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

== 2.4 Economic Drivers and Their Trends by Regions and Sectors ==

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This section provides a summary of the main economic drivers of GHG emissions (mostly territorial) by regions and sectors, including those that are more indirect drivers related to economic activity, such as inequality and rapid urbanisation. Trade as a driver of global GHG emissions is described in the [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 Chapter 2] Supplementary Material. Socio-demographic drivers are described in [[#2.6|Section 2.6]] . The Kaya decomposition presented in this section is based on the International Energy Agency (IEA) and Emissions Database for Global Atmospheric Research (EDGAR) v6 databases and tracks global, regional, and sectoral GHG emissions from 1990 to 2019 ( [[#Crippa--2021|Crippa et al. 2021]] ; [[#IEA--2021c|IEA 2021c]] ; [[#Lamb--2021b|Lamb et al. 2021b]] ; [[#Minx--2021|Minx et al. 2021]] ). It shows main contributors to GHG emissions as independent factors, although these factors also interact with each other.

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=== 2.4.1 Economic Drivers at Global and Regional Levels ===

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Economic growth (measured as GDP) and its main components – GDP per capita and population growth – remained the strongest drivers of GHG emissions in the last decade, following a long-term trend ( ''robust evidence'' , ''high agreement'' ) ( [[#Liddle--2015|Liddle 2015]] ; [[#Malik--2016|Malik et al. 2016]] ; [[#Sanchez--2016|Sanchez and Stern 2016]] ; [[#Chang--2019|Chang et al. 2019]] ; Dong et al. 2019; Liobikiene and Butkus 2019; [[#Liu--2019a|Liu et al. 2019a]] ; [[#Mardani--2019|Mardani et al. 2019]] ; [[#Pan--2019|Pan et al. 2019]] ; [[#Dong--2020|Dong et al. 2020]] ; [[#Parker--2020|Parker and Bhatti 2020]] ; [[#Xia--2021|Xia et al. 2021]] ). Globally, GDP per capita remained by far the strongest upward driver, increasing almost in tandem with energy consumption and CO 2 emissions up until 2015, after which some modest decoupling occurred ( [[#Deutch--2017|Deutch 2017]] ; [[#Wood--2018|Wood et al. 2018]] ) ( [[#2.3.3|Section 2.3.3]] ). The main counteracting, yet insufficient, factor that led to emissions reductions was decreased energy use per unit of GDP in almost all regions (–2.0% yr –1 between 2010 and 2019 globally) (see also [[#Lamb--2021b|Lamb et al. 2021b]] ) (Figure 2.16) ( ''robust evidence'' , ''high agreement'' ). These reductions in energy intensity are a result of technological innovation, structural changes, regulation, fiscal support, and direct investment, as well as increased economic efficiency in underlying sectors ( [[#Yao--2015|Yao et al. 2015]] ; [[#Sanchez--2016|Sanchez and Stern 2016]] ; [[#Chang--2019|Chang et al. 2019]] ; [[#Dong--2019a|Dong et al. 2019a]] ; [[#Mohmmed--2019|Mohmmed et al. 2019]] ; [[#Stern--2019|Stern 2019]] ; [[#Azhgaliyeva--2020|Azhgaliyeva et al. 2020]] ; [[#Goldemberg--2020|Goldemberg 2020]] ; [[#Gao--2021|Gao et al. 2021]] ; [[#Liddle--2021|Liddle and Huntington 2021]] ; [[#Liu--2019b|Liu et al. 2019b]] ; [[#Xia--2021|Xia et al. 2021]] ).

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[[File:8e1b78d741c08125db0c862ae577c8ba IPCC_AR6_WGIII_Figure_2_16.png]]

'''Figure 2.16''' '''|''' '''Trends and drivers of global GHG emissions, including: (a) trends of GHG emissions by sectors 1990–2019; (b) share of total and per capita GHG emissions by world region in 2019; and (c) Kaya decomposition of CO''' 2 '''emissions drivers.''' The Kaya decomposition is based on the equation F = P(G/P)(E/G)(F/E), where F is CO 2 emissions, P is population, G/P is GDP per capita, E/G is the energy intensity of GDP and F/E is the carbon intensity of energy. The indicated annual growth rates are averaged across the years 2010–2019 (in panel (c), these are for fossil fuel CO 2 emissions only, in order to ensure compatibility with underlying energy data). Note that the energy consumption by itself (primary energy supply) is not part of the decomposition, but is listed here for comparison with the Kaya factors. Source: data from [[#Crippa--2021|Crippa et al. (2021)]] , [[#IEA--2021c|IEA (2021c)]] , [[#Minx--2021|Minx et al. (2021)]] .

The decades-long trend that efficiency gains were outpaced by an increase in worldwide GDP (or income) per capita continued unabated in the last 10 years ( ''robust evidence'' , ''high agreement'' ) ( [[#Wiedmann--2020|Wiedmann et al. 2020]] ; [[#Xia--2021|Xia et al. 2021]] ). In addition, the emissions-reducing effects of energy efficiency improvements are diminished by the energy rebound effect, which has been found in several studies to largely offset any energy savings ( ''robust evidence'' , ''high agreement'' ) ( [[#Rausch--2018|Rausch and Schwerin 2018]] ; [[#Colmenares--2020|Colmenares et al. 2020]] ; [[#Stern--2020|Stern 2020]] ; [[#Brockway--2021|Brockway et al. 2021]] ; [[#Bruns--2021|Bruns et al. 2021]] ). The rebound effect is discussed extensively in [[IPCC:Wg3:Chapter:Chapter-9#9.9.2|Section 9.9.2]] .

