Life-cycle assessment of non-domestic building stocks: A meta-analysis of current modelling methods
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2.1 CIVIL ENGINEERING
Building stock models (BSMs) are essential for simulating the contributions of regional and national building sectors to climate change under different policy scenarios, and for identifying pathways to climate change mitigation. To date, BSMs have focused on the operational life-cycle impacts of domestic dwellings; there has been less emphasis either on non-domestic buildings (NDBs) or full life-cycle analysis. This paper provides a first review of the theory and practice of NDB stock modelling which considers life-cycle energy, emissions and costs. A meta-analysis of the literature was undertaken involving a structured search of relevant articles in key scientific repositories. 98 in-scope studies were identified and data collected on their aims and objectives, methodologies, data sources, system boundaries, considered impacts, representativeness, uncertainty analysis, validation and verification techniques, further research identified, model transparency and software tools employed. The review necessitated the classification of modelling methodologies. The existing ‘bottom-up’ and ‘top-down’ groups were found to be ambiguous and led to confusion. Therefore, an alternative methodology classification is proposed, considering both the modelling technique and model simulation data used. The findings of the analysis indicate that most approaches use engineering models employing archetype data. However, almost all current life-cycle models of NDB stocks are incomplete. Only one study considered the full building life-cycle and most did not include uncertainty analysis. The reproducibility of study results is poor since most do not provide sufficiently-detailed information on the models and data used. Critically, there is a lack of representative input data which limits their usefulness as evidence in policymaking.
Julian Bischof, Aidan Duffy, Life-cycle assessment of non-domestic building stocks: A meta-analysis of current modelling methods,Renewable and Sustainable Energy Reviews, Volume 153, 2022, 111743, ISSN 1364-0321, DOI: 10.1016/j.rser.2021.111743.