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

== 2.2 Past and Present Trends of Territorial GHG Emissions ==

<div id="h1-3-siblings" class="h1-siblings"></div>

Total anthropogenic greenhouse gas (GHG) emissions as discussed in this chapter comprise CO 2 emissions from fossil fuel combustion and industrial (FFI) processes, [[#footnote-009|5]] net CO 2 emissions from land use, land-use change, and forestry (CO 2 -LULUCF) (often named FOLU – forestry and other land-use – in previous IPCC reports), methane (CH 4 ), nitrous oxide (N 2 O) and fluorinated gases (F-gases) comprising hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF 6 ) as well as nitrogen trifluoride (NF 3 ). 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--2021a|Szopa et al. 2021a]] ; 2021b).

A growing number of global GHG emissions inventories have become available since AR5 ( [[#Minx--2021|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--2021|Crippa et al. 2021]] ), PRIMAP (Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths) ( [[#Gütschow--2021a|Gütschow et al. 2021a]] ), CAIT (Climate Analysis Indicators Tool) ( [[#WRI--2019|WRI 2019]] ) and CEDS (A Community Emissions Data System for Historical Emissions) ( [[#Hoesly--2018|Hoesly et al. 2018]] ). None of these inventories presently cover CO 2 -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 ( [[#2.2.1|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 [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 Chapter 2] Supplementary Material (2.SM.1) and in [[#Minx--2021|Minx et al. (2021)]] .

<div id="_idContainer009" class="Basic-Text-Frame"></div>

[[File:1f0757f6644fdb918164933e19584629 IPCC_AR6_WGIII_Figure_2_2.png]]

'''Figure 2.2''' ''': Estimates of global anthropogenic greenhouse gasemissions from different data sources 1970–2019.''' '''Panel (a):''' CO 2 FFI emissions from: EDGAR – Emissions Database for Global Atmospheric Research (this dataset) ( [[#Crippa--2021|Crippa et al. 2021]] ); GCP – Global Carbon Project ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; [[#Andrew--2021|Andrew and Peters 2021]] ); CEDS – Community Emissions Data System ( [[#Hoesly--2018|Hoesly et al. 2018]] ; [[#O’Rourke--2021|O’Rourke et al. 2021]] ); CDIAC Global, Regional, and National Fossil-Fuel CO 2 Emissions ( [[#Gilfillan--2020|Gilfillan et al. 2020]] ); PRIMAP-hist – Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths ( [[#Gütschow--2016|Gütschow et al. 2016]] , 2021b); EIA – Energy Information Administration International Energy Statistics ( [[#EIA--2021|EIA 2021]] ); BP – BP Statistical Review of World Energy ( [[#BP--2021|BP 2021]] ); IEA – International Energy Agency ( [[#IEA--2021a|IEA 2021a]] , 2021b); IPPU refers to emissions from industrial processes and product use. '''Panel (b):''' Net anthropogenic CO 2 -LULUCF emissions from: BLUE – Bookkeeping of land-use emissions ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ); DGVM-mean – multi-model mean of CO 2 -LULUCF emissions from dynamic global vegetation models ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ); OSCAR – an earth system compact model ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; [[#Gasser--2020|Gasser et al. 2020]] ); HN – Houghton and Nassikas Bookkeeping Model ( [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ); for comparison, the net CO 2 flux from FAOSTAT (FAO Tier 1) is plotted, which comprises net emissions and removals on forest land and from net forest conversion ( [[#FAOSTAT--2021|FAOSTAT 2021]] ; [[#Tubiello--2021|Tubiello et al. 2021]] ), emissions from drained organic soils under cropland/grassland ( [[#Conchedda--2020|Conchedda and Tubiello 2020]] ), and fires in organic soils ( [[#Prosperi--2020|Prosperi et al. 2020]] ), as well as a net CO 2 flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC, which include land-use change, and fluxes in managed lands ( [[#Grassi--2021|Grassi et al. 2021]] ). '''Panel (c):''' Anthropogenic CH 4 emissions from: EDGAR (above); CEDS (above); PRIMAP-hist (above); GAINS – The Greenhouse gas – Air pollution Interactions and Synergies Model ( [[#Höglund-Isaksson--2020|Höglund-Isaksson et al. 2020]] ); EPA-2019: Greenhouse gas emission inventory ( [[#US-EPA--2019|US-EPA, 2019]] ); FAO –FAOSTAT inventory emissions ( [[#Tubiello--2013|Tubiello et al. 2013]] ; Tubiello 2018; [[#FAOSTAT--2021|FAOSTAT 2021]] ); '''Panel (d):''' Anthropogenic N 2 O emissions from: GCP – global nitrous oxide budget (Tian et al. 2020); CEDS (above); EDGAR (above); PRIMAP-hist (above); GAINS ( [[#Winiwarter--2018|Winiwarter et al. 2018]] ); EPA-2019 (above); FAO (above). Differences in emissions across different versions of the EDGAR dataset are shown in the Supplementary Material (Figure 2.SM.2). Source: [[#Minx--2021|Minx et al. (2021)]] .

Across this report, version 6 of EDGAR ( [[#Crippa--2021|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 CO 2 -FFI, CH 4 , N 2 O, and F-gas (HFCs, PFCs, SF 6 , NF 3 ) 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 [[#2.2.1|Section 2.2.1]] as well as in the [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 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|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 CO 2 -LULUCF estimates are added to the dataset as the average of estimates from three bookkeeping models of land-use emissions ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Gasser--2020|Gasser et al. 2020]] ) following the Global Carbon Project ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). This is different to AR5, where land-based CO 2 emissions from forest fires, peat fires, and peat decay, were used as an approximation of the net-flux of CO 2 -LULUCF (Blanco et al. 2014). Note that the definition of CO 2 -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--2018|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--2021|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 CO 2 fluxes from lands considered managed ( [[IPCC:Wg3:Chapter:Chapter-7#7.2.2|Section 7.2.2]] ). Finally, note that the CO 2 -LULUCF estimate from bookkeeping models as provided in this chapter is indistinguishable from the CO 2 from agriculture, forestry and other land use (AFOLU) as reported in Chapter 7, because the CO 2 emissions component from agriculture is negligible.

The resulting synthetic dataset used here has undergone additional peer review and is publicly available ( [[#Minx--2021|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--2021|Minx et al. (2021)]] .

In this chapter and the report as a whole, different GHGs are frequently converted into common units of CO 2 equivalent (CO 2 -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|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--2013|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 [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 Chapter 2] Supplementary Material. Throughout the chapter GHG emissions are reported (in GtCO 2 -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.

<div id="2.2.1" class="h2-container"></div>

<span id="uncertainties-in-ghg-emissions"></span>
=== 2.2.1 Uncertainties in GHG Emissions ===

<div id="h2-1-siblings" class="h2-siblings"></div>

Estimates of historical GHG emissions – CO 2 , CH 4 , N 2 O and F-gases – are uncertain to different degrees. Assessing and reporting uncertainties is crucial in order to understand whether available estimates are sufficiently robust to answer policy questions – for example, if GHG emissions are still rising, or if a country has achieved an emission reduction goal ( [[#Marland--2008|Marland 2008]] ). These uncertainties can be of scientific nature, such as when a process is not sufficiently understood. They also arise from incomplete or unknown parameter information (e.g., activity data, or emission factors), as well as estimation uncertainties from imperfect modelling techniques. There are at least three major ways to examine uncertainties in emission estimates ( [[#Marland--2009|Marland et al. 2009]] ): (i) by comparing estimates made by independent methods and observations (e.g., comparing atmospheric measurements with bottom-up emissions inventory estimates) ( [[#Saunois--2020|Saunois et al. 2020]] ; [[#Petrescu--2020|Petrescu et al. 2020]] a and 2020b; Tian et al. 2020); (ii) by comparing estimates from multiple sources and understanding sources of variation ( [[#Macknick--2011|Macknick 2011]] ; [[#Andres--2012|Andres et al. 2012]] ; [[#Andrew--2020|Andrew 2020]] ; [[#Ciais--2021|Ciais et al. 2021]] ); and (iii) by evaluating estimates from a single source ( [[#Hoesly--2018|Hoesly and Smith 2018]] ), for instance via statistical sampling across parameter values (e.g., [[#Monni--2007|Monni et al. 2007]] ; Robert J. [[#Andres--2014|Andres et al. 2014]] ; [[#Tian--2019|Tian et al. 2019]] ; [[#Solazzo--2021|Solazzo et al. 2021]] ).

