AI-powered knowledge integration to Carbon-neutral Cities
The aim of the project is to develop, test, and apply coupled complex systems models in a novel, artificial intelligence-based decision support system (AI-DSS) for handling complex socio-physical interactions, their implications, and potential feedbacks. The development with be done collaboratively by a network of researchers, urban planners, and other stakeholders (local NGOs, residents, and businesses). The Stockholm region in Sweden will be used as a test case to apply and train the models.
The trained modeling system will analyse various scenarios of urban transformation and their environmental impacts over both short- and long-term time horizons. The trained models and AI-DSS will then be prepared for application to different test cities (Gothenburg, Sweden; Chicago, USA; and Nanjing, China) for comparison of models, comparison of potential climate mitigation solutions, and testing of deep transfer learning techniques.
Ongoing project activities
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We are actively engaging with planners at Swedish regions (including Stockholm and Uppsala ) and municipalities (such as Trelleborg ) and conducting research to understand how we can make the AI-DSS model useful for practical planning applications. We know that there are many barriers to the uptake of planning support systems in real-world planning, such as: a lack of trust for the results, lots of learning being required to use the tool, and institutional inertia. In this project, we are striving to develop and model and tool which is user-friendly and meets the needs of planners to implement concrete spatial planning for climate action.
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We at KTH and SU are working closely with our colleagues at the University of Illinois at Urbana-Champaign in the USA to combine our learnings and expertise around greenhouse gas emissions and carbon sequestration in Sweden , with their cutting-edge research and modelling of emissions and sequestrations in Illinois . Combining the research on these two very different places helps us to highlight and identify strengths, weaknesses, and opportunities in both regions for a complete understanding of the relationships between land use change and emissions, and how we can use this to shape sustainable development .
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We are also collaborating with our colleagues in the USA to incorporate our learnings about greenhouse gases into an dynamic spatial land use change model for both Stockholm and Illinois.
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Finally, we are working with a student from the computer science department at KTH to develop and test the AI optimization algorithm which we will use in the AI-DSS model to generate scenario plans which use planning and policy measures, combined with strategies such as nature-based solutions (an area into which we are conducting complementary research with colleagues at MIT), to minimize future greenhouse gas emissions.
Funding and participation
This project is funded by Formas and is a collaboration between Stockholm University (SU), KTH, Shanghai Jiao Tong University (SJTU), The University of Illinois at Urbana-Champaign (UIUC), Region Stockholm, and WSP.