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Entity Normalization in Life Cycle Assessment: Hybrid Schemes Applied to a Transportation Agency Case Study

Authors :
Quinn Langfitt
Liv Haselbach
Source :
Journal of Industrial Ecology. 21:1090-1102
Publication Year :
2016
Publisher :
Wiley, 2016.

Abstract

Summary Government agencies, companies, and other entities are using environmental assessments, like life cycle assessment (LCA), as an input to decision-making processes. Communicating the esoteric results of an LCA to these decision makers can present challenges, and interpretation aids are commonly provided to increase understanding. One such method is normalizing results as a means of providing context for interpreting magnitudes of environmental impacts. Normalization is mostly carried out by relating the environmental impacts of a product (or process) under study to those of another product or a spatial reference area (e.g., the United States). This research is based on the idea that decision makers might also benefit from normalization that considers comparisons to their entity's (agency, company, organization, etc.) total impacts to provide additional meaning and aid in comprehension. Two hybrid normalization schemes have been developed, which include aspects of normalization to both spatially based and entity-based impacts. These have been named entity-overlaid and entity-accentuated normalization, and the schemes allow for performance-based planning or emphasizing environmental impact types that are most relevant to an entity's operational profile, respectively. A hypothetical case study is presented to demonstrate these schemes, which uses environmental data from a U.S. transportation agency as the basis for entity normalization factors. Results of this case study illustrate how entity-related references may be developed, and how this additional information may enhance the presentation of LCA results using the hybrid normalization schemes.

Details

ISSN :
10881980
Volume :
21
Database :
OpenAIRE
Journal :
Journal of Industrial Ecology
Accession number :
edsair.doi...........5bc8925c5bb522aa43100ff00ca71703