Uncertainty estimates can be rather different depending on the method chosen. For example, the range of estimates from multiple sources is bounded by their interdependency; they can be lower than true structural plus parameter uncertainty, or than estimates made by independent methods. In particular, it is important to account for potential bias in estimates, which can result from using common methodological or parameter assumptions, or from missing sources (systemic bias). It is further crucial to account for differences in system boundaries – that is, which emissions sources are included in a dataset and which are not, otherwise direct comparisons can exaggerate uncertainties ( [[#Macknick--2011|Macknick 2011]] ; [[#Andrew--2020|Andrew 2020]] ). Independent top-down observational constraints are, therefore, particularly useful to bound total emission estimates, but are not yet capable of verifying emission levels or trends ( [[#Petrescu--2021a|Petrescu et al. 2021a]] , 2021b). Similarly, uncertainties estimates are influenced by specific modelling choices. For example, uncertainty estimates from studies on the propagation of uncertainties associated with key input parameters (activity data, emissions factors) following the IPCC Guidelines (IPCC 2006) are strongly determined by assumptions on how these parameters are correlated between sectors, countries, and regions ( [[#Janssens-Maenhout--2019|Janssens-Maenhout et al. 2019]] ; [[#Solazzo--2021|Solazzo et al. 2021]] ). Assuming (full) covariance between source categories, and therefore dependence between them, increases uncertainty estimates. Estimates allowing for some covariance as in Sollazo et al. (2021) also tend to yield higher estimates than the range of values from ensemble of dependent inventories ( [[#Saunois--2016|Saunois et al. 2016]] , 2020).

For this report, a comprehensive assessment of uncertainties is provided in the Supplementary Material (2.SM.2) to this chapter based on [[#Minx--2021|Minx et al. (2021)]] . The uncertainties reported here combine statistical analysis, comparisons of global emissions inventories and an expert judgement of the likelihood of results lying outside a defined confidence interval, rooted in an understanding gained from the relevant literature. This literature has improved considerably since AR5, with a growing number of studies that assess uncertainties based on multiple lines of evidence ( [[#Saunois--2016|Saunois et al. 2016]] , 2020; Tian et al. 2020; [[#Petrescu--2021a|Petrescu et al. 2021a]] , 2021b).

To report the uncertainties in GHG emissions estimates, a 90% confidence interval (5th–95th percentile) is adopted – that is, there is a 90% likelihood that the true value will be within the provided range if the errors have a Gaussian distribution, and no bias is assumed. This is in line with previous reporting in IPCC AR5 (Blanco et al. 2014; Ciais et al. 2014). Note that national emissions inventory submissions to the UNFCCC are requested to report uncertainty using a 95% confidence interval. The use of this broader uncertainty interval implies, however, a relatively high degree of knowledge about the uncertainty structure of the associated data, particularly regarding the distribution of uncertainty in the tails of the probability distributions. Such a high degree of knowledge is not present over all regions, emission sectors and species considered here.

Based on the assessment of relevant uncertainties above, a constant, relative, global uncertainty estimates for GHGs are applied at a 90% confidence interval that range from relatively low values for CO 2 -FFI (±8%), to intermediate values for CH 4 and F-gases (±30%), to higher values for N 2 O (±60%) and CO 2 -LULUCF (±70%). Uncertainties for aggregated total GHG emissions in terms of CO 2 -eq emissions are calculated as the square root of the squared sums of absolute uncertainties for individual gases (taking F-gases together), using GWP100 to weight emissions of non-CO 2 gases but excluding uncertainties in the metric itself.

This assessment of uncertainties is broadly in line with AR5 WGIII (Blanco et al. 2014), but revises individual uncertainty judgements in line with the more recent literature ( [[#Saunois--2016|Saunois et al. 2016]] , 2020; [[#Janssens-Maenhout--2019|Janssens-Maenhout et al. 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; Tian et al. 2020; [[#Solazzo--2021|Solazzo et al. 2021]] ) as well as the underlying synthetic analysis provided here (e.g., Figures 2.2 and 2.3 in this chapter; and [[#Minx--2021|Minx et al. 2021]] ). As such, reported changes in these estimates do not reflect changes in the underlying uncertainties, but rather a change in expert judgement based on an improved evidence base in the scientific literature. Uncertainty estimates for CO 2 -FFI and N 2 O remain unchanged compared to AR5. The change in the uncertainty estimates for CH 4 from 20% to 30% is justified by larger uncertainties reported for EDGAR emissions ( [[#Janssens-Maenhout--2019|Janssens-Maenhout et al. 2019]] ; [[#Solazzo--2021|Solazzo et al. 2021]] ) as well as the wider literature ( [[#Kirschke--2013|Kirschke et al. 2013]] ; [[#Tubiello--2015|Tubiello et al. 2015]] ; [[#Saunois--2016|Saunois et al. 2016]] , 2020). As AR6 – in contrast to AR5 – uses CO 2 -LULUCF data from global bookkeeping models, the respective uncertainty estimate is based on the reporting in the underlying literature ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ) as well as Working Group I (Canadell et al. 2021). The 70% uncertainty value is at the higher end of the range considered in AR5 (Blanco et al. 2014).

Finally, for F-gas emissions top-down atmospheric measurements from the 2018 World Meteorological Organization’s (WMO) Scientific Assessment of Ozone Depletion (Engel and Rigby 2018; [[#Montzka--2018|Montzka and Velders 2018]] ) are compared to the data used in this report ( [[#Crippa--2021|Crippa et al. 2021]] ; [[#Minx--2021|Minx et al. 2021]] ) as shown in Figure 2.3. Due to the general absence of natural F-gas fluxes, there is a sound understanding of global and regional F-gas emissions from top-down estimates of atmospheric measurements with small and well-understood measurement, lifetime and transport model uncertainties (Engel and Rigby 2018; [[#Montzka--2018|Montzka and Velders 2018]] ). However, when species are aggregated into total F-gas emissions, EDGARv6.0 emissions are around 10% lower than the [[#WMO--2018|WMO 2018]] values throughout, with larger differences for individual F-gas species, and further discrepancies when comparing to older EDGAR versions. Based on this, the overall uncertainties for aggregate F-gas emissions is judged conservatively at 30% – 10 percentage points higher than in AR5 (Blanco et al. 2014).

<div id="_idContainer012" class="Basic-Text-Frame"></div>

[[File:5b9a37d6792965b3d0d736bd32459e16 IPCC_AR6_WGIII_Figure_2_3.png]]

'''Figure 2.3''' '''|''' '''Comparison between top-down estimates and bottom-up EDGAR inventory data on GHG emissions for 1980–2016. Left panel:''' Total GWP100-weighted emissions based on IPCC AR6 (Forster et al. 2021a) of F-gases in Olivier and Peters (2020) [EDGARv5FT] (dark-red dotted line, excluding C 4 F 10 , C 5 F 12 , C 6 F 14 and C 7 F 16 ) and EDGARv6 (bright red dashed line) compared to top-down estimates based on AGAGE and NOAA data from [[#WMO--2018|WMO (2018)]] (blue lines; Engel and Rigby (2018); [[#Montzka--2018|Montzka and Velders (2018)]] ). '''Right panel:''' Top-down aggregated emissions for the three most abundant CFCs (–11, –12 and –113) and HCFCs (–22, –141b, –142b) not covered in bottom-up emissions inventories are shown in dark blue and yellow. For top-down estimates the shaded areas between two respective lines represent 1 σ uncertainties. Source: [[#Minx--2021|Minx et al. (2021)]] .

Aggregate uncertainty across all GHGs is approximately ±11% depending on the composition of gases in a particular year. AR5 applied a constant uncertainty estimates of ±10% for total GHG emissions. The upwards revision applied to the uncertainties of CO 2 -LULUCF, CH 4 and F-gas emissions therefore has a limited overall effect on the assessment of GHG emissions.

GHG emissions metrics such as GWP100 have their own uncertainties, which has been largely neglected in the literature so far. [[#Minx--2021|Minx et al. (2021)]] report the uncertainty in GWP100 metric values as ±50% for methane and other short-lived climate forcers (SLCFs), and ±40% for non-CO 2 gases with longer atmospheric lifetimes (specifically, those with lifetimes longer than 20 years). If uncertainties in GHG metrics are considered, and are assumed independent (which may lead to an underestimate) the overall uncertainty of total GHG emissions in 2019 increases from ±11% to ±13%. Metric uncertainties are not further considered in this chapter, but are referred to in Cross-Chapter Box 2 in this chapter, and [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 Chapter 2] Supplementary Material on GHG metrics (2.SM.3).

The most appropriate metric to aggregate GHG emissions depends on the objective (Cross-Chapter Box 2). One such objective can be to understand the contribution of emissions in any given year to warming, while another can be to understand the contribution of cumulative emissions over an extended time period to warming. In Figure 2.4 the modelled warming from emissions of each gas or group of gases is also shown – calculated using the reduced-complexity climate model Finite Amplitude Impulse Response (FaIR) model v1.6, which has been calibrated to match several aspects of the overall WGI assessment (Forster et al. 2021a; specifically Cross-Chapter Box 7 in [[IPCC:Wg3:Chapter:Chapter-10|Chapter 10]] therein). Additionally, its temperature response to emissions with shorter atmospheric lifetimes such as aerosols, methane or ozone has been adjusted to broadly match those presented in [[#Szopa--2021a|Szopa et al. (2021a)]] . There are some differences in actual warming compared to the GWP100 weighted emissions of each gas (Figure 2.4), in particular a greater contribution from CH 4 emissions to historical warming. This is consistent with warming from CH 4 being short-lived and hence having a more pronounced effect in the near-term during a period of rising emissions. Nonetheless, Figure 2.4 highlights that emissions weighted by GWP100 do not provide a fundamentally different information about the contribution of individual gases than modelled actual warming over the historical period, when emissions of most GHGs have been rising continuously, with CO 2 being the dominant and CH 4 being the second most important contributor to GHG-induced warming. Other metrics such as GWP* (or GWP star) ( [[#Cain--2019|Cain et al. 2019]] ) offer an even closer resemblance between cumulative CO 2 -eq emissions and temperature change. Such a metric may be more appropriate when the key objective is to track temperature change when emissions are falling, as in mitigation scenarios.

