Regional electricity sector employment and skills under different European policy scenarios

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To boost climate action while maintaining a just transition in the electricity sector, we assess the regional employment implications in 2035 of two policy ambitions, the ‘Fit for 55’ package of the European Union (EU) and a hypothetical EU-wide coal phaseout. We account for changes in employment and associated skills from electricity generation, storage and transmission, covering the whole supply chain from resource extraction to decommissioning. We then distinguish employment transfers from employment gains to identify skill needs at a NUTS 2 regional scale. We find that net employment creation is higher and more evenly distributed under the ‘Fit for 55’ policy plan compared to the hypothetical EU coal phaseout. Under both policy scenarios, the skill requirements for the workers in transition (many of whom are transferring from fossil fuel technologies to low-carbon technologies) depend on the specific region, while the skillsets needed for employment gains are similar across all European regions as they are driven by employment growth in technologies that require similar skills (wind and solar power). This information can support the design of re-skilling programs, helping to ensure that regions are not left behind in the energy transition. Environmental Policy Energy Engineering electricity system model employment skills just transition regional analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction European energy and climate policy efforts are expected to bring about job creation in renewable energy (Pai et al., 2021 ). However, decarbonization policies have been considered insufficient (Rogelj et al., 2016 ) and there have been calls to boost efforts for fossil fuel phaseout (International Energy Agency, 2023 ; WWF et al., 2024). The electricity sector is of particular importance to climate policies as it has high greenhouse gas (GHG) emissions (Eurostat, 2024a ; Sasse and Trutnevyte, 2023a ), and is expected to decarbonize earlier to allow other sectors to decarbonize (European Commission, Directorate-General for Climate Action, 2020 ; Pietzcker et al., 2021 ; Sasse and Trutnevyte, 2023a ). To ensure net-zero emissions by 2050 (European Commission. Directorate-General for Climate Action, 2019), it is thus important to advance electricity sector policy ambitions at an earlier date than 2050 (this is also expected to lower system costs (Victoria et al., 2020 )). The global climate crisis has emphasized the concept of a just and equitable transition in employment distribution (evident in the EU Just Transition Mechanism (European Commission, 2020 )), with skill requirements being an important determinant (García-García et al., 2020 ). This is reflected in the increasing integration of justice and equity objectives in electricity systems models (Goforth et al., 2025 ). Coal mining regions are likely to be severely affected (Borgonovi et al., 2023 ) by strongly concentrated impacts (European Commission. Directorate-General for Economic and Financial Affairs., 2022) during the transition. Historically, a faster transition from coal in Germany would most likely have resulted in a faster recovery of the mining regions (Oei et al., 2020 ). When comparing the coal phaseout to that of nuclear power in Germany, coal workers exhibit a low resilience for transformation due to their larger numbers, lower skills and a lack of employment mobility (Selje, 2022 ). Thus, the inclusion of employment distribution, mining regions and regional skill requirements is paramount when comparing policy ambitions. With the limited historical mobility of fossil fuel workers, and their geographical situation in areas where green jobs are potentially less likely to emerge, location is an obstacle when finding employment in a decarbonized system (Lim et al., 2023 ). It has been shown through skill-integrated, spatial-temporal analysis that, although few coal power plant workers can easily transfer to green jobs, upstream sectors of renewable energy can alleviate job loss due to their compatible skill requirements and flexible location options (Wu et al., 2024 ). In Europe, there is an expected surge in construction and manufacturing jobs which will decline significantly following 2035 when there will be a shift towards operation and maintenance jobs (Černý et al., 2022 ). Although there has been increasing policy attention on regional impacts of the energy transition (Alves Dias et al., 2018 ; Kapetaki et al., 2020 ), few studies have integrated spatially explicit employment data—covering both fossil fuel and renewable technologies—with sector-wide electricity systems models to assess impacts holistically, including evolving skill needs. Demand in the labor market for renewable electricity is expected to grow rapidly for highly qualified, highly skilled, non-manual workers with the implementation of the European Green Deal (European Centre for the Development of Vocational Training., 2023). Similar findings have been presented in the US, where the lowest-skilled workforce may experience more varied gains and losses than their high-skilled counterparts (Xie et al., 2023 ). But as far as we are aware, a regionalized and more detailed assessment of skills does not exist yet for Europe. In this study, we thus investigate: (1) How do regional employment levels in the European electricity sector compare in the case of current decarbonization ambitions (European Environment Agency, 2024 ) and the hypothetical EU-wide phaseout of coal and lignite (European Court of Auditors, 2022 )? (2) What are the key skill-related barriers for workers transitioning between jobs in the electricity sector under these different policies? How do skill demands vary across regions? (3) How do re-skilling requirements compare with the skill requirements of new workers in the electricity sector? To answer these research questions, we model the regional impacts on employment and skills in Europe in 2035, accounting for the whole supply chain from resource extraction to decommissioning across electricity generation, storage and transmission. The regional employment changes in resource extraction are linked to the changes in electricity generation using a novel spatial allocation method (with separate methods for biogas, woody biomass, coal and lignite, and oil and gas). 