1 Introduction

The transport sector has been a critical economic area for the world from the industrialisation era to the present. It is an essential financial sector as it employs more than 11 million people, enabling international trade both in Europe and developing countries (Maparu & Mazumder, 2017). The trade-off of advanced transport infrastructures is their environmental impact. Although the greenhouse emissions in EU decreased by 22.4% between 1990 and 2014, the greenhouse gas (GHG) contribution of the transport sector has considerably increased, amounting to more than 20.8% and rated as the second most important source of emissions in the EU (Andrés & Padilla, 2018). The pollutants emitted by endothermic engines powered by fossil fuels can also elicit harmful health effects like heart disease, asthma, and cancer. In Europe, the transport sector is the second sector for greenhouse gas (GHG) emissions after the power system sector. In the USA, the emissions caused by the transport sector have overtaken the environmental impact of electricity generation (Fan et al., 2018). Road transport is responsible for 72.9% of emissions within the transport sector, followed by aviation and maritime, which account for 13.3% and 12.8%, respectively. The growing demand for transport services could determine increased air pollution, reducing the sustainability of the whole sector. However, the pandemic has disrupted the status quo offering a cause for reflection on how transportation needs can evolve in the coming future. Significantly during times of restricting travel and activity measures, the travel behaviour has radically changed, showing a slight increase in shares of cycling and walking while public transport usage dropped significantly.

At the same time, private cars remained the preferred travel mode across countries (Eisenmann et al., 2021). It is also interesting to evaluate how the pandemic has provided alternative solutions to reduce the transport demand, such as blended, flexible, and hybrid working, and it has established a new essential travel baseline. At the same time, technology is presenting imminent breakthroughs in the transportation sector. Electric vehicles and scooters have already been used throughout Europe but are not yet perceived as a potential replacement for endothermic cars because of their range or safety limitations (Kopplin et al., 2021). Autonomous vehicles and drones are the future technologies announced as having a high potential to reduce energy consumption and emissions, especially in the last mile operations (Figliozzi, 2020; Staat, 2018). The deployment of these technologies is imminent, but social, economic, and technological barriers and untested negative implications are slowing down the adoption. Among the concerns of these technologies, there is a potential increase in congestion, unanswered ethical questions on the control of the vehicle, and excessive travel demand (EU Directorate-General for Communication, 2020). Therefore, it is essential to assess these innovations’ potential impact and evaluate possible already available alternatives if the technology will not deliver what has been envisioned. The scalability of analysed advanced mobility solutions is probably one of the leading global challenges, especially for developing countries lacking infrastructure with less structured and competent governmental bodies. Another possible alternative tested during the pandemic could be to establish policies that could, in some ways, limit the mobility of the population. However, such an approach could become incompatible with the concept of democracy and freedom of movement. The relevance of these issues is underlined by the related policy questions in a recently published European Commission policy report (Bertoni et al., 2022, pp. 136–140).

The current chapter tries to reflect on the points mentioned above, and it is structured into six main sections. Section 24.2 provides a detailed overview of the future and already present technologies that could positively impact the sector, including the integration perspective. Section 24.3 highlights the potential of the illustrated technologies to reduce emissions and improve the sector’s sustainability. Section 24.4 will give an overview of the impact of the pandemic on the transport sector and highlights some findings. Section 24.5 will assess the applicability of technologies to developing countries and their challenges. Section 24.6 shows how policies can further contribute to transport sustainability, while Sect. 24.7 will summarise the chapter and provide some recommendations.

