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Data noise often does not allow definitive results to be drawn from life cycle assessments (LCAs). The use of possibility theory to model data uncertainty led to the development of an LCA model that is able to derive useful conclusions to a specified level of confidence. The specific decision domain in this study involves the identification and selection of the best environmental option from ten different automotive fuels.

Possibilistic Uncertainty Propagation and Compromise Programming in the Life Cycle Analysis of Alternative Motor Vehicle Fuels