Sustainable urban mobility transition - Costs and Benefits

Glossary: Policy measures: range of options that cities currently have at their disposal to lead the transition to sustainable urban mobility. The study model can simulate their impact individually or as part of a broader policy group. For more details please refer to Annex 1.

Policy groups: coherent sets of policy measures. 6 policy groups have been modelled in the study: shared mobility and demand management, innovative services, green public transport and logistics fleets & charging infrastructure, pricing schemes, transport infrastructure, traffic management and control. For more details please refer to Annex 1. Policy scenarios: also referred to as transition pathways in the study, the policy scenarios consist of different combinations of policy measures. 3 policy scenarios have been modelled in the study (Promote and regulate, plan and build, mixed). For more details please refer to Annex 1. Net balance: the sum of revenues and externalities generated by measures, minus their implementation costs. Externalities: social costs linked to CO2 emissions, air pollutant emissions (considering NOx, VOC, CO and PM2.5), noise and accidents (fatalities and injured people). In this report, the applied monetary values are those adopted by the European Commission handbook of 2019 (Sustainable Transport Infrastructure Charging and Internalisation of Transport Externalities). Executive summary

The present report is a short version of a study on costs and benefits of the sustainable urban mobility transition, realised by transport and modelling experts from TRT Trasporti e Territorio.1 Based on 12 city prototypes, costs and benefits have been evaluated compared to a business-as-usual baseline. Three potential policy scenarios have been modelled, building pathways to decarbonisation and sustainable mobility in cities, in line with EIT Urban Mobility strategic agenda, the Sustainable and Smart Mobility Strategy objectives, and the Green Deal targets for the transport sector of -55% and -90% greenhouse gas emissions by 2030 and 2050 compared to 1990 levels. Policy Scenario 1 (named “Promote and Regulate”) assumes mostly promotion and regulation of 19 sustainable mobility options, Policy Scenario 2 (“Plan and Build”) focuses on 14 infrastructure building and technology related actions, and Policy Scenario 3 (“Mixed”) is a mix of the two approaches, with 23 measures related to both technological innovations and behavioural change. In all three policy scenarios, the group of 779 EU-27 cities are able to reach the Green Deal CO2 reduction targets in 2050 thanks to the implementation of policy measures and an ambitious fleet decarbonisation. On the other hand, in the Business-as-Usual (BAU) scenario (simulated with the EU Reference assumptions on vehicle technology evolution) emission would only be be reduced of 65% in 2050, compared to 2019 levels. The main differences between the scenarios are their ability to meet the Green Deal 2030 target, as well as the impacts of implemented measures on other indicators such as cost effectiveness, modal split, private car ownership, and fatalities. Depending on the policies implemented, the sustainable urban mobility transition in European cities could lead to net benefits of up to €177bn by 2030 and €698bn by 2050 (Scenario 3). Of these net benefits, saved costs from reduced CO2 emissions, pollution, noise, and fatalities (externalities) amount to €79bn in 2030 and €264bn in 2050. In order to achieve that, European cities will need extra investments in sustainable mobility measures compared to business-as-usual scenario of €86bn by 2030 and €150bn by mid-century. On average, each euro invested in the transition can generate up to 3,06€ (2,14 in revenues and 0,92 in externalities) by 2030, and from €2,32 to €5,66by mid-century (i.e. up to €3,90 in revenues, and €1,76 in externalities). All costs and revenues, as well as externalities, are discounted and cumulated from 2019. Both the Promote and Regulate and the Plan and Build scenarios fail to meet the 2030 Green Deal objectives, before overcoming the target by 2050. The analysis shows that meeting the 2030 target requires ambitious reduction of private motorised transport in urban areas, which is one of Scenario 3’s most distinctive feature compared to the Promote and Regulate and the Plan and Build scenarios.