Table 24.1 Recent and upcoming low emission technologies for a more sustainable mobility
Full size table

2 Background: Computational, Environmental, and Data Aspects of Sustainable Mobility Technologies

This section analyses the main characteristics of the more imminent and high-potential technologies in the mobility sector that will lead to the decarbonisation of the transport system, trying to estimate the adoption rate, the economic and environmental impact, and the data dimension. All the low emission technologies identified are compared across different characteristics and summarised in Table 24.1. The table compares each technology’s advantages, barriers, and travel range and highlights its scalability and the economic impact. A set of labels for each technology analysed is assigned to assess the solution’s effects and define an uptaking timeline. The labels identified are leverage, application timeline and potential risks. Leverage identifies a technology/set of technologies that can solve one or more mobility challenges and it has been divided as high leverage and low leverage. High leverage identifies technologies that could significantly solve one or more challenges identified as critical or bottlenecks to the uptake. The low leverage label identifies technologies that could solve a limited subset of broader mobility challenges. Application timeline evaluates the temporal applicability of the technology, and it has been identified as long term, medium or short term. When a solution is tagged long term, the technology will have its immediate impact after 2030. In the medium-term, it could already impact, but the adoption phase could be delayed after 2030. In short term, the technology has already impacted the mobility sector, and it is in the adoption phase. Short- and long-term solutions are necessary to improve the sector’s sustainability. In potential risk assesses the uncertainty of the impact of the technology on the emissions reduction and side effects of the full-scale deployment. It is divided in high risk, when the technology is risky because of its uncertain emission reduction, or it is not yet at commercialisation maturity or could lead to adverse side effects. If is tagged medium risk, the emission reduction impact at the full scale has been modelled, and a contingency plan for adverse risks is outlined. In case of low risk the technology could positively impact the emission reductions, and there are no relevant adverse risks on a full-scale deployment. The labels represent an evaluation based on the literature, and they should not be considered definitive or specific. However, such a classification will provide a quick overview of what is coming, the timeline, and the potential impact. The following section adds further details to Table 24.1 on data and computational perspectives for the upcoming and future mobility technologies, starting from the imminent uptaking of electric vehicles and extending to multimodal sharing mobility concepts.

2.1 Hybrid and Plug-In Vehicles

Electric vehicles provide an alternative to meet the needs for a green and clean source of transportation with fewer emissions and better fuel economy. There are three main categories of EVs: fully electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEV). As illustrated in Table 24.1, the technology is low risk and commercially available across Europe, with Norway leading the uptake of EVs with a share of 75% (Wangsness et al., 2020). In the EU, HEVs and PHEV reached the penetration of 1.25% over the 3.46% share of the whole car sector (European Environmental Agency, n.d.). The EV’s lifetime energy consumption costs are significantly lower than conventional, between 45% and 70%. EVs are between 60% and 70% more efficient than gas vehicles; however, the benefit is offset by the high capital cost of the EVs’ battery technology (Habib et al., 2018). One of the adoption barriers is the battery capacity which is not enough to provide a comparable driving range to internal combustion engine (ICE) vehicles and requires a lengthy charging duration (Capuder et al., 2020). Another major obstacle to the mass deployment of EVs is the slow implementation of charging infrastructures such as fast-charging stations and the challenges of integrating the power system. One of the primary data challenges is data interoperability between power systems, mobility providers, parking data, and charging infrastructure (Karpenko et al., 2018). Since cars are parked approximately 90% of the time, interoperability can support grid services by modulating/injecting/absorbing electricity based on grid operators’ needs and market opportunities. At the same time, operators can deliver local benefits via behind-the-meter optimisation leading to a maximised energy efficiency and local use of renewables, fostering customers’ involvement through new services and tools. From the life cycle analysis of critical components such as EV batteries, the charging and travel historical data could lead to a life extension of the battery or innovative business models focused on second-life battery applications (Shahjalal et al., 2022).

2.2 Connected Autonomous Vehicles (CAV)

Autonomous vehicles (AVs) have been identified as a possible solution to various modern transport issues. The adoption of autonomous cars can provide environmental benefits of up to 60% and economic and social advantages (Kopelias et al., 2020). Table 24.1 shows that the benefits of connected electric autonomous vehicles involve reducing emissions and energy consumption through their ability to implement eco-driving, which continuously optimises the engine to run consistently at the most efficient operating points. As a result, it will also reduce emissions (Wadud et al., 2016). Additionally, the environmental advantages begin from the reduced demand for vehicles to the car’s standard maintenance and optimal operation. AVs can also provide more significant economic benefits by offering ridesharing services (Bahamonde-Birke et al., 2018). The ridesharing economy allows greater efficiencies by reallocating underutilised resources for more productive purposes, such as achieving new sources of supply at a lower cost. It requires integrated datasets and intense computational resources. Legislations for all road traffic aim to ensure the best road safety; therefore, autonomous vehicles must meet their predecessors’ complex and new strict requirements. The legal challenges include public policies, traffic codes, technological standards, and ethical dilemmas (Barabás et al., 2017).

