LCA data quality: sensitivity and uncertainty analysis.

Life cycle assessment (LCA) data quality issues were investigated by using case studies on products from starch-polyvinyl alcohol based biopolymers and petrochemical alternatives. The time horizon chosen for the characterization models was shown to be an important sensitive parameter for the environmental profiles of all the polymers. In the global warming potential and the toxicity potential categories the comparison between biopolymers and petrochemical counterparts altered as the time horizon extended from 20 years to infinite time. These case studies demonstrated that the use of a single time horizon provide only one perspective on the LCA outcomes which could introduce an inadvertent bias into LCA outcomes especially in toxicity impact categories and thus dynamic LCA characterization models with varying time horizons are recommended as a measure of the robustness for LCAs especially comparative assessments. This study also presents an approach to integrate statistical methods into LCA models for analyzing uncertainty in industrial and computer-simulated datasets. We calibrated probabilities for the LCA outcomes for biopolymer products arising from uncertainty in the inventory and from data variation characteristics this has enabled assigning confidence to the LCIA outcomes in specific impact categories for the biopolymer vs. petrochemical polymer comparisons undertaken. Uncertainty combined with the sensitivity analysis carried out in this study has led to a transparent increase in confidence in the LCA findings. We conclude that LCAs lacking explicit interpretation of the degree of uncertainty and sensitivities are of limited value as robust evidence for decision making or comparative assertions.