For small and medium city prototypes in the short term (2030), pricing schemes are the most cost-effective measures to reduce urban transport CO2 emissions in line with the Green Deal objectives. For large city prototypes, innovative services and shared mobility and demand management are the better choice. Looking in more details at the urban mobility transition for the next decade, the following policies per city size are the most cost-effective ones to decarbonise urban mobility: • For small cities (from 50 000 to 100 000 inhabitants), pricing schemes (congestion and pollution charging, parking pricing, public transport integrated ticketing and tariff schemes) and transport infrastructure (bus & tram network and facilities, walking and cycling networks and facilities, urban delivery centres) are the most effective revenue-generating options to address the Green Deal objectives by 2030. On the contrary, traffic management and control (legal framework for logistics and new mobility, traffic calming measures, prioritising public transport) are the least effective tools to reduce CO2 emissions. • For medium cities (from 100 000 to 500 000 inhabitants), pricing schemes, innovative services (DRT, autonomous vehicles, ITS), as well as shared mobility and demand management policies, are the most effective groups. By contrast, transport infrastructure measures are the least effective ones. • For large cities (above 500 000 inhabitants), innovative services, shared mobility and demand management and pricing schemes are the best levers to reduce CO2 emissions in a cost- effective way. On the opposite, transport infrastructure is the least effective policy group. By 2050, the effectiveness of the different policy groups2 to meet the 90% CO2 emission reduction objective for the transport sector slightly evolves compared to 2030: • For small cities, pricing schemes are the most effective revenue-generating options to address Green Deal objectives by 2050. On the contrary, traffic management and control are the least effective tools to reduce CO2 emissions. • For medium cities, innovative mobility services, pricing schemes, and shared mobility and demand management policies are the most effective groups. By contrast, traffic management and control measures are the least effective measures. • For large cities, innovative services and shared mobility and demand management policies are the best levers to reduce CO2 emissions in a cost- effective way. On the opposite, transport infrastructure is the least effective policy group. The transition will also impact mobility behaviours and safety in urban areas. Scenario 3 is the one that would help reduce the most the use of private motorized modes, with a modal share being reduced to 14% in the four prototype regions by 2050. Interestingly, this reduction is accompanied by the highest percentage of carsharing modal split. Looking at car ownership in 2050, Scenario 2 and Scenario 3 are the two most effective policy mixes, attaining a 23% and 44% reduction respectively. The highest drops in car ownership levels are encountered in large cities, where many alternatives to the private car are available. In 2050, Scenario 3 brings a 63% reduction in urban fatalities (from 2,6 per 100 000 inhabitants in 2019 to 1 per 100 000 inhabitants in 2050), compared to base year levels. For Scenario 2, this trend is mainly driven by the construction of active mobility infrastructure (walking and cycling lanes) which significantly improves the safety of pedestrian and cyclists. On the other hand, the reduction on the number of deaths of Scenario 1 and 3 is mainly due to the implementation of traffic management and control measures (30km/h speed limits, pedestrian areas, etc.) Analytical framework The MOMOS assessment tool provides estimations of mobility trends in urban areas quantifying indicators on transport (modal split, vehicle fleet evolution, car ownership, congestion, etc.), safety & environmental (air pollutant and GHG emissions, energy consumption, fatalities, etc.) as well as economic impacts (cost and revenues for the city, externalities, etc.) of transport policy measures from the base year (set as 2019) until 2030 and 2050. The adaptation of the model for the application to the various city prototypes listed in table 1 is performed through a set of transport parameters, that allow the model to reproduce the most appropriate urban transport patterns. For example, small cities might have in general a reduced availability of public transport infrastructures compared to large cities, and so on. By differently combining the policy measures, three transition scenarios have been built through subsets of policies, whose combination and interaction define the scenario itself: - Scenario 1 “Promote and Regulate” is mostly based on transforming transport demand towards a more sustainable mobility behaviour of citizens through information, regulations, and promotion of innovative and shared mobility services. The approach of this scenario is aimed at the short to medium term. Some of its measures are relatively fast to implement with few investments costs associated, others need some more time and resources but basically there are no long-term programmes. - Scenario 2 “Plan and Build” is focused on transport supply investments in technologies and infrastructures. The aim is to change the urban environment and its existing transport facilities, with a more long-term strategy. The focus is especially on public transport, with less emphasis on other transport alternatives (e.g. shared mobility). Autonomous vehicles and Demand-Responsive Transport are also a crucial part of this scenario. This strategy is ambitious and long term, as many of its measures need time to be implemented and