Workforce Shifts and Skills Disruption in Automotive Manufacturing

The automotive industry is in the middle of a labor transformation that is quieter than product launches but louder in its consequences. When executives talk about electrification, automation, and supply-chain resilience, they usually frame these as engineering and procurement problems. Yet the hardest constraint is increasingly human: who will build, maintain, program, and continuously improve the new factory? The transition is not simply “jobs lost versus jobs gained.” It is a rewiring of what “automotive work” even means—moving from mechanical repetition toward electro-mechanical integration, software discipline, data fluency, and systems thinking, while still demanding the craft knowledge that keeps quality high and downtime low. The result is a split-screen reality: some traditional roles are shrinking, some are being redesigned, and entirely new roles are appearing faster than training pipelines can supply them. The OECD has described this as green and digital transitions reshaping the automotive ecosystem, with impacts that propagate far beyond final assembly into suppliers, logistics, energy, and services.*1
The uncomfortable truth is that many companies are still treating workforce strategy as a downstream HR function—something to “support” the transformation. In practice it is upstream. If you cannot secure the right skills mix, your electrification roadmap becomes an aspiration, your automation investment underperforms, and your reshoring story turns into a press release with staffing gaps. Labor is not a cost line to minimize in a transition like this; it is the capacity that determines whether you win or stall. Multiple institutions have been warning that the pace of technological change is colliding with skills formation systems that move far more slowly, creating bottlenecks in technical talent and mid-skill roles alike.*2
This article examines what is shifting inside automotive manufacturing work, why the disruption is happening now, and how OEMs, suppliers, policymakers, and workers can respond with strategies that are realistic rather than rhetorical. The goal is not to soothe the reader with generic optimism. It is to show, with real examples, where the pressure points are and how to build advantage through workforce design.

The Triple Shock Reshaping Auto Labor: Electrification, Automation, and Industrial Rebalancing
Three forces are converging, and it is the convergence—not any single force—that makes today’s labor disruption structurally different from previous waves. Electrification changes the product architecture and the value chain, shifting labor away from internal-combustion powertrain complexity and toward batteries, power electronics, thermal management, and high-voltage safety. Automation and AI change the production system itself, expanding what machines can do and altering what humans do when machines do more. Meanwhile, industrial policy and geopolitical risk are pulling supply chains closer to end markets, accelerating investments in new plants and new tiers of suppliers—often in regions that do not yet have the needed talent base.*1
Executives sometimes treat these as independent programs: an “EV program,” an “Industry 4.0 program,” a “localization program.” That separation is a strategic mistake. Electrification increases software content and electronics, which increases the need for automation in testing and traceability, which increases the need for data engineers and controls specialists, which then changes the type of supplier you need and the kind of labor market you are recruiting from. The OECD’s work on the green and digital transitions emphasizes these interconnections, showing how the automotive ecosystem’s boundaries widen into adjacent sectors.*1
At the same time, employers are reporting that skills gaps are the biggest barrier to business transformation across industries, and the green transition is now directly shaping the skills employers prioritize.*3 The workforce problem is not only the shortage of elite software engineers. It is also the shortage of maintenance technicians who can troubleshoot sensors and networks, production supervisors who can read dashboards and act on SPC signals, and quality engineers who understand both electrochemistry-driven failure modes and software-driven defects.

Why Traditional Automotive Jobs Are Disappearing (and Which Ones Are Being Rewritten)
A common simplification is that EVs require fewer parts than ICE vehicles, therefore fewer workers, therefore job losses. Reality is messier. Yes, the powertrain has fewer moving components, and certain machining-intensive supplier segments face structural decline. But new labor demand appears in battery cell production, module/pack assembly, power electronics, charging systems, and increasingly in software and electronics integration. The net outcome depends on where in the value chain your region sits, what gets localized, and whether your industrial base can capture the new activities rather than importing them.*4
Research continues to interrogate the “fewer workers” claim using plant-level data and process-based modeling rather than slogans.*5 One of the most important insights from this body of work is that the transition is less about the number of workers in “assembly” and more about the redistribution of work across components, testing, and upstream manufacturing. Battery production can be labor-intensive in different ways, but it is not the same work as engine assembly, and it does not automatically appear in the same place.
In Europe, the labor stress is visible at suppliers whose portfolios are tied to traditional powertrain systems and whose customers are simultaneously demanding cost cuts and electrification investment. Job-cut announcements and restructuring plans at major suppliers reflect this pressure, especially in divisions linked to powertrain technologies.*6 The lesson is not that EVs “kill jobs.” The lesson is that product architecture changes the geography of employment—often faster than communities can adapt.

