AI and Climate Action: Opportunities, Risks, and Challenges for Developing Countries
A recent technical paper by the Technology Executive Committee (TEC) under the UNFCCC Technology Mechanism examines how Artificial Intelligence (AI) can accelerate climate action in developing countries that face the greatest climate risks.
Leveraging AI to Tackle Climate Change
The paper, launched at the AI for Good Summit in Geneva in July 2025, was developed with the United Nations Industrial Development Organisation and supported by the Korea International Cooperation Agency. It offers a clear guide for policymakers, practitioners, and researchers on the benefits and pitfalls of applying AI for climate mitigation and adaptation in vulnerable regions.
How AI Supports Climate Mitigation
AI can significantly cut greenhouse gas emissions by optimizing energy use and identifying emission hotspots. For example, AI-driven energy management improves grid efficiency, forecasts demand, and smartly integrates renewables like solar and wind. In transportation, AI analyzes traffic data to reduce fuel consumption through better route planning and traffic flow.
AI’s Role in Climate Adaptation
On the adaptation side, AI enhances early warning systems for extreme weather events such as hurricanes, floods, and droughts, enabling timely disaster responses. It also helps urban planners identify infrastructure vulnerabilities and optimize land use. Combined with satellite imagery, AI supports biodiversity preservation, sustainable water management, and land restoration.
Risks and Challenges for Developing Countries
Despite these advantages, AI poses risks if not implemented thoughtfully. Biased algorithms can reinforce social inequalities and erode trust. Additionally, AI systems consume significant energy and water, which raises sustainability concerns in resource-scarce regions.
Many developing countries also face hurdles like limited digital infrastructure, unreliable internet, and insufficient computing power. A lack of quality climate data limits AI model training and decision-making. Without clear data-sharing protocols and cybersecurity safeguards, AI outputs risk being inaccurate or misused.
Practical Recommendations for Scaling AI in Developing Countries
- Invest in digital infrastructure and AI training to build local expertise and institutional capacity.
- Improve climate data collection and promote open-data initiatives to enhance AI model accuracy.
- Develop AI governance frameworks that ensure transparency, fairness, accountability, and ethics.
- Design AI systems with inclusive approaches to address gender bias and social inequalities.
- Promote energy- and water-efficient AI technologies to minimize environmental impacts.
- Encourage global cooperation among governments, UN agencies, private sector, academia, and civil society to share knowledge and harmonize standards.
By focusing on these areas, developing countries can adopt AI solutions that support effective climate action while addressing their unique challenges. Strengthening infrastructure and governance will also boost innovation and collaboration across sectors.
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