provide results. With respect to the first scenario, this one requires higher investments, due to the infrastructures that need to be build and maintained. • Scenario 3 “Mixed” is a mix between the two previous scenarios. It considers policies from each of the two previous scenarios and intensifies their reach in order to reach the target of -55% of CO2 emissions reduction by 2030. It assumes changes in urban mobility (as well as in the other transport sectors) and extreme shifts in how people move (with also related acceptability issues) in addition to the foreseen trend of fleet decarbonisation. This is done including regulations and behavioural incentives as well as the provision of infrastructures and services. Economic instruments play a key role in this approach and their role would be twofold. On the one hand, they are used for changing the behaviour of citizens by adopting the “user pays” or “polluter pays” principle (for example, road pricing policies are a cornerstone of this scenario). On the other hand, they are used to generate resources to support sustainable mobility by improving public transport, walking, and cycling facilities. Input Data In order to represent the characteristics at the base year, as well as the trends in place in each city prototype, the modelling tool requires a set of input data to reproduce different city circumstances, related to socio-demographic aspects as well as mobility features. These cover characteristics such as population, urban growth, average income, congestion level, as well as public transport infrastructure, innovative services, parking, traffic management solutions, etc. Annex 2 lists and describes all the data inputs that have been collected for the 30 reference cities and used to define the representative inputs of the 12 city prototypes. Inputs related to base year refers to 2019. Building on the input data, the implementation of a range of sustainable urban policy measures has been modelled. Measures have been consolidated into six policy groups: 1. Shared mobility and demand management (Mobility as a Service, vehicle sharing, delivery plans, teleworking) 2. Innovative services (Autonomous vehicles, Demand Responsive Transport, Intelligence Transport Services) 3. Greening public transport and logistics (Green fleets and charging infrastructure) 4. Pricing schemes (Congestion and pollution charging, parking pricing, public transport integrated ticketing and tariff schemes) 5. Transport infrastructure (Bus and tram network and facilities, walking and cycling networks and facilities, urban delivery centres) 6. Traffic management and control (legal framework for logistics and new mobility, traffic calming measures, prioritising public transport) Impact of the sustainable mobility transition in EU27 cities In quantifying the cost of the sustainable urban mobility transition, the study considers the cost of externalities. These costs are estimated taking into account CO2 emissions, air pollutant emissions (considering NOx, VOC, CO and PM2.5), noise and accidents (fatalities and injured people). The applied monetary values are those adopted by the European Commission handbook of 20198 . The impacts of sustainable mobility measures on cities in the EU 27 is broken down in the following indicator categories: • Modal split • Car ownership • Fatalities • CO2 emissions • Economic outputs 2.1 Modal Split Private motorized (private cars, both as driver or passenger, and motorbikes) modal share decreases in all three scenarios by 2030 and 2050 but with an important differentiation: while Scenario 1 and 2 show a reduction of -11% and -8% respectively, Scenario 3 reduces the modal share of private cars by 24%, more than halving the private car use. This significant reduction is the consequence of an extreme intensification of policies implemented in the “Mixed” scenario (compared to the other two) to allow it reaching the Green Deal target already by 2030. In fact, such short-term objective would only be possible through drastic changes in how people move (i.e., the modal split). The need to achieve such a drastic impact on modal split could be reduced if policies were designed to push faster fleet turnover in favour of EVs. The potential for policy measures feedback on fleet turnover is something that should be further investigated. Also, active modes (walking, cycling and micro-mobility) show a significant growth in both Scenario 1 and 3 by 2030 (+6% and +9% respectively). Then, the share remains constant until 2050. For what concerns public transport (metro, tram, buses, and DRT, when implemented) modal share, Scenario 2 is definitely the one with the largest increase in 2050 (+ 18%). This impact is justified by the implementation of new public transport infrastructures foreseen in this scenario. This substantial growth in 2050 hinders the scenario’s uptake of active modes, which indeed are stable over time. In particular, in large cities Scenario 2 will bring the public transport modal share to values between 37% (Central/Western) and 56% (Eastern). In addition, it is worth highlighting the growth of car sharing, which is only implemented in Scenario 1 and 3. This growth is more evident in the Mixed scenario, where car sharing utilization is definitely boosted by the parallel implementation of autonomous vehicles. Finally, looking at the different city prototypes, Scenario 3 is the one that reduces the most the use of private motorized modes. In 2050, in large cities, this percentage drops to around 10% in the four prototype regions. This reduction is accompanied by the highest percentage of carsharing modal split though. Of course, the (lower) initial car modal share compared to small and medium cities also plays a crucial role in explaining this number.

