Editing IPCC:AR6/WGIII/Chapter-2 (section)

=== 2.6.2 Factors Affecting Household Consumption Patterns and Behavioural Choices ===

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Households’ carbon emissions are closely linked to activities and consumption patterns of individuals and as a group in households. Individual and group behaviour, in turn, is shaped by economic, technological, and psychological factors, social contexts (such as family ties, friends and peer pressure) and cultural contexts (social identity, status, and norms) as well as the natural environment (number of hot and cold days) and physical infrastructure, or geography ( [[#Jorgenson--2019|Jorgenson et al. 2019]] ). For example, a city with an excellent bicycle infrastructure will make it safer and easier for citizens to become highly mobile by using their bikes; a city that has less density and is dominated by automobile infrastructure induces more people to travel by car (Chapters 8 and 10). As a consequence, many climate relevant consumption acts are not consciously decided on or deliberately made part of a lifestyle, but are strongly influenced by the factors listed above. [[IPCC:Wg3:Chapter:Chapter-5|Chapter 5]] provides a more in-depth discussion on behavioural drivers and examples of behavioural interventions and policies that can be used to reduce emissions.

Demographic characteristics such as age, sex, and education constitute an important set of determinants influencing emissions patterns. People of different genders have different consumption patterns. For example, men tend to consume more food (especially meat) than women, leading to higher food-related emissions. Also, men spend more money on vehicles and driving ( [[#Wang--2018|Wang et al. 2018]] ). Similar evidence has been found in Germany, Greece, Norway, and Sweden, where men’s energy use is 8%, 39%, 6%, and 22% higher than women’s, respectively ( [[#Räty--2010|Räty and Carlsson-Kanyama 2010]] ).

'''Income.''' Due to the differences that shape individuals’ consumption patterns, there are enormous differences in the associated carbon footprints – with income being one of the most important predictors. Globally, households with income in the top 10% – income higher than USD23.03 purchasing power parity (PPP) per capita per day – are responsible for 34–45% of GHG emissions, while those in the bottom 50% – income less than USD2.97 PPP per capita per day – are responsible for only 13–15% of emissions, depending on the study ( [[#Chancel--2015|Chancel and Piketty 2015]] ; [[#Hubacek--2017b|Hubacek et al. 2017b]] ) (Figure 2.25). The average carbon footprint of the high household incomes is more than an order of magnitudes larger than that of the lowest expenditure group ( [[#Feng--2021|Feng et al. 2021]] ). For example, [[#Zhang--2016|Zhang et al. (2016)]] analysed the impact of household consumption across different income households on CO 2 emissions in China and concluded that the impact on CO 2 emissions generated by urban households’ consumption is 1.8 times as much as that of rural ones. High-income households have higher emissions related to transport and entertainment – such as recreational expenditure, travel, and eating out – than low-income households. Low-income households tend to have a larger share on necessities such as fuel for heating and cooking ( [[#Kerkhof--2009|Kerkhof et al. 2009]] ). Figure 2.25 shows the carbon footprint per capita ranked by per capita income.

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'''Figure 2.25''' '''|''' '''Carbon footprints per capita income and expenditure category for 109 countries ranked by per capita income (consumption-based emissions).'''

'''Age.''' The effect of population ageing on emissions is contested in the literature. Ageing when accompanied by shrinking household size and more energy-intensive consumption and activity patterns results in increased emissions. However, an ageing labour force can also dampen economic growth and result in less energy-intensive activity such as driving, which decreases emissions ( [[#Liddle--2010|Liddle and Lung 2010]] ; [[#Liddle--2011|Liddle 2011]] ). Ageing of the population characterises the demographic transition in both developed and developing countries. The implications of ageing for emissions depend on labour force participation of the elderly and differences in the consumption and investment patterns of different age groups ( [[#O’Neill--2012|O’Neill et al. 2012]] ). Analysis using panel macro data from OECD countries suggests that shifts in age and cohort composition have contributed to rising GHG emissions since the 1960s ( [[#Menz--2012|Menz and Welsch 2012]] ; Nassen 2014). Household-level data over time for the USA provides evidence that residential energy consumption increases over the lifetime of household members, largely due to accompanying changes in household size ( [[#Estiri--2019|Estiri and Zagheni 2019]] ). Similar insights emerge from Japan, where analysis shows that those in their 70s or older, a group that is growing in size in Japan, have higher emissions than other age groups ( [[#Shigetomi--2014|Shigetomi et al. 2014]] , 2018, 2019). Recent analysis from China suggests that the shift to smaller and ageing households is resulting in higher carbon emissions because of the accompanying time-use and consumption shifts ( [[#Yu--2018|Yu et al. 2018]] ; [[#Li--2019|Li and Zhou 2019]] ). An increase in the dependency ratio – that is, the proportion of children aged under 15 and people over 65 relative to the working-age population – in other analyses, has been shown to lead to reduced CO 2 emissions in China ( [[#Wei--2018|Wei et al. 2018]] ; [[#Li--2019|Li and Zhou 2019]] ). Implications of the nature of this relationship are important to policy discussions of working hours and retirement age that are likely to have an influence on emissions. For example, children and youth tend to emit more education-related emissions than adults ( [[#Han--2015|Han et al. 2015]] ). Older people tend to have higher emissions related to heating and cooling being more sensitive to temperature ( [[#Meier--2010|Meier and Rehdanz 2010]] ).

