example where successful initiatives are linked to the convergence of multiple policies that include import tariffs, research incentives for R&D, job creation programmes, policies to widen health and education services, and strategies for increased safety for women and children (Kattumuri and Kruse 2019; Gebreslassie 2020).

The energy-efficient lighting transition in Europe represents a good case of the formation of policy coalitions that led to the development of policy packages. As attention to energy efficiency in Europe increased in the 1990s, policymakers attempted to stimulate energy-saving lamp diffusion through voluntary measures. But policies stimulated only limited adoption. Consumers perceived compact fluorescent lamps (CFLs) as giving ‘cold’ light, being unattractively shaped, taking too long to achieve full brightness, unsuitable for many fixtures, and unreliable (Wall and Crosbie 2009). Still, innovations by major CFL and LED multinationals continued. Increasing political attention to climate change and criticisms from environmental NGOs (e.g. WWF, Greenpeace) strengthened awareness about the inefficiency of incandescent light bulbs (ILBs), which led to negative socio-cultural framings that associated ILBs with energy waste (Franceschini and Alkemade 2016). The combined pressures from the lighting industry, NGOs and member states led the European Commission to introduce the 2009 ban of ILBs of more than 80W, progressing to lower-wattage bans in successive years. While the ILB ban initially mainly boosted CFL diffusion, it also stimulated LED uptake. LED prices decreased quickly by more than 85% between 2008 and 2012 (Sanderson and Simons 2014), because of scale economies, standardisation and commoditisation of LED chip technology, and improved manufacturing techniques. Because of further rapid developments to meet consumer tastes, LEDs came to be seen as the future of domestic lighting (Franceschini et al. 2018). Acknowledging these changing views, the 2016 and 2018 European bans on directional and non-directional halogen bulbs explicitly intended to further accelerate the LED transition and reduce energy consumption for residential lighting.

In summary, more equitable societies are associated with high levels of social trust and enable actions that reduce GHG emissions. To this end, people play an important role in the delivery of demand-side mitigation options within which efficiency and ‘Improve’ options dominate. Policies that are aimed at behaviour and lifestyle changes come with political risks for policymakers. However, the potential exists for broadening demand-side interventions to include ‘Avoid’ and ‘Shift’ policies. Longer term thinking and implementation that involves careful sequencing of policies as well as designing policy packages that address multiple co-benefits would be critical to manage interactions among supply-side and demand-side options to accelerate mitigation.

Knowledge on climate action that starts with the social practices and how people live in various environments, cultures, contexts and attempts to improve their well-being, is still in its infancy. In models, climate solutions remain supply-side oriented, and evaluated against GDP, without acknowledging the reduction in well-being due to climate impacts. GDP is a poor metric of human well-being, and climate policy evaluation requires better grounding in relation to decent living standards and/or similar benchmarks. Actual solutions will invariably include demand, service provisioning and end use. Literature on how gender, informal economies mostly in developing countries, and solidarity and care frameworks translate into climate action, but also how climate action can improve the life of marginalised groups, remains scarce. The working of economic systems under a well-being-driven rather than GDP-driven paradigm requires better understanding.

The digital economy, as well as shared and circular economy, is emerging as a template for great narratives, hopes and fears. Yet, there are few systematic evaluations of what is already happening and what can govern it towards a better narrative. Research needs to better gauge energy trends for rapidly evolving systems like data centres, increased use of social media and influence of consumption and choices, AI, blockchain; and implications of digital divides among social groups and countries on well-being. Governance decisions on AI, indirectly fostering either climate harming or climate mitigating activities remain unexplored. Better integration of mitigation models and consequential lifecycle analysis is needed for assessing how digitalisation, shared economy and circular economy change material and energy demand.

Scenarios start within parameter-rich models carrying more than a decade-long legacy of supply-side technologies that are not always gauged in recent technological developments. Service provisioning systems are not explicitly modelled, and diversity in concepts and patterns of lifestyles rarely considered. A new class of flexible and modular models with focus on services and activities, based on a variety of data sources including big data collected and compiled, is needed. There is scope for more sensitivity analysis on two aspects to better guide further detailed studies on societal response to policy. These aspects need to explore which socio-behavioural aspects and/or organisation changes has the biggest impact on energy/emissions reductions, and on the scale for take-back effects, due to interdependence on inclusion or exclusion of groups of people. Models mostly consider behavioural change free, and don’t account for how savings due to ‘Avoid’ measures may be re-spent. Most quantitatively measurable service indicators, for example passenger-kilometres travelled or tonne-kilometres of freight transport are also inadequate to measure services in the sense of well-being contributions. More research is needed on how to measure, for example, accessibility, social inclusion etc. Otherwise, services will also be poorly represented in scenarios.

