Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen: http://dx.doi.org/10.18419/opus-13974
Autor(en): Kiemel, Steffen
Rietdorf, Chantal
Schutzbach, Maximilian
Miehe, Robert
Titel: How to simplify life cycle assessment for industrial applications : a comprehensive review
Erscheinungsdatum: 2022
Dokumentart: Zeitschriftenartikel
Seiten: 26
Erschienen in: Sustainability 14 (2022), No. 15704
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-139938
http://elib.uni-stuttgart.de/handle/11682/13993
http://dx.doi.org/10.18419/opus-13974
ISSN: 2071-1050
Zusammenfassung: Life cycle assessment (LCA) has established itself as the dominant method for identifying the environmental impact of products or services. However, conducting an LCA is labor and time intensive (especially regarding data collection). This paper, therefore, aims to identify methods and tools that enhance the practicability of LCA, especially with regard to product complexity and variance. To this end, an initial literature review on the LCA of complex products was conducted to identify commonly cited barriers and potential solutions. The obtained information was used to derive search strategies for a subsequent comprehensive and systematic literature review of approaches that address the identified barriers and facilitate the LCA process. We identified five approaches to address the barriers of time and effort, complexity, and data intensity. These are the parametric approach, modular approach, automation, aggregation/grouping, and screening. For each, the concept as well as the associated advantages and disadvantages are described. Especially, the automated calculation of results as well as the automated generation of life cycle inventory (LCI) data exhibit great potential for simplification. We provide an overview of common LCA software and databases and evaluate the respective interfaces. As it was not considered in detail, further research should address options for automated data collection in production by utilizing sensors and intelligent interconnection of production infrastructure as well as the interpretation of the acquired data using artificial intelligence.
Enthalten in den Sammlungen:04 Fakultät Energie-, Verfahrens- und Biotechnik

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
sustainability-14-15704.pdf3,8 MBAdobe PDFÖffnen/Anzeigen


Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons