Author
Listed:
- Nadeem, Waqar
- Ashraf, Abdul R.
- Khan, Huda
- Kumar, V.
Abstract
Addressing the Sustainable Development Goal related to climate change through artificial intelligence (AI) is an important area of interest for scholars, practitioners, and policymakers. This study examines how AI based strategies, hereafter AI strategies – including AI data management and quality, AI analytics, and AI-driven insights employed by the firms – impacting the climate change performance. It emphasizes the mediating role of climate crisis management (risk identification, risk assessment, and crisis response monitoring and treatment) and the moderating role of responsible AI. Using survey data from 235 managers of firms in the USA and Canada, findings reveal that climate risk identification and assessment significantly mediate the positive effects of AI strategies on climate change performance. These indirect effects are stronger under conditions of high responsible AI embeddedness. While crisis response monitoring and treatment also show a positive indirect relationship with climate change performance, this effect does not significantly differ based on the level of responsible AI. The research contributes to crisis management literature by highlighting the critical role of embedding responsible AI strategies for effective climate crisis management, especially in accurately identifying crisis types and assessing their severity. Additionally, we provide a structured 3x3 matrix that offers managerial guidelines drawing insights from data-derived findings and present critical research avenues for future exploration. Practically, these findings assist managers in effectively integrating responsible AI practices into crisis management processes to enhance firms’ climate performance and resilience.
Suggested Citation
Nadeem, Waqar & Ashraf, Abdul R. & Khan, Huda & Kumar, V., 2026.
"Impact of AI strategies on climate-change performance: Responsible AI and crisis management perspectives,"
Technovation, Elsevier, vol. 150(C).
Handle:
RePEc:eee:techno:v:150:y:2026:i:c:s0166497225002226
DOI: 10.1016/j.technovation.2025.103390
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