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

Abstract

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.

Uncertainty Analysis of Embedded Energy and Greenhouse Gas Emissions Using BIM in Early Design Stages

“…One way to counteract this would be to subdivide the building into more parts but thereby losing the early design stage simplicity. In addition, this phenomenon relates to material uncertainties in early design stages, which were not included in this study, but are subject to current (e.g., Tecchio et al [51]) and future research.…”

“…Neither are LCA models, such as characterization methods, varied in our study, as the employed database, Oekobaudat [26], does not provide data for this, and designers cannot influence these choices. Therefore, the underlying datasets are fixed in this study, in contrast to Tecchio et al [50,51], which employed the method of structured under-specification to capture uncertainty in material choice in early design stages.…”

Gaussian Process Regression as a Replicable, Streamlined Approach to Inventory and Uncertainty Analysis in Life Cycle Assessment

“…For example, Olivetti et al put forward the structured underspecification and probabilistic triage framework, which quantifies the uncertainty of proxy data usage by categorizing an item within an aggregate group . This approach has been applied recently in the building sector and for electrical products. − Meron et al proposed a selection process based on the characteristic space among available and missing data . However, such methods still rely on data compiled via the semi-quantitative pedigree approach that depends in part on subjective evaluations to determine temporal and geographic representativeness.…”

Leveraging life cycle assessment and simplex lattice design in optimizing fossil fuel blends for sustainable desalination

“…In addition, mixture design-based regression is applied to postulate system governing equations that establish a metamodel or "model of models" to act as a stand-alone simplified mockup for the LCA that can be expanded to predict the environmental impact indicators for different DPs to be utilized for system optimization. The concept of LCA metamodels has been discussed in the literature under the topics of LCA predictions (Huijbregts et al 2006), streamlining (Hanes et al 2013;Hunt et al 1998;Olivetti et al 2013;Tecchio et al 2018), LCA meta-analysis (Berger and Finkbeiner 2011;Padey et al 2012Padey et al , 2013, and others (Birkved and Heijungs 2011;Gardezi et al 2016). Metamodeling the LCA through regression has demonstrated great advantages in abstracting the timeconsuming LCA model building (Padey et al 2012(Padey et al , 2013, providing an approach for selecting proxy life cycle impact categories in streamlined LCA, factor screening (Hunt et al 1998;Pascual-Gonzalez et al 2015), and model simplification (Abanda et al 2013;Birkved and Heijungs 2011;De Soete et al 2014;Gardezi et al 2016;Hanes et al 2013) and better interpretation and correlation (Berger and Finkbeiner 2011;Grant et al 2016;Park and Seo 2003).…”