Shared mobility and lifestyles as mechanisms to reduce environmental impacts from passenger transportation

We used travel behavior to determine travel demand, which was then used as an input to calculate the environmental impacts of passenger transportation at the baseline timepoint and in the various scenarios. Travel demand was used as a measure that directly reflects changes in travel due to the use of shared mobility. We quantified environmental impacts using multiregional input-output analysis using EXIOBASE, version 3.8.2 (Stadler et al 2018). The environmental footprint indicators we used include carbon (GWP100), energy use, and air pollution (PM_2.5) (Stadler et al 2018).

2.1.1. Travel demand

We quantified travel demand as the number of road kilometers travelled per year by each country's inhabitants, itemized by transportation mode (public transportation, private vehicles, electric bikes, pedal bikes, and walking). We calculated the total kilometers of travel (Tkm) for each country (k) by multiplying the average road distance travelled per year per inhabitant (Dpy) by the total traveling population (PT), using the equation:

$$\begin{equation*}{\text{Tk}}{{\text{m}}_k} = {\text{Dp}}{{\text{y}}_k} \times {\text{P}}{{\text{T}}_k}.\end{equation*}$$

Total kilometers of travel per inhabitant can be further disaggregated by transportation mode (i) for each country (k) as follows:

$$\begin{equation*}{\text{TMk}}{{\text{m}}_{i,k}} = {\text{Tk}}{{\text{m}}_k} \times {\text{t}}{{\text{m}}_{i,k}}\end{equation*}$$

where ${\text{t}}{{\text{m}}_{i,k}}$ corresponds to the modal share of each specific transportation mode.

For every country, we controlled for differences in the private car fleet by distinguishing among electric, internal combustion and hybrid cars. The fuel economy of the passenger fleet together with the fuel type, (differentiating between gasoline and diesel) are also accounted for. Data sources for these calculations are specified in SI5.

2.1.2. Monetary expenditure associated with travel demand

2.1.3. Quantification of environmental impacts

This model did not consider feedback loops between variables, such as how a reduction in car ownership and use could potentially decrease fuel demand, also leading to a drop in fuel prices. Additionally, our model excluded changes in prices both over time and due to potential taxes. Carbon taxes are one mechanism that could be used to accelerate the transition to electrical vehicles powered by clean energy. In order to model car sharing in the input and output tables we use the best proxy product that was available. This might introduce some uncertainty into the results.

We modeled three scenarios to calculate the environmental impacts of passenger transportation in the case countries (table 1) (for more information about the scenarios, see SI5). Similar to Barrett et al (2022) and Grubler et al (2018), we identified the drivers of the scenarios, including digitalization, shared mobility, well-being, and energy efficiency. Digitalization gives transportation users access to timely information covering all available transportation modes and also enables smart ticketing and online payments. Digitalization enables mobility-as-a-service, where public, private, and shared transportation modes are integrated into a single mobility package that can respond to customer preferences and choices (Leviäkangas 2016, Franssens et al 2021). This improved access to transportation information shapes how citizens choose to travel and this can enable a reduction in environmental impacts (Anagnostopoulou et al 2020). Shared mobility modes complement existing public transportation, enabling people to give up car ownership with the help of access to shared cars for special errands such as furniture purchases. Well-being refers to the benefits from active and public transportation as a motivator for change: e.g., improvements in health, equity, and environmental awareness. Active transportation includes walking and riding a pedal or electric bike. Energy efficiency refers to the shift from pollutant-emitting vehicles to electric ones, as well as a shift to lighter-weight vehicles.

Brand et al (2021) developed transportation scenarios using the avoid-shift-improve (ASI) framework that included both travel distance avoided and a shift to lower-emission transportation modes. The scenarios we propose here follow a similar logic and are summarized in table 1.

The United States, the Netherlands, and Sweden were selected as the case countries due to their high car ownership levels and the wide variation among the three in terms of their modal breakdowns, especially in terms of personal car, public, and active transportation (table 2). The transportation landscape is different in these countries: inhabitants of the United States depend on personal cars, as is reflected in the high rate of vehicle ownership and the high annual travel distance per capita. The Netherlands and Sweden have similar rates of car ownership, but the Dutch are relatively greater users of active transportation, while Swedes are relatively greater users of public transportation. Both of these countries have achieved a decrease in car ownership in recent years (Miljiobarometern 2022a) (for more detail about the cases, see SI1).

We collected indicators that represent the current state of passenger transportation for each case (table 3), which we grouped into well-being, attractiveness of public transportation, digitalization and readiness for electrification of the private fleet (for a detailed description of the indicators, see SI2). Although our selection of these indicators was subjective, together they represent a reasonable assessment of the transportation system in each country and provide a complementary perspective to the environmental assessment of personal mobility at the baseline timepoint (figure 1).

