Researching and applying the potential of Artificial Intelligence to foster climate resilience at the regional and local levels

Expected outcome

AI has the potential to support or facilitate virtually every aspect of regions’ climate adaptation efforts. From risk assessment to climate forecasting, from infrastructure planning to resource management, and more . Contributing to the objectives of the AI Continent Action Plan and the EU Mission on Adaptation to Climate Change, projects are expected to contribute one of the following: AI is used to improve actionable climate adaptation knowledge for European regions and local authorities by integrating climate data into decision-making processes. Specific sectors in the selected regions become more resilient to climate change thanks to the use of AI to improve their processes or technologies, while simultaneously advancing their digital transformation.

Scope

Rationale Over the past few years, a rapid and disruptive acceleration of progress in Artificial Intelligence has occurred, driven by significant advances in widespread data availability, computing power and machine learning . While the potential of Artificial Intelligence is being uncovered each day and its possibilities are expanding exponentially, such technological revolution can significantly accelerate Europe’s efforts towards climate resilience and contribute to the objectives of the Adaptation Mission. Considering the ever-changing nature of AI growth, proposals should demonstrate that they go beyond state of the art, they should identify a specific gap that can be addressed by an AI-powered tool, and explain why it would improve existing models, tools or applications or, alternatively, justify the need to develop entirely new solutions. In particular, the proposal should address one of the following two objectives: Objective 1: “ AI for more accessible data ” AI can quickly process and identify patterns from big datasets that would otherwise be too complex. Proposals should explore how AI can be further integrated, including via AI techniques such as deep learning, to make data more accessible and understandable, to facilitate informed decision-making by regions and local authorities. Improvements should be tested with at least 3 region al and local authorities to ensure that they provide a concrete added value to end-users (i.e. decision-makers). Moreover, proposals are expected to apply such analysis to concretely improve data integrity and accessibility . Objective 2: “ AI for sectoral adaptation ” Use and application of machine learning and AI tools to help regions and local authorities optimize their management of resources and improve adaptation technologies in key sector(s). Proposals should develop and test the tools in the territory of at least 5 regional or local authorities . The work should engage private sector actors with relevant expertise in the sector chosen (e.g. smart agriculture, construction and buildings sector, water management, waste management, transports, resilience of energy systems, etc.), to ensure that such AI-powered tools are designed for scalability and uptake. Regardless of the objective addressed, all proposals should provide training and dissemination material for different target groups, including regions and local authorities, on how to best take advantage of the AI-powered improvements. Attention should be given to avoiding biases and ensuring that the data is representative of diverse populations, as well as taking into account possible limitations and potential misinformation when using generative AI tools. To ensure a balanced portfolio covering both objectives, grants will be awarded to proposals not only in order of ranking but at least also to one proposal that is the highest ranked within each objective, provided that the applications attain all thresholds.

What this means for cities and regions

Regions and local authorities as the end users and test beds, alongside research partners. Smaller projects, so a manageable entry point for a mid-sized city. Topic budget €12 million; 4 grants expected; around €3 million per project. Opens 9 February 2027; closes 21 September 2027. AI for adaptation runs on climate and Earth-observation data; Copernicus climate projections and Sentinel time series are the natural training and input data.