Anzeige
Erschienen in:
01.01.2013 | DATA AVAILABILITY, DATA QUALITY IN LCA
Comparison of two versions of an EPD, using generic and specific data for the foreground system, and some methodological implications
Erschienen in: The International Journal of Life Cycle Assessment | Ausgabe 1/2013
EinloggenAktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Purpose
Differences in the practice of inclusion and the definition of specific and generic data when performing an LCA for an Environmental Product Declaration (EPD) may lead to incomparable EPDs. The purpose of this paper is to illuminate the importance of precise definitions regarding data quality in EPDs.
Method
The authors define relevant terminology before describing methodological differences between two versions of EPDs for an office chair. The analyses performed for one EPD use generic data for the foreground system, while the other uses specific data. Results for some impact categories as well as inventory findings are shown, and the reasons for differences are investigated and discussed.
Results
Relevant dilemmas are examined with regard to the choice of generic or specific data. These include practical hindrances and the promotion of environmental improvement. Some preliminary methodological and organisational implications are described, followed by an outline of further research.
Conclusions
This paper shows the substantial variations arising from using two datasets with different degrees of specificity, and concludes that they increase in relation to the distinctiveness of the process or material. This highlights the importance of EPD programmes in establishing precise, unambiguous definitions and vocabulary with regard to specific as against generic data, when combined with foreground and background processes. It is essential to take this into consideration so as to avoid misunderstandings or false agreement when discussing data quality. It is also necessary in order to avoid comparisons of products based on very different assumptions.