Harmonising equity and efficiency in allocating global post-Kyoto GHG emissions

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.

Why carbon leakage matters and what can be done against it

Transportation has emerged as a major contributor to greenhouse gas (GHG) emissions worldwide. With increasing population growth and food demand, the spatial decoupling of production and consumption has intensified, driving a surge in transnational agricultural products transportation. However, existing research lacks a systematic assessment and future projection of GHG emissions from agricultural product transportation across multiple transport modes worldwide. To address this gap, we developed a global trade-linked transport GHG emission database by integrating multisource data and modeling frameworks. Compared with the research method in this paper, the previous great circle distance as the GHG emission of agricultural product transportation mileage was underestimated by 34%. This study systematically evaluates the spatiotemporal evolution of agricultural transport GHG emissions from 2000 to 2021 and explores their mitigation potential under future scenarios. Our findings reveal that global GHG emissions from agricultural transport increased by 1.6-fold over the 21-year period, with rising GHG emission intensity and trade density collectively shaping a high-carbon flow pattern dominated by exports from the Americas to Asia. Future scenario analyses indicate that the localization strategies proposed in this study are not particularly effective in reducing transport-related GHG emissions as inefficiencies inherent in localized agricultural production can increase production-stage emissions and, in turn, result in a net rise in total GHG outputs. These results suggest that future strategies should prioritize optimizing trade structures while simultaneously enhancing domestic agricultural productivity and promoting low-carbon farming technologies to achieve net emission reductions at the source.

Advanced wastewater treatment processes and novel technologies are adopted to improve nutrient removal from wastewater so as to meet stringent discharge standards. Municipal wastewater treatment plants are one of the major contributors to the increase in the global greenhouse gas (GHG) emissions and therefore it is necessary to carry out intensive studies on quantification, assessment and characterization of GHG emissions in wastewater treatment plants, on the life cycle assessment from GHG emission prospective, and on the GHG mitigation strategies. Greenhouse Gas Emission and Mitigation in Municipal Wastewater Treatment Plants summarises the recent development in studies of greenhouse gases’ (CH4 and N2O) generation and emission in municipal wastewater treatment plants. It introduces the concepts of direct emission and indirect emission, and the mechanisms of GHG generations in wastewater treatment plants’ processing units. The book explicitly describes the techniques used to quantify direct GHG emissions in wastewater treatment plants and the protocol used by the Intergovernmental Panel on Climate Change (IPCC) to estimate GHG emission due to wastewater treatment in the national GHG inventory. Finally, the book explains the life cycle assessment (LCA) methodology on GHG emissions in consideration of the energy and chemical usage in municipal wastewater treatment plants. In addition, the strategies to mitigate GHG emissions are discussed. The book provides an overview for researchers, students, water professionals and policy makers on GHG emission and mitigation in municipal wastewater treatment plants and industrial wastewater treatment processes. It is a valuable resource for undergraduate and postgraduate students in the water, climate, and energy areas; for researchers in the relevant areas; and for professional reference by water professionals, government policy makers, and research institutes. ISBN: 9781780406305 (Print) ISBN: 9781780406312 (eBook) ISBN: 9781780409054 (ePUB)

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Livestock and greenhouse gas emissions: The importance of getting the numbers right

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.

Contributions of natural systems and human activity to greenhouse gas emissions

Household consumption has become a primary contributor to greenhouse gas (GHG) emissions. Population aging influences the scale and structure of consumption, which in turn affects GHG emissions both domestically and internationally through complex global supply chains. Despite the importance of this demographic shift, the impact of China’s aging population on global and national GHG mitigation efforts remains unclear. This study estimates and analyzes the effects of changes in China’s age structure on both the global GHG footprint and the disparities in GHG emission trends across countries from 2010 to 2020, with projections extending to the 2020–2030 period. Our findings indicate that China’s aging population reduced the global GHG footprint by 188.14 Mt CO 2 -equivalent from 2010 to 2020 and is projected to further decrease emissions by 142.90 Mt CO 2 -equivalent from 2020 to 2030. These reductions vary significantly across countries, influenced by trade volume with China and differences in sector-specific emission intensities. China’s aging demographic has a substantial impact on the global GHG emission landscape, offering insights for policy formulation aimed at addressing the dual challenges of aging and climate change.

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.

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.

