Greenhouse-gas emission targets for limiting global warming to 2 °C

Abstract. To track progress towards keeping global warming well below 2 ∘C or even 1.5 ∘C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO2 emissions, CH4 emissions, N2O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO2 eq. consisting of CO2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO2, CO2-LULUCF 5.7 ± 4.0 GtCO2, CH4 10 ± 3.1 GtCO2 eq., N2O 2.6 ± 1.6 GtCO2 eq., and F-gases 1.3 ± 0.40 GtCO2 eq. Initial estimates suggest further growth of 1.3 GtCO2 eq. in GHG emissions to reach 59 ± 6.6 GtCO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr−1) compared to 2000–2009 (2.4 % yr−1), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.

Contributions of natural systems and human activity to greenhouse gas emissions

Plastics and climate change—Breaking carbon lock-ins through three mitigation pathways

The refining industry is the third-largest source of global greenhouse gas (GHG) emissions from stationary sources, so it is at the forefront of the energy transition and net zero pathways. The dynamics of contributors in this sector such as crucial countries, leading enterprises, and key emission processes are vital to identifying key GHG emitters and supporting targeted emission reduction, yet they are still poorly understood. Here, we established a global sub-refinery GHG emission dataset in a long time series based on life cycle method. Globally, cumulative GHG emissions from refineries reached approximately 34.1 gigatons (Gt) in the period 2000-2021 with an average annual increasing rate of 0.7%, dominated by the United States, EU27&UK, and China. In 2021, the top 20 countries with the largest GHG emissions of oil refining accounted for 83.9% of global emissions from refineries, compared with 79.5% in 2000. Moreover, over the past two decades, 53.9-57.0% of total GHG emissions came from the top 20 oil refining enterprises with the largest GHG emissions in 12 of these 20 countries. Retiring or installing mitigation technologies in the top 20% of refineries with the largest GHG emissions and refineries with GHG emissions of more than 0.1 Gt will reduce the level of GHG emissions by 38.0%-100.0% in these enterprises. Specifically, low-carbon technologies installed on furnaces and boilers as well as steam methane reforming will enable substantial GHG mitigation of more than 54.0% at the refining unit level. Therefore, our results suggest that policies targeting a relatively small number of super-emission contributors could significantly reduce GHG emissions from global oil refining.

Permafrost regions are an important source of greenhouse gases. However, the effects of different permafrost wetland types on greenhouse gas emissions and the driving factors are still unclear in the permafrost region. Here, we selected three typical permafrost wetlands from the Daxing'an Mountains to investigate the effects of permafrost wetland types on greenhouse gas emissions. The cumulative N2O, CO2, and CH4 emissions were 84–122, 657,942–1,446,121, and 173–16,924 kg km−2, respectively. The linear mixed effects model indicated that N2O emissions were significantly affected by the NO3−‐N content, whereas CO2 emissions were mainly driven by soil temperature, water table level, and NO3−‐N content. CH4 emissions were affected by soil temperature and water table level. Permafrost wetland types significantly affected the average and cumulative N2O, CO2, and CH4 emissions. The cumulative N2O emissions were highest in the Larix gmelinii ‐ Carex appendiculata (LC) wetland and lowest in the Betula fruticosa Pall. (B) wetland, driven by NO3−‐N content. The cumulative CO2 emissions were highest in the B wetland and lowest in the L. gmelinii ‐ Ledum palustre var. dilatatum (LL) wetland. The cumulative CH4 emissions from B wetland were significantly higher than those from LL and LC wetlands. The differences in cumulative CO2 and CH4 emissions were driven by the water table level. Our findings indicate that NO3−‐N content affect the spatial‐temporal variation of N2O emissions, whereas water table level influence the spatial‐temporal variation of CO2 and CH4 emissions in the permafrost region of the Daxing'an Mountains.

China is one of the largest contributors to global greenhouse gas (GHG) emissions, and the livestock sector is a major source of non-CO2 GHG emissions. Mitigation of GHG emissions from the livestock sector is beneficial to the sustainable development of the livestock sector in China. This study investigated the provincial level of GHG emissions from the livestock sector between 2000 and 2020 in China, to determine the driving factors affecting the provincial-level GHG emissions from the livestock sector, based on the logarithmic mean Divisia index (LMDI) model, which took into account of technological progress, livestock structure, economic factor, and agricultural population. Moreover, a gray model GM (1, 1) was used to predict livestock GHG emissions in each province until 2030 in China. The results showed that the GHG of Chinese livestock sector was decreased from 195.1 million tons (MT) CO2e in 2000 to 157.2 MT CO2e in 2020. Henan, Shandong, and Hebei provinces were the main contributors to the reduction in Chinese livestock GHG emissions, with their livestock GHG emissions reduced by 60.1%, 53.5% and 45.5%, respectively, in 2020 as compared to 2000. The reduction in GHG emissions from the Chinese livestock sector can be attributed to two main factors: technological progress and the shrinking of the agricultural laborers. In contrast, the agricultural economic development model with high input and high emissions showed a negative impact on GHG emission reduction in China’s livestock sector. Furthermore, the different livestock structure in each province led to different GHG reduction effects on the livestock sector. Under the gray model GM (1,1), the GHG emissions of the livestock sector will be reduced by 33.7% in 2030 as compared with 2020 in China, and the efficiency factor will account for 76.6% of the positive effect of GHG reduction in 2030. The eastern coastal region will be the main contributor to the reduction of GHG emissions from the Chinese livestock sector in 2030. Moreover, recommendations (such as upgrading livestock management methods and promoting carbon emission mitigation industries) should be proposed for the environmentally sustainable development of the livestock sector in the future.