2. Methods EXPANSE spatial electricity sector model The European EXPANSE (Sasse and Trutnevyte, 2023a , 2023b , 2020 , 2019 ; Trutnevyte et al., 2012 ) electricity sector model is a spatially and temporally detailed, technology rich, single-year optimization model of the European electricity system in 2035 that covers 33 countries (European Union minus Cyprus and Malta and plus Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Norway, Serbia, Switzerland, and the United Kingdom). The model includes electricity generation at the level of 296 NUTS-2 regions and the electricity demand, storage, and transmission of 128 transmission grid nodes. EXPANSE balances electricity generation, storage, and transmission with inelastic electricity demand at each transmission grid node and time step. The model represents each hour of the year and optimizes every sixth hour to strike a balance between computational costs and accuracy (Schyska et al., 2021 ). Employment assessment This study builds on the earlier employment assessment in EXPANSE (Sasse and Trutnevyte, 2023a , 2020 ), which accounts for direct employment in construction, operation and maintenance, and decommissioning of electricity generation, storage, and transmission. These jobs are calculated for the EXPANSE model outputs using employment factors (see Appendix). The employment factors (excluding extraction) are technology-specific and are applied to the installed capacity in each NUTS 2 region (jobs/MW). Construction, operation and maintenance and decommissioning jobs are retained within the NUTS 2 region where there is capacity installed. Job losses are constrained using the current scenario, ensuring that jobs that do not exist in the current scenario cannot be removed. The nuclear decommissioning process can take 15–20 years (International Atomic Energy Agency, 2023 ), thus the nuclear decommissioning jobs are retained after plant closures. In this study, the extraction jobs are calculated at the continental level by applying fuel-specific employment factors (Ram et al., 2020 ) (jobs/MWh) to the annual fuel use of each plant using woody biomass, biogas, waste, oil, natural gas, coal, and lignite. These jobs are then spatially allocated to the NUTS 2 regions of extraction through newly created spatial allocation methods specific to each technology. This allocation method thereby links the change in fuel use of each plant with the change in employment at the regions of extraction. In the current scenario, employment levels for oil, gas, coal and lignite extraction are allocated to each NUTS 2 region proportionally to existing employment in each region. That is, all oil and gas extraction jobs calculated by the model are aggregated and then distributed proportionally to the number of oil and gas extraction workers in each region (Eurostat, 2024b ). The same method is conducted separately for coal and lignite mining (Alves Dias et al., 2018 ). The employment factors are applied to the installed capacity of the cost-optimal electricity mix in 2035 to find the number of extraction workers per technology. The change in extraction jobs due to a policy scenario is calculated at the continent level and then distributed spatially to each NUTS 2 region with separate methods for each technology. Coal and lignite extraction job changes are mapped using a risk rating (Alves Dias et al., 2018 ), with the highest risk regions losing the highest proportion of jobs; peat regions (Alves Dias et al., 2021 ) are accounted for here with the highest risk rating. Oil and gas extraction jobs are mapped using supply cost data (Black et al., 2023 ), with the most expensive regions losing the highest fraction of jobs. Woody biomass extraction jobs stay within the same region as the generation unless there are significant wood imports at the country level (see Appendix). Skills Analysis The skills data is obtained from the O*NET database (O*NET and U.S. Department of Labor, Employment and Training Administration, 2024) where the individual skills are assigned to groups (Greenspon and Raimi, 2024 ; O*NET and U.S. Department of Labor, Employment and Training Administration, 2024) (social, content, resource management, complex problem solving, systems, process and technical; see Appendix). Based on similar studies (Greenspon and Raimi, 2024 ; Lim et al., 2023 ; Soydan et al., 2024 ), the importance and level of each skill are combined to create a set of skill scores for each job. A list of jobs per supply chain sector group (extraction, construction, operation and maintenance, decommissioning) and technology is first created (see Appendix). The skill scores are weighted for each technology through employment factors from each of these supply chain groups and are normalized to one for each technology (e.g. 3.8% of all skills needed for oil and gas generation jobs are in operations monitoring). As such, the skill scores from each technology are linked to the number of jobs in each region (they are calculated through the combination of employment factors and installed capacity). Skill