SPATIAL ECONOMETRIC ANALYSIS OF LIVING-ENERGY CARBON EMISSIONS IN CHINA AND ITS DRIVING FACTORS

This study examined urbanization rate, resident consumption level, energy structure and energy intensity of per capital carbon emissions of residents' living energy (CERLE) with STIRPAT model incorporating ridge regression. Then, we measured per capital CERLE of China's 30 provinces from 2007 to 2012 and analyzed its distribution by using quartile maps, and finally used Moran's I index to conduct spatial correlation diagnoses. Empirical results indicate that energy structure can lead to a decrease in per capital CERLE while urbanization rate, resident consumption level and energy intensity are opposite. The estimated elastic coefficients suggest that energy structure is the most important driving factor of per capital CERLE. Additionally, the coefficients increase after the spatial correlation factors in the regions are introduced, which indicated that it will underestimate the effect of the driving factors on per capital CERLE if the spatial factors are not taken into account.