Clustering Strategies for Defining Archetypes to Support Integrated Simulations of Environmental Impacts

Abstract

Life cycle assessment (LCA)’s inherent data-intensiveness hampers application to neighbourhood environmental assessments, particularly for built stock modelling. Data collection can be reduced to a manageable amount by grouping a large number of buildings into a limited set of aggregates with similar characteristics and defining exemplars (archetypes) that represent each group. LCAs would be performed for the archetypes only and their results extended to the represented buildings. As archetype definition is seldom detailed in the literature, this paper tests, and details different procedures that could enable neighbourhood LCAs. K-medoids and CLARA partition algorithms, as well as agglomerative hierarchical clustering techniques, were applied to group over 300 buildings into a limited number of clusters. A building representative of each cluster was identified to proceed to bottom-up LCA. K-medoids clustering stands out for the quality of clusters and their representatives. Restraining the maximum number of clusters to keep subsequent LCA work manageable imposes some quality loss yet allows for achieving satisfactory division results. Regardless of the clustering technique used, data was the best divided the larger the number of clusters used, for the various factors in the database depicting the studied area resulted in several possible data combinations. Although detailed representation is desirable in LCA modelling, limiting the number of variables facilitates data pre-treatment and an optimal balance should be pursued in future studies.