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

== 5.2 Services, Well-being and Equity in Demand-side Mitigation ==

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As outlined in section 5.1, mitigation, equity and well-being go hand in hand to motivate actions. Global, regional, and national actions and policies that advance inclusive well-being and build social trust strengthen governance. There is ''high evidence'' and ''high agreement'' that demand-side measures cut across all sectors, and can bring multiple benefits ( [[#Mundaca--2019|Mundaca et al. 2019]] ; [[#Wachsmuth--2019|Wachsmuth and Duscha 2019]] ; [[#Geels--2020|Geels 2020]] ; [[#Niamir--2020b|Niamir et al. 2020b]] ; [[#Garvey--2021|Garvey et al. 2021]] ; [[#Roy--2021|Roy et al. 2021]] ). Since effective demand requires affordability, one of the necessary conditions for acceleration of mitigation through demand-side measures is wide and equitable participation from all sectors of society. Low-cost low-emissions technologies, supported by institutions and government policies, can help meet service demand and advance both climate and well-being goals ( [[#Steffen--2018a|Steffen et al. 2018a]] ; [[#Khosla--2019|Khosla et al. 2019]] ). This section introduces metrics of well-being and their relationship to GHG emissions, and clarifies the concept of service provisioning.

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=== 5.2.1 Metrics of Well-being and their Relationship to Greenhouse Gas Emissions ===

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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--2014|Rao et al. 2014]] ; [[#Lamb--2015|Lamb and Rao 2015]] ).

Current socioeconomic systems are based on high-carbon economic growth and resource use ( [[#Steffen--2018b|Steffen et al. 2018b]] ). Several systematic reviews confirm that economic growth is tightly coupled with increasing CO 2 emissions ( [[#Ayres--2005|Ayres and Warr 2005]] ; [[#Tiba--2017|Tiba and Omri 2017]] ; [[#Mardani--2019|Mardani et al. 2019]] ; [[#Wiedenhofer--2020|Wiedenhofer et al. 2020]] ) although the level of emissions depends on inequality ( [[#Baležentis--2020|Baležentis et al. 2020]] ; [[#Liu--2020b|Liu et al. 2020b]] ), and on geographic and infrastructural constraints that force consumers to use fossil fuels ( [[#Pottier--2021|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|Ozturk 2010]] ). In a systematic review, [[#Mardani--2019|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 CO 2 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|Komiyama 2014]] ; [[#Brockway--2017|Brockway et al. 2017]] ; [[#Shuai--2019|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|Jackson 2009]] ; [[#Krausmann--2009|Krausmann et al. 2009]] ; [[#Ward--2016|Ward et al. 2016]] ; [[#Jackson--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--2019|Heun and Brockway 2019]] ; [[#Hickel--2019|Hickel and Kallis 2019]] ; [[#Vadén--2020|Vadén et al. 2020]] ; [[#Wiedenhofer--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%20Quéré--2019|Le Quéré et al. 2019]] ), albeit at scales insufficient for mitigation pathways ( [[#Vadén--2020|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--2021|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|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|IEA 2020a]] ). Energy-related CO 2 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 GtCO 2 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--2018|Singh et al. 2018]] ; [[#Jacobs--2019|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|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--2009|Roy and Pal 2009]] ; [[#Fleurbaey--2014|Fleurbaey and Tadenuma 2014]] ; [[#Komiyama--2014|Komiyama 2014]] ; [[#Dietrich--2016|Dietrich and List 2016]] ; [[#Mattauch--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--2003|Nyborg and Rege 2003]] ; [[#Stuber--2008|Stuber et al. 2008]] ; [[#Brownell--2009|Brownell and Warner 2009]] ). Further evidence on changing preferences in consumption choices pertinent to decarbonisation includes [[#Grinblatt--2008|Grinblatt et al. (2008)]] and [[#Weinberger--2010|Weinberger and Goetzke (2010)]] for mobility; [[#Erb--2016|Erb et al. (2016)]] , [[#Muller--2017|Muller et al. (2017)]] , and [[#Costa--2019|Costa and Johnson (2019)]] for diets; and [[#Baranzini--2017|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--2012|Jacobsen et al. 2012]] ; [[#Schumacher--2015|Schumacher 2015]] ; [[#Dasgupta--2016|Dasgupta et al. 2016]] ; [[#Daube--2016|Daube and Ulph 2016]] ; [[#Ulph--2021|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--2016b|Creutzig et al. 2016b]] ; [[#Mattauch--2016|Mattauch et al. 2016]] ; [[#Mattauch--2018|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--2017|Lamb and Steinberger 2017]] ). Many dimensions of well-being and ‘basic needs’ are social, not individual, in character ( [[#Schneider--2016|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--2019|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|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--2017|Atkinson et al. 2017]] ; [[#Cramton--2017|Cramton et al. 2017]] ; [[#Milkoreit--2017|Milkoreit 2017]] ; [[#Tvinnereim--2017|Tvinnereim et al. 2017]] ; [[#Smith--2018|Smith and Reid 2018]] ; [[#Carattini--2019|Carattini et al. 2019]] ).

Because of data limitations, which can make cross-country comparisons difficult, health-based indicators and in particular life expectancy ( [[#Lamb--2014|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--2009|Decancq et al. 2009]] ; [[#Sager--2017|Sager 2017]] ; [[#Burstein--2019|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--2017|Lamb and Steinberger 2017]] ; [[#Porter--2017|Porter et al. 2017]] ; [[#Smith--2018|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|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|Sirgy 2012]] ; [[#Brand-Correa--2017|Brand-Correa and Steinberger 2017]] ; [[#Lamb--2017|Lamb and Steinberger 2017]] ; [[#Ganglmair-Wooliscroft--2019|Ganglmair-Wooliscroft and Wooliscroft 2019]] ). Eudaimonic well-being focuses on the individual in the broader context, associating happiness with virtue ( [[#Sirgy--2012|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--2019|Fanning and O’Neill 2019]] ) such as the capabilities ( [[#Sen--1985|Sen 1985]] ), human needs ( [[#Doyal--1991|Doyal and Gough 1991]] ; [[#Max-Neef--1991|Max-Neef et al. 1991]] ) and models of psychosocial well-being ( [[#Ryan--2001|Ryan and Deci 2001]] ). Measures of well-being differ somewhat in developed and developing countries ( [[#Sulemana--2016|Sulemana et al. 2016]] ; [[#Ng--2019|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--2019|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|Requena 2016]] ); and income inequality is negatively associated with subjective well-being in developed countries, but positively so in less-developed countries ( [[#Ngamaba--2018|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'' ) ''.''

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==== 5.2.1.1 Services for Well-being ====

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Well-being needs are met through services. Provision of services associated with low energy demand is a key component of current and future efforts to reduce carbon emissions. Services can be provided in various culturally-appropriate ways, with diverse climate implications. There is ''high evidence'' and ''high agreement'' in the literature that many granular service provision systems can make ‘demand’ more flexible, provide new options for mitigation, support access to basic needs, and enhance human well-being. Energy services offer an important lens to analyse the relationship between energy systems and human well-being ( [[#Jackson--2008|Jackson and Papathanasopoulou 2008]] ; [[#Druckman--2010|Druckman and Jackson 2010]] ; [[#Mattioli--2016|Mattioli 2016]] ; [[#Walker--2016|Walker et al. 2016]] ; [[#Fell--2017|Fell 2017]] ; [[#Brand-Correa--2018|Brand-Correa et al. 2018]] ; [[#King--2019|King et al. 2019]] ; [[#Pagliano--2019|Pagliano and Erba 2019]] ; [[#Whiting--2020|Whiting et al. 2020]] ). Direct and indirect services provided by energy, rather than energy itself, deliver well-being benefits ( [[#Kalt--2019|Kalt et al. 2019]] ). For example, illumination and transport are intermediary services in relation to education, health care, meal preparation, sanitation, and so on, which are basic human needs. Sustainable consumption and production revolve around ‘doing more and better with the same’ and thereby increasing well-being from economic activities ‘by reducing resource use, degradation and pollution along the whole lifecycle, while increasing quality of life’ ( [[#UNEP--2010|UNEP 2010]] ). Although energy is required for delivering human development by supporting access to basic needs ( [[#Lamb--2015|Lamb and Rao 2015]] ; [[#Lamb--2017|Lamb and Steinberger 2017]] ), a reduction in primary energy use and/or shift to low-carbon energy, if associated with the maintenance or improvement of services, can not only ensure better environmental quality but also directly enhance well-being ( [[#Roy--2012|Roy et al. 2012]] ). The correlation between human development and emissions is not necessarily coupled in the long term, which implies there is a need to prioritise human well-being and the environment over economic growth ( [[#Steinberger--2020|Steinberger et al. 2020]] ). At the interpersonal and community levels, cultural specificities, infrastructure, norms, and relational behaviours differ (Box 5.3). For example, demand for space heating and cooling depends on building materials and designs, urban planning, vegetation, clothing and social norms as well as geography, incomes, and outside temperatures ( [[#Brand-Correa--2018|Brand-Correa et al. 2018]] ; [[#Campbell--2018|Campbell et al. 2018]] ; [[#Ivanova--2018|Ivanova et al. 2018]] ; [[#IEA--2019b|IEA 2019b]] ; [[#Dreyfus--2020|Dreyfus et al. 2020]] ). In personal mobility, different variable needs satisfiers (e.g., street space allocated to cars, buses or bicycles) can help satisfy human needs, such as accessibility to jobs, health care, and education. Social interactions and normative values play a crucial role in determining energy demand. Hence, demand-side and service-oriented mitigation strategies are most effective if geographically and culturally differentiated ( [[#Niamir--2020a|Niamir et al. 2020a]] ).