In the TAU scenario, transportation activity continues to rise over time due to rising annual per capital travel distances (figure 2). Most of this distance is travelled by private car, followed by public transportation. The Netherlands has the highest share of active transportation, at 11.4%, while Sweden has 3.8% and the United States has 1.3%. The United States has the highest car ridership (figure 3). In the ASI scenario, total travel distance decreases due to the availability of car sharing, causing a change in the modal split, including a reduction in private car travel and an increase in active, public, and shared transportation use. The magnitude of the modal shift varies in each country, depending on their initial modal distribution (figures 2 and 3). The ASI + ST scenario shows a more drastic decrease in annual travel distance, which drops by 22%, 23%, and 35% for the Netherlands, Sweden, and the United States (figure 2). In all countries, active and public transportation become the dominant modes, taking over 30% of the modal split (figure 3).

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In the TAU scenario, the number of cars increases in all countries, reaching 673, 687, and 942 per 1000 inhabitants in 2035 for the Netherlands, Sweden, and the United States, respectively. In the ASI scenario there is a decrease in car ownership: fewer than 500 cars per 1000 inhabitants in the Netherlands and Sweden, and fewer than 690 cars per 1000 inhabitants in the United States. The decrease in car ownership is more radical in the ASI + ST scenario, where the combination of car sharing and lifestyle changes allow the rate to drop to less than 200 cars per 1000 inhabitants in the Netherlands and Sweden, and fewer than 470 in the United States (figure 4).

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Environmental impacts from passenger transportation at the baseline are above the thresholds for all indicators in all countries. The United States generates higher environmental impacts than the other two countries due its higher modal share of private car driving and lower fuel economy of its passenger vehicle fleet. The Netherlands has a higher energy footprint than Sweden, as well as a slightly higher PM_2.5 footprint, given the characteristics of its fleet (figure 5).

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Although the TAU scenario shows a decrease over time for all environmental impacts, this decrease is not enough to meet most of the environmental target thresholds. Demand-side mechanisms enable a greater decrease in these impacts, with the potential benefits of the ASI and ASI + ST scenarios varying depending on the strength of demand-side interventions. The ASI scenario shows a moderate decrease in all impact categories due to car sharing. The ASI + ST scenario yields higher saving potentials because it results in a sharp reduction in car use and ownership and an increase in the modal share of active and public transportation, in addition to a reduction in travel distances (figure 6).

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The speed at which environmental impacts achieve the threshold targets varies by country. We found that very strong demand-side-mechanisms would be necessary to achieve target environmental thresholds in the United States. If the United States is to reach these environmental thresholds, drastic actions are needed on both the demand and the supply side. For Sweden and the Netherlands, the moderate demand-side mechanisms included in the ASI scenario were sufficient to achieve the target thresholds by 2035 for most impact categories, since these two countries have already supported substantial developments in active and public transportation and the electrification of their passenger fleets (figure 6).

Context indicators (introduced in table 1) are part of the support system that enables changes in travel behavior, car ownership, and car purchasing rates when people engage in shared mobility. The results from these indicators are included in table 4. Out of the three countries included in this study, the United States has the highest number of road fatalities per million people and the lowest level of satisfaction with public transportation (United States Department of Transport 2018, Sustainable Development Report 2022). In addition, it has the lowest share of electric vehicles in the fleet and the lowest number of charging stations per electrical vehicle. The United States also has the lowest modal share of active and public transportation coupled with one of the highest car ownerships levels in the world, as well as a high modal share for car ridership. These contextual indicators and travel behavior features contribute to the high environmental impacts from passenger transportation in the United States compared to Sweden and the Netherlands (figure 5). Sweden stands out for its low rate of pedestrian and cyclist fatalities and high satisfaction with public transportation, both of which encourage people to use public transit and active forms of transportation. The Netherlands, for its part, has a widespread culture of cycling.

One of the contributions of this study is in proposing possible pathways for reducing the environmental impacts from passenger transportation through the drivers of shared mobility, digitalization, well-being, and passenger fleet electrification in three countries. The results show that relying on fleet electrification is not enough to reach and stay under carbon and energy target thresholds and improve air quality. The pathways with higher potential for benefits are those that combine the passenger fleet electrification with other supply- and demand-side mechanisms that enable changes in travel behavior and car ownership. Other studies have also found great potential for environmental benefits from combining supply- and demand-side mechanisms (van Vuuren et al 2018, Raugei et al 2021, Barrett et al 2022).

The speed and intensity at which changes in the supply and demand side need to take place to make the scenarios feasible and achieve the target thresholds in time is context dependent (Grubler et al 2018). Examples of such changes are the deployment of electric vehicle charging infrastructure, the building of robust public transportation networks and a shift to active and public transportation modes. Some countries have already started working on implementing some of the actions considered in the scenarios, such as establishing emission-free zones for example in Amsterdam with the Clean Air policy, or offering free public transit as in Luxemburg (Mobilitegratuite 2023).

