Data Quality Assessment of the Uncertainty Analysis Applied to the Greenhouse Gas Emissions of a Dairy Cow System

The results of an uncertainty analysis are achieved by the statistical information (standard error, type of probability distributions, and range of minimum and maximum) of the selected input parameters. However, there are limitations in identifying sufficient data samples for the selected input parameters for statistical information in the field of life cycle assessment (LCA). Therefore, there is a strong need for a consistent screening procedure to identify the input parameters for use in uncertainty analysis in the area of LCA. The conventional procedure for identifying input parameters for the uncertainty analysis method includes assessing the data quality using the pedigree method and the contribution analysis of the LCA results. This paper proposes a simplified procedure for ameliorating the existing data quality assessment method, which can lead to an efficient uncertainly analysis of LCA results. The proposed method has two salient features: (i) a simplified procedure based on contribution analysis followed by a data quality assessment for selecting the input parameters for the uncertainty analysis; and (ii) a quantitative data quality assessment method is proposed, based on the pedigree method, that adopts the analytic hierarchy process (AHP) method and quality function deployment (QFD). The effects of the uncertainty of the selected input parameters on the LCA results were assessed using the Monte Carlo simulation method. A case study of greenhouse gas (GHG) emissions from a dairy cow system was used to demonstrate the applicability of the proposed procedure.