Global net anthropogenic greenhouse gas (GHG) emissions during the last decade (2010–2019) were higher than at any previous time in human history (high confidence). Since 2010, GHG emissions have continued to grow, reaching 59 ± 6.6 GtCO2-eq in 2019, 1 but the average annual growth in the last decade (1.3%, 2010–2019) was lower than in the previous decade (2.1%, 2000–2009) ( high confidence). Average annual GHG emissions were 56 ± 6.0 GtCO2-eq yr –1 for the decade 2010–2019 growing by about 9.1 GtCO2-eq yr –1 from the previous decade (2000–2009) – the highest decadal average on record ( high confidence). {2.2.2, Table 2.1, Figure 2.2, Figure 2.5}

Emissions growth has varied, but persisted across all groups of GHGs (high confidence). The average annual emission levels of the last decade (2010–2019) were higher than in any previous decade for each group of GHGs ( high confidence). In 2019, CO2 emissions were 45 ± 5.5 GtCO2, 2 CH411 ± 3.2 GtCO2-eq, N2O 2.7 ± 1.6 GtCO2-eq and fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) 1.4 ± 0.41 GtCO2-eq. Compared to 1990, the magnitude and speed of these increases differed across gases: CO2 from fossil fuel and industry (FFI) grew by 15 GtCO2-eq yr –1 (67%), CH4 by 2.4 GtCO2-eq yr –1 (29%), F-gases by 0.97 GtCO2-eq yr –1 (254%), and N2O by 0.65 GtCO2-eq yr –1 (33%). CO2 emissions from net land use, land-use change and forestry (LULUCF) have shown little long-term change, with large uncertainties preventing the detection of statistically significant trends. F-gases excluded from GHG emissions inventories such as chlorofluorocarbons and hydrochlorofluorocarbons are about the same size as those included ( high confidence). {2.2.1, 2.2.2, Table 2.1, Figures 2.2, 2.3 and 2.5}

Globally, gross domestic product (GDP) per capita and population growth remained the strongest drivers of CO2 emissions from fossil fuel combustion in the last decade (robust evidence, high agreement). Trends since 1990 continued in the years 2010 to 2019 with GDP per capita and population growth increasing emissions by 2.3% and 1.2% yr –1, respectively. This growth outpaced the reduction in the use of energy per unit of GDP (–2% yr –1, globally) as well as improvements in the carbon intensity of energy (–0.3% yr –1) ( high confidence). {2.4.1, Figure 2.16}

The global COVID-19 pandemic led to a steep drop in CO2 emissions from fossil fuel and industry (high confidence). Global CO2-FFI emissions dropped in 2020 by about 5.8% (5.1–6.3%) or about 2.2 (1.9–2.4) GtCO2 compared to 2019. Emissions, however, have rebounded globally by the end of December 2020 (medium confidence). {2.2.2, Figure 2.6}

Cumulative net CO2 emissions of the last decade (2010–2019) are about the same size as the remaining carbon budget for keeping warming to 1.5°C (medium confidence). Cumulative net CO2 emissions since 1850 are increasing at an accelerating rate: about 62% of total cumulative CO2 emissions from 1850 to 2019 occurred since 1970 (1500 ± 140 GtCO2); about 43% since 1990 (1000 ± 90 GtCO2); and about 17% since 2010 (410 ± 30 GtCO2). For comparison, the remaining carbon budget for keeping warming to 1.5°C with a 67% (50%) probability is about 400 (500) ± 220 GtCO2 (medium confidence). {2.2.2, Figure 2.7; AR6 WGI 5.5; AR6 WGI Table 5.8}

A growing number of countries have achieved GHG emission reductions longer than 10 years – a few at rates that are broadly consistent with climate change mitigation scenarios that limit warming to well below 2°C (high confidence). There are at least 18 countries that have reduced CO2 and GHG emissions for longer than 10 years. Reduction rates in a few countries have reached 4% in some years, in line with rates observed in pathways that limit warming to 2°C (>67%). However, the total reduction in annual GHG emissions of these countries is small (about 3.2 GtCO2-eq yr –1) compared to global emissions growth observed over the last decades. Complementary evidence suggests that countries have decoupled territorial CO2 emissions from GDP, but fewer have decoupled consumption-based emissions from GDP. This decoupling has mostly occurred in countries with high per capita GDP and high per capita CO2 emissions. {2.2.3, 2.3.3, Figure 2.11, Table 2.3, Table 2.4}

