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AI-driven sustainable energy saving: Pathways for enhancing energy efficiency in Chinese listed firms

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  • Wu, Chuntao
  • Li, Haoran
  • Yuan, Bingbing

Abstract

Artificial intelligence (AI) offers unprecedented opportunities for energy management and optimization through data-driven, precision decision-making. This paper investigates how AI development influences energy efficiency (EE) by analyzing an unbalanced panel dataset of 54,657 observations from 4453 listed firms in China over the period 2007 to 2023. Studies have found that the development and diffusion of AI can significantly enhance EE at the firm level, while also inducing short-term energy rebound effects. Mechanistic analysis suggests that AI enhances EE mainly through innovative inputs and green innovation outputs. However, the relationship between AI and labor—where AI serves as a substitute for human labor—limits the role of human capital investment in fostering innovation. Further, heterogeneity analysis reveals that firms in non-high-tech, labor-intensive, and privately owned sectors, as well as those located in the Midwest or with established market positions, are particularly likely to benefit from AI-driven EE improvements. This study not only extends the application of technology diffusion theory to the domain of AI, but also draws comparative insights from other BRICS nations, aiming to offer actionable guidance for China's transition toward intelligent manufacturing and a low-carbon economy.

Suggested Citation

  • Wu, Chuntao & Li, Haoran & Yuan, Bingbing, 2025. "AI-driven sustainable energy saving: Pathways for enhancing energy efficiency in Chinese listed firms," Applied Energy, Elsevier, vol. 401(PA).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pa:s0306261925013376
    DOI: 10.1016/j.apenergy.2025.126607
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