• Energy Research

Abstract This study analyses the changes in energy-related carbon dioxide (CO2) emissions of the agricultural sector in China from 2005 to 2013. Using the logarithmic mean Divisia index (LMDI) decomposition method, this study attributes the changes in agricultural CO2 emissions to agricultural CO2 emissions intensity, agricultural productive income intensity, rural residents’ income structure, the distribution pattern of residential income, the distribution pattern of national income, economic development, provincial population distribution, and population scale, and treats these factors as technology, distribution, and population effects. Based on this, the nested decomposition problem, which has not been mentioned in related studies, is solved. To emphasize the importance of the logarithmic mean weight functions, two different chain LMDI decomposition methods are developed that are based on differences in the logarithmic mean weight functions. The results show that the distribution pattern of national income and rural residents’ income structure are two key factors that separately stimulate and suppress the changes in China's agricultural energy-related CO2 emissions. After nested decomposition of the distribution pattern of residential income, the suppressing influence from the rural population proportion is stronger than the stimulating influence from rural-urban income inequity. Although the results of the two chain LMDI decomposition methods are similar, only the distribution pattern of national income and rural residents’ income structure maintain positive impacts on the changes in China's agricultural CO2 emissions by year, while the rural residents’ income structure, distribution pattern of residential income, and rural population proportion continue to have negative impacts on changes in China's agricultural CO2 emissions by year. Furthermore, the technology, distribution, and population effects could not suppress China's agricultural CO2 emissions simultaneously in most years.

Understanding the relationship between carbon emissions and vegetation carbon sequestration is essential for reducing the greenhouse effect. In this study, we constructed a carbon balance pressure index to measure the eco-environment pressure caused by carbon emissions in 77 countries from 2000 to 2015, and the logarithmic mean Divisia index decomposition method was used to identify the key factors related to carbon balance pressure. As the change in vegetation carbon sequestration is relatively stable, carbon emissions have become the direct cause of the rise in the global carbon balance pressure. The carbon balance pressure in advanced economies decreased slowly, while that in emerging economies increased but the growth rate decreased. The decomposition results showed that carbon intensity is the main factor restraining the rise of carbon balance pressure, while GDP per capita and land population pressure are the main driving forces, and vegetation carbon sequestration intensity plays only a small role. Further analysis showed that the restraining effect of carbon intensity can offset the incremental effect of GDP per capita in advanced economies, and the vegetation carbon sequestration intensity also has a positive impact, but not in emerging economies. Besides, different factors play different roles depending on the country. The conclusions were also supported by various robustness tests.

Abstract PM2.5 emissions have become a source of severe air pollution in China during recent years, and an increasing number of studies have focused on these emissions and their drivers’ impacts. However, most studies have relied on econometric models and only show the raw relationship between different socioeconomic drivers and PM2.5 pollution, ignoring the regional, sectoral and temporal heterogeneity. Hence, this study adopts the index decomposition method to analyze the determinants of the changes in PM2.5 emissions in different regions and sectors in China, between 2005 and 2015. By deriving an extended chain and nested refined Laspeyres index decomposition analysis method, the effects of determinants could be analyzed and compared annually. The results show: (1) the emission intensity and energy intensity in the Industry sector significantly reduced PM2.5 emissions, whereas other industrial sectors failed to do so; (2) the improvement in residential income contributed to increasing PM2.5 emissions rather than facilitating reductions at the present stage; (3) the rural per capita income and urban-rural income gap are two key factors stimulating PM2.5 emissions and the effects observed largely due to coal consumption.

AbstractIn this paper, we respectively decompose and study different effects of technological progress on carbon emissions in China based on the combination of the logarithmic mean Divisia index method, the Solow residual model, and spatial econometrics. Furthermore, we propose an improved approach to estimate the rebound effect index. By comparing the different effects of technological progress on carbon emissions, our results indicate that China's overall domestic technological progress reduced its carbon emissions over this period. As for the rebound effect index, the estimated results are higher than in previous studies because of the spatial rebound effect, which was ignored by previous studies. Regionally, although the eastern region had high rebound effects, the western region is at the greatest risk from the rebound effects. Finally, we present specific environmental policy proposals for China's sustainable development based on empirical results.