changes are calculated on a regional basis. Re-skilling scores are the skill requirements for a worker with the opportunity to transfer jobs within the electricity sector and the same NUTS 2 region. They are calculated as the positive difference between the skills of an average job gained in the region and an average job lost. Policy Scenarios In this study, two policy scenarios are compared for 2035: one that is in line with the accepted EU decarbonization policy (the ‘Fit for 55’ package (European Environment Agency, 2024 )) and a hypothetical EU-wide phaseout of coal for electricity generation. Although other European countries are modelled, the policy constraints are applied only to those in the EU. Given the recent research on the vulnerability of coal mining regions (Alves Dias et al., 2018 ) and the need for a decline in coal (Diluiso et al., 2021 ; European Court of Auditors, 2022 ), coal and lignite are chosen for the technology-focused phaseout. The objective of the EU ‘Fit for 55’ package (European Environment Agency, 2024 ) is to reduce GHG emissions by 55% by 2030 compared to 1990 levels. For 2035, a constraint of an emissions target aligning with the ‘Fit for 55’ policy package of the EU is derived by multiplying the estimated GHG emission intensity of electricity generation required in 2030 (European Environment Agency, 2024 ) (110 g \(\:{\text{C}\text{O}}_{2,eq}\) ) with the electricity demand of 2030 (Kättlitz and Buyuk, 2024 ). The phaseout target is implemented as generation capacity constraints, with the upper limit for each coal and lignite generation capacity in the EU set to zero. The demand data for the modelled year 2035 is obtained from the “National Trends” scenario of the TYNDP (Kättlitz and Buyuk, 2024 ) and is calculated as an average between 2030 and 2040 data. For comparison, a third, current scenario is added too, using generation and storage capacity from the 2018 grid (Sasse and Trutnevyte, 2023a ) while transmission capacity is allowed to increase to accommodate the demand of 2020 (Hörsch et al., 2018 ; Open Power System Data, 2020 ). It is assumed that no new nuclear and fossil fuel generation capacities can be built from the current scenario, except for planned expansions as of 2022 (Sasse and Trutnevyte, 2023a ). It is also assumed that all nuclear generation capacities are decommissioned in Belgium and Germany by 2035 (Sasse and Trutnevyte, 2023a ). Transmission capacity can increase up to four times between 2018 and 2035. 3. Results Technology mixes, emissions, and costs The two policy scenarios, both with the objective of decarbonization, vary in terms of technology mixes, emissions and system costs. The smaller coal and lignite capacities across Europe under the EU phaseout coincide with larger capacities in hydrogen storage, gas power, open-field solar power, nuclear power, waste power, and hydropower compared to the ‘Fit for 55’ package. The cumulative European electricity sector emissions reductions are 211.2 MtCO 2eq (EU: 228.0 MtCO 2eq ) under the ‘Fit for 55’ scenario and 290.2 MtCO 2eq (EU: 306.5 MtCO 2eq ) under the EU coal phaseout, representing reductions of 31% and 43% respectively. The additional costs compared to the current scenario are 38.0 Billion Euro (EU: 25.0 Billion Euro) under the ‘Fit for 55’ package and 45.4 Billion Euro (EU: 33.2 Billion Euro) under the EU coal phaseout. Employment To address inequality in opportunities across European regions, the two policy scenarios are compared in terms of employment changes overall and across NUTS-2 regions (Fig. 2 ). As compared to the current scenario, ‘Fit for 55’ adds a net 236.7 thousand jobs and the coal phaseout adds 175.2 thousand jobs overall. 75% and 69% of these jobs are in the European Union respectively, while others occur in other modelled countries, such as Switzerland, Norway, United Kingdom, and the Balkan countries. More regions have net employment losses under the EU coal phaseout scenario, particularly in the coal and lignite mining regions of Germany, Poland, Czech Republic, Hungary, Romania and Bulgaria. Employment gains are highest in Northern Scotland (driven by offshore wind power) and Southern Spain (driven by onshore wind and open-field solar power). There are higher employment gains in hydrogen and waste in the case of coal phaseout, particularly in Germany. The largest increase in net employment changes is in operation and maintenance (‘Fit for 55’: 192.6 thousand, EU Coal Phaseout: 180.8 thousand), and onshore wind power creates the largest share of these jobs. As shown in Fig. 2 b), the next largest increase in net employment is in construction (‘Fit for 55’: 94.8 thousand, EU Coal Phaseout: 87.5 thousand), with open-field solar power driving this growth. These changes are followed by those in decommissioning (Fit for 55’: 26.3 thousand, EU Coal Phaseout: 23.9 thousand) and extraction (‘Fit for 55’: -46.7 thousand, EU Coal Phaseout: -83.1 thousand). Regional re-skilling needs Within the NUTS 2 regions across Europe, gross job gains and losses in the electricity sector determine the number of workers with opportunities for employment transfer. For these workers, we estimate the necessary re-skilling requirements (Fig. 3 ). Throughout the results and discussion, workers experiencing employment loss who have the potential to gain employment in the electricity sector within the same NUTS 2 region are referred to as “potential employment transfers”. If there are more job losses than job gains within the region, then the number of potential employment transfers is equal to the number of employment gains and vice versa. Skill scores are applied to the jobs gained and lost per technology as shown in Fig. 3 c). The NUTS 2 regional re-skilling needs are