Artificial intelligence, or AI, is quickly becoming a fundamental tool in how we understand and address climate change. As climate-related challenges become more complex and urgent, AI for climate action can enhance sustainability assessments, enable the development of more accurate climate models, and inform practical choices for mitigation or adaptation. What sets AI apart is its ability to quickly process immense amounts of climate data, helping build more flexible and precise models. This empowers decision makers and businesses to better understand risks, optimise resources, and design innovative solutions for effective climate resilience and adaptation.
Sector-specific impacts and the role of digital tools
AI’s transformative power is being applied across climate solutions, supporting sector-wide decarbonisation, greater efficiency, and the development of new technologies. In energy systems, AI improves forecasting, supports waste reduction, and manages renewable microgrids; in transport, it optimises routes, streamlines freight, and enhances electric vehicle integration. Tools such as RETScreen help organisations assess, plan, and monitor clean energy projects using integrated climate data, while directories like Swiss Sustainable Finance connect practitioners with further resources. Together, these digital tools advance the decarbonisation of economies.
Bridging data gaps: AI for climate action and the future of climate risk assessments
While advances in mitigation are essential for limiting future climate impacts, addressing the immediate and escalating effects of climate change calls for an equally strong focus on adaptation. Realising the full potential of AI for climate action and adaptation requires overcoming fundamental data challenges. Traditional risk models are built on the expectation that the future will reflect the past, but today’s climate extremes, from record-breaking heatwaves to severe flooding, are breaking these patterns. As a result, many conventional forecasts have become less reliable, which makes it essential to find new ways of anticipating climate risks. Even with the latest technology, we only gain one year of global climate data each year, making it difficult for historical records to keep pace with the scale and speed of current changes. Artificial intelligence and machine learning overcome some of these constraints by integrating vast and varied data sources such as satellite imagery, environmental sensors, and field observations. These AI-driven tools allow to create synthetic datasets that are both detailed and local, filling gaps in existing records and making simulations of extreme weather more accurate.
By generating such detailed, localised weather datasets and enabling much more precise modelling of extreme events, AIs drive the development of new climate risk solutions.
Practical implications: Addressing real problems with real solutions
How these technologies can truly enhance decision-making and build climate resilience, shows the example of Reask, a leader in utilising synthetic data to transform climate risk forecasting. Using advanced climate physics, Reask generates highly detailed, global tropical cyclone event sets, enabling accurate simulation in areas where real data are scarce. This technology helps insurers, reinsurers, asset managers, and governments better measure, price, and manage climate risks, particularly in emerging markets where quality catastrophe data are limited. By integrating its data products into modelling and portfolio decisions, and by offering parametric insurance and real-time early warnings, Reask supports financial and public sector clients in strengthening their resilience to severe weather.
Equally innovative is another organisation in this area: Previsico. The company specialises in AI-powered, real-time flood risk forecasting and warnings at the property level up to 48 hours in advance. By aggregating live data from sensors, satellites, and weather feeds, Previsico provides highly accurate and granular surface water flood predictions. This enables insurers, utilities, and public authorities to proactively mitigate risk and minimise losses. As global flood losses increase rapidly, Previsico’s platform addresses the growing need for reliable, scalable, and advanced predictive solutions, providing greater detail and foresight compared to traditional models.
Towards responsible AI: Balancing innovation, inclusion, and sustainability
Despite these advances, AI brings challenges that require careful management. Training and running AI models consume significant energy, and, if powered by fossil fuels, add to emissions. Large data centres also withdraw substantial water for cooling and rely on minerals whose extraction can harm local environments. Socially, the benefits of AI may be concentrated in wealthier countries with strong digital infrastructure, risking wider gaps with developing economies. Ensuring inclusion and access will be crucial, so AI does not leave climate-vulnerable communities further behind.
To fully realise the promise of AI for climate action, responsible development is essential. This means managing AI’s environmental footprint and investing in digital infrastructure and capacity-building in developing economies too, to ensure that all countries can benefit from new technologies. With this approach, AI I can both support innovation and adaptation, while also offering attractive opportunities for sustainable investors and helping drive global climate resilience.
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