Consumption-based CO2 emissions in Developed Countries and the Asia and Pacific region are higher than in other regions (high confidence). In Developed Countries, consumption-based CO2 emissions peaked at 15 GtCO2 in 2007, declining to about 13 GtCO2 in 2018. The Asia and Pacific region, with 52% of current global population, has become a major contributor to consumption-based CO2 emission growth since 2000 (5.5% yr –1 for 2000–2018); it exceeded the Developed Countries region, which accounts for 16% of current global population, as the largest emitter of consumption-based CO2. {2.3.2, Figure 2.14}

Carbon intensity improvements in the production of traded products have led to a net reduction in CO2 emissions embodied in international trade (robust evidence, high agreement). A decrease in the carbon intensity of traded products has offset increased trade volumes between 2006 and 2016. Emissions embodied in internationally traded products depend on the composition of the global supply chain across sectors and countries and the respective carbon intensity of production processes (emissions per unit of economic output). {2.3, 2.4}

Developed Countries tend to be net CO2 emission importers, whereas developing countries tend to be net emission exporters (robust evidence, high agreement). Net CO2 emission transfers from developing to Developed Countries via global supply chains have decreased between 2006 and 2016. Between 2004 and 2011, CO2 emission embodied in trade between developing countries have more than doubled (from 0.47 to 1.1 Gt) with the centre of trade activities shifting from Europe to Asia. {2.3.4, Figure 2.15}

Emissions from developing countries have continued to grow, starting from a low base of per capita emissions and with a lower contribution to cumulative emissions than Developed Countries (robust evidence, high agreement). Average 2019 per capita CO2-FFI emissions in three developing regions – Africa (1.2 tCO2 per capita), Asia and Pacific (4.4 tCO2 per capita), and Latin America and Caribbean (2.7 tCO2 per capita) – remained less than half that of Developed Countries (9.5 tCO2 per capita) in 2019. CO2-FFI emissions in the three developing regions together grew by 26% between 2010 and 2019, compared to 260% between 1990 and 2010, while in Developed Countries emissions contracted by 9.9% between 2010 and 2019, and by 9.6% between 1990 and 2010. Historically, the three developing regions together contributed 28% to cumulative CO2-FFI emissions between 1850 and 2019, whereas Developed Countries contributed 57% and Least-Developed Countries contributed 0.4%. {2.2.3, Figures 2.9 and 2.10}

Globally, GHG emissions continued to rise across all sectors and subsectors; most rapidly in transport and industry (high confidence). In 2019, 34% (20 GtCO2-eq) of global GHG emissions came from the energy sector, 24% (14 GtCO2-eq) from industry, 22% (13 GtCO2-eq) from agriculture, forestry and other land use (AFOLU), 15% (8.7 GtCO2-eq) from transport and 5.6% (3.3 GtCO2-eq) from buildings. Once indirect emissions from energy use are considered, the relative shares of industry and buildings emissions rise to 34% and 16%, respectively. Average annual GHG emissions growth during 2010 to 2019 slowed compared to the previous decade in energy supply (from 2.3% to 1.0%) and industry (from 3.4% to 1.4%, direct emissions only), but remained roughly constant at about 2% per year in the transport sector ( high confidence). Emission growth in AFOLU is more uncertain due to the high share of CO2-LULUCF emissions (medium confidence). {2.4.2, Figure 2.13, Figures 2.16 to 2.21}

Average annual growth in GHG emissions from energy supply decreased from 2.3% for 2000–2009 to 1.0% for 2010–2019 (high confidence). This slowing of growth is attributable to further improvements in energy efficiency (annually, 1.9% less energy per unit of GDP was used globally between 2010 and 2019). Reductions in global carbon intensity by –0.2% yr –1 contributed further – reversing the trend during 2000 to 2009 (+0.2% yr –1) (medium confidence). These carbon intensity improvements were driven by fuel switching from coal to gas, reduced expansion of coal capacity, particularly in Eastern Asia, and the increased use of renewables. {2.2.4, 2.4.2.1, Figure 2.17}

GHG emissions in the industry, buildings and transport sectors continue to grow, driven by an increase in the global demand for products and services (high confidence). These final demand sectors make up 44% of global GHG emissions, or 66% when the emissions from electricity and heat production are reallocated as indirect emissions to related sectors, mainly to industry and buildings. Emissions are driven by the large rise in demand for basic materials and manufactured products, a global trend of increasing floor space per capita, building energy service use, travel distances, and vehicle size and weight. Between 2010 and 2019, domestic and international aviation were particularly fast growing at average annual rates of +3.3% and +3.4%. Global energy efficiencies have improved in all three demand sectors, but carbon intensities have not. {2.2.4; Figures 2.18 to 2.20}