The EV Factory: Where Work Shrinks, Where It Expands, and Where It Mutates
Walk through an ICE-era plant and you see a manufacturing story centered on stamping, welding, machining, fuel systems, exhaust systems, transmissions, and the choreography of hundreds of mechanical interfaces. Walk through an EV-era plant and the story moves toward battery packs, high-voltage harnessing, electric drive units, inverter assembly, thermal systems, and software configuration. The build sequence changes, the failure modes change, and the safety rules change.
High-voltage safety alone forces a rethink of tasks, training, and standard work. Roles that once relied on mechanical competence now require high-voltage qualification, lockout/tagout discipline at a new level, and diagnostic routines involving electronic control units and firmware versions. This is not an “add-on skill.” It reshapes job design.
At the component level, the shift can be seen in expected demand changes for wiring and electronics architectures as EV platforms expand and software-defined systems grow in importance.*7 What matters for workforce planning is that these changes redistribute labor demand toward electronics manufacturing, embedded systems testing, and end-of-line software validation. A plant can hit takt time and still ship a defective vehicle if software configuration and sensor calibration are wrong. That risk pulls labor demand toward roles that look more like test engineering and IT operations than traditional final assembly.

Battery Supply Chains: The New Employment Engine—With a Different Skill DNA
Battery manufacturing is frequently marketed as the job-creation counterweight to ICE decline, but it comes with two underappreciated realities. First, battery jobs are not “engine jobs relocated.” They require a different mix of skills—electrochemistry awareness, dry-room protocols, statistical control of sensitive processes, and rigorous contamination control. Second, many battery plants are being built in places without existing battery talent pools, so staffing becomes the critical path.
In the United States and Europe, battery investments have been tied to industrial policy and strategic autonomy goals, creating urgency to build domestic capacity.*8 But the labor transition creates friction: the workers available locally often have relevant manufacturing experience but not the specific process knowledge of cell production. Companies that underestimate the training load learn quickly that hiring alone does not create competence.
A useful real-life example is the way labor institutions and companies are negotiating job quality and transfer pathways as battery plants scale. In the U.S., arrangements enabling workers from legacy facilities to move into battery operations while preserving wages and seniority have been highlighted as a mechanism to reduce social backlash and speed staffing.*9 That kind of pathway matters because it turns workforce transition from a political fight into a managed pipeline.
The deeper strategic point is this: battery plants operate like advanced process industries. If you treat them like a conventional assembly facility, you will pay for it in yield losses, scrap, and reliability problems. The workforce must be designed around process control, preventive discipline, and cross-functional problem-solving from day one.

Automation and “Physical AI”: Why the Factory Talent Stack Is Changing
Automation in automotive manufacturing is not new. What is new is the combination of cheaper sensing, richer data, AI-enabled perception, and more flexible robotics that can handle variability rather than only repetition. This is pushing automation beyond classic welding cells into inspection, material handling, and adaptive tasks. Recent reporting has emphasized how AI-enhanced robotics are moving toward more autonomous operation in dynamic environments, changing the nature of production work and increasing the value of roles that supervise, maintain, and program automated systems.*10
The labor implication is not simply displacement. It is polarization and redesign. Some task bundles disappear; others become “exception handling” roles where humans intervene when systems fail, which paradoxically demands higher skill because you are dealing with rare, complex problems rather than routine repetition. If you automate 80% of a process, the remaining 20% can become the hardest part—requiring deep system understanding, not less.
From a training perspective, this creates a trap. Many firms try to “upskill” line workers with short courses while leaving job structures unchanged. The result is frustration: workers gain theory but are not given roles that use it. The right approach is job redesign first, training second. If you cannot describe what the new role does differently, training becomes a box-ticking exercise.
OECD work on automation and training has long argued that technology adoption and skill use are linked: firms that redesign work to use skills tend to realize better returns from both training and technology.*2 That is a warning against superficial reskilling programs that ignore how work is actually organized.