AbstractChina’s carbon peak greatly impacts global climate targets. Limited studies have comprehensively analyzed the influence of the COVID-19 pandemic, changing emission network, and recent carbon intensity (CI) reduction on the carbon peak and the corresponding mitigation implications. Using a unique dataset at different levels, we project China’s CO2 emission by 2035 and analyze the time, volume, driver patterns, complex emission network, and policy implications of China’s carbon peak in the post- pandemic era. We develop an ensemble time-series model with machine learning approaches as the projection benchmark, and show that China’s carbon peak will be achieved by 2021–2026 with > 80% probability. Most Chinese cities and counties have not achieved carbon peaks response to the priority-peak policy and the current implementation of CI reduction should thus be strengthened. While there is a "trade off" between the application of carbon emission reduction technology and economic recovery in the post-pandemic era, a close cooperation of interprovincial CO2 emission is also warranted.

Abstract Although energy technological progress has been regarded as an important driver for reducing carbon emissions, the existence of carbon rebound effect prevents a portion of the potential carbon reductions to be realized. Compared with the energy rebound effect, research on the carbon rebound effect is scarce because it is always equated with the energy rebound effect. However, the carbon rebound effect is more complex. Given that the traditional method for carbon rebound effect assessment only reflects energy rebound effects, our study proposed an improved production-theoretical decomposition analysis (PDA)-Meta-frontier Malmquist index (MMI)-based method and explored carbon rebound effects in China from 2006 to 2015. Our results show that (1) the eastern and western regions faced fewer carbon rebound effect risks compared with those of the central region due to decreasing emission intensity associated with energy technological progress; (2) the reductions in emission intensity in the eastern region relied both on coal and non-coal technology, whereas the western region only relied on coal technology; and (3) the non-coal technology in the eastern region was at the meta-frontier, whereas the non-coal technology of other regions exhibited catch-up effects.

Data from China’s Ministry of Civil Affairs show that more than 75% of the country’s poor live in rural areas. Therefore, to achieve the goal of poverty alleviation by 2020, the problem of rural poverty requires urgent attention. Based on the entropy method, we herein assess the level of poverty vulnerability in each province in China to guide the direction of future poverty alleviation efforts. In addition, based on the logarithmic mean Divisia index method and the grey relational analysis method, we studied the effects and contributions of rural poverty incidence, rural agricultural outcomes per capita, the proportion of agricultural outcomes, gross domestic product per capita, and total population on rural poverty. We found that, when it comes to natural resources, human resources, physical assets, financial assets, and social resources, the most vulnerable areas are concentrated in western China. We suggest the government pay close attention to the interests of individuals in this region to balance economic development, distribute the benefits of economic development to the rural poor, and narrow the income gap between urban and rural areas.

Abstract Coal is one of the main fuel sources in China. This paper sheds light on the evolution of China's interregional differences in CO2 emissions from coal by constructing a Gini coefficient and decoupling elasticity index for emissions from 1997 to 2012 and explains why emission differences deviate from economic growth differences. The study decomposed the Gini coefficient of CO2 emissions from coal by source, incremental source, and region. It also divided the decoupling elasticity of carbon emissions into two components: effects of environmental expenditure and effects of emission reduction policy. The findings of the study are as follows: First, interregional differences in China's overall CO2 emissions from coal are characterized by periodic fluctuation. Second, the differences in emissions from raw coal, the concentration effect of emissions, and the emission differences within regions are the three main factors in the overall difference changes in coal's carbon emissions in China. Last but not least, the decoupling between provincial CO2 emissions from coal and economic growth is on the whole weak. Based on the above findings, the author offers four suggestions for emission reduction.