Providing access to modern energy services universally would increase global GHG emissions by, at most, a few percent (medium confidence). The additional energy demand needed to support decent living standards 3 for all is estimated to be well below current average energy consumption (medium evidence, high agreement ). More equitable income distributions can reduce carbon emissions, but the nature of this relationship can vary by level of income and development (limited evidence, medium agreement ). {2.4.3}

Evidence of rapid energy transitions exists, but only at sub-global scales (medium evidence, medium agreement). Emerging evidence since the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) on past energy transitions identifies a growing number of cases of accelerated technology diffusion at sub-global scales and describes mechanisms by which future energy transitions may occur more quickly than those in the past. Important drivers include technology transfer and cooperation, intentional policy and financial support, and harnessing synergies among technologies within a sustainable energy system perspective (medium evidence, medium agreement ). A fast global low-carbon energy transition enabled by finance to facilitate low-carbon technology adoption in developing, and particularly in least-developed countries, can facilitate achieving climate stabilisation targets (robust evidence, high agreement ). {2.5.2, Table 2.5}

Multiple low-carbon technologies have shown rapid progress since AR5 – in cost, performance, and adoption – enhancing the feasibility of rapid energy transitions (robust evidence, high agreement). The rapid deployment and cost decrease of modular technologies like solar, wind, and batteries have occurred much faster than anticipated by experts and modelled in previous mitigation scenarios (robust evidence, high agreement ). The political, economic, social, and technical feasibility of solar energy, wind energy and electricity storage technologies has improved dramatically over the past few years. In contrast, the adoption of nuclear energy and carbon capture and storage (CCS) in the electricity sector has been slower than the growth rates anticipated in stabilisation scenarios. Emerging evidence since AR5 indicates that small-scale technologies (e.g., solar, batteries) tend to improve faster and be adopted more quickly than large-scale technologies (nuclear, CCS) (medium evidence, medium agreement ). {2.5.3, 2.5.4, Figures 2.22 and 2.23}

Robust incentives for investment in innovation, especially incentives reinforced by national policy and international agreements, are central to accelerating low-carbon technological change (robust evidence, medium agreement). Policies have driven innovation, including instruments for technology push (e.g., scientific training, research and development) and demand pull (e.g., carbon pricing, adoption subsidies), as well as those promoting knowledge flows and especially technology transfer. The magnitude of the scale-up challenge elevates the importance of rapid technology development and adoption. This includes ensuring participation of developing countries in an enhanced global flow of knowledge, skills, experience, and equipment. Also, technology itself requires strong financial, institutional, and capacity-building support (robust evidence, high agreement ). {2.5.4, 2.5, 2.8}

The global wealthiest 10% contribute about 36–45% of global GHG emissions (robust evidence, high agreement). The global 10% wealthiest consumers live in all continents, with two-thirds in high-income regions and one-third in emerging economies (robust evidence, medium agreement ). The lifestyle consumption emissions of the middle-income and poorest citizens in emerging economies are between 5 and 50 times below their counterparts in high-income countries (medium evidence, medium agreement ). Increasing inequality within a country can exacerbate dilemmas of redistribution and social cohesion, and affect the willingness of rich and poor to accept lifestyle changes for mitigation and policies to protect the environment (medium evidence, medium agreement ) {2.6.1, 2.6.2, Figure 2.25}

Estimates of future CO2 emissions from existing fossil fuel infrastructures already exceed remaining cumulative net CO2 emissions in pathways limiting warming to 1.5°C with no or limited overshoot (high confidence). Assuming variations in historical patterns of use and decommissioning, estimated future CO2 emissions from existing fossil fuel infrastructure alone are 660 (460–890) GtCO2 and from existing and currently planned infrastructure 850 (600–1100) GtCO2. This compares to overall cumulative netCO2 emissions until reaching net zero CO2 of 510 (330–710) Gt in pathways that limit warming to 1.5°C with no or limited overshoot, and 890 (640–1160) Gt in pathways that limit warming to 2°C (<67%) ( high confidence). While most future CO2 emissions from existing and currently planned fossil fuel infrastructure are situated in the power sector, most remaining fossil fuel CO2 emissions in pathways that limit warming to 2°C (<67%) and below are from non-electric energy – most importantly from the industry and transportation sectors ( high confidence). Decommissioning and reduced utilisation of existing fossil fuel installations in the power sector as well as cancellation of new installations are required to align future CO2 emissions from the power sector with projections in these pathways ( high confidence). {2.7.2, 2.7.3, Figure 2.26, Table 2.6, Table 2.7}

