Spatio-Temporal Heterogeneity of Regional Carbon Emission Drivers in China: Evidence from an Integrated Random Forest and GTWR Model

Precisely identifying the key drivers of regional carbon emissions and their spatiotemporal heterogeneity is critical for formulating differentiated strategies under China’s “Dual Carbon” goals. To address the limitations of traditional models in variable screening and handling non-stationarity, this study constructs an analytical framework that integrates a Random Forest (RF) model for preliminary variable screening, Geographically and Temporally Weighted Regression (GTWR) for spatiotemporal quantification, and the CRITIC method for multidimensional evaluation. Based on panel data from 30 Chinese provinces spanning 2005 to 2023, this study investigates the spatiotemporal evolution of carbon emission drivers. The findings reveal significant regional disparities. In the eastern region, the emission-increasing effect driven by population continues to intensify. Although economic growth shows signs of decoupling from emissions, the emission reduction benefits of industrial upgrading are diminishing. Notably, provinces such as Jiangsu have even experienced a rebound in energy consumption, which is potentially linked to the expansion of digital infrastructure. In the central region, a “pollution haven” effect has emerged due to the relocation of energy-intensive industries. Furthermore, the impacts of population, urbanization, and energy consumption structure exhibit an inverted U-shaped trend, with green urbanization beginning to yield initial emission reductions. In the western region, the suppressive effect of energy intensity on emissions continues to strengthen, particularly around Shaanxi. For northern energy-rich areas, economic growth acts as a prominent driver, while the impact of population displays a clear “positive in the south, negative in the north” spatial pattern. Moreover, northern provinces have successfully leveraged agglomeration effects to achieve emission reductions. Ultimately, these findings provide robust empirical support for constructing a spatially differentiated governance system to facilitate carbon neutrality.