Abstract
Life cycle assessment (LCA) will always involve some subjectivity and uncertainty. This reality is especially true when the analysis concerns new technologies. Dealing with uncertainty can generate richer information and minimize some of the result mismatches currently encountered in the literature. As a way of analyzing future fuel cell vehicles and their potential new fuels, the Fuel Upstream Energy and Emission Model (FUEEM) developed at the University of California—Davis, pioneered two different ways to incorporate uncertainty into the analysis. First, the model works with probabilistic curves as inputs and with Monte Carlo simulation techniques to propagate the uncertainties. Second, the project involved the interested parties in the entire process, not only in the critical review phase. The objective of this paper is to present, as a case study, the tools and the methodologies developed to acquire most of the knowledge held by interested parties and to deal with their — eventually conflicted—interests. The analysis calculation methodology, the scenarios, and all assumed probabilistic curves were derived from a consensus of an international expert network discussion, using existing data in the literature along with new information collected from companies. The main part of the expert discussion process uses a variant of the Delphi technique, focusing on the group learning process through the information feedback feature. A qualitative analysis indicates that a higher level of credibility and a higher quality of information can be achieved through a more participatory process. The FUEEM method works well within technical information and also in establishing a reasonable set of simple scenarios. However, for a complex combination of scenarios, it will require some improvement. The time spent in the process was the major drawback of the method and some alternatives to share this time cost are suggested.
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Contadini, J.F., Moore, R.M. & Mokhtarian, P.L. Life cycle assessment of fuel cell vehicles a methodology example of input data treatment for future technologies. Int J LCA 7, 73–84 (2002). https://doi.org/10.1007/BF02978850
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DOI: https://doi.org/10.1007/BF02978850