Digitalization holds one of the main elements in driving change: access to information. Access to transportation information and how it is presented to users influences how they chose to travel (Anagnostopoulou et al 2020, Franssens et al 2021). Research shows that transportation users who receive information about the environmental impacts of transportation modes are likely to change their travel behavior (Franssens et al 2021). Many stakeholders (i.e., shared mobility organizations or city planners) are interested in shaping how people choose to travel, and tensions from differing interests can arise. Digitalization can allow people to have access about location of shared vehicles as well as information about changes in public transit schedule. Digitalization should unfold with the goal of reducing the environmental impacts of passenger transportation and increasing the accessibility and quality of transportation services.

For more transformative scenarios to materialize, we must challenge the structures that support and enable private car dependency (Mattioli et al 2020). These structures include the automotive industry, the built environment, and cultural and societal norms, all of which incentivize pro-car policy and culture (Mattioli et al 2020). These structures are embedded differently in each society, and thus the process of challenging and ultimately breaking them down will be specific for each country.

Differences in the perceived monetary value of car ownership and public and active transportation are barriers to the shift away from car ownership. Car ownership's perceived monetary value is higher than its real cost, because car owners value the convenience and flexibility that this transportation mode offers (Moody et al 2021). Public transportation and shared mobility modes are perceived as having a much lower value than car ownership; thus, a shift away from car ownership will require an increase in the value that people perceive in transportation modes that are alternatives to car ownership (Moody et al 2021).

Cultural perceptions about car ownership and car sharing constitute another barrier to challenging the car regime. Beyond the perceived convenience and flexibility that car owners experience, car ownership is a symbol of status and modernization (Verhoef and van Wee 2000, Gatersleben 2011). A collective abandonment of this symbol supposes a deep cultural transformation and a shift in social norms. People that today are challenging this norm and live a car-free lifestyle do so because they perceive car ownership as a burden (including the economic cost and the time used to take care of the car), are environmentally conscious, and live in a built environment that allows them to live this lifestyle (Paijmans and Pojani 2021, Iacobucci 2022).

A tension between the industries that enable the car regime and the need to transform passenger transportation is one systemic barrier that is present in all societies. Mattioli et al (2020) mentions job creation and economic growth from car manufacturing and the industries that support automotive infrastructure as a barriers to a shift away from the car regime. Regions that rely on car sales for economic growth and jobs may therefore view decreasing car reliance as negative and threatening (de Vries 2022).

Granularity in context indicators can highlight how different population groups or urban areas have specific vulnerabilities or challenges regarding passenger transportation (Martiskainen et al 2021). Such context indicators include access to public transportation stations (a part of transportation poverty), coverage, and availability of public transportation (Sustrans 2012). However, publicly available data on such context indicators is limited, which is an obstacle to truly understanding transportation landscapes at a granular level.

Transport policies that incentives compact cities, robust networks of public and share transportation as well as active transportation infrastructure can enable a decrease in car ownership and use. This in combination with policies that accelerate a transition from fossil-fuel based transportation options to electric and low emission intensity energy sources, are policies that facilitate a decrease in environmental impacts from passenger transportation. Other alternatives to further decrease these impacts are policies that establish car free areas, speed limits and moratorias (Fitzpatrick et al 2022).

Policies in the areas of housing, employment, welfare and credit are relevant to decrease forced car ownership and use (Mattioli 2017). This shows the need to consider synergies between policies that address different areas to maximize their potential and decrease the possibility of t reducing potential environmental and social gains.

Transportation policies need to be equitable and must consider special needs such as disabled users or a lack of other transportation options, such as those of isolated or rural populations. We need transportation policies that decrease car ownership and generates a shift to active, public and shared transportation in urban areas if we are to achieve target environmental thresholds. However, such policies need to be flexible and adaptable to particular cases where people's well-being is dependent on car ownership and ridership (Kett et al 2020, Remillard et al 2022).

Although we found that the target thresholds could be met in the transformative scenarios, these scenarios necessitate radical actions on both the supply and demand sides. From the demand side, changes in travel behavior and lifestyles were required, namely a dramatic decrease in private car use and ownership and increase active, public and shared transportation ridership, decreasing at the same time the annual distance travel. For the supply side, the electrification of the public transportation fleet as well as the provision of robust public transportation networks and infrastructure for active transportation. These actions should come from a variety of actors, including policymakers, industries, and consumers. Alignment and coordination among these actions is key to enabling systemic change. The precise content of change-enabling policies will differ in each country, as will the level of investment needed to implement them.