the positive difference between the average skill set (skills and scores) gained in the region and the average skill set lost, scaled to the number of potential transfers. Following the workflow in Fig. 3 , the re-skilling needs of each region are obtained (see Appendix). As seen in Fig. 3 for the example of the EU coal phaseout scenario in Germany, Poland, the Czech Republic, Slovakia and Hungary, coal mining regions show particularly large and localized employment loss. Several coal and lignite mining regions require mostly content and systems re-skilling. The regions in Germany that require a higher share of resource management re-skilling are driven by potential transfers from nuclear power plants. The loss of predominantly fossil fuel employment (Fig. 5 ) drives a shift in the demanded skills of transitioning workers across Europe. Under both policy scenarios, countries with the largest aggregate of potential same-region employment transfers are Germany (‘Fit for 55’: 21.0 thousand, EU Coal Phaseout: 27.4 thousand), Spain (‘Fit for 55’: 8.9 thousand, EU Coal Phaseout: 7.2 thousand) and Poland (‘Fit for 55’: 8.1 thousand, EU Coal Phaseout: 11.4 thousand). Workers transferring from nuclear and oil and gas technologies (non-extraction) drive the demand for resource management skills in several countries. Coal, lignite, oil and gas extraction workers have low content and systems skills compared to other technologies and have high process and technical skills. Within most countries, potential employment transfers require additional content skills the most to fulfill the requirements of their new employment. Content skills are the foundational skills necessary for applying and gaining more specific skills, such as reading comprehension, active listening, writing, speaking, mathematics and science (O*NET and U.S. Department of Labor, Employment and Training Administration, 2024). This is also reflected across all employment gains at the continental scale, including transfers within the electricity sector, from outside the electricity sector, and newly qualified workers (Fig. 5 ). Critical thinking, reading comprehension, active listening, speaking and writing are amongst the most needed individual skills for employment gains under both policy scenarios. In contrast, technical skills, such as operations monitoring and operations and control, appear amongst the highest skills of lost jobs, mainly driven by losses in fossil fuels extraction and nuclear power. Within each NUTS 2 region, both the positive and negative changes in employment determine the required skills for potential employment transfers creating strong regional variations in re-skilling needs. Employment gains are predominantly in solar and wind power capacities and thus drive demand for similar skills across all NUTS 2 regions in the model (see Appendix). On the other hand, re-skilling needs for workers with the potential to transition are highly diverse across regions and depend on the technologies driving the gains and losses. Brandenburg in Germany (DE40) is the region with the largest number of potential transfers under both policy scenarios (‘Fit for 55’: 2.5 thousand, ‘EU Coal Phaseout’: 4.0 thousand). The transfers are driven by the shift from coal and lignite jobs (in extraction and generation) to jobs in onshore wind power, hydrogen and rooftop solar power, while the highest re-skilling needs are in the content group (Fig. 3 ), such as science, writing, mathematics and reading comprehension. On the contrary, Andalusia (ES61), another region with many potential transfers (1.7 thousand under both policy scenarios) experiences a significant shift from oil, gas and coal employment (non-extraction) to employment in offshore and onshore wind power, and open-field and rooftop solar power. Wind power workers have higher resource management skills than workers in oil, gas and coal non-extraction roles driving the predominant need for skills in this group. Nuclear power represents a significant share of gross job losses across the continent (‘Fit for 55: -15.7 thousand, EU Coal Phaseout: -14.3 thousand), along with coal and lignite extraction (‘Fit for 55: -59.8 thousand, EU Coal Phaseout: -113.0 thousand), non-extraction coal and lignite (‘Fit for 55: -19.1 thousand, EU Coal Phaseout: -34.4 thousand), non-extraction oil and gas (‘Fit for 55: -9.3 thousand, EU Coal Phaseout: -7.6 thousand), and oil and gas extraction (‘Fit for 55: -16.9 thousand, EU Coal Phaseout: -3.7 thousand). However, unlike these technologies, nuclear power provides net job gains (‘Fit for 55: 8.9 thousand, EU Coal Phaseout: 10.3 thousand) overall from capacity expansion in some regions. The technologies that create the largest number of gross employment gains are onshore wind power (‘Fit for 55’: 110.3 thousand, EU Coal Phaseout: 105.4 thousand), open-field solar power (‘Fit for 55’: 90.0 thousand, EU Coal Phaseout: 98.2 thousand) and offshore wind power (‘Fit for 55’: 44.7 thousand, EU Coal Phaseout: 27.8 thousand). Considering a nuclear worker transferring to an onshore wind plant, the most required skills are in the resource management group. A coal and lignite extraction worker following this transition would require skills the most in the content group with the skill gaps being quantitatively larger. This example is relevant for several regions where coal and lignite extraction workers have the potential to transfer to onshore wind power in Germany, Estonia, Spain, Finland, Italy, Lithuania, Latvia, Poland, Romania, Sweden, Slovakia, and the UK. The regions where nuclear power workers have the potential to transfer to onshore wind power are in Belgium, Germany, and Spain. This analysis of specific types of workers in transition highlights the technology-specific re-skilling needs of workers in employment transition. 