4.4 Article

Environmental assessment of future technologies: how to trim LCA to fit this goal?

Journal

INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT
Volume 14, Issue 6, Pages 584-588

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s11367-009-0120-6

Keywords

Electricity-generating technologies; Emerging energy technologies; Future technologies; Long-term LCI modeling; NEEDS project; New Energy Externality Developments for Sustainability project; External cost assessment

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New and innovative technologies may claim substantial efficiency gains in the future. However, they are often assessed based on their current performance, measured in the laboratory or in pilot plants. The goal of discussion forum 38 was, on one hand, to shed light on the main drivers and principles that ensure a sensible and fair assessment of far future technologies. On the other hand, the most recent European developments in prospective technology assessment of emerging energy technologies and the related quantification of external costs were touched upon. The discussion forum started with three talks dedicated to external costs and multicriteria decisions presenting results of the New Energy Externality Developments for Sustainability project. After three presentations considering long-term LCI modeling aspects, lectures were held covering industry implementation and case studies. The following main conclusions were drawn at the end of discussion forum 38: (a) life cycle assessment (LCA) is considered a useful tool for environmental assessments of future energy technology, (b) consistency in LCA modeling of future situations is achieved by adapting data in the foreground (electricity-generating technology) and in the background (electricity supply mix, material manufacture, transport services, etc.), (c) external cost assessments and multicriteria decision analysis involve value judgments and thus do lead to a variety of different conclusions, (d) the present situation must be known properly to be able to model possible future situations, and (e) challenges are the data availability and definition of consistent scenarios of the future.

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