A Method to Facilitate Uncertainty Analysis in LCAs of Buildings
2017
Francesco Pomponi | Bernardino D’Amico | Alice Moncaster
Life cycle assessment (LCA) is increasingly becoming a common technique to assess the embodied energy and carbon of buildings and their components over their life cycle. However, the vast majority of existing LCAs result in very definite, deterministic values which carry a false sense of certainty and can mislead decisions and judgments. This article tackles the lack of uncertainty analysis in LCAs of buildings by addressing the main causes for not undertaking this important activity. The research uses primary data for embodied energy collected from European manufacturers as a starting point. Such robust datasets are used as inputs for the stochastic modelling of uncertainty through Monte Carlo algorithms. Several groups of random samplings between 101 and 107 are tested under two scenarios: data are normally distributed (empirically verified) and data are uniformly distributed. Results show that the hypothesis on the data no longer influences the results after a high enough number of random samplings (104). This finding holds true both in terms of mean values and standard deviations and is also independent of the size of the life cycle inventory (LCI): it occurs in both large and small datasets. Findings from this research facilitate uncertainty analysis in LCA. By reducing significantly the amount of data necessary to infer information about uncertainty, a more widespread inclusion of uncertainty analysis in LCA can be encouraged in assessments from practitioners and academics alike.
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