Adaptation for Just and Sustainable Transitions (ADJUST)

Climate change affects us all, but not equally. To prepare Austria for fair and resilient climate adaptation, researchers from the University of Vienna, specializing in spatial planning, population geography, and geographic artificial intelligence (GeoAI), are launching a new project in collaboration with partners from industry and society. This project aims to study the impact of climate adaptation measures holistically, equitably, and based on data-driven approaches. Current climate adaptation measures often encounter societal resistance because they do not always sufficiently consider how different regions, sectors, and demographic groups are affected differently. Climate adaptation measures that target major cities like Vienna, for example, might disproportionately burden rural communities. Such inequalities can negatively impact the long- term acceptance of important climate adaptation measures. Therefore, a new approach is needed that integrates fair, data-based climate adaptation policy more strongly in spatial and societal contexts. The project will, therefore, develop a comprehensive, transdisciplinary strategy combining spatial planning, population geography, and modern spatial data science technologies, such as artificial intelligence. The projects goal is to make inequalities more transparently visible, ensure fairer climate adaptation (measures), and offer more targeted support for Austria`s diverse regions and sectors. Hence, the focus is not on uniform one-size-fits-all solutions, but on customized strategies that account for regional needs and diverse societal groups, using both theories from spatial planning and data-driven methods. The following three central questions are at the project`s core: How can climate adaptation measures be planned spatially fairly, ensuring no region is disproportionately burdened? How do climate policies impact different population groups, such as older individuals or migrants? Moreover, how can data-driven AI decisions be made fairer without unintentionally reinforcing existing inequalities (which may be encoded in the data as biases)? The project will emphasize avoiding data and model biases that might otherwise disadvantage rural and less-represented populations. Today, rural regions often lack current and balanced data, whereas metropolitan areas like Vienna are much better equipped in terms of data availability and are studied more frequently. These data gaps should be closed, and algorithmic inequalities minimized or at least documented. In a collaborative workshop, the project`s initial phase brings together experts from academia and practice to discuss concrete steps for an interconnected approach explicitly considering Austrias diversity in planning climate adaptation measures.