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

== 2.3 Past and Present Trends of Consumption-based CO 2 Emissions (CBEs) and Emissions Embodied in Trade ==

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=== 2.3.1 Scope, Variability and Uncertainty of CBEs ===

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Consumption is increasingly met by global supply chains often involving large geographical distances and causing emissions in producing countries ( [[#Hubacek--2014|Hubacek et al. 2014]] , 2016; [[#Wiedmann--2018|Wiedmann and Lenzen 2018]] ). Therefore, accounting for emissions from production along the entire supply chain to fulfil final demand, – so-called consumption-based emissions (CBEs), – is necessary to understand why emissions occur and to what extent consumption choices and associated supply chains contribute to total emissions, and ultimately how to influence consumption to achieve climate mitigation targets and environmental justice (Vasconcellos 2020).

Production-based emissions (PBEs) and territorial emissions resulting from the production and consumption of goods and services within a region (for both domestic use and export) are often used by authorities to report carbon emissions ( [[#Peters--2008|Peters 2008]] ) ( [[#2.2|Section 2.2]] ). PBEs also include emissions from international activities (e.g., international aviation/shipping and non-resident activities), which are excluded from territorial emissions ( [[#Karstensen--2018|Karstensen et al. 2018]] ; [[#Shan--2018|Shan et al. 2018]] ). In contrast, CBEs refer to emissions along the entire supply chains induced by consumption, irrespective of the place of production ( [[#Liu--2015|]] [[#Liu--2015|Liu et al. 2015]] b). This reflects a shared understanding that a wider system boundary going beyond territorial emissions is important to avoid outsourcing of pollution and to achieve global decarbonisation. CBEs allow for the identification of new policy levers through information on a country’s trade balance of embodied emissions, households’ carbon implications of their lifestyle choices, companies’ upstream emissions as input for supply chain management, and cities’ footprints outside their administrative boundaries ( [[#Davis--2010|Davis and Caldeira 2010]] ; [[#Feng--2013|Feng et al. 2013]] ). [[#Kander--2015|Kander et al. (2015)]] proposed a technology-adjusted consumption-based emission accounting (TCBA) approach to address the issue of carbon intensity in exports. TCBA incorporates emissions embodied in trade but also adjusted for differences in carbon efficiency in exports of different countries. Unlike PBEs, there are no internationally agreed approaches to calculate CBEs, making it a major drawback for mainstreaming the use of this indicator in policymaking.

There are other proposed emission accounting approaches used in different circumstances. Historical cumulative emissions (HCEs) are used when analysing countries’ historic contribution to emissions and responsibility for emission reduction. HCEs account for a country’s cumulative past emissions, which may be different from the country’s current annual emissions ( [[#Botzen--2008|Botzen et al. 2008]] ; [[#Ritchie--2019|Ritchie 2019]] ), but are sensitive to the choice of cut-off period. For example, the USA and EU-27 countries plus the UK contributed respectively 13.3% and 8.7% to global PBEs in 2019 ( [[#Crippa--2020|Crippa et al. 2020]] ), however, they emitted around 25% and 22% of global historical PBEs since 1751 ( [[#Ritchie--2019|Ritchie 2019]] ). Extraction-based emissions (EBEs) accounting allocates all emissions from burning fossil fuels throughout the supply chains to the country where the fuels were extracted ( [[#Steininger--2015|Steininger and Schinko 2015]] ). EBEs can be calculated by multiplying primary energy extraction of fossil fuels with their respective carbon content ( [[#Erickson--2013|Erickson and Lazarus 2013]] ). Another approach for accounting emissions is income-based emission (IBE), which traces emissions throughout all supply chains and allocates emissions to primary inputs (e.g., capital and labour). In other words, IBEs investigate a country’s direct and indirect downstream GHG emissions enabled by its primary inputs ( [[#Liang--2017|Liang et al. 2017]] a). All these approaches provide complementary information and different angles to assigning responsibility for emissions reductions.

