Research on the Spatiotemporal Correlation Characteristics Between Artificial Intelligence and Energy Transition in China

Artificial intelligence (AI), which is advancing rapidly, offers a novel and important tool for driving sustainable energy transition, although the spatiotemporal correlation between the two is complex. Taking China’s 30 provinces as the study subjects, this research constructs an evaluation index system from the perspective of energy transition outcomes to assess the level of China’s energy transition. It evaluates the level of AI development based on the foundation of AI development, AI technological innovation, and AI application, and analyzes its spatiotemporal evolution characteristics. Pearson correlation analysis and bivariate local spatial autocorrelation are employed to investigate the spatiotemporal associations between energy transition and AI. In addition, the dynamic mechanisms linking the two are further investigated using a geographically and temporally weighted regression (GTWR) model. The results indicate that, first, innovation and application in AI were on the rise, while regional disparities were widening and a polarization phenomenon was emerging; AI development was concentrated in the eastern regions, with a decreasing trend toward the northwestern inland areas. Second, the overall level of China’s energy transition continued to rise, with a box-shaped clustering pattern observed across regions; Beijing, Inner Mongolia, Jiangsu, and Shandong had achieved a relatively high level of energy transition. Third, the development of AI did not always correlate positively with the energy transition. There was a significant positive correlation between AI technological innovation and application and the energy transition. There were significant differences in the spatial patterns linking AI development and the energy transition. The positive correlation between the two was significant and widespread, concentrated in the central and eastern provinces.