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Streamlining the Life Cycle Assessment of Buildings by Structured Under‐Specification and Probabilistic Triage
Author(s) -
Tecchio Paolo,
Gregory Jeremy,
Olivetti Elsa,
Ghattas Randa,
Kirchain Randolph
Publication year - 2019
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1111/jiec.12731
Subject(s) - scope (computer science) , triage , computer science , probabilistic logic , data collection , life cycle assessment , operations research , environmental resource management , risk analysis (engineering) , environmental science , business , engineering , artificial intelligence , mathematics , production (economics) , statistics , medicine , emergency medicine , economics , macroeconomics , programming language
Summary Life cycle thinking plays an important role in sustainable development in the building sector. However, the complexity of data collection and scope definition limits life cycle assessment (LCA) applications. Even if the inventory data have already been collected, tabulated, and indexed, the method is still time‐consuming, which may be discouraging for designers. This study demonstrates how the LCA of buildings can be robustly streamlined using structured underspecification of impact data combined with an effective and efficient triage of the data collection. Tests were conducted with a series of building typologies that were analyzed with a cradle‐to‐gate approach. The probabilistic triage approach was tested to identify selected activities requiring detailed specification because they contribute most to total impact, thereby reducing data gathering effort. Impacts such as global warming, acidification, eutrophication, and smog creation were assessed, and results showed that 40% to 46% of the bill of materials components represent 75% of total impacts of single‐family houses and multifamily buildings. By specifying only a prioritized subset of the bill of materials to the highest level of specificity, results proved to be reasonably accurate and obtainable with less effort.