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=== Box 2.1 | Policy Applications of Consumption-based Emissions ===

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Consumption-based emissions provide additional or complementary information to production-based emissions that can be used for a variety of policy applications. These include:

'''•''' Complementary national-level emissions accounting and target or budget setting

'''•''' Raising awareness and increasing understanding of the GHG effects of consumption

'''•''' Accounting for and understanding of distributional and responsibility issues in GHG emissions mitigation, both nationally and internationally

'''•''' Incentives to change consumption patterns or reduce consumption (e.g., through taxation policies)

'''•''' Accounting for and understanding of carbon leakage and emissions embodied in trade*

'''•''' International emissions trading schemes or linked national schemes

'''•''' Trade policies addressing emissions embodied in trade and international supply chains (e.g., border tax adjustments and clean technology transfers, carbon offsetting or financing, etc.)

'''•''' Including embodied emissions in product performance standards and labelling

'''•''' Policies of public and private procurement

'''•''' Agreements with international suppliers

'''•''' Discussing the climate impacts of lifestyles and inequalities in consumption and associated emissions.

The points above are based on a synopsis of studies ( [[#Steininger--2014|Steininger et al. 2014]] ; [[#Afionis--2017|Afionis et al. 2017]] ; [[#Hubacek--2017b|Hubacek et al. 2017b]] ; [[#Wang--2018|Wang and Zhou 2018]] ; [[#Bolea--2020|Bolea et al. 2020]] ).

\* Note, however, that comparing embodied emissions in trade between countries is further complicated by the fact that emission intensities differ across countries. Approaches to adjust for these differences and facilitate comparisons have been suggested ( [[#Kander--2015|Kander et al. 2015]] ; [[#Baumert--2019|Baumert et al. 2019]] ; [[#Dietzenbacher--2020|Dietzenbacher et al. 2020]] ; [[#Jakob--2021|Jakob 2021]] ). Many different approaches on how to share responsibility between producers and consumers have been proposed in designing effective integrated global climate policies ( [[#Liu--2017|Liu and Fan 2017]] ; [[#Khajehpour--2019|Khajehpour et al. 2019]] ; [[#Jakob--2021|Jakob et al. 2021]] ). Ultimately, assigning responsibility is normative.

The dominant method for calculating nations’ CBEs is global multi-region input-output (GMRIO) analysis ( [[#Wiedmann--2018|Wiedmann and Lenzen 2018]] ). Other frequently used approaches include analysing bilateral trade flows of products and their lifecycle emission factors ( [[#Sato--2014|Sato 2014]] ). Generally, the uncertainties associated with CBEs depends on the choice of the dataset/model used for calculation, which differs according to: (i) the national economic and trade data used; (ii) the emissions data used; (iii) the sector or product-level aggregation; (iv) the regional aggregation; (v) the conceptual scope (e.g., residential vs territorial accounting principle); and (vi) the model construction techniques, which include table-balancing algorithms and ways of dealing with missing or conflicting data ( [[#Moran--2014|Moran and Wood 2014]] ; [[#Owen--2017|Owen, 2017]] ; [[#Wieland--2018|Wieland et al. 2018]] ; [[#Wood--2018|Wood et al. 2018]] , 2019). When excluding systematic error sources, research has shown that the stochastic relative standard deviation (RSD) of total national CBEs is not significantly different to that from PBEs accounts and in the region of 5–15% ( [[#Wood--2018|Wood et al. 2018]] , 2019).

Six global accounts for consumption-based GHG emissions at the country level are widely used (Table 2.2). Each dataset has been constructed by different teams of researchers, covering different time periods and containing CBEs estimates for different sets of countries and regions ( [[#Owen--2017|Owen 2017]] ).

[[#Wood--2019|Wood et al. (2019)]] present a comprehensive and systematic model intercomparison and find a variation of 5–10% for both PBE and CBE accounts of major economies and country groups (e.g., EU-28, OECD). The estimates for the USA were the most closely aligned, with 3.7% RSD. For smaller countries, variability is in the order of 20–30% and can reach more than 40% in cases of very small, highly trade-exposed countries such as Singapore and Luxembourg ( [[#Wood--2019|Wood et al. 2019]] ). It is recommended that CBEs results for such countries be interpreted with care.

Overall, production accounts showed a slightly higher convergence (8% average of RSD) than consumption-based accounts (12%). The variation across model results can be approximately halved, when normalising national totals to one common value for a selected base year. The difference between PBEs result variation (4% average RSD after normalisation) and CBEs results (7%) remains after normalisation.