The Software-Defined Vehicle Is Quietly Reclassifying Automotive Labor
The manufacturing workforce shift is not only inside factories. It is inside the definition of the product. As vehicles become software-defined, more value comes from code, electronics architectures, and continual updates. That forces OEMs to build capabilities traditionally associated with tech companies: software engineering, cybersecurity, data management, and digital product operations.*7
The workforce disruption shows up in unusual places. Quality now includes software regressions. Warranty now includes OTA update failures. Manufacturing now includes software flashing, calibration routines, and secure key management. Supply chains now include semiconductor constraints and software licensing. None of this fits comfortably into traditional automotive org charts.
This is why some OEMs have struggled with software transitions: they tried to “bolt on” software teams without changing governance, incentives, and career ladders. The consequence is talent attrition. High-demand software talent does not stay in organizations where they cannot ship, where bureaucracy blocks toolchains, and where compensation is not competitive.
The strategic opportunity is that manufacturing organizations can become an advantage in the software era—if they integrate digital traceability, closed-loop quality data, and rapid feedback from the field into engineering. But that requires a workforce that can work across boundaries: manufacturing engineers who understand data pipelines, quality engineers who understand software failure modes, and production teams who can execute digital standard work.

Reshoring and “Friend-Shoring”: When Supply Strategy Becomes a Talent Crisis
Reshoring is often discussed as a matter of incentives, tariffs, and risk management. Yet the binding constraint is increasingly labor: can you staff and sustain a modern facility at scale? Industrial rebalancing creates surges in demand for skilled trades, controls engineers, and experienced production leaders, often in the same regions at the same time.
In Europe, the employment debate is now central to public policy discussions about competitiveness, EV adoption, and investment timing.*11 In the U.S., reshoring and foreign direct investment announcements have been tracked as part of a broader reindustrialization narrative, though these announcements do not automatically translate into staffed, productive capacity.*8 The reason is straightforward: plants can be financed faster than talent can be developed.
Trade blocs and industrial policies also shape where jobs go. If battery content rules or local-content requirements push new investments into a region, that region’s education and training systems become strategic infrastructure. Countries that treat skills formation as slow-moving bureaucracy will lose the race to those that treat it like an industrial capability.

Europe’s Supplier Squeeze: A Real-Time Stress Test of the Transition
Europe offers a live case study in the human consequences of a compressed transition amid global competition. Supplier organizations have reported job losses linked to lower production levels, cost pressures, and structural shifts in demand.*12 While each company’s situation differs, the pattern is consistent: suppliers tied to traditional components face a shrinking addressable market and must invest in new technologies while simultaneously absorbing margin pressure.
Large supplier restructurings underscore how transition pain concentrates in specific portfolios. Announced job reductions in powertrain-related divisions reflect both cyclical and structural forces, including weaker-than-expected EV demand in some periods and the long-term direction of travel away from ICE.*6 The strategic takeaway is that the workforce challenge is not evenly distributed. It clusters in the middle of the value chain—especially among Tier 1 and Tier 2 suppliers whose legacy products funded the skills and capital base of the last era.
For policymakers, this means “EV transition jobs” narratives that focus only on new battery plants miss the employment reality of suppliers. For companies, it means the workforce plan must include supplier workforce transformation, not only internal headcount planning.

The EV Demand Whiplash Problem: Why Workforce Planning Keeps Failing
Many OEMs and suppliers are experiencing a workforce planning failure mode that has nothing to do with incompetence and everything to do with volatility. When EV demand growth is slower than forecast in a given market, companies pause investments, cut costs, and freeze hiring. Then, as regulations tighten or demand rebounds, they scramble again—only to find the talent pipeline has moved on.
Public reporting on job cuts tied to weaker EV sales in Europe illustrates how demand variability translates directly into employment decisions and political debate.*13 This whiplash undermines training systems because apprenticeships and reskilling programs require stable multi-year commitments. If companies treat training as discretionary and cyclical, the talent base decays right when it is needed most.
The mature strategy is to decouple capability-building from short-term demand swings. You can modulate production volumes without dismantling your skill-building engine, but only if leadership treats workforce capability as core infrastructure, not a variable cost.

