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When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment
Author(s) -
Mendoza Beltran Angelica,
Cox Brian,
Mutel Chris,
Vuuren Detlef P.,
Font Vivanco David,
Deetman Sebastiaan,
Edelenbosch Oreane Y.,
Guinée Jeroen,
Tukker Arnold
Publication year - 2020
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1111/jiec.12825
Subject(s) - life cycle assessment , electricity , baseline (sea) , industrial ecology , consistency (knowledge bases) , environmental economics , computer science , environmental science , production (economics) , sustainability , engineering , artificial intelligence , economics , ecology , oceanography , electrical engineering , biology , macroeconomics , geology
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.