Shared mobility options, such as car sharing, are often claimed to be more sustainable, although evidence at an individual or city level may contradict these claims. This study aims to improve understanding of the effects of car sharing on transport-related emissions at an individual and city level. This is done by quantifying the greenhouse gas (GHG) emissions of the travel habits of individuals before and after engaging with car sharing. The analysis uses a well-to-wheel (WTW) approach, including both business-to-consumer (B2C) and peer-to-peer (P2P) car-sharing fleets. Changes in GHG emissions after engaging in car sharing vary among individuals. Transport-related GHG emissions caused by car-free individuals tend to increase after they engage in car sharing, while emissions caused by previous car owners tend to fall. At the city level, GHG emissions savings can be achieved by using more efficient cars in sharing systems and by implementing greener mobility policies. Changes in travel habits might help to reduce GHG emissions, providing individuals migrate to low-carbon transport modes. The findings can be used to support the development and implementation of transport policies that deter car ownership and support shared mobility solutions that are integrated in city transport systems.

No abstract

Despite the deployment of low- or zero-emission technologies, achieving emissions reductions in the passenger transportation sector remains challenging. Demand-side mechanisms can be instrumental in reducing environmental impacts of transportation and reconfiguring transportation systems in a way that shifts users away from private car ownership.

In this article we look at the Netherlands, Sweden, and the United States to quantify the environmental benefits from such shifts in passenger transportation, considering socio-technological drivers of transportation including well-being, digitalization, shared mobility, and electrification. We establish pathways for each of these countries considering their context. We frame these pathways using the avoid-shift-improve (ASI) framework which shapes the scenarios that we quantify in our analysis. We use a travel demand model as an input to calculate carbon, energy, and air pollution footprints. We quantify direct emissions considering the characteristics of the private fleet and indirect using multiregional input-output (MRIO) analysis.

The results show that target thresholds can be reached under the proposed supply and demand initiatives. For the United States, these actions are more dramatic than for the Netherlands and Sweden due to that country’s stronger car dependence. A deep social transformation is needed to make these scenarios possible and enable a shift towards public, active and shared transportation in urban areas.