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Using Standard Statistics to Consider Uncertainty in Industry-Based Life Cycle Inventory Databases (7 pp)

Sugiyama, Hirokazu ; Fukushima, Yasuhiro ; Hirao, Masahiko ; Hellweg, Stefanie ; Hungerbühler, Konrad

In: The International Journal of Life Cycle Assessment, 2005, vol. 10, no. 6, p. 399-405

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    Titel
    • Using Standard Statistics to Consider Uncertainty in Industry-Based Life Cycle Inventory Databases (7 pp)
    Autor
    • Sugiyama, Hirokazu. Hirokazu Sugiyama, BEng MEng Institute for Chemical and Bioengineering Swiss Federal Institute of Technology ETH Hönggerberg 8093 Zürich SWITZERLAND
    • Fukushima, Yasuhiro. Yasuhiro Fukushima Department of Chemical System Engineering The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 JAPAN
    • Hirao, Masahiko. Masahiko Hirao Department of Chemical System Engineering The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 JAPAN
    • Hellweg, Stefanie. Dr. Stefanie Hellweg Institute for Chemical and Bioengineering Swiss Federal Institute of Technology ETH Hönggerberg 8093 Zürich SWITZERLAND
    • Hungerbühler, Konrad. Prof. Dr. Konrad Hungerbühler Institute for Chemical and Bioengineering Swiss Federal Institute of Technology ETH Hönggerberg 8093 Zürich SWITZERLAND
    Dokumententyp
    • Postprint
    Sprache
    • Englisch
    Veröffentlicht in
    • The International Journal of Life Cycle Assessment, 2005, vol. 10, no. 6, p. 399-405. Ecomed
    Andere elektronische Ausgabe
    OAI-PMH-ID
    • oai:doc.rero.ch:312200
    Summary
    Goal, Scope and Background: Decision-makers demand information about the range of possible outcomes of their actions. Therefore, for developing Life Cycle Assessment (LCA) as a decision-making tool, Life Cycle Inventory (LCI) databases should provide uncertainty information. Approaches for incorporating uncertainty should be selected properly contingent upon the characteristics of the LCI database. For example, in industry-based LCI databases where large amounts of up-to-date process data are collected, statistical methods might be useful for quantifying the uncertainties. However, in practice, there is still a lack of knowledge as to what statistical methods are most effective for obtaining the required parameters. Another concern from the industry's perspective is the confidentiality of the process data. The aim of this paper is to propose a procedure for incorporating uncertainty information with statistical methods in industry-based LCI databases, which at the same time preserves the confidentiality of individual data. Methods: The proposed procedure for taking uncertainty in industry-based databases into account has two components: continuous probability distributions fitted to scattering unit process data, and rank order correlation coefficients between inventory flows. The type of probability distribution is selected using statistical methods such as goodness-of-fit statistics or experience based approaches. Parameters of probability distributions are estimated using maximum likelihood estimation. Rank order correlation coefficients are calculated for inventory items in order to preserve data interdependencies. Such probability distributions and rank order correlation coefficients may be used in Monte Carlo simulations in order to quantify uncertainties in LCA results as probability distribution. Results and Discussion: A case study is performed on the technology selection of polyethylene terephthalate (PET) chemical recycling systems. Three processes are evaluated based on CO2 reduction compared to the conventional incineration technology. To illustrate the application of the proposed procedure, assumptions were made about the uncertainty of LCI flows. The application of the probability distributions and the rank order correlation coefficient is shown, and a sensitivity analysis is performed. A potential use of the results of the hypothetical case study is discussed. Conclusion and Outlook: The case study illustrates how the uncertainty information in LCI databases may be used in LCA. Since the actual scattering unit process data were not available for the case study, the uncertainty distribution of the LCA result is hypothetical. However, the merit of adopting the proposed procedure has been illustrated: more informed decision-making becomes possible, basing the decisions on the significance of the LCA results. With this illustration, the authors hope to encourage both database developers and data suppliers to incorporate uncertainty information in LCI databases