Decent living standards (DLS) serves as a socio-economic benchmark as it views human welfare not in relation to consumption but rather in terms of services which together help meet human needs (e.g., nutrition, shelter, health, etc.), recognising that these service needs may be met in many different ways (with different emissions implications) depending on local contexts, cultures, geography, available technologies, social preferences, and other factors. Therefore, one key way of thinking about providing well-being for all with low carbon emissions centres around prioritising ways of providing services for DLS in a low-carbon way (including choices of needs satisfiers, and how these are provided or made accessible). They may be supplied to individuals or groups or communities, both through formal markets and/or informally, for example by collaborative work, in coordinated ways that are locally appropriate, designed and implemented in accordance with overlapping local needs.

The most pressing DLS service shortfalls, as shown in Figure 5.2, lie in the areas of nutrition, mobility, and communication. Gaps in regions such as Africa and the Middle East are accompanied by current levels of service provision in the highly industrialised countries at much higher than DLS levels for the same three service categories. The lowest population quartile by income worldwide faces glaring shortfalls in housing, mobility, and nutrition. Meeting these service needs using low-emissions energy sources is a top priority. Reducing GHG emissions associated with high levels of consumption and material throughput by those far above DLS levels has potential to address both emissions and inequality in energy and emission footprints ( [[#Otto--2019|Otto et al. 2019]] ). This, in turn, has further potential benefits; under the conditions of ‘fair’ income reallocation to public services, this can reduce national carbon footprint by up to 30% while allowing the consumption of those at the bottom to increase ( [[#Millward-Hopkins--2021|Millward-Hopkins and Oswald 2021]] ). The challenge then is to address the upper limits of consumption. When consumption only just supports the satisfaction of basic needs, any decrease causes deficiencies in human-need satisfaction. This is quite unlinke the case of consumption that exceeds the limits of basic needs, in which deprivation causes a subjective discomfort ( [[#Brand-Correa--2020|Brand-Correa et al. 2020]] ). Therefore, to collectively remain within environmental limits, the establishment of minimum and maximum standards of consumption, or sustainable consumption corridors, ( [[#Wiedmann--2020|Wiedmann et al. 2020]] ) has been suggested, depending on the context. In some countries, carbon-intensive ways of satisfying human needs have been locked-in, for example via car-dependent infrastructures ( [[#Jackson--2008|Jackson and Papathanasopoulou 2008]] ; [[#Druckman--2010|Druckman and Jackson 2010]] ; [[#Mattioli--2016|Mattioli 2016]] ; [[#King--2019|King et al. 2019]] ), and both infrastructure reconfiguration and adaptation are required to organise need satisfaction in low-carbon ways (see also [[IPCC:Wg3:Chapter:Chapter-10#10.2|Section 10.2]] ).

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[[File:a7c3b34a9e34a6cb0cf7ba886618ef22 IPCC_AR6_WGIII_Figure_5_2.png]]

'''Figure 5.2''' [[#footnote-000|2]] '''| Heterogeneity in access to and availability of services for human well-being within and across countries.''' Panel '''(''' '''a)''' Across-country differences in panel (a) food (meat and other), (b) housing, (c) mobility, (d) communication (mobile phones and high-speed internet access). Variation in service levels across countries within a region is shown as error bars (black). Values proposed as decent standards of living threshold ( [[#Rao--2019b|Rao et al. 2019b]] ) are shown as red dashed lines. Global average values are shown as blue dashed lines. Panel '''(b)''' Within-country differences in service levels as a function of income differences for the Netherlands (bottom and top 10% of incomes) and India (bottom and top 25% of incomes) ( [[#Grubler--2012b|Grubler et al. 2012b]] ) (data update 2016). Panel '''(c)''' Decent living energy (DLE) scenario using global, regional and DLS dimensions for final energy consumption at 149 EJ (15.3 GJ cap –1 yr –1 ) in 2050 ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ), requiring advanced technologies in all sectors and radical demand-side changes. Values are shown for five world regions based on the AR6 WGIII Regional breakdown. We use passenger kilometres per day per capita ( km day '''–1''' cap '''–1''' ) as a metric for mobility only as a reference, however, transport and social inclusion research suggest the aim is to maximise accessibility and not travel levels or travelled distance.

There is ''high evidence'' and ''high agreement'' in the literature that vital dimensions of human well-being correlate with consumption, but only up to a threshold. High potential for mitigation lies in using low-carbon energy for new basic needs satisfaction while cutting emissions of those whose basic needs are already met ( [[#Grubler--2018|Grubler et al. 2018]] ; [[#Rao--2018b|Rao and Min 2018b]] ; [[#Rao--2019b|Rao et al. 2019b]] ; [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ;

[[#Keyßer--2021|Keyßer and Lenzen 2021]] ). Decent living standards indicators serve as tools to clarify this socio-economic benchmark and identify well-being for all compatible mitigation potential. Energy services provisioning opens up avenues of efficiency and possibilities for decoupling energy services demand from primary energy supply, while needs satisfaction leads to the analysis of the factors influencing the energy demand associated with the achievement of well-being ( [[#Brand-Correa--2017|Brand-Correa and Steinberger 2017]] ; [[#Tanikawa--2021|Tanikawa et al. 2021]] ). Vital dimensions of well-being correlate with consumption, but only up to a threshold: decent living energy thresholds range from about 13 to 18.4 GJ cap –1 yr – 1 of final energy consumption but the current consumption ranges from under 5 GJ cap –1 yr –1 to over 200 GJ cap –1 yr –1 ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ), thus a mitigation strategy that protects minimum levels of essential-goods service delivery for DLS, but critically views consumption beyond the point of diminishing returns of needs satisfaction, is able to sustain well-being while generating emissions reductions ( [[#Goldemberg--1988|Goldemberg et al. 1988]] ; [[#Jackson--1999|Jackson and Marks 1999]] ; [[#Druckman--2010|Druckman and Jackson 2010]] ; [[#Girod--2010|Girod and De Haan 2010]] ; [[#Vita--2019a|Vita et al. 2019a]] ; [[#Baltruszewicz--2021|Baltruszewicz et al. 2021]] ). Such relational dynamics are relevant both within and between countries, due to variances in income levels, lifestyle choice (see also [[#5.4.4|Section 5.4.4]] ), geography, resource assets and local contexts. Provisioning for human needs is recognised as participatory and inter-relational; transformative mitigation potential can be found in social as well as technological change ( [[#Mazur--1974|Mazur and Rosa 1974]] ; [[#Goldemberg--1985|Goldemberg et al. 1985]] ; [[#Lamb--2017|Lamb and Steinberger 2017]] ; [[#O’Neill--2018|O’Neill et al. 2018]] ; [[#Hayward--2019|Hayward and Roy 2019]] ; [[#Vita--2019a|Vita et al. 2019a]] ). More equitable societies which provide DLS for all can devote attention and resources to mitigation ( [[#Richards--2003|Richards 2003]] ; [[#Dubash--2013|Dubash 2013]] ; [[#Rafaty--2018|Rafaty 2018]] ; [[#Oswald--2021|Oswald et al. 2021]] ). For further exploration of these concepts, see [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I.

