Analysing the Socio-Economic Effects

This ambitious phase in the project is dedicated to exploring the ripple effects of CCAM on employment, skills, and society at large. The work is structured around three core pillars:

• Employment Effects: Using advanced macroeconomic modelling, the team forecasts how CCAM will impact jobs across sectors and occupations, from logistics to public transport.
• Skills Forecasting: Through a blend of data analysis and stakeholder foresight, the work will identify the emerging skills landscape and pinpoints where reskilling and upskilling will be most critical.
• Socio-Economic Impact: Beyond the workplace, CCAM will influence communities, accessibility, and equity. The work will develop a dynamic model to assess these broader societal effects, ensuring that the human dimension of mobility innovation is not overlooked.

Read on to find out more details on each of these areas.

What Are We Doing to Understand CCAM’s Impact on Employment?

As part of the CCAM-ERAS project, Cambridge Econometrics’ role focuses on understanding how Connected, Cooperative and Automated Mobility (CCAM) technologies will reshape Europe’s employment landscape. These technologies are changing how goods and people move, and they’re going to change the labour market too.

In this phase of the project, we build a robust, data-driven approach to assess how CCAM will affect jobs across sectors and occupations, helping policymakers and educators plan for a fair, future-ready transition.

The existing research on the deployment of autonomous vehicles is inconclusive on the exact employment effects, particularly in Europe due to low commercial deployment and too few pilots specific to the European context. This specific work uses a macroeconomic model that allows building several scenarios about the implementation and the impact of CCAM on employment by sector and employment in occupations.

In order to estimate the future employment impacts, supply chains for CCAM were mapped to standard sector classification (NACE), assumptions are being gathered for scenarios, and finally an employment forecast is generated for each scenario.

Mapping the CCAM supply chain

The first step is to define what CCAM really means in terms of the traditional transport sector. We began by mapping out the supply-chain of traditional transport using Input-Output tables and evaluating the key sectors that would differ with the increase in shares of autonomous vehicles. For instance, relative to traditional transportation, CCAM would require increased investment in Light Detection and Ranging (LIDAR) and Radio Detection and Ranging (RADAR) capabilities in public transport infrastructure. This would involve an increased role of the ICT sector in CCAM deployment.

Designing scenarios and gathering data

To explore different ways CCAM may be rolled out across Europe, scenario analysis is performed. For this, we gathered use cases based on commercial viability and scope as a starting point to produce different scenarios. The use cases include:

• Freight transport: ground transportation for last-mile delivery, bus depot, port/terminal;
• Passenger transport: public shared, private shared, shared transportation targeting specific groups.

These use cases would help streamline assumptions gathering for CCAM deployment. The two key levers of scenarios would be:

Speed of adoption

Deployment of CCAM will vary depending on the context. For example, closed environments such as ports and warehouses are likely to see faster adoption.

• Barriers to adoption in passenger transport:
• Safety concerns: The complexity of open-road environments raises higher safety standards.
• Regulatory barriers: Approval processes and compliance requirements are more stringent for passenger-focused applications, slowing down implementation.

Investment in CCAM

The pace and effectiveness of deployment also depends on the timing, size, and ownership of investments. These factors can differ widely across different scenarios.

• Barriers to investment:
  • High initial costs: Infrastructure upgrades and vehicle retrofitting require significant capital.
  • Uncertainty in ROI: Unclear market timelines or regulatory outcomes may deter private investment.
  • Fragmented ownership models: Differing interests among public, private, and consortium stakeholders can complicate funding and coordination

Additionally, the levers can be differentiated by levels of automation, region, imposing, e.g. differential speeds of adoption of levels of investments to model its impact on the national and EU-wide economy.

Employment forecast scenarios

This step would begin with finding consensus on the timeline of the forecast for the study from members of CCAM-ERAS to ensure that it is use-case relevant. Next, using the E3ME macro-econometric model, we will produce the different employment projections until 2030 (or to time horizon that is more pertinent to the use-case) by sectors (mapped to the use cases) and translate the sectoral outcomes towards occupations and qualifications. Other project members will then link to skills requirements, as well as education and reskilling efforts. This analysis of the changes in employment by sector and occupations across the defined scenarios is provided to understand the impact of divergent investments and technology adoption strategies. This will provide a clearer understanding of the specific impact of such choices have on the outcome in terms of the allocation of jobs and the overall employment effects.