Furthermore, AVs pose a significant threat to the job of professional drivers as they would change the required skills for workers whose careers are linked to mobility systems. It also may impact taxi drivers and other on-demand driver services as corporations have already begun experimenting with offering driverless experiences (Sousa et al., 2018). One of AVs’ risks is cybersecurity, which could lead to terrorist attacks and privacy intrusion (Ahangar et al., 2021). Therefore, in Table 24.1, the technology has been identified as high risk and high leverage. However, its impact on society is still projected in the long term. From the data perspective, AV requires a different approach to mobility data, such as seamless integration across data providers and social media to forecast trajectories, optimise routes, and understand common mobility patterns (Giannotti et al., 2016).

2.3 Compressed Natural Gas (CNG) Vehicles

Over 23.5 million natural gas vehicles (NGV) are on roads worldwide. The leading countries in natural gas are the Asian countries with 15.7 million natural gas vehicles, closely followed by the Latin American countries with 5.4 million natural gas vehicles (Khan et al., 2015). NGV have been identified as leading candidates for green transportation among sustainable fuel alternatives. CNG is a clean energy fuel when used as motor fuel, and there are relatively low particulate emissions and toxicity of exhaust gasses (Agarwal et al., 2018). However, there are high costs with developing the refuelling infrastructure, such as pipelines and filling stations, which are the more significant disadvantage of the technology (Imran Khan, 2017). The considerable challenge of natural gas vehicles is the lower efficiency compared to gasoline vehicles and longer refilling time. Other environmental challenges to the adoption of CNG vehicles concern fuel treatment and natural gas distribution (Chala et al., 2018). From the data perspective, geographical data can reduce waiting time at gas stations for refilling, and satellite data analysis can support the identification of leakages. Despite advances, the technology is highly dependent on gas imports, and it is unlikely to scale, so in Table 24.1, it has been classified as low leverage.

2.4 Hydrogen Fuel Cell Vehicles

Fuel cell vehicles result in nearly zero tailpipe emissions during vehicle operations (Sharma & Strezov, 2017). The implementation and use of hydrogen fuel cell vehicles were found to have a positive impact and result in economic savings over internal combustion engine vehicles (ICEVs) (Watabe & Leaver, 2021). The study found that hydrogen fuel cell vehicles using hydrogen from solar and wind electrolysis will have positive economic benefits beyond 2050. The first significant barrier concerns the safety of hydrogen vehicles and linked awareness campaigns. The concern for safety arises as hydrogen can burn in lower concentrations, and a possible spark or fire may occur if there is a mixture of hydrogen and air (Manoharan et al., 2019). The second barrier involves the storage of hydrogen. A sizeable onboard storage tank is required to transport the fuel. The barrier to adoption is concerning finding the appropriate material for the storage container. As described in Table 24.1, another barrier is a lack of hydrogen infrastructure that could lead to slow adoption, the inability to charge from home, and cost-related issues to the adoption. From the social data perspective, the technology requires strong awareness campaigns to limit the focus on security concerns, and integrated data on the infrastructure could support adoption in the long term.

