Probing the carbon emissions in 30 regions of China based on symbolic regression and Tapio decoupling.

Against the background of energy shortages and severe air pollution, countries around the world are aware of the importance of energy conservation and emission reduction; China is actively achieving emission reduction targets. In this study, we use a symbolic regression to classify China's regions according to the degree of influencing factors and calculate and analyze the inherent decoupling relationship between carbon emissions and economic growth in each region. Based on our results, we divided the 30 regions of the country into six categories according to the main influencing factors: GDP (13 regions), energy intensity (EI; 7 regions), industrial structure (IS; 3 regions), urbanization rate (UR; 3 regions), car ownership (CO; 2 regions), and household consumption level (HCL; 2 regions). Then, according to the order of the average carbon emissions in each region from high to low, these regions were further categorized as Type-EI, Type-UR, Type-GDP, Type-IS, Type-CO, or Type-HCL regions. The decoupling coefficient of the Type-UR region was the smallest with an expansive coupling and weak decoupling, whereas the other regions showed expansive negative decoupling, expansive coupling, and weak decoupling. Among them, the reduction rate of the decoupling coefficient in the Type-EI region was the largest at 6.65%. EI and GDP regions were the most notable contributors to emissions, based on which we provide policy recommendations.