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=== 5.2.2 Inequity in Access to Basic Energy Use and Services ===

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==== 5.2.2.1 Variations in Access to Needs-satisfiers for Decent Living Standards ====

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There is very ''high evidence'' and ''very high agreement'' that globally, there are differences in the amount of energy that societies require to provide the basic needs for everyone. At present nearly one-third of the world’s population are ‘energy poor’, facing challenges in both access and affordability, that is, more than 2.6 billion people have little or no access to energy for clean cooking. About 1.2 billion lack energy for cleaning, sanitation and water supply, lighting, and basic livelihood tasks ( [[#Sovacool--2016|Sovacool and Drupady 2016]] ; [[#Rao--2017|Rao and Pachauri 2017]] ).The current per capita energy requirement to provide a decent standard of living range from around 5 to 200 GJ cap –1 yr –1 ( [[#Steckel--2013|Steckel et al. 2013]] ; [[#Lamb--2017|Lamb and Steinberger 2017]] ; [[#Rao--2019b|Rao et al. 2019b]] ; [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ), which shows the level of inequality that exists; this depends on the context, such as geography, culture, infrastructure or how services are provided ( [[#Brand-Correa--2018|Brand-Correa et al. 2018]] ) (Box 5.3). However, through efficient technologies and radical demand-side transformations, the final energy requirements for providing DLS by 2050 is estimated at 15.3 GJ cap –1 yr –1 ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ). Recent DLS estimates for Brazil, South Africa, and India are in the range between 15 and 25 GJ cap –1 yr –1 ( [[#Rao--2019b|Rao et al. 2019b]] ).The most gravely energy poor are often those living in informal settlements, particularly women, in sub-Saharan Africa and developing Asia, whose socially-determined responsibilities for food, water, and care are highly labour-intensive and made more intense by climate change ( [[#Guruswamy--2016|Guruswamy 2016]] ; [[#Wester--2019|Wester et al. 2019]] ). In Brazil, India and South Africa, where inequality is extreme ( [[#Alvaredo--2018|Alvaredo et al. 2018]] ) mobility (51–60%), food production and preparation (21–27%) and housing (5–12%) dominate total energy needs ( [[#Rao--2019b|Rao et al. 2019b]] ). Minimum requirements of energy use consistent with enabling well-being for all is between 20 and 50 GJ cap –1 yr –1 depending on context ( [[#Rao--2019b|Rao et al. 2019b]] ). Inequality in access to and availability of services for human well-being varies in extreme degree across countries and income groups. In developing countries, the bottom 50% receive about 10% of the energy used in land transport and less than 5% in air transport, while the top 10% use about 45% of the energy for land transport and around 75% for air transport ( [[#Oswald--2020|Oswald et al. 2020]] ). Within-country analysis shows that particular groups in China – women born in the rural West with disadvantaged family backgrounds – face unequal opportunities for energy consumption ( [[#Shi--2019|Shi 2019]] ). Figure 5.3 shows the wide variation across world regions in people’s access to some of the basic material prerequisites for meeting DLS, and variations in energy consumption, providing a starting point for comparative global analysis.

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[[File:a97cf7ea27309c90d16a458792152cd8 IPCC_AR6_WGIII_Figure_5_3.png]]

'''Figure 5.3 | Energy use per capita per year of three groups of countries ranked by socio-economic development and displayed for each country based on four or five different income groups (according to data availability) as well as geographical representation.''' The final energy use for decent living standards (20–50 GJ cap –1 yr –1 ) ( [[#Rao--2019b|Rao et al. 2019b]] ) is indicated in the blue column as a reference for global range, rather than dependent on each country. Source: data based on [[#Oswald--2020|Oswald et al. (2020)]] .

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=== Box 5.3 | Inequities in Access to and Levels of End-use Technologies and Infrastructure Services ===

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Acceleration in mitigation action needs to be understood from a societal perspective. Technologies, access and service equity factors sometimes change rapidly. Access to technologies, infrastructures and products, and the services they provide, are essential for raising global living standards and improving human well-being ( [[#Alkire--2014|Alkire and Santos 2014]] ; [[#Rao--2018b|Rao and Min 2018b]] ). Yet access to and levels of service delivery are distributed extremely inequitably as of now. How fast such inequities can be reduced by granular end-use technologies is illustrated by the cellphone (households with mobiles), comparing the situation between 2000 and 2018. In this eighteen-year period, cellphones changed from a very inequitably-distributed technology to one with almost universal access, bringing accessibility benefits especially to populations with very low disposable income and to those whose physical mobility is limited ( [[#Porter--2016|Porter 2016]] ). Every human has the right to a dignified decent life, to live in good health and to participate in society. This is a daunting challenge, requiring that in the next decade governments build out infrastructure to provide billions of people with access to a number of services and basic amenities in comfortable homes, nutritious food, and transit options ( [[#Rao--2018b|Rao and Min 2018b]] ). For a long time, this challenge was thought to also be an impediment to developing countries’ participation in global climate mitigation efforts. However, recent research shows that this need not be the case ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ; [[#Rao--2019b|Rao et al. 2019b]] ).

Several of the Sustainable Development Goals (SDGs) ( [[#UN--2015|UN 2015]] ) deal with providing access to technologies and service infrastructures to the share of population so far excluded, showing that the UN 2030 Agenda has adopted a multidimensional perspective on poverty. Multidimensional poverty indices, such as the Social Progress Indicator and the Individual Deprivation Measure, go beyond income and focus on tracking the delivery of access to basic services by the poorest population groups, both in developing countries ( [[#Fulton--2009|Fulton et al. 2009]] ; [[#Alkire--2014|Alkire and Santos 2014]] ; [[#Alkire--2017|Alkire and Robles 2017]] ; [[#Rao--2018b|Rao and Min 2018b]] ), and in developed countries ( [[#Townsend--1979|Townsend 1979]] ; [[#Aaberge--2015|Aaberge and Brandolini 2015]] ; [[#Eurostat--2018|Eurostat 2018]] ). At the same time, the SDGs, primarily SDG 10 on reducing inequalities within and among countries, promote a more equitable world, both in terms of inter- as well as intra-national equality.

Access to various end-use technologies and infrastructure services features directly in the SDG targets and among the indicators used to track their progress ( [[#UN--2015|UN 2015]] ; [[#UNESC--2017|UNESC 2017]] ): Basic services in households (SDG 1.4.1), Improved water sources (SDG 6.1.1); Improved sanitation (SDG 6.1.2); Electricity (SDG 7.1.1); Internet – fixed broadband subscriptions (SDG 17.6.2); Internet – proportion of population using (SDG 17.8.1). Transport (public transit, cars, mopeds or bicycles) and media technologies (mobile phones, TVs, radios, PCs, Internet) can be seen as proxies for access to mobility and communication, crucial for participation in society and the economy ( [[#Smith--2015|Smith et al. 2015]] ). In addition, SDG 10 is a more conventional income-based inequality goal, referring to income inequality (SDG 10.1), social, economic and political inclusion of all (SDG 10.2.), and equal opportunities and reduced inequalities of outcome (SDG 10.3).

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[[File:725e60c55314112dba5f6a4bc5191da5 IPCC_AR6_WGIII_Box_5_3_Figure_1.png]]

'''Box 5.3, Figure 1 | International inequality in access and use of goods and services.''' '''Upper panel:''' International Lorenz curves and Gini coefficients accounting for the share of population living in households without access (origin of the curves on the y-axis), multiple ownership not considered. '''Lower panel:''' Gini, number of people without access, access rates and coverage in terms of share of global population and number of countries included. *Reduced samples lead to underestimation of inequality. A sample, for example, of around 80% of world population (taking the same 43 countries as for mobiles and cars) led to a lower Gini of around 0.48 (–0.04) for electricity. The reduced sample was kept for mobiles in 2018 to allow for comparability with 2000. Source: [[#Zimm--2019|Zimm (2019)]] .

<div id="5.2.2.2" class="h3-container"></div>

<span id="variations-in-energy-use"></span>
==== 5.2.2.2 Variations in Energy Use ====

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There is ''high evidence'' and ''high agreement'' in the literature that through equitable distribution, well-being for all can be assured at the lowest-possible energy consumption levels ( [[#Steinberger--2010|Steinberger and Roberts 2010]] ; [[#Oswald--2020|Oswald et al. 2020]] ) by reducing emissions related to consumption as much as possible, while assuring DLS for everyone ( [[#Annecke--2002|Annecke 2002]] ; [[#de%20Zoysa--2011|de Zoysa 2011]] ; [[#Ehrlich--2013|Ehrlich and Ehrlich 2013]] ; [[#Spangenberg--2014|Spangenberg 2014]] ; [[#Toroitich--2014|Toroitich and Kerber 2014]] ; [[#Kenner--2015|Kenner 2015]] ; [[#Toth--2016|Toth and Szigeti 2016]] ; [[#Smil--2017|Smil 2017]] ; [[#Otto--2019|Otto et al. 2019]] ; [[#Baltruszewicz--2021|Baltruszewicz et al. 2021]] ). For example, at similar levels of human development, per capita energy demand in the US was 63% higher than in Germany ( [[#Arto--2016|Arto et al. 2016]] ); those patterns are explained by context in terms of various climate, cultural and historical factors influencing consumption. Context matters even in within-country analysis, for example, electricity consumption in the US shows that efficiency innovations do exert positive influence on savings of residential energy consumption, but the relationship is mixed; on the contrary, affluence (household income and home size) and context (geographical location) drive resource utilisation significantly ( [[#Adua--2019|Adua and Clark 2019]] ); affluence is central to any future prospect in terms of environmental conditions ( [[#Wiedmann--2020|Wiedmann et al. 2020]] ). In China, inequality of energy consumption and expenditure varies highly depending on the energy type, end-use demand and climatic region ( [[#Wu--2017|Wu et al. 2017]] ).

