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<p>Using data from Chinese listed manufacturing firms (2008–2022), this study investigates the impact of AI on corporate energy efficiency within China’s institutional context. Through a cost–benefit analysis framework, we identify a U-shaped relationship: AI initially reduces energy efficiency but later enhances it. Managerial short-sightedness weakens this U-shaped effect, while environmental concern strengthens it. The AI effect is more pronounced in technology-intensive firms, with no significant difference between state-owned and non-state-owned enterprises. Based on these findings, we propose three policy recommendations for Chinese policymakers: (i) provide financial subsidies, tax incentives, and low-interest loans to help firms overcome the short-term costs of AI adoption; (ii) integrate environmental performance metrics into executive evaluation systems and expand green financing channels (e.g., green bonds) to encourage long-term managerial incentives; and (iii) prioritize targeted R&amp;D support for technology-intensive firms and foster cross-ownership alliances between SOEs and private firms to facilitate knowledge sharing and joint infrastructure projects.</p>

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The impact of artificial intelligence on corporate energy efficiency: the role of managerial traits

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Hui Tian,
  • Zhe Huang

摘要

Using data from Chinese listed manufacturing firms (2008–2022), this study investigates the impact of AI on corporate energy efficiency within China’s institutional context. Through a cost–benefit analysis framework, we identify a U-shaped relationship: AI initially reduces energy efficiency but later enhances it. Managerial short-sightedness weakens this U-shaped effect, while environmental concern strengthens it. The AI effect is more pronounced in technology-intensive firms, with no significant difference between state-owned and non-state-owned enterprises. Based on these findings, we propose three policy recommendations for Chinese policymakers: (i) provide financial subsidies, tax incentives, and low-interest loans to help firms overcome the short-term costs of AI adoption; (ii) integrate environmental performance metrics into executive evaluation systems and expand green financing channels (e.g., green bonds) to encourage long-term managerial incentives; and (iii) prioritize targeted R&D support for technology-intensive firms and foster cross-ownership alliances between SOEs and private firms to facilitate knowledge sharing and joint infrastructure projects.