 

Forecasting skills for the CCAM future

The Evolving Skills Landscape for CCAM

As the transportation sector embraces the shift towards Connected, Cooperative, and Automated Mobility (CCAM), the demand for new skills is rising rapidly. The integration of autonomous vehicles (AVs), smart infrastructure, and digital technologies in the mobility ecosystem is transforming how transportation works and reshaping the skillsets required across the sector. This transformation, however, does not come without challenges, particularly in aligning education, training, and workforce development with the evolving demands of CCAM. In this blog post, we explore the evolving skills landscape, focusing on the policy context, the impact of CCAM on jobs, tasks, and skills, and the potential mismatches in skills that may arise as the sector undergoes this transformation.

Policy Landscape: A Framework for Skills Development

The policy environment is a key driver in shaping the skills landscape for CCAM. At the European Union level, various initiatives and frameworks are being developed to support the digital transformation of the transport sector and ensure that the workforce is equipped with the skills needed for the future. Some of the most notable EU policies include:

The European Digital Strategy and the Digital Decade

The European Union’s Digital Decade aims to ensure that Europe is fully equipped for digital transformation by 2030. This includes increasing digital skills across the workforce. The strategy prioritises the deployment of high-speed digital infrastructure, such as 5G networks, to support the development and integration of CCAM technologies. It also calls for greater investments in digital skills education and vocational training programs to prepare workers for roles in emerging technologies such as autonomous vehicles, smart infrastructure, and data analytics.

The European Skills Agenda

Launched in 2020, the European Skills Agenda emphasises the need for reskilling and upskilling in response to both the green and digital transitions. This initiative is designed to align workforce skills with the changing demands of the economy, with a strong focus on increasing digital literacy and creating pathways for workers to acquire the skills necessary to thrive in sectors like CCAM. The agenda also includes the Pact for Skills, which aims to foster collaboration between businesses, educational institutions, and policymakers to address skills gaps and improve access to training.

EU Mobility Strategy and the Digital Single Market

The EU Mobility Strategy envisions a future of smart, sustainable, and connected transport systems. In line with this vision, the Digital Single Market promotes the use of digital technologies to make mobility systems more efficient and accessible. The deployment of CCAM technologies—such as AVs, electric vehicles (EVs), and connected infrastructure—necessitates significant investment in skills development. These include not only technical skills but also regulatory and compliance skills, as the deployment of CCAM systems will require new safety and privacy regulations.

National and Regional Policy Initiatives

National policies play an equally important role in fostering skills development for CCAM. For example, countries like France and Germany have implemented comprehensive national strategies that focus on digital transformation and skills development, with specific attention to CCAM-related technologies. These strategies aim to bridge the gap between labour market needs and the skills available in the workforce, particularly in emerging fields like AI, machine learning, and cybersecurity.

The policy landscape surrounding CCAM is evolving rapidly, with policymakers recognising that the workforce must be prepared to meet the demands of an increasingly digital and automated transport sector. The EU’s focus on digital skills and workforce development creates a supportive framework for ensuring that CCAM technologies are implemented successfully.

 

The Impact of CCAM on Jobs, Tasks, and Skills

As CCAM technologies become more widespread, they are set to disrupt the transportation industry, affecting jobs, tasks, and skills across multiple sectors. The impact of these changes will vary, with some roles being automated, others being transformed, and new roles emerging to support the deployment and maintenance of CCAM systems.

Automotive Industry

The automotive sector is at the heart of CCAM deployment. The shift towards autonomous vehicles and electrification is expected to significantly impact traditional job roles. For example, roles in vehicle manufacturing that rely on mechanical engineering are at risk of diminishing as the focus shifts to software engineering, cybersecurity, and systems integration. AVs require a different set of skills, particularly in software development, AI, and sensor technology. Maintenance and repair workers will also need to adapt, as AVs require expertise in software diagnostics, cybersecurity, and complex electronic systems.

Transport and Freight Sectors

In the transport and logistics sectors, the introduction of CCAM technologies, such as driverless trucks and automated cargo handling systems, will lead to a reduction in demand for traditional driving and manual labour roles. However, while job displacement is expected in certain areas (e.g., truck drivers, taxi drivers), new roles will emerge in areas such as fleet management, data analysis, cybersecurity, and logistics optimisation. For instance, truck drivers may transition into remote fleet management or vehicle monitoring roles, where they oversee fleets of autonomous vehicles from centralised control centres.

Passenger Transport

In passenger transport, the advent of automated buses, taxis, and ride-sharing platforms is expected to change the nature of jobs in the sector. While some traditional driving roles will be automated, new roles will emerge in customer service, safety, vehicle maintenance, and fleet management. Bus drivers, for instance, may transition into roles that focus more on passenger assistance, as automated buses will require fewer drivers. Similarly, taxi drivers may shift towards roles such as vehicle stewards, helping passengers with special needs or assisting with troubleshooting AVs.

