How to Handle Uncertainties and Assumptions in Interpreting LCA Results?

Paper #:
  • 982210

Published:
  • 1998-11-30
Citation:
Saur, K., Finkbeiner, M., Hoffmann, R., Eyerer, P. et al., "How to Handle Uncertainties and Assumptions in Interpreting LCA Results?," SAE Technical Paper 982210, 1998, https://doi.org/10.4271/982210.
Pages:
12
Abstract:
LCA studies aim at an integrating system assessment as a comprehensive and holistic approach to prevent tradeoffs and guide users and decision makers for better informed decisions. The total life cycle approach aims at informing and supporting decision making and management support.LCA, like other management techniques as well, has inherent limitations, making choices, assumptions etc. inevitable. Before using the findings of life cycle studies, a consideration of those uncertainties, the effects of value choices and assumptions, as well as the inherent data inaccuracies must be examined in more detail.Traditional error and uncertainty analysis failed in practical use due to the specific system modeling, the data availability and the respective data collection procedures in life cycle studies. New approaches to identify and understand the system specific uncertainties are necessary for this purpose.The aim of this paper is to demonstrate a methodological approach reflecting the need for the inclusion of uncertainty. By using sensitivity analysis, error assumptions and statistical techniques to determine the influence of value choices, assumptions and data inaccuracies, much more reliable information can be provided to the decision makers.By systematically determining the various influencing factors and by describing their real impact on the overall result, the most significant variables can be identified. This then is an essential input into decision making and the planning of future life cycle studies.This methodological approach is shown exemplarily by a case study on a material comparison for automotive body applications. The paper concludes with a recommendation on data collection requirements, an adequate study planning and use of the findings.
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