A significant decarbonisation of the energy system was only noticeable in North America, Europe and Eurasia. Globally, the amount of CO 2 per unit of energy used has practically remained unchanged over the last three decades ( [[#Tavakoli--2018|Tavakoli 2018]] ; [[#Chang--2019|Chang et al. 2019]] ), although it is expected to decrease more consistently in the future ( [[#Xia--2021|Xia et al. 2021]] ). Population growth has also remained a strong and persistent upward driver in almost all regions (+1.2% yr –1 globally from 2010 to 2019) (Lamb et al. 2021) (Figure 2.16), although per capita emission levels are very uneven across world regions. Therefore, modest population increases in wealthy countries may have a similar impact on emissions as high population increases in regions with low per capita emission levels.

Developing countries remained major accelerators of global CO 2 emissions growth since 2010, mostly driven by increased consumption and production, in particular in East Asia ( ''robust evidence'' , ''high agreement'' ) ( [[#Jiborn--2020|Jiborn et al. 2020]] ). While energy intensity declined to a similar extent in countries of the Organisation for Economic Co-operation and Development (OECD) and non-OECD countries over the last 30 years, economic growth has been much stronger in non-OECD countries ( [[#González-Torres--2021|González-Torres et al. 2021]] ). This led to an average annual growth rate of 2.8% of CO 2 emissions in these countries, whereas they decreased by 0.3% yr –1 in OECD countries ( [[#UNEP--2019|UNEP 2019]] ). The majority of developed economies reduced both production-based and consumption-based CO 2 emissions modestly ( [[#Jiborn--2020|Jiborn et al. 2020]] ; [[#Xia--2021|Xia et al. 2021]] ). This was due to slower economic growth, increased energy efficiency (less energy per unit of GDP), fuel switching from coal to gas (mostly in North America) ( [[#Wang--2020|Wang et al. 2020]] b), and the use of less and cleaner energy from renewables in Europe (Peters et al. 2017; [[#Karstensen--2018|Karstensen et al. 2018]] ; [[#Chang--2019|Chang et al. 2019]] ; [[#Wood--2019|Wood et al. 2019]] c).

Economic growth as the main driver of GHG emissions is particularly strong in China and India ( ''robust evidence'' , ''high agreement'' ) ( [[#Liu--2019b|Liu et al. 2019b]] ; [[#Ortega-Ruiz--2020|Ortega-Ruiz et al. 2020]] ; Z. [[#Wang--2020|Wang et al. 2020]] b; [[#Yang--2020|Yang et al. 2020]] ; [[#Zheng--2020|Zheng et al. 2020]] ; [[#Xia--2021|Xia et al. 2021]] ), although both countries show signs of relative decoupling because of structural changes ( [[#Marin--2019|Marin and Mazzanti 2019]] ). A change in China’s production structure (with relatively less heavy industry and lower-carbon manufacturing) and consumption patterns (i.e., the type of goods and services consumed) has become the main moderating factor of emissions after 2010, while economic growth, consumption levels, and investment remain the dominating factors driving up emissions ( [[#Wang--2019|Wang and Jiang 2019]] ; [[#Jiborn--2020|Jiborn et al. 2020]] ; [[#Zheng--2020|Zheng et al. 2020]] ). In India, an expansion of production and trade as well as a higher energy intensity between 2010 and 2014 caused increased emissions ( [[#Kanitkar--2015|Kanitkar et al. 2015]] ; Wang and Zhou 2020; Z. [[#Wang--2020|Wang et al. 2020]] b).

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=== 2.4.2 Sectoral Drivers ===

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GHG emissions continued to rise since 2010 across all sectors and subsectors, most rapidly in electricity production, industry, and transport. Decarbonisation gains from improvements in energy efficiency across different sectors and worldwide have been largely wiped out by increases in demand for goods and services. Prevailing consumption patterns have also tended to aggravate energy use and emissions, with the long-term trend led by developed regions. Decarbonisation trends in some developed regions are limited in size and geographically. Globally, there are enormous unexploited mitigation potentials from adopting best available technologies.

The following subsections discuss main emissions drivers by sector. More detailed analyses of sectoral emissions and mitigation options are presented in Chapters 6–11.

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==== 2.4.2.1 Energy Systems ====

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Global energy system emissions growth has slowed down in recent years, but global oil and gas use was still growing ( [[#Jackson--2019|Jackson et al. 2019]] ) and the sector remained the single largest contributor to global GHG emissions in 2019 with 20 GtCO 2 -eq (34%) ( ''high confidence'' ) (Figure 2.17). Most of the 14 GtCO 2 -eq from electricity and heat generation (23% of global GHG emissions in 2019) were due to energy use in industry and in buildings, making these two sectors also prominent targets for mitigation ( [[#Davis--2018|Davis et al. 2018]] ; Crippa et al. 2019) (see subsections 2.4.2.2 and 2.4.2.3 below).

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[[File:a9fbb7e28d049e20bdcbeab8226f3368 IPCC_AR6_WGIII_Figure_2_17.png]]

'''Figure 2.17''' '''|''' '''Trends and drivers of global energy sector emissions (see Figure 2.16 caption for details) with energy measured as primary energy supply.'''

