• Energy Research

A straightforward global layout of carbon capture, utilization and storage (CCUS) is imperative for limiting global warming well below 2 °C. Here, we propose a cost-effective strategy for matching carbon sources and sinks on a global scale. Results show 3,093 carbon clusters and 432 sinks in 85 countries and regions are selected to achieve 92 GtCO2 mitigation by CCUS, 64% of which will be sequestered into sedimentary basins for aquifer storage and 36% will be used for CO2-EOR (enhanced oil recovery). Of the identified source–sink matching, 80% are distributed within 300 km and are mainly located in China, the United States, the European Union, Russia and India. The total cost is ~0.12% of global cumulative gross domestic product. Of countries with CO2-EOR, 75% will turn into profitable at the oil price over US$100 per barrel. These findings indicate our proposed layout is economically feasible. However, its implementation requires global collaboration on financial and technological transfer. Carbon capture, utilization and storage (CCUS) will be required to meet climate targets. An economically feasible global CCUS layout can be achieved by capturing the carbon sources in 85 regions and mitigating with 59 GtCO2 sequestration and aquifer storage and 33 GtCO2-enhanced oil recovery.

A straightforward global layout of carbon capture, utilization and storage (CCUS) is imperative for limiting global warming well below 2 °C. Here, we propose a cost-effective strategy for matching carbon sources and sinks on a global scale. Results show 3,093 carbon clusters and 432 sinks in 85 countries and regions are selected to achieve 92 GtCO2 mitigation by CCUS, 64% of which will be sequestered into sedimentary basins for aquifer storage and 36% will be used for CO2-EOR (enhanced oil recovery). Of the identified source–sink matching, 80% are distributed within 300 km and are mainly located in China, the United States, the European Union, Russia and India. The total cost is ~0.12% of global cumulative gross domestic product. Of countries with CO2-EOR, 75% will turn into profitable at the oil price over US$100 per barrel. These findings indicate our proposed layout is economically feasible. However, its implementation requires global collaboration on financial and technological transfer. Carbon capture, utilization and storage (CCUS) will be required to meet climate targets. An economically feasible global CCUS layout can be achieved by capturing the carbon sources in 85 regions and mitigating with 59 GtCO2 sequestration and aquifer storage and 33 GtCO2-enhanced oil recovery.

Abstract An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO–FCM–Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO–FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020.

Abstract An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO–FCM–Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO–FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020.

There are substantial differences in carbon footprints across households. This study applied an environmentally extended multiregional input–output approach to estimate household carbon footprints for 12 different income groups of China’s 30 regions. Subsequently, carbon footprint Gini coefficients were calculated to measure carbon inequality for households across provinces. We found that the top 5% of income earners were responsible for 17% of the national household carbon footprint in 2012, while the bottom half of income earners caused only 25%. Carbon inequality declined with economic growth in China across space and time in two ways: first, carbon footprints showed greater convergence in the wealthier coastal regions than in the poorer inland regions; second, China’s national carbon footprint Gini coefficients declined from 0.44 in 2007 to 0.37 in 2012. We argue that economic growth not only increases income levels but also contributes to an overall reduction in carbon inequality in China.

There are substantial differences in carbon footprints across households. This study applied an environmentally extended multiregional input–output approach to estimate household carbon footprints for 12 different income groups of China’s 30 regions. Subsequently, carbon footprint Gini coefficients were calculated to measure carbon inequality for households across provinces. We found that the top 5% of income earners were responsible for 17% of the national household carbon footprint in 2012, while the bottom half of income earners caused only 25%. Carbon inequality declined with economic growth in China across space and time in two ways: first, carbon footprints showed greater convergence in the wealthier coastal regions than in the poorer inland regions; second, China’s national carbon footprint Gini coefficients declined from 0.44 in 2007 to 0.37 in 2012. We argue that economic growth not only increases income levels but also contributes to an overall reduction in carbon inequality in China.

AbstractA strategy that informs on countries’ potential losses due to lack of climate action may facilitate global climate governance. Here, we quantify a distribution of mitigation effort whereby each country is economically better off than under current climate pledges. This effort-sharing optimizing approach applied to a 1.5 °C and 2 °C global warming threshold suggests self-preservation emissions trajectories to inform NDCs enhancement and long-term strategies. Results show that following the current emissions reduction efforts, the whole world would experience a washout of benefit, amounting to almost 126.68–616.12 trillion dollars until 2100 compared to 1.5 °C or well below 2 °C commensurate action. If countries are even unable to implement their current NDCs, the whole world would lose more benefit, almost 149.78–791.98 trillion dollars until 2100. On the contrary, all countries will be able to have a significant positive cumulative net income before 2100 if they follow the self-preservation strategy.

AbstractA strategy that informs on countries’ potential losses due to lack of climate action may facilitate global climate governance. Here, we quantify a distribution of mitigation effort whereby each country is economically better off than under current climate pledges. This effort-sharing optimizing approach applied to a 1.5 °C and 2 °C global warming threshold suggests self-preservation emissions trajectories to inform NDCs enhancement and long-term strategies. Results show that following the current emissions reduction efforts, the whole world would experience a washout of benefit, amounting to almost 126.68–616.12 trillion dollars until 2100 compared to 1.5 °C or well below 2 °C commensurate action. If countries are even unable to implement their current NDCs, the whole world would lose more benefit, almost 149.78–791.98 trillion dollars until 2100. On the contrary, all countries will be able to have a significant positive cumulative net income before 2100 if they follow the self-preservation strategy.