A broad range of climate policies, including instruments like carbon pricing, play an increasing role in GHG emissions reductions. The literature is in broad agreement, but the magnitude of the reduction rate varies by the data and methodology used, country, and sector (robust evidence, high agreement). Countries with a lower carbon pricing gap (higher carbon price) tend to be less carbon intensive (medium confidence). {2.8.2, 2.8.3}

Climate-related policies have also contributed to decreasing GHG emissions. Policies such as taxes and subsidies for clean and public transportation, and renewable policies have reduced GHG emissions in some contexts (robust evidence, high agreement). Pollution control policies and legislations that go beyond end-of-pipe controls have also had climate co-benefits, particularly if complementarities with GHG emissions are considered in policy design (medium evidence, medium agreement ). Policies on AFOLU and sector-related policies such as afforestation can have important impacts on GHG emissions (medium evidence, medium agreement ). {2.8.4}

As demonstrated by the contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6 WGI) (IPCC 2021a), greenhouse gas 4 (GHG) concentrations in the atmosphere and annual anthropogenic GHG emissions continue to grow and have reached a historic high, driven mainly by continued fossil fuels use (Jackson et al. 2019; Friedlingstein et al. 2020; Peters et al. 2020). Unsurprisingly, a large volume of new literature has emerged since AR5 on the trends and underlying drivers of anthropogenic GHG emissions. This chapter provides a structured assessment of this new literature and establishes the most important thematic links to other chapters in this report.

While AR5 has mostly assessed GHG emissions trends and drivers between 1970 and 2010, this assessment focuses on the period 1990–2019 with the main emphasis on changes since 2010. Compared to Chapter 5 in the contribution of WG III to AR5 (Blanco et al. 2014), the scope of the present chapter is broader. It presents the historical background of global progress in climate change mitigation for the rest of the report and serves as a starting point for the assessment of long-term as well as near- and medium-term mitigation pathways in Chapters 3 and 4, respectively. It also provides a systemic perspective on past emissions trends in different sectors of the economy (Chapters 6–12), and relates GHG emissions trends to past policies (Chapter 13) and observed technological development (Chapter 16). There is also a greater focus on the analysis of consumption-based sectoral emissions trends, empirical evidence of emissions consequences of behavioural choices and lifestyles, and the social aspects of mitigation (Chapter 5). Finally, a completely new section discusses the mitigation implications of existing and planned long-lived infrastructure and carbon lock-in.

Figure 2.1 presents the road map of this chapter. It is a simplified illustration of the causal chain driving emissions along the black arrows. It also highlights the most important linkages to other chapters in this volume (blue lines). The logic of the figure is that the main topic of this chapter is GHG emissions trends (discussed only in this chapter at such level of detail), hence they are at the top of the figure in grey-outlined boxes. The secondary theme is the drivers behind these trends, depicted in the second line of grey-outlined boxes. Four categories of drivers highlight key issues and guide readers to chapters in which more details are presented. Finally, in addition to their own motivations and objectives, climate and non-climate policies and measures shape the aspirations and activities of actors in the main driver categories, as shown in the grey-outlined box below.

Accordingly, the grey-outlined boxes at the top of Figure 2.1 show that the first part of the chapter presents GHG emissions from two main perspectives: their geographical locations; and the places where goods are consumed and services are utilised. A complicated chain of drivers underlie these emissions. They are linked across time, space, and various segments of the economy and society in complex non-linear relationships. Sections shown in the second row of grey-outlined boxes assess the latest literature and improve the understanding of the relative importance of these drivers in mitigating GHG emissions. A huge mass of physical capital embodying immense financial assets and potentially operating over a long lifetime produces vast GHG emissions. This long-lived infrastructure can be a significant hindrance to fast and deep reductions of emissions; it is therefore also shown as an important driver. A large range of economic, social, environmental, and other policies has been shaping these drivers of GHG emissions in the past and are anticipated to influence them in the future, as indicated by the grey-outlined policies box and its manifold linkages. As noted, blue lines show linkages of sections to other chapters that discuss these drivers and their operating mechanisms in detail.