Growth in CO 2 emissions from energy systems has closely tracked rising GDP per capita globally ( [[#Lamb--2021b|Lamb et al. 2021b]] ), affirming the substantial literature describing the mutual relationship between economic growth and demand for energy and electricity ( ''robust evidence'' , ''high agreement'' ) ( [[#Khanna--2009|Khanna and Rao 2009]] ; [[#Stern--2011|Stern, 2011]] ). This relationship has played out strongly in developing regions, particularly in Asia, where a massive scale up of energy supply has accompanied economic growth – with average annual increases of energy demand between 3.8–4.3% in 2010–2019 (Figure 2.17). The key driver for slowing the growth of energy systems CO 2 emissions has been declining energy intensities in almost all regions. Annually, 1.9% less energy per unit of GDP was used globally between 2010 and 2019.

The carbon intensity of power generation varies widely between (and also within) regions (Chapter 6). In North America, a switch from coal to gas for power generation (Peters et al. 2017, 2020; [[#Feng--2019|Feng 2019]] ; [[#Mohlin--2019|Mohlin et al. 2019]] ) as well as an overall decline in the share of fossil fuels in electricity production (from 66% in 2010 to 59% in 2018) ( [[#Mohlin--2019|Mohlin et al. 2019]] ) has decreased carbon intensity and CO 2 emissions. Since 2007, Europe’s carbon intensity improvements have been driven by the steady expansion of renewables in the share of electricity generation ( ''medium evidence'' , ''high agreement'' ) (Peters et al. 2017, 2020; [[#Le%20Quéré--2019|Le Quéré et al. 2019]] ; [[#Rodrigues--2020|Rodrigues et al. 2020]] ). Some studies attribute these effects to climate policies, such as the carbon floor price in the UK, the EU emissions trading scheme, and generous renewable energy subsidies across the continent ( [[#Dyrstad--2019|Dyrstad et al. 2019]] ; H. [[#Wang--2020|Wang et al. 2020]] ). South-East Asian developed countries and Australia, Japan and New Zealand stand out in contrast to other developed regions, with an increase of regional carbon intensity of 1.8 and 1.9% yr –1 , respectively (Figure 2.17). Generally, the use of natural gas for electricity production is growing strongly in most countries and gas has contributed to the largest increase in global fossil CO 2 emissions in recent years ( [[#Jackson--2019|Jackson et al. 2019]] ; [[#Peters--2020|Peters et al. 2020]] ). Furthermore, gas brings the risk of increased methane (CH 4 ) emissions from fugitive sources, as well as large cumulative emissions over the lifetime of new gas power plants that may erase early carbon intensity reductions ( [[#Shearer--2020|Shearer et al. 2020]] ).

The growth of emissions from coal power slowed after 2010, and even declined between 2011 and 2019, primarily due to a slowdown of economic growth and fewer coal capacity additions in China ( [[#Friedlingstein--2019|Friedlingstein et al. 2019]] ; [[#Peters--2020|Peters et al. 2020]] ). Discussions of a global ‘peak coal’, however, may be premature, as further growth was observed in 2019 ( [[#Friedlingstein--2019|Friedlingstein et al. 2019]] ; [[#Peters--2020|Peters et al. 2020]] ). Large ongoing and planned capacity increases in India, Turkey, Indonesia, Vietnam, South Africa, and other countries has become a driver of thermal coal use after 2014 ( [[#UNEP--2017|UNEP 2017]] ; [[#Edenhofer--2018|Edenhofer et al. 2018]] ; Steckel et al. 2019).

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==== 2.4.2.2 Industry Sector ====

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When indirect emissions from electricity and heat production are included, industry becomes the single highest emitting sector of GHGs (20.0 GtCO 2 -eq in 2019) ( ''high confidence'' ). Facilitated by globalisation, East Asia has been the main source and primary driver of global industry emissions growth since 2000 ( ''robust evidence'' , ''high agreement'' ) (Lamb et al. 2021). However, while East Asia has emitted 45% of the world’s industry GHG emissions in 2019, a remarkable decrease of 5.0% yr –1 in energy intensity and 1.6% in carbon intensity helped to stabilise direct industrial CO 2 emissions in this region (–0.3% yr –1 between 2010 and 2019; Figure 2.18). Direct industry CO 2 emissions have also declined in Latin America, Europe and Australia, Japan and New Zealand, and – to a smaller extent – in North America. In all other regions, they were growing – most rapidly in southern Asia (+4.3% annually for direct CO 2 emissions since 2010) (Figure 2.18).

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[[File:e4ffa05f76cc6e1b98e6bc52bd25ed42 IPCC_AR6_WGIII_Figure_2_18.png]]

'''Figure 2.18''' '''|''' '''Trends and drivers of global industry sector emissions (see Figure 2.16 caption for details) with energy measured as total final energy consumption.'''

The main global driver of industry emissions has been a massive rise in the demand for products that are indirectly used in production, such as cement, chemicals, steel, aluminium, wood, paper, plastics, lubricants, fertilisers, and so on. This demand was driven by economic growth, rising affluence, and consumption, as well as a rapid rise in urban populations and associated infrastructure development ( ''robust evidence'' , ''high agreement'' ) ( [[#Krausmann--2018|Krausmann et al. 2018]] ). There is strong evidence that the growing use of concrete, steel, and other construction materials is particularly tightly coupled to these drivers ( [[#Pauliuk--2013|Pauliuk et al. 2013]] ; [[#Cao--2017|Cao et al. 2017]] ; [[#Krausmann--2017|Krausmann et al. 2017]] ; [[#Plank--2018|Plank et al. 2018]] ; [[#Haberl--2020|Haberl et al. 2020]] ). Per capita stocks of cement and steel show a typical pattern of rapid take-off as countries urbanise and industrialise, before slowing down to low growth at high levels of GDP. Hence, in countries that have recently been industrialising and urbanising – that is Eastern, Southern and South-Eastern Asia – a particularly strong increase of emissions from these subsectors can be observed. Selected wealthy countries seem to stabilise at high per capita levels of stocks, although it is unclear if these stabilisations persist and if they result in significant absolute reductions of material use ( [[#Wiedenhofer--2015|Wiedenhofer et al. 2015]] ; [[#Cao--2017|Cao et al. 2017]] ; [[#Krausmann--2018|Krausmann et al. 2018]] ). Opportunities for prolonging lifetimes and improving end of life recycling in order to achieve absolute reductions in extraction activities are as yet unexploited ( [[#Krausmann--2017|Krausmann et al. 2017]] ; [[#Zink--2017|Zink and Geyer, 2017]] ).