IT and Data Management

With the increased reliance on digital technologies in the transport sector, there will be a growing demand for IT professionals with expertise in data management, cybersecurity, and system integration. As CCAM technologies generate vast amounts of data, the need for data scientists and analysts will rise, particularly in the context of big data analytics, predictive maintenance, and optimising transport networks. Additionally, cybersecurity specialists will play a critical role in ensuring the safety and security of connected and autonomous vehicles.

 

Possible Skills Mismatches and Gaps

As the CCAM revolution unfolds, there is a risk of significant skills mismatches between the supply of available skills and the demand for the specialised skills required in the new mobility ecosystem. The following are some of the key challenges associated with these mismatches:

Skills Gaps in Emerging Technologies

While demand for AI, machine learning, and cybersecurity skills is growing rapidly, there is a lag in the availability of trained professionals to fill these roles. Educational systems are struggling to keep pace with the evolving needs of the transport sector, and many workers lack the technical expertise required for advanced roles in CCAM. This skills gap is particularly evident in fields like AI, autonomous systems, and data management, where there is a shortage of workers with the necessary qualifications.

Reskilling and Upskilling Needs

One of the most pressing challenges is the need to reskill and upskill the existing workforce to meet the demands of CCAM. Many workers in the transport sector, particularly those in traditional roles, lack the digital skills required to operate and maintain advanced CCAM technologies. Reskilling programs must be developed to help these workers transition into new roles. This includes retraining drivers for fleet management or customer service roles, as well as providing technical training for vehicle maintenance workers in AV technologies.

Geographic Disparities in Skills Availability

Skills mismatches are not only a matter of availability but also geography. Certain regions, particularly rural or less digitally connected areas, may face greater challenges in attracting workers with the necessary digital and technical skills. This disparity can create uneven access to opportunities in the CCAM sector, exacerbating existing inequalities in the workforce.

Mismatch Between Education Systems and Industry Needs

The skills required for CCAM are highly specialised, and many educational institutions are not adequately prepared to provide the training necessary for these roles. Industry collaboration with academic institutions is essential to ensure that curricula are aligned with real-world needs. Without this collaboration, workers may struggle to gain the qualifications necessary for emerging roles, and employers may face difficulties finding the talent they need.

Key Findings from the International Transport Forum Roundtable

In February 2025, we participated in the International Transport Forum's roundtable on AI's impact on transport skills. The event underscored the rapid pace of digital transformation within the sector, highlighting the increasing need for skills in artificial intelligence, machine learning, and cybersecurity. Experts from around the globe discussed the importance of a well-rounded skill set for workers, stressing that both technical proficiency and an understanding of ethical considerations in AI will be vital.

The roundtable also emphasised the importance of aligning educational curricula with the emerging needs of the transport industry. It was noted that many educational institutions are still playing catch-up, struggling to integrate CCAM technologies into their programs. As such, a proactive approach is needed to ensure that future transport professionals are equipped with the necessary skills.

 

Gathering Data for Skills Forecasting

To further refine our understanding of the skills that will be in demand, we have launched a comprehensive survey with transport sector and educational stakeholders. This survey is pivotal in identifying gaps between the skills available in the current workforce and those required for the CCAM future. You can participate in the survey now by accessing it here.

The results will feed into our ongoing skills forecasting activities, allowing us to create a more nuanced picture of how the workforce needs to evolve.

In May 2025, we will also be attending the ITS Europe conference, where we will engage with key stakeholders in the transport industry to discuss skills development and the future of mobility. This will be an important opportunity to share insights from our research and collaborate on strategies to address the emerging skills needs.

Later in the year, we will host a workshop at the UITP conference in Hamburg, where we will focus on the practical implications of CCAM on skills. Our goal will be to bring together policymakers, educators, and industry leaders to develop concrete solutions for reskilling and upskilling the workforce in response to the ongoing digital transformation.

Conclusion: Building the Future Workforce for CCAM

As CCAM technologies continue to develop, it is clear that the skills required in the transport sector will evolve dramatically. To ensure that the workforce is ready for the future, proactive steps must be taken to identify skills gaps and provide targeted training programs. Our ongoing skills survey, along with insights from key events will be instrumental in shaping the future of transport education and workforce development.

We look forward to sharing more insights from these events and continuing our work on forecasting the skills needed to thrive in the age of CCAM.

Socio-economic impact

Please return to this page at a later date when findings from later stages of the project will be added.