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Confronting Uncertainty in Life Cycle Assessment Used for Decision Support
Author(s) -
Herrmann Ivan T.,
Hauschild Michael Z.,
Sohn Michael D.,
McKone Thomas E.
Publication year - 2014
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1111/jiec.12085
Subject(s) - life cycle assessment , industrial ecology , decision support system , computer science , context (archaeology) , decision maker , variance (accounting) , scale (ratio) , operations research , management science , risk analysis (engineering) , sustainability , data mining , production (economics) , engineering , business , ecology , economics , paleontology , physics , accounting , quantum mechanics , biology , macroeconomics
Summary The aim of this article is to help confront uncertainty in life cycle assessments (LCAs) used for decision support. LCAs offer a quantitative approach to assess environmental effects of products, technologies, and services and are conducted by an LCA practitioner or analyst (AN) to support the decision maker (DM) in making the best possible choice for the environment. At present, some DMs do not trust the LCA to be a reliable decision‐support tool—often because DMs consider the uncertainty of an LCA to be too large. The standard evaluation of uncertainty in LCAs is an ex‐post approach that can be described as a variance simulation based on individual data points used in an LCA. This article develops and proposes a taxonomy for LCAs based on extensive research in the LCA, management, and economic literature. This taxonomy can be used ex ante to support planning and communication between an AN and DM regarding which type of LCA study to employ for the decision context at hand. This taxonomy enables the derivation of an LCA classification matrix to clearly identify and communicate the type of a given LCA. By relating the LCA classification matrix to statistical principles, we can also rank the different types of LCA on an expected inherent uncertainty scale that can be used to confront and address potential uncertainty. However, this article does not attempt to offer a quantitative approach for assessing uncertainty in LCAs used for decision support.