2.5 Unmanned Aerial Vehicles (UAVs)

Drones, also known as unmanned aerial vehicles (UAVs), combine three critical principles of technology: data processing, autonomy, and boundless mobility. They enable new access to new spaces and analysis with data collection aid (Kellermann et al., 2020). UAVs have the potential to reduce energy consumption and emissions in some scenarios significantly. Current UAVs are approximately 47 times more CO2 efficient than US delivery vehicles in terms of energy consumption and approximately over 1000 times concerning emissions. Drone delivery will also significantly shift energy and greenhouse gas consumption (Figliozzi, 2020). For instance, drones will shift energy usage and greenhouse gas emissions from vehicle fuels such as diesel and gasoline to varying regional sources of electricity to be charged. The wide-scale implementation of drones will lead to economic and commercial benefits. Drones can be deployed in several contexts and for varying purposes; however, drones for parcel delivery services are still in infancy, along with their “air taxi” services to transport passengers between cities. As a result of its ability to serve multiple needs, the European Commission estimates that drones will have an economic impact of 10 billion euros annually by 2035 and expects approximately 250,000 to 450,000 jobs to be created (de Miguel Molina & Santamarina Campos, 2018). Despite being identified as high leverage, there are several barriers to the public adoption of drones (Table 24.1). The most significant anticipated obstacles to adopting drones are concerning the technical, legal, and public acceptance of drones (Kellermann et al., 2020). The technical concerns refer to autonomous flying, airspace integration, and questions about battery capacity and data communication. UAV trips can flood the suburb and city airspace, providing traffic and safety concerns. Therefore, prioritising accurate, centralised data acquisition and control of airspace traffic is required to fully deploy the technology. The second biggest potential barrier is ethical aspects, which are heavily related to privacy threats. Drones may threaten privacy because of their ability to capture imagery and collect sensitive data (Merkert & Bushell, 2020).

2.6 Carsharing

Carsharing significantly impacts car usage and ownership, enabling a reduction in environmental impacts. The annual environmental benefit per capita is between 240 and 390 kilograms (Nijland & van Meerkerk, 2017). The same study found that the total impact of carsharing versus ownership leads to an annual emission reduction between 13% and 18%. Similar findings have been highlighted in other studies, which have found an emission reduction between 35% when hybrid vehicles are utilised and 65% when utilising electric vehicles (Baptista et al., 2014; Te & Lianghua, 2020). Carsharing is a short-term technology (Table 24.1), and it enables users to gain economic benefits such as reducing travel costs associated with travel style and car ownership. Car owners saved approximately 74%, and public transit car owners saved around 60% by adopting carsharing in Ireland (Rabbitt & Ghosh, 2016). However, non-car owners may have to adapt to a multimodal active traveller lifestyle, which was found to have incurred additional costs. Safety was highlighted as an area of concern as respondents had commented that security is one of the most significant inhibitors of carsharing. Carsharing requires a data-driven approach to learning the mobility habits of users, providing flexibility in case of delays or route changes. Additionally, carsharing requires reassurance to users on the reliability and safety of the trips and drivers. Such a reassurance and safety layer can be entrusted by social network data and previous users’ feedback.

2.7 Micromobility

Micromobility aims at providing short-distance, flexible, sustainable, and cost-effective on-demand short-distance transport (between 3 and 20 km). Micromobility involves a range of small vehicles that operate at approximately 20 to 25 km/h, such as bicycles, scooters, skateboards, and electric bikes. These vehicles encourage a shift towards low-carbon and sustainable modes of transport that can reduce carbon emissions from 40 to 70 per cent compared to an ICE (Abduljabbar et al., 2021).

2.7.1 Cycling and Electric Bikes

Cycling with traditional bicycles is environmentally friendly as it does not emit emissions and is economically viable to produce (Pucher & Buehler, 2017). E-bikes are substantially more efficient, with an average CO2 emission for km of 22 g, which is significantly lower than ICE vehicles (Elliot et al., 2018; Philips et al., 2020). However, the benefits of electric bikes are also varied depending on the mode of transport they are replacing (Edge et al., 2018). The wide-scale implementation and encouragement of cycling as a sustainable mode of transportation is not without its drawbacks. Cycling has been marginalised in many cities’ transports planning, and significant barriers to adopting and implementing pro-cycling policies are caused by a lack of infrastructure, funding, and leadership (Wang, 2018). There are often compact urban structure and a lack of street space in European cities, especially inner-city areas, therefore making it challenging to implement cycling infrastructure. Concerning electric bikes, the disposal of their batteries and their manufacturing emissions is the most significant environmental concern (Liu et al., 2021). Besides the lack of cycling infrastructure, other relevant barriers to cycling are the limited feasible trip range, personal safety concerns, the safety of bike storage, and lack of flexibility for sudden route extension beyond a particular length or passenger transport. However, as identified in Table 24.1, a low-risk technology could significantly contribute to a sustainable transport system if the ecosystem is enriched with data-driven technologies for charging, sharing, and improving the infrastructure.

