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

=== 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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'''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)]] .

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==== 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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'''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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==== 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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