A Comparative Decomposition Analysis of the Factors Driving Energy-Related Carbon Emissions from Three Typical Provinces in China: Jiangsu, Henan and Inner Mongolia

Abstract

An accurate understanding of the real situation of energy-related carbon emissions and the main factors driving the carbon emissions increments are crucial for China to realize its emission mitigation targets. Adopting the comparative decomposition of an extended LMDI (Log-Mean Divisia Index) approach, this study decomposed the changes in carbon emissions of Jiangsu, Henan, and Inner Mongolia, which are located in the eastern, central and western parts of China. This analysis led to three main findings. 1) During the period of 1996–2017, the energy-related carbon emissions in the examined provinces exhibited upward trends, but with some differences among the provinces. 2) The influences of driving factors on carbon emissions varied distinctly in different provinces and economic stages. Economic growth had the largest positive effect on provincial carbon emissions increases. From 1996 to 2017, the contribution rates of economic development to emissions growth in Henan, Jiangsu and Inner Mongolia were 307.19%, 205.08% and 161.26%, respectively. This influence was followed by urbanization and population size. 3) Energy intensity played a leading role in facilitating emissions-reduction in the examined provinces, except for during the tenth Five-Year Plan, followed by the energy structure. The effect of rural population proportion was the weakest among all the curbing factors. Furthermore, urban and rural resident′s energy consumption per capita demonstrated relatively minor impacts and disparate directions of influence in the different provinces and economic periods, but began to play increasing roles in driving up provincial emissions changes. For example, residential energy consumption in Jiangsu contributed over 7.9% to the total carbon emission growth in 1996-2017, among which urban residents' per-capita energy consumption contributed more than 3.8%. In view of these findings, policy makers should formulate targeted emission reduction measures that are based on the distinct situations and key factors which affect carbon emissions in each province.