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Artificial Intelligence and Climate Adaptation

“The Intersection between AI and Climate Risk”

The climate crisis is increasing at an alarming pace. In 2023, global carbon dioxide emissions exceeded 36 billion metric tons, pushing the world closer to surpassing the 1.5°C warming threshold outlined in the Paris Agreement. Climate-related disasters are also escalating: between 2000 and 2019, 7,348 major disasters were recorded worldwide, causing over 1.2 million deaths and affecting more than 4 billion people. The economic toll is staggering, with climate disasters generating nearly $3 trillion in global losses over the past two decades.

These risks are not evenly distributed. Least Developed Countries (LDCs) and Small Island Developing States (SIDS) contribute less than 1% of global greenhouse gas emissions, yet they bear some of the heaviest adaptation burdens. Limited access to finance, technology, and infrastructure leaves these regions disproportionately exposed to climate shocks.

At the same time, technological innovation offers new pathways for resilience. Artificial Intelligence (AI), with its ability to process vast datasets, detect patterns, and generate forecasts, has the potential to transform how societies anticipate, prepare for, and respond to climate risks. Whether through powering early warning systems, optimizing energy use, or monitoring ecosystems, AI is becoming a vital tool at the intersection of climate adaptation and risk management.

AI as a Catalyst for Climate Adaptation

Enhancing Early Warning Systems

One of the most promising applications of AI lies in early warning systems (EWS). By analyzing climate, weather, and geospatial data at unprecedented speed and scale, AI can improve the accuracy and timeliness of disaster forecasts. This means communities can receive more reliable alerts about floods, droughts, and hurricanes, enabling life-saving evacuation measures and risk-informed planning. Recent work by the UNFCCC Technology Executive Committee (TEC) highlights how AI can power next-generation EWS, especially when combined with foundation models trained on diverse datasets. These systems could enable more proactive disaster risk management, reducing losses and protecting vulnerable populations.

Smarter Energy and Urban Systems

AI technologies are also being deployed to make energy and urban systems more resilient. In the energy sector, AI can forecast power demand, optimize grid operations, and accelerate the integration of renewables such as wind and solar. In cities, AI-powered models simulate the impacts of sea-level rise, heatwaves, and extreme rainfall, supporting climate-resilient urban planning and infrastructure investments.

Supporting Biodiversity, Land, and Water Management

AI, powered by satellite imagery and geospatial data, is transforming biodiversity, land, and water management by enabling real-time monitoring at scale. It can detect early signs of deforestation, desertification, or illegal logging, support efficient irrigation and water quality tracking, and monitor species populations and migration routes. These capabilities allow governments and conservationists to act proactively, safeguarding ecosystems while optimizing natural resource use. For developing countries, where traditional monitoring is costly, AI offers a cost-effective way to advance nature-based solutions, strengthen climate adaptation, and ensure the resilience of vital ecosystem services such as clean air, fertile soil, and freshwater.

Opportunities for Developing Countries

For developing countries, Least Developed Countries (LDCs), and Small Island Developing States (SIDS), the stakes are particularly high. These regions face the brunt of climate impacts while often lacking robust infrastructure and technical capacity. The #AI4ClimateAction initiative under the UNFCCC emphasizes AI’s potential to:

  • Strengthen resilience by improving disaster response.
  • Boost efficiency in agriculture, energy, and water management.
  • Support decision-making through better climate data analysis.

If supported with the right policies and partnerships, AI could become a strategic tool to help these countries adapt more effectively and participate fully in global climate efforts.

Risks and Challenges

Despite its promise, deploying AI for climate adaptation comes with significant challenges:

  • Bias and Inequality: Algorithms that are not designed inclusively may reinforce existing disparities, undermining trust.
  • Resource Intensity: AI systems consume considerable energy and water, raising sustainability concerns in resource-scarce regions.
  • Data Gaps: Many developing countries lack access to high-quality, comprehensive datasets required to train accurate AI models.
  • Digital Divide: Limited connectivity, inadequate computing power, and capacity shortages hinder effective deployment.
  • Governance Gaps: Without strong frameworks, AI outputs may be inaccurate, opaque, or misused.

Pathways Forward

To harness AI responsibly for climate adaptation:

  • Close the Digital Divide: Invest in infrastructure, connectivity, and AI capacity-building programs that empower local experts and institutions.
  • Expand Climate Data Access: Promote open-data initiatives and cross-border sharing frameworks to strengthen the datasets that power AI models.
  • Embed Inclusive and Ethical Design: Develop AI systems with fairness, transparency, and accountability at their core, tackling gender bias and social inequalities.
  • Promote Sustainable AI: Encourage energy and water-efficient AI practices to ensure technology use aligns with climate goals.
  • Foster Global Collaboration: Strengthen partnerships between governments, UN bodies, the private sector, academia, and civil society to align standards, build trust, and accelerate innovation.

Conclusion

AI holds immense potential to transform how societies anticipate, adapt to, and manage climate risks. From smarter early warning systems to resilient energy grids and sustainable land use, AI can unlock solutions that scale globally. Yet, realizing this potential requires more than technology alone. It demands inclusive governance, equitable access, and sustainable design, particularly for countries most vulnerable to climate shocks. By bridging digital divides, closing data gaps, and embedding responsible innovation, AI can become a cornerstone of climate adaptation. The intersection of AI and climate risk is not only a technological frontier but also a test of global solidarity. If deployed wisely, AI can help ensure that no community is left behind in the urgent journey toward a more sustainable future.

References

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  • World Bank (2023). Climate-Smart Agriculture. https://www.worldbank.org/en/topic/climate-smart-agriculture
  • Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2019). Green AI. arXiv. https://arxiv.org/abs/1907.10597
  • https://www.statista.com/statistics/526002/energy-related-carbon-dioxide-emissions-worldwide/?utm_s
  • https://www.nasa.gov/news-release/temperatures-rising-nasa-confirms-2024-warmest-year-on-record/?utm_s
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  • Hua Yin, Xieyu Yin, Fenghua Wen, Artificial intelligence and climate risk: A double machine learning approach, International Review of Financial Analysis, Volume 103, 2025, 104169, ISSN 1057-5219, https://doi.org/10.1016/j.irfa.2025.104169.
  • https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117
  • https://www.techpolicy.press/the-overlooked-climate-risks-of-artificial-intelligence/
  • https://unfccc.int/news/ai-and-climate-action-opportunities-risks-and-challenges-for-developing-countries