Structured Under‐Specification of Life Cycle Impact Assessment Data for Building Assemblies

Abstract

SummaryThe existence of uncertainties and variations in data represents a remaining challenge for life cycle assessment (LCA). Moreover, a full analysis may be complex, time-consuming, and implemented mainly when a product design is already defined. Structured under-specification, a method developed to streamline LCA, is here proposed to support the residential building design process, by quantifying environmental impact when specific information on the system under analysis cannot be available. By means of structured classifications of materials and building assemblies, it is possible to use surrogate data during the life cycle inventory phase and thus to obtain environmental impact and associated uncertainty. The bill of materials of a building assembly can be specified using minimal detail during the design process. The low-fidelity characterization of a building assembly and the uncertainty associated with these low levels of fidelity are systematically quantified through structured under-specification using a structured classification of materials. The analyst is able to use this classification to quantify uncertainty in results at each level of specificity. Concerning building assemblies, an average decrease of uncertainty of 25% is observed at each additional level of specificity within the data structure. This approach was used to compare different exterior wall options during the early design process. Almost 50% of the comparisons can be statistically differentiated at even the lowest level of specificity. This data structure is the foundation of a streamlined approach that can be applied not only when a complete bill of materials is available, but also when fewer details are known. Keywords:buildings design process industrial ecology streamlined life cycle assessment structured under-specification uncertainty analysis Supporting information is linked to this article on the JIE website

Streamlining the Life Cycle Assessment of Buildings by Structured Under‐Specification and Probabilistic Triage

“…Note : Each life cycle inventory (LCI) data set related to construction materials was classified and organized into four hierarchical levels of specificity. Four levels of specification (M1, M2, M3, and M4) were used, as suggested by Tecchio and colleagues (). EI = environmental impact; CO 2 = carbon dioxide; eq/m 2 = equivalent per square meter; ICF = insulated concrete form; kg = kilograms; N = nitrogen; O 3 = trioxide; SO 2 = sulfur dioxide.…”

“…The authors developed a structured underspecification hierarchy for building materials in a previous work (Tecchio et al. ). Several LCI databases (ecoinvent, USLCI, Athena Sustainable Materials Institute, and Gabi) were used to increase the number of options available to the user and to account for the variation in results across databases.…”

“…In this method, each LCI data set related to construction materials was classified and organized into four hierarchical levels of specificity. Four levels of specification (M1, M2, M3, and M4) were used, as suggested by Tecchio and colleagues (). Groups of a given level (e.g., M2) can be aggregated to form a wider group of higher rank (e.g., M1), creating therefore a classification hierarchy.…”

“…The classification of LCI data sets is therefore a strictly finite, 1‐to‐n hierarchical tree, which means that each entry at M4 (the terminal level in the tree) is univocally assigned to a preceding group level (M3) and by extension therefore to an M2 and M1 (Tecchio et al. ). The full database is provided in a spreadsheet (supporting information available on the Journal's website).…”

“…This approach is referred to as structured underspecification and probabilistic triage (Patanavanich 2011;Olivetti et al 2013;Reis 2013). This involves first characterizing the system in terms of low-fidelity data using a technique called structured underspecification (Tecchio et al 2018). Based on this initial characterization and iteratively thereafter, statistical methods are used to identify the most influential aspects of the system life cycle.…”