2.7.2 Electric Scooter

During the last few years, electric scooters’ uptake has been soaring. The transport solution has been identified as economic, clean, and sustainable (Table 24.1). However, introducing electric scooters as a transportation mode has caused several conflicts, such as problems with space, speed, and safety (Gössling, 2020; O’Keeffe, 2019). Some researchers found that the barriers varied greatly on whether respondents had used an e-scooter and how often they used it in the last month. In the survey, 46% of non-riders were satisfied with the current modes of transport and were not interested in e-scooters (Sanders et al., 2020). Issues with e-scooter equipment, such as being hard to find or easy to break, were a significant barrier among e-scooter users. Safety-related barriers were found to be more even between both groups. From the data perspective, localisation and the computation of optimal collection routes of dead scooters require accurate GPS data. Additional parking verification through advanced computer vision techniques requires heavy computational capabilities.

2.7.3 Mobility as a Service (MaaS)

Shared mobility refers to the shared use of a vehicle. These vehicles can range from scooters to bikes or electric bikes. It is a modern and innovative transportation strategy that enables users to have short-term access to a mode of transport when required. Thus, it may increase multimodality, minimise vehicle ownership and distance travelled, and provide new ways to access goods and services. Shared mobility has an extensive and wide range of modalities; however, the development of newer mobility options, alongside the development of new technology, led to the development of the service concept known as mobility as a service (MaaS) (Machado et al., 2018). Such a concept is often described as a one-stop management platform that unifies and links the purchase and delivery of mobility services such as bike sharers, share riders, and car sharers (Wong et al., 2020).

Additionally, the subscription to MaaS enables tailoring and developing mobility services around an individual’s preferences, which may be beneficial to both transport users and providers. Therefore, the seamless and affordable travel experience MaaS provides may play a significant role in pursuing sustainable transport. Its goals are to create an integrated multimodal system and substitution private vehicles with alternative options (Jittrapirom et al., 2017). The efficient running of MaaS platforms requires seamless data interoperability between operators and mobility data providers such as navigation systems to forecast demand and dynamically allocate resources such as vehicles or public transport routes.

3 Questions and Challenges: Decarbonisation of the Transport Sector with the Currently Available Technology

As illustrated in the previous section, three leading technologies could disrupt the personal transport sector: unmanned aerial vehicles (drones) and connected autonomous and hydrogen cars. Although these technologies have been classified as high leverage technologies with a potentially disruptive impact on the industry, critical technological and social barriers exist and rely on research and development progress and political and financial factors. Additionally, without an environmental analysis of mobility behaviour and clear directives on how to shift towards more sustainable mobility solutions, it is impossible to outline a feasible roadmap to the decarbonisation of the transport system. For example, it is essential to understand if low leverage technologies can achieve the same benefit as the leading future technologies illustrated. For this purpose, the aggregated impact of low leverage technologies on the annual per capita carbon footprint measured in tons of CO2 emissions is considered and computed with an open mobility dataset. It is further compared with the potential impact of the appraised high leverage technologies to assess the viability of the combined solutions.

It should be noted that the low leverage and low-risk solutions such as cycling, electric bike, and electric scooters have a constraint on the trip length. At the same time, carsharing and electric vehicles can cover virtually any distance, as demonstrated by numerous examples of successful carsharing initiatives such as BlaBlaCar (Quirós et al., 2021).

The first step in the analysis was to evaluate if short trips within the 20 km range represent a significant percentage of the overall total of car trips. Users would switch to micromobility transports such as bicycles, e-scooters, electric bikes, and so on for trips between 3 and 20 km (Fiorello et al., 2016). The distance below 3 km can easily be covered by walking, so the uptake is much lower. As per literature, above the 20 km range, micromobility is not suitable and more comfortable transport modes are required.