'''Household size.''' Per capita emissions tend to decrease with family size, as living together becomes more energy efficient ( [[#Qu--2013|Qu et al. 2013]] ). The household size in most countries is decreasing ( [[#Liu--2011|Liu et al. 2011]] ), but the degree differs across countries – for example, there is a higher decrease rate in China than in Canada and the UK ( [[#Maraseni--2015|Maraseni et al. 2015]] ). The evidence shows that shifts to smaller households are associated with larger per-capita footprints ( [[#Liddle--2014|Liddle and Lung 2014]] ; [[#Underwood--2015|Underwood and Zahran 2015]] ; [[#Ivanova--2017|Ivanova et al. 2017]] ; [[#Wiedenhofer--2018|Wiedenhofer et al. 2018]] ), at least in developed countries ( [[#Meangbua--2019|Meangbua et al. 2019]] ).

'''Urban living.''' The carbon footprint of individuals and households is also significantly influenced by urban-rural differences ( [[#Ivanova--2018|Ivanova et al. 2018]] ; [[#Wiedenhofer--2018|Wiedenhofer et al. 2018]] ). In some cases, the difference can be explained by the effect of locational and spatial configuration characteristics, such as levels of compactness/density, centrality, proximity and ease of access to services. In all these parameters, urban areas score higher compared with rural or peri-urban (outlying and suburban) areas, thus influencing household emissions in different ways. Urban households tend to have higher emissions than rural households ( [[#O’Neill--2010|O’Neill et al. 2010]] ; [[#Liu--2011|Liu et al. 2011]] ), but with adifferent energy and consumption structures. For example, rural households have more diverse energy inputs, such as biomass, biogas, solar, wind, small hydro and geothermal in addition to coal ( [[#Maraseni--2016|Maraseni et al. 2016]] ).

In terms of indirect emissions, urban households have more service-related emissions – such as from education and entertainment – than rural households, while rural households tend to have higher emissions related to food consumption or transportation ( [[#Büchs--2013|Büchs and Schnepf 2013]] ; [[#Maraseni--2016|Maraseni et al. 2016]] ) but this is strongly dependent on the specific situation of the respective country, as in poorer regions, rural transport might be mainly based on public transport with lower carbon emissions per capita. Centrality and location also play a role on the level of urban household emissions. Studies on US households found that residents in the urban core have 20% lower household emissions than residents in suburbs, which show a large range of household emissions (from –50% to +60%) ( [[#Kahn--2000|Kahn 2000]] ; [[#Jones--2014|Jones and Kammen 2014]] ). Higher population density tends to be associated with lower per capita emissions ( [[#Liddle--2014|Liddle and Lung 2014]] ; [[#Liu--2017|Liu et al. 2017]] ).

Location choices are a significant contributor to household emissions. Suburbanites tend to own larger, spacious homes with larger heating and cooling requirements. Commuting distance and access to public transportation, recreation areas, city centres, public services, and shops are other important neighbourhood-specific determinants of carbon emissions ( [[#Baiocchi--2010|Baiocchi et al. 2010]] ) (see more on this in Chapters 8 and 10).

'''Time use.''' A study on the emissions implications of time use ( [[#Wiedenhofer--2018|Wiedenhofer et al. 2018]] ) found that the most carbon-intensive activities are personal care, eating and drinking and commuting. Indirect emissions are also high for repairs and gardening. In contrast, home-based activities, such as sleep and resting, cleaning and socialising at home, have low carbon intensities per hour of time use. The same study also found that households in cities and areas with higher incomes tend to substitute personal activities for contracted services, thus shifting away from households to the service sector ( [[#Wiedenhofer--2018|Wiedenhofer et al. 2018]] ). Improvements in the efficiency of time or resource use are diminished by rebound effects that have been shown to reduce emissions savings by 20–40% on average (Gillingham et al. 2015), while other authors argue that, potentially, the size of the rebound effect could be larger ( [[#Saunders--2015|Saunders 2015]] ) (see more coverage of the rebound effect in Chapters 9 and 16). Lifestyle shifts brought about by using information technologies and socio-technological changes are inducing alterations in people’s daily activities and time-use patterns.

