Nonlinear Dynamics and Spatial Correlation Pattern of the Digital Economy on Energy Efficiency: Evidence from Ensemble Learning and Spatio-Temporal Graph Neural Network

Achieving synergy between the digital economy and energy efficiency is pivotal for realizing high-quality development under the “Dual Carbon” targets. However, traditional econometric methods struggle to capture the complex nonlinear and spatio-temporal dependencies inherent in this relationship. To address this issue, this study develops a two-stage framework using Chinese provincial panel data. It combines LightGBM/CatBoost and SHAP for critical factor identification, and employs STGNN for capturing nonlinear and spatial correlation patterns, to systematically decode the driving mechanisms of the digital economy on energy efficiency. The results reveal three key findings: (1) Complex Nonlinearity: The impact manifests in distinct U-shaped, inverted U-shaped, and weak correlation patterns, accompanied by significant spatial clustering. (2) Structural Heterogeneity: The dimensions of the digital economy show differential associations with energy efficiency. Industrial digitization and infrastructure are associated with more direct improvements in efficiency, whereas digital industrialization functions primarily through indirect technological supply. (3) Spatial Correlation Pattern: Higher levels of digital development correspond to higher local energy efficiency and are linked to positive predicted adjustments in neighboring regions, with notable regional heterogeneity. Combining machine learning-based feature selection with deep learning-based spatiotemporal modeling provides a scientific basis for formulating location-specific digital economy strategies and coordinated energy-saving policies.