Review of archetype development strategies for energy assessment at urban scale

Abstract

Understanding the built environment's impacts is essential to support strategic planning and policy design for sustainable development now, and in the future. Modelling individual buildings and infrastructure at high level of detail is resource intensive. Thus, urban scale analyses demand simplifications that balance level of detail and scope broadness. For combining simplified modelling and extended scope, classification by archetypes emerge as a promising methodological approach to extend assessment scope beyond energy use simulation. Archetypes that include life cycle assessment (LCA) parameters can support circularity challenges diagnosis, mapping and predictions, strategies to close material and energy loops and their monitoring within urban built environments. We hypothesized that, upon limited complementation, operational and pre/post construction (embodied) datasets coupled with building grouping techniques satisfactorily represent the built stock to support cradle to grave LCA of built environments. Studies on the use of archetypes for this purpose are scarce, so this article reports findings of a Systematic Literature Review (SLR) on archetypes-based approaches for energy assessment that could inspire application in LCA studies at urban scale. The SLR highlighted a lack of methodological consensus, and that data availability seems to be the major limitation for archetype creation in most studies, which rarely present in-depth information on their development. The few investigations providing consistent methodological procedures actually detail the initial classification process. Transposing the archetype strategy from energy assessment to LCA at urban scale faces practical limitations. Several databases support operational energy studies, but the same does not apply to LCA. Urban building energy models typically overlook infrastructure. Also, statistical results depend directly on data input quality and data availability may compromise the quality of variable selection.