The reduction of working hours is increasingly discussed as an approach to improve well-being and reduce emissions ( [[#Fitzgerald--2015|Fitzgerald et al. 2015]] , 2018; [[#Melo--2018|Melo et al. 2018]] ; [[#Wiedenhofer--2018|Wiedenhofer et al. 2018]] ; [[#Smetschka--2019|Smetschka et al. 2019]] ). For instance, analysis of differences in working hours across the USA for the period 2007–2013 shows that there is a strong positive relationship between carbon emissions and working hours. This relationship holds, even after controlling for other differences in political, demographic and economic drivers of emissions ( [[#Fitzgerald--2018|Fitzgerald et al. 2018]] ). In other analyses, this relationship is seen to hold in both developed and developing countries ( [[#Fitzgerald--2015|Fitzgerald et al. 2015]] ). One recent study, however, finds evidence of nonlinear relationships between working time and environmental pressure in EU15 countries between 1970 and 2010, in cases where non-work time is spent instead in carbon-intensive leisure activities ( [[#Shao--2017|Shao and Shen 2017]] ).

'''Social norms.''' Evidence from experiments in the US shows that social norms cannot only help in reducing a household’s absolute level of electricity use but also shift the time of use to periods when more renewable electricity is in the system ( [[#Horne--2017|Horne and Kennedy, 2017]] ). Analysis from Sweden shows that adoption of sustainable innovations like solar panels is influenced by perceived behaviour and expectations of others ( [[#Palm--2017|Palm, 2017]] ). Similar conclusions emerge from analysis in the Netherlands on the adoption of electric vehicles and smart energy systems ( [[#Noppers--2019|Noppers et al. 2019]] ).

Broader contextual factors and cultural trends towards consumerism, individualisation and defining self-worth through conspicuous consumption can drive emissions up ( [[#Chancel--2015|Chancel and Piketty, 2015]] ). However, cohort and generational shifts can drive emissions down. For instance, evidence, from millennials in the OECD shows that fewer younger people have driving licenses compared to older generations ( [[#Kuhnimhof--2012|Kuhnimhof et al. 2012]] ). Similar, findings are evident from analysis for the US, where changing attitudes, decreased employment and rising virtual mobility explain decreased travel by Millennials ( [[#McDonald--2015|McDonald, 2015]] ). Analysis for France shows that baby boomers are higher emitters than other generations ( [[#Chancel--2014|Chancel, 2014]] ). A change in social norms is taking place with the spread of the sharing economy by which consumers share or borrow goods from other consumers. Sharing opportunities are more advanced within the mobility sector ( [[#Greenblatt--2015|Greenblatt and Shaheen, 2015]] ). Successful car and bike sharing have rapidly expanded in countries such as China, Indonesia, Mexico, Brazil and Turkey. Technology and data advances are currently barriers to spreading of sharing in low- and lower middle-income cities but the potential offered by these technologies to allow poor countries to leapfrog to more integrated, efficient, multimodal transport systems is important ( [[#Yanocha--2020|Yanocha et al. 2020]] ). Despite this potential it is unclear how much shared mobility contributes to transport decarbonisation or to make it worse as it takes away riders from public transit ( [[#ITF--2019|ITF, 2019]] ). The evidence so far shows that the potential positive impacts of shared mobility with pooled rides in lowering travel costs, abating congestion, and reduced GHG emissions have not materialised to date ( [[#Merlin--2019|Merlin, 2019]] ) (Chapter 5).

'''Education and environmental knowledge.''' A positive relationship was found between general and carbon-specific knowledge and the attitude towards carbon-specific behaviours in US consumers ( [[#Polonsky--2012|Polonsky et al. 2012]] ). One example, pertaining to students, found that the gain of environmental knowledge resulted in more environmentally favourable attitude among these high school students ( [[#Bradley--1999|Bradley et al. 1999]] ). A comparison across states in the USA, for example, shows that environmental awareness can be a mitigating factor of territorial GHG emissions ( [[#Dietz--2015|Dietz et al. 2015]] ). A 1% increase in ‘environmentalism’ – defined as the ‘environmental voting record of the state’s Congressional delegation’ ( [[#Dietz--2015|Dietz et al. 2015]] ) – leads to a 0.45% decrease in emissions.

