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Leveraging AI to enhance firms’ resource efficiency: ecological modernization theory and resource-based view perspectives

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  • Zhao, Lang
  • Xu, Jiawei
  • Zhang, Baofeng
  • Lu, Jianjun

Abstract

How to leverage advanced technologies to enhance resource efficiency under conditions of intensified resource scarcity is becoming a pressing issue that urgently needs to be addressed. Drawing on ecological modernization theory and resource-based view, this study explores the relationship between AI and firms’ resource efficiency under different environmental pressures. Using a comprehensive dataset of Chinese listed firms, the findings reveal that artificial intelligence (AI) adoption significantly improves resource efficiency, primarily reflected in more efficient management of energy, materials, and waste. Green continuous innovation capabilities fully mediates the relationship between AI and resource efficiency. Moreover, the positive effect of AI on resource efficiency is amplified under stringent environmental pressures, indicating that firms facing higher pollution governance pressure and carbon emission pressure derive greater benefits from AI technologies. Moreover, the study further reveals four combination patterns of pollution governance pressure and carbon emission pressure at different levels, which result in differentiated outcomes in the relationship between AI and resource efficiency. Among these, high carbon emission pressure is a necessary condition for driving firms to use AI technology to enhance resource efficiency. Our study not only contributes to the theoretical understanding of the relationship between AI, external environmental pressures, and resource efficiency, but also provides some valuable insights for managers and policymakers on how to adopt AI and formulate effective environmental regulations to enhance resource efficiency.

Suggested Citation

  • Zhao, Lang & Xu, Jiawei & Zhang, Baofeng & Lu, Jianjun, 2026. "Leveraging AI to enhance firms’ resource efficiency: ecological modernization theory and resource-based view perspectives," International Journal of Production Economics, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:proeco:v:291:y:2026:i:c:s0925527325002087
    DOI: 10.1016/j.ijpe.2025.109723
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