A hybrid Data Quality Indicator and statistical method for improving uncertainty analysis in LCA of complex system – application to the whole-building embodied energy analysis

Uncertainty analysis has been recommended when using LCA for choosing sustainable products. The existing uncertainty analysis methods are helpful but have more or less inherent deficiency. The goal of this paper is to present a hybrid stochastic method to improve the uncertainty estimate in LCA with data limitations. This method can be a valuable tool especially to evaluate deterministic results of LCA of complex product system (e.g. building) when uncertain information is needed for decision-making. Compared to deterministic results, probabilistic results were often considered more reliable when large data uncertainties existed, such as data uncertainties in embodied energy coefficients of building materials. Both the statistical and Data Quality Indicator methods have been used to estimate data uncertainties in LCA. However, neither of those alone is adequate to address the challenges in LCA of complex product system, due to the large quantity of material types and data scarcity. This paper presents a hybrid method, which combines Data Quality Indicator and the statistical method by using a prescreening process based on Monte Carlo rank-order correlation sensitivity analysis. By optimizing the utilization effect of the available statistical data, this hybrid method can increase the reliability of the uncertainty estimate compared to the pure data indicator method. In the presented case study which performed the stochastic estimating of whole-building embodied energy, improved results from the hybrid method were observed compared to the pure Data Quality Indicator method. In conclusion, the presented hybrid method can be used as a feasible alternate for evaluating deterministic LCA results like whole-building embodied energy, when more reliable results are desired with limited data availability. Although this approach is presented in the context of building embodied energy uncertainty analysis, it can be used for LCA uncertainty analysis for conveniently making more reliable decision in the case of choosing complex “greener” products in other fields.

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