Current life-cycle assessment (LCA) methodologies rarely include economic analysis—even though this element is almost always crucial to any company's decision making. This article discusses practical tools for integrating economic considerations into LCA. © 2001 John Wiley & Sons, Inc.
This article describes the work carried out by the Promoting Sound Practices (PSP) working group of SPOLD (Society for the Promotion of Lifecycle Development) on the development of a common format for reporting life cycle inventory data in a comparable and transparent way, and hence towards the eventual goal of a decentralised network of life cycle inventory databases. Establishing such a database network depends on the achievement of consensus amongst potential users, data owners and data generators. Accordingly, building consensus has been, and will continue to be given, a high priority in this work. As well as a summary of the consensus building activities, this article provides an outline of the developing format and an indication of the next steps planned, some of which are already underway.
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Traditionally, critical reviews - or peer reviews - are known from the international scientific journals, where submitted articles are subjected to critical scrutiny by anonymous colleagues (peers) before being accepted for publication - often after considerable adjustments. Since it is difficult to determine objective criteria for scientific quality, the subjective - but professional - judgement of peers becomes the ultimate quality assurance for scientific work.
Life cycle assessments have in common with scientific work the difficulty of establishing objective quality criteria. Many of the judgements a practitioner will have to make in the course of a life cycle assessment cannot be said to be true or false, but only more or less justifiable. Therefore, the ultimate quality judgement can only be subjective - although based on professional experience.
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Different lists of application areas for life cycle assessment are reviewed together with some suggestions for a typology of these application areas. It is concluded that the scope of a life cycle assessment is determined by the area of validity of the decision with respect to time, space, and interest groups affected. On this basis, six application areas are distinguished. It is further concluded that the application area has limited influence on the inventory analysis and impact assessment phases, although these may be influenced significantly by the decision-maker and the complexity of the trade-offs between the involved environmental impacts. The reporting format for a life cycle assessment depends on the socio-economic importance of the decision, the intended audience, and the time available for decision making.
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The data quality matrix for product life cycle inventory data proposed in Weidema & Wesnæs (J. Cleaner Prod. (1996), 4: 167-174) was subjected to a multi-user test, in which 7 persons scored the same 10 datasets representing 10 different processes. Deviations among scores were listed, and the causes for deviations were determined and grouped into a limited number of well-defined classes. For the majority of the scores, the different test persons arrived at the same score. Deviations occur most often among neighbouring scores. Only a smaller number of the deviations (less than 10% of all scores) affect the overall assessment of the data quality and/or uncertainty of the corresponding dataset. Based on the analysis of the causes of the deviations, improvements to the matrix and its accompanying explanations were suggested and implemented (reported in the appendix to this paper). The average time consumption for the scoring by the different test persons was less than 10 minutes per data set. It is concluded that the time consumption and the number of deviating scores can be kept at an acceptable level for the pedigree matrix to be recommended for internal data quality management and for comprehensive communication of quality assessments of large amounts of data.
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Many methodological choices in a life cycle assessment (LCA) depend on the goal of the study, i.e. its application area. A better definition of the application areas and especially a clear distinction between retrospective and prospective applications allows a more unambiguous description of the methodology to apply for different applications.
Marginal technologies are defined as the technologies actually affected by the small changes in demand typically studied in prospective, comparative life cycle assessments. Using data on marginal technologies thus give the best reflection of the actual consequences of a decision. Furthermore, data on marginal technologies are easier to collect, more precise, and more stable in time than data on average technologies. A 5-step procedure is suggested to identify the marginal technologies. The step-wise procedure first clarifies the situation in which the marginal should apply, and then identifies what specific technology is marginal in this situation. The procedure is illustrated in two examples: European electricity production and pulp and paper production.
