Developing an integrated circularity model at the urban scale requires considering the intrinsic complexity of territorial systems, encompassing different elements, processes and mechanisms, to model the flows of energy and materials. Following an innovative and integrated modeling approach supported by machine learning algorithms, the CircEUlar research team is designing a comprehensive framework that combines physical urban features, socio-economic and behavioral variables.

This framework investigates the nexus between energy and materials use on a high-resolution – the block scale. With a view at estimating materials intensity at such level of detail, the Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI) is combing data from multiple sources (e.g. statistical microdata) with a definition of building typologies to characterize the building stock – the so-called “Archetypes.”

For the application of the methodology to the city of Porto, INEGI is trying to match diverse available information from former European projects (e.g. TABULA) and past in-house work on the characterization of the Northern-Portugal built stock (the later including a set of 84 “archetypes”). The main challenges are related to data integration (with census data) and reconciliation of spatial scales. This is a crucial step towards deploying the integrated circularity model for the Porto case study, where the “archetypes” will provide a key basis to model the materials stocks embedded in the city’s buildings.