4. Discussion This analysis of the European electricity system under two policy scenarios (the EU ‘Fit for 55’ package of legislation and a hypothetical EU-wide coal phaseout) shows the tradeoff between costs, employment and GHG emissions. The EU coal phaseout results in higher system costs, more employment losses, more regional disparities in unemployment, and yet boosts GHG emissions reductions by 37% compared to the ‘Fit for 55’ package. Under the EU coal phaseout, the additional costs of the electricity system are 19% higher compared to the ‘Fit for 55’ package. A previous estimate for electricity sector employment creation driven by the ‘Fit for 55’ package estimated 17% increase by 2030 (Fragkiadakis et al., 2023 ), which is comparable with the 22% increase by 2035 from our analysis aligned with the same policy. Net employment creation is higher under the implemented ‘Fit for 55’ policy plan compared to the hypothetical EU coal phaseout, but the associated GHG emissions reductions are significantly higher under the EU coal phaseout. Coal phaseout also has additional benefits, such as reduction in local air pollution and improved health (Pehle et al., 2025 ). In combination with previous retrospective work that suggests that a timely coal phaseout in the past would have led to lower costs, less environmental damage and a faster recovery in regions (Oei et al., 2020 ), this study provides more insight into such ambitions at the European level. The phaseout of coal and lignite for electricity generation would exacerbate the need for re-skilling coal and lignite workers who could transition to technologies of high growth (especially solar and wind power) across the continent. For coal and lignite extraction workers, there is an increased need for systems and content skills (as seen in Fig. 3 ) but not social, technical or process skills in which these workers are rich. This aligns with prior findings that fossil fuel extraction workers surpass the required level of technical skills in areas of employment growth (Greenspon and Raimi, 2024 ). Overall, re-skilling needs for electricity sector workers who transfer jobs within the same NUTS 2 region vary greatly based on the nature of their regional energy transition. In contrast, the skill needs of employment gains, i.e. of workers who gain their first employment in the electricity sector and those transferring from other sectors, are homogeneous across European regions as they are driven by technologies requiring similar skills (solar and wind power). This study hence is useful as it identifies regional employment gains, losses and potential transfers, including the most in-demand skills for both new jobs and potential employment transfers (re-skilling). The methods can be used to identify regions that are at risk of adverse effects of the energy transition and the re-skilling support needed to ensure that these regions are not disadvantaged. This information can support the design of re-skilling programs (e.g. through the Just Transition Mechanism (European Commission, 2020 )), ensuring regional equality. This work links a European electricity sector model with an employment and skill study to identify the employment and skill-related barriers for workers transitioning between electricity sector jobs under two policy scenarios. The novelty of this work also lies in the quantification of employment impacts in the whole supply chain at a NUTS 2 regional scale. In addition, the study distinguishes the skill requirements of transitioning workers from those associated with overall employment gains in the power sector. Future work can extend the scope beyond the electricity sector and outside of Europe to allow the integration of employment data from other steps in the supply chain, such as manufacturing, or to investigate potential job transfers from the electricity sector to others. A majority of fossil fuel workers who transition to green jobs will do so without relocating (Lim et al., 2023 ). However, spillover effects across NUTS 2 regions could be added to the study to account for employment mobility between regions (including data on commuting distance (Wu et al., 2024 ) and precise plant locations). Uncertainties and long-term changes in employment factors could be included as employment factors are expected to decrease over time (Fragkos and Paroussos, 2018 ). Finally, only two policy scenarios for 2035 have been analyzed here and future work could extend the analysis to a larger suite of policies and their implementation scenarios. Declarations Acknowledgments This work received funding from the Swiss State Secretariat for Education, Research, and Innovation SEFRI for the project IAM COMPACT “Expanding Integrated Assessment Modelling: Comprehensive and Comprehensible Science for Sustainable, Co-Created Climate Action” (Project no. 101056306) and for the project PRISMA “Net-zero pathway research through integrated assessment model advancements” (Project no. 101081604). Development of the EXPANSE model was supported by the partnership between the University of Geneva and Services Industriels de Genève. The computations were performed at the University of Geneva using Bamboo High Performance Computing service. The authors bear sole responsibility for the conclusions and results. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Lessons from Germany’s hard coal mining phase-out: policies and transition from 1950 to 2018. Climate Policy 20, 963–979. https://doi.org/10.1080/14693062.2019.1688636 O*NET, U.S. Department of Labor, Employment and Training Administration, 2024. O*NET 29.0 Database. Open Power System Data, 2020. Open Power System Data platform. Pai, S., Emmerling, J., Drouet, L., Zerriffi, H., Jewell, J., 2021. Meeting well-below 2°C target would increase energy sector jobs globally. One Earth 4, 1026–1036. https://doi.org/10.1016/j.oneear.2021.06.005 Pehle, H., Sasse, J.