Consumption is energy- and materials-intensive and expands along with income. About half of the energy used in the world is consumed by the richest 10% of people, most of whom live in developed countries, especially when one includes the energy embodied in the goods they purchase from other countries and the structure of consumption as a function of income level ( [[#Arto--2016|Arto et al. 2016]] ; [[#Wolfram--2016|Wolfram et al. 2016]] ; [[#Santillán%20Vera--2021|Santillán Vera et al. 2021]] ). International trade plays a central role, being responsible for shifting burdens in most cases from low-income developing countries producers to high-income developed countries as consumers ( [[#Wiedmann--2020|Wiedmann et al. 2020]] ). China is the largest exporter to the EU and United States, and accounts for nearly half and 40% of their imports in energy use respectively ( [[#Wu--2019|Wu et al. 2019]] ). Wealthy countries have exported or outsourced their climate and energy crisis to low- and middle-income countries ( [[#Baker--2018|Baker 2018]] ), exacerbated by intensive international trade ( [[#Steinberger--2012|Steinberger et al. 2012]] ; [[#Scherer--2018|Scherer et al. 2018]] ). Therefore, issues of total energy consumption are inseparably related to the energy inequity among the countries and regions of the world.

Within the energy use induced by global consumer products, household consumption is the biggest contributor, contributing to around three-quarters of the global total ( [[#Wu--2019|Wu et al. 2019]] ). A more granular analysis of household energy consumption reveals that the lowest two quintiles in countries with average annual income below USD15,000 cap –1 yr –1 consume less energy than the international energy requirements for DLS (20–50 GJ cap –1 ); 77% of people consume less than 30 GJ cap –1 yr –1 and 38% consume less than 10 GJ cap –1 yr –1 ( [[#Oswald--2020|Oswald et al. 2020]] ). Many energy-intensive goods have high price elasticity (&gt;1.0), implying that growing incomes lead to over-proportional growth of energy footprints in these consumption categories. Highly unequally distributed energy consumption is concentrated in the transport sector, ranging from vehicle purchase to fuels, and most unequally in package holidays and aviation ( [[#Gössling--2019|Gössling 2019]] ; [[#Oswald--2020|Oswald et al. 2020]] ).

Socio-economic dynamics and outcomes affect whether provisioning of goods and services is achieved at low energy demand levels (Figure 5.4). Specifically, multivariate regression shows that public service quality, income equality, democracy, and electricity access enable higher need satisfaction at lower energy demand, whereas extractivism and economic growth beyond moderate levels of affluence reduce need satisfaction at higher energy demand ( [[#Vogel--2021|Vogel et al. 2021]] ). Altogether, this demonstrates that at a given level of energy provided, there is large scope to improve service levels for well-being by modifying socio-economic context without increasing energy supply (Figure 5.4).

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[[File:2e52df68d01befeff2f6531a1b1ef945 IPCC_AR6_WGIII_Figure_5_4.png]]

'''Figure 5.4 | Improving services for well-being is possible, often at huge margin, at a given (relatively low) level of energy use.''' Source: reused with permission from [[#Vogel--2021|Vogel et al. (2021)]] .

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<span id="variations-in-consumption-based-emissions"></span>
==== 5.2.2.3 Variations in Consumption-based Emissions ====

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The carbon footprint of a nation is equal to the direct emissions occurring due to households’ transport, heating and cooking, as well as the impact embodied in the production of all consumed goods and services ( [[#Wiedmann--2008|Wiedmann and Minx 2008]] ; [[#Davis--2010|Davis and Caldeira 2010]] ; [[#Hübler--2017|Hübler 2017]] ; [[#Vita--2019a|Vita et al. 2019a]] ). There are large differences in carbon footprints between the poor and the rich. As a result of energy use inequality, the lowest global emitters (the poorest 10% in developing countries) in 2013 emitted about 0.1 tCO 2 cap –1 yr –1 , whereas the highest global emitters (the top 1% in the richest countries) emitted about 200–300 tCO 2 cap –1 yr –1 ( [[#World%20Bank--2019|World Bank 2019]] ). The poorest 50% of the world’s population are responsible for only about 10% of total lifetime consumption emissions, in contrast about 50% of the world’s GHG emissions can be attributed to consumption by the world’s richest 10%, with the average carbon footprint of the richest being 175 times higher than that of the poorest 10% ( [[#Chancel--2015|Chancel and Piketty 2015]] ). This richest 10% consumed the global carbon budget by nearly 30% during the period 1990–2015 ( [[#Kartha--2020|Kartha et al. 2020]] ; [[#Gore--2020|Gore 2020]] ). While mitigation efforts often focus on the poorest, the lifestyle and consumption patterns of the affluent often influence the growing middle class ( [[#Otto--2019|Otto et al. 2019]] ). Across EU countries, only 5% of households are living within 1.5°C climate limits and the top 1% emit more than 22 times the target on average, with land and air transport being particular characteristics of the highest-emitting countries ( [[#Ivanova--2020|Ivanova and Wood 2020]] ).

In low-income nations – which can exhibit per-capita carbon footprints 30 times lower than wealthy nations ( [[#Hertwich--2009|Hertwich and Peters 2009]] ) – emissions are predominantly domestic and driven by provision of essential services (shelter, low-meat diets, clothing). Per capita carbon footprints average 1.6 tonnes per year for the lowest income category, then quickly increase to 4.9 and 9.8 tonnes for the two middle-income categories and finally to an average of 17.9 tonnes for the highest income category. Global CO 2 emissions remain concentrated: the top 10% of emitters contribute about 35–45% of the total, while the bottom 50% contribute just 13–15% of global emissions ( [[#Chancel--2015|Chancel and Piketty 2015]] ; [[#Hubacek--2017|Hubacek et al. 2017]] ). In wealthy nations, services such as private road transport, frequent air travel, private jet ownership, meat-intensive diets, entertainment and leisure add significant emissions, while a considerable fraction of the carbon footprint is imported from abroad, embedded in goods and services ( [[#Hubacek--2017|Hubacek et al. 2017]] ).

High-income households consume and demand energy at an order of magnitude greater than what is necessary for DLS ( [[#Oswald--2020|Oswald et al. 2020]] ). Energy-intensive goods, such as package holidays, have a higher income elasticity of demand than less energy-intensive goods like food, water supply and housing maintenance, which results in high-income individuals having much higher energy footprints ( [[#Oswald--2020|Oswald et al. 2020]] ). Evidence highlights highly unequal GHG emissions in aviation: only 2–4% of the global population flew internationally in 2018, with 1% of the world population emitting 50% of CO 2 from commercial aviation ( [[#Gössling--2020|Gössling and Humpe 2020]] ). Some individuals may add more than 1600 tCO 2 yr –1 individually by air travel ( [[#Gössling--2019|Gössling 2019]] ).

The food sector dominates in all income groups, comprising 28% of households’ carbon footprint, with cattle and rice the major contributors ( [[#Scherer--2018|Scherer et al. 2018]] ); food also accounts for 48% and 70% of household impacts on land and water resources respectively, and consumption of meat, dairy, and processed food rise fast asincomes increase ( [[#Ivanova--2016|Ivanova et al. 2016]] ). Roughly 20–40% of food produced worldwide is lost to waste before it reaches the market, or is wasted by households, the energy embodied in wasted food was estimated at around 36 EJ yr –1 , and during the period 2010–2016 global food loss and waste equalled 8–10% of total GHG emissions ( [[#Godfray--2014|Godfray and Garnett 2014]] ; [[#Springmann--2018|Springmann et al. 2018]] ; [[#Mbow--2019|Mbow et al. 2019]] ). Global agri-food supply chains are crucial in the variation of per capita food consumption-related-GHG footprints, mainly in the case of red meat and dairy ( [[#Kim--2020|Kim et al. 2020]] ) since the highest per capita food-consumption-related GHG emissions do not correlate perfectly with the income status of countries. Thus, it is also crucial to focus on high-emitting individuals and groups within countries, rather than only those who live in high-emitting countries, since the top 10% of emitters live on all continents and one-third of them are from the developing world ( [[#Chakravarty--2009|Chakravarty et al. 2009]] ; [[#Pan--2019|Pan et al. 2019]] ).