<div id="_idContainer014" class="Basic-Text-Frame"></div>

[[File:59c543168b88b39268bec2d454e47046 IPCC_AR6_WGIII_Figure_2_4.png]]

'''Figure 2.4''' '''|''' '''Contribution of different GHGs to global warming over the period 1750 to 2018.''' Top row: contributions estimated with the FaIR reduced-complexity climate model. Major GHGs and aggregates of minor gases as a timeseries in '''(a)''' and as a total warming bar chart with 90% confidence interval added in '''(b)''' . Bottom row: contribution from short-lived climate forcers as a time series in '''(c)''' and as a total warming bar chart with 90% confidence interval added in '''(d)''' . The dotted line in (c) gives the net temperature change from short-lived climate forcers other than CH 4 . F-Kyoto/Paris includes the gases covered by the Kyoto Protocol and Paris Agreement, while F-other includes the gases covered by the Montreal Protocol but excluding the HFCs. Source: [[#Minx--2021|Minx et al. (2021)]] .

<div id="cross-chapter-box-2" class="h2-container box-container"></div>

<span id="cross-chapter-box-2-ghg-emissions-metrics"></span>
=== Cross-Chapter Box 2 | GHG Emissions Metrics ===

<div id="h2-2-siblings" class="h2-siblings"></div>

'''Authors:''' Andy Reisinger (New Zealand), Alaa Al Khourdajie (United Kingdom/Syria), Kornelis Blok (the Netherlands), Harry Clark (New Zealand), Annette Cowie (Australia), Jan S. Fuglestvedt (Norway), Oliver Geden (Germany), Veronika Ginzburg (the Russian Federation), Céline Guivarch (France), Joanna I. House (United Kingdom), Jan Christoph Minx (Germany), Rachid Mrabet (Morocco), Gert-Jan Nabuurs (the Netherlands), Glen P. Peters (Norway/Australia), Keywan Riahi (Austria), Roberto Schaeffer (Brazil), Raphael Slade (United Kingdom), Anders Hammer Strømman (Norway), Detlef P. van Vuuren (the Netherlands)

Comprehensive mitigation policy relies on consideration of all anthropogenic forcing agents, which differ widely in their atmospheric lifetimes and impacts on the climate system. GHG emission metrics [[#footnote-008|6]] provide simplified information about the effects that emissions of different GHGs have on global temperature or other aspects of climate, usually expressed relative to the effect of emitting CO 2 (see emission metrics in Annex I: Glossary). This information can inform prioritisation and management of trade-offs in mitigation policies and emission targets for non-CO 2 gases relative to CO 2 , as well as for baskets of gases expressed in CO 2 -eq. This assessment builds on the evaluation of GHG emission metrics from a physical science perspective by WGI (Forster et al. 2021b). For additional details and supporting references, see [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 Chapter 2] Supplementary Material (2.SM.3) and Annex II.8.

The global warming potential (GWP) and the global temperature change potential (GTP) werethe main metrics assessed in AR5 ( [[#Myhre--2013|Myhre et al. 2013]] ; Kolstad et al. 2014). The GWP with a lifetime of 100 years (GWP100) continues to be the dominant metric used in the scientific literature on mitigation assessed by WGIII. The assessment by WGI (Forster et al. 2021) includes updated values for these metrics based on updated scientific understanding of the response of the climate system to emissions of different gases, including changing background concentrations. It also assesses new metrics published since AR5. Metric values in AR6 include climate-carbon cycle feedbacks by default; this provides an important update and clarification from AR5 which reported metric values both with and without such feedbacks.

The choice of metric, including time horizon, should reflect the policy objectives for which the metric is applied ( [[#Plattner--2009|Plattner et al. 2009]] ). Recent studies confirm earlier findings that the GWP is consistent with a cost-benefit framework (Kolstad et al. 2014), which implies weighting each emission based on the economic damages that this emission will cause over time, or conversely, the avoided damages from avoiding that emission. The GWP time horizon can be linked to the discount rate used to evaluate economic damages from each emission. For methane, GWP100 implies a social discount rate of about 3–5% depending on the assumed damage function, whereas GWP20 implies a much higher discount rate, greater than 10% ( ''medium confidence'' ) (Mallapragada and Mignone 2019; [[#Sarofim--2018|Sarofim and Giordano 2018]] ). The dynamic GTP is aligned with a cost-effectiveness framework, as it weights each emission based on its contribution to global warming in a specified future year (e.g., the expected year of peak warming for a given temperature goal). This implies a shrinking time horizon and increasing relative importance of SLCF emissions as the target year is approached (Johansson 2011; [[#Aaheim--2017|Aaheim and Mideksa 2017]] ). The GTP with a static time horizon (e.g., GTP100) is not well-matched to either a cost-benefit or a cost-effectiveness framework, as the year for which the temperature outcome is evaluated would not match the year of peak warming, nor the overall damages caused by each emission ( [[#Edwards--2014|Edwards and Trancik 2014]] ; [[#Strefler--2014|Strefler et al. 2014]] ; [[#Mallapragada--2017|Mallapragada and Mignone 2017]] ).

A number of studies since AR5 have evaluated the impact of various GHG emission metrics and time horizons on the global economic costs of limiting global average temperature change to a pre-determined level (e.g. [[#Strefler--2014|Strefler et al. 2014]] ; [[#Harmsen--2016|Harmsen et al. 2016]] ; Tanaka et al. 2021) (see 2.SM.3 for additional detail). These studies indicate that, for mitigation pathways that limit warming to 2°C (&lt;67%) above pre-industrial levels or lower, using GWP100 to inform cost-effective abatement choices between gases would achieve such long-term temperature goals at close to least global cost within a few percent ( ''high confidence'' ). Using the dynamic GTP instead of GWP100 could reduce global mitigation costs by a few percent in theory ( ''high confidence'' ), but the ability to realise those cost savings depends on the temperature limit, policy foresight and flexibility in abatement choices as the weighting of SLCF emissions increases over time ( ''medium confidence'' ) ( [[#van%20den%20Berg--2015|van den Berg et al. 2015]] ; [[#Huntingford--2015|Huntingford et al. 2015]] ). Similar benefits as for the dynamic GTP might be obtained by regularly reviewing and potentially updating the time horizon used for GWP in light of actual emissions trends compared to climate goals ( [[#Tanaka--2020|Tanaka et al. 2020]] ).

The choice of metric and time horizon can affect the distribution of costs and the timing of abatement between countries and sectors in cost-effective mitigation strategies. Sector-specific lifecycle assessments find that different emission metrics and different time horizons can lead to divergent conclusions about the effectiveness of mitigation strategies that involve reductions of one gas but an increase of another gas with a different lifetime (e.g., [[#Tanaka--2019|Tanaka et al. 2019]] ). Assessing the sensitivity of conclusions to different emission metrics and time horizons can support more robust decision-making ( [[#Levasseur--2016|Levasseur et al. 2016]] ; [[#Balcombe--2018|Balcombe et al. 2018]] ) (see 2.SM.3 for details). Sectoral and national perspectives on GHG emission metrics may differ from a global least-cost perspective, depending on other policy objectives and equity considerations, but the literature does not provide a consistent framework for assessing GHG emission metrics based on equity principles.

Literature since AR5 has emphasised that the GWP100 is not well-suited to estimating the warming effect at specific points in time from sustained SLCF emissions (e.g., [[#Allen--2016|Allen et al. 2016]] ; [[#Cain--2019|Cain et al. 2019]] ; [[#Collins--2019|Collins et al. 2019]] ). This is because the warming caused by an individual SLCF emission pulse diminishes over time and hence, unlike CO 2 , the warming from SLCF emissions that are sustained over multiple decades to centuries depends mostly on their ongoing rate of emissions rather than their cumulative emissions. Treating all gases interchangeably based on GWP100 within a stated emissions target therefore creates ambiguity about actual global temperature outcomes ( [[#Fuglestvedt--2018|Fuglestvedt et al. 2018]] ; [[#Denison--2019|Denison et al. 2019]] ). Supplementing economy-wide emission targets with information about the expected contribution from individual gases to such targets would reduce the ambiguity in global temperature outcomes.

