中国省域碳强度空间依赖格局及其影响因素的空间异质性研究

Spatial Dependence Pattern of Carbon Emission Intensity in China’s Provinces and Spatial Heterogeneity of Its Influencing Factors

在估算各省域碳强度的基础上,利用探索性空间数据分析（ESDA）和时空跃迁测度方法以及地理加权回归（GWR）模型分析了1995～2015年中国省域（不包括西藏、港、澳、台地区）能源消费碳强度的空间依赖格局及其驱动因素的空间异质性。结果显示：（1）中国省域碳强度存在显著的空间正相关性,表现为先下降后上升再到小幅波动的特征,碳强度相似的省域趋向于集聚,表明中国省域碳强度具有明显的空间依赖特征;（2）省域碳强度存在不均衡的发展格局,高-高集聚的省域主要分布在中国西北部,低-低集聚的省域多分布于中国东南部。（3）碳强度空间集聚总体呈优化态势,高-高集聚的省域在减少,低-低集聚的省域在不断增多,但不同省域在碳强度的空间集聚中所起的作用不同。（4）碳强度影响因素（解释变量）的回归系数均为正值,4个解释变量对碳强度的影响程度依次为：能源强度〉能源结构〉产业结构〉人均GDP;且各因素对碳强度的影响在不同省域具有明显的空间异质性。

The carbon emissions intensities of China＇s thirty provinces caused by energy consumption were calculated according to the reference approach provided by IPCC. Exploratory spatial data analysis（ESDA）,space-time transition measurement method and geographically weighted regression（GWR） model were employed to analyze the spatial dependence of provincial carbon emissions intensity and spatial heterogeneity of its driving factors from 1995 to 2015. The results were shown as follows： 1） There was a significant positive spatial correlation in carbon emissions intensity among provinces. Global spatial autocorrelation decreased first and then increased and last fluctuated slightly. The provinces with similar carbon intensity tended to be agglomerate, indicating that provincial carbon intensity had an obvious spatial dependence characteristics. 2） An uneven development pattern of carbon emission intensity existed in China＇s provinces. The provinces with H-H agglomeration were mainly distributed in the northwest of China, while the ones with L-L agglomeration mainly distributed in the southeast of China. 3） The spatial agglomeration of carbon intensity presented an overall trend of optimization, the provinces with H-H agglomeration decreased, while ones with L-L agglomeration increased. However, different provinces played different roles in the spatial agglomeration of carbon intensity. 4） The driving factors of carbon emissions intensity had obvious spatial heterogeneity among China＇s provinces, and there was a positive correlation between the 4 explanatory variables and carbon intensity. The influence degree of 4 explanatory variables on carbon intensity was as follows： energy intensity〉energy structure〉industrial structure〉per capita GDP. Different policies of carbon intensity reduction should be formulated according to the actual situation of each province. Therefore, in order to achieve regional differences in carbon emission reduction, it is necessary to take full account of the actual situation of carbon intensity in each province and the spatial differences of carbon intensity affected by different factors.