The environmental impact of increasing equity across income groups can be either positive or negative ( [[#Hubacek--2017|Hubacek et al. 2017]] ; [[#Rao--2018a|Rao and Min 2018a]] ; [[#Scherer--2018|Scherer et al. 2018]] ; [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ). Projections for achieving equitable levels of service provision globally predict large increases in global GHG emissions and demand for key resources ( [[#Blomsma--2017|Blomsma and Brennan 2017]] ), especially in passenger transport, which is predicted to increase nearly three-fold between 2015 and 2050, from 44 trillion to 122 trillion passenger-kilometres ( [[#OECD--2019a|OECD 2019a]] ), and associated infrastructure needs, increasing freight ( [[#Murray--2017|Murray et al. 2017]] ), increasing demand for cooling ( [[#IEA--2018|IEA 2018]] ), and shifts to carbon-intensive high-meat diets ( [[#OECD/FAO--2018|OECD/FAO 2018]] ).

Increasing incomes for all to attain DLS raises emissions and energy footprints, but only slightly ( [[#Chakravarty--2009|Chakravarty et al. 2009]] ; [[#Jorgenson--2016|Jorgenson et al. 2016]] ; [[#Scherer--2018|Scherer et al. 2018]] ; [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ; [[#Oswald--2020|Oswald et al. 2020]] ; [[#Oswald--2021|Oswald et al. 2021]] ). The amount of energy needed for a high global level of human development is dropping ( [[#Steinberger--2010|Steinberger and Roberts 2010]] ) and could by 2050 be reduced to 1950 levels ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ) requiring a massive deployment of technologies across the different sectors as well as demand-side reduction consumption. The consumption share of the bottom half of the world’s population represents less than 20% of all energy footprints, which is less than what the top 5% of people consume ( [[#Oswald--2020|Oswald et al. 2020]] ).

Income inequality itself also raises carbon emissions ( [[#Hao--2016|Hao et al. 2016]] ; [[#Sinha--2016|Sinha 2016]] ; [[#Uzar--2019|Uzar and Eyuboglu 2019]] ; [[#Baloch--2020|Baloch et al. 2020]] ; [[#Oswald--2020|Oswald et al. 2020]] ; [[#Wiedmann--2020|Wiedmann et al. 2020]] ; [[#Vogel--2021|Vogel et al. 2021]] ). Wide inequality can increase status-based consumption patterns, where individuals spend more to emulate the standards of the high-income group (the Veblen effect); inequality also diminishes environmental efforts by reducing social cohesion and cooperation ( [[#Jorgenson--2017|Jorgenson et al. 2017]] ) and finally, inequality also operates by inducing an increase in working hours that leads to higher economic growth and, consequently, higher emissions and ecological footprint, so working time reduction is key for policy to both reduce emissions and protect employment ( [[#Fitzgerald--2015|Fitzgerald et al. 2015]] ; [[#Fitzgerald--2018|Fitzgerald et al. 2018]] ).

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<span id="equity-trust-and-participation-in-demand-side-mitigation"></span>

=== 5.2.3 Equity, Trust, and Participation in Demand-side Mitigation ===

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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). [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I (Section 5.SM.1) contains more detail on these links, drawing from social science literature.

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[[File:9933371913cf126df091ba54c5966b92 IPCC_AR6_WGIII_Figure_5_5.png]]

'''Figure 5.5 | Well-being, equity, trust, governance and climate mitigation: positive feedbacks.''' Well-being for all, increasingly seen as the main goal of sustainable economies, reinforces emissions reductions through a network of positive feedbacks linking effective governance, social trust, equity, participation and sufficiency. This diagram depicts relationships noted in this chapter text and explained further in the Social Science Primer ( [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 Chapter 5] Supplementary Material I). The width of the arrows corresponds to the level of confidence and degree of evidence from recent social sciences literature.

Economic growth in equitable societies is associated with lower emissions than in inequitable societies ( [[#McGee--2018|McGee and Greiner 2018]] ), and income inequality is associated with higher global emissions ( [[#Ravallion--1997|Ravallion et al. 1997]] ; [[#McGee--2018|McGee and Greiner 2018]] ; [[#Rao--2018c|Rao and Min 2018c]] ; [[#Diffenbaugh--2019|Diffenbaugh and Burke 2019]] ; [[#Fremstad--2019|Fremstad and Paul 2019]] ; [[#Liu--2020|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--2010|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--2021|Vogel et al. 2021]] ).

Well-designed climate mitigation policies ameliorate constituents of well-being ( [[#Creutzig--2021b|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--2021b|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.

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[[File:67aeaa733bb1eccecd825984b1f35f0c IPCC_AR6_WGIII_Figure_5_6.png]]

'''Figure 5.6 | Two-way link between demand-side climate mitigation strategies and multiple dimensions of human well-being and SDGs.''' All demand-side mitigation strategies improve well-being in sum, though not necessarily in each individual dimension. Incumbent business (in contrast to overall economic performance) may be challenged. Source: [[#Creutzig--2021b|Creutzig et al. (2021b)]] .

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--2017|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--2009|Tanner et al. 2009]] ; [[#Lorenz--2013|Lorenz 2013]] ; [[#Chu--2015|Chu 2015]] ; [[#Cloutier--2015|Cloutier et al. 2015]] ; [[#Mitchell--2015|Mitchell 2015]] ; [[#Martin--2016|Martin and Shaheen 2016]] ; [[#Vandeweerdt--2016|Vandeweerdt et al. 2016]] ; [[#Turnheim--2018|Turnheim et al. 2018]] ). Moreover, these factors are the ones identified as enablers of high need satisfaction at lower energy use ( [[#Vogel--2021|Vogel et al. 2021]] ).

