Abstract
LCA data quality issues were investigated by using case studies on products from starch-PVOH biopolymers and petrochemical alternatives. These case studies demonstrated that the parameters and assumptions in the database as well as the characterization and normalization methods need to be addressed in sensitivity analysis in order to draw robust LCA conclusions. This study also presents an approach to integrate statistical methods into LCA models for analyzing uncertainty in industrial and computer-simulated datasets. This chapter 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 has led to a transparent increase in confidence in the LCA findings. It is concluded 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.
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Guo, M. (2012). Sensitivity and Uncertainty Analysis. In: Life Cycle Assessment (LCA) of Light-Weight Eco-composites. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35037-5_7
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DOI: https://doi.org/10.1007/978-3-642-35037-5_7
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