Towards a Reduced Set of Indicators in Buildings LCA Applications: A Statistical Based Method
Résumé
Several Life Cycle Assessment (LCA) tools for buildings have been developed over the past few years. Recent reviews show that most of the tools use between 8 and 16 Life Cycle Impact Assessment (LCIA) and Life Cycle Inventory (LCI) based indicators. This high number of indicators is often seen as a problematic issue for decision makers. In this paper, we propose a statistical methodology to solve the problem of identifying a simplified but relevant set of environmental indicators. It is based on a Principal Component Analysis (PCA) applied on a French LCI/LCIA database of building materials and products. Results show that only 4 dimensions can be sufficient to explain 86 % of the variance (also called inertia) for an original set of 13 indicators. Rotation techniques such as varimax proved to be quite powerful to extract relevant environmental components. In a subsequent building case study, the weightings of the functional units of the different materials and products also confirm the conclusions drawn at the database scale.