Total anthropogenic greenhouse gas (GHG) emissions as discussed in this chapter comprise CO2 emissions from fossil fuel combustion and industrial (FFI) processes, 5 net CO2 emissions from land use, land-use change, and forestry (CO2-LULUCF) (often named FOLU – forestry and other land-use – in previous IPCC reports), methane (CH4), nitrous oxide (N2O) and fluorinated gases (F-gases) comprising hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) as well as nitrogen trifluoride (NF3). There are other major sources of F-gas emissions that are regulated under the Montreal Protocol such as chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) that also have considerable warming impacts (Figure 2.4), however they are not considered here. Other substances, including ozone and aerosols, that further contribute climate forcing are only treated very briefly, but a full chapter is devoted to this subject in the Working Group I contribution to AR6 (Szopa et al. 2021a; 2021b).

A growing number of global GHG emissions inventories have become available since AR5 (Minx et al. 2021). However, only a few are comprehensive in their coverage of sectors, countries and gases – namely EDGAR (Emissions Database for Global Atmospheric Research) (Crippa et al. 2021), PRIMAP (Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths) (Gütschow et al. 2021a), CAIT (Climate Analysis Indicators Tool) (WRI 2019) and CEDS (A Community Emissions Data System for Historical Emissions) (Hoesly et al. 2018). None of these inventories presently cover CO2-LULUCF, while CEDS excludes F-gases. For individual gases and sectors, additional GHG inventories are available, as shown in Figure 2.2, but each has varying system boundaries leading to important differences between their respective estimates (Section 2.2.1). Some inventories are compiled bottom-up, while others are produced synthetically and are dependent on other inventories. A more comprehensive list and discussion of different datasets is provided in the Chapter 2 Supplementary Material (2.SM.1) and in Minx et al. (2021).

Across this report, version 6 of EDGAR (Crippa et al. 2021) provided by the Joint Research Centre of the European Commission, is used for a consistent assessment of GHG emissions trends and drivers. It covers anthropogenic releases of CO2-FFI, CH4, N2O, and F-gas (HFCs, PFCs, SF6, NF3) emissions by 228 countries and territories and across five sectors and 27 subsectors. EDGAR is chosen because it provides the most comprehensive global dataset in its coverage of sources, sectors and gases. For transparency, and as part of the uncertainty assessment, EDGAR is compared to other global datasets in Section 2.2.1 as well as in the Chapter 2 Supplementary Material (2.SM.1). For individual country estimates of GHG emissions, it may be more appropriate to use inventory data submitted to the United Nations Framework Convention on Climate Change (UNFCCC) under the common reporting format (CRF) (UNFCCC 2021). However, these inventories are only up to date for Annex I countries and cannot be used to estimate global or regional totals. As part of the regional analysis, a comparison of EDGAR and CRF estimates at the country-level is provided, where the latter is available (Figure 2.9).

Net CO2-LULUCF estimates are added to the dataset as the average of estimates from three bookkeeping models of land-use emissions (Hansis et al. 2015; Houghton and Nassikas 2017; Gasser et al. 2020) following the Global Carbon Project (Friedlingstein et al. 2020). This is different to AR5, where land-based CO2 emissions from forest fires, peat fires, and peat decay, were used as an approximation of the net-flux of CO2-LULUCF (Blanco et al. 2014). Note that the definition of CO2-LULUCF emissions by global carbon cycle models, as used here, differs from IPCC definitions (IPCC 2006) applied in national greenhouse gas inventories (NGHGI) for reporting under the climate convention (Grassi et al. 2018, 2021) and, similarly, from estimates by the Food and Agriculture Organization of the United Nations (FAO) for carbon fluxes on forest land (Tubiello et al. 2021). The conceptual difference in approaches reflects different scopes. We use the global carbon cycle models’ approach for consistency with Working Group I (Canadell et al. 2021) and to comprehensively distinguish natural from anthropogenic drivers, while NGHGI generally report as anthropogenic all CO2 fluxes from lands considered managed (Section 7.2.2). Finally, note that the CO2-LULUCF estimate from bookkeeping models as provided in this chapter is indistinguishable from the CO2 from agriculture, forestry and other land use (AFOLU) as reported in Chapter 7, because the CO2 emissions component from agriculture is negligible.

The resulting synthetic dataset used here has undergone additional peer review and is publicly available (Minx et al. 2021). Comprehensive information about the dataset as well as underlying uncertainties (including a comparison with other datasets) can be found in the Supplementary Material to this chapter and in Minx et al. (2021).