-P., Trutnevyte, E., 2025. Regional inequalities in air quality and health co-benefits due to climate change mitigation in the European electricity sector. Climatic Change 178, 27. https://doi.org/10.1007/s10584-024-03851-x Pietzcker, R.C., Osorio, S., Rodrigues, R., 2021. Tightening EU ETS targets in line with the European Green Deal: Impacts on the decarbonization of the EU power sector. Applied Energy 293, 116914. https://doi.org/10.1016/j.apenergy.2021.116914 Ram, M., Aghahosseini, A., Breyer, C., 2020. Job creation during the global energy transition towards 100% renewable power system by 2050. Technological Forecasting and Social Change 151, 119682. https://doi.org/10.1016/j.techfore.2019.06.008 Rogelj, J., den Elzen, M., Höhne, N., Fransen, T., Fekete, H., Winkler, H., Schaeffer, R., Sha, F., Riahi, K., Meinshausen, M., 2016. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639. https://doi.org/10.1038/nature18307 Sasse, J.-P., Trutnevyte, E., 2023a. A low-carbon electricity sector in Europe risks sustaining regional inequalities in benefits and vulnerabilities. Nat Commun 14, 2205. https://doi.org/10.1038/s41467-023-37946-3 Sasse, J.-P., Trutnevyte, E., 2023b. Cost-effective options and regional interdependencies of reaching a low-carbon European electricity system in 2035. Energy 282, 128774. https://doi.org/10.1016/j.energy.2023.128774 Sasse, J.-P., Trutnevyte, E., 2020. Regional impacts of electricity system transition in Central Europe until 2035. Nat Commun 11, 4972. https://doi.org/10.1038/s41467-020-18812-y Sasse, J.-P., Trutnevyte, E., 2019. Distributional trade-offs between regionally equitable and cost-efficient allocation of renewable electricity generation. Applied Energy 254, 113724. https://doi.org/10.1016/j.apenergy.2019.113724 Schyska, B.U., Kies, A., Schlott, M., Von Bremen, L., Medjroubi, W., 2021. The sensitivity of power system expansion models. Joule 5, 2606–2624. https://doi.org/10.1016/j.joule.2021.07.017 Selje, T., 2022. Comparing the German exit of nuclear and coal: Assessing historical pathways and energy phase-out dimensions. Energy Research & Social Science 94, 102883. https://doi.org/10.1016/j.erss.2022.102883 Soydan, H., Düzgün, H.Ş., Brune, J., 2024. A Novel Job Similarity Index for Career Transition in the Mining Industry. Mining, Metallurgy & Exploration 41, 2257–2278. https://doi.org/10.1007/s42461-024-01017-y Trutnevyte, E., Stauffacher, M., Schlegel, M., Scholz, R.W., 2012. Context-Specific Energy Strategies: Coupling Energy System Visions with Feasible Implementation Scenarios. Environ. Sci. Technol. 46, 9240–9248. https://doi.org/10.1021/es301249p Victoria, M., Zhu, K., Brown, T., Andresen, G.B., Greiner, M., 2020. Early decarbonisation of the European energy system pays off. Nat Commun 11, 6223. https://doi.org/10.1038/s41467-020-20015-4 Wu, H., Liu, J., Hu, X., He, G., Zhou, Y., Wang, X., Liu, Y., Ma, J., Tao, S., 2024. Fewer than 15% of coal power plant workers in China can easily shift to green jobs by 2060. One Earth 7. https://doi.org/10.1016/j.oneear.2024.10.006 WWF, Greenpeace, Transport & Environment, CAN Europe, EEB, E3G, Global Citizen, Carbon Market Watch, 2024. Open letter: fossil fuel phase out date needed in EU 2040 target. Xie, J.J., Martin, M., Rogelj, J., Staffell, I., 2023. Distributional labour challenges and opportunities for decarbonizing the US power system. Nat. Clim. Chang. 13, 1203–1212. https://doi.org/10.1038/s41558-023-01802-5 Additional Declarations The authors declare no competing interests. Supplementary Files SI.docx Supplementary Information for "Regional electricity sector employment and skills under different European policy scenarios" Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7232815","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491909193,"identity":"0c6e9786-653d-4cfa-b098-9fc64996899b","order_by":0,"name":"Alison Maguire","email":"","orcid":"","institution":"University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"","lastName":"Maguire","suffix":""},{"id":491909194,"identity":"15b07052-68fb-4ba0-a26e-dff7d688bf72","order_by":1,"name":"Alexandre Torné","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACdjDJDOfLMTAwNoAYCTi1MMNJCMuYdC2JDVAJnFr4m5mPSfzcYZ3Pz8B/8HHlDpv0Dbebm198YLDLw6VF4jBbsmHvmXTLmQ3MzIZnz6TlbrhzsM1yBkNyMU6HHeYxfMDbdtjA4AAzm2Rj2+HcDTcS24x5GA7AXYgO5A/zfzj4F6jFHqLlf7oBSMsfPFoMDvMwPgbbwgDWciABqKX5MQMeLYaH2YyNZdvSDSQOMxsbNrYlG84E2sLYY5CMU4vc8eZnkm/brA342xsfPmxss5Pnu5H++MOPCjucWhAAkQAY2CQYDAiqR9P9gUQNo2AUjIJRMLwBAB72V4iLKW9LAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-0557-0350","institution":"University of Geneva","correspondingAuthor":true,"prefix":"","firstName":"Alexandre","middleName":"","lastName":"Torné","suffix":""},{"id":491909195,"identity":"6822137c-e11a-411a-b17d-7150eddf5d03","order_by":2,"name":"Evelina Trutnevyte","email":"","orcid":"https://orcid.org/0000-0002-1716-6192","institution":"University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Evelina","middleName":"","lastName":"Trutnevyte","suffix":""}],"badges":[],"createdAt":"2025-07-28 10:35:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7232815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7232815/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87802039,"identity":"bb2e8482-2a7f-4c30-bfdd-b228a6e82102","added_by":"auto","created_at":"2025-07-29 07:57:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178598,"visible":true,"origin":"","legend":"\u003cp\u003eEuropean generation and storage technology capacities and emissions by policy scenario in 2035.