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Abstract This study analyzes the determinants of changes in electricity generation intensity in China and further uncovers the reasons for the differences between the changes in electricity generation intensity in the thermal and sustainable power sectors. By developing a factorial-intertemporal nested decomposition technique using the refined Laspeyres index, we demonstrate the contributions of electricity generation structure, electricity generation-to-consumption ratio, production electricity consumption intensity, residential electricity consumption intensity, and electricity consumption loss intensity effects. Although the electricity generation intensity of the thermal power sector has been lower than that of the sustainable power sector, the latter has declined remarkably and has remained the key sector driving the overall changes in electricity generation intensity. Meanwhile, the effect of electricity consumption intensity is the main factor that reduces electricity generation intensity. Moreover, the impact of production electricity consumption intensity in the thermal power sector exceeds its impact in the sustainable power sector. Ultimately, we find that the differences between the changes in electricity generation intensity in the thermal and sustainable power sectors are mainly due to their differences in production electricity consumption intensity.

Abstract Understanding the coupling mechanisms between carbon emissions and the eco-environment is essential for building sustainable economies. Based on the coupling coordination model, this paper investigates the coordination degree of carbon emissions and the eco-environment in China from 2009 to 2015. The Logarithmic Mean Divisia Index decomposition method was used to identify the key factors affecting the coordination degree. Furthermore, different scenarios were simulated to show the impact of changes in government priorities on the coordination degree. The results show that the dynamic relationship between carbon emissions and the eco-environment in China has reached a barely balanced stage, but a few provinces are still struggling with imbalances. The coordination level of carbon emissions and the eco-environment becomes the key factor affecting coordinated development, while the interaction intensity has a limited effect. Further analysis indicated that although the carbon emission evaluation value has a positive effect on promoting an increase in the coordination degree, the eco-environment evaluation value determines the trend of coordinated regional development. Moreover, the priorities of government policymakers have a substantial impact on coordinated development, but these impacts gradually diminish as the differences in evaluation values decrease. These findings contribute to the formulation of effective regional sustainable development strategies.

AbstractAccurate, long-term, full-coverage carbon dioxide (CO2) data in units of prefecture-level cities are necessary for evaluations of CO2 emission reductions in China, which has become one of the world’s largest carbon-emitting countries. This study develops a novel method to match satellite-based Defense Meteorological Satellite Program’s Operational Landscan System (DMSP/OLS) and Suomi National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data, and estimates the CO2 emissions of 334 prefecture-level cities in China from 1992 to 2017. Results indicated that the eastern and coastal regions had higher carbon emissions, but their carbon intensity decreased more rapidly than other regions. Compared to previous studies, we provide the most extensive and long-term CO2 dataset to date, and these data will be of great value for further socioeconomic research. Specifically, this dataset provides a foundational data source for China’s future CO2 research and emission reduction strategies. Additionally, the methodology can be applied to other regions around the world.

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.

Abstract Research on China’s coal consumption addresses global concerns related to clean energy, sustainable communities, and climate action, among others (i.e., Targets 7, 11, and 13 in the new United Nations Sustainable Development Goals). Most existing literature has focused on industrial coal rather than residential coal consumption. This study analyzed the Chinese urban–rural residential coal consumption disparity during 2000–2015 by the expanded calculating approach of the Gini coefficient combined with the Log Mean Divisia Index (LMDI). Analysis of the urban–rural residential coal consumption disparity and its elasticity to LMDI decomposition effects have not been undertaken in other literature. The empirical results demonstrated that the increased disparity during the study period mainly resulted from the increased ratio of rural to urban coal consumption per capita. Meanwhile, the decreasing per capita urban coal proportion and increasing rural per capita income were the leading factors for their respective coal consumption variations. Furthermore, urban residential coal consumption showed strong decoupling, while rural residential coal consumption showed weak decoupling, and the variation in the urban–rural gap was sensitive to rural energy intensity and population. The study explored the underlying reasons for the increasing disparity and supported the “energy ladder” theory.

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.

Abstract As a main method for achieving environmental protection, improvement of environmental efficiency is vitally important for reducing environmental risk and level of ecological scarcity. Thus, quantitative analysis of environmental efficiency is not only an important premise for understanding the situation of regional environmental protection, but also a prerequisite for designing and adjusting relevant policies. In this paper, we treat the undesirable output in an economic activity by a linear monotonic decreasing transformation approach. We employ data envelopment analysis (DEA) technique to evaluate environmental efficiency in 30 provinces of China during the period 2001–2010, and conduct hypothesis tests on these environmental efficiencies. Furthermore, we investigate the horizontal differences of environmental efficiency in six regions of China and the vertical differences among different years. The results show that our empirical tests have a high statistical reliability that provides support for prompting environmental policies in different parts of China.