Therefore, we have analysed the share of domestic trips with private vehicles that can be replaced with micromobility transport services. The data for the analysis were extracted from the US national travel survey in 2009 and 2017 (Federal Highway Administration, 2020), which details more than 1.9 million private trips from participants across the USA, and similar results have been found throughout Europe.Footnote 1 The participants, during the trial, have logged detailed data for each trip, such as distance, type of vehicle, starting and end time, duration, and destination. As illustrated in Fig. 24.1, the cumulative percentage of car trips versus distance reveals that 47.9% of trips are within the 3 km to 20 km range. The low leverage micromobility solutions could uptake a significant percentage of the transport needs in such a range. Above the 20 km range, electric vehicles and carsharing can increase their market share until reaching their full potential.

Fig. 24.1
figure 1

Percentage of domestic trips within the range for modal shifting. The blue line is the total cumulative percentage of the trips, while the orange line is the cumulative percentage of trips between 3 and 20 km that a low leverage/low-risk technology could replace

Full size image
Fig. 24.2
figure 2

Annual per capita potential EU emission reduction for each low leverage technology where 100% is the average EU emissions per capita baseline for transport

Full size image

The impact of each low leverage technology has been evaluated through the literature and in terms of annual per capita carbon footprint reduction. Four scenarios for each technology have been considered and their carbon emission reduction compared to the average EU carbon emissions per capita, as illustrated in Fig. 24.2 . The four scenarios illustrate a stepwise increased share of a single technology from 10% (Scenario A) to 75% (Scenario D). In the graph, carsharing does not assume any vehicle upgrading. Still, it calculates the reduction of emissions caused by fewer vehicles on the road and shared mobility, and it includes the whole range of trips above 3 km. The remaining low leverage technologies can significantly impact the share of trips between 3 and 20 km, while between 20 and 30 km, the number of trips affected by the modal switch was reduced by 50%.

Interestingly, there is a marginal positive impact of station-based bike-sharing uptake compared to dockless bike-sharing. The potential emission reduction of these technology spans from 13% associated with a carsharing penetration of 75% in Scenario D up to 33% of station-based bike-sharing in the same scenario. The scenarios do not separate the electric versus not electric bike-sharing because the two results averaged. Each scenario also considers a 5% uncertainty derived from slightly different results in the literature. Although the technologies analysed can significantly impact the emission reduction, the interaction and uptake of each technology are uncertain, and further analysis requires a higher level of complexity and different perspectives.

Moreover, the study and the literature clearly show that reducing the total number of vehicles on the road could not necessarily lead to a proportional emission reduction (Commission & Centre, 2019). To systematically mitigate the carbon emissions from transport, focusing on a few disruptive technologies or assessing the currently available technologies to reduce the number of vehicles is not enough. It is essential to develop a holistic and data-driven approach to transport emissions (Giannotti et al., 2016). As illustrated in Wang et al., a profound discovery of phenomena that have led to emission reductions using essential temporal-spatial data is the first step towards developing sustainable and interoperable mobility solutions. The second step is the identification of the underlying explanations that have led to the events exploiting machine learning techniques of social-economic and temporal-spatial data. The third step is the predictive assessment of solutions’ impact achievable by combining mobility data and subject-related data (Wang et al., 2021). An interesting example of the effectiveness of this methodology is represented by the impact of the pandemic on transport.