Recently developed step/pulse metrics such as the combined global temperature change potential (CGTP) ( [[#Collins--2019|Collins et al. 2019]] ) and GWP* ( [[#Allen--2018|Allen et al. 2018]] ; [[#Cain--2019|Cain et al. 2019]] ) recognise that a sustained increase/decrease in the rate of SLCF emissions has a similar effect on global surface temperature over multiple decades as a one-off pulse emission/removal of CO 2 . These metrics use this relationship to calculate the CO 2 emissions or removals that would result in roughly the same temperature change as a sustained change in the rate of SLCF emissions (CGTP) over a given time period, or as a varying time series of CH 4 emissions (GWP*). From a mitigation perspective, these metrics indicate greater climate benefits from rapid and sustained methane reductions over the next few decades than if such reductions are weighted by GWP100, while conversely, sustained methane increases have greater adverse climate impacts ( [[#Collins--2019|Collins et al. 2019]] ; [[#Lynch--2020|Lynch et al. 2020]] ). The ability of these metrics to relate changes in emission rates of short-lived gases to cumulative CO 2 emissions makes them well-suited, in principle, to estimating the effect on the remaining carbon budget from more, or less, ambitious SLCF mitigation over multiple decades compared to a given reference scenario ( ''high confidence'' ) ( [[#Collins--2019|Collins et al. 2019]] ; Forster et al. 2021).

The potential application of GWP* in wider climate policy (e.g., to inform equitable and ambitious emission targets or to support sector-specific mitigation policies) is contested, although relevant literature is still limited ( [[#Rogelj--2019|Rogelj and Schleussner 2019]] , 2021; [[#Schleussner--2019|Schleussner et al. 2019]] ; [[#Allen--2021|Allen et al. 2021]] ; [[#Cain--2021|Cain et al. 2021]] ). Whereas GWP and GTP describe the marginal effect of each emission relative to the absence of that emission, GWP* describes the equivalent CO 2 emissions that would give the same temperature change as an emissions trajectory of the gas considered, starting at a (user-determined) reference point. The warming based on those cumulative CO 2 -equivalent emission at any point in time is relative to the warming caused by emissions of that gas before the reference point. Because of their different focus, GWP* and GWP100 can equate radically different CO 2 emissions to the same CH 4 emissions: rapidly declining CH 4 emissions have a negative CO 2 -warming-equivalent value based on GWP* (rapidly declining SLCF emissions result in declining temperature, relative to the warming caused by past SLCF emissions at a previous point in time) but a positive CO 2 -equivalent value based on GWP or GTP (each SLCF emission from any source results in increased future radiative forcing and global average temperature than without this emission, regardless of whether the rate of SLCF emissions is rising or declining). The different focus in these metrics can have important distributional consequences, depending on how they are used to inform emission targets ( [[#Lynch--2021|Lynch et al. 2021]] ; [[#Reisinger--2021|Reisinger et al. 2021]] ), but this has only begun to be explored in the scientific literature.

A key insight from WGI is that, for a given emissions scenario, different metric choices can alter the time at which net zero GHG emissions are calculated to be reached, or whether net zero GHG emissions are reached at all (2.SM.3). From a mitigation perspective, this implies that changing GHG emission metrics but retaining the same numerical CO 2 -equivalent emissions targets would result in different climate outcomes. For example, achieving a balance of global anthropogenic GHG emissions and removals, as stated in Article 4.1 of the Paris Agreement could, depending on the GHG emission metric used, result in different peak temperatures and in either stable, or slowly or rapidly declining temperature after the peak ( [[#Allen--2018|Allen et al. 2018]] ; [[#Fuglestvedt--2018|Fuglestvedt et al. 2018]] ; [[#Tanaka--2018|Tanaka and O’Neill 2018]] ; [[#Schleussner--2019|Schleussner et al. 2019]] ). A fundamental change in GHG emission metrics used to monitor achievement of existing emission targets could therefore inadvertently change their intended climate outcomes or ambition, unless existing emission targets are re-evaluated at the same time ( ''very'' ''high confidence'' ).

The WGIII contribution to AR6 reports aggregate emissions and removals using updated GWP100 values from AR6 WGI unless stated otherwise. This choice was made on both scientific grounds (the alignment of GWP100 with a cost-benefit perspective under social discount rates and its performance from a global cost-effectiveness perspective) and for procedural reasons, including continuity with past IPCC reports and alignment with decisions under the Paris Agreement Rulebook (Annex II.8). A key constraint in the choice of metric is also that the literature assessed by WGIII predominantly uses GWP100 and often does not provide sufficient detail on emissions and abatement of individual gases to allow translation into different metrics. Presenting such information routinely in mitigation studies would enable the application of more diverse GHG emission metrics in future assessments to evaluate their contribution to different policy objectives.

All metrics have limitations and uncertainties, given that they simplify the complexity of the physical climate system and its response to past and future GHG emissions. No single metric is well-suited to all applications in climate policy. For this reason, the WGIII contribution to AR6 reports emissions and mitigation options for individual gases where possible; CO 2 -equivalent emissions are reported in addition to individual gas emissions where this is judged to be policy-relevant. This approach aims to reduce the ambiguity regarding mitigation potentials for specific gases and actual climate outcomes over time arising from the use of any specific GHG emission metric.

<div id="2.2.2" class="h2-container"></div>

<span id="trends-in-the-global-ghg-emissions-trajectories-and-short-lived-climate-forcers"></span>

=== 2.2.2 Trends in the Global GHG Emissions Trajectories and Short-lived Climate Forcers ===

<div id="h2-2-siblings" class="h2-siblings"></div>

<div id="2.2.2.1" class="h3-container"></div>

<span id="anthropogenic-greenhouse-gas-emissions-trends"></span>
==== 2.2.2.1 Anthropogenic Greenhouse Gas Emissions Trends ====

<div id="h3-1-siblings" class="h3-siblings"></div>

Global GHG emissions continued to rise since AR5, but the rate of emissions growth slowed ( ''high confidence'' ). GHG emissions reached 59 ± 6.6 GtCO 2 -eq in 2019 (Table 2.1 and Figure 2.5). In 2019, CO 2 emissions from the FFI were 38 (±3.0) Gt, CO 2 from LULUCF 6.6 ± 4.6 Gt, CH 4 11 ± 3.2 GtCO 2 -eq, N 2 O 2.7 ± 1.6 GtCO 2 -eq and F-gases 1.4 ± 0.41 GtCO 2 -eq. There is ''high confidence'' that average annual GHG emissions for the last decade (2010–2019) were the highest on record in terms of aggregate CO 2 -eq emissions, but ''low confidence'' for annual emissions in 2019 as uncertainties are large considering the size and composition of observed increases in the most recent years ( [[#UNEP--2020a|UNEP 2020a]] ; [[#Minx--2021|Minx et al. 2021]] ).

'''Table 2''' '''.1 |''' '''Total anthropogenic GHG emissions (GtCO''' 2 '''-eq yr''' –1 ''') 1990–2019.''' CO 2 from fossil fuel combustion and industrial processes (FFI); CO 2 from Land Use, Land Use Change and Forestry (LULUCF); methane (CH 4 ); nitrous oxide (N 2 O); fluorinated gases (F-gases: HFCs, PFCs, SF 6 , NF 3 ). Aggregate GHG emissions trends by groups of gases reported in GtCO 2 '''-''' eq converted based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report (AR6). Uncertainties are reported for a 90% confidence interval. Source: [[#Minx--2021|Minx et al. (2021)]] .

{| class="wikitable"
|-
| rowspan="2"|
| colspan="6"| Average annual emissions (GtCO 2 -eq)

|-
| CO 2 FFI

| CO 2 LULUCF

| CH 4

| N 2 O

| Fluorinated gases

| GHG

|-
| 2019

| 38 ± 3.0

| 6.6 ± 4.6

| 11 ± 3.2

| 2.7 ± 1.6

| 1.4 ± 0.41

| 59 ± 6.6

|-
| 2010–2019

| 36 ± 2.9

| 5.7 ± 4.0

| 10 ± 3.0

| 2.6 ± 1.5

| 1.2 ± 0.35

| 56 ± 6.0

|-
| 2000–2009

| 29 ± 2.4

| 5.3 ± 3.7

| 9.0 ± 2.7

| 2.3 ± 1.4

| 0.81 ± 0.24

| 47 ± 5.3

|-
| 1990–1999

| 24 ± 1.9

| 5.0 ± 3.5

| 8.2 ± 2.5

| 2.1 ± 1.2

| 0.49 ± 0.15

| 40 ± 4.9

|-
| 1990

| 23 ± 1.8

| 5.0 ± 3.5

| 8.2 ± 2.5

| 2.0 ± 1.2

| 0.38 ± 0.11

| 38 ± 4.8

|}

<div id="_idContainer017" class="Basic-Text-Frame"></div>

[[File:0d5efdd376c1afc4183a6931dc85857c IPCC_AR6_WGIII_Figure_2_5.png]]

'''Figure 2.5''' '''|''' '''Total anthropogenic GHG emissions (GtCO''' 2 '''-eq yr''' –1 ''') 1990–2019.''' CO 2 from fossil fuel combustion and industrial processes (FFI); net CO 2 from land use, land use change and forestry (LULUCF); methane (CH 4 ); nitrous oxide (N 2 O); fluorinated gases (F-gases: HFCs, PFCs, SF 6 , NF 3 ). '''Panel (a):''' Aggregate GHG emissions trends by groups of gases reported in GtCO 2 -eq converted based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. '''Panel (b):''' Waterfall diagrams juxtaposes GHG emissions for the most recent year (2019) in CO 2 equivalent units using GWP100 values from the IPCC’s Second, Fifth, and Sixth Assessment Reports, respectively. Error bars show the associated uncertainties at a 90% confidence interval. '''Panel (c):''' individual trends in CO 2 -FFI, CO 2 -LULUCF, CH 4 , N 2 O and F-gas emissions for the period 1990–2019, normalised to 1 in 1990. Source: data from [[#Minx--2021|Minx et al. (2021)]] .

