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Prioritisation of modelling parameters of a free-floating car sharing system according to their sensitivity to the environmental impacts

Olivier Guyon, Dominique Millet, Julien Garcia, Manuele Margni, Sophie Richet, Nicolas Tchertchian
Journal of cleaner production 2021 v.296 pp. 126081
area, automobiles, electricity, environmental impact, eutrophication, exhibitions, experimental design, global warming potential, life cycle assessment, models, oxidation, paper, photochemistry, prioritization
Relying on life cycle assessment (LCA) to evaluate product-service systems (PSSs), and more specifically car sharing systems, involves many challenges. Car sharing services include free-floating car sharing, which enables users to take and leave vehicles anytime and anywhere within a limited service area. This paper proposes a model of a free-floating electric car sharing system in which eight parameters that influence environmental impacts may be identified. Among these parameters are the rate of use of the vehicles, standard trip representative of the service’s actual use, vehicle model used within the service, and electric mix used to charge the vehicles. Adapting the life cycle assessment methodology to the studied system makes it possible to link the studied parameters to the indicator values of the service’s environmental impact. The environmental indicators considered are global warming potential (GWP), photochemical oxidation potential (POCP), eutrophication potential (EP), and abiotic resource depletion potential (ADP). As a result, by using a design of experiments, it is possible to prioritise the eight system parameters according to their influence on the four environmental impact indicators. More specifically, the experiment demonstrates that the electricity mix has a major influence on the GWP and POCP indicators. With regard to the ADP indicator, the vehicle model used in the service is the most influential parameter by far. The use rate and trip type parameters have significant effects on the four environmental indicators. Finally, the experiment also shows that the results heavily rely on the study’s methodological choices.