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

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Abstract

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.

Introduction

The automotive industry is undergoing a revolution. Tackling the energy transition, appropriating technological advances to increase their competitiveness and the attractiveness of their products, and having to anticipate new uses for them, car manufacturers must adapt quickly (Delhi, 2016). Among the new uses of vehicles are innovative mobility services, such as car sharing and carpooling (Kamargianni et al., 2016). With the arrival of these new services, most car manufacturers are responding by offering their own mobility services (Gao et al., 2016). One of the most common is car sharing. There are several types of car sharing, including the B2C (business-to-consumer) free-floating car sharing that is currently being developed in large cities (Becker et al., 2017).

The environmental performance of automotive vehicles is gradually becoming a design criterion for manufacturers. More specifically, current regulations call for exhaust emission thresholds that must not be exceeded under financial penalties for carbon dioxide emissions (Regulation, 2009) or a market ban (Parliament and Union, 2007). It seems likely that these regulations will eventually apply over the entire life cycle of products (Manfredi et al., 2012) and even organisations themselves (Pelletier et al., 2012). Also, customers are increasingly sensitive to issues related to global warming and the environment in general. It is legitimate to think that it will soon become essential to also analyse and optimise the environmental performances of new mobility solutions through a life cycle approach, as is essential today for the use of personal vehicles. Car manufacturers will have to stand out from the competition by offering services with the best possible environmental performances. While these different services are often presented as virtuous for the environment (Katzev, 2003), few studies have undertaken their environmental analysis. This is mainly due to a lack of methods and tools to analyse the environmental performances of the services. Indeed, methodologies such as life cycle assessment in compliance with ISO 14040 standards (Finkbeiner et al., 2006) are not directly applicable to these product-service systems (PSSs) or car-sharing services (Doualle et al., 2015). It is, therefore, necessary to develop methodologies to analyse and optimise these services from an environmental perspective. To our knowledge, there are only a few studies in the literature that attempt to demonstrate the environmental benefits of new mobility solutions as compared to traditional solutions such as private vehicle use (Luna et al., 2020). Concerning the environmental analysis and quantification of the impacts of these services, there is a real gap that this paper seeks to address.

This paper presents an original approach to modelling a free-floating car sharing system to which a life cycle assessment methodology was adapted. System parameters are based on this modelling. By adapting the LCA methodology, these parameters may be linked to the output values of environmental indicators. Four environmental indicators are considered: global warming potential (GWP), photochemical oxidation potential (POCP), eutrophication potential (EP), and abiotic depletion potential (ADP). These are the four environmental indicators most commonly observed by automotive manufacturers (Garcia et al., 2015). GWP is an indicator that aims to group under a single value the added effect of all the substances contributing to the increase in the greenhouse effect. Conventionally, we are limited for the moment to direct greenhouse gases (GHGs), that is to say, the six gases (CO2, CH4, N2O, CFC, HFC, SF6) taken into account in the Kyoto protocol. This indicator is expressed in “CO2 equivalent” because, by definition, the greenhouse effect attributed to CO2 is set at 1 and that of other substances relative to CO2. POCP is one indicator of the possible contribution of an organic compound to the formation of ground-level ozone; EP indicator represents the modification and degradation of an aquatic environment linked to an excessive supply of nutrients, in particular nitrogen and phosphorus. Finally, ADP refers to the measure of the use of non-renewable sources for energy production.

In the literature, there are few studies on the environmental assessment of PSS. The objective of this paper is to propose a modeling of a car-sharing PSS allowing an environmental assessment based on the traditional methodology of vehicle LCA. Thanks to this modelling, it will be possible to prioritise the identified system parameters according to their influence on the environmental impact indicators of the service by using a design of experiments. The results obtained can help in decision-making to design the business model of a car-sharing service to minimize these environmental impacts.

Section snippets

Free-floating car sharing product-service system

Car sharing is part of the product-service systems (PSSs) in automotive mobility category. The concept of PSS is neither new nor exclusive to the automotive sector. For the past two decades, many authors have tackled PSSs, and there are several definitions in the literature. Mont (2002) explains that a PSS should be defined as a system of products, services, networks, and supporting infrastructure designed to be competitive, meet customer needs, and have less environmental impact than

System boundaries

In this paper, the studied business model is the free-floating car sharing service presented earlier. Fig. 1 is a map of the system and its constituting elements.

The mapping (Fig. 1) illustrates the eight remaining blocks shown in Fig. 2.

Modelling assumptions

To carry out the experimentation to study the influence of system parameters on the environmental impacts, it was necessary to model the system and identify the system parameters to be analysed. In this session, the LCA methodology adapted to the system will

Identification of system parameters (factors)

By adapting the LCA methodology to the system, two new parameters are introduced: the allocation coefficient α and theoretical end-of-life distance of vehicles. These two parameters are in addition to the nine parameters derived from the system modelling, thus yielding eleven modelling parameters, three of which are fixed parameters. As input data, a study duration of two years and a fixed trip demand over this duration are imposed. It is also assumed that the distance travelled by the vehicles

Results

The order of the levels of the eight factors detailed in Table 4 is used to present the results. To study the influence, calculations of averages over balanced (i.e. orthogonal) sets of results are used. This is the notion of effect. The effect of a factor associated with a level is defined by the arithmetic difference between the mean of the indicator values associated with the factor level in question (mean of 16 values for this design of experiments) and the overall mean calculated from all

Discussion

It is important to emphasize that these results are based on the study’s methodology and assumptions. Indeed, the allocation coefficient parameter ranks among the most influential parameters for each environmental indicator. The strong influence of this parameter is related to its wide range of variation (between 0.25 and 0.75). Seeing as the choice of the coefficient value is subjective, it seemed necessary to select a broad range. Indeed, the value of this coefficient reflects the share of

Conclusion

This study ranks the various parameters according to their influence on the values of the four indicators of the service’s environmental impacts. The loss of accuracy of the results due to the use of the design of experiments may, in some cases, postpone the prioritisation of the parameters. However, it is still possible to draw interesting conclusions from the results. First, the importance of the choice of allocation coefficient, which has a major influence on the results (for all four

CRediT authorship contribution statement

Olivier Guyon: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft, Writing - review & editing. Dominique Millet: Validation, Resources, Writing - review & editing, Supervision, Project administration. Julien Garcia: Conceptualization, Methodology, Validation, Resources, Writing - review & editing, Supervision. Manuele Margni: Validation, Writing - review & editing, Supervision. Sophie Richet: Validation, Supervision, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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