Figure 2: Aggregated Modal Split for Scenario 2 “Plan and Build” in 2019, 2030, 2050. All EU27 cities

Car Ownership First of all, Figure 4 shows that Scenario 3 is the one with the highest reduction in car ownership (-31% in 2030, - 44% in 2050). On the other hand, Scenarios 1 and 2 entail smaller reduction in the number of cars owned. In particular, the reduction associated to the second scenarios are more substantial by 2050, when the effects of alternative transport infrastructures, which characterize the “Plan and Build” scenario kick in. For the “Promote and Regulate” scenario, the number of cars per inhabitants will only decline by 16% by 2050. A possible explanation might be that policies mostly focused on promotion and taxation have impact on the cost of cars use (and this impact declines with the increase EVs penetration). However, these policies are not as capable of changing long-term decision on car ownership, as, for example, the uptake of autonomous vehicles combined with a large diffusion of shared mobility services. The highest drops in car ownership levels are encountered in large cities. This might be explained by the wide range of alternative modes that large cities offer, making cars redundant for urbanites

CO2 emissions (tank-to-wheel) CO2 emissions level is key to assess the impact of the different scenarios on the sustainable mobility transition. Figure 6 shows that all three policy scenarios are able to gradually reach and overtake the Green Deal target by 2050. However, only Scenario 3 (with its intensified policy measures mainly resulting in substantial change in the modal split) allows to reach the objective also by 2030. In particular, by 2030, Scenario 1, more focussed on short-term actions in comparison to Scenario 2, gets quite close to the target (0.278 ton CO2 /capita, whereas the estimated target is 0.237 ton CO2 /capita). On the other hand, the Plan and Build scenario, relying on long-term infrastructural actions, still generates a higher amount of greenhouse gases (0.319 ton CO2 / capita). By 2050, the three scenarios attain CO2 emissions reductions of about -94% (compared to 2019 levels). In case no policy measure foreseen in the three transition scenarios is implemented (BAU scenario with the EU Reference assumptions on vehicle technology evolution), the CO2 emissions will only decline from 0.657 ton per capita in 2019 to 0.490 ton per capita in 2030 and to 0.231 ton per capita in 2050, respectively about -25% and -65% compared to 2019 levels. Considering the 12 prototypes (see Annex I – Output Indicators), the results show similar trends, with all three policy scenarios reaching the Green Deal target in 2050, and Scenario 3 doing it also in 2030. The lowest values of CO2 per capita will be reached in small cities in northern and eastern Europe, where in 2050 each citizen will be responsible for the emission of as low as 0,016 t per year of greenhouse gases.