Phasing out the blast furnace to meet global climate targets

Adding Corbicula fluminea altered the effect of plant species diversity on greenhouse gas emissions and nitrogen removal from constructed wetlands in the low-temperature season

Changes in land use management practices may have multiple effects on microclimate and soil properties that affect soil greenhouse gas (GHG) emissions. Soil surface GHG emissions need to be better quantified in order to assess the total environmental costs of current and possible alternative land uses in the Missouri River Floodplain (MRF). The objective of this study was to evaluate soil GHG emissions (CO2, CH4, N2O) in MRF soils under long-term agroforestry (AF), row-crop agriculture (AG) and riparian forest (FOR) systems in response to differences in soil water content, land use, and N fertilizer inputs. Intact soil cores were obtained from all three land use systems and incubated under constant temperature conditions for a period of 94 days using randomized complete block design with three replications. Cores were subjected to three different water regimes: flooded (FLD), optimal for CO2 efflux (OPT), and fluctuating. Additional N fertilizer treatments for the AG and AF land uses were included during the incubation and designated as AG-N and AF-N, respectively. Soil CO2 and N2O emissions were affected by the land use systems and soil moisture regimes. The AF land use resulted in significantly lower cumulative soil CO2 and N2O emissions than FOR soils under the OPT water regime. Nitrogen application to AG and AF did not increase cumulative soil CO2 emissions. FLD resulted in the highest soil N2O and CH4 emissions, but did not cause any increases in soil cumulative CO2 emissions compared to OPT water regime conditions. Cumulative soil CO2 and N2O emissions were positively correlated with soil pH. Soil cumulative soil CH4 emissions were only affected by water regimes and strongly correlated with soil redox potential.

Global production of building materials is a primary contributor to greenhouse gas (GHG) emissions, but the production of these materials is necessary for modern infrastructure and society. Understanding the GHG emissions from building materials production in the context of their function is critical to decarbonizing this important sector. In this work, we present estimates of global production, approximate ranges of GHG emissions, and ranges of material properties of 12 critical building material classes to provide a unified dataset across material types. This dataset drew from industry analyses of production and emissions, ranges of emission factors within a material type, and broad reporting of thermal and mechanical properties to compare both within and between material types. Globally, in 2019, we estimate 42.8 Gt of these 12 materials were produced, with 38.6 Gt used in the building and construction industry. As a result of this production, 9.3 Gt of CO2 was emitted, or 25% of global fossil GHG emissions, with 5.8 Gt CO2 (16% of global GHG emissions) due to materials used in construction applications. Both construction material production and emissions are primarily driven by structural materials, such as concrete and steel. Material selection can play a key role in reducing emissions in the context of the function, with variation in emissions of structural materials per unit strength between 0.001–0.1 kg CO2/kg/MPa and in insulation materials per R-value/thickness of 0.018–0.14 kg CO2/kg/(K⋅m2W−1))). The developed dataset can play a key role in supporting decision-making in materials by providing a unified source for examining emissions, material properties, and quantity of material produced.

Anthropogenic activities have altered approximately two-thirds of the Earth's land surface. Urbanization, industrialization, agricultural expansion, and deforestation are increasingly impacting the terrestrial landscapes, leading to shifts of areas in artificial surface (i.e., humanmade), cropland, pasture, forest, and barren land. Land use patterns and associated greenhouse gas (GHG) emissions play a critical role in global climate change. Here we synthesized 29 years of global historical data and demonstrated how land use impacts global GHG emissions using structural equation modeling. We then obtained predictive estimates of future global GHG emissions using a deep learning model. Our results show that, from 1992 to 2020, the global terrestrial areas covered by artificial surface and cropland have expanded by 133% and 6% because of population growth and socioeconomic development, resulting in 4.0% and 3.8% of declines in pasture and forest areas, respectively. Land use was significantly associated with GHG emissions (p < 0.05). Artificial surface dominates global GHG emissions, followed by cropland, pasture, and barren land. The increase in artificial surfaces has driven up global GHG emissions through the increase in energy consumption. Conversely, improved agricultural management practices have contributed to mitigating agricultural GHG emissions. Forest, on the other hand, serves as a sink of GHG. In total, global GHG emissions increased from 31 to 46 GtCO2eq from 1992 to 2020. Looking ahead, if current trends in global land use continue at the same rates, our model projects that global GHG emissions will reach 76 ± 8 GtCO2eq in 2050. In contrast, reducing the rates of land use change by half could limit global GHG emissions to 60 ± 3 GtCO2eq in 2050. Monitoring and analyzing these projections allow a better understanding of the potential impacts of various land use scenarios on global climate and planning for a sustainable future.

