Spatial Dependence Pattern of Energy-Related Carbon Emissions and Spatial Heterogeneity of Influencing Factors in China: Based on ESDA-GTWR Model

To find out the spatial dependence of carbon emissions and its evolution characteristics is the key to achieving regional differential emission reduction strategy. In this study, 30 provinces with different population sizes and in different stages of development in China, were selected to explore the spatial heterogeneity of carbon emissions by exploratory spatial data analysis (ESDA), combined with geographically and temporally weighted regression (GTWR). The findings revealed that (1) energy-related carbon emissions at the province-level in China increased from 1997 to 2016, with an increment of 8,893 million tons; (2) there is a significant positive spatial correlation between provincial carbon emissions, which showed the characteristics of rising first and then falling; this indicated that provincial carbon emissions have obvious spatial dependent characteristics; (3) the tertiary industry ratio had a restraining effect on carbon emissions, whereas the other three variables, namely GDP, urbanization rate, and energy intensity had a positive effect on carbon emissions of provinces in China; and (4) province-scale spatial differences in and distribution patterns of carbon emissions within the same countrywide, which will help decision making in terms of carbon trading and ecological compensation mechanisms. Therefore, we suggested that in the formulation of reduction policies for carbon emissions, policymakers need to adapt to local conditions which accord to the characteristics of the province. © 2021 Technoscience Publications. All rights reserved.