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M-1A-5 - Shared Mobility

Shared mobilty and Mobility as a Service (MaaS) covers a wide range of solutions centered around the concept of reduced or non-ownership of vehicles. Examples of shared mobility include: e-scooter sharing, car sharing and on-demand mobility.

Shared mobility is outlined in section 10.2.3 (shared economy) of (IPCC AR6 WG3 2022)1.

Mitigation Objective

The primary goal is for a efficiency shift to increase the average number of passengers per light-duty vehicle for short and medium journeys (25km or less).

Mitigation Potential

While the theoretical potential for this mitigation option is a halving of emissions (ITF 2020)2, by means of shifting all light-duty vehicle travel to carpooling, we align with the IPCC conclusion that a realistic goal is 20% of vehicle travel shifted to carpooling.

Potential

The AR6 report and the literature referenced by it indicate that of all shared mobility options, only carpooling has any significant potential with an estimated emission reduction potential of 12%.

Shared mobility could increase utilisation rates of LDVs, thus improving the efficiency of the system. However, shared mobility could also divert users from transit systems or active transport modes. Studies on ride-sourcing have reported both potential for reductions and increases in transport-related emissions (Schaller 2018; Ward et al. 2021). Other case studies suggests that carpooling to replace 20% of private car trips could result in a 12% reduction in GHG emissions (ITF 2020a; ITF 2020b). Thus, the effect of shared mobility on transport-related GHG emissions is highly uncertain.

- (IPCC AR6 WG3 2022)1

Replacing 20% of private car trips with shared modes would still result in an 11% reduction in vehicle-kilometres and a 12% reduction in emissions while the impact on congestion would be even greater (-16%), as carpooling requires less empty or non-productive passenger-kilometres.

- (ITF 2020)2

... results show that MaaS-impacts are even stronger when substantial fleets of car-sharing, bike-sharing and ride-hailing are introduced into the network. In fact, integration of shared modes may even allow substantial efficiency gains on the supply 570 side, when used to provide accessibility to areas, in which demand is too low to support line-based public transportation. Combined with unbiased mode choice decisions, system efficiency can be increased by up to 11 % and total energy consumption reduced by up to 31 %.

- (Becker et al. 2019)3

Modelling

This mitigation method has been modelled with the Transition Element: T-1A1a-5 - More efficient commuting by car.

Primary Reference

The primary reference for this mitigation measure is (IPCC AR6 WG3 2022)1.

Secondary References

TBD

Shared Mobility Simulations for Lyon

This study (ITF 2020)2 looked at several forms of shared mobility (carpooling, shared taxi and taxi-bus) and simulated five scenarios of varying levels of trip replacement in order to determine carbon mitigation potential. Scenario 1, replacement of all private vehicle kilometers with shared mobility alternatives, sets the maximum theoretical potential of this mitigation option to 50.6% reduction in GHG emissions.

Assessing the welfare impacts of Shared Mobility and Mobility as a Service (MaaS)

This study (Becker et al. 2019)3 used computer model simulations to determine the potentially effectiveness of various shared mobility solutions on overall travel efficiency. The paper concluded that a 31% resource efficiency improvement was possible. The correlating GHG emission reduction was not included in this study - however we make the assumption here that a reduction in total energy consumption should lead to a proportional reduction in GHG emissions; i.e. approximately 30%.

  1. IPCC AR6 WG3. 2022. Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by Priyadarshi R. Shukla, Jim Skea, Raphael Slade, Alaa Al Khourdajie, Renée van Diemen, David McCollum, Minal Pathak, et al. https://doi.org/10.1017/9781009157926. ↩↩↩

  2. ITF. 2020. “Shared Mobility Simulations for Lyon.” No. 74. OECD Publishing. https://doi.org/10.1787/031951c3-en. ↩↩↩

  3. Becker, Henrik, Milos Balac, Francesco Ciari, and Kay Axhausen. 2019. “Assessing the Welfare Impacts of Shared Mobility and Mobility as a Service (MaaS).” Transportation Research Part A: Policy and Practice 131 (September). https://doi.org/10.1016/j.tra.2019.09.027. ↩↩

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