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7232815/v1/edef91d98ce588983f3d5394.png"},{"id":87802537,"identity":"f35648d4-cf37-40ed-bc4c-f1c59bf4d9b2","added_by":"auto","created_at":"2025-07-29 08:05:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":619234,"visible":true,"origin":"","legend":"\u003cp\u003ea) Net employment changes (number of jobs) at NUTS 2 spatial resolution from the ‘Fit for 55’ and EU coal phaseout policy scenarios as compared to the current scenario. The employment impacts modelled overall differ from those in the EU because EXPANSE models some European countries that are not part of the EU. b) Net technology-specific and supply chain sector-specific employment changes in number of jobs.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7232815/v1/b5571bdd3dee1f1fb6b4d378.png"},{"id":87802540,"identity":"f0dc40c6-a95b-4dfe-8956-9703e57068e3","added_by":"auto","created_at":"2025-07-29 08:05:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":548115,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic showing the workflow of the skills analysis for a subregion of Germany, Poland, Czech Republic, Slovakia and Hungary under the coal phaseout scenario. a) Job gains and losses per technology, b) Regional job gains, losses and potential transfers within NUTS 2 region, c) workflow from skills per job to skills per technology (see Appendix for the list of jobs per technology), d) net skills from job gains, job losses and re-skilling needs for each NUTS 2 region, where circle size is proportional to the number of jobs.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7232815/v1/c3b9558b49e8cc148363d3bd.png"},{"id":87802040,"identity":"50013f8b-678f-4f36-9220-467ee6b01134","added_by":"auto","created_at":"2025-07-29 07:57:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":430601,"visible":true,"origin":"","legend":"\u003cp\u003eRe-skilling needs by 2035 showing country-level aggregates.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7232815/v1/cf9feb43c5c03889a4369913.png"},{"id":87804153,"identity":"66f5161c-69b1-4448-800f-ab97d7542a56","added_by":"auto","created_at":"2025-07-29 08:13:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":214107,"visible":true,"origin":"","legend":"\u003cp\u003eThe most gained and lost skills by 2035, with a breakdown for each technology causing the changes.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7232815/v1/4716459d59fa5d7f8b304a72.png"},{"id":87804758,"identity":"d4379816-023a-40ce-b4e1-dedbdbf03d3a","added_by":"auto","created_at":"2025-07-29 08:21:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1961274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7232815/v1/f3a59ba9-a09d-474d-bcac-37ba56c78526.pdf"},{"id":87802539,"identity":"e62abd22-a80b-4847-8570-77bcc383e1d3","added_by":"auto","created_at":"2025-07-29 08:05:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4686354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information for\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\"Regional electricity sector employment and skills under different European policy scenarios\"\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7232815/v1/960068d9dfb5f965ed7e3c94.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRegional electricity sector employment and skills under different European policy scenarios\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEuropean energy and climate policy efforts are expected to bring about job creation in renewable energy (Pai et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, decarbonization policies have been considered insufficient (Rogelj et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and there have been calls to boost efforts for fossil fuel phaseout (International Energy Agency, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; WWF et al., 2024). The electricity sector is of particular importance to climate policies as it has high greenhouse gas (GHG) emissions (Eurostat, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Sasse and Trutnevyte, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), and is expected to decarbonize earlier to allow other sectors to decarbonize (European Commission, Directorate-General for Climate Action, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pietzcker et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sasse and Trutnevyte, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). To ensure net-zero emissions by 2050 (European Commission. Directorate-General for Climate Action, 2019), it is thus important to advance electricity sector policy ambitions at an earlier date than 2050 (this is also expected to lower system costs (Victoria et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)). The global climate crisis has emphasized the concept of a just and equitable transition in employment distribution (evident in the EU Just Transition Mechanism (European Commission, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)), with skill requirements being an important determinant (Garc\u0026iacute;a-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is reflected in the increasing integration of justice and equity objectives in electricity systems models (Goforth et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Coal mining regions are likely to be severely affected (Borgonovi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) by strongly concentrated impacts (European Commission. Directorate-General for Economic and Financial Affairs., 2022) during the transition. Historically, a faster transition from coal in Germany would most likely have resulted in a faster recovery of the mining regions (Oei et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). When comparing the coal phaseout to that of nuclear power in Germany, coal workers exhibit a low resilience for transformation due to their larger numbers, lower skills and a lack of employment mobility (Selje, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the inclusion of employment distribution, mining regions and regional skill requirements is paramount when comparing policy ambitions.