GHG emissions levels in 2019 were higher compared to 10 and 30 years earlier ( ''high confidence'' ): about 12% (6.5 GtCO 2 -eq) higher than in 2010 (53 ± 5.7 GtCO 2 -eq) (the last year of AR5 reporting) and about 54% (21 GtCO 2 -eq) higher than in 1990 (38 ± 4.8 GtCO 2 -eq) (the baseline year of the Kyoto Protocol and frequent nationally determined contribution (NDC) reference). GHG emissions growth slowed compared to the previous decade ( ''high confidence'' ): From 2010 to 2019, GHG emissions grew on average by about 1.3% per year compared to an average annual growth of 2.1% between 2000 and 2009. Nevertheless the absolute increase in average annual GHG emissions for 2010–2019 compared to 2000–2009 was 9.1 GtCO 2 -eq and, as such, the largest observed in the data since 1970 (Table 2.1) – and most likely in human history ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; [[#Gütschow--2021b|Gütschow et al. 2021b]] ). Decade-by-decade growth in average annual GHG emissions was observed across all (groups of) gas as shown in Table 2.1, but for N 2 O and CO 2 -LULUCF emissions this is much more uncertain.

Reported total annual GHG emission estimates differ between the WGIII contributions in AR5 (Blanco et al. 2014) and AR6 (this chapter) mainly due to differing global warming potentials ( ''high confidence'' ). For the year 2010, total GHG emissions were estimated at 49 ± 4.9 GtCO 2 -eq in AR5 (Blanco et al. 2014), while we report 53 ± 5.7 GtCO 2 -eq here. However, in AR5 total GHG emissions were weighted based on GWP100 values from IPCC’s Second Assessment Report. Applying those GWP values to the 2010 emissions from AR6 yields 50 GtCO 2 -eq (Forster et al. 2021a). Hence, observed differences are mainly due to the use of most recent GWP values, which have higher warming potentials for methane (29% higher for biogenic and 42% higher for fugitive methane) and 12% lower values for nitrous oxide (Cross-Chapter Box 2 in this chapter).

Emissions growth has been persistent but varied in pace across gases. The average annual emission levels of the last decade (2010–2019) were higher than in any previous decade for each group of GHGs: CO 2 , CH 4 , N 2 O, and F-gases ( ''high confidence'' ). Since 1990, CO 2 -FFI have grown by 67% (15 GtCO 2 -eq), CH 4 by 29% (2.4 GtCO 2 -eq), and N 2 O by 33% (0.65 GtCO 2 -eq), respectively (Figure 2.5). Growth in fluorinated gases (F-gas) has been by far the highest with about 254% (1.0 GtCO 2 -eq), but it occurred from low levels. In 2019, total F-gas levels were no longer negligible with a share of 2.3% of global GHG emissions. Note that the F-gases reported here do not include CFCs and HCFCs, which are groups of substances regulated under the Montreal Protocol. The aggregate CO 2 -eq emissions of HFCs, HCFCs and CFCs were each approximately equal in 2016, with a smaller contribution from PFCs, SF 6 , NF 3 and some more minor F-gases. Therefore, the GWP-weighted F-gas emissions reported here (HFCs, PFCs, SF 6 , NF 3 ), which are dominated by the HFCs, represent less than half of the overall CO 2 -eq F-gas emissions in 2016 (Figure 2.3).

The only exception to these patterns of GHG emissions growth is net anthropogenic CO 2 -LULUCF emissions, where there is no statistically significant trend due to high uncertainties in estimates (Figures 2.2 and 2.5; [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 Chapter 2] Supplementary Material). While the average estimate from the bookkeeping models report a slightly increasing trend in emissions, NGHGI and FAOSTAT estimates show a slightly decreasing trend, which diverges in recent years (Figure 2.2). Similarly, trends in CO 2 -LULUCF estimates from individual bookkeeping models differ: while two models (BLUE and OSCAR) show a sustained increase in emissions levels since the mid-1990s, emissions from the third model (Houghton and Nassikas (HN)) declined (Figure 2.2 in this chapter; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). Differences in accounting approaches and their impacts CO 2 emissions estimates from land use is covered in [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] and in the [https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2 Chapter 2] Supplementary Material (2.SM.2). Note that anthropogenic net emissions from bioenergy are covered by the CO 2 -LULUCF estimates presented here.

The CO 2 -FFI share in total CO 2 -eq emissions has plateaued at about 65% in recent years and its growth has slowed considerably since AR5 ( ''high confidence'' ). The CO 2 -FFI emissions grew at 1.1% during the 1990s and 2.5% during the 2000s. For the last decade (2010s) – not covered by AR5 – this rate dropped to 1.2%. This included a short period between 2014 and 2016 with little or no growth in CO 2 -FFI emissions, mainly due to reduced emissions from coal combustion ( [[#Jackson--2016|Jackson et al. 2016]] ; [[#Qi--2016|Qi et al. 2016]] ; [[#Peters--2017a|Peters et al. 2017a]] ; Canadell et al. 2021). Subsequently, CO 2 -FFI emissions started to rise again ( [[#Peters--2017b|Peters et al. 2017b]] ; [[#Figueres--2018|Figueres et al. 2018]] ; [[#Peters--2020|Peters et al. 2020]] ).

Starting in the spring of 2020 a major break in global emissions trends was observed due to lockdown policies implemented in response to the COVID-19 pandemic ( ''high confidence'' ) ( [[#Forster--2020|Forster et al. 2020]] ; [[#Le%20Quéré--2020|Le Quéré et al. 2020]] , 2021; Z. [[#Liu--2020b|]] [[#Liu--2020b|Liu et al. 2020b]] ; [[#Bertram--2021|Bertram et al. 2021]] ). Overall, global CO 2 -FFI emissions are estimated to have declined by 5.8% (5.1%–6.3%) in 2020, or about 2.2 (1.9–2.4) GtCO 2 in total ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; Z. [[#Liu--2020b|]] [[#Liu--2020b|Liu et al. 2020b]] ; [[#BP--2021|BP 2021]] ; [[#Crippa--2021|Crippa et al. 2021]] ; [[#IEA--2021a|IEA 2021a]] ). This exceeds any previous global emissions decline since 1970, both in relative and absolute terms (Figure 2.6). Daily emissions, estimated based on activity and power-generation data, declined substantially compared to 2019 during periods of economic lockdown, particularly in April 2020 – as shown in Figure 2.6 – but rebounded by the end of 2020 ( ''medium confidence'' ) ( [[#Le%20Quéré--2020|Le Quéré et al. 2020]] , 2021; Z. [[#Liu--2020b|]] [[#Liu--2020b|Liu et al. 2020b]] ). Impacts were differentiated by sector, with road transport and aviation particularly affected. Inventories estimate the total power sector CO 2 reduction from 2019 to 2020 at 3% ( [[#IEA--2021a|IEA 2021a]] ) and 4.5% ( [[#Crippa--2021|Crippa et al. 2021]] ). Approaches that predict near real-time estimates of the power sector reduction are more uncertain and estimates range more widely, between 1.8% ( [[#Le%20Quéré--2020|Le Quéré et al. 2020]] , 2021), 4.1% (Z. [[#Liu--2020b|]] [[#Liu--2020b|Liu et al. 2020b]] ) and 6.8% ( [[#Bertram--2021|Bertram et al. 2021]] ); the latter taking into account the over-proportional reduction of coal generation due to low gas prices and merit order effects. Due to the very recent nature of this event, it remains unclear what the exact short- and long-term impacts on future global emissions trends will be.

<div id="_idContainer019" class="Basic-Text-Frame"></div>

[[File:c9cdfa6eb4d9c0d64b7eebd78e29045d IPCC_AR6_WGIII_Figure_2_6.png]]

'''Figure 2.6''' '''|''' '''Global CO''' 2 '''emissions from fossil fuel combustion and industry (FFI) in 2020 and the impact of COVID-19. Panel (a)''' depicts CO 2 -FFI emissions over the past five decades (GtCO 2 yr –1 ). The single year declines in emissions following major economic and geopolitical events are shown, as well as the decline recorded in five different datasets for emissions in 2020 (COVID-19) compared to 2019 (no COVID-19). '''Panel (b)''' depicts the change in global daily carbon emissions (MtCO 2 per day) in 2020 compared to 2019, showing the impact of COVID-19 lockdown policies. Source: [[#Friedlingstein--2020|Friedlingstein et al. (2020)]] , [[#Le%20Quéré--2020|Le Quéré et al. (2020)]] , Carbon Monitor ( [[#Liu--2020b|]] [[#Liu--2020b|Liu et al. 2020b]] ), [[#BP--2021|BP (2021)]] , [[#Crippa--2021|Crippa et al. (2021)]] , [[#IEA--2021a|IEA (2021a)]] .