What Skills Are Actually Rising—and Why “Reskilling” Often Misses the Point
It is fashionable to say “we need more software skills.” True, but insufficient. The skills rising in automotive manufacturing are best understood as a layered stack.
At the foundation are modern manufacturing basics: statistical thinking, process discipline, safety, and quality problem-solving. On top of that sits digital fluency: understanding sensors, PLCs, networks, MES systems, and data capture. Above that sits systems integration: how mechanical, electrical, and software subsystems interact. Then come specialized domains: battery processes, cybersecurity, functional safety, and AI-assisted automation.
Global employer surveys consistently show that skill disruption is accelerating and that employers expect significant changes in required skills within a few years, including skills linked to technology and the green transition.*3 The OECD’s work on digital transition skills highlights labor market bottlenecks in digital occupations and the need for upskilling pathways.*14 The implication for automotive leaders is that the scarcest talent is often hybrid talent: the person who can bridge manufacturing reality and digital systems, not only the pure software engineer.
This is where most reskilling programs fail. They train people in isolated content areas—coding basics, robotics modules—without building the integrative competence required to deliver results on the shop floor. Integration is the skill. A plant does not need 200 Python beginners. It needs a smaller number of digitally fluent manufacturing leaders who can redesign processes, implement systems, and coach teams through change.

A Case of Managed Transition: Turning Layoffs Into Pathways Instead of Headlines
A labor transition becomes socially and operationally manageable when workers can see credible pathways from old roles into new ones. In the U.S., examples of transfer arrangements into battery facilities—where experienced auto workers can move into new plants with protections—show how institutional design can reduce resistance and speed ramp-up.*9
This matters because opposition to electrification is often framed as cultural or political. In reality, it is frequently rational risk assessment by workers who have seen “transition” promises fail before. If a community has lived through plant closures, it will not trust abstract job-creation claims. It will trust signed pathways, recognized credentials, and wages that support a family.
For OEMs, the lesson is brutally simple: if you want to move fast, you cannot treat workforce transition as collateral damage. You have to build legitimacy with workers and communities through concrete mechanisms.

The Volkswagen “Employment 2030” Style Approach: Scenario Planning for Skills, Not Just Volumes
One of the most practical models for workforce transition is scenario-based planning that links product strategy to job families and training needs. Work conducted on future employment and training requirements in the context of automotive transformation has emphasized structured scenario thinking as a way to avoid panic narratives and instead plan redeployment and qualification pathways.*15
The value of scenario planning is that it forces leadership to quantify where skills will move, not merely state that “reskilling is important.” When you map roles likely to decline, roles likely to grow, and roles likely to mutate, you can design training capacity, credentialing, and internal mobility mechanisms before the crisis hits.
The most overlooked benefit is cultural: scenario planning signals to the workforce that leadership is thinking beyond the next quarter. That credibility reduces attrition among the very people you need to retain to train others.

The New Roles Booming in Automotive Manufacturing (and Why They’re Hard to Fill)
New roles are emerging across four clusters.
The first cluster is electrification operations: battery process technicians, high-voltage safety leads, power electronics test engineers, thermal systems specialists, and end-of-line calibration experts. These roles blend safety, process discipline, and electronics knowledge.
The second cluster is automation and controls: robotics programmers, PLC and SCADA engineers, machine vision specialists, reliability engineers, and predictive maintenance analysts. As AI-enabled automation expands, these roles become more central to throughput and quality.*10
The third cluster is digital manufacturing: MES architects, data engineers for shop-floor data, cybersecurity specialists for OT networks, and digital quality engineers who can connect field data to manufacturing traceability. As software and electronics content expands, digital manufacturing capability becomes a competitive lever, not an IT support function.*7
The fourth cluster is supply chain and localization: supplier development engineers who understand new technologies, quality auditors for battery supply chains, and industrial planners who can balance resiliency with cost. As the automotive ecosystem broadens into energy and digital domains, these boundary roles become critical.*1
They are hard to fill because they do not sit neatly in existing education pathways. A robotics programmer with automotive safety discipline is rare. A battery process technician with quality instincts is rare. A manufacturing data engineer who respects the realities of uptime and takt time is rare. Firms that insist on perfect candidates will lose. Firms that build internal academies and apprenticeship-style pipelines will win.

