A novel geographic evolution tree based on econometrics for analyzing regional differences in determinants of Chinese CO2 emission intensity

Abstract

Recent studies have increasingly focused on China's CO2 emission intensity (CEI). However, specific or sufficient guidance is needed with regard to China's complex regional differences and the underlying relations between drivers and CEI. Herein, we develop a novel evolution tree based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to spatially visualize and quantify regional development patterns and the impact of determinants on CEI in China. The results showed that China's CEI spatially decreased from the northwest inland to the southeast coast and showed an overall annual decrease. Different regions with various regional development patterns have varying impact mechanisms on local CEI. In the highly developed region, affluence and industrial structure had the greatest effect, with an elasticity coefficient of −0.63, and 0.63, respectively. In the upper-middle developed, lower-middle developed, and developing regions, the energy structure exerted the greatest effect on local CEI with elasticity coefficients of 0.98%, 2.06%, and 0.95%, respectively. In the underdeveloped region, population had the greatest promotion effect with an impact of 1.42; however, affluence exerts a pronounced inhibitory effect with an impact of −0.63. Factors affecting China's CEI have regionally varied effects. Affluence had a significant inhibitory effect on CEI in all five regions, especially in the underdeveloped region; population had a negative effect in the relatively developed regions and a positive effect in the less developed regions. Other factors exerted a positive effect in all five regions, but their significance varied regionally. These results can help policymakers adopt effective regional energy conservation and emission reduction measures.