From 1850 until around 1950, anthropogenic CO 2 emissions were mainly (&gt;50%) from land use, land-use change and forestry (Figure 2.7). Over the past half-century CO 2 emissions from LULUCF have remained relatively constant around 5.1 ± 3.6 GtCO 2 but with a large spread across estimates ( [[#Le%20Quéré--2018|Le Quéré et al. 2018]] a; [[#Friedlingstein--2019|Friedlingstein et al. 2019]] , 2020). By contrast, global annual FFI-CO 2 emissions have continuously grown since 1850, and since the 1960s from a decadal average of 11 ± 0.9 GtCO 2 to 36 ± 2.9 GtCO 2 during 2010–2019 (Table 2.1).

<div id="_idContainer021" class="Basic-Text-Frame"></div>

[[File:42dd9ed63404adf8021acc48b41abfb9 IPCC_AR6_WGIII_Figure_2_7.png]]

'''Figure 2.7''' '''|''' '''Historic anthropogenic CO''' 2 '''emission and cumulative CO''' 2 '''emissions (1850–2019) as well as remaining carbon budgets for limiting warming to 1.5°C and 2°C. Panel (a)''' shows historic annual anthropogenic CO 2 emissions (GtCO 2 yr –1 ) by fuel type and process. '''Panel (b)''' shows historic cumulative anthropogenic CO 2 emissions for the periods 1850–1989, 1990–2009, and 2010–2019 as well as remaining future carbon budgets as of 1 January 2020 to limit warming to 1.5°C and 2°C at the 67th percentile of the transient climate response to cumulative CO 2 emissions. The whiskers indicate a budget uncertainty of ±220 GtCO 2 -eq for each budget and the aggregate uncertainty range at one standard deviation for historical cumulative CO 2 emissions, consistent with Working Group 1. Sources: [[#Friedlingstein--2020|Friedlingstein et al. (2020)]] and Canadell et al. (2021).

Cumulative CO 2 emissions since 1850 reached 2400 ± 240 GtCO 2 in 2019 ( ''high confidence'' ). [[#footnote-007|7]] More than half (62%) of total emissions from 1850 to 2019 occurred since 1970 (1500 ± 140 GtCO 2 ), about 42% since 1990 (1000 ± 90 GtCO 2 ) and about 17% since 2010 (410 ± 30 GtCO 2 ) ( [[#Friedlingstein--2019|Friedlingstein et al. 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; Canadell et al. 2021) (Figure 2.7). Emissions in the last decade are about the same size as the remaining carbon budget of 400 ± 220 (500, 650) GtCO 2 for limiting global warming to 1.5°C and between one-third and half the 1150 ± 220 (1350, 1700) GtCO 2 for limiting global warming below 2°C with a 67% (50%, 33%) probability, respectively ( ''medium confidence'' ) (Canadell et al. 2021). At current (2019) levels of emissions, it would only take 8 (2–15) and 25 (18–35) years to emit the equivalent amount of CO 2 for a 67th percentile 1.5°C and 2°C remaining carbon budget, respectively. Related discussions of carbon budgets, short-term ambition in the context of Nationally Determined Contributions (NDCs), pathways to limiting warming to well below 2°C and carbon dioxide removals are mainly discussed in Chapters 3, 4, and 12, but also [[#2.7|Section 2.7]] of this chapter.

Even when taking uncertainties into account, historical emissions between 1850 and 2019 constitute a large share of total carbon budgets from 2020 onwards for limiting warming to 1.5°C with a 50% probability as well as for limiting warming to 2°C with a 67% probability. Based on central estimates only, historical cumulative net CO 2 emissions between 1850–2019 amount to about four fifths of the total carbon budget for a 50% probability of limiting global warming to 1.5°C (central estimate about 2900 GtCO 2 ), and to about two thirds of the total carbon budget for a 67% probability to limit global warming to 2°C (central estimate about 3550 GtCO 2 ). The carbon budget is the maximum amount of cumulative net global anthropogenic CO 2 emissions that would result in limiting global warming to a given level with a given likelihood, taking into account the effect of other anthropogenic climate forcers. This is referred to as the total carbon budget when expressed starting from the pre-industrial period, and as the remaining carbon budget when expressed from a recent specified date. The total carbon budgets reported here are the sum of historical emissions from 1850 to 2019 and the remaining carbon budgets from 2020 onwards, which extend until global net zero CO 2 emissions are reached. Uncertainties for total carbon budgets have not been assessed and could affect the specific calculated fractions (IPCC 2021 [Working Group 1 SPM], Canadell et al., 2021 [Working Group 1 Ch5]).

Comparisons between historic GHG emissions and baseline projections provide increased evidence that global emissions are not tracking high-end scenarios ( [[#Hausfather--2020|Hausfather and Peters 2020]] ), and rather followed ‘middle-of-the-road’ scenario narratives in the earlier series, and by combinations of ‘global-sustainability’ and ‘middle-of-the-road’ narratives in the most recent series (IPCC Special Report on Emissions Scenarios (SRES) and Shared Socioeconomic Pathways (SSP)-baselines) ( [[#Pedersen--2020|Pedersen et al. 2020]] ; Strandsbjerg Tristan [[#Pedersen--2021|Pedersen et al. 2021]] ). As countries increasingly implement climate policies and technology costs continue to evolve, it is expected that emissions will continually shift away from scenarios that assume no climate policy but remain insufficient to limit warming to below 2°C ( [[#Vrontisi--2018|Vrontisi et al. 2018]] ; [[#Hausfather--2020|Hausfather and Peters 2020]] ; [[#Roelfsema--2020|Roelfsema et al. 2020]] ; [[#UNEP--2020b|UNEP 2020b]] ).

The literature since AR5 suggests that compared to historical trends baseline scenarios might be biased towards higher levels of fossil fuel use compared to what is observed historically (Cross-Chapter Box 1 in Chapter 1; [[#Ritchie--2017|Ritchie and Dowlatabadi 2017]] , 2018; [[#Ritchie--2019|Ritchie 2019]] ; [[#Creutzig--2021|Creutzig et al. 2021]] ;). [[#Ritchie--2017|Ritchie and Dowlatabadi (2017)]] show that per-capita primary energy consumption in baseline scenarios tends to increase at rates faster than those observed in the long-term historical evidence – particularly in terms of coal use. For example, SSP5 envisions a six-fold increase in per capita coal use by 2100 – against flat long-term historical observations – while the most optimistic baseline scenario SSP1-Sustainability is associated with coal consumption that is broadly in line with historical long-term trends ( [[#Ritchie--2017|Ritchie and Dowlatabadi 2017]] ). In contrast, models have struggled to reproduce historical upscaling of wind and solar and other granular energy technologies ( [[#Wilson--2013|Wilson et al. 2013]] ; [[#van%20Sluisveld--2015|van Sluisveld et al. 2015]] ; [[#Creutzig--2017|Creutzig et al. 2017]] ; [[#Shiraki--2020|Shiraki and Sugiyama 2020]] ; [[#Sweerts--2020|Sweerts et al. 2020]] ; [[#Wilson--2020b|Wilson et al. 2020b]] ).

<div id="2.2.2.2" class="h3-container"></div>

<span id="other-short-lived-climate-forcers-slcfs"></span>
==== 2.2.2.2 Other Short-lived Climate Forcers (SLCFs) ====

<div id="h3-2-siblings" class="h3-siblings"></div>

There are other emissions with shorter atmospheric lifetimes that contribute to climate changes. Some of them (aerosols, sulphur emissions or organic carbon) reduce forcing, while others – such as black carbon, carbon monoxide or non-methane volatile organic compounds (NMVOC) – contribute to warming (Figure 2.4) as assessed in WGI (Forster et al. 2021c; [[#Szopa--2021a|Szopa et al. 2021a]] ). Many of these other SLCFs are co-emitted during combustion processes in power plants, cars, trucks, airplanes, but also during wildfires and household activities such as traditional cooking with open biomass burning. As these co-emissions have implications for net warming, they are also considered in long-term emission reduction scenarios as covered in the literature ( [[#Harmsen--2020|Harmsen et al. 2020]] ; [[#Rauner--2020b|Rauner et al. 2020b]] ; [[#Smith--2020|Smith et al. 2020]] ; [[#Vandyck--2020|Vandyck et al. 2020]] ) as well as [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] of this report. These air pollutants are also detrimental to human health (e.g., [[#Lelieveld--2015|Lelieveld et al. 2015]] , 2018; [[#Vohra--2021|Vohra et al. 2021]] ). For example, [[#Lelieveld--2015|Lelieveld et al. (2015)]] estimate a total of 3.3 (1.6–4.8) million premature deaths in 2010 from outdoor air pollution. Reducing air pollutants in the context of climate policies therefore leads to substantial co-benefits of mitigation efforts (Von Stechow et al. 2015; [[#Rao--2017|Rao et al. 2017]] ; [[#Lelieveld--2019|Lelieveld et al. 2019]] ; [[#Rauner--2020a|Rauner et al. 2020a]] ). Here we briefly outline the major trends in emissions of SLCFs.

