Analysis of China's regional thermal electricity generation and CO 2 emissions: Decomposition based on the generalized Divisia index

Sci Total Environ. 2019 Sep 10;682:737-755. doi: 10.1016/j.scitotenv.2019.05.143. Epub 2019 May 17.


Even though Chinese government has been promoting the development of renewable energy, coal-fired thermal electricity generation still accounts for nearly 70% of the total electricity generation, proving to be the largest carbon dioxide (CO2) emitter in China. Uncovering the driving forces of CO2 emissions, thus, is of great significance to provide appropriate mitigation policies for the sustainable development of China's thermal electricity generation. In this regard, this study aims to fill a research gap by applying Index Decomposition Analysis (IDA) approach via the Generalized Divisia Index Model (GDIM) to examine the driving factors behind the CO2 emission changes in China's thermal electricity generation during 2000-2016. The decomposition results indicate that the factors contributing to the growth in CO2 emission can be ranked as follows: economic activity (52.0%), electricity demand (45.8%) and energy use (36.2%), whereas factors suppressing the growth in the mission are carbon intensity change (-17.7%), technology (-11.3%), energy mix (-2.4%), energy efficiency (-1.7%) and electricity efficiency (-0.9%). Noteworthy, the promoting effect of the economic activity varied little with time, whereas that of electricity demand and energy use exhibited a downward trend in general. Besides, though the progress in technology contributed a lot to the decrease of CO2 emission, its decreasing effects tended to diminish since 2013. Northeast and East regions appeared as those contributing to the mitigation of the CO2 emissions from China's thermal electricity generation, whereas the North and Northwest regions exerted a lag to the abatement of CO2 emission.

Keywords: Carbon dioxide emissions; Generalized Divisia Index Model; Index Decomposition Analysis; Thermal electricity generation.