Developing non-residential building stock archetypes for LCI\u2014a German case study of office and administration buildings

Abstract

PurposeTo accomplish the national and international climate goals, building renovation and optimisation of their energy and resource efficiency are essential. Thus, reliable information on the building stock (BS) is necessary. Most previous building typologies are focussing on residential buildings and the operational phase. This paper shows the development of a methodology for generating non-residential building (NRB) typologies for life cycle inventory analysis (LCI) of building constructions. Hereby, archetypes of office, administration and department (OAD) buildings are developed, exemplarily for the German NRB stock.The methodology can further be utilised for quantity surveying of urban material stocks, related recycling scenarios and waste management. Furthermore, the exemplarily generated archetypes provide necessary information for the estimation of realistic refurbishment scenarios.MethodsApproaches for the development of NRB archetypes, the descriptions of associated building materials and the LCI of BS were analysed and integrated into a methodology. It provides a clear path on the classification in building usage categories and determination of relevant building parameters for conducting LCI studies. Its aim is the creation of NRB typologies, presenting construction materials and building geometry in a useful way for life-cycle assessments (LCA).To demonstrate the methodology’s usability, it is applied to a case study with the sample of 161 OAD buildings, provided by the German NRB database ENOB:dataNWG. In combination with relevant literature on BS archetypes and materials, a sample OAD building typology has been created.Results and discussionMinimum data requirements for conducting simplified LCI calculation of BSs were identified by analysing existing LCA methods, like the German BNB system. Important clusters for developing NRB archetypes were determined: building usage category, building construction types and building age. These data gaps between required information for simplified LCA studies and available information in ENOB:dataNWG were identified, and solutions for closing these data gaps were proposed and tested. Since building archetypes must reflect the overall BS, uncertainties were discussed. The ENOB:dataNWG database was not completed at the time this paper was written, so comprehensive uncertainty analyses are important next steps.ConclusionsThis methodology development forms the groundwork for creating LCI building typologies for simplified LCA studies. It shows practically how to deal with a BS database and illustrates which typical values can be chosen for closing data gaps. The methodology was tested on an exemplary sample of OAD buildings. Based on this case study, the methodology concept was proven useful for the generation of a NRB typology.