Conventional air pollutants that are subject to significant emission controls in many countries include sulphur dioxide (SO 2 ), nitrogen oxides (NO x ), black carbon (BC) and carbon monoxide (CO). From 2015 to 2019, global SO 2 and NOx emissions declined, mainly due to reductions in energy systems (Figure 2.8). Reductions in BC and CO emissions appear to have occurred over the same period, but trends are less certain due to the large contribution of emissions from poorly quantified traditional biofuel use. Emissions of CH 4 , OC and NMVOC have remained relatively stable in the past five years. OC and NMVOC may have plateaued, although there is additional uncertainty due to sources of NMVOCs that may be missing in current inventories ( [[#McDonald--2018|McDonald et al. 2018]] ).

<div id="_idContainer023" class="Basic-Text-Frame"></div>

[[File:2ea1f275249202b7225d866dd3a4d0ce IPCC_AR6_WGIII_Figure_2_8.png]]

'''Figure 2.8''' '''|''' '''Air pollution emissionsby major sectors from CEDS (1970–2019) and EDGAR (1970–2015) inventories.''' Source: Crippa et al. (2019a, 2018); [[#O’Rourke--2020|O’Rourke et al. (2020)]] ; [[#McDuffie--2020|McDuffie et al. (2020)]] .

<div id="2.2.3" class="h2-container"></div>

<span id="regional-ghg-emissions-trends"></span>
=== 2.2.3 Regional GHG Emissions Trends ===

<div id="h2-3-siblings" class="h2-siblings"></div>

Regional contributions to global GHG emissions have shifted since the beginning of the international climate negotiations in the 1990s ( ''high confidence'' ). As shown in Figure 2.9, developed countries (North America, Europe, and Australia, Japan, New Zealand) as a group have not managed to reduce GHG emissions substantially, with fairly stable levels at about 15 GtCO 2 -eq yr –1 between 1990 and 2010, while countries in Asia and Pacific (Eastern Asia, Southern Asia, and South-East Asia and Pacific) have rapidly increased their share of global GHG emissions – particularly since the 2000s ( [[#Jackson--2019|Jackson et al. 2019]] ; [[#Peters--2020|Peters et al. 2020]] ; UNEP 2020c; [[#Crippa--2021|Crippa et al. 2021]] ; [[#IEA--2021b|IEA 2021b]] ).

<div id="_idContainer025" class="Basic-Text-Frame"></div>

[[File:2ffc49cdd1dbf8ff27a3f0d1a1e9289c IPCC_AR6_WGIII_Figure_2_9.png]]

'''Figure 2.9''' ''': Change in regional GHGs from multiple perspectives and their underlying drivers.''' '''Panel (a):''' Regional GHG emissions trends (in GtCO 2 -eq yr –1 ) for the time period 1990–2019. GHG emissions from international aviation and shipping are not assigned to individual countries and shown separately. '''Panels (b) and (c):''' Changes in GHG emissions for the 20 largest emitters (as of 2019) for the post-AR5 reporting period 2010–2019 in relative (% annual change) and absolute terms (GtCO 2 -eq). '''Panels (d) and (e):''' GHG emissions per capita and per GDP in 2019 for the 20 largest emitters (as of 2019). GDP estimated using constant international purchasing power parity (USD2017). Emissions are converted into CO 2 -equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report (Forster et al. 2021a). The black dots represent the emissions data from UNFCCC-CRFs (2021) that were accessed through [[#Gütschow--2021a|Gütschow et al. (2021a)]] . Net LULUCF CO 2 emissions are included in panel (a), based on the average of three bookkeeping models ( [[#2.2|Section 2.2]] ), but are excluded in panels (b–e) due to a lack of country resolution.

Most global GHG emission growth occurred in Asia and Pacific, which accounted for 77% of the net 21 GtCO 2 -eq increase in GHG emissions since 1990, and 83% of the net 6.5 GtCO 2 -eq increase since 2010. [[#footnote-006|8]] Africa contributed 11% of GHG emissions growth since 1990 (2.3 GtCO 2 -eq) and 10% (0.7 GtCO 2 -eq) since 2010. The Middle East contributed 10% of GHG emissions growth since 1990 (2.1 GtCO 2 -eq) and also 10% (0.7 GtCO 2 -eq) since 2010. Latin America and the Caribbean contributed 11% of GHG emissions growth since 1990 (2.2 GtCO 2 -eq), and 5% (0.3 GtCO 2 -eq) since 2010. Two regions, Developed Countries, and Eastern Europe and West Central Asia, reduced emissions overall since 1990, by –1.6 GtCO 2 -eq and –0.8 GtCO 2 -eq, respectively. However, emissions in the latter started to grow again since 2010, contributing to 5% of the global GHG emissions change (0.3 GtCO 2 -eq).

Average annual GHG emission growth across all regions slowed between 2010 and 2019 compared to 1990–2010, with the exception of Eastern Europe and West Central Asia. Global emissions changes tend to be driven by a limited number of countries, principally the G20 Group ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; UNEP 2020c; [[#Xia--2021|Xia et al. 2021]] ). For instance, the slowing of global GHG emissions between 2010 and 2019, compared to the previous decade, was primarily triggered by substantial reductions in GHG emissions growth in China. Ten countries jointly contributed about 75% of the net 6.5 GtCO 2 -eq yr –1 increase in GHG emissions during 2010–2019, of which two countries contributed more than 50% (Figure 2.9) (see also [[#Minx--2021|Minx et al., 2021]] ; [[#Crippa--2021|Crippa et al., 2021]] ).

GHG and CO 2 -FFI levels diverge starkly between countries and regions ( ''high confidence'' ) ( [[#Jackson--2019|Jackson et al. 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ; UNEP 2020c; [[#Crippa--2021|Crippa et al. 2021]] ). Developed Countries sustained high levels of per capita CO 2 -FFI emissions at 9.5 tCO 2 per capita in 2019 (but with a wide range of 1.9–16 tCO 2 per capita). This is more than double that of three developing regions: 4.4 (0.3–12.8) tCO 2 per capita in Asia and Pacific; 1.2 (0.03–8.5) tCO 2 per capita in Africa; and 2.7 (0.3–24) tCO 2 per capita in Latin America. [[#footnote-005|9]] Per capita CO 2 -FFI emissions were 9.9 (0.89–15) tCO 2 per capita in Eastern Europe and West Central Asia, and 8.6 (0.36–38) tCO 2 per capita in the Middle East. CO 2 -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 2010–2019 and by 9.6% between 1990–2010.

Least-Developed Countries and Small Island Developing States contributed only a negligible proportion of historic GHG emissions growth and have the lowest per capita emissions. As of 2019 Least Developed Countries contribute 3.3% of global GHG emissions, excluding LULUCF CO 2 , despite making up 13% of the global population. Small Island Developing States contributed 0.6% of global GHG emissions in 2019, excluding LULUCF CO 2 , with 0.9% of the global population. Since the start of the industrial revolution in 1850 up until 2019, Least Developed Countries contributed 0.4% of total cumulative CO 2 emissions, while Small Island Developing States contributed 0.5% (Figure 2.10). Conversely, Developed Countries have the highest share of historic cumulative emissions ( [[#Rocha--2015|Rocha et al. 2015]] ; [[#Gütschow--2016|Gütschow et al. 2016]] ; [[#Matthews--2016|Matthews 2016]] ), contributing approximately 57% (Figure 2.10), followed by Asia and Pacific (21%), Eastern Europe and West Central Asia (9%), Latin America and the Caribbean (4%), the Middle East (3%), and Africa (3%). Developed Countries still have the highest share of historic cumulative emissions (45%) when CO 2 -LULUCF emissions are included, which typically account for a higher proportion of emissions in developing regions (Figure 2.10).

<div id="_idContainer028" class="Basic-Text-Frame"></div>

[[File:fe73fdcc75d64e159b1369019ce01098 IPCC_AR6_WGIII_Figure_2_10.png]]

'''Figure 2.10''' '''|''' '''Different perspectives on historic emissions andequity. Panel (a)''' shows the regional proportion (%) of total global population or emissions in 2018 or 2019, for five categories: population (persons); consumption-based CO 2 -FFI emissions (GtCO 2 ); production-based CO 2 -FFI emissions (GtCO 2 ); production-based GHG emissions excluding CO 2 -LULUCF (GtCO 2 -eq); and production-based GHG emissions including CO 2 -LULUCF (GtCO 2 -eq). '''Panel (b)''' shows the regional proportion (%) of total cumulative production-based CO 2 emissions from 1850 to 2019, including and excluding CO 2 -LULUCF (GtCO 2 ). In the lower panels, the proportion of each population or emissions category attributable to Least-Developed Countries and Small Island Developing States (SIDS) are shown, where available (CO 2 -LULUCF data is not available for these regions). GHG emissions are converted into CO 2 -equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report (Forster et al. 2021a). Source: data from Friedglinstein et al. (2020).

