Bottom‐up estimation of material stocks and flows in Toronto's road network

Improving resource efficiency is critical to reduce the environmental impacts of constructing and maintaining the built environment. A growing body of research has been investigating where and for what purposes construction materials are consumed in cities. Much of this existing research relies on archetypes to simplify the analysis of the large number of structures, and city‐scale bottom‐up material stock assessments mostly examine buildings to the exclusion of other urban structures. In this research we examine the effectiveness of pavement archetypes compared to spatially disaggregate pavement material data from a municipal pavement management database for stock estimation and find that the use of archetypes can lead to asymmetric estimation errors (i.e., variability in prediction error among materials and across the city's geography). We use Toronto's road network as a case study and estimate the stocks of concrete, asphalt, and granular materials. The stock of materials is unevenly distributed, in terms of both material type (uneven distribution of concrete vs. asphalt) and when normalized by area and population. In general, the use of archetypes tends to obscure spatial variability in material use, even across a relatively limited geographical area like Toronto and relatively consistent product like roads. We argue that the uncertainty inherent in the use of archetypes is significant and should be accounted for in bottom‐up urban material flow analysis, that governments are in a unique position to provide high quality data for study, and that efforts should be made to capture, collate, and share this information more widely.