Environmental knowledge is not always directly translating into decreased ecological footprint ( [[#Csutora--2012|Csutora 2012]] ). While pro-environmental action is lagging behind, research shows that this is not caused by people undervaluing the environment, but rather by people structurally underestimating how much others care ( [[#Bouman--2019|Bouman and Steg 2019]] ). Other evidence shows that there are multiple causal pathways through which a more educated population can effect emissions, some of which may be positive and others negative ( [[#Lutz--2019|Lutz et al. 2019]] ). A more educated population is more productive and can drive higher economic growth and therefore emissions ( [[#Lenzen--2013|Lenzen and Cummins 2013]] ). Moreover, education that is designed to specifically inform decision makers of the impacts of their decisions and provide behavioural nudges can be a way to reduce emissions ( [[#Duarte--2016|Duarte et al. 2016]] ).

'''Status competition.''' As part of a larger consumer society and consumer culture, based on consumer-oriented lifestyles, products frequently provide a source for identity and fulfilment ( [[#Stearns--2001|Stearns 2001]] ; [[#Baudrillard--2017|Baudrillard 2017]] ; [[#Jorgenson--2019|Jorgenson et al. 2019]] ). People pursue cultural constructs such as status, comfort, convenience, hygiene, nutrition, and necessity. Consumption is, by and large, not an end in itself but a means to achieve some other end, and those ends are diverse and not necessarily connected to one another ( [[#Wilk--2010|Wilk 2010]] ). This shows that consumption patterns cannot be sufficiently understood without also considering the context – for example, the cultural and social contexts leading to status competition and status-related consumption ( [[#Veblen--2009|Veblen 2009]] ; Ehrhardt-Martinez, K. et al. 2015; [[#Wilk--2017|Wilk 2017]] ). Status seeking can work to reduce emissions when ‘green products’ such as an electric car or photovoltaics on the roof become a sign for high-status ( [[#Griskevicius--2010|Griskevicius et al. 2010]] ). It also can work to increase emissions through visible and high-carbon intensive consumption items, such a larger homes, fuel-inefficient sport utility vehicles (SUVs), and long-distance vacations ( [[#Schor--1998|Schor 1998]] ), driven by a notion of having ‘to keep up with the Joneses’ ( [[#Hamilton--2011|Hamilton 2011]] ). This can lead to formation of new habits and needs, where products and services become normalised and are quickly perceived as needed, reinforced through social networks and advertisement, making it psychologically easy to convert a luxury item to a perceived necessity ( [[#Assadour--2012|Assadour 2012]] ). For example, the share of adults who consider a microwave a necessity was about one-third in 1996 but had increased to more than two-thirds in 2006, but retreated in importance during the recession years 2008–2009 ( [[#Morin--2009|Morin and Taylor 2009]] ). Similar ups and downs have been observed for television sets, air conditioning, dishwashers or clothes dryers. ( [[#Druckman--2009|Druckman and Jackson 2009]] ). Basic needs and luxury items are subject to change over one’s lifetime and in relation to others ( [[#Horowitz--1988|Horowitz 1988]] ). This shows that the boundaries of the public’s luxury-versus-necessity perceptions are malleable ( [[#Morin--2009|Morin and Taylor 2009]] ).

'''Inequality.''' Global inequality within and between countries has shifted over the last decade’s expanding consumption and consumer culture ( [[#Castilhos--2016|Castilhos and Fonseca 2016]] ; [[#Alvaredo--2018|Alvaredo et al. 2018]] ; [[#Short--2020|Short and Martínez 2020]] ). The rise of income of middle-class in countries, mostly in Asia – for example, China, India, Indonesia and Vietnam – and the stagnating incomes of the middle classes in developed economies reduced between countries’ income differences; meanwhile, the population under extreme poverty (a threshold of USD1.9 per person per day) is now concentrated in Sub-Saharan Africa and South Asia ( [[#Milanović--2016|Milanović 2016]] ). A major gap between top and bottom incomes occurred in parallel within countries. Since 1980, the top 1% richest individuals in the world captured twice as much growth as the bottom 50% individuals (Friedman and Savage 2017; [[#Alvaredo--2018|Alvaredo et al. 2018]] ). The influence of these dual inequality trends on lifestyles, new consumption patterns and carbon emissions at regional, local and global scale are large and have led to the fastest growth of global carbon emissions, in particular, for fast emerging economies (Sections 2.2. and 2.3). Emissions remain highly concentrated, with the top 10% per capita emitters contributing to between 35–45% of global emissions, while the bottom 50% emitters contribute to 13–15% of global emissions ( [[#Hubacek--2017a|Hubacek et al. 2017a]] ). Furthermore, the top 1% of income earners by some estimates could have an average carbon footprint 175 times that of an average person in the bottom 10% (Otto et al. 2020). The top 10% high emitters live in all continents, and one-third of them live in emerging countries ( [[#Chancel--2015|Chancel and Piketty 2015]] ; [[#Hubacek--2017a|Hubacek et al. 2017a]] ; [[#Semieniuk--2020|Semieniuk and Yakovenko 2020]] ). Mitigation pathways need to consider how to minimise the impacts of inequality on climate change and the different mechanisms and effects coming into play between the inequality of income and emissions ( [[#Baek--2013|Baek and Gweisah 2013]] ; [[#Berthe--2015|Berthe and Elie 2015]] ; [[#Hao--2016|Hao et al. 2016]] ; [[#Grunewald--2017|Grunewald et al. 2017]] ) ( [[#2.4.3|Section 2.4.3]] ).