On the production side, improvements in the efficiency of material extraction, processing, and manufacturing have reduced industrial energy use per unit of output (J. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al. 2019]] ). These measures, alongside improved material substitution, lightweight designs, extended product and servicing lifetimes, improved service efficiency, and increased reuse and recycling will enable substantial emissions reductions in the future ( [[#Hertwich--2019|Hertwich et al. 2019]] ). In absence of these improvements in energy intensity, the growth of population and GDP per capita would have driven the industrial CO 2 emissions to rise by more than 100% by 2017 compared with 1990, instead of 56% ( [[#Lamb--2021b|Lamb et al. 2021b]] ). Nonetheless, many studies point to deep regional differences in efficiency levels and large globally unexploited potentials to improve industrial energy efficiency by adopting best available technologies and practices for metal, cement, and chemical production ( [[#Gutowski--2013|Gutowski et al. 2013]] ; [[#Schulze--2016|Schulze et al. 2016]] ; [[#Hernandez--2018|Hernandez et al. 2018]] ; [[#Talaei--2018|Talaei et al. 2018]] ).

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==== 2.4.2.3 Buildings Sector ====

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Global direct and indirect GHG emissions from the buildings sector reached 9.7 GtCO 2 -eq in 2019, or 16% of global emissions). Most of these emissions (66%, or 6.4 GtCO 2 -eq) were upstream emissions from power generation and commercial heat (Figure 2.19). The remaining 33% (3.3 GtCO 2 -eq) of emissions were directly produced in buildings, for instance by gas and coal boilers, and cooking and lighting devices that burn kerosene, biomass, and other fuels (Lamb et al. 2021). Residential buildings accounted for the majority of this sector’s emissions (64%, 6.3 GtCO 2 -eq, including both direct and indirect emissions), followed by non-residential buildings (35%, 3.5 GtCO 2 -eq) ( ''high confidence'' ).

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[[File:7de478fa2c86c00402dd31b7a3bcd8df IPCC_AR6_WGIII_Figure_2_19.png]]

'''Figure 2.19''' '''|''' '''Trends and drivers of global buildings sector emissions (see Figure 2.16 caption for details) with energy measured as total final energy consumption.'''

Global buildings sector GHG emissions increased by 0.7% yr –1 between 2010 and 2019 (Figure 2.19), growing the most in absolute terms in East and South Asia, whereas they declined the most in Europe, mostly due to the expansion of renewables in the energy sector and increased energy efficiency (Lamb et al. 2021). North America has the highest per capita GHG emissions from buildings and the second highest absolute level after East Asia (Figure 2.19).

Rising wealth has been associated with more floor space being required to service growing demand in the retail, office, and hotel sectors ( ''medium evidence'' , ''high agreement'' ) ( [[#Daioglou--2012|Daioglou et al. 2012]] ; [[#Deetman--2020|Deetman et al. 2020]] ). In addition, demographic and social factors have driven a cross-national trend of increasing floor space per capita. As populations age and decrease in fertility, and as individuals seek greater privacy and autonomy, households declined in size, at least before the COVID-19 pandemic ( [[#Ellsworth-Krebs--2020|Ellsworth-Krebs 2020]] ). These factors led to increased floor space per capita, even as populations stabilise. This in turn is a key driver for building sector emissions, because building characteristics such as size and type, rather than occupant behaviour, tend to explain the majority of energy use within dwellings ( [[#Guerra%20Santin--2009|Guerra Santin et al. 2009]] ; [[#Ürge-Vorsatz--2015|Ürge-Vorsatz et al. 2015]] ; [[#Huebner--2017|Huebner and Shipworth 2017]] ) (Chapter 9).

Energy activity levels further drive regional differences. In Eurasia, Europe and North America, thermal demands for space heating dominate building energy use, at 66%, 62% and 48% of residential energy demand, respectively ( [[#IEA--2020a|IEA 2020a]] ). In contrast, cooking has a much higher share of building energy use in regions of the Global South, including China ( [[#Cao--2016|Cao et al. 2016]] ). And, despite temperatures being on average warmer in the Global South, electricity use for cooling is a more prominent factor in the Global North ( [[#Waite--2017|Waite et al. 2017]] ). This situation is changing, however, as rapid income growth and demographic changes in the Global South enable households to heat and cool their homes ( [[#Ürge-Vorsatz--2015|Ürge-Vorsatz et al. 2015]] , 2020).