Better understanding is required on: (i) more detailed causal mechanisms in the mutual interactions between individual, social, and structural drivers of change and how these vary over time, that is, what is their relative importance in different transition phases; (ii) how narratives associated with specific technologies, group identities, and climate change influence each other and interact over time to enable and constrain mitigation outcomes; (iii) how social media influences the development and impacts of narratives about low-carbon transitions; (iv) the effects of social movements (for climate justice, youth climate activism, fossil fuel divestment, and climate action more generally) on social norms and political change, especially in less developed countries; (v) how existing provisioning systems and social practices destabilise through the weakening of various lock-in mechanisms, and resulting deliberate strategies for accelerating demand-side transitions; (vi) a dynamic understanding of feasibility, which addresses the dynamic mechanisms that lower barriers or drive mitigation options over the barriers; (vii) how shocks like prolonged pandemic impact willingness and capacity to change and their permanency for various social actors and country contexts. The debate on the most powerful leverage points and policies for speeding up change in social and technological systems need to be resolved with more evidence. Discussion on the policy interdependence and implications of end-user and efficiency focused strategies have only just started suggesting an important area for future research.

Table 5.7 | Examples of policies to enable‘Improve’options

People can be educated through knowledge transfer so they can act in different roles, and in each role everyone can contribute to limit global warming to 1.5°C. Citizens with enough knowledge can organise and put political pressure on the system. Role models can set examples to others. Professionals (e.g., engineers, urban planners, teachers, researchers) can change professional standards in consistency with decarbonisation; for example urban planners and architects can design physical infrastructures to facilitate low-carbon mobility and energy use by making walking and cycling safe for children. Rich investors can make strategic plans to divest from fossils and invest in carbon-neutral technologies. Consumers, especially those in the top 10% of the world population in terms of income, can limit consumption, especially in mobility, and explore the good life consistent with sustainable consumption.

Policymakers support individual actions in certain contexts, not only by economic incentives, such as carbon pricing, but also by interventions that understand complex decision-making processes, habits, and routines. Examples of such interventions include, but are not limited to, choice architectures and nudges that set green options as default, shift away from cheap petrol or gasoline, increasing taxes on carbon-intensive products, or substantially tightening regulations and standards to support shifts in social norms, and thus can be effective beyond the direct economic incentive.

Humaninduced global warming, together with other global trends and events, such as digitalisation and automation, and the COVID-19 pandemic, induce changes in labour markets, and bring large uncertainty and ambiguity. History and psychology reveal that societies can thrive in these circumstances if they openly embrace uncertainty on the future and try out ways to improve life. Tolerating ambiguity can be learned, for example by interacting with history, poetry and the arts. Sometimes religion and philosophy also help.

As a key enabler, novel narratives created in a variety of ways, such as by advertising, images and the entertainment industry, help to break away from the established meanings, values and discourses and the status quo. For example, discourses that frame comfortable public transport services to avoid stress from driving cars on busy, congested roads help avoid car driving as a status symbol and create a new social norm to shift to public transport. Discourses that portray plant-based protein as healthy and natural promote and stabilise particular diets. Novel narratives and inclusive processes help strategies to overcome multiple barriers. Case studies demonstrate that citizens support transformative changes if participatory processes enable a design that meets local interests and culture. Promising narratives specify that even as speed and capabilities differ, humanity embarks on a joint journey towards well-being for all and a healthy planet.

There is a growing realisation that mere monetary value of income growth is insufficient to measure national welfare and individual well-being. Hence, any action towards climate change mitigation is best evaluated against a set of indicators that represent a broader variety of needs to define individual well-being, macroeconomic stability, and planetary health. Many solutions that reduce primary material and fossil energy demand, and thus reduce GHG emissions, provide better services to help achieve well-being for all.

Economic growth measured by total or individual income growth is a main driver of GHG emissions. Only a few countries with low economic growth rates have reduced both territorial and consumption-based GHG emissions, typically by switching from fossil fuels to renewable energy and by reduction in energy use and switching to low/zero carbon fuels, but until now at insufficient rates and levels for stabilising global warming at 1.5°C. High deployment of low/zero carbon fuels and associated rapid reduction in demand for and use of coal, gas, and oil can further reduce the interdependence between economic growth and GHG emissions.