The Double-Edged Sword of AI in Climate Change: Opportunities, Risks, and Responsible Governance
Bibliographic record
Abstract
Artificial intelligence (AI) has emerged as a powerful tool in climate action, enhancing strategies for mitigation, adaptation, and resilience-building.AI technologies enable real-time emissions tracking, improved climate forecasting, renewable energy optimization, and disaster preparedness, significantly improving climate response capabilities.However, despite these benefits, AI's deployment presents critical ethical, environmental, and governance challenges.AI's high energy consumption, reliance on resource-intensive hardware, and potential bias in climate models raise concerns about its long-term sustainability and equitable access.Additionally, AI is paradoxically being used to enhance fossil fuel extraction, prolonging reliance on carbon-intensive industries.These challenges necessitate a balanced approach that prioritizes sustainable AI development, fair distribution of AI-driven climate solutions, and transparent governance frameworks.This article provides a comprehensive analysis of AI's applications in climate change mitigation and adaptation, critically evaluating its benefits, risks, and ethical considerations.It also outlines policy recommendations to ensure that AI remains a responsible, sustainable, and globally accessible tool for addressing climate challenges, including the implementation of transparent energy reporting standards for AI systems and the development of global governance frameworks to prevent technological monopolies and ensure equitable access.This article finds that while AI holds transformative potential for climate mitigation and adaptation, its benefits can only be fully realized through equitable access, sustainable infrastructure, and ethical governance.By aligning AI innovation with global climate justice goals, it can become a powerful force for building longterm climate resilience and environmental sustainability.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".