能源碳排放的空间依赖与经济增长——基于广东的空间面板模型分析

The Spatial Dependence of Carbon Emissions from Energy Consumption and Economic Growth——Based on the Spatial Analysis of Panel Data in Guangdong

利用探索性空间数据,分析了广东省碳排放的空间格局及其空间跃迁状态,并进一步采用空间滞后模型(SLM)和空间误差模型(SEM)分析碳排放与经济增长的关系。结果表明:广东省市域人均碳排放存在显著的空间自相关性,绝大部分城市属于"高——高"和"低——低"类型,碳排放集群格局表现出高度的空间稳定性和路径依赖性。人均碳排放的主导驱动因素是人均GDP,但人均碳排放与人均GDP增长未呈现显著的倒"U"型关系;能源效率、产业结构调整与城市化水平提升对人均碳排放具有显著的削减作用,而对外贸易和技术进步对碳排放的影响在统计上不显著。

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 de- pendence. 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.