Steady improvements in building energy intensities across regions can be attributed to baseline improvements in building fabrics, appliance efficiencies, energy prices, and fuel shifts. Many countries have adopted a mix of relevant policies, such as energy labelling, building energy codes, and mandatory energy performance requirements ( [[#Nie--2014|Nie and Kemp 2014]] ; [[#Nejat--2015|Nejat et al. 2015]] ; [[#Economidou--2020|Economidou et al. 2020]] ). Efforts towards building refurbishments and retrofits have also been pursued in several nations, especially for historical buildings in Europe, but evidence suggests that the recent retrofit rates have not made a significant dent on emissions ( [[#Corrado--2016|Corrado and Ballarini 2016]] ). The Chinese central government launched various policies, including command and control, economic incentives, and technology measures, but a big gap remains between the total rate of building green retrofit in the nation and the future retrofit potential (G. [[#Liu--2020a|Liu et al. 2020a]] , 2020b). Still, one major global factor driving down energy intensities has been the global transition from inefficient coal and biomass use in buildings for heating and cooking, towards natural gas and electricity, in part led by concerted policy action in Asian countries ( [[#Ürge-Vorsatz--2015|Ürge-Vorsatz et al. 2015]] ; [[#Kerimray--2017|Kerimray et al. 2017]] ; [[#Thoday--2018|Thoday et al. 2018]] ). As developing countries construct new buildings, there is sizable potential to reduce and use less carbon-intensive building materials and adopt building designs and standards that lower lifecycle buildings energy use and allow for passive comfort. [[IPCC:Wg3:Chapter:Chapter-9|Chapter 9]] describes the mitigation options of the buildings sector.

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==== 2.4.2.4 Transport Sector ====

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With a steady, average annual growth of +1.8% yr –1 between 2010 and 2019, global transport GHG emissions reached 8.9 GtCO 2 -eq in 2019 and accounted for 15% of all direct and indirect emissions (Figure 2.20). Road transport passenger and freight emissions represented by far the largest component and source of this growth (6.1 GtCO 2 -eq, 69% of all transport emissions in 2019) ( ''high confidence'' ). National plus international shipping and aviation emissions together accounted for 2.0 GtCO 2 -eq or 22% of the sector’s total in 2019. North America, Europe and Eastern Asia stand out as the main regional contributors to global transport emissions and together account for 50% of the sector’s total.

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[[File:3bd3dbbc23cef753f9070ddb5c997a7f IPCC_AR6_WGIII_Figure_2_20.png]]

'''Figure 2.20''' '''|''' '''Trends and drivers of global transport sector emissions (see Figure 2.16 caption for details) with energy measured as total final energy consumption.'''

The proportion of total final energy used in transport (28%) and its fast expansion over time weighs heavily on climate mitigation efforts, as 92% of transport energy comes from oil-based fuels ( [[#IEA--2020b|IEA 2020b]] ). These trends situate transport as one of the most challenging sectors for climate change mitigation – no country has so far been able to realise significant emissions reductions in the sector. North America’s absolute and per capita transport emissions are the highest amongst world regions, but those of South, South-East and East Asia are growing the fastest ( ''high confidence'' ) (between +4.6% and +5.2% yr –1 for CO 2 between 2010 and 2019) (Figure 2.20).

More so than any other sector, transport energy use has tracked GDP per capita growth (Figure 2.20), (Lamb et al. 2021). With the exception of road gasoline demand in OECD countries, the demand for all road fuels generally increases at least as fast as the rate at which GDP per capita increases ( [[#Liddle--2020|Liddle and Huntington 2020]] ). Developments since 1990 continue a historical trend of increasing travel distances and a shift from low- to high-speed transport modes that goes along with GDP growth ( [[#Schäfer--2009|Schäfer et al. 2009]] ; [[#Gota--2019|Gota et al. 2019]] ). Modest improvements in energy efficiency have been realised between 2010 and 2019, averaging –1.5% yr –1 in energy intensity globally, while carbon intensities of the transport sector have remained stable in all world regions (Figure 2.20). Overall, global increases in passenger and freight travel activity levels have outpaced energy efficiency and fuel economy improvements, continuing a long-term trend for the transport sector ( ''medium evidence'' , ''high agreement'' ) ( [[#Gucwa--2013|Gucwa and Schäfer 2013]] ; Grübler 2015; [[#McKinnon--2016|McKinnon 2016]] ).

Despite some policy achievements, energy use in the global transport system remains to the present deeply rooted in fossil fuels ( ''robust evidence'' , ''high agreement'' ) ( [[#Figueroa--2014|Figueroa et al. 2014]] ; [[#IEA--2019|IEA 2019]] ). In part this is due to the increasing adoption of larger, heavier combustion-based vehicles in some regions, which have tended to far outpace electric and hybrid vehicle sales (Chapter 10). Yet, stringent material efficiency and lightweight design of passenger vehicles alone would have the potential to cut cumulative global GHG emissions until 2060 by 16–39 GtCO 2 -eq ( [[#Pauliuk--2021|Pauliuk et al. 2021]] ).

While global passenger activity has expanded in all world regions, great disparities exist between low- and high-income regions, and within countries between urban and rural areas ( [[#ITF--2019|ITF 2019]] ). While private car use is dominant in OECD countries ( [[#EC--2019|EC 2019]] ), the growth of passenger-km (the product of number of travellers and distance travelled) has considerably slowed there, down to an increase of just 1% yr –1 between 2000 and 2017 ( [[#SLoCaT--2018|SLoCaT 2018]] ) (Chapter 10). Meanwhile, emerging economies in the Global South are becoming more car-dependent, with rapidly growing motorisation, on-demand private transport services, urban sprawl, and the emergence of local automotive production, while public transport struggles to provide adequate services ( [[#Dargay--2007|Dargay et al. 2007]] ; [[#Hansen--2017|Hansen and Nielsen 2017]] ; [[#Pojani--2017|Pojani and Stead 2017]] ).

