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Geospatial Characterization of Material Stock in the Residential Sector of a Latin‐American City

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  • Carlos Mesta
  • Ramzy Kahhat
  • Sandra Santa‐Cruz

Abstract

Building stock constitutes a huge repository of construction materials in a city and a potential source for replacing primary resources in the future. This article describes the application of a methodological approach for analyzing the material stock (MS) in buildings and its spatial distribution at a city‐wide scale. A young Latin‐American city, the city of Chiclayo in Peru, was analyzed by combining geographical information systems (GIS) data, census information, and data collected from different sources. Application of the methodology yielded specific indicators for the physical size of buildings (i.e., gross floor area and number of stories) and their material composition. The overall MS in buildings, in 2007, was estimated at 24.4 million tonnes (Mt), or 47 tonnes per capita. This mass is primarily composed of mineral materials (97.7%), mainly concrete (14.1 Mt), while organic materials (e.g., 0.15 Mt of wood) and metals (e.g., 0.40 Mt of steel) constitute the remaining share (2.3%). Moreover, historical census data and projections were used to evaluate the changes in the MS from 1981 to 2017; showing a 360% increase of the MS in the last 36 years. This study provides essential supporting information for urban planners, helping to provide a better understanding of the availability of resources in the city and its future potential supply for recycling as well as to develop strategies for the management of construction and demolition waste.

Suggested Citation

  • Carlos Mesta & Ramzy Kahhat & Sandra Santa‐Cruz, 2019. "Geospatial Characterization of Material Stock in the Residential Sector of a Latin‐American City," Journal of Industrial Ecology, Yale University, vol. 23(1), pages 280-291, February.
    • Handle: RePEc:bla:inecol:v:23:y:2019:i:1:p:280-291
    DOI: 10.1111/jiec.12723
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    4. Jakob Lederer & Johann Fellner & Andreas Gassner & Karin Gruhler & Georg Schiller, 2021. "Determining the material intensities of buildings selected by random sampling: A case study from Vienna," Journal of Industrial Ecology, Yale University, vol. 25(4), pages 848-863, August.
    5. Hadi Arbabi & Maud Lanau & Xinyi Li & Gregory Meyers & Menglin Dai & Martin Mayfield & Danielle Densley Tingley, 2022. "A scalable data collection, characterization, and accounting framework for urban material stocks," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 58-71, February.
    6. Xaysackda Vilaysouk & Savath Saypadith & Seiji Hashimoto, 2022. "Semisupervised machine learning classification framework for material intensity parameters of residential buildings," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 72-87, February.
    7. Franco Donati & Sébastien M. R. Dente & Chen Li & Xaysackda Vilaysouk & Andreas Froemelt & Rohit Nishant & Gang Liu & Arnold Tukker & Seiji Hashimoto, 2022. "The future of artificial intelligence in the context of industrial ecology," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1175-1181, August.

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