Climate change is happening. As mitigation efforts can no longer stop climate change, adaptation is needed to ensure climate resiliency. National regulations and policies often fail to meet the specific contexts of the local scales, and thus, communities need to render themselves capable of adapting through Community-Based Climate Adaptation (CBA) projects. Artificial Intelligence (AI) is seen as a powerful technology with much potential for aiding communities in their adaptation goals, but how this collaboration is to be designed is still unclear. This project uses a More-than-Human-Centred (MtHC) approach, as well as the CreaTures framework and the Augmented Collective Intelligence framework, to explore the collaboration between urban communities and AI technology. This exploration is done to assess how AI systems can enhance the capabilities of communities, and how residents can be motivated to adopt MtHC approaches in their adaptation measures. The process, stakeholders, and challenges of CBA projects, as well as opportunities for AI-based interventions, are explored through literature research, expert interviews and a thematic analysis. Following this, design concepts are explored in collaboration with an expert panel, prototypes of further developed iterations are tested, and a final concept is created. The final concept, called Entangled Intelligence, is tested both with a participant group and the expert panel, in order to retrieve insights on the research questions. The project and its limitations are discussed, and a set of characteristics of AI that support MtHC approaches to CBA is presented.

Urban forests are now digitalized as smart green infrastructures to mitigate environmental change. Cities are proactively planting and replacing trees considering their future ecosystem benefits, such as carbon sequestration, urban heat reduction, and water retention. Digital twin simulation is a key technology enabling this particular mode of governance, which generates future scenarios under various management schemes. These data inform decisions on tree selection and placement, thereby shaping forests into uniquely efficient and responsive urban organisms. Despite its positive prospects, however, the underlying ontologies of these digital forests are rarely questioned, often lacking consideration for the flora and fauna inhabiting the forests. The models built upon anthropocentric biases and values pose a risk of selectively consolidating futures where the forests are optimized only for humans, which does not always guarantee the same good future for multi-species.
Acknowledging that humans coexist with other forms of life, Eco-Urban Futures takes a more-than-human approach in designing the computational model and a simulation platform. The notion of more-than-human bodies was constructed as a strong concept to explore alternative modes of making sense of and acting upon data toward more-than-human forest governance. The designed interface enables users to navigate the forest data across diverse temporal, spatial, and agential scales, thereby urging policymakers, urban planners, and citizens to reimagine healthier futures for us-with-the-forest.