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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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.
LCANET Food, European Network for Life Cycle Assessment Research and Development within the food chain, Concerted action PL-97-3079 of the Food and Agriculture programme (FAIR).
The book can be ordered from LEI. Price: NLG 120. Please order: Report 2.00.01. ISBN 90-5242-563-9.
You may download the Preface, Introduction, Conclusions (chapter 30) and the conclusions of the 4 working groups in Word 97 format as one zipped file.
The SPOLD Information System (SIS) was released in January 2000. This poster gives a quick survey of the system.
Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group ‘Data Availability and Quality’ presents a framework for data uncertainty assessment in LCI. Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.
LCA has traditionally been performed as a bottom-up process analysis, based on linking the specific processes in a supply chain. Exceptions to this approach may be found, especially in the early LCA work in Japan, which was often based on IOA. The process-based method is explained in more detail by Marianne Wesnæs in Chapter 3, who also points out its capability for detail as a significant advantage of this approach. However, a major problem in process-based LCA is the likelihood that important parts of the product systems are left out of the analysis, simply because it is a very difficult task to follow the entire supply chain in detail. As pointed out by Manfred Lenzen in Chapter 4, up to 50% of the environmental exchanges related to a product can be left out, thus possibly leading to erroneous conclusions.
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Main objectives of the project are:
1. To introduce the environmental data in the design processes
2. To facilitate data exchange and independence from any computer system
Those objectives are both “key points” and “bottle necks” for a wider diffusion of LCA and eco- design tools.
They are achieved adapting the standards developed for the communication of product, process and property data in design and manufacturing to the requirements for LCA. Three types of standards for computerised information representation and communication have been adopted:
• ISO 10303 provides entity-relationship models for product, property and process data;
• ISO 15926 provides classification structures for the description of industrial data
• Web ontology provide web-based classification structures for the description of information.
These standards are used for the representation of examples of LCA data in accordance with ISO 14048 and their communication between different systems.
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