Herrmann, Ivan Tengbjerg1; Hauschild, Michael Zwicky1; Sohn, Michael D.8; McKone, Thomas E.7
1 Department of Management Engineering, Technical University of Denmark2 Systems Analysis, Department of Management Engineering, Technical University of Denmark3 DTU Climate Centre, Systems Analysis, Department of Management Engineering, Technical University of Denmark4 Energy Systems Analysis, Systems Analysis, Department of Management Engineering, Technical University of Denmark5 Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark6 Lawrence Berkeley National Laboratory7 University of California at Berkeley8 Lawrence Berkeley National Laboratory
Developing and Proposing a Taxonomy for LCA Studies
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 decisionsupport 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.
Journal of Industrial Ecology, 2014, Vol 18, Issue 3
Data quality; Decision support; Industrial ecology; Life cycle assessment (LCA); Transparency; Variability