Reducing supply chain costs is a primary concern of every organization. Organizations have implemented offshore outsourcing as an effective strategy towards reducing supply chain costs. However, neither government nor corporate organizations have sufficiently taken into account the effects of this strategy on global greenhouse gas (GHG) emissions. The purpose of this research is to analyze the impact of offshore outsourcing on global GHG emissions, and the effect of changes in fuel prices in addition to a carbon price on additional emissions on supply chain costs. The purpose is supported by five key objectives. The objectives are addressed through a systematic methodology. The analysis is supported by a literature review, and the development and testing of mathematical models. Finally, a framework to reduce global GHG emissions through a carbon price on differential emissions from manufacturing and additional emissions from international transportation is proposed. The findings suggest that offshore outsourcing has increased global emissions. The fuel prices are increasing at a rate higher than the overall rate. A carbon price on excess emissions due to outsourcing coupled with increasing fuel prices impacts supply chain costs adversely and leads to bigger lot-sizes. As an illustration at the national level, the framework showed that emissions for the USA increased by about 20% for every year between 2007 and 2010. As another example from a corporate organization, the net profit (profit after tax) for Wal-Mart was reduced by about 19% for 2006 due to a carbon price on manufacturing emissions alone. The suggested framework is a major contribution for quantifying the extent of changes in the emissions due to offshore outsourcing and the value of these emissions at a prevailing rate of carbon tax in North America. It is intended to provide a basis for reducing emissions and costs from global supply chains. The proposed framework provides a level playing field to manufacturers in different countries using different technologies, provides incentives to organizations for manufacturing in locations where net emissions are low, helps national governments determine how they can generate revenue for dealing with emissions, and, most importantly, aids in reducing overall global GHG emissions.

Reducing supply chain costs is a primary concern of every organization. Organizations have implemented offshore outsourcing as an effective strategy towards reducing supply chain costs. However, neither government nor corporate organizations have sufficiently taken into account the effects of this strategy on global greenhouse gas (GHG) emissions. The purpose of this research is to analyze the impact of offshore outsourcing on global GHG emissions, and the effect of changes in fuel prices in addition to a carbon price on additional emissions on supply chain costs. The purpose is supported by five key objectives. The objectives are addressed through a systematic methodology. The analysis is supported by a literature review, and the development and testing of mathematical models. Finally, a framework to reduce global GHG emissions through a carbon price on differential emissions from manufacturing and additional emissions from international transportation is proposed. The findings suggest that offshore outsourcing has increased global emissions. The fuel prices are increasing at a rate higher than the overall rate. A carbon price on excess emissions due to outsourcing coupled with increasing fuel prices impacts supply chain costs adversely and leads to bigger lot-sizes. As an illustration at the national level, the framework showed that emissions for the USA increased by about 20% for every year between 2007 and 2010. As another example from a corporate organization, the net profit (profit after tax) for Wal-Mart was reduced by about 19% for 2006 due to a carbon price on manufacturing emissions alone. The suggested framework is a major contribution for quantifying the extent of changes in the emissions due to offshore outsourcing and the value of these emissions at a prevailing rate of carbon tax in North America. It is intended to provide a basis for reducing emissions and costs from global supply chains. The proposed framework provides a level playing field to manufacturers in different countries using different technologies, provides incentives to organizations for manufacturing in locations where net emissions are low, helps national governments determine how they can generate revenue for dealing with emissions, and, most importantly, aids in reducing overall global GHG emissions.

Agricultural activities are a substantial contributor to global greenhouse gas (GHG) emissions, accounting for about 58% of the world's anthropogenic non-carbon dioxide GHG emissions and 14% of all anthropogenic GHG emissions, and agriculture is often viewed as a potential source of relatively low-cost emissions reductions. We estimate the costs of GHG mitigation for 36 world agricultural regions for the 2000–2020 period, taking into account net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. For croplands and rice cultivation, we use biophysical, process-based models (DAYCENT and DNDC) to capture the net GHG and yield effects of baseline and mitigation scenarios for different world regions. For the livestock sector, we use information from the literature on key mitigation options and apply the mitigation options to emission baselines compiled by EPA.

Agricultural activities are a substantial contributor to global greenhouse gas (GHG) emissions, accounting for about 58% of the world's anthropogenic non‐carbon dioxide GHG emissions and 14% of all anthropogenic GHG emissions, and agriculture is often viewed as a potential source of relatively low‐cost emissions reductions. We estimate the costs of GHG mitigation for 36 world agricultural regions for the 2000–2020 period, taking into account net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. For croplands and rice cultivation, we use biophysical, process‐based models (DAYCENT and DNDC) to capture the net GHG and yield effects of baseline and mitigation scenarios for different world regions. For the livestock sector, we use information from the literature on key mitigation options and apply the mitigation options to emission baselines compiled by EPA.