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

Abstract

• There was a significant positive spatial correlation between provincial CO 2 emissions in China. • Spatio-temporal heterogeneity analysis of driving factors of CO 2 emissions was carried out using GTWR model. • Inter-provincial differences in the impact of industrial structure on CO 2 emissions were minimal. • Energy intensity is a key factor in achieving dual reduction in provincial CO 2 emissions, followed by trade openness. To achieve the 2060 carbon neutrality target, each province in China needs to ensure rapid reduction in carbon dioxide (CO 2 ) 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 CO 2 emissions and CO 2 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 CO 2 emissions in each province and determined the spatial autocorrelation of provincial CO 2 emissions. We used the geographically and temporally weighted regression (GTWR) model to analyze the spatio-temporal evolution of the driving factors of provincial CO 2 emissions. The results showed that CO 2 emission intensity of each province gradually decreased, and the CO 2 emissions between provinces were spatially autocorrelated. Energy intensity had the highest influence on total CO 2 emissions, and the influence of trade openness on CO 2 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 CO 2 emissions and CO 2 emission intensity.