Digital intelligence and energy sustainability in China: Spatial dynamics, threshold effects, and policy implications

Digital intelligence has become a driving force for energy transformation and low-carbon sustainable development. Unlike digitization, intelligence focuses more on the application of digital technologies. In this study, we assessed the level of digital intelligence development (Dig) and energy sustainability efficiency (EE) in China using the entropy weight-TOPSIS method and super-efficiency SBM model. Through integrated kernel density analysis and trend surface modeling, we observe that EE shows a continuous upward trajectory over time, but stagnation is obvious in the central region, and there is a spatial phenomenon of “center collapse”. Based on these basic research results, this study utilizes the spatial Durbin model and the panel threshold model to investigate the direct, spatial, and threshold effects of Dig on EE. The findings suggest that Dig greatly promotes EE. However, this favorable impact exhibits limited spatial spillovers, with its advantages concentrated within specific regions. Whereas there are disparities in the level of EE across localities, region-specific investments in digital infrastructure are needed to maximize energy sustainability benefits. In addition, our analysis finds that the level of economic development and industrial structure are key threshold variables affecting Dig for EE. It is worth noting that Dig can only contribute substantially to promoting EE if both the level of economic development and industrial structure exceed the specified thresholds. Meanwhile, this study contributes to the literature by combining spatial econometric modeling with digital transformation indicators to assess their heterogeneous impact on energy sustainability.