• Energy Research

Abstract Although technological progress has greatly reduced energy use, the actual energy savings have always been lower than the potential conservations. This is known as the “energy rebound effect.” This paper estimated energy rebound effects of China's fossil and non-fossil fuel consumption. Additionally, this study derived an improved approach for decomposition of energy rebound effects without presupposing production function forms. Results shows that the fossil fuel rebound effect was higher than the non-fossil fuel, which may be due to the heterogeneous impacts of technology on potential fossil and non-fossil energy savings. Furthermore, fossil and non-fossil energy rebound effects were both predominantly caused by the substitution effect instead of the output effect. Hence, vigorously improving non-fossil energy efficiency will help reduce the fossil energy rebound effect.

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.

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.

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.

AbstractWith the implementation of China’s top-down CO2 emissions reduction strategy, the regional differences should be considered. As the most basic governmental unit in China, counties could better capture the regional heterogeneity than provinces and prefecture-level city, and county-level CO2 emissions could be used for the development of strategic policies tailored to local conditions. However, most of the previous accounts of CO2 emissions in China have only focused on the national, provincial, or city levels, owing to limited methods and smaller-scale data. In this study, a particle swarm optimization-back propagation (PSO-BP) algorithm was employed to unify the scale of DMSP/OLS and NPP/VIIRS satellite imagery and estimate the CO2 emissions in 2,735 Chinese counties during 1997–2017. Moreover, as vegetation has a significant ability to sequester and reduce CO2 emissions, we calculated the county-level carbon sequestration value of terrestrial vegetation. The results presented here can contribute to existing data gaps and enable the development of strategies to reduce CO2 emissions in China.