4 Impact of the Pandemic

The Covid-19 pandemic has had an enormous impact on social life and transport. The lockdowns and restrictions imposed by governments worldwide to reduce transmissions have drastically affected the transport sector. Such measure was evident as global road transport had fallen below 50% compared to 2019, and commercial flight transport had dropped below 75% by mid-April of 2020 (Abu-Rayash & Dincer, 2020). The pandemic has altered people’s social life routines, travel, and working behaviours. The government-imposed restrictions caused a surge of immediate change towards remote working. Remote working has dramatically affected our mobility, reducing congestion and improving productivity in several sectors (Philips et al., 2020). Global road transport has decreased by more than 50% compared to March 2019. April 2020 flight activity dropped by almost 75% compared to 2019 due to a reduction in transport demand due to restrictions imposed by the government. By the end of April 2020, the total number of passenger transport had declined by 77% compared to January 2020. Lastly, air passenger transport was the least used mode of transportation because of restrictions. On utilising public transport, when conditions began to ease, the public had become more cautious with their transportation choice due to anxiety and fear of infection of Covid-19 (Campisi et al., 2020). As a result, the preference for travelling employing private vehicles increased as they felt public transport was unsafe. As reported in a survey from Jenelius et al., 25% of respondents have entirely resigned from using public transport. These results suggest that people’s perception of their well-being in public transport is essential in determining their willingness to use it. After several months, the general perception identified it as risky (Jenelius & Cebecauer, 2020; Przybylowski et al., 2021). The pandemic has resulted in developing a preference for private vehicles as their mode of transport rather than public transport. In parallel, different private transport modes such as e-scooter and bikes had peak sales trends (Eisenmann et al., 2021; Nundy et al., 2021). It has been deemed the most appropriate mode of road transport in several countries. Berlin has expanded its yellow tapes on its roads to encourage and allow more room for cyclists. In Budapest, cycles were implemented, and there has been a 300% reduction in tariffs for bikes. Lastly, in the UK, the government supported bicycles as a mode of transport.

5 Developing Countries

Developing countries tend to suffer from a variety of issues. These countries face significant environmental challenges due to rapid urbanisation, population growth, climate and environmental issues, and inefficient governance and environmental management, therefore making it extremely difficult for these countries to pursue sustainable transportation (Ameen & Mourshed, 2017). However, an interesting perspective is that developing countries could implement measures to avoid the same path as developed countries. For instance, during the pandemic, the municipality of Bogota transformed a car road lane of 100 km into a bike lane to facilitate citizens to commute by bicycle (Rodriguez-Valencia et al., 2021). In most developing countries, urban areas are affected by prevailing global megatrends such as population growth and urbanisation. The main problem in achieving sustainable transportation in developing countries is a lack of quality infrastructure (Gordon, 2012). Poor infrastructure contributes to a high quantity of accidents and mortality rates. For instance, Bangladesh’s fatality rates are the highest globally at 85.6 per 10,000 vehicles in 2004, which was double the South Asian average of 40.56. Secondly, pollution is exponentially increasing and affecting population health. Lack of necessary transport infrastructure and planning leads to high traffic congestion. Therefore, it is challenging to design the infrastructure to match the current needs (Kyriacou et al., 2019). However, digitalisation and the availability without strong privacy concerns of large mobility datasets could open up to test innovative solutions. For instance, the city of Manila (Philippines) has made an effort to propel digital transition and sustainable transport modes to respond to the pandemic measures. The government has pushed towards systematic data collection and establishing an open database system for all governmental transport agencies to adopt other MaaS solutions (Hasselwander et al., 2022).

6 Policy Restrictions

Covid-19 has forced society to partially renounce its freedom of movement, especially during the early stage of the pandemic when limited studies on the virus and reckless citizens’ behaviour threatened public health. From the economic perspective, the externalities cost of the restrictions posed a significant burden on society in unequal shares (Zivin & Sanders, 2020). In such a context, personal and individual decisions no longer match the greater benefit of society. Therefore, policy interventions such as the shutdown of businesses and limitations to mobility and personal freedom have caused significant backlashes. These periods have further stressed the evidence that subsidies and incentives for sustainable and virtuous behaviours are more effective than restrictions on personal freedom. Because the climate crisis is reaching an emergency level and reducing the burden for citizens of such externality, policymakers could develop a subsidy infrastructure for environmentally friendly behaviours exploiting big data such as mobile data, metering, and location data. Privacy concerns, cybersecurity, and reliability of the data sources are still open challenges to reaching such ambitious objectives. If the policy framework is accurately planned, some technologies such as edge computing, anonymisation, gamification, and distributed ledgers reduce the security risks and mitigate the consequences of opening the data to the public.

