Better climate risk management with functional tools

In a panel of 51 countries between 1996 and 2023, it was found that using detection, capture and integrated action tools significantly reduces physical climate risks. The effect is larger for functional applications than for basic technology, and it is amplified in agreements that combine political and economic integration. In addition, the ability of these tools to govern risk grows as climate hazards increase.

Governments around the world are under scrutiny to show that the millions they put into artificial intelligence are not just a technological spectacle but a tool able to cushion the damage of droughts, floods and heat waves. In a context where extreme weather events are becoming more frequent and costly, the question shifts from whether AI can help to how to do it in a way that actually reduces the physical risk facing communities and economies.

The authors of the paper argue that AI can govern those risks through three interrelated mechanisms: early detection of threats, capture of information to mobilize resources, and integrated action that coordinates the response. They argue that the effect does not come from the basic technology itself, but from the functional applications that put those mechanisms into practice, and that its power multiplies when countries take part in agreements that combine political and economic integration, beyond simple trade blocs. They also point out that, as climate hazards increase, the ability of these tools to contain risk grows, which suggests a positive feedback between threat and response.

To back up that thesis, the authors rely on case studies showing how learning-based warning systems anticipate river floods or forest fires, and on work comparing the performance of AI platforms launched by multinational consortia against isolated initiatives. They highlight that alliances that combine regulatory standards, shared funds and data standards allow information detected by one country to translate quickly into concrete actions in another, while purely economic agreements lack that synergy. The argument is completed by the observation that, as the intensity of hazards grows, the margin of improvement AI provides becomes more pronounced, which challenges the view that the technology only produces rebound effects or ethical dilemmas with no tangible compensation.

Policymakers who want to turn these findings into policy should prioritize investment in functional AI applications (sensing, analysis and activation systems) and encourage cooperation frameworks that link political decisions with financial mechanisms and shared data. At the same time, they will need to watch that these schemes do not create new risks of dependence, bias or displaced responsibility, keeping open the debates on distributive justice and democratic governance. The tension that remains unresolved is how far these benefits can scale without side effects that erode public trust or deepen gaps between countries with different technological capacities.

Conclusions

• Functional applications of analysis tools add more value for reducing climate risks than generic technology approaches.
• Blocs that combine political and economic cooperation amplify the effect of these tools on risk management.
• As physical climate risks intensify, the contribution of these tools to their governance becomes stronger.
• Governments can prioritize investment in functional solutions within political integration frameworks to improve resilience to climate change.