Streamlined Life Cycle Assessment Through Materials Classification and Under-Specification

<p>Data gaps in life cycle assessment (LCA) lead to a reliance on proxy data and introduce uncertainty into assessments. This work shows how data mining techniques can be used to quantitatively understand the important characteristics driving environmental impacts of materials when more than one impact is considered. The results can subsequently be used to fill data gaps and predict a material’s environmental performance. This paper explores the use of clustering and principal component analysis to identify potential material classifications, and then the use of regression trees and other statistical methods to evaluate the classifications and develop a material taxonomy. This method is applied to a life cycle inventory dataset for several metals, and the TRACI 2.0 impact assessment method is used to illustrate the observed reduction in uncertainty as more information is specified across the hierarchy using this proposed classification. The analysis indicates that function, price, and recycled content are significant classifiers for metals. Applying these factors to create taxonomies demonstrates both an effective and efficient reduction in uncertainty. This is represented by a 68-78% reduction in the sum of squares error within (SSW) of the worst case groups from level 1 to level 2 of the taxonomy and an 84-91% overall reduction in SSW of the worst case groups from level 1 to level 3. Using this materials taxonomy reduces the amount of information required to specify a product, therefore enabling one component of streamlined life cycle assessment.