In 2024, the World Meteorological Organization (WMO) recorded 617 extreme weather events reported by countries around the world. Of these, 152 were classified as unprecedented and 297 as unusual. According to the WMO’s State of the Global Climate report, these disasters displaced over 824,000 people, injured 1.1 million, and caused 1,700 deaths. Yet, these figures represent only part of the overall toll of the climate crisis. As global climate change accelerates, its financial and developmental impacts deepen—especially in vulnerable regions—making it harder for communities to invest in adaptation and mitigation measures.
Beyond immediate damage, the economic burden of climate change is worsened by financial systems that often overlook the value of ecosystems like forests and wetlands, which provide essential services and act as buffers against natural hazards. A study on European banks revealed that ecosystem degradation and biodiversity loss significantly affect financial stability, highlighting the need to integrate environmental factors into risk assessments. At the 2025 Pan-European Summit on Climate Resilience, experts and policymakers called for urgent, science-based, and equitable strategies that strengthen resilience by engaging local communities and leveraging natural systems.
The International Day for Disaster Risk Reduction emphasizes the theme “Fund Resilience, Not Disasters,” calling for financial systems to better consider the complex links between climate risks, land degradation, and biodiversity loss. Artificial intelligence (AI) is emerging as a key tool in this effort. By processing massive datasets, AI can identify patterns across environmental, social, and economic systems, helping forecast and mitigate risks. Machine learning is already improving heatwave prediction, cyclone forecasting, and agricultural adaptation strategies. Initiatives such as AlphaEarth Foundations use satellite and climate data to map land degradation and soil health, while other studies apply AI to model forest fire risks and promote more resilient ecosystems.
AI’s capacity to analyze interconnected risks can also reshape how climate finance operates. Instead of focusing on short-term disaster relief, AI-driven systems can model complex, multi-hazard events to guide long-term investments in resilience. It can translate climate-biodiversity-land data into measurable financial metrics, helping investors quantify and price risks. For the private sector, AI offers insights to stabilize returns while promoting nature-based solutions. For public institutions, it can identify where funding would best de-risk investments, enabling blended finance strategies that boost both climate and nature resilience.
To fully realize AI’s potential, equitable access to reliable, standardized data and computational infrastructure is essential. Equally important is building trust through transparency. Explainable AI (xAI) ensures that AI systems can justify their predictions, grounding decisions in verifiable data and scientific principles. This transparency is vital when AI informs financial and policy decisions that affect communities and ecosystems.
As the global community marks this year’s International Day for Disaster Risk Reduction, the message is clear: resilience must be prioritized in every funding decision. Integrating AI into long-term climate and financial planning can help shift global systems from reactive to proactive. International collaboration will play a pivotal role, with initiatives such as the Global Initiative on Resilience to Natural Hazards through AI Solutions and its Working Group on AI for Climate Applications leading the way. Co-led by UN agencies including the ITU, UNEP, UNFCCC, UPU, WMO, and UNESCO, this global effort aims to harness AI for climate adaptation, particularly supporting developing and vulnerable nations. Together, these collaborations demonstrate how AI can be a transformative force—funding resilience instead of disasters, and helping humanity navigate toward a safer, more sustainable future.
