Impact of AI strategies on climate-change performance: Responsible AI and crisis management perspectives
Why this work is in the frame
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Bibliographic record
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. • Integrates AI strategy with a climate crisis management framework to assess climate performance. • Responsible AI enhances risk identification but not crisis response effectiveness. • AI's strongest impact lies in proactive risk management rather than real-time crisis handling. • Offers a framework aligning organizational AI strategy in addressing SDG 13. • Offers 3X3 matrix based managerial guidelines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it