Analysing the Socio-Economic Effects of CCAM

Mapping the social road ahead for automated mobility

CCAM won't arrive as a single event — it will arrive unevenly, shaped by who gets access, who gets displaced, and who gets left out of the design conversation. This strand of research within the CCAM-ERAS project was set up to track that unevenness across three stages: first by listening to stakeholders and citizens directly, then by turning what was heard into a working forecast of possible futures, and finally by bringing that forecast back to the people who gave the original evidence, to check it still holds.

The three reports below should be read as one continuous line of work rather than three separate studies. Each builds directly on the data, model, or stakeholder feedback produced by the one before it.

Stakeholders' & Population Groups' Impact Analysis & Mapping

This deliverable lays the empirical groundwork for everything that follows. It combines a literature review of CCAM's economic and social dimensions with 14 semi-structured interviews across four stakeholder groups — education and training providers, road transport operators, representative organisations, and industry — alongside a public survey carried out in the Netherlands, Belgium, Norway, Germany, and Cyprus.

Major findings:

• Economic upside is concentrated: stakeholders see the clearest gains in freight logistics, e-commerce, and shared mobility, through efficiency and new data-driven business models.
• Jobs will shift, not just vanish: professional drivers and low-skilled transport roles face real displacement risk, while other roles evolve — reskilling in AI, cybersecurity, and systems integration is flagged as urgent.
• Education has a central role: HEIs and VEIs are expected to adapt curricula and keep access to mobility careers inclusive as the sector changes.
• Equity is not automatic: CCAM could help elderly, disabled, and rural populations — but early rollout is likely to be urban and costly, risking exclusion without targeted policy.
• Public sentiment is cautious optimism: respondents recognise convenience, safety and environmental upside, but trust is uneven — lower among rural and lower-income groups, and consistently limited by a lack of clear public information.

Read the full D4.6 report here D4.6 Stakeholders’ & population groups impact analysis & mapping

Social Impact Assessment

D4.6 established what people expect. D4.7 takes that same survey data — 1,083 responses across five countries — and asks a harder question: what would actually happen to different population groups under different rates of CCAM rollout?

This deliverable builds a system dynamics model spanning self-driving taxis, private automated cars, automated public buses, on-demand shuttles, and automated delivery. It forecasts low-, reference-, and high-penetration scenarios out to 2030, 2040, and 2050, tracking impact across adoption, accessibility, affordability, employment, inclusion, well-being, safety, and long-term spatial change — with the population itself modelled by age, gender, education, employment, location, and existing access to transport.

Major findings:

• Scale cuts both ways: greater CCAM penetration increases the potential size of both benefits and harms — it doesn't only make things better.
• Availability ≠ accessibility: automated taxis, buses and shuttles could genuinely help non-drivers and older people, but only if cost, coverage, and vehicle design are addressed — technology alone doesn't close the gap.
• Adoption will be uneven from the start: technologically confident users and those in early-deployment areas move first, and financial, digital, physical and territorial barriers persist unless deployment is deliberately inclusive.
• Employment effects are mixed, not one-directional: task displacement, occupational change, and new technical/supervisory roles all appear together; faster reskilling shortens the disruption.
• Long-term spatial risk: scenarios weighted toward private automated vehicles show potential for longer commutes and changed residential patterns over time.

Read the full D4.7 report here D4.7 - Social Impact Assessment_v_1.0_Final

Final Social Impact Assessment

 D4.8 closes the loop, taking that forecast back to the stakeholders behind D4.6 to ask: does this still hold up against what you know?

The validation took place at the CCAM-ERAS stakeholder workshop in Brussels on 19 November 2025 — a presentation of the model's outputs followed by structured breakout discussion testing the plausibility, coherence, and decision-making value of forecasts to 2050. Regional case studies from Helmond, Düsseldorf, and Limassol grounded the discussion in real conditions rather than averages.

Major findings:

• Direction confirmed, pace tempered: stakeholders backed the overall trajectory but stressed change will be gradual and cumulative, not sudden — diffusion, infrastructure, and regulation all take time.
• No single CCAM pathway: Helmond shows early proportional uptake and strong charging infrastructure growth; Düsseldorf drives the largest absolute taxi volumes alongside a shift toward public transport; Limassol adopts more gradually, addressing specific needs first.
• Benefits are conditional, not automatic: safety, accessibility and mobility gains are real possibilities, but so are increased travel demand, empty-vehicle trips, congestion, and digital exclusion if deployment lacks proper policy and design.
• Sharper causal language: feedback led the team to separate contextual factors (GDP, income, infrastructure) more clearly from effects directly attributable to CCAM adoption.
• The core conclusion: CCAM's societal benefit depends on governance, public trust, infrastructure readiness, affordability, and inclusive design — not on automation alone.

Read the full D4.8 report here D4.8 - Final Report on Social Impact Assessment_v_1.0_Final