4.7 Article

Spatial characterization of construction material stocks: The case of the Paris region

Journal

RESOURCES CONSERVATION AND RECYCLING
Volume 170, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.resconrec.2021.105512

Keywords

Construction materials; Anthropogenic stocks; Urbanization; Urban mining; Circular economy

Funding

  1. Regional Council of the Paris region
  2. Regional and Interdepartmental Office for the Environment and Energy of Ile-de-France (DRIEE)

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This article examines the spatial distribution of construction materials in the Paris region in 2013, using a GIS modeling approach to estimate and locate different types of stocks. The study found a higher proportion of underground materials in anthropogenic stocks, with higher stock density in central Paris. Data suggests that systematic assessment of construction materials stocks in French regions and cities can be conducted using statistical data and GIS modeling.
Better knowledge of the spatial distribution of anthropogenic stocks is key for the implementation of circular economy policies. This article focuses on the case of construction materials in the Paris region in 2013. A bottomup approach using GIS modeling is applied to estimate and locate the stocks of 27 materials and 24 building archetypes, three transport networks and six energy and water networks. The stocks of three sub-urban areas inside the region (Paris, PC and GC) and three neighborhoods representative of urban forms are compared. The anthropogenic stocks of the Paris region amount to 204 t/capita and contain 96% of non-metallic minerals. Buildings make up 72% of the stocks. Materials located underground represent 47% of the stocks. The density of total stocks is much higher in central Paris (4.6 t/m(2) of the urbanized area) than in the outskirts of GC (0.7 t/m(2)). Higher stock density is associated with lower shares of single-family houses and networks in total stocks. This study suggests the possibility of systematically assessing stocks of construction materials using statistical data and GIS modeling over French regions and cities. As studying stocks with a bottom-up approach is dataintensive, to provide more accurate information, the availability and quality of data are crucial.

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