Project ResultsAutomotive Sector

The European automotive industry is currently undergoing profound structural transformation, driven by the transition to low-carbon technologies and increasing global competition. This shift is reshaping production processes, value chains, and market dynamics, creating both challenges and opportunities for the sector.

The transition to electromobility, together with disruptions in global supply chains and rising geopolitical tensions, is placing significant pressure on European manufacturers. At the same time, new policy frameworks and industrial strategies aim to support the sector's adaptation and long-term competitiveness.

Transition to electromobility is thus for now a bumpy road, and that is why we prepared different scenarios for automotive production in Slovakia, and looked at its impact on labour demand and labour composition by 2050.

The figures present alternative scenarios for the future development of the automotive sector, focusing on the evolution of production volumes and the shift from internal combustion engine vehicles (ICEV) to battery electric vehicles (BEV). The scenarios differ in both the speed of electrification and the overall scale of production.

In the Business-as-usual (BAU) scenario, production remains broadly stable at around 1 million vehicles, while the transition towards electrification is gradual. By 2050, BEVs account for approximately 30% of production, while ICE vehicles still represent around 70%, indicating a relatively slow structural shift.

The Best-case scenario assumes a rapid and successful transition to electromobility. Total production increases to approximately 1.3 million vehicles (+18%), with a full transition to 100% BEV production by 2045–2050. This reflects strong technological adoption, expanding battery production capacity, and favourable market conditions.

A similar technological pathway is observed in the Plausible scenario, where production remains stable at around 1 million vehicles, but the sector still achieves a complete transition to BEVs by 2045–2050. This scenario represents a balanced trajectory with gradual but consistent electrification.

In contrast, the Worst-case scenario reflects a situation of global disruption and loss of competitiveness. Production declines significantly to around 400 thousand vehicles, and although electrification progresses, BEVs account for only about 27% of production by 2050. This indicates both reduced industrial capacity and a delayed technological transition.

Overall, the results highlight that the future of the automotive sector depends critically on both the speed of electrification and the ability to maintain or expand production capacity. While rapid electrification can support growth and competitiveness, delayed transition may lead to significant declines in output.

The figure presents the total labour demand (direct and upstream) in the automotive sector under four alternative scenarios: BAU, Best-case, Likely-case, and Worst-case. Values are expressed in thousands of jobs and illustrate how employment evolves between 2019 and 2050 depending on the pace of technological transition and changes in production volumes.

In the BAU scenario, labour demand remains relatively stable over time, increasing slightly from approximately 168 thousand jobs in 2019 to around 171 thousand jobs by 2050. This reflects a situation with limited structural change and stable production levels.

The Best-case scenario shows a strong increase in employment, driven by both higher production volumes and rapid electrification. Labour demand rises from around 168 thousand jobs in 2019 to approximately 214 thousand jobs by 2050, indicating significant growth in the sector.

A similar but more moderate trend is observed in the Likely-case scenario, where employment increases to approximately 181 thousand jobs by 2050. This reflects a balanced transition with stable production and gradual technological change.

In contrast, the Worst-case scenario shows a sharp decline in labour demand. While employment initially increases to around 201 thousand jobs by 2030, it subsequently drops significantly to approximately 49 thousand jobs by 2050. This reflects a loss of competitiveness, declining production capacity, and an incomplete transition to new technologies.

Overall, the results highlight that labour demand in the automotive sector is highly sensitive to both production levels and the success of the technological transition. While successful electrification can support employment growth, delayed or disrupted transition may lead to substantial job losses.

The figure provides a more detailed decomposition of total labour demand in the automotive sector by vehicle type (ICEV vs. BEV) and battery production, across the four scenarios. Values are expressed in thousands of jobs.

In the BAU scenario, employment remains dominated by internal combustion engine vehicles (ICEVs) throughout the entire period. Although BEV-related employment gradually increases, reaching around 47 thousand jobs by 2050, it is not sufficient to offset the decline in ICE-related employment, which remains the primary source of labour demand.

In the Best-case scenario, a strong structural shift is observed. Employment rapidly transitions from ICEV production to BEV and battery production, with BEV-related jobs becoming dominant already after 2030. By 2050, BEV production accounts for the largest share of employment (approximately 185 thousand jobs), while battery production also expands significantly (around 29 thousand jobs). ICE-related employment declines sharply.

The Likely-case scenario shows a similar but more gradual transition. ICE-related employment decreases over time, while BEV and battery production increase steadily. By 2050, BEV-related employment reaches approximately 157 thousand jobs, accompanied by moderate growth in battery production (around 24 thousand jobs), indicating a balanced structural shift.