In general, the largest contributors to uncertainty of CBEs results are – in descending order of priority – the total of territorial GHG emission accounts, the allocation of emissions to economic sectors, the total and composition of final demand, and lastly the structure of the economy. Harmonising territorial emissions across GMRIO datasets is the single most important factor that reduces uncertainty by about 50% ( [[#Tukker--2020|Tukker et al. 2020]] ). More work is required to optimise or even institutionalise the compilation of multi-region input-output data and models to enhance the accuracy of consumption-based accounting ( [[#Tukker--2018|Tukker et al. 2018]] ; [[#Wood--2018|Wood et al. 2018]] ).

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=== 2.3.2 Trends in Global and Regional CBEs Trajectories ===

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In comparison to territorial emissions discussed in [[#2.2|Section 2.2]] , Figure 2.14 shows the trends of global and regional CBEs from 1990 to 2018. This section uses the PBEs and CBEs data from the Global Carbon Budget 2020 ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ), which are slightly different from the PBEs used in [[#2.2|Section 2.2]] . The Global Carbon Budget only includes CO 2 emissions from fossil fuels and cement production.

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[[File:dde9459729b38edd1b63c6367dd75a15 IPCC_AR6_WGIII_Figure_2_14.png]]

'''Figure 2.14''' '''|''' '''Consumption-based CO''' 2 '''emissions trends for the period 1990–2018.''' The CBEs of countries are collected from the Global Carbon Budget 2020 ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). Source: this figure is modified based on [[#Hubacek--2021|Hubacek et al. (2021)]] .

The two panels at left in Figure 2.14 show total and per capita CBEs for six regions. The three panels on the right show additional information for the 18 top-emitting countries with the highest CBEs in 2018. In Developed Countries, consumption-based CO 2 emissions peaked at 15 GtCO 2 in 2007 with a subsequent 16% decline until 2016 (to 12.7 GtCO 2 ) and a slight rebound of 1.6% until 2018 (to 12.9 GtCO 2 ). Asia and Pacific has been a major contributor to consumption-based CO 2 emissions growth since 2000 and exceeded Developed Countries as the global largest emissions source in 2015. From 1990 to 2018, the average growth rate of Asia and Pacific was 4.8% per year, while in other regions emissions declined by –1.1%–4.3% per year on average. In 2018, 35% of global consumption-based CO 2 emissions were from Developed Countries and 39% from Asia and Pacific, 5% from Latin American and Caribbean, 5% from Eastern Europe and West Central Asia, 5% from Middle East, and 3% from Africa ( [[#Hubacek--2021|Hubacek et al. 2021]] ). Global CBEs kept growing over the period with a short-lived decline in 2008 due to the global financial crisis. In 2020, lockdowns associated with COVID-19 significantly reduced global emissions ( [[#2.2.2|Section 2.2.2]] ), including CBEs ( [[#Shan--2021a|Shan et al. 2021a]] ).

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<span id="decoupling-of-emissions-from-economic-growth"></span>
=== 2.3.3 Decoupling of Emissions from Economic Growth ===

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There has been a long-standing discussion on whether environmental impacts such as carbon emissions and use of natural resources can be decoupled from economic growth. It is controversial whether absolute decoupling can be achieved at a global scale ( [[#Ward--2016|Ward et al. 2016]] ; [[#Hickel--2020|Hickel and Kallis 2020]] ; [[#Haberl--2020|Haberl et al. 2020]] ). However, a number of studies found that it is feasible to achieve decoupling at the national level, and they have explored the reasons for such decoupling ( [[#Schandl--2016|Schandl et al. 2016]] ; [[#Ward--2016|Ward et al. 2016]] ; [[#Deutch--2017|Deutch 2017]] ; [[#Roinioti--2017|Roinioti and Koroneos 2017]] ; [[#Vadén--2020|Vadén et al. 2020]] ; [[#Habimana%20Simbi--2021|Habimana Simbi et al. 2021]] ; [[#Shan--2021b|Shan et al. 2021b]] ).

Table 2.3 shows the extent of decoupling of CBEs and GDP of countries based on CBEs from the Global Carbon Budget ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ) and GDP data from the World Bank. Table 2.4 also presents countries’ degree of decoupling of PBEs and GDP. These data allow a comparison of decoupling between GDP and both PBEs and CBEs.

'''Table 2.''' '''4 |''' '''Country groups with different degree of PBE–GDP decoupling from 2015 to 2018.'''

