Archetype generation for the district-scale Life Cycle Assessment of buildings

The determination of buildings’ environmental impacts is relevant for decision-making processes in policy, urban planning, and finance. Life Cycle Assessment (LCA) is a viable method to this end. However, its inherently high data demand results in an unfeasible degree of complexity. For this matter, statistical representations of buildings, i.e. archetypes, can accelerate the modelling and simulation process. They provide relevant information on the thermo-physical and environmental properties of building components, and can be allocated to building models by a limited range of input data such as the construction age class [1].

Conversely, the generation of such archetypes proves laborious in countries with rigorous data privacy regulations and highly heterogeneous local data storage policies. In Germany, a broad range of residential building archetypes is available for the purpose of Building Energy Performance Simulations (BEPS). Archetypes for building LCA have been developed, however they either have not been fully validated or are not publicly available [2]. Furthermore, there is a general lack of archetypes for non-residential buildings. Therefore, a representative use case of data acquisition and processing for residential and non-residential building LCA archetypes gives insight into the methodology and may serve as a blueprint for other countries.

For this purpose, this contribution demonstrates the procurement and statistical evaluation of building data for archetype generation in Germany. Specifically, this paper shows the derivation of relevant building templates from a range of local databases, and quantitative as well as qualitative deviations from current archetype sets. This is accompanied by a database architecture, or schema, for district-scale building LCA data. (Both the archetypes and the database schema will be made available open-source upon final submission.) Notably, the statistical evaluation of the procured data shows that buildings’ properties depend on their location within the respective country, a property that has not been considered in previous archetype sets. Moreover, the newly developed non-residential building archetypes do not show the same degree of sensitivity to building age class as residential buildings.

The presented archetypes include information on their building components’ environmental impacts. However, the district-scale determination of such impacts necessitates the LCA of a broad range of buildings. This could be achieved by complementing the presented archetypes with Geographical Information System (GIS)-based building geometries. Additionally, the presented overview of country-specific data availability and the methodology can be extended towards other countries in an in-depth review.