AI integration for climate risk mitigation: The role of organizational context

Muhammad Kaleem Khan, Muhammad Jameel Hussain, Muhammad Wasim Hussan, Afifa Qadeer, Anona Armstrong and Shanshan Li

Technological Forecasting and Social Change, 2025, vol. 220, issue C

Abstract: This study examines the relationship between artificial intelligence (AI) adoption and Firm-Level Climate Change Risk (FLCCR) among Chinese enterprises. Using comprehensive firm-level data on AI implementation and FLCCR exposure, we analyze the contextual effectiveness of AI across diverse ownership structures, industry sectors, and corporate governance frameworks. Our empirical analysis reveals a robust association between AI adoption and reduced FLCCR, with findings consistent with established economic theories. The results remain statistically significant after addressing potential endogeneity concerns through multiple robustness checks. Our findings reveal that AI's climate risk-reduction potential is not uniform but context-dependent, varying significantly across ownership types, sectors, and governance characteristics. Notably, the risk-mitigating effects of AI appear particularly pronounced in state-owned enterprises, firms operating in pollution-intensive or high-technology sectors, and organizations with strong corporate governance mechanisms, specifically those characterized by board independence and gender diversity. These findings contribute to the growing literature on technological solutions for environmental challenges while providing actionable insights for corporate decision-makers and policymakers seeking to enhance climate resilience through strategic AI integration. The study underscores the potential role of AI as a tool for sustainable development while acknowledging the complex interplay between technological adoption and organizational factors in risk mitigation outcomes.

Keywords: AI adoption; Firm-level climate change risk; State-ownership; Polluting industries; High tech industries; Gender diversity; Board independence (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:220:y:2025:i:c:s0040162525003580

DOI: 10.1016/j.techfore.2025.124327

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