AI-powered Knowledge Integration to Carbon-Neutral Cities

Transition of cities towards carbon neutrality requires an improved understanding of the interlinkages between land-use and energy planning, and of the complexity of coupled socio-ecological patterns, processes, and feedbacks in urban-regional systems.

Artificial intelligence-based decision support systems (AI-DSS) can help develop and improve this understanding through learning, mapping, and forecasting complex urban interactions in a scenario-based analytical framework.

The general aims of this project are to initiate a collaborative learning process and develop an AI-DSS for handling the complex interactions, and their effects and feedbacks, in urban transformation and associated land-use changes and climate change impacts.

The project will provide new scientific knowledge on the interactions between human and natural systems and support sustainable urban spatial planning and carbon-neutrality goals in urban areas.

The highly replicable decision support system developed (including a deep transfer learning approach) will increase the ability to improve climate change mitigation and adaptation strategies, by helping different stakeholders in different places to share experiences and learn from each other.

The AI-DSS will enable a wide range of users and stakeholders to access, operate, and feed back into the models relatively easily, to test various urban planning and policy scenarios, and to assess effects on GHG emissions.