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<p>Enhancing energy efficiency is a key solution for alleviating environmental crises and promoting sustainable development. As a strategic future-oriented technology, the rise of artificial intelligence (AI) opens new pathways for improving energy efficiency. However, research exploring the role of AI on regional energy efficiency from spatial perspectives remains scarce. To address these gaps, this study establishes a novel AI evaluation system and applies a combination weighting method to measure the AI indices of 30 Chinese provinces from 2006 to 2021. Furthermore, spatial econometric models are employed to detect the effect and mechanisms of AI on energy efficiency. The results highlight that AI significantly improves provincial energy efficiency, and its spatial spillover effects exhibit regional heterogeneity. In the eastern, high-intelligence and developed regions, AI generates positive spillovers through technological diffusion, while it induces negative externalities in low-intelligence and developing regions. Notably, compared to resource-based region, non-resource-based region receives more pronounced AI dividend. Further mechanism analysis indicates that industrial structure upgrading and research and development (R&amp;D) elements flow are crucial channels for AI to enhance energy efficiency. These findings enrich the understanding of AI’s influence in the energy sector and provide practical implications for building a modern energy system to achieve sustainable development goals.</p>

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The pathway to enhancing energy efficiency: Is artificial intelligence important?

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Nan Ke,
  • Jiamin Shen

摘要

Enhancing energy efficiency is a key solution for alleviating environmental crises and promoting sustainable development. As a strategic future-oriented technology, the rise of artificial intelligence (AI) opens new pathways for improving energy efficiency. However, research exploring the role of AI on regional energy efficiency from spatial perspectives remains scarce. To address these gaps, this study establishes a novel AI evaluation system and applies a combination weighting method to measure the AI indices of 30 Chinese provinces from 2006 to 2021. Furthermore, spatial econometric models are employed to detect the effect and mechanisms of AI on energy efficiency. The results highlight that AI significantly improves provincial energy efficiency, and its spatial spillover effects exhibit regional heterogeneity. In the eastern, high-intelligence and developed regions, AI generates positive spillovers through technological diffusion, while it induces negative externalities in low-intelligence and developing regions. Notably, compared to resource-based region, non-resource-based region receives more pronounced AI dividend. Further mechanism analysis indicates that industrial structure upgrading and research and development (R&D) elements flow are crucial channels for AI to enhance energy efficiency. These findings enrich the understanding of AI’s influence in the energy sector and provide practical implications for building a modern energy system to achieve sustainable development goals.