Advice for OEMs: Treat Workforce Capability Like a Product You Engineer
If you are leading an OEM transformation, the first discipline is to stop thinking of workforce planning as headcount. Headcount is an accounting artifact. Capability is what matters. Your plan should begin with a “capability bill of materials” for the future factory: the roles, proficiency levels, and cross-functional interfaces required to run EV and software-rich production at quality.
Second, redesign work before you train. Automation changes tasks; EV architectures change validation steps; software changes end-of-line processes. If you train people for yesterday’s job titles with tomorrow’s buzzwords, they will either quit or stagnate. OECD research on automation and training supports the view that skill use and organizational design shape training outcomes.*2
Third, build internal mobility as a system, not a slogan. Most companies have “career paths,” but few have high-throughput transition pathways with real incentives, transparent criteria, and protected time for learning. The strongest programs treat internal mobility like a supply chain: intake, assessment, training, placement, feedback, and continuous improvement.
Fourth, professionalize the mid-skill layer. The biggest bottleneck in many plants is not senior leadership or entry-level hiring; it is the layer of team leads, technicians, and supervisors who translate strategy into daily execution. Invest in them, pay them like they matter, and give them real authority with accountability.
Finally, measure success with operational metrics, not training metrics. The goal is not “people trained.” The goal is fewer hours of unplanned downtime, faster ramp to stable yield, fewer software-related quality escapes, and safer high-voltage operations.

Advice for Suppliers: Don’t Let Your Portfolio Decide Your Fate
Suppliers face a harsher version of the transition because they often lack OEM-scale balance sheets and branding power. The temptation is to defend the legacy portfolio for as long as possible. That strategy usually ends with abrupt downsizing when customer volumes fall and pricing pressure intensifies, as reflected in restructuring dynamics across parts of the supplier landscape.*6
A better strategy starts with ruthless portfolio truth-telling. Identify which product lines are structurally declining, which can be repurposed, and which new lines you can credibly enter. Then align workforce strategy to that portfolio shift, not to hope.
Suppliers should also prioritize “adjacent skill conversion.” A precision machining workforce can move into components for e-axles, thermal systems, and battery housings, but only if you invest early in process capability and quality systems. Waiting until the order book collapses is too late.
Finally, suppliers should cooperate on training infrastructure. Competing firms can still share pre-competitive training platforms—especially in regions where the alternative is everyone losing to labor scarcity.

Advice for Policymakers and Trade Blocs: Skills Are Industrial Policy
If a region wants automotive investment, it must offer more than subsidies. It must offer talent at scale and at speed. This requires three moves.
First, build credential systems that are modular and portable. Workers need proof of competence that travels across employers—especially when the transition involves plant closures and new plant openings. Without portability, workers resist retraining because it feels like gambling on one employer.
Second, co-invest in training capacity tied to real hiring commitments. Public training programs fail when they are detached from employer demand. The most credible models link funding to outcomes: placements, wage levels, retention, and progression.
Third, address geographic inequality. Research on “just transition” dynamics in Europe highlights how shifts can reproduce regional and social inequalities if investments and benefits concentrate unevenly.*16 A region that loses ICE supplier jobs and does not attract battery or electronics work experiences a one-way shock. Policy must anticipate this, not react to it.
Trade-bloc strategy also matters. If localization policies pull new investments into a region but the skill infrastructure is weak, companies will import talent temporarily, raising costs and creating political backlash. Long-term competitiveness requires developing local capability.

Advice for Workers: How to Stay Valuable as the Definition of “Automotive Skill” Changes
If you are a worker navigating this shift, the honest message is: loyalty to a job title is risky. Loyalty to a capability is powerful. The market is rewarding people who can operate at the intersection of mechanical systems, electrical systems, and digital control.
Start by choosing a “spine skill” that will remain in demand: industrial maintenance for automated systems, quality engineering with data skills, controls and robotics, high-voltage safety and testing, or manufacturing operations leadership. Then add a “bridge skill” that connects you to the new world: basic networking, data interpretation, PLC familiarity, or software configuration understanding. You do not have to become a software engineer. You do have to become fluent enough to work effectively in software-rich environments.
Also, insist on real pathways. Ask employers what roles trained workers actually move into, what wage progression looks like, and what credentials are recognized. If the answers are vague, the program is marketing.
Finally, protect your health and bargaining power. New industries can replicate old mistakes. Battery plants and high-voltage work introduce new safety risks, and job quality debates are part of the transition.*4 Choose environments that treat safety and progression as real commitments.

