Autonomous mobility and electric vehicles as drivers of sustainable mobility: a quantitative analysis of the acceptability of disruptive technologies and possible decarbonization pathways for the transport sector

The transport sector is currently undergoing a major paradigm shift, driven by an unprecedented convergence of environmental, economic, and social challenges. Globally, transport accounts for approximately one-third of total energy consumption and greenhouse gas (GHG) emissions, making its decarbonization a top priority in international and European climate agendas. Within this transformative landscape, “disruptive technologies” (as specified below) such as Battery Electric Vehicles (BEVs) and Autonomous Vehicles (AVs) are increasingly regarded as key enablers of cleaner, smarter and more inclusive mobility systems. This thesis focuses on BEVs and AVs as two central pillars of this technological transition, each offering distinct contributions. Despite their different levels of technological maturity, both require a fundamental shift in the way mobility is conceived and delivered, a new paradigm that redefines how people interact with vehicles, infrastructure, and transport services, and plan their trips (in this sense was intended for “disruptive technologies”). BEVs represent an already mature and deployed solution that plays a central role in transport decarbonization, transforming both energy use and travel behavior, while AVs, though still in a nascent stage with respect to real-case applications, promise substantial impacts not only on decarbonization, but also on safety, accessibility, and transport equity, particularly through shared mobility services such as the Shared Autonomous Vehicles (SAVs). However, the actual contribution of BEVs and AVs to decarbonization and sustainability targets is not automatic. Their large-scale adoption depends on user acceptance, especially in early stages of deployment. Without public acceptance, even the most advanced innovations may fail to generate meaningful system-wide benefits. Building on this premise, the thesis pursues two overarching objectives: (i) to analyze user acceptance of autonomous mobility solutions and its evolution over time, and (ii) to assess the potential contribution of “disruptive technologies” to the national transport decarbonization trajectories. Although some studies have explored user acceptance of BEVs and Avs at specific moments of their market penetration, the literature remains fragmented due to the heterogeneity of contexts, objectives, samples, and methodological approaches. This limits cross-study comparability and, above all, prevents a temporal assessment of how acceptance evolves as technologies become more familiar to users. The present research tries to contribute to addressing this gap by adopting a longitudinal before-and-after design that enables a controlled comparison of user preferences over time. Therefore, to address the first aim, a longitudinal before-and-after analysis was conducted in Naples in 2018 and 2024 using a (Stated Preference -SP) Discrete Choice Experiment (DCE). The survey targeted regular public transport users and season-ticket holders, specifically bus passengers. The experiment compared two bus services identical in all relevant attributes (e.g., path, frequency, vehicle type, engine, and onboard quality comfort) except for the presence or absence of a human driver (traditional bus service vs. autonomous bus service), enabling a controlled evaluation of user preferences. A Mixed Multinomial Logit (MMNL) model was estimated to quantify heterogeneity in Willingness to Pay (WTP), ensuring comparability over time and avoiding biases associated with latent attitudinal variables, which are often unstable across multi-year periods and would have limited the validity of the before-and-after comparison. A consolidated model such as the MMNL was therefore preferred over more recent and complex approaches, as one aim of this research was not to propose a new/more advanced acceptance estimation model but to evaluate the temporal evolution of user preferences. A complementary real-world survey was carried out in Luxembourg, where an SAV pilot project has been operating since 2021, to evaluate user satisfaction, perceived benefits, and the social value of autonomous mobility services. The estimations results reveal a significant shift in user acceptance of SAVs compared to its traditional counterpart (with human driver): the average WTP for this service increase from -2.31 €/trip in 2018 to -0.63 €/trip in 2024, corresponding to a reduction in perceived disutility (reluctance to change) of approximately 70%. This trend indicates a transition from an initial high reluctance for SAV to a near-indifference stance, suggesting that AV-based solutions are moving beyond the early skepticism phase and are progressively approaching wider public acceptability. In confirmation of this result, the investigations conducted in the Luxembourg case show that more than 85% of respondents reported being satisfied or very satisfied with the SAV service, and over 60% were aged over 60 or had reduced mobility, confirming the potential of SAVs to enhance accessibility for vulnerable groups and to act as socially inclusive last-mile transport solutions. Starting from these results, the second aim of the research evaluated the contribution of possible disruptive technologies (as intended in this thesis) to the transport sector decarbonization. A technology-driven modeling framework was developed, combining bottom-up and top-down demand estimation methods within the Avoid–Shift–Improve (ASI) approach to scenario design. The model was applied to the Italian road transport sector to assess the emissions impact of business-as-usual (BAU) trends versus additional technology-driven scenarios. Results show that even under the most optimistic assumptions within the BAU trajectory, the projected reduction in GHG emissions by 2030 reaches no more than about 30% compared to 2005, remaining significantly below the National Effort Sharing Regulation (ESR) target of -43.7%. By contrast, the technology-driven additional scenario grounded in evidence from current pilot projects, field studies, and state-of-the-art and technologically feasible assumptions (e.g., accelerated automation uptake, improved eco-driving, higher vehicle occupancy, better traffic management, and efficiency gains), suggests a potential GHG reduction of up to 40%. This highlights the strategic role of the “disruptive technologies” in aligning with national and EU climate objectives. Nevertheless, some limitations of the performed research should be acknowledged. First, the empirical evidence is based on only two specific case studies (Naples and Luxembourg), which limit the generalizability of the findings to other geographical and cultural contexts. Second, the Stated Preference approach may be affected by an inherent “status quo bias,” i.e. respondents may state a preference for their current travel option not because it truly reflects their choices, but simply due to reluctance to abandon habitual behavior or skepticism toward unfamiliar hypothetical scenarios in which they struggle to imagine themselves. This can reduce the reliability of the elicited preferences, even if methodological measures were adopted to mitigate this risk. Third, the MMNL modeling framework, although robust for estimating WTP and suitable for longitudinal analysis, does not allow to capture latent psychological constructs such as trust, perceived safety, or environmental concern, which may influence user choices. In addition, the survey was conducted only among regular public transport users, potentially overlooking the preferences of other user groups. Fourth, the decarbonization analysis is limited to the road transport sector and considers only TTW and WTW emissions, without encompassing an overall life-cycle assessment (LCA). Finally, future research may expand the empirical base to additional contexts, incorporate latent variables and real-world behavioral data through advanced modelling approaches, adopt multimodal system perspectives, and integrate full LCA and equity indicators. In conclusion, this research offers an integrated and policy-relevant contribution by combining behavioral analysis and scenario-based modeling to evaluate the role of disruptive technologies in supporting sustainable mobility transitions. By assessing user acceptance dynamics, identifying the social value of SAVs in real-case contexts, and quantifying the decarbonization potential of BEVs and AVs at a time when transport decarbonization is both urgent and strategic, the study provides actionable insights for policymakers, planners, and stakeholders committed to designing mobility systems that are environmentally sustainable, socially inclusive, and economically viable.