Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling

• Published in: The international journal of life cycle assessment Vol. 25; no. 12; pp. 2325 - 2341
• Main Authors: Patouillard, Laure, Lorne, Daphné, Collet, Pierre, Bulle, Cécile, Margni, Manuele
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2020; Springer Nature B.V; Springer Verlag
• Subjects: Acidification; Case studies; Climate change; Confidence intervals; Decision making; Earth and Environmental Science; Economic analysis; Economic models; Economics; Economics and Finance; Energy transition; Environment; Environmental assessment; Environmental Chemistry; Environmental Economics; Environmental Engineering/Biotechnology; Environmental Sciences; Eutrophication; Global warming; Humanities and Social Sciences; Interpretation of LCA Studies for Decision Support; Inventory management; Life cycle analysis; Life cycle assessment; Life cycles; Marine pollution; Mathematical models; Oxidants; Oxidizing agents; Particulate matter; Photochemical oxidants; Photochemicals; Public policy; Reduction; Sensitivity analysis; Transportation; Uncertainty analysis; Variability; Water pollution; France; Life cycle assessment; Uncertainty; Regionalization; Partial equilibrium economic modeling,Global sensitivity analysis; Interpretation; Transport; Public policy; partial equilibrium economic modelling; interpretation; Life Cycle Assessment; modèle d'équilibre partiel; transport; uncertainty; global sensitivity analysis; public policy; regionalization
• ISSN: 0948-3349; 1614-7502
• DOI: 10.1007/s11367-020-01785-x

Purpose
Consequential life cycle assessment (C-LCA) aims to assess the environmental consequences of a decision. It differs from traditional LCA because its inventory includes all the processes affected by the decision which are identified by accounting for causal links (physical, economic, etc.). However, C-LCA results could be quite uncertain which makes the interpretation phase harder. Therefore, strategies to assess and reduce uncertainty in C-LCA are needed. Part of uncertainty in C-LCA is due to spatial variability that can be reduced using regionalization. However, regionalization can be complex and time-consuming if straightforwardly applied to an entire LCA model.
Methods
The main purpose of this article is to prioritize regionalization efforts to enhance interpretation in C-LCA by assessing the spatial uncertainty of a case study building on a partial equilibrium economic model. Three specific objectives are derived: (1) perform a C-LCA case study of alternative transportation scenarios to investigate the benefits of implementing a public policy for energy transition in France by 2050 with an uncertainty analysis to explore the strength of our conclusions, (2) perform global sensitivity analyses to identify and quantify the main sources of spatial uncertainty between foreground inventory model from partial equilibrium economic modeling, background inventory model and characterization factors, (3) propose a strategy to reduce the spatial uncertainty for our C-LCA case study by prioritizing regionalization.
Results and discussion
Results show that the implementation of alternative transport scenarios in compliance with public policy for the energy transition in France is beneficial for some impact categories (ICs) (global warming, marine acidification, marine eutrophication, terrestrial acidification, thermally polluted water, photochemical oxidant formation, and particulate matter formation), with a confidence level of 95%. For other ICs, uncertainty reduction is required to determine conclusions with a similar level of confidence. Input variables with spatial variability from the partial equilibrium economic model are significant contributors to the C-LCA spatial uncertainty and should be prioritized for spatial uncertainty reduction. In addition, characterization factors are significant contributors to the spatial uncertainty results for all regionalized ICs (except land occupation IC).
Conclusions
Ways to reduce the spatial uncertainty from economic modeling should be explored. Uncertainty reduction to enhance the interpretation phase and the decision-making should be prioritized depending on the goal and scope of the LCA study. In addition, using regionalized CFs in C-LCA seems to be relevant, and C-LCA calculation tools should be adapted accordingly.