Spatio-temporal evolution relationships between provincial CO2 emissions and driving factors using geographically and temporally weighted regression model

To achieve the 2060 carbon neutrality target, each province in China needs to ensure rapid reduction in carbon dioxide (CO2) emission according to its own developmental characteristics. Meanwhile, to achieve sustainable emission reduction, it is important to explore the development path of dual reduction of total CO2 emissions and CO2 emission intensity in each province. Based on the data of 30 provinces in China for the period 2005-2019, in this study, we analyzed the spatial and temporal evolution trends of CO2 emissions in each province and determined the spatial autocorrelation of provincial CO2 emissions. We used the geographically and temporally weighted regression (GTWR) model to analyze the spatio-temporal evolution of the driving factors of provincial CO2 emissions. The results showed that CO2 emission intensity of each province gradually decreased, and the CO2 emissions between provinces were spatially autocorrelated. Energy intensity had the highest influence on total CO2 emissions, and the influence of trade openness on CO2 emission intensity had the largest inter-provincial differences. At present, reducing energy intensity and the proportion of secondary industries, improving trade openness, and using electricity alternatives are the key for some provinces to achieve dual reduction of total CO2 emissions and CO2 emission intensity.

Automated summary

To achieve the 2060 carbon neutrality target, each province in China needs to ensure rapid reduction in carbon dioxide (CO2) emissions based on their own characteristics. This study analyzed the spatial and temporal trends of CO2 emissions in each province and identified spatial autocorrelation. The results showed a gradual decrease in CO2 emission intensity, with energy intensity having the highest influence on total CO2 emissions.