In this chapter and the report as a whole, different GHGs are frequently converted into common units of CO2 equivalent (CO2-eq) emissions using 100-year global warming potentials (GWP100) from AR6 WGI (Forster et al. 2021a). This reflects the dominant use in the scientific literature and is consistent with decisions made by Parties to the Paris Agreement for reporting and accounting of emissions and removals (UNFCCC 2019). Other GHG emissions metrics exist, all of which, like GWP100, are designed for specific purposes and have limitations and uncertainties. The appropriate choice of GHG emissions metrics depends on policy objective and context (Myhre et al. 2013; Kolstad et al. 2015). A discussion of GHG metrics is provided in a Cross-Chapter Box later in the chapter (Cross-Chapter Box 2) and at length in the Chapter 2 Supplementary Material. Throughout the chapter GHG emissions are reported (in GtCO2-eq) at two significant digits to reflect prevailing uncertainties in emissions estimates. Estimates are subject to uncertainty, which we report for a 90% confidence interval.

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 Chapter 2 Supplementary Material. Socio-demographic drivers are described in 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 et al. 2021; IEA 2021c; Lamb et al. 2021b; Minx et al. 2021). It shows main contributors to GHG emissions as independent factors, although these factors also interact with each other.

Technological change for climate change mitigation involves improvement in and adoption of technologies, primarily those associated with energy production and use. Technological change has had a mitigating effect on emissions over the long term and is central to efforts to achieving climate goals ( high confidence). Progress since AR5 shows that multiple low-carbon technologies are improving and falling in cost ( high confidence); technology adoption is reaching substantial shares, and small-scale technologies are particularly promising on both (medium confidence). Faster adoption and continued technological progress can play a crucial role in accelerating the energy transition. However, the historical pace of technological change is still insufficient to catalyse a complete and timely transition to a low-carbon energy system: technological change needs to accelerate ( high confidence). This section assesses the role of technological change in driving emissions reductions and the factors that drive technological change, with an emphasis on the speed of transitions. Incentives and support for technological change affect technology outcomes (Sivaram et al. 2018; Wilson et al. 2020a). Work since AR5 has focused on evaluating the effectiveness of policies: those that accelerate technological change by enhancing knowledge (technology push) and those that increase market opportunities for successful technologies (demand pull) (Nemet 2013); as well as the importance of tailoring support to country contexts (Barido et al. 2020; Rosenbloom et al. 2020), including the limits of carbon-pricing policies to date (Lilliestam et al. 2020). Section 2.8 and Chapter 13 describe how these polices affect emissions; Chapter 14 and Cross-Chapter Box 12 in Chapter 16 discuss transition dynamics; and Chapter 16 provides a more detailed assessment of the evolution and mitigation impacts of technology development, innovation, and transfer.

•Global GHG emissions estimates are published less frequently and with greater reporting lags than, for example, CO2 from fossil fuel and industry. Data quality and reporting frequency remains an issue, particularly in developing countries where the statistical infrastructure is not well developed. Efforts to compile a global GHG emissions inventory by country, sector, and across time, that is annually updated based on the best-available inventory information, similar to ongoing activities for carbon dioxide (CO2), methane (CH4) or nitrous oxide (N2O), could fill this gap. Uncertainties and their methodological treatment in GHG emissions estimates are still not comprehensively understood.

•There is a more fundamental data gap for F-gas emissions, where data quality in global inventories is poor due to considerable gaps in the underlying activity data – particularly in developing countries. Comprehensive tracking of fluorinated gases (F-gas) emissions would also imply the inclusion of other gases not covered under the Paris Agreement, such as chlorofluorocarbons, hydrochlorofluorocarbons and others.

•Currently, despite advances in terms of data availability, sectoral and spatial resolution, the results in consumption-based emission estimates are dependent on the database used, the level of sectoral aggregation and country resolution. More fine-grained data at spatial resolution as well as the product level would support exploring the mitigation options at the sub-national level, companies and households.

•Consumption-based emission accounts suffer from lack of quantification of uncertainties at the subnational level and especially in data-scarce environments, such as for developing countries. A better understanding of drivers that caused decoupling of emissions at the national and especially sub-national level are important to explore.

