An empirical spatiotemporal decomposition analysis of carbon intensity in China's industrial sector

Abstract

Abstracts In order to develop efficient industrial CO2 emissions' strategies for China, it is important to compare the performance of carbon intensity and its major driving factors among different provinces. However, such studies are relatively limited so far. The present study describes the features of industrial aggregate carbon intensity (IACI) as well as its driving factors for China's thirty provinces based on the spatiotemporal logarithmic mean Divisia index (ST-LMDI) method. This method allows comparing all provinces against a common benchmark. The empirical results show that Beijing, Tianjin, Shanghai, Guangdong and Heilongjiang rank as the top five provinces while Hebei, Shanxi, Inner Mongolia, Ningxia and Xinjiang perform the worst. From 1999 to 2015, the IACI of most industrial sectors tends to decrease except in Ningxia and Xinjiang, with energy intensity playing a decisive role in all provinces, and both energy structure and emission coefficients yielding mixed effects across provinces and over time. Additionally, this study employs spatial autocorrelation to divide China's thirty provinces into four categories, combining the economic development level and geographical location into a common framework. Then the ST-LMDI method is used to explore how the four regions perform in IACI when the influences of neighbors are taken into account. The results show that the regions with high level of economic development perform better and the regions with the same level of economic development but which are surrounded by less-developed regions have lower IACI. Based on the results, differentiated policies in energy intensity, energy structure and emission coefficient for the local and central governments are recommended.