The Strategic Rethink: Workforce Is Becoming the Deciding Advantage
As product differentiation becomes harder and supply chains become more contested, the decisive advantage may be execution: ramping new plants faster, stabilizing quality sooner, and continuously improving processes in a world of software updates and electronics complexity. That execution is built on people.
The industry will still talk about gigafactories, AI, and industrial policy. But the winners will be those who build a coherent talent system: redesigned work, credible training, internal mobility, and a culture that respects hybrid expertise. The losers will be those who treat workforce transition as an HR project, cut training during demand dips, and then wonder why their automation investment underdelivers and their EV program misses milestones.
The automotive industry is not just transforming technology. It is transforming the social contract of industrial work. Companies and countries that engineer this transition deliberately will not only reduce disruption—they will capture the growth that comes after it.





References

*1 Organisation for Economic Co-operation and Development. (2023). How the green and digital transitions are reshaping the automotive ecosystem. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/03/how-the-green-and-digital-transitions-are-reshaping-the-automotive-ecosystem_c10d29a2/f1874cab-en.pdf
*2 Organisation for Economic Co-operation and Development. (2018). Automation, skills use and training. https://www.oecd.org/content/dam/oecd/en/publications/reports/2018/03/automation-skills-use-and-training_b611ef08/2e2f4eea-en.pdf
*3 World Economic Forum. (2025). The Future of Jobs Report 2025. https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf
*4 World Resources Institute. (2024). How the EV transition will impact auto manufacturing jobs. https://www.wri.org/insights/ev-transition-auto-manufacturing-jobs
*5 Cotterman, T. (2024). The transition to electrified vehicles: Evaluating the labor implications of changing powertrain systems. Energy Policy. https://www.sciencedirect.com/science/article/pii/S0301421524000843
*6 Reuters. (2025, October 1). Auto supplier ZF Group to cut 7,600 jobs in powertrain unit by 2030. https://www.reuters.com/world/auto-supplier-zf-group-cut-7600-jobs-powertrain-unit-by-2030-2025-10-01/
*7 McKinsey & Company. (2019). Automotive software and electronics 2030 (full report PDF). https://www.mckinsey.com/~/media/mckinsey/industries/automotive%20and%20assembly/our%20insights/mapping%20the%20automotive%20software%20and%20electronics%20landscape%20through%202030/outlook%20on%20the%20automotive%20software%20and%20electronics%20market%20through%202030/automotive-software-and-electronics-2030-full-report.pdf
*8 Reshoring Initiative. (2025). 2024 annual report including Q1 2025 data report (PDF). https://reshorenow.org/content/pdf/2024-1Q2025_RI_DATA_Report.pdf
*9 Trades Union Congress. (n.d.). Ex-auto workers win job deal at electric vehicle factory in Ohio. https://www.tuc.org.uk/workplace-guidance/case-studies/ex-auto-workers-win-job-deal-electric-vehicle-factory-ohio
*10 Financial Times. (2026, January). Physical AI: robotics are poised to revolutionise business. https://www.ft.com/content/3449e77c-721b-4fc9-8082-c584d8f74848
*11 Eurofound. (2025). Employment in the EU’s automotive sector. https://www.eurofound.europa.eu/en/publications/all/employment-eus-automotive-sector
*12 CLEPA. (2025, January 15). Job losses escalate as demand stays below expectation. https://www.clepa.eu/insights-updates/data-digests/clepa-data-digest-18-job-losses-escalate-as-demand-stays-below-expectation/
*13 Associated Press. (2024, November). Ford, facing economic headwinds and weak EV sales, to cut 4,000 jobs in Europe. https://apnews.com/article/e4d1ba2796b069dca26a6b79bef4c67c
*14 Organisation for Economic Co-operation and Development. (2023). Skills for the digital transition (PDF). https://www.oecd.org/content/dam/oecd/en/publications/reports/2022/10/skills-for-the-digital-transition_6b5e0b05/38c36777-en.pdf
*15 Fraunhofer IAO. (2020). Employment 2030: Summary (Volkswagen study) (PDF). https://www.iao.fraunhofer.de/content/dam/iao/images/iao-news/employment-2030-summary.pdf
*16 Szabó, J. (2024). Driving towards a just transition? The case of the European automotive industry. Energy Research & Social Science. https://www.sciencedirect.com/science/article/pii/S2214629624002408