Intended nationally determined contributions (INDCs) are a new strategy for mitigating climate change. Many international organizations and scholars have assessed the possibility of holding the increase in global average temperature to well below 2°C based on INDCs. Although the conclusions of these assessments are consistent, there are still large differences among the assessment results. For example, the global greenhouse gas emissions in 2030 estimated by INDCs are between 47.1–66.5 GtCO2 eq, and the temperature increase at the end of the 21st century estimated by INDCs is between 2.4–4.0°C; the inconsistency represented by these ranges is not conducive to an accurate assessment of the contributions of the current INDCs to global warming mitigation or to the further development of emissions reduction programs. By summarizing the existing studies, we found that the main reasons for the differences in estimates of global greenhouse gas emissions in 2030 made using INDCs are as follows: (1) The studies interpreted INDCs differently, which is attributable to three reasons: The studies (a) made different assumptions for the unquantifiable INDCs; (b) ignored or used different methods to estimate the emissions not covered by INDCs; and (c) used different amounts of INDCs because the studies were performed at different times. (2) The studies used different databases that include different greenhouse gases, accounting methods and data sources to estimate historical greenhouse gas emissions. (3) The studies used different methods for estimating greenhouse gas emissions and removals related to land use, land-use change and forestry (LULUCF). (4) The studies used different values of the global warming potential. Additionally, the main reasons for the differences in the predictions of the temperature increase at the end of the 21st century based on INDCs are as follows: (1) Differences in the estimations of greenhouse gas emissions in 2030 based on INDCs and (2) different methods of extrapolating global greenhouse gas emissions to 2100. There are three main extrapolation methods: one is to maintain the net present value of the carbon price in 2030 and then extrapolate the greenhouse gas emissions to 2100; another is to maintain the decarbonization rate of a certain period of history and then extrapolate the greenhouse gas emissions to 2100; the third is to match the emissions reduction scenario with the current INDC emissions reduction scenario from the IPCC AR5 scenario database and then use the matching emissions reduction scenario as the current INDC emissions reduction scenario. The use of different methods of extrapolating carbon emissions is one of the main reasons for the differences in the prediction results. (3) Differences in the methods for predicting the effects of greenhouse gas emissions on temperature. Statistical methods and simulation methods are the two main prediction methods; they use different calculation methods, which led to the difference in the prediction results. Therefore, the following points are worth noting: (1) Most importantly, to the extent possible, countries should submit absolute emissions reduction targets as much as possible; nonquantifiable INDCs without detailed methods descriptions and data introductions should not be submitted; (2) authorities should recommend certain data sets that are the most suitable for INDC accounting; (3) a global warming potential should be designated to avoid differences in greenhouse gas estimates due to the use of different criteria; and (4) to the extent possible, future research should adopt simulation methods for predicting the impact of global greenhouse gas emissions on temperature.

Studies on the contribution of milk production to global greenhouse gas (GHG) emissions are rare (FAO 2010) and often based on crude data which do not appropriately reflect the heterogeneity of farming systems. This article estimates GHG emissions from milk production in different dairy regions of the world based on a harmonised farm data and assesses the contribution of milk production to global GHG emissions. The methodology comprises three elements: (1) the International Farm Comparison Network (IFCN) concept of typical farms and the related globally standardised dairy model farms representing 45 dairy regions in 38 countries; (2) a partial life cycle assessment model for estimating GHG emissions of the typical dairy farms; and (3) standard regression analysis to estimate GHG emissions from milk production in countries for which no typical farms are available in the IFCN database. Across the 117 typical farms in the 38 countries analysed, the average emission rate is 1.50 kg CO(2) equivalents (CO(2)-eq.)/kg milk. The contribution of milk production to the global anthropogenic emissions is estimated at 1.3 Gt CO(2)-eq./year, accounting for 2.65% of total global anthropogenic emissions (49 Gt; IPCC, Synthesis Report for Policy Maker, Valencia, Spain, 2007). We emphasise that our estimates of the contribution of milk production to global GHG emissions are subject to uncertainty. Part of the uncertainty stems from the choice of the appropriate methods for estimating emissions at the level of the individual animal.

Climatic temperature controls the geographical patterns of coastal marshes greenhouse gases emissions over China

The Trans-Pacific Partnership (TPP) is one of the proposed mega-free trade agreements. While several previous studies have measured the economic impact of the trade liberalization resulting from the TPP, the TPP may have not only a very large economic impact, but also a significant environmental impact, such as changes in greenhouse gas (GHG) emissions. The purpose of this paper is to contribute to the debate over TPP and GHG emissions by asking the following question: Will the TPP increase or decrease GHG emissions? We estimate the potential impact on GHG emissions changes caused by the TPP using the Global Trade Analysis Project (GTAP) model, and the GTAP CO2 and non-CO2 emissions databases. Our results suggest that the TPP is likely to increase the total amount of GHG emissions in the 12 TPP member countries, as well as global emissions. The main reason for increasing TPP member and global GHG emissions is non-CO2 emissions growth in Australia and the US.