县域尺度下湖南省碳排放空间分异特征与影响因素

Spatial Differentiation Characteristics of Carbon Emission and Its Influencing Factors in Hunan Province,China at the County Scale

开展县域尺度下的碳排放空间分异与影响因素研究,是助推实现县域等多尺度区域“双碳”目标的重要环节。以湖南省122个县(市、区)为研究对象,通过夜间灯光数据估算湖南省各区县的碳排放量,采用探索性空间数据分析方法刻画了县域碳排放时空格局,运用地理探测器和时空地理加权回归(GTWR)模型,从政策、经济、能源、社会和产业维度对县域碳排放空间分异与影响因素进行了定量研究。结果表明:①2012~2020年,湖南省县域碳排放总体呈减弱趋势,且差异较为明显,空间格局分布为北高南低,东西差距较小;②莫兰指数(Moran's I)逐年下降,空间集聚特征较为显著,总体成正相关关系;③财政支出、人口规模、城镇化率、能源碳排放强度和农业发展水平是影响县域碳排放的主导因子;④通过时空地理加权回归模型对主导因子进行分析,发现同一指标对不同区县碳排放的影响存在显著的时空差异。

Carrying out the research on the spatial heterogeneity of carbon emissions and influencing factors at the county scale is an important link to help achieve the goal of“double carbon”for counties and districts and other multi-scale regions.Using 122 counties in Hunan province as the research object,the carbon emissions of each county in Hunan province were estimated through nightlight data,the exploratory spatial data analysis methods were used to depict the spatial and temporal patterns of carbon emissions in the counties,and the geodetector and geographically and temporally weighted regression(GTWR)model were used to quantitatively investigate the spatial differentiation of carbon emissions in the counties and the factors influencing in terms of policy,economy,energy,society,and industry.The results show that①from 2012 to 2020,the overall trend of carbon emission in the counties of Hunan province is weakening and the difference is more obvious,the spatial pattern distribution is high in the north and low in the south,and the gap between the east and west is relatively small;②the Moran's I value decreases year by year,the spatial clustering feature is more significant,and the overall positive correlation trend is shown;③the financial expenditures,the size of population,the urbanization rate,energy carbon intensity and agricultural development level are the dominant factors affecting carbon emissions in the counties;④based on GTWR model,there are significant spatial and temporal differences in the impact of the same indicator on carbon emissions in different counties.