•Understanding how socio-economic drivers modulate emission mitigation is crucial. Technological improvements (e.g., improved energy or land-use intensity of the economy) have shown a persistent pattern over the last few decades, but gains have been outpaced by increases in affluence (GDP per capita) and population growth, leading to continued emissions growth. Therefore the key gap in knowledge is how these drivers of emissions can be mitigated by demand management, alternative economic models, population control and rapid technological transition to different extents and in different settings. More research on decoupling and sustainability transformations would help to answer these questions. Key knowledge gaps also remain in the role of trade – in particular, how supporting low-carbon technologies in developing and exporting countries can counteract the upward-driving effect of trade, and how to achieve decoupling without outsourcing emissions to others and often to less developed regions.

•Understanding of how inequality affects emissions is in a nascent stage. Less is known about the causal mechanisms by which different dimensions of inequality – such as income, socio-economic, spatial, socio-cultural-gender and ethnicity – affect emissions. In particular, limited knowledge exists on the linkages between dimensions of inequality other than income or wealth and emissions arising from different service demands. Research gaps are apparent on how inequalities in living standards relate to emissions and how changes in inequalities between genders, social groups, and other marginalised communities impact emissions trends.

•Digitalisation of the economy is often quoted as providing new mitigation opportunities, but knowledge and evidences are yet limited – such as understanding of the role of smart apps and the potential and influence of disruptive technologies at the demand and supply side on GHG emissions.

•Despite growing evidence of technological progress across a variety of mitigation areas and the availability of increasingly precise datasets, knowledge gaps remain on technological change and innovation and evidence on speed of transitions to clarify what would make them fast or slow. Innovation is an inherently uncertain process and there will always be imperfect ex ante knowledge on technological outcomes and their effects on mitigation. The extent to which a low-carbon transition can proceed faster than historical examples is crucial to aid future mitigation. That depends on a better understanding of the speed of building, updating and replacing infrastructure. Additionally, how and whether financing for low-carbon technology investment in low- and middle-income countries can be delivered at low-cost and sustained over time are important questions. The emerging findings that small-scale technologies learn faster and are adopted more quickly need to be tested against a broader set of cases, and in particular against the large dispersion in data.

•Future CO2 emissions from existing and planned infrastructure is not well understood and quantified outside the power sector. Further integration of bottom-up accounting and scenario approaches from integrated assessment seems promising. Comprehensive assessments of hard-to-abate residual fossil fuel emissions and their relationship to CO2 removal activities are lacking, but will be important for informing net-zero emissions strategies.

•Empirical evidence of emission impacts from climate policies, including carbon pricing, is not sufficient for unambiguous attribution assessment, mainly due to the limited experience with climate-related policy experiments to date. More attention to the methodology for comprehensive evaluation of climate policies and measures, such as effective carbon rates is apparent. Key knowledge gaps also exist on ex-post evaluations of climate and non-climate policies and measures for their impact on emissions, particularly at the global scale, considering national circumstances and priorities.

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1 Emissions of GHGs are weighed by global warming potentials with a 100-year time horizon (GWP100) from the Sixth Assessment Report (Forster et al. 2021). GWP100 is commonly used in wide parts of the literature on climate change mitigation and is required for reporting emissions under the United Nations Framework Convention on Climate Change (UNFCCC). All metrics have limitations and uncertainties. (Cross-Chapter Box 2 in Chapter 2 and Annex II, Part II, Section 8).

2 In 2019, CO2 from fossil fuel and industry (FFI) were 38 ± 3.0 Gt, CO2 from net land use, land-use change and forestry (LULUCF) 6.6 ± 4.6 Gt.

3 Decent Living Standards (DLS) – a benchmark of material conditions for human well-being – overlaps with many Sustainable Development Goals (SDGs). Minimum requirements of energy use consistent with enabling well-being for all is between 20 and 50 GJ per capita yr –1 depending on the context. {5.2.2, 5.2.2, Box 5.3, Figure 5.6}

4 Greenhouse gases are gaseous constituents of the atmosphere that absorb and emit radiation at specific wavelengths within the spectrum of radiation emitted by the Earth’s surface, by the atmosphere itself, and by clouds. This property causes the greenhouse effect. Water vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and ozone (O3) are the primary GHGs in the Earth’s atmosphere. Human-made GHGs include sulphur hexafluoride (SF6), hydrofluorocarbons (HFCs), chlorofluorocarbons (CFCs), and perfluorocarbons (PFCs); see Annex I: Glossary.

5 Industrial processes relate to CO2 releases from fossil fuel oxidation and carbonate decomposition.

6 Emission metrics also exist for aerosols, but these are not commonly used in climate policy. This assessment focuses on GHG emission metrics only.