Figure 6: Emissions of CO2 (Tank-to-wheel) for the study scenarios in 2019, 2030, 2050. All EU27 cities Policy measures’ effectiveness While the results presented above consider the urban mobility transition scenarios in their totality, it is also possible to evaluate costs and revenues of single policy groups (e.g., shared mobility and demand management, innovative services, pricing schemes, etc.), as well as the CO2 reduction that is attributable to it. In particular, these calculations are performed by comparing an “empty” scenario with no policy measures activated10, and scenarios in which each group of policy measures is activated separately. This way, it is possible to understand how much each group of policy measures is responsible in terms of reduction of CO2 emissions and to calculate the net costs associated to it. Looking at the costs and revenues of the five policy groups, the following two figures highlight how each of them results into a profit (positive) or a loss (negative) in each of the 12 city prototypes. This is represented with cumulated costs as of 2030 (Figure 13) and as of 2050 (Figure 14). As of 2030, innovative services followed by shared mobility and demand management and pricing schemes are the group of measures that accounts for the highest revenues. This is true for each city prototype. On the other hand, transport infrastructure is particularly expensive in large cities. Looking at the values in 2050, it is possible to notice how both shared mobility and demand management and innovative services significantly increase their revenues compared to pricing schemes (particularly in large cities). On the other hand, transport infrastructure remains the most expensive group of measure, especially in large cities where the implementation of additional metro lines has a considerable impact on their net balance. Interestingly, traffic management and control is the group of policies that entails the smallest differences among the 12 city prototypes. Urban mobility in EU27 cities can reach the Green Deal target in terms of greenhouse gas emissions provided investments in sustainable mobility policies are increased by an estimated €86 billion until 2030 (Scenario 3) and by €92-161 billion until 2050 (all scenarios). On average, each euro invested in the transition can generate up to €3,06 (€2,14 in revenues and €0,92 in externalities) by 2030, and from €2,32 to €5,66 by 2050 (i.e. up to €3,90 in revenues, and €1,76 in externalities). The implementation of appropriate sustainable transport policy measures is necessary to reach the Green Deal target, as the BAU scenarios would only lead to 25% CO2 reduction by 2030 and 65% CO2 reduction by 2050 compared to 2019 levels. Results show that only Scenario 3 can reach the 2030 target,highlighting the magnitude of the challenge. Scenario 3 does so thanks to intensified policies that generate more drastic changes in mobility behaviour and substantially reduce the private car modal split, which might entail important acceptability issues. Still, the need to achieve such a drastic impact on modal split could be reduced if policies were designed to push faster fleet turnover in favour of EVs. The potential for policy measures feedback on fleet turnover, which is something that goes beyond this study, is something that should be further investigated. In terms of CO2 reduction, the policy effectiveness analysis shows that, for all city sizes, shared mobility and demand management is the policy group that generates the highest impacts by 2030 and 2050. After that, there are some differences, based on city dimensions, in terms of policy groups affecting greenhouse gas emissions. In small cities, pricing schemes is preferrable over the other groups. In medium cities, transport infrastructure is the second “greenest” policy measure. Finally, in large cities, traffic management and control is the second-best option. In terms of economic results of the policy scenarios, which considers costs, revenues, and the external cost savings generated by the implementation of sustainable policy measures, Scenarios 1 and 3 have positive net balances for all prototypes. Their magnitude increases with the city dimension, while the value per capita does not entail substantial differences due to city dimension. On the other hand, in 2030 Scenario 2 results into a negative net balance in more than half of the 12 city prototypes. However, the benefits of the infrastructural investments kick-in in the longer term resulting into positive net balances for almost all prototypes by 2050. Looking at the economic results of single policy groups, innovative services and shared mobility and demand management are the two groups guaranteeing the highest revenues in large cities, while pricing schemes is the better option in small and medium cities (especially in the shorter period). On the other hand, transport infrastructure results to be most expensive group of policy measures, especially in large cities where the implementation of additional metro lines (not foreseen in small and medium cities) might have a considerable share on their net balance. Finally, it is interesting to look at the costs/revenues and the CO2 monetization attributable to each policy group (policy effectiveness). In the short term, pricing schemes is the most effective group for small and medium cities, while innovative services win in large cities and medium cities. On the other side, transport infrastructure is the least effective in large cities, while traffic management and control is the worst in small and medium cities. In the long term, pricing schemes remains the favoured options for small cities, while innovative services followed by shared mobility and demand management are more effective for medium and large cities.

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