Blessing or Peril? The Impact of Artificial Intelligence on China's Energy Transition

 Energy transition is essential for combating climate change and achieving sustainability, with artificial intelligence (AI) playing a key role in advancing this transition and developing a modern energy system. This paper uses data from 261 prefecture-level cities across China to explore the core aspects of energy transition from three perspectives: quantity, quality, and structure. By linking AI with energy transition, this study investigates the impacts and mechanisms through which AI influences the energy transition within an integrated framework. Additionally, considering the characteristics of AI's pervasiveness, integration, and synergy, the paper examines the spatial spillover effects of AI on energy transition, offering a novel perspective for policy discussions on AI and green energy. The findings show that AI can reduce energy consumption, enhance energy efficiency, and optimize energy structure, thereby promoting the energy transition across three dimensions: quantity, quality, and structure. After conducting robustness and endogeneity tests, the conclusions remain robust. Mechanism analysis reveals that AI improves human-machine alignment by leveraging the complementary strengths of both machines and workers, fostering coordination and ultimately supporting energy transition. Furthermore, AI can generate positive externalities, such as economies of scale, technological spillovers, and knowledge sharing, by facilitating economic agglomeration, further advancing energy transition. The moderating effect analysis indicates that AI is more effective in promoting energy transition in regions with strong digital infrastructure, high technological absorption capacity, and labor-intensive economies. The spatial spillover effects demonstrate that energy transition exhibits significant geographic clustering. As globalization and information technology evolve, inter-regional interactions are increasing, and AI has the potential to overcome geographic barriers, generating spillover effects on energy transition across regions. However, the siphon effect, which concentrates technological advancements in certain areas, is stronger than the trickle-down effect, which benefits surrounding regions. As a result, AI may foster local technological growth hubs, advancing energy transition in those areas while indirectly depleting human resources and other factors in neighboring regions, thus hindering energy transition in less developed areas. This study enhances the understanding of the opportunities presented by AI, providing valuable insights for promoting energy transition and ecological civilization construction.