Uncertainty in the life cycle assessment of building emissions: A comparative case study of stochastic approaches

Abstract Life cycle assessment of buildings has become popular recently, due to the significant potential of emission reduction. Previous studies have contributed much to the quantification of emissions, yet the issue of uncertainties needs to be further investigated. In general, the uncertainty in life cycle assessment is mainly due to the errors in input parameters, definition of system boundary and scenario assumptions, and choice of analytical models, which could be summarized as parameter, scenario, and model uncertainties. In the present study, data quality indicators were adopted and a semi-quantitative method was modeled accordingly, relevant to the uncertainty analysis of building emission assessment. A case study building was analyzed comparing the deterministic and stochastic approaches, and the results indicate that the uncertainty in input parameters could result in a standard deviation of 3106 tCO2e for the sample mean of 61504 tCO2e, which is in line with the deterministic results. Further scenario analyses investigated the influence of scenario and modelling alternatives. Relevant results emphasized the definition of system boundary and energy efficiency to control uncertainties, and suggested applying statistical distribution for key parameters in analysis. Overall, the present study could provide useful information on the uncertainty of life cycle emission assessment, and, therefore, be helpful in decision-making regarding the low-carbon development of building industry.

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