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Life cycle assessment and data envelopment analysis approach for the selection of building components according to their environmental impact efficiency: a case study for external walls

Abstract Environmental criteria have to be taken into account when it comes to selecting a specific building component among a set of candidates with the same function. This article presents a methodological approach – based on both Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) – for the selection of building components according to their environmental impact efficiency. A three-step LCA + DEA approach is proposed and tested through a case study for 175 external walls. The three steps of this approach involve data collection, life cycle impact assessment, and DEA of the sample of building components using environmental impacts as DEA inputs. Overall, from the availability of multiple data on the material and energy flows of each building component, the method provides decision makers with eco-efficiency scores and environmental benchmarks. A cautious definition of the set of candidates is critical, as relative efficiency scores are calculated. Data availability and functional homogeneity regarding the building components evaluated are the key requirements for the general use of the method. The three-step LCA + DEA approach proposed is proven to be a useful method to enhance decision making and environmental benchmarking in the building sector.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).57 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
