A study of dominant factors in the coupled coordination of population-ecology-energy-digital economy in China based on random forest algorithm.

This study aims to examine the spatiotemporal patterns and dominant influencing factors of the coupling coordinated development (CCD) among population, ecology, energy, and digital economy (PEED) systems in China, contributing to the broader goal of sustainable regional development. Using panel data from 31 Chinese provinces over the period 2011-2020, we construct a PEED coordination index and analyze its evolution through coupling coordination models, spatial autocorrelation (Moran's I), the Geodetector model, and a Random Forest algorithm with SHAP analysis. Results show a steady improvement in the overall CCD across provinces, although significant regional disparities persist-eastern provinces such as Guangdong and Beijing lead in coordination, while western and northeastern regions lag behind. Among the four subsystems, the ecological subsystem shows the greatest spatial variation, while the digital economy subsystem is more homogeneous. The Nighttime Light Index, Urbanization Rate, and Green Coverage Rate are identified as the most important drivers, with the Nighttime Light Index consistently exhibiting the strongest influence on CCD. SHAP analysis reveals nonlinear effects of all drivers, highlighting the complexity of subsystem interactions. The findings provide policy-relevant insights for promoting balanced and sustainable development. Policymakers should focus on enhancing urban planning, ecological protection, renewable energy adoption, and digital infrastructure investment, especially in less-developed regions, to further strengthen PEED coordination and support the achievement of Sustainable Development Goals (SDGs).