Freight travel activity grew across the globe by 68% in the last two decades, driven by global GDP increases, together with the proliferation of online commerce and rapid (i.e., same-day and next-day) delivery ( [[#SLoCaT--2018|SLoCaT 2018]] ). Growth has been particularly rapid in heavy-duty road freight transport.

While accounting for a small share of total GHG emissions, domestic and international aviation have been growing faster than road transport emissions, with average annual growth rates of +3.3% and +3.4%, respectively, between 2010 and 2019 ( [[#Crippa--2021|Crippa et al. 2021]] ; [[#Minx--2021|Minx et al. 2021]] ;). Energy efficiency improvements in aviation were considerably larger than in road transport, but were outpaced by even larger increases in activity levels ( [[#SLoCaT--2018|SLoCaT 2018]] ; [[#Lee--2021|Lee et al. 2021]] ) (Chapter 10).

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==== 2.4.2.5 AFOLU Sector ====

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GHG emissions from agriculture, forestry and other land use (AFOLU) reached 13 GtCO 2 -eq globally in 2019 ( ''medium confidence'' ) (Figure 2.21). AFOLU trends, particularly those for CO 2 -LULUCF, are subject to a high degree of uncertainty ( [[#2.2.1|Section 2.2.1]] ). Overall, the AFOLU sector accounts for 22% of total global GHG emissions, and in several regions – Africa, Latin America, and South-East Asia – it is the single largest emitting sector, which is also significantly affected itself by climate change (AR6 WGI Chapters 8, 11, and 12; and AR6 WGII Chapter 5). Latin America has the highest absolute and per capita AFOLU GHG emissions of any world region (Figure 2.21). CO 2 emissions from land-use change and CH 4 emissions from enteric fermentation together account for 74% of sector-wide GHGs. Note that CO 2 -LULUCF estimates included in this chapter are not necessarily comparable with country GHG inventories, due to different approaches to estimating anthropogenic CO 2 sinks ( [[#Grassi--2018|Grassi et al. 2018]] ) (Chapter 7).

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'''Figure 2.21''' '''|''' '''Trends and drivers of global AFOLU sector emissions: (a) trends of GHG emissions by subsectors 1990–2019; (b) share of total sector and per capita GHG emissions by world region in 2019; and (c) Kaya decomposition of GHG emissions drivers.''' Based on the equation H=P(A/P)(L/A)(H/L), where P is population, A/P is agricultural output per capita, L/A is the land required per unit of agricultural output (land efficiency), and H/L is GHG emissions per unit of land (GHG intensity) ( [[#Hong--2021|Hong et al. 2021]] ). GHG emissions H comprise agricultural CH 4 and N 2 O emissions from EDGAR v6.0. The indicated annual growth rates are averaged across the years 2010–2019 – LULUCF CO 2 emissions are excluded in panel (c). (Note: due to different datasets, the population breakdown for AFOLU emissions is slightly different than that in the other sector figures above).

Unlike all other sectors, AFOLU emissions are typically higher in developing compared to developed regions ( ''medium confidence'' ). In Africa, Latin America, and South-East Asia, CO 2 emissions associated with land-use change and management predominate, dwarfing other AFOLU and non-AFOLU sources and making AFOLU the single largest sector with more than 50% of emissions in these regions ( [[#Lamb--2021b|Lamb et al. 2021b]] ). Land-use and land-management emissions are associated with the expansion of agriculture into carbon-dense tropical forest areas ( [[#Vancutsem--2021|Vancutsem et al. 2021]] ), where large quantities of CO 2 emissions result from the removal and burning of biomass and draining of carbon rich soils ( [[#Pearson--2017|Pearson et al. 2017]] ; [[#IPCC--2018|IPCC 2018]] ; [[#Hong--2021|Hong et al. 2021]] ). Ruminant livestock rearing takes place on vast tracts of pasture land worldwide, contributing to large quantities of CH 4 emissions from enteric fermentation in Latin America (0.8 GtCO 2 -eq in 2018), Southern Asia (0.6 GtCO 2 -eq), and Africa (0.5 GtCO 2 -eq), while also playing a sizable role in the total AFOLU emissions of most other regions ( [[#Lamb--2021b|Lamb et al. 2021b]] ).

In all regions, the amount of land required per unit of agricultural output has decreased significantly from 2010 to 2019, with a global average of –2.2% yr –1 (land efficiency metric in Figure 2.21). This reflects agricultural intensification and technological progress. However, in most regions this was mirrored by an increase in output per capita, meaning that absolute GHG emissions in most regions increased over the last decade. A significant increase in total AFOLU emissions occurred in Africa, driven by both increased GHG emissions per unit of land and increased populations (Figure 2.21).

The AFOLU sector and its emissions impacts are closely tied to global supply chains, with countries in Latin America and South-East Asia using large portions of their land for agricultural and forestry products exported to other countries (Chapter 7). The strong increases in production per capita and associated GHG emissions seen in these regions are at least partly attributable to growing exports and not national food system or dietary changes. At the same time, efforts to promote environmental sustainability in regions such as the EU and the USA (but also fast-growing emerging economies such as China) can take place at the cost of increasing land displacement elsewhere to meet their own demand ( [[#Meyfroidt--2010|Meyfroidt et al. 2010]] ; [[#Yu--2013|Yu et al. 2013]] ; [[#Creutzig--2019|Creutzig et al. 2019]] ).