Inequality trends catalyse impact at a demand level, mobilising rapid lifestyles changes, symbolic consumption and ideals of material improvements and upward mobility ( [[#Castilhos--2017|Castilhos et al. 2017]] ) and emulation of high-carbon emissions intensive lifestyle of the wealthy ( [[#Gough--2017|Gough 2017]] ). Decoupling energy use and emissions from income growth and the decarbonisation of energy services have not counteracted these trends ( [[#2.4.1|Section 2.4.1]] ). Alternative options to deal with carbon inequality, such as sharing global carbon emissions among high emitters ( [[#Chakravarty--2009|Chakravarty et al. 2009]] ; [[#Chakravarty--2013|Chakravarty and Tavoni 2013]] ) or addressing the discourse of income distribution and the carbon intensity of high emitters lifestyles ( [[#Hubacek--2017b|Hubacek et al., 2017b]] ; [[#Gössling--2019|Gössling 2019]] ; [[#Otto--2019|Otto et al. 2019]] ) are met with caution that such alternatives may necessitate hard-to-implement institutional changes ( [[#Semieniuk--2020|Semieniuk and Yakovenko 2020]] ). Growing inequality within countries may make recomposition of emission intensive consumption more difficult and, it may also exacerbate redistribution and social cohesion dilemmas ( [[#Gough--2017|Gough 2017]] ; [[#Römpke--2019|Römpke et al. 2019]] ). Climate mitigation action has different motivational departures in unequal context. An emerging global ‘middle class’ strengthens consumption at the margin as evidence by first-time purchases of white goods with likely impacts on energy demand ( [[#Wolfram--2012|Wolfram et al. 2012]] ), and with a warming climate, the increased use of air conditioning ( [[#Davis--2015|Davis and Gertler 2015]] ). Inequality may affect the willingness of rich and poor to pay for environmental goods or accept policies to protect the environment ( [[#Baumgärtner--2017|Baumgärtner et al. 2017]] ). Unequal departure for action is strongly manifested in cities of all sizes in developing countries with low-income urban residents hardest hit in lock-in situations such as lack of access to transportation and jobs ( [[#Altshuler--2013|Altshuler 2013]] ; [[#Mattioli--2017|Mattioli 2017]] ), lack of green spaces ( [[#Joassart-Marcelli--2011|Joassart-Marcelli et al. 2011]] ), poor access to waste collection ( [[#King--2013|King and Gutberlet 2013]] ) and to energy and clean water provision. The exacerbation of these conditions constrains the feasibility for achieving emissions reductions through lifestyle or behavioural changes alone ( [[#Baiocchi--2010|Baiocchi et al. 2010]] ; [[#Oxfam--2015|Oxfam 2015]] ). High inequality limits mitigation efforts and conversely, advancing mitigation should not contribute to deepen existing inequalities ( [[#Rao--2018|Rao and Min 2018]] ; [[#Saheb--2019|Saheb et al. 2019]] ). It is critically important to account for varying demands and affordability across heterogeneous household groups in access to quality energy, education, health, decent jobs and services, while recomposing consumption and balancing societal trade-offs via policies to boost the inclusion of low-income and energy-poor population groups ( [[#Pachauri--2013|Pachauri et al. 2013]] ). Further, there is a need to reduce inequalities and improve the capabilities people have to live the lives they value ( [[#Sen--1999|Sen 1999]] ; [[#Gough--2011|Gough et al. 2011]] ; [[#Gough--2017|Gough, 2017]] ; [[#Aranoff--2019|Aranoff et al. 2019]] ).

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