Estimating Materials Stocked by Land‐Use Type in Historic Urban Buildings Using Spatio‐Temporal Analytical Tools. (11th September 2015)

• Record Type: Journal Article
• Authors: Marcellus‐Zamora, Kimberlee A.; Gallagher, Patricia M.; Spatari, Sabrina; Tanikawa, Hiroki
• Is Part Of: Journal of industrial ecology. Volume 20:Number 5(2016:Oct.)
• Page Start: 1025
• Page End: 1037
• Publication Date: 2015-09-11
• Subjects: building stock -- construction materials -- geographical information systems (GIS) -- industrial ecology -- land use -- material stock analysis (MSA); Industrial ecology -- Periodicals; Product life cycle -- Environmental aspects -- Periodicals; Industrial management -- Environmental aspects -- Periodicals; Écologie industrielle -- Périodiques; 658.56
• DOI: 10.1111/jiec.12327
• Languages: English
• ISSNs: 1088-1980

Abstract

Summary: The construction industry is an important contributor to urban economic development and consumes large volumes of building material that are stocked in cities over long periods. Those stocked spaces store valuable materials that may be available for recovery in the future. Thus quantifying the urban building stock is important for managing construction materials across the building life cycle. This article develops a new approach to urban building material stock analysis (MSA) using land‐use heuristics. Our objective is to characterize buildings to understand materials stocked in place by: (1) developing, validating, and testing a new method for characterizing building stock by land‐use type and (2) quantifying building stock and determining material fractions. We conduct a spatial MSA to quantify materials within a 2.6‐square‐kilometer section of Philadelphia from 2004 to 2012. Data were collected for buildings classified by land‐use type from many sources to create maps of material stock and spatial material intensity. In the spatial MSA, the land‐use type that returned the largest footprint (by percentage) and greatest (number) of buildings were civic/institutional (42%; 147) and residential (23%; 275), respectively. The model was validated for total floor space and the absolute overall error (n = 46; 20%) in 2004 and (n = 47; 24%) in 2012. Typically, commercial and residential land‐use types returned the lowest overall error and weighted error. We present a promising alternative method for characterizing buildings in urban MSA that leverages multiple tools (geographical information systems [GIS], design codes, and building models) and test the method in historic Philadelphia.