{| class="wikitable"
|-
| rowspan="2" colspan="2"| Number of countries

| Absolute decoupling

| Relative decoupling

| No decoupling

| Economic recession

|-
| 32

| 41

| 36

| 6

|-
| rowspan="2"| CBEs (gigatonnes)

| Total

| 6.41

| 23.43

| 2.83

| 0.85

|-
| Global share

| 19.1%

| 69.9%

| 8.4%

| 2.5%

|-
| rowspan="2"| PBEs (gigatonnes)

| Total

| 5.33

| 24.36

| 3.04

| 0.84

|-
| Global share

| 15.9%

| 72.6%

| 9.1%

| 2.5%

|-
| rowspan="2"| Population (million)

| Total

| 857

| 4518

| 1213

| 270

|-
| Global share

| 12.5%

| 65.9%

| 17.7%

| 3.9%

|-
| rowspan="2"| GDP (billion)

| Total

| 27091

| 45255

| 4086

| 2997

|-
| Global share

| 34.1%

| 57.0%

| 5.1%

| 3.8%

|-
| rowspan="4"| Per capita GDP (1000 USD2010)

| Average

| 28.83

| 19.53

| 6.00

| 17.78

|-
| Median

| 26.36

| 12.04

| 3.64

| 13.12

|-
| Max

| 79.23

| 110.70

| 63.93

| 33.11

|-
| Min

| 1.09

| 0.57

| 0.49

| 5.80

|-
| rowspan="4"| Per capita CBEs (tonnes)

| Average

| 7.70

| 6.98

| 3.99

| 12.55

|-
| Median

| 6.78

| 6.00

| 1.95

| 11.33

|-
| Max

| 23.22

| 37.95

| 25.35

| 23.21

|-
| Min

| 0.43

| 0.09

| 0.18

| 2.33

|-
| rowspan="4"| CBEs intensity

(tonnes per 1000 USD2010)

| Average

| 0.41

| 0.50

| 0.77

| 0.66

|-
| Median

| 0.31

| 0.44

| 0.52

| 0.69

|-
| Max

| 2.41

| 1.68

| 4.10

| 1.22

|-
| Min

| 0.12

| 0.10

| 0.20

| 0.21

|-
| rowspan="4"| Per capita PBEs (tonnes)

| Average

| 6.02

| 5.69

| 4.33

| 14.15

|-
| Median

| 5.36

| 4.88

| 1.67

| 13.22

|-
| Max

| 20.13

| 16.65

| 39.27

| 27.24

|-
| Min

| 0.30

| 0.09

| 0.01

| 2.23

|-
| rowspan="4"| PBEs intensity

(tonnes per 1000 USD2010)

| Average

| 0.33

| 0.45

| 0.71

| 0.75

|-
| Median

| 0.20

| 0.31

| 0.44

| 0.68

|-
| Max

| 1.47

| 1.76

| 4.83

| 1.80

|-
| Min

| 0.05

| 0.10

| 0.13

| 0.20

|}

Note: CBEs are obtained from the Global Carbon Budget 2020 ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ), GDP and population are from the World Bank. One country (Venezuela) does not have GDP data after 2015, so the degree of decoupling was only calculated for 115 countries. In order to be consistent with the results of CBEs, we calculate the decoupling of PBE until 2018.

Absolute decoupling refers to a decline of emissions in absolute terms or as being stable while GDP grows (i.e., a decoupling index [[#footnote-003|11]] greater than 1); relative decoupling refers to growth of emissions being lower than growth of GDP (a decoupling index between 0 and 1); and no decoupling, which refers to a situation where emissions grow to the same extent or faster than GDP (a decoupling index of less than 0) ( [[#Wu--2018|Wu et al. 2018]] ).

