Abstract
Life-cycle impact assessments (LCIAs) are complex because they almost always involve uncertain consequences relative to multiple criteria. Several authors have noticed that this is precisely the sort of problem addressed by methods of decision analysis. Despite several experiences of using multiple-attribute decision analysis (MADA) methods in LCIA, the possibilities of MADA methods in LCIA are rather poorly elaborated in the field of life-cycle assessment. In this article we provide an overview of the commonly used MADA methods and discuss LCIA in relation to them. The article also presents how different frames and tools developed by the MADA community can be applied in conducting LCIAs. Although the exact framing of LCIA using decision analysis still merits debate, we show that the similarities between generic decision analysis steps and their LCIA counterparts are clear. Structuring of an assessment problem according to a value tree offers a basis for the definition of impact categories and classification. Value trees can thus be used to ensure that all relevant impact categories and interventions are taken into account in the appropriate manner. The similarities between multiattribute value theory (MAVT) and the current calculation rule applied in LCIA mean that techniques, knowledge, and experiences derived from MAVT can be applied to LCIA. For example, MAVT offers a general solution for the calculation of overall impact values and it can be applied to help discern sound from unsound approaches to value measurement, normalization, weighting, and aggregation in the LCIA model. In addition, the MAVT framework can assist in the methodological development of LCIA because of its well-established theoretical foundation. The relationship between MAVT and the current LCIA methodology does not preclude application of other MADA methods in the context of LCIA. A need exists to analyze the weaknesses and the strengths of different multiple-criteria decision analysis methods in order to identify those methods most appropriate for different LCIA applications.