Global diets are a key driver of production per capita, and thus land pressure and AFOLU emissions (Chapter 7). As per capita incomes rise and populations urbanise, traditional, low-calorie diets that emphasise starchy foods, legumes, and vegetables transition towards energy-intensive products such as refined sugars, fats, oils, and meat ( [[#Pradhan--2013|Pradhan et al. 2013]] ; [[#Tilman--2014|Tilman and Clark 2014]] ). At a certain point in national development, affluence and associated diets thus override population growth as the main driver of AFOLU emissions ( [[#Kastner--2012|Kastner et al. 2012]] ). Very high calorie diets have high total GHG emissions per capita ( [[#Heller--2015|Heller and Keoleian 2015]] ) and are common in the developed world ( [[#Pradhan--2013|Pradhan et al. 2013]] ). Over the last few decades, a ‘westernisation’ of diets has also been occurring in developing countries ( [[#Pradhan--2013|Pradhan et al. 2013]] ). Low- and middle-income countries such as India, Brazil, Egypt, Mexico, and South Africa have experienced a rapid dietary shift towards western-style diets (De [[#Carvalho--2013|Carvalho et al. 2013]] ; [[#Pradhan--2013|Pradhan et al. 2013]] ; [[#Popkin--2015|Popkin 2015]] ). Another driver of higher food requirements per capita is food waste, which has increased more or less continuously since the 1960s in all regions but Europe ( [[#Porter--2016|Porter and Reay 2016]] ).

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=== 2.4.3 Poverty and Inequality ===

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Increasing economic inequality globally has given rise to concern that unequal societies may be more likely to pollute and degrade their environments ( [[#Masud--2018|Masud et al. 2018]] ; [[#Chancel--2020|Chancel 2020]] ; [[#Hailemariam--2020|Hailemariam et al. 2020]] ; [[#Millward-Hopkins--2021|Millward-Hopkins and Oswald 2021]] ). The nature of this relationship has important implications for the design of income redistribution policies aiming to reduce inequalities ( [[#2.6|Section 2.6]] presents evidence on how affluence and high consumption relate to emissions). Income inequality and carbon intensity of consumption differs across countries and individuals ( [[#Baležentis--2020|Baležentis et al. 2020]] ) ( [[#2.3.3|Section 2.3.3]] ). Reduced income inequality between nations can reduce emissions intensity of global income growth, if energy intensity reductions from income growth in some nations offset increases in energy and emissions from higher growth in other nations ( [[#Rao--2018|Rao and Min 2018]] ). Increasing income inequality between individuals can translate into larger energy and emissions inequality if higher incomes are spent on more energy-intensive consumption and affluent lifestyles ( [[#Oswald--2020|Oswald et al. 2020]] ; [[#Wiedmann--2020|Wiedmann et al. 2020]] ) ( [[#2.6|Section 2.6]] ).

Literature shows that more equitable income distributions can improve environmental quality, but the nature of this relationship can vary by level of development ( ''low evidence'' , ''medium agreement'' ) ( [[#Knight--2017|Knight et al. 2017]] ; [[#Chen--2020|Chen et al. 2020]] ; [[#Hailemariam--2020|Hailemariam et al. 2020]] ; [[#Huang--2020|Huang and Duan 2020]] ; [[#Liobikienė--2020|Liobikienė and Rimkuvienė 2020]] ; [[#Rojas-Vallejos--2020|Rojas-Vallejos and Lastuka 2020]] ; [[#Uddin--2020|Uddin et al. 2020]] ). Differences in the energy and carbon intensities of consumption and the composition of consumption baskets across populations and nations matter for emissions. ( [[#Jorgenson--2016|Jorgenson et al. 2016]] ; [[#Grunewald--2017|Grunewald et al. 2017]] ). There is evidence to suggest that more equal societies place a higher value on environmental public goods ( [[#Baumgärtner--2017|Baumgärtner et al. 2017]] ; [[#Drupp--2018|Drupp et al. 2018]] ). Additional research shows that reducing top income inequality in OECD countries can reduce carbon emissions and improve environmental quality ( [[#Hailemariam--2020|Hailemariam et al. 2020]] ) and that the effect of wealth inequality, measured as the wealth share of the top decile, on per capita emissions in high-income countries, is positive ( [[#Knight--2017|Knight et al. 2017]] ). Evidence from 40 sub-Saharan African countries suggests that a rise in income inequality contributed to increasing CO 2 emissions between 2010 and 2016, controlling for other drivers such as economic growth, population size, and inflation ( [[#Baloch--2020|Baloch et al. 2020]] ).

The key development objective of eradicating extreme poverty ( [[#Chakravarty--2013|Chakravarty and Tavoni 2013]] ; [[#Hubacek--2017a|Hubacek et al. 2017a]] ; [[#Malerba--2020|Malerba 2020]] ) and providing universal access to modern energy services ( [[#Pachauri--2013|Pachauri et al. 2013]] , 2018; [[#Pachauri--2014|Pachauri 2014]] ; [[#Singh--2017|Singh et al. 2017]] ) only marginally affects GHG emissions ( ''medium evidence'' , ''high agreement'' ). Shifts from biomass to more efficient energy sources and collective provisioning systems for safe water, health, and education are associated with reduced energy demand ( [[#Baltruszewicz--2021|Baltruszewicz et al. 2021]] ). Efforts to alleviate multi-dimensional poverty by providing minimum decent living standards universally, however, may require more energy and resources. Recent estimates of the additional energy needed are still within bounds of projections of energy demand under climate stabilisation scenarios ( [[#Hubacek--2017a|Hubacek et al. 2017a]] , 2017b; [[#Rao--2019|Rao et al. 2019]] ; [[#Pascale--2020|Pascale et al. 2020]] ; [[#Kikstra--2021|Kikstra et al. 2021]] ). Bottom-up estimates suggest that achieving decent living standards requires 13–40 GJ per capita annually, much less than the current world average energy consumption of 80 GJ per capita in 2020 ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ) ( ''medium evidence'' , ''high agreement'' ). Aggregate top-down estimates suggest that achieving a high Human Development Index (HDI) score above 0.8 requires energy consumption between 30–100 GJ per capita yr –1 ( [[#Lamb--2015|Lamb and Rao 2015]] ). There is some evidence, however, of a decoupling between energy consumption and HDI over time ( [[#Akizu-Gardoki--2018|Akizu-Gardoki et al. 2018]] ). The emissions consequences of poverty alleviation and decent living also depend on whether improvements in well-being occur via energy- and carbon-intensive industrialisation or low-carbon development ( [[#Semieniuk--2020|Semieniuk and Yakovenko 2020]] ; [[#Fu--2021|Fu et al. 2021]] ; [[#Huang--2021|Huang and Tian 2021]] ).