<div id="_idContainer028" class="Basic-Text-Frame"></div>

[[File:11aa52cbb4d47caec7d17f60b4195008 IPCC_AR6_WGIII_Figure_2_11.png]]

'''Figure 2.11''' '''|''' '''Recent average annual GHG emissions changes of countries (left panel) versus rates of reduction in 1.5°C and 2°C mitigation scenarios.''' Scenario data is taken from [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] of this report with the scenario categories defined and summarised in Table 3.2 in Chapter 3. Emissions are converted into CO 2 -equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report (Forster et al. 2021a). Circles indicate countries (left panel) or individual scenarios (right panel), the former scaled by total emissions in 2019. Horizontal lines indicate the region average emissions change (left panel), or scenario category average emissions change (right panel). Source: data from [[#Minx--2021|Minx et al. (2021)]] .

A growing number of countries have reduced CO 2 and GHG emissions for longer than 10 years ( ''high confidence'' ) ( [[#Le%20Quéré--2019|Le Quéré et al. 2019]] ; [[#Burck--2021|Burck et al. 2021]] ; [[#Lamb--2021a|Lamb et al. 2021a]] ; [[#Wu--2021|Wu et al. 2021]] ). Data up to 2018 indicates that about 24 countries have reduced territorial CO 2 and GHG emissions (excluding LULUCF CO 2 ), as well as consumption-based CO 2 emissions, for at least 10 years ( [[#Lamb--2021a|Lamb et al. 2021a]] ). Uncertainties in emissions levels and changes over time prevents a precise assessment of reductions in some cases. Of these 24 countries, 12 peaked emissions in the 2000s; six have sustained longer reductions since the 1970s; and six are former members of the Eastern Bloc, where emissions dropped rapidly in the 1990s and continued declining at a slower pace thereafter. Country emissions reductions have been driven by both climate and non-climate policies and factors, including structural changes. To date, most territorial emissions reductions were realised in the electricity and heat sector, followed by industry and buildings, while in many cases transport emissions have increased since countries reached their overall emissions peak ( [[#Climate%20Transparency--2021|Climate Transparency 2021]] ; [[#Lamb--2021a|Lamb et al. 2021a]] ). One estimate of the total reduction in annual GHG emissions – from peak years to 2018 – sums to 3.2 GtCO 2 -eq across all decarbonising countries ( [[#Lamb--2021a|Lamb et al. 2021a]] ). These reductions have therefore been far outweighed by recent emissions growth. However, climate policy related reductions may be even larger when compared against a counterfactual case of emissions growth across different sectors ( [[#Eskander--2020|Eskander and Fankhauser 2020]] ) (Cross-Chapter Box 1 in Chapter 1; [[#2.8|Section 2.8]] ).

The recent (2010–2019) emissions changes of some countries are in line with pathways that limit warming to below 2°C (&lt;67%) '''(''' e.g., –4% average annual reductions) (Figure 2.10). Overall, there are first country cases emerging that highlight the feasibility of sustained emission reductions outside of periods of economic disruption ( [[#Lamb--2021a|Lamb et al. 2021a]] ). However, such pathways will need to be taken by many more countries to keep the goals of the Paris Agreement in reach ( [[#Höhne--2020|Höhne et al. 2020]] ; [[#Roelfsema--2020|Roelfsema et al. 2020]] ; [[#Kriegler--2018a|Kriegler et al. 2018a]] ; [[#den%20Elzen--2019|den Elzen et al. 2019]] ) as analysed by [[IPCC:Wg3:Chapter:Chapter-4|Chapter 4]] of this report. Moreover, observed reductions are not yet consistent and long-term, nor achieved across all sectors, nor fully aligned with country NDC targets ( [[#Le%20Quéré--2019|Le Quéré et al. 2019]] ; [[#Lamb--2021a|Lamb et al. 2021a]] ; [[#den%20Elzen--2019|den Elzen et al. 2019]] ; [[#Burck--2021|Burck et al. 2021]] ; [[#Climate%20Transparency--2021|Climate Transparency 2021]] ).

<div id="2.2.4" class="h2-container"></div>

<span id="sectoral-ghg-emissions-trends"></span>
=== 2.2.4 Sectoral GHG Emissions Trends ===

<div id="h2-4-siblings" class="h2-siblings"></div>

In 2019, 34% (20 GtCO 2 -eq) of the 59 GtCO 2 -eq GHG emissions came from the energy sector, 24% (14 GtCO 2 -eq) from industry, 22% (13 GtCO 2 -eq) from AFOLU, 15% (8.7 GtCO 2 -eq) from transport and 6% (3.3 GtCO 2 -eq) from buildings (Figure 2.12). The relative size of each sector depends on the exact definition of sector boundaries ( [[#de%20la%20Rue%20du%20Can--2015|de la Rue du Can et al. 2015]] ; [[#Lamb--2021b|Lamb et al. 2021b]] ). The largest individual subsector contributing to global GHG emissions in 2019 was electricity and heat generation at 14 GtCO 2 -eq. This subsector can be reallocated to consuming sectors as indirect (scope 2) emissions to emphasise the role of final energy demand and demand-side solutions in climate change mitigation ( [[#Creutzig--2018|Creutzig et al. 2018]] ) (Chapter 5). This increases the emission share of the industry sector to 34% and of the buildings sector to 16%.

<div id="_idContainer032" class="Basic-Text-Frame"></div>

[[File:2109a3504f43bcce8e5a122c7e73983a IPCC_AR6_WGIII_Figure_2_12.png]]

'''Figure 2.12''' '''|''' '''Total anthropogenic direct and indirect GHG emissions for the year 2019 (in GtCO''' 2 '''-eq) by sector and subsector.''' Direct emissions estimates assign emissions to the sector in which they arise (scope 1 reporting). Indirect emissions – as used here – refer to the reallocation of emissions from electricity and heat to the sector of final use (scope 2 reporting). Note that cement refers to process emissions only, as a lack of data prevents the full reallocation of indirect emissions to this sector. More comprehensive conceptualisations of indirect emissions including all products and services (scope 3 reporting) are discussed in [[#2.3|Section 2.3]] of this chapter. Emissions are converted into CO 2 -equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. Percentages may not add up to 100 across categories due to rounding at the second significant digit. Source: based on [[#Lamb--2021b|Lamb et al. (2021b)]] ; data: [[#Minx--2021|Minx et al. (2021)]] .

Average annual GHG emissions growth has been fastest in the transport sector with about 1.8% for the most recent period 2010–2019, followed by direct emissions in the industry sector (1.4%) and the energy sector (1%) (Figure 2.13). This is different to growth patterns observed in the previous decade as reported in AR5 (IPCC 2014a; Blanco et al. 2014). Between 2000 and 2009 fastest GHG emissions growth was observed for industry with 3.4% followed by the energy sector with 2.3%. GHG emissions growth in the transport sector has been stable across both periods at about 1.8%, while direct building emissions growth averaged below 1% during 2010–2019. Ranking of high-emitting subsectors by direct emissions highlights the importance of CO 2 emissions from LULUCF (6.6 GtCO 2 -eq; but with low confidence in magnitude and trend), road transport (6.1 GtCO 2 -eq), metals (3.1 GtCO 2 -eq), and other industry (4.4 GtCO 2 -eq). Overall, some of the fastest growing sources of subsector emissions from 2010 to 2019 have been international aviation (+3.4%), [[#footnote-004|10]] domestic aviation (+3.3%), inland shipping (+2.9%), metals (+2.3%), international shipping (+1.7%), and road transport (+1.7%).

<div id="_idContainer034" class="Basic-Text-Frame"></div>

[[File:e2d9b794578cb7471258d6ae5ba5fe86 IPCC_AR6_WGIII_Figure_2_13.png]]

'''Figure 2.13''' '''|''' '''Total annual anthropogenic GHG emissions by major economic sector and their underlying trends by region. Panel (a):''' Trends in total annual anthropogenic GHG emissions (in GtCO 2 -eq yr –1 ) by major economic sector. '''Panel (b):''' Trends in total annual anthropogenic GHG emissions (in GtCO 2 -eq yr –1 ) by major economic sector and region. Panels c and d: Largest subsectoral changes in GHG emissions for the reporting period 2010–2019 in relative (% annual change) and absolute terms (GtCO 2 -eq yr –1 ). Emissions are converted into CO 2 -equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. Source: based on [[#Lamb--2021b|Lamb et al. (2021b)]] ; Data: [[#Crippa--2021|Crippa et al. (2021)]] ; [[#Minx--2021|Minx et al. (2021)]] .

<div id="2.3" class="h1-container"></div>

<span id="past-and-present-trends-of-consumption-based-co-2-emissions-cbes-and-emissions-embodied-in-trade"></span>