Abstract This paper uses the exploratory spatial data analysis to explore the spatial pattern of carbon emissions and its spatial dynamics transition state in Guangdong, and then empirically analyses the driving force factors of carbon emissions by using spatial lag model (SLM) and spatial error model (SEM).It shows that, per capita carbon emissions of the cities exists significant spatial autocorrelation, most cities are “high-high” and “low-low” type of carbon cluster patterns, and it means a high degree of spatial stability and path dependence. GDP per capita is the dominant driver of per capita carbon emissions, and they didn’t show significant inverted “U”-shaped relationship. Energy efficiency, Industrial restructuring and upgrading the level of urbanization have a significant role in the reduction of the per capita carbon emissions, while foreign trade and technological progress are not statistically significant.
HU Xin-Yan,LIN Wen-Qing. The Spatial Dependence of Carbon Emissions from Energy Consumption and Economic Growth ——Based on the Spatial Analysis of Panel Data in Guangdong. Economic Survey, 2014, 31(4): 013.
2014, Vol. 31 