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=== 2.4.4 Rapid and Large-scale Urbanisation as a Driver of GHG Emissions ===

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Economic growth and urbanisation go hand in hand and are both influencing GHG emissions. However, the exact role of urban development in driving emissions is multi-faceted and heterogeneous, depending on development status and other regional factors ( ''medium evidence'' , ''high agreement'' ) ( [[#Jorgenson--2014|Jorgenson et al. 2014]] ; [[#Lamb--2014|Lamb et al. 2014]] ; [[#Liddle--2014|Liddle and Lung 2014]] ; [[#Creutzig--2015|Creutzig et al. 2015]] ; [[#Pincetl--2017|Pincetl 2017]] ; [[#Azizalrahman--2019|Azizalrahman and Hasyimi 2019]] ; [[#Muñoz--2020|Muñoz et al. 2020]] ). This calls for a differentiated assessment. This section assesses the process of rapid urban growth in developing countries and how emissions change over time when cities’ urban populations and infrastructure expand at fast speed and at a massive scale ( [[#Seto--2017|Seto et al. 2017]] ; [[#Elmqvist--2021|Elmqvist et al. 2021]] ). To distinguish, [[#2.6|Section 2.6]] includes the carbon footprint of urban lifestyles and the difference in emissions profiles between already urbanised and less urbanised areas. [[IPCC:Wg3:Chapter:Chapter-8|Chapter 8]] deals with urban strategies for climate change mitigation.

Urban development is most significant and rapid in developing and transition countries, accompanied by a substantial migration of rural populations to urban areas ( [[#Apergis--2016|Apergis and Li 2016]] ; [[#Azizalrahman--2019|Azizalrahman and Hasyimi 2019]] ; Z. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al. 2019]] ) and associated impacts on land use ( [[#Richardson--2015|Richardson et al. 2015]] ). If the trend of developing countries following infrastructure stock patterns in industrialised nations continues until 2050, this could cause approximately 350 GtCO 2 from the production of materials ( [[#Müller--2013|Müller et al. 2013]] ). This would be equivalent to 70% of the 500 GtCO 2 estimated remaining carbon budget from the beginning of 2020 to limit global warming to 1.5°C with a likelihood of 50% ( [[#IPCC--2021b|IPCC 2021b]] ).

In many developing countries across the world, the process of urban expansion leads to higher per capita consumption-based GHG emissions ( ''medium evidence'' , ''high agreement'' ) ( [[#Jorgenson--2014|Jorgenson et al. 2014]] ; [[#Yao--2015|Yao et al. 2015]] ; [[#Zhang--2016|Zhang et al. 2016]] ; [[#Wood--2018|Wood et al. 2018]] a; [[#Muñoz--2020|Muñoz et al. 2020]] ). The high disparity between rural and urban personal carbon footprints in these countries ( [[#Wiedenhofer--2017|Wiedenhofer et al. 2017]] ) ( [[#2.6|Section 2.6]] ) means that migration to urban areas increases overall emissions as levels of income and expenditure rise, leading to further economic growth and infrastructure development in urban areas ( [[#Müller--2013|Müller et al. 2013]] ; [[#Li--2015|Li et al. 2015]] ; [[#Wang--2016|Wang and Yang 2016]] ; [[#Zhang--2016|Zhang et al. 2016]] ; [[#Wiedenhofer--2017|Wiedenhofer et al. 2017]] ; Cetin and Bakirtas 2019; [[#Fan--2019|Fan et al. 2019]] ; [[#Li--2019|Li and Zhou 2019]] ; [[#Xia--2019|Xia et al. 2019]] ; [[#Sarkodie--2020|Sarkodie et al. 2020]] ).

For total production-based emissions in general, urbanisation is thought to have a smaller effect than changes in population, GDP per capita, and energy and emissions intensities, which are all more influential ( [[#Lin--2017|Lin et al. 2017]] ). Another driver of urban emissions is rising ambient air temperature caused by urban land expansion, which will likely drive a substantive increase in air conditioning use and cold storage for food ( [[#Huang--2019|Huang et al. 2019]] ). Specific emission drivers, however, depend on city- and place-specific circumstances such as income, household size, density, or local climate ( [[#Baiocchi--2015|Baiocchi et al. 2015]] ; H. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al. 2019]] ). Geographical factors, urban form, and transport/fuel costs are dependent on each other, and, together with economic activity, have been found to explain 37% of urban direct energy use and 88% of urban transport energy use in a global sample of 274 cities ( [[#Creutzig--2015|Creutzig et al. 2015]] ).

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