There is less policy lock-in in more equitable societies ( [[#Seto--2016|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--2012|Scheffran et al. 2012]] ). Climate-related injustices are addressed where equity is prioritised ( [[#Klinsky--2014|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--2011|Okvat and Zautra 2011]] ; [[#Bentley--2014|Bentley 2014]] ; [[#Klinsky--2016|Klinsky et al. 2016]] ; [[#Roy--2018a|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--2001|Cazorla and Toman 2001]] ; [[#Dulal--2009|Dulal et al. 2009]] ; [[#Chuku--2010|Chuku 2010]] ; [[#Shonkoff--2011|Shonkoff et al. 2011]] ; [[#Navroz--2019|Navroz 2019]] ; [[#Hofstad--2020|Hofstad and Vedeld 2020]] ; [[#Muttitt--2020|Muttitt and Kartha 2020]] ; [[#Roy--2020|Roy and Schaffartzik 2020]] ; [[#Temper--2020|Temper et al. 2020]] ; [[#Waller--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--2016|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--2009|Chhatre and Agrawal 2009]] ; [[#Garg--2011|Garg 2011]] ; [[#Shaw--2014|Shaw et al. 2014]] ; [[#Serrao-Neumann--2015|Serrao-Neumann et al. 2015]] ; [[#Klausbruckner--2016|Klausbruckner et al. 2016]] ; [[#Salas--2019|Salas and Jha 2019]] ) (Figure 5.6). Limiting climate change risks is fundamental to collective well-being ( [[#Max-Neef--1989|Max-Neef et al. 1989]] ; [[#Yamin--2005|Yamin et al. 2005]] ; [[#Nelson--2013|Nelson et al. 2013]] ; [[#Gough--2015|Gough 2015]] ; [[#Gough--2017|Gough 2017]] ; [[#Pecl--2017|Pecl et al. 2017]] ; [[#Tschakert--2017|Tschakert et al. 2017]] ). [[#5.6|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--2004|Arrow et al. 2004]] ; [[#Ramakrishnan--2021|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|Jackson 2008]] ; [[#Fedrigo--2010|Fedrigo et al. 2010]] ; [[#Schroeder--2013|Schroeder 2013]] ; [[#Figge--2014|Figge et al. 2014]] ; [[#Spangenberg--2016|Spangenberg and Germany 2016]] ; [[#Spengler--2016|Spengler 2016]] ; [[#Burke--2020|Burke 2020]] ; [[#Mont--2020|Mont et al. 2020]] ). Beyond a threshold, increased material consumption is not closely correlated with improvements in human progress ( [[#Frank--1999|Frank 1999]] ; [[#Kahneman--2010|Kahneman and Deaton 2010]] ; [[#Steinberger--2010|Steinberger and Roberts 2010]] ; [[#Roy--2012|Roy et al. 2012]] ; [[#Oishi--2018|Oishi et al. 2018]] ; [[#Xie--2018|Xie et al. 2018]] ; [[#Vita--2019b|Vita et al. 2019b]] ; [[#Wang--2019|Wang et al. 2019]] ; [[#Vita--2020|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|Kenner 2019]] ; [[#Otto--2019|Otto et al. 2019]] ; [[#Pascale--2020|Pascale et al. 2020]] ; [[#Stratford--2020|Stratford 2020]] ) ( [[#5.2.2.3|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--2008|Brand and Boardman 2008]] ; [[#Roy--2009|Roy and Pal 2009]] ; [[#Roy--2012|Roy et al. 2012]] ; [[#Brand--2010|Brand and Preston 2010]] ; [[#Gore--2015|Gore 2015]] ; [[#Hubacek--2017|Hubacek et al. 2017]] ; [[#Jorgenson--2017|Jorgenson et al. 2017]] ; [[#Sahakian--2018|Sahakian 2018]] ; [[#Gössling--2019|Gössling 2019]] ; [[#Kenner--2019|Kenner 2019]] ; [[#Lynch--2019|Lynch et al. 2019]] ; [[#Osuoka--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--2018|O’Neill et al. 2018]] ; [[#Vogel--2021|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--2011|Grunewald et al. 2011]] ; [[#Golley--2012|Golley and Meng 2012]] ; Chancel et al. 2015; [[#Grunewald--2017|Grunewald et al. 2017]] ; [[#Jorgenson--2017|Jorgenson et al. 2017]] ; [[#Sager--2017|Sager 2017]] ; [[#Klasen--2018|Klasen 2018]] ; [[#Liu--2019|Liu et al. 2019]] ); reducing inequality in high-income countries helps to reduce emissions ( [[#Klasen--2018|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--2003|Adger et al. 2003]] ; [[#Hammar--2007|Hammar and Jagers 2007]] ; [[#Van%20Vossole--2012|Van Vossole 2012]] ; [[#Bulkeley--2015|Bulkeley and Newell 2015]] ; [[#Smith--2015|Smith and Howe 2015]] ; ISSC et al. 2016; [[#Alvaredo--2018|Alvaredo et al. 2018]] ; [[#Smith--2018|Smith and Mayer 2018]] ; [[#Fairbrother--2019|Fairbrother et al. 2019]] ; [[#Hayward--2019|Hayward and Roy 2019]] ; [[#Kulin--2019|Kulin and Johansson Sevä 2019]] ; [[#Liao--2019|Liao et al. 2019]] ).

Populism and politics of fear are less prevalent under conditions of more income equality ( [[#Chevigny--2003|Chevigny 2003]] ; [[#Bryson--2016|Bryson and Rauwolf 2016]] ; [[#O’Connor--2017|O’Connor 2017]] ; [[#Fraune--2018|Fraune and Knodt 2018]] ; [[#Myrick--2019|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|Swyngedouw 2011]] ; [[#Lockwood--2018|Lockwood 2018]] ; [[#Huber--2020|Huber et al. 2020]] ). ‘Climate populism’ movements are driven by an impetus for justice ( [[#Beeson--2019|Beeson 2019]] ; [[#Hilson--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--2020|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|Barnett 2003]] ; [[#Mearns--2009|Mearns and Norton 2009]] ; [[#Inderberg--2015|Inderberg et al. 2015]] ; [[#Sovacool--2015|Sovacool et al. 2015]] ; [[#Hunsberger--2017|Hunsberger et al. 2017]] ; [[#Soliev--2020|Soliev and Theesfeld 2020]] ). Leadership is also important in fostering environmentally-responsible group behaviours ( [[#Liu--2020|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|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 2010]] ; [[#Guruswamy--2020|Guruswamy 2020]] ). In developing countries, livelihood improvements do not necessarily cause increases in emissions ( [[#Peters--2012|Peters et al. 2012]] ; [[#Reusser--2013|Reusser et al. 2013]] ; [[#Creutzig--2015a|Creutzig et al. 2015a]] ; [[#Chhatre--2009|Chhatre and Agrawal 2009]] ; [[#Baltruszewicz--2021|Baltruszewicz et al. 2021]] ) and poverty alleviation causes negligible emissions ( [[#Chakravarty--2009|Chakravarty et al. 2009]] ). Greater equity is an important step towards sustainable service provisioning ( [[#Godfray--2018|Godfray et al. 2018]] ; [[#Dorling--2019|Dorling 2019]] ; [[#Timko--2019|Timko 2019]] ).

As discussed in [[#5.6|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--2017|Burke and Stephens 2017]] ; [[#Frank--2017|Frank 2017]] ; [[#Healy--2017|Healy and Barry 2017]] ; [[#Sen--2017|Sen 2017]] ; [[#Chapman--2018|Chapman et al. 2018]] ; [[#La%20Viña--2018|La Viña et al. 2018]] ; [[#Chapman--2019|Chapman and Fraser 2019]] ; [[#Piggot--2019|Piggot et al. 2019]] ; [[#Sunderland--2020|Sunderland et al. 2020]] ). Global and intergenerational climate inequities impact people’s well-being, which affects their consumption patterns and political actions ( [[#Albrecht--2007|Albrecht et al. 2007]] ; [[#Fritze--2008|Fritze et al. 2008]] ; [[#Gori-Maia--2013|Gori-Maia 2013]] ; [[#Clayton--2015|Clayton et al. 2015]] ; [[#Pizzigati--2018|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--2006|Mulder et al. 2006]] ; [[#Harriss--2010|Harriss and Shui 2010]] ; [[#Figge--2014|Figge et al. 2014]] ; [[#Grinde--2018|Grinde et al. 2018]] ; [[#Spangenberg--2019|Spangenberg and Lorek 2019]] ; [[#Vita--2020|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--2012|Kallis et al. 2012]] ; [[#Jackson--2016|Jackson and Victor 2016]] ; [[#Stuart--2017|Stuart et al. 2017]] ; [[#Chapman--2019|Chapman and Fraser 2019]] ; [[#D’Alessandro--2019|D’Alessandro et al. 2019]] ; [[#Gabriel--2019|Gabriel and Bond 2019]] ; [[#Huang--2019|Huang et al. 2019]] ; [[#Victor--2019|Victor 2019]] ). Such degrowth pathways may be crucial in combining technical feasibility of mitigation with social development goals ( [[#Hickel--2021|Hickel et al. 2021]] ; [[#Keyßer--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--2009|Kern and Bulkeley 2009]] ; [[#Lidskog--2009|Lidskog and Elander 2009]] ; [[#Amundsen--2010|Amundsen et al. 2010]] ; [[#Keskitalo--2010|Keskitalo 2010]] ; [[#Lee--2015|Lee and Koski 2015]] ; [[#Jokinen--2016|Jokinen et al. 2016]] ; [[#Lepeley--2017|Lepeley 2017]] ; [[#Marquardt--2017|Marquardt 2017]] ; [[#Di%20Gregorio--2019|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%20Tàbara--2018|David Tàbara et al. 2018]] ; [[#Patterson--2019|Patterson and Huitema 2019]] ; [[#Barnes--2020|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--2005|Thomas and Twyman 2005]] ; [[#Mearns--2009|Mearns and Norton 2009]] ; [[#Klinsky--2014|Klinsky and Winkler 2014]] ; [[#Lamb--2014|Lamb et al. 2014]] ; [[#Lamb--2017|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|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|Dombrowski 2010]] ; [[#Wise--2014|Wise et al. 2014]] ; [[#Haque--2015|Haque et al. 2015]] ; [[#Jodoin--2015|Jodoin et al. 2015]] ; [[#Mitchell--2015|Mitchell 2015]] ; [[#Kaiser--2020|Kaiser 2020]] ; [[#Alegria--2021|Alegria 2021]] ). This sometimes involves complex policy discussions that can lead to governance innovations, also influencing social norms ( [[#Martinez--2020|Martinez 2020]] ). A specific example are citizen assemblies, deliberating public policy challenges, such as climate change ( [[#Devaney--2020|Devaney et al. 2020]] ). Activist climate movements are changing policies as well as normative values ( [[#5.4|Section 5.4]] and the Social Science Primer, [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-5 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|Goodman 2009]] ; [[#Schlosberg--2014|Schlosberg and Collins 2014]] ; [[#Jafry--2019|Jafry 2019]] ; [[#Cheon--2020|Cheon 2020]] ). Youth climate activists, and Indigenous leaders, are also exerting growing political influence towards mitigation ( [[#Helferty--2009|Helferty and Clarke 2009]] ; [[#White--2011|White 2011]] ; [[#Powless--2012|Powless 2012]] ; [[#Petheram--2015|Petheram et al. 2015]] ; [[#UN--2015|UN 2015]] ; [[#Curnow--2016|Curnow and Gross 2016]] ; [[#Grady-Benson--2016|Grady-Benson and Sarathy 2016]] ; [[#Claeys--2017|Claeys and Delgado Pugley 2017]] ; [[#O’Brien--2018|O’Brien et al. 2018]] ; [[#Rowlands--2019|Rowlands and Gomez Peña 2019]] ; [[#Bergmann--2020|Bergmann and Ossewaarde 2020]] ; [[#Han--2020|Han and Ahn 2020]] ; [[#Nkrumah--2021|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|Wildcat 2014]] ; [[#Chanza--2016|Chanza and De Wit 2016]] ; [[#Whyte--2017|Whyte 2017]] ; [[#Whyte--2018|Whyte 2018]] , [[#Temper--2020|Temper et al. 2020]] ). Related trends include recognition of the value of traditional ecological knowledge, Indigenous governance principles, decentralisation, and appropriate technologies ( [[#Lange--2007|Lange et al. 2007]] ; [[#Goldthau--2014|Goldthau 2014]] ; [[#Whyte--2017|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--2008|Rothstein and Teorell 2008]] ; [[#Carattini--2015|Carattini et al. 2015]] ; [[#Klenert--2018|Klenert et al. 2018]] ; [[#Patterson--2018|Patterson et al. 2018]] ). Inter-personal trust among citizens often promotes pro-environment behaviour by influencing perceptions ( [[#Harring--2013|Harring and Jagers 2013]] ), enhancing cooperation, and reducing free-riding and opportunistic behaviour ( [[#Gür--2020|Gür 2020]] ). Individual support for carbon taxes and energy innovations falls when collective community support is lacking ( [[#Bolsen--2014|Bolsen et al. 2014]] ; [[#Smith--2018|Smith and Mayer 2018]] ; [[#Simon--2020|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--2015|Nayak et al. 2015]] ).

