PREreview of In-depth Analysis of Artificial Intelligence for Climate Change Mitigation

Review by Mehdi Zoghaib of: “In-depth Analysis of Artificial Intelligence for Climate Change Mitigation”

Liu, Lucy, (2025). In-depth Analysis of Artificial Intelligence for Climate Change Mitigation, Preprint

Key words: AI, Climate Change, Renewable energy systerm

Abstract:

This paper explores how artificial intelligence (AI) contributes to mitigating climate change by enhancing climate modeling, optimizing renewable energy systems, promoting sustainable practices, and supporting carbon capture and storage (CCS) technologies. It also discusses challenges related to AI adoption, including ethical concerns, data quality, and transparency.

1. AI’s Role in Climate Change Mitigation

Climate Modeling: AI helps improve climate models by analyzing vast datasets, making predictions more accurate and useful for policy decisions.

Renewable Energy Optimization: AI-driven smart grids enhance energy distribution, balance supply and demand, and improve renewable energy integration.

Agriculture: AI aids in precision agriculture by optimizing irrigation, reducing pesticide use, and increasing crop yields while minimizing environmental impact.

Carbon Capture and Storage (CCS): AI helps identify ideal carbon capture sites and optimizes CCS operations to reduce carbon emissions.

2. Evolution of AI Methods in Climate Mitigation

Traditional AI:

Rule-based systems and symbolic AI relied on predefined rules but lacked adaptability.

Expert systems emulated human decision-making but struggled with complex environments.

Recent AI Advances

Machine Learning & Deep Learning: AI now learns from large datasets to identify patterns and make accurate predictions.

Transfer Learning: Pre-trained AI models improve climate research by leveraging knowledge from related domains.

3. Challenges in Applying AI to Climate Change

Data Quality & Availability: Climate models require large, high-quality datasets, which may not always be accessible or accurate.

Transparency & Interpretability:

AI decisions need to be explainable to ensure trust among policymakers and stakeholders.

Ethical Considerations: AI should be designed to avoid biases and ensure equitable distribution of benefits.

4. Future Prospects & Recommendations

Transparent AI policies and frameworks are necessary to guide responsible AI use in climate action. Interdisciplinary Collaboration Governments, industries, and researchers must work together to integrate AI effectively into climate strategies.

Scaling AI Solutions: Future advancements should focus on making AI-driven climate solutions more accessible and impactful globally.

Conclusion

AI has significant potential to aid in climate change mitigation by improving climate predictions, optimizing renewable energy use, and promoting sustainability. However, challenges related to data, ethics, and transparency must be addressed through interdisciplinary collaboration and governance frameworks. This paper emphasizes that AI is not a standalone solution but a powerful tool that, when used responsibly, can drive significant progress in the fight against climate change.

Competing interests

The author declares that they have no competing interests.