Map of Porto

Building from a past machine-learning (ML) model analysing the impact of urban form on energy demand, CircEUlar partner INEGI is now working on expanding it to a circularity perspective, with a view at informing policies and strategies towards carbon neutral urban areas. Such integrated model is expected to allow capturing potential trade-offs and synergies between mobility and building and household services, contributing to inform circularity strategies and bringing about wider impacts.

The first step comprises the identification of circularity determinants in urban context and of indicators and relevant metrics to translate these factors. The main challenges refer to i) the selection of the right features and respective indicators; as well as ii) the availability of suitable data to measure them. This expanded and integrated ML circularity model will be spatially-explicit and developed for two case study cities (Porto and Berlin). INEGI is currently focused on collecting high-resolution data to be used in the modelling process as well as on model design and architecture. Cross-consortium collaboration will be highly valuable in this process. Once all relevant information is collected, the next steps include the selection of the most relevant circularity variables so that it can then be applied to the case studies for preliminary findings.


Map of Berlin