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.
Highlights
Introduction and aim of the projectIn climate policies, building stocks (BS), including energy use as well as material use, are of great relevance, due to their share on the energy consumption and material use
A sample dataset of OAD buildings is used as a case study to create an exemplary OAD building typology
Like residential buildings (RB), non-residential building (NRB) can be described by several archetypes, which are distinguished by their functional, structural and energy-relevant characteristics (BMVBS 2011)
Summary
Introduction and aim of the projectIn climate policies, building stocks (BS), including energy use as well as material use, are of great relevance, due to their share on the energy consumption and material use. 36% of the final energy use and for 39% of the energy-related CO2-emissions, including upstream power generation and the manufacturing of materials and products for building construction (UN Environment and International Energy Agency 2017). Building construction has a share in global CO2-emissions of 11% (UN Environment and International Energy Agency 2017). The medium-term EU objectives are for example reducing GHG emissions by 40% and increasing the energy efficiency by about 27% compared with 1990 until 2030 (European Commission 2016). The long-term German and EU climate objectives are to reduce GHG emissions by 80 to 95% or even 100% until 2050 compared with 1990 (BMU 2018; European Commission 2016). The German national targets regarding climate protection in the BS are to reduce the primary energy use by 80% in 2050 compared to the year 2008 as well as GHG emission by 40% compared to 2010 (BMUB 2015)
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