Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities

The acceleration of urbanization has significantly exacerbated climate change due to excessive anthropogenic carbon emissions and air pollutants. Based on data from 281 prefecture-level cities in China between 2015 and 2021. The spatiotemporal co-evolution of urban carbon emissions and air pollutants was analyzed through map visualization and kernel density estimation, revealing non-equilibrium and heterogeneity. Extreme gradient boosting (XGBoost) multiscale geographically weighted regression models(MGWR) and SHAP theory from game theory were employed to deeply investigate the disparities in relevance, spatial heterogeneity, and multiscale fluctuations of carbon emissions and air pollution. The main results are summarized as follows: (1) Between 2015 and 2018, CO2 emissions exhibited significant fluctuations, while SO2 and PM2.5 concentrations decreased markedly. (2) The XGBoost-SHAP model identified seven key driving factors, demonstrating high precision, the SHAP model is used to explain the model and reveal the influence of driving factors on carbon emissions. (3) The concentrations of CO2, SO2, and PM2.5 were positively correlated, the influence of each factor exhibited significant spatiotemporal differences, with varying directions of fluctuation across different regions. Thus, the symbiotic relationship between carbon emissions and air pollutants can inform decision-making for regional planning and sustainable urban development.