During the most recent three-year period from 2015 to 2018, 23 countries (or 20% of the 116 sample countries) have achieved absolute decoupling of CBEs and GDP, while 32 countries (or 28%) achieved absolute decoupling of PBEs and GDP: 14 of them (e.g., the UK, Japan, and the Netherlands) also decoupled PBEs and GDP. Countries with absolute decoupling of CBEs tend to achieve decoupling at relatively high levels of economic development and high per capita emissions. Most of EU and North American countries are in this group. Decoupling was not only achieved by outsourcing carbon-intensive production, but also improvements in production efficiency and energy mix, leading to a decline of emissions. Structural Decomposition Analysis shows that the main driver for decoupling has been a reduction in carbon intensity (i.e., change in energy mix and energy efficiency) from both domestic production and imports ( [[#Hubacek--2021|Hubacek et al. 2021]] ). Similarly, [[#Wood--2020b|Wood et al. (2020b)]] found that EU countries have reduced their overall consumption-based GHG emissions by 8% between 1995 and 2016, mainly due to the use of more efficient technology. The literature also shows that changes in the structure of economy with a shift to tertiary sectors of production may contribute to such decoupling ( [[#Kanitkar--2015|Kanitkar et al. 2015]] ; Jiang et al. 2021).

A total of 67 (or 58%) countries, including China and India, have relatively decoupled GDP and CBEs between 2015 and 2018, reflecting a slower growth in emissions than GDP. It is worth noting that the USA shows relative decoupling of emissions (both CBEs and PBEs) and GDP over the most recent period, although it strongly decoupled economic growth from emissions between 2005 and 2015. Thus decoupling can be temporary and countries’ emissions may again increase after a period of decoupling.

Another 19 (or 16%) countries, such as South Africa and Nepal, have experienced no decoupling between GDP and CBEs from 2015 to 2018, meaning the growth of their GDP is closely tied with the consumption of emission-intensive goods. As a result, a further increase of GDP in these countries will likely lead to higher emissions, if they follow the historical trend without substantive improvement in efficiency of production and energy use.

It is important to note that a country’s degree of decoupling changes over time. For example, 32countries achieved absolute decoupling from 2010 to 2015 but only 10 of them remained decoupled over the next three years. More importantly, although absolute decoupling has reduced annual emissions, the remaining emissions are still contributing to an increase in atmospheric carbon concentration. Absolute decoupling is not sufficient to avoid consuming the remaining CO 2 emission budget under the global warming limit of 1.5°C or 2°C and to avoid climate breakdown ( [[#Stoknes--2018|Stoknes and Rockström 2018]] ; [[#Hickel--2020|Hickel and Kallis 2020]] ). Even if all countries decouple in absolute terms this might still not be sufficient and thus can only serve as one of the indicators and steps toward fully decarbonising the economy and society.

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=== 2.3.4 Emissions Embodied in Trade (EET) ===

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As global trade patterns have changed over recent decades, so have emissions embodied in trade (EET) (Jiang &amp; Green 2017). EET refers to emissions associated with production of traded goods and services and is equal to the difference between PBEs and CBEs ( [[#Wiebe--2016|Wiebe and Yamano 2016]] ). EET includes two parts: emissions embodied in imports (EEI); and emissions embodied in exports (EEE). For a given country or region with CBEs higher than PBEs, it is a net importer with a higher EEI than EEE, and vice versa.

EET have been rising faster since the 1980s due to an increase in trade volume ( [[#Xu--2014|Xu and Dietzenbacher 2014]] ; [[#Wood--2018|Wood et al. 2018]] ). CO 2 emissions from the production of internationally traded products peaked in 2006 at about 26% of global CO 2 emissions. Since then, international CO 2 emissions transfers declined but are likely to remain an important part of the climate policy agenda ( [[#Wood--2020a|Wood et al. 2020a]] ). About 24% of global economic output and 25% of global CO 2 emissions are embodied in the international trade of goods and services as of 2014 ( [[#Hubacek--2021|Hubacek et al. 2021]] ).

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==== 2.3.4.1 Net Emission Transfers ====

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Located downstream in global supply chains, developed countries (mostly in Western Europe and North America) tend to be net emission importers, that is, EEI are larger than EEE. For example, over 40% of national CO 2 footprints in France, Germany, Italy, and Spain are from imports ( [[#Fan--2017|Fan et al. 2017]] ). Developing countries tend to be net emission exporters with higher PBEs than their CBEs ( [[#Peters--2011a|Peters et al. 2011a]] ), especially for Asia and Pacific (as shown in Figure 2.15). That is to say, there is a net emission transfer and outsourcing of carbon-intensive production from developed to developing economies via global trade ( [[#Jiang--2018|]] [[#Jiang--2018|Jiang et al. 2018]] ), mainly caused by cheap labour costs ( [[#Tate--2017|Tate and Bals 2017]] ) and cheap raw materials ( [[#Mukherjee--2018|Mukherjee 2018]] ). Increasing openness to trade ( [[#Fernández-Amador--2016|Fernández-Amador et al. 2016]] ) and less stringent environmental legislation (acting as so-called pollution havens) are also possible reasons ( [[#Hoekstra--2016|Hoekstra et al. 2016]] ; [[#Malik--2016|Malik and Lan 2016]] ; [[#Banerjee--2020|Banerjee and Murshed 2020]] ).

