Driving Factors of CO2 Concentration in Mainland China Based on GWR

Abstract

Considering that the complexity and dynamicity of CO2 emissions, the spatiotemporal distribution pattern of atmospheric CO2 and its drivers remain unclear. In this study, we used the Geographically Weighted Regression (GWR) method to analyze the comprehensive distribution of CO2 concentration in mainland China from 2015 to 2019. We considered the relationship between nine factors, including natural and human activities, and CO2 concentration. To clarify the correlation between CO2 concentration and drivers, we utilized Pearson’s correlation coefficient. Then, the GWR analysis revealed the spatial heterogeneity across provinces, which reflects the extent to which impact factors influence CO2 concentrations. Finally, we analysed CO2 concentration spatiotemporal variation characteristics and predicted future trends of CO2 concentration in 31 provinces in China. According to our research, GDP has a major impact on CO2 growth, while natural factors have a minor influence on CO2 concentration. Our study found significant regional differences in the effects of combined variables on CO2 concentrations, with monthly rotational patterns temporally and clustering of high growth rates spatially and CO2 concentration in mainland China will continue to steadily increase. The findings of this research are crucial for China’s future energy low-carbon transition and policy-making.