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Artificial intelligence-driven environmental risk assessment and green investment efficiency: Evidence from Chinese listed companies

Author

Listed:
  • Luo, Kaikai
  • Zhu, Xiangcheng

Abstract

Against the backdrop of increasingly stringent environmental governance requirements, this study examines the impact of artificial intelligence-driven environmental risk assessment on the green investment efficiency of Chinese listed companies. Adopting a green finance perspective and utilising panel data from 2015 to 2024 concerning green investment and environmental risk assessment among Chinese listed firms, empirical testing is conducted through the construction of a mediation effect model. Findings reveal that environmental risk enhances green investment efficiency via the critical pathway of corporate data governance capabilities; Artificial intelligence risk assessment technology, acting as a moderating variable, both amplifies the direct positive impact of environmental risk on green investment efficiency and enhances the transmission efficiency of the data governance capability mediation mechanism. Heterogeneity analysis indicates that these effects are more pronounced in state-owned enterprises and high-pollution industries. Endogeneity and robustness tests further validate the reliability of these conclusions. This research provides new empirical evidence and practical insights for enhancing corporate green investment effectiveness through intelligent technologies and data governance amid environmental uncertainty.

Suggested Citation

  • Luo, Kaikai & Zhu, Xiangcheng, 2026. "Artificial intelligence-driven environmental risk assessment and green investment efficiency: Evidence from Chinese listed companies," Finance Research Letters, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:finlet:v:90:y:2026:i:c:s154461232502625x
    DOI: 10.1016/j.frl.2025.109376
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