Research On The Factors Influencing Carbon Emissions From Provincial Energy Consumption

With the development of economy and population growth,China’s energy consumption demand is increasing,and the carbon emission from energy consumption is also increasing year by year,which leads to serious environmental pollution problems.In order to control carbon emissions,the Chinese government has proposed the goals of achieving carbon peaking by 2030 and carbon neutrality by 2060.Therefore,to understand the current situation of carbon emissions from provincial energy consumption,to investigate the decoupling effect of provincial carbon emissions and economic growth,and to clarify the relationship between various influencing factors and carbon emissions,it is helpful to take targeted measures to control carbon emissions.Firstly,based on the panel data of 30 provinces in China from 2006 to 2020,we measure the carbon emissions of energy consumption in China’s provinces and analyze the decoupling of per capita carbon emissions from economic development.Secondly,a panel quantile regression model is constructed to investigate the differences of carbon emissions per capita at different quantile points based on six indicators:urbanization rate,industrial structure,population size,energy intensity,economic development level,and science and technology innovation,and compare them with the fixed-effect model.Finally,based on the spatial perspective,a spatial Durbin model was constructed to investigate the spatial spillover effects of per capita carbon emissions in the provincial areas.The results show that(1)the decoupling status of China’s per capita carbon emission decoupling index has been fluctuating during the period 2006-2020,but the overall decoupling status is relatively stable,mainly weak decoupling.The decoupling status of each province(urban area)is mainly weak decoupling and expansionary negative decoupling,while strong decoupling occurs only occasionally.(2)From the regression results of fixed effects,the coefficients of energy intensity and economic development level are the largest,while the coefficients of industrial structure and urbanization rate are smaller in promoting per capita carbon emissions.In contrast,population size and science and technology innovation have a suppressive effect on per capita carbon emissions.From the results of the panel quantile regression,the influence of energy intensity at the two ends of the quantile is smaller than that at the middle,with an“inverted U-shaped” distribution.The effect of economic development level at both ends of the quantile is larger than that in the middle,with a “U-shaped” distribution.The strongest inhibitory effect of science and technology innovation on per capita carbon emissions is found at the 0.25 quantile.(3)In terms of spatial correlation,there is a significant positive spatial correlation of per capita carbon emissions from 2006 to2020,and the spatial correlation is decreasing year by year,and the clustering characteristics of per capita carbon emissions are mostly H-H clustering and L-L clustering.From the regression results of the spatial Durbin model,industrial structure,energy intensity and science and technology innovation have spillover effects on per capita carbon emissions,which not only directly affect the per capita carbon emissions of the region,but also influence the per capita carbon emissions of neighboring regions.The above findings can provide an important reference for the formulation of inter-regional low-carbon synergistic development policies,which can promote the achievement of national green sustainable development goals.