When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment. (21st November 2018)

• Record Type: Journal Article
• Authors: Mendoza Beltran, Angelica; Cox, Brian; Mutel, Chris; van Vuuren, Detlef P.; Font Vivanco, David; Deetman, Sebastiaan; Edelenbosch, Oreane Y.; Guinée, Jeroen; Tukker, Arnold
• Other Names: Bergerson Joule guestEditor.; Cucurachi Stefano guestEditor.; Seager Thomas guestEditor.
• Is Part Of: Journal of industrial ecology. Volume 24:Number 1(2020:Feb.)
• Page Start: 64
• Page End: 79
• Publication Date: 2018-11-21
• Subjects: background changes -- epistemological uncertainty -- industrial ecology -- integrated assessment models -- life cycle assessment -- prospective LCA; Industrial ecology -- Periodicals; Product life cycle -- Environmental aspects -- Periodicals; Industrial management -- Environmental aspects -- Periodicals; Écologie industrielle -- Périodiques; 658.56
• DOI: 10.1111/jiec.12825
• Languages: English
• ISSNs: 1088-1980

Abstract

Summary: Prospective life cycle assessment (LCA) needs to deal with the large epistemological uncertainty about the future to support more robust future environmental impact assessments of technologies. This study proposes a novel approach that systematically changes the background processes in a prospective LCA based on scenarios of an integrated assessment model (IAM), the IMAGE model. Consistent worldwide scenarios from IMAGE are evaluated in the life cycle inventory using ecoinvent v3.3. To test the approach, only the electricity sector was changed in a prospective LCA of an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using six baseline and mitigation climate scenarios until 2050. This case study shows that changes in the electricity background can be very important for the environmental impacts of EV. Also, the approach demonstrates that the relative environmental performance of EV and ICEV over time is more complex and multifaceted than previously assumed. Uncertainty due to future developments manifests in different impacts depending on the product (EV or ICEV), the impact category, and the scenario and year considered. More robust prospective LCAs can be achieved, particularly for emerging technologies, by expanding this approach to other economic sectors beyond electricity background changes and mobility applications as well as by including uncertainty and changes in foreground parameters. A more systematic and structured composition of future inventory databases driven by IAM scenarios helps to acknowledge epistemological uncertainty and to increase the temporal consistency of foreground and background systems in LCAs of emerging technologies.