[[#5.6|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--2012|Atteridge et al. 2012]] ; [[#Patterson--2018|Patterson et al. 2018]] ; [[#Jewell--2020|Jewell and Cherp 2020]] ; [[#Singh--2020|Singh et al. 2020]] ; [[#Singh--2021|Singh et al. 2021]] ).

<div id="box-5.4" class="h2-container box-container"></div>

<span id="box-5.4-gender-race-intersectionality-and-climate-mitigation"></span>
=== Box 5.4 | Gender, Race, Intersectionality and Climate Mitigation ===

<div id="h2-10-siblings" class="h2-siblings"></div>

There is ''high evidence'' and ''high agreement'' that empowering women benefits both mitigation and adaptation, because women prioritise climate change in their voting, purchasing, community leadership, and work, both professionally and at home ( ''high evidence'' , ''high agreement'' ). Increasing voice and agency for those marginalised in intersectional ways by indigeneity, race, ethnicity, dis/ability, and other factors has positive effects for climate policy ( ''high evidence'' , ''high agreement'' ).

Climate change affects people differently along all measures of difference and identity, which have intersectional impacts linked to economic vulnerability and marginalisation ( [[#Morello%20Frosch--2009|Morello Frosch et al. 2009]] ; [[#Dankelman--2010|Dankelman 2010]] ; [[#Habtezion--2013|Habtezion 2013]] ; Godfrey and Torres 2016; [[#Walsh--2016|Walsh 2016]] ; [[#Flatø--2017|Flatø et al. 2017]] ; [[#Goodrich--2019|Goodrich et al. 2019]] ; [[#Perkins--2019|Perkins 2019]] ; [[#Gür--2020|Gür 2020]] ). Worldwide, racialised and Indigenous people bear the brunt of environmental and climate injustices through geographic location in extraction and energy ‘sacrifice zones’, areas most impacted by extreme weather events, and/or through inequitable energy access ( [[#Aubrey--2019|Aubrey 2019]] ; [[#Jafry--2019|Jafry 2019]] ; [[#Gonzalez--2020|Gonzalez 2020]] ; [[#Lacey-Barnacle--2020|Lacey-Barnacle et al. 2020]] ; [[#Porter--2020|Porter et al. 2020]] ; [[#Temper--2020|Temper et al. 2020]] ) Disparities in climate change vulnerability not only reflect pre-existing inequalities, they also reinforce them. For example, inequities in income and in the ownership and control of household assets, familial responsibilities due to male out-migration, declining food and water access, and increased disaster exposure can undermine women’s ability to achieve economic independence, enhance human capital, and maintain physical and mental health and well-being ( [[#Chandra--2017|Chandra et al. 2017]] ; [[#Eastin--2018|Eastin 2018]] ; [[#Das--2019|Das et al. 2019]] ). Studies during the COVID-19 crisis have found that, in general, women’s economic and productive lives have been affected disproportionately to men’s ( [[#Alon--2020|Alon et al. 2020]] ; [[#ILO--2020|ILO 2020]] ). Women have less access to social protections and their capacity to absorb economic shocks is very low, so they face a ‘triple burden’ during crises – including those

Box 5.4

resulting from climate change – and this is heightened for women in the less-developed countries and for those who are intersectionally vulnerable ( [[#Coates--2020|Coates et al. 2020]] ; [[#McLaren--2020|McLaren et al. 2020]] ; [[#Wenham--2020|Wenham et al. 2020]] ; [[#Azong--2021|Azong and Kelso 2021]] ; [[#Erwin--2021|Erwin et al. 2021]] ; [[#Maobe--2021|Maobe and Atela 2021]] ; [[#Nicoson--2021|Nicoson 2021]] ; [[#Sultana--2021|Sultana 2021]] ; [[#Versey--2021|Versey 2021]] ). Because men currently hold the majority of energy-sector jobs, energy transition will impact them economically and psychologically; benefits, burdens and opportunities on both the demand and supply sides of the mitigation transition have a range of equity implications ( [[#Pearl-Martinez--2017|Pearl-Martinez and Stephens 2017]] ; [[#Standal--2020|Standal et al. 2020]] ; [[#Mang-Benza--2021|Mang-Benza 2021]] ). Mitigating gendered climate impacts requires addressing inequitable power relations throughout society ( [[#Wester--2019|Wester and Lama 2019]] ).

Women’s well-being and gender-responsive climate policy have been emphasised in international agreements including the Paris Agreement (UNFCCC 2015), Convention on the Elimination of all Forms of Discrimination Against Women General Recommendation 37 ( [[#Vijeyarasa--2021|Vijeyarasa 2021]] ), and the 2016 Decision 21/CP.22 on Gender and Climate Change ( [[#UNFCCC--2016|]] [[#UNFCCC--2016|UNFCCC 2016]] ; [[#Larson--2018|Larson et al. 2018]] ). Increasing the participation of women and marginalised social groups, and addressing their special needs, helps to meet a range of SDGs, improve disaster and crisis response, increase social trust, and improve climate mitigation policy development and implementation ( [[#Alber--2009|Alber 2009]] ; [[#Whyte--2014|Whyte 2014]] ; [[#Elnakat--2015|Elnakat and Gomez 2015]] ; [[#Salehi--2015|Salehi et al. 2015]] ; [[#Buckingham--2017|Buckingham and Kulcur 2017]] ; [[#Cohen--2017|Cohen 2017]] ; [[#Kronsell--2017|Kronsell 2017]] ; [[#Lee--2019|Lee and Zusman 2019]] ).

Women have a key role in the changing energy economy due to their demand for and end use of energy resources in socially-gendered productive roles in food production and processing, health, care, education, clothing purchases and maintenance, commerce, and other work, both within and beyond the home ( [[#Räty--2009|Räty and Carlsson-Kanyama 2009]] ; [[#Oparaocha--2011|Oparaocha and Dutta 2011]] ; [[#Bob--2014|Bob and Babugura 2014]] ; [[#Macgregor--2014|Macgregor 2014]] ; [[#Perez--2015|Perez et al. 2015]] ; [[#Bradshaw--2018|Bradshaw 2018]] ; [[#Clancy--2019|Clancy and Feenstra 2019]] ; [[#Clancy--2019|Clancy et al. 2019]] ; [[#Fortnam--2019|Fortnam et al. 2019]] ; [[#Rao--2019a|Rao et al. 2019a]] ; [[#Quandt--2019|Quandt 2019]] ; [[#Horen%20Greenford--2020|Horen Greenford et al. 2020]] ; [[#Johnson--2020|Johnson 2020]] ). Women’s work and decision-making are central in the food chain and agricultural output in most developing countries, and in household management everywhere. Emissions from cooking fuels can cause serious health damage, and unsustainable extraction of biofuels can also hurt mitigation ( [[#Bailis--2015|Bailis et al. 2015]] ), so considering health, biodiversity and climate tradeoffs and co-benefits is important ( [[#Rosenthal--2018|Rosenthal et al. 2018]] ; [[#Aberilla--2020|Aberilla et al. 2020]] ; [[#Mazorra--2020|Mazorra et al. 2020]] ). Policies on energy use and consumption are often focused on technical issues related to energy supply, thereby overlooking demand-side factors such as household decision-making, unpaid work, livelihoods and care ( [[#Himmelweit--2002|Himmelweit 2002]] ; [[#Perch--2011|Perch 2011]] ; [[#Fumo--2014|Fumo 2014]] ; [[#Hans--2019|Hans et al. 2019]] ; [[#Huyer--2020|Huyer and Partey 2020]] ). Such gender-blindness represents the manifestation of wider issues related to political ideology, culture and tradition ( [[#Carr--2014|Carr and Thompson 2014]] ; [[#Thoyre--2020|Thoyre 2020]] ; [[#Perez--2015|Perez et al. 2015]] ; [[#Fortnam--2019|Fortnam et al. 2019]] ).

Women, and all those who are economically and/or politically marginalised, often have less access to energy and use less, not just because they may be poorer but case studies show because their consumption choices are more ecologically inclined and their energy use is more efficient ( [[#Lee--2013|Lee et al. 2013]] ; [[#Permana--2015|Permana et al. 2015]] ; [[#Li--2019|Li et al. 2019]] ). Women’s carbon footprints are about 6–28% lower than men’s (with high variation across countries), mostly based on their lower meat consumption and lower vehicle use ( [[#Isenhour--2009|Isenhour and Ardenfors 2009]] ; [[#Räty--2009|Räty and Carlsson-Kanyama 2009]] ; [[#Räty--2010|Räty and Carlsson-Kanyama 2010]] ; [[#Barnett--2012|Barnett et al. 2012]] ; [[#Medina--2016|Medina and Toledo-Bruno 2016]] ; [[#Ahmad--2017|Ahmad et al. 2017]] ; [[#Fernström%20Nåtby--2018|Fernström Nåtby and Rönnerfalk 2018]] ; [[#Li--2019|Li et al. 2019]] ). Gender-based income redistribution in the form of pay equity for women could reduce emissions if the redistribution is revenue neutral ( [[#Terry--2009|Terry 2009]] ; [[#Dengler--2018|Dengler and Strunk 2018]] ). Also, advances in female education and reproductive health, especially voluntary family planning, can contribute greatly to reducing world population growth ( [[#Abel--2016|Abel et al. 2016]] ; [[#Dodson--2020|Dodson et al. 2020]] ).

Carbon emissions are lower per capita in countries where women have more political ‘voice’, controlling for GDP per capita and a range of other factors ( [[#Ergas--2012|Ergas and York 2012]] ). While most people recognise that climate change is happening ( [[#Lewis--2018|Lewis et al. 2018]] ; [[#Ballew--2019|Ballew et al. 2019]] ), climate denialism is more prevalent among men ( [[#McCright--2011|McCright and Dunlap 2011]] ; [[#Anshelm--2014|Anshelm and Hultman 2014]] ; [[#Nagel--2015|Nagel 2015]] ; [[#Jylhä--2016|Jylhä et al. 2016]] ), while women are more likely to be environmental activists, and to support stronger environmental and climate policies ( [[#Stein--2004|Stein 2004]] ; [[#McCright--2014|McCright and Xiao 2014]] , [[#Whyte--2014|Whyte 2014]] ). Racialised groups are more likely to be concerned about climate change and to take political action to support climate mitigation policies ( [[#Leiserowitz--2010|Leiserowitz and Akerlof 2010]] ; Godfrey and Torres 2016; [[#Schuldt--2016|Schuldt and Pearson 2016]] ; [[#Pearson--2017|Pearson et al. 2017]] ; [[#Ballew--2020|Ballew et al. 2020]] ; [[#Johnson--2020|Johnson 2020]] ). This underscores the important synergies between equity and mitigation. The contributions of women, racialised people, and indigenous people, who are socially positioned as those first and most affected by climate change – and therefore experts on appropriate climate responses – are substantial ( [[#Dankelman--2010|Dankelman and Jansen 2010]] ; [[#Wickramasinghe--2015|Wickramasinghe 2015]] ; [[#Black--2016|Black 2016]] ; [[#Vinyeta--2016|Vinyeta et al. 2016]] ; [[#Pearse--2017|Pearse 2017]] ). Equitable power, participation, and agency in climate policymaking is hence an effective contribution for improving governance and decision-making on climate change mitigation ( [[#Reckien--2017|Reckien et al. 2017]] ; [[#Collins--2019|Collins 2019]] ). Indigenous knowledge is an important source of guidance for biodiversity conservation, impact assessment, governance, disaster preparedness and resilience ( [[#Salick--2009|Salick and Ross 2009]] ; [[#Green--2010|Green and Raygorodetsky 2010]] ; [[#Speranza--2010|Speranza et al. 2010]] ; [[#Mekuriaw%20Bizuneh--2013|Mekuriaw Bizuneh 2013]] ; Mekuriaw 2017), and women are often the local educators, passing on and utilising traditional and indigenous knowledge ( [[#Ketlhoilwe--2013|Ketlhoilwe 2013]] ; [[#Onyige--2017|Onyige 2017]] ; [[#Azong--2018|Azong et al. 2018]] ).

Higher female political participation, controlled for other factors, leads to higher stringency in climate policies, and results in lower GHG emissions ( [[#Cook--2019|Cook et al. 2019]] ). Gender equity is also correlated with lower per capita CO 2 -eq emissions ( [[#Ergas--2012|Ergas and York 2012]] ).

Box 5.4

In societies where women have more economic equity, their votes push political decision-making in the direction of environmental and sustainable development policies, less high-emission militarisation, and more emphasis on equity and social policies such as via wealth and capital gains taxes ( [[#Ergas--2012|Ergas and York 2012]] ; [[#Resurrección--2013|Resurrección 2013]] ; [[#UNEP--2013|UNEP 2013]] ; [[#Glemarec--2016|Glemarec et al. 2016]] ; [[#Bryan--2018|Bryan et al. 2018]] ; [[#Crawford--2019|Crawford 2019]] ). Changing social norms on race and climate are linked and policy-relevant ( [[#Benegal--2018|Benegal 2018]] ; [[#Elias--2018|Elias et al. 2018]] ; [[#Slocum--2018|Slocum 2018]] ; [[#Gach--2019|Gach 2019]] ; [[#Wallace-Wells--2019|Wallace-Wells 2019]] ; [[#Temple--2020|Temple 2020]] ; [[#Drolet--2021|Drolet 2021]] ). For all these reasons, climate policies are strengthened by including more differently-situated knowledge and diverse perspectives, such as feminist expertise in the study of power ( [[#Bell--2020|Bell et al. 2020]] ; [[#Lieu--2020|Lieu et al. 2020]] ); clarifying equity goals (e.g., distinguishing among ‘reach, ‘benefit’, and ‘empowerment’; obtaining disaggregated data and using clear empirical equity measures; and confronting deeply-ingrained inequities in society ( [[#Lau--2021|Lau et al. 2021]] ). Inclusivity in climate governance spans mitigation–adaptation, supply–demand and formal–informal sector boundaries in its positive effects ( [[#Morello%20Frosch--2009|Morello Frosch et al. 2009]] ; [[#Dankelman--2010|Dankelman 2010]] ; [[#Bryan--2013|Bryan and Behrman 2013]] ; [[#Habtezion--2013|Habtezion 2013]] ; Godfrey and Torres 2016; [[#Walsh--2016|Walsh 2016]] ; [[#Flatø--2017|Flatø et al. 2017]] ; [[#Wilson--2018|Wilson et al. 2018]] ; [[#Goodrich--2019|Goodrich et al. 2019]] ; [[#Perkins--2019|Perkins 2019]] ; [[#Bell--2020|Bell et al. 2020]] ; [[#Gür--2020|Gür 2020]] ).

<div id="5.3" class="h1-container"></div>

<span id="mapping-the-opportunity-space"></span>