In contrast, the Worst-case scenario highlights a dual challenge. While ICE employment declines substantially after 2030, the expansion of BEV and battery production is insufficient to compensate for this loss. By 2050, total employment is significantly reduced, with BEV-related jobs reaching only around 42 thousand and battery production remaining limited (approximately 7 thousand jobs).

Overall, the results illustrate that the transition from ICE to BEV fundamentally reshapes the structure of labour demand, with battery production emerging as a key new source of employment. However, the extent to which these new activities can compensate for declining ICE-related jobs depends strongly on the success and speed of the transition.

The figure presents the percentage structure of upstream employment across sectors, illustrating how labour demand is distributed along the supply chain in each scenario over time. Unlike previous results, values are expressed as shares (%), capturing structural rather than absolute changes.

Across all scenarios, the metal and industrial manufacturing sector dominates upstream employment, accounting for the largest share throughout the entire period. However, their relative importance declines over time, particularly in more transformative scenarios. For instance, in the Best-case scenario, the share decreases from approximately 52.5% in 2020 to around 43.2% by 2050, reflecting a gradual diversification of the supply chain.

At the same time, retail and trade and other service-related sectors gain importance across all scenarios. Their combined share increases steadily, indicating a structural shift towards more service-oriented activities as the automotive sector evolves. This trend is most pronounced in the Likely-case and Worst-case scenarios, where services absorb a growing portion of employment.

Energy and construction sectors maintain relatively stable shares, with only moderate increases in some scenarios, reflecting their supporting role in infrastructure development and system operation. Similarly, transport remains relatively constant or slightly declining, suggesting efficiency gains in logistics over time.

In the Worst-case scenario, structural changes are more pronounced. The share of traditional industrial sectors drops more significantly (down to approximately 35.5% by 2050), while fossil-related production and other sectors temporarily gain importance, reflecting a less efficient and more fragmented transition pathway.

Overall, the results indicate that the automotive transition leads not only to changes in total employment but also to a reallocation of labour across sectors, with a gradual shift from heavy industry towards services and supporting activities along the value chain.

The figure presents the evolution of total costs (capital and labour) in the automotive sector across scenarios, disaggregated by skill level. Values are expressed in million €.

In the BAU scenario, total costs remain relatively stable over time, with a slight decline by 2050. This reflects a continuation of the current production structure, where labour costs—especially for high-skilled workers—remain dominant, while capital costs increase moderately. The overall cost structure does not undergo significant transformation.

In the Best-case scenario, a clear structural shift is observed. While labour costs decrease in the medium term, particularly for high-skilled workers, capital costs increase substantially, reaching approximately 7,159 million € by 2050. This reflects the capital-intensive nature of electrification and battery production. At the same time, labour costs partially recover by 2050, indicating new employment opportunities in emerging segments.

The Likely-case scenario shows a similar but more moderate pattern. Total costs decline slightly over time, driven mainly by reductions in labour costs, while capital costs increase steadily. This suggests a gradual transition where investments are spread over time, leading to a more balanced cost structure.

In contrast, the Worst-case scenario exhibits a sharp decline in total costs after 2030. By 2050, both labour and capital costs drop significantly, with total expenditures falling to a fraction of initial levels. This reflects a contraction of the automotive sector, where reduced production volumes and limited investment lead to a shrinking economic footprint.

Across all scenarios, a common trend emerges: capital costs become increasingly important relative to labour costs, highlighting the shift towards more capital-intensive production processes associated with electrification and battery manufacturing.

The figures present the evolution of the skill structure of employment in the automotive sector under the Best-case scenario, with a particular focus on gender dynamics and female employment growth.

The figure shows that the automotive transition is associated with a clear shift towards higher-skilled labour. The share of high-skilled workers increases significantly, from approximately 37.2% in 2020 to 47.1% by 2050, while the share of medium-skilled workers declines from 57.4% to 48.4%. At the same time, low-skilled employment remains marginal and continues to decrease slightly (from 5.4% to 4.5%). This confirms a strong skill-upgrading effect driven by technological change, particularly electrification and digitalisation.

The figure decomposes employment changes by gender and skill level. While employment increases for both men and women, the growth is particularly pronounced among high-skilled workers, especially in absolute terms for male employment. However, female employment also increases across all skill categories, with visible gains in medium- and high-skilled positions.

The figure highlights percentage growth in female employment by skill level, providing a clearer view of structural change. The results show that high-skilled female employment grows most strongly, reaching an increase of approximately +70% by 2050 in the Best-case scenario. This reflects the fact that space is being created in the automotive sector for highly qualified women. Medium-skilled female employment shows only modest growth, while low-skilled female employment remains volatile and relatively limited.

Taken together, the results indicate that the automotive transition not only increases demand for skilled labour but also contributes to a gradual rebalancing of gender representation, particularly in high-skilled occupations.