There is often a lack of adequate planning and regulation in developing countries, leading to problems such as congestion problems and high costs and travel times. In some of these developing countries, this is caused by a poor public transport system, sense of community, and education. In contrast, mobility bottlenecks could be caused by high motorisation rates and private car use in other countries, leading to economic, social, and environmental problems (Sánchez-Atondo et al., 2020).

7 Conclusions and Recommendations

This chapter analysed a set of future/imminent transport technologies, and they were classified based on their suitability to solve a specific transport issue (high/low leverage), deployment time (short/long term), and associated risk (high/low risk). Among the high leverage and long-term technologies, connected autonomous vehicles, hydrogen fuel cells, and unmanned aerial vehicles have high disruptive potential and the necessary features to reduce carbon emissions from transport globally. Carsharing, shared electric mobility (MaaS), electric scooter, and cycling have been classified as low leverage and short-term technologies that can improve the transport sector’s sustainability. One of the main questions addressed was determining if low leverage sustainable transport modes could replace future high leverage solutions if the technology advancements do not deliver what they have promised. The study indicated that the expected average emission reduction of 9.3% can be associated with the micromobility shared technologies identified if adequately promoted at the European level.

It should be noted that the combination of micromobility solutions, EV adoption, and carsharing could reach a similar level of decarbonisation for passenger transport expected by high leverage technologies. However, the path towards full decarbonisation of the transport sector is still long. Thus, waiting for future technologies will not bring any additional benefit.

As a first recommendation, we can state that a sustainable transport system requires all stakeholders to work together towards the common objective of adopting low leverage technologies and creating a new data-driven infrastructure to reward sustainable mobility behaviours. As described above, it is fundamental to collect data to analyse patterns, establish a baseline, and test and verify new technologies and measures. A shared and open data repository could support the analysis of positive and negative phenomena that impact the system. The security risks of such an open data repository are well known and should be carefully considered; however, nowadays, several data distributed infrastructures based on semantic interoperability are rising and could be good candidates to be scaled across the EU.

The second recommendation is to implement a reward system to promote sustainable mobility behaviours. EV owners have been rewarded with free motorway tolls, reduced parking tariffs and taxes in several cities. Such a reward mechanism could be extended to exploiting recurrent mobility patterns. A reward for using a planning tool for short/long trips or utilising a MaaS infrastructure instead of a personal car can be awarded, and shared data can be utilised for further optimisation.Footnote 2 The technology to implement a flexible transport system is low leverage; Google’s Matrix APIs already provide timing, traffic, and distance for different transport systems such as bikes, public transport, and cars. In the USA, Google Maps started to embed public transport tickets. The Uber CEO clearly stated they wanted to become the leader in a safe, electric, shared, and connected transportation system for cities. Private efforts should be backed up by policies to reduce mobility needs, foster remote working, and pave the way to fast, steady, and effective adoption of technologies.

The third recommendation for a sustainable transport system is to exploit the heterogeneous public and big data to forecast demand and optimise road capacity, reduce peak hours’ traffic, and integrate with the rewards mechanisms. Using data collected through different sources and gamification can promote sustainable behaviours, especially if combined with social media and linked to a reward mechanism. Data integration and harmonisation are essential for the mass adoption of existing low leverage and future technologies. One of the limits of the low leverage technologies and EV adoption is to rely on the decarbonisation of the power system to deliver an essential contribution to the sector emissions. Therefore, data interoperability is necessary also for sector coupling and integration. The situation is more complex in developing countries because of the lack of regulation and infrastructure. In this case, probably the most appropriate solution to reduce congestion and emissions is adopting a combination of low and high leverage technologies that do not require massive investment in infrastructures such as MaaS, UAVs and waterborne or air transport. The decarbonisation of the sector for these countries will be undoubtedly further delayed in time compared to the more economically developed countries because of a slower orchestration between public and private interests.

As a final recommendation, the pandemic has also highlighted the importance of remote working and provided an estimated baseline for essential travel requirements. These data should be seen as a reference scenario and used to develop subsidies and incentives towards more sustainable mobility. Although government personal freedom restrictions are not compatible with democracy unless there is a tangible health and safety risk, the associated risks of climate change emergency could justify implementing more decisive policies and actions to reduce anthropogenic carbon emissions.