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[[File:10d6b78ee6ba746ec370c66c51e6b5d7 IPCC_AR6_WGIII_Figure_2_15.png]]

'''Figure 2.15''' '''|''' '''Total annual CO''' 2 '''emissions for 116 countries by global region based on consumption- and production-based emissions.''' The shaded areas are the net CO 2 trade balances (differences) between each of the regions. Yellow shading indicates that the region is a net importer of embodied CO 2 emissions, leading to consumption-based emission estimates that are higher than traditional territorial emission estimates. Blue shading indicates the reverse. Production-based emissions are collected from EDGAR and consumption-based emissions from the Global Carbon Budget 2020 ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). Source: this figure is modified based on [[#Hubacek--2021|Hubacek et al. (2021)]] .

Net emissions transferred between developing and developed countries peaked at 7.3% of global CO 2 emissions in 2006 and then subsequently declined ( [[#Wood--2020a|Wood et al. 2020a]] ). The main reason for the decline was an improvement in the carbon intensity of traded products, rather than a decline in trade volume ( [[#Wood--2020a|Wood et al. 2020a]] ). Despite continued improvements, developing economies tend to have higher emission intensity than developed economies due to less efficient technologies and a carbon-intensive fuel mix ( [[#Jiang--2017|Jiang and Guan 2017]] ).

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==== 2.3.4.2 Geographical Shifts of Emissions Embodied in Trade ====

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With the rapid growth of developing countries, the geographical centre of global trade as well as emissions embodies in trade is changing. The fast growth of Asian countries is shifting the global trade centre from Europe to Asia ( [[#Zhang--2019|Zhang et al. 2019]] ). Asian exports in monetary units increased by 235% from 1996 to 2011, and its share in global exports increased from 25% to 46%, whereas Europe’s share in global exports decreased from 51% in 1996 to 39% in 2011. After 2011, global trade has stalled, but Asia’s share of global exports further increased to 42% in 2020 ( [[#UNCTAD--2021|UNCTAD 2021]] ).

In addition to changes in trade volume, trading patterns have also been changing significantly in Asian countries. These countries are replacing traditional trading hubs (such as Russia and Germany) due to the fast growth in trade flows, especially with countries of the Global South ( [[#Zhang--2019|Zhang et al. 2019]] ). The largest geographical shifts in trade-embodied emissions between 1995 and 2011 occurred in high-tech, electronics, and machinery ( [[#Malik--2016|Malik and Lan 2016]] ; [[#Jiang--2018|]] [[#Jiang--2018|Jiang et al. 2018]] ). For example, China is shifting its exports to include more low-carbon and higher value-added goods and services. As a result, China’s exported emissions declined by 20% from 2008 to 2015 ( [[#Mi--2018|Mi et al. 2018]] ).

Developing countries are increasingly playing an important role in global trade. EET between developing countries, so-called South-South trade, has more than doubled between 2004 (0.47 Gt) and 2011 (1.11 Gt), which is seen as a reflection of a new phase of globalisation ( [[#Meng--2018|Meng et al. 2018]] ). Developing countries, therefore, have gained importance as global suppliers of goods and services and have also become more relevant as global consumers as they grow their domestic demand ( [[#Fernández-Amador--2016|Fernández-Amador et al. 2016]] ). Since 2014, CO 2 emission transfer between developing countries has plateaued and then slightly declined and seems to have stabilised at around the same level of transfers between non-OECD and OECD countries at around 2.4 GtCO 2 yr –1 ( [[#Wood--2020a|Wood et al. 2020a]] ). In both cases, a decrease in carbon intensity of trade just about offset increased trade volumes ( [[#Wood--2020a|Wood et al. 2020a]] ).

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<span id="economic-drivers-and-their-trends-by-regions-and-sectors"></span>