7 For consistency with WGI, uncertainties in this paragraph are reported at a 68% confidence interval. This reflects the difficulty in the WGI context of characterising the uncertainty in the CO2 fluxes between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well as the difficulty of updating the emissions from land-use change.

8 Note that GHG emissions from international aviation and shipping could not be attributed to individual regions, while CO2 emissions from AFOLU could not be attributed to individual countries. Change in GHG emissions that can be easily assigned to regions is 20.3 of 20.8 GtCO2-eq for 1990–2019 and 6.3 of 6.5 GtCO2-eq for 2010–2019.

9 In all cases, constraining countries within the emissions range to those larger than 1 million population.

10 Note that this does not include the additional warming impacts from aviation due to short-lived climate forcers, which are assessed in Chapter 10 (Section 10.5).

11 The decoupling index can be calculated based on changes of a country’s GDP and CO2 emissions (Akizu-Gardoki et al. 2018; Wu et al. 2018). See the equation below. DI refers to decoupling index; G1 refers to the GDP of reporting year while G0 refers to the base year; E1 refers to emissions of the reporting year while E0 refers to emissions of the base year.

12 This section only reviews the emission impacts of selected policy instruments. Other important aspects such as equity and cost-effectiveness are assessed in Chapter 13, presenting comprehensive evaluations of policies and measures.

13 Refer to Chapter 13 on policies and institutions for a detailed discussion of emissions leakages and complex interactions from policy mixes.

14 The OECD (2018) measures carbon prices using the effective carbon rate (ECR), which is the sum of three components: specific taxes on fossil fuels; carbon taxes; and prices of tradable emissions permits. The carbon pricing gap measures the difference between actual ECRs and benchmark rates. The carbon pricing gap indicates the extent to which polluters do not pay for the damage from carbon emissions.

Table 2.2 | Features of six global datasets for consumption-based emissions accounts.

Table 2. 3| Country groups with different degree of CBE–GDP decoupling from 2015 to 2018.

Note: CBEs are obtained from the Global Carbon Budget 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. This table is modified from Hubacek et al. (2021).

Figure 2.25: Carbon footprints per capita income and expenditure category for 109 countries ranked by per capita income (consumption-based emissions). Notes: countries and income categories are dependent on data availability. Light blue dots represent income quintiles (lowest, low, middle, higher, and highest) of EU countries and the USA. Yellow dots are for the developing country group provided by the World Bank for four expenditure categories: lowest, low, middle and higher (Hubacek et al. 2017b). Dark blue diamonds represent average per capita carbon footprints. Countries are ranked from the lowest per capita income (bottom) to the highest income (top) within each country group. Countries are grouped using the IPCC’s six high-level classification categories. Footprint values for higher income groups in the World Bank data are less reliable.

Table 2.6 | Comparing cumulative future CO2 emissions estimates from existing andproposed long-lived infrastructures by sector. Future CO2 emissions estimates are reported from the ‘year of dataset’. Note that, in some cases, the totals may not correspond to the sum of underlying sectors due to rounding (based on Tong et al. 2019). Initial estimates of future CO2 emissions from fossil fuel infrastructures by Davis et al. (2010) are considerably lower than more recent estimates by Smith et al. (2019) and Tong et al. (2019) due to substantial growth in fossil energy infrastructure, as represented by more recent data. Estimates presented here are rounded to two significant digits.

Table 2.7 | Residual (gross) fossil fuel emissions (GtCO2) in climate change mitigation scenarios strengthening mitigation action after 2020 (‘early strengthening’), compared to scenarios that keep Nationally Determined Contribution (NDC) ambition level until 2030 and only strengthen thereafter. Cumulative gross CO2 emissions from fossil fuel and industry until reaching net zero CO2 emissions are given in terms of the mean as well as minimum and maximum (in parentheses) across seven participating models: AIM/CGE, GCAM, IMAGE, MESSAGES, POLES, REMIND, WITCH. Scenario design prescribes a harmonised, global carbon price in line with long-term carbon budget. Delay scenarios follow the same price trajectory, but 10 years later. Carbon dioxide removal requirements represent ex-post calculations that subtract gross fossil fuel emissions from the carbon budget associated with the respective long-term warming limit. We take the carbon budget for limiting warming to 1.5°C with a 50% probability and to 2°C with a 67% probability (Canadell et al. 2021). Hence, carbon dioxide removal (CDR) requirements reflect a minimum amount of CDR for a given mitigation trajectory. Results are reported at two significant digits. Sources: Luderer et al. (2018); Tong et al. (2019).