\u003c/p\u003e\u003cp\u003eWith the limited historical mobility of fossil fuel workers, and their geographical situation in areas where green jobs are potentially less likely to emerge, location is an obstacle when finding employment in a decarbonized system (Lim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It has been shown through skill-integrated, spatial-temporal analysis that, although few coal power plant workers can easily transfer to green jobs, upstream sectors of renewable energy can alleviate job loss due to their compatible skill requirements and flexible location options (Wu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Europe, there is an expected surge in construction and manufacturing jobs which will decline significantly following 2035 when there will be a shift towards operation and maintenance jobs (Čern\u0026yacute; et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although there has been increasing policy attention on regional impacts of the energy transition (Alves Dias et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kapetaki et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), few studies have integrated spatially explicit employment data\u0026mdash;covering both fossil fuel and renewable technologies\u0026mdash;with sector-wide electricity systems models to assess impacts holistically, including evolving skill needs. Demand in the labor market for renewable electricity is expected to grow rapidly for highly qualified, highly skilled, non-manual workers with the implementation of the European Green Deal (European Centre for the Development of Vocational Training., 2023). Similar findings have been presented in the US, where the lowest-skilled workforce may experience more varied gains and losses than their high-skilled counterparts (Xie et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). But as far as we are aware, a regionalized and more detailed assessment of skills does not exist yet for Europe.\u003c/p\u003e\u003cp\u003eIn this study, we thus investigate: (1) How do regional employment levels in the European electricity sector compare in the case of current decarbonization ambitions (European Environment Agency, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the hypothetical EU-wide phaseout of coal and lignite (European Court of Auditors, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)? (2) What are the key skill-related barriers for workers transitioning between jobs in the electricity sector under these different policies? How do skill demands vary across regions? (3) How do re-skilling requirements compare with the skill requirements of new workers in the electricity sector? To answer these research questions, we model the regional impacts on employment and skills in Europe in 2035, accounting for the whole supply chain from resource extraction to decommissioning across electricity generation, storage and transmission. The regional employment changes in resource extraction are linked to the changes in electricity generation using a novel spatial allocation method (with separate methods for biogas, woody biomass, coal and lignite, and oil and gas).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cb\u003eEXPANSE spatial electricity sector model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe European EXPANSE (Sasse and Trutnevyte, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Trutnevyte et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) electricity sector model is a spatially and temporally detailed, technology rich, single-year optimization model of the European electricity system in 2035 that covers 33 countries (European Union minus Cyprus and Malta and plus Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Norway, Serbia, Switzerland, and the United Kingdom). The model includes electricity generation at the level of 296 NUTS-2 regions and the electricity demand, storage, and transmission of 128 transmission grid nodes. EXPANSE balances electricity generation, storage, and transmission with inelastic electricity demand at each transmission grid node and time step. The model represents each hour of the year and optimizes every sixth hour to strike a balance between computational costs and accuracy (Schyska et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEmployment assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study builds on the earlier employment assessment in EXPANSE (Sasse and Trutnevyte, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which accounts for direct employment in construction, operation and maintenance, and decommissioning of electricity generation, storage, and transmission. These jobs are calculated for the EXPANSE model outputs using employment factors (see Appendix). The employment factors (excluding extraction) are technology-specific and are applied to the installed capacity in each NUTS 2 region (jobs/MW). Construction, operation and maintenance and decommissioning jobs are retained within the NUTS 2 region where there is capacity installed. Job losses are constrained using the current scenario, ensuring that jobs that do not exist in the current scenario cannot be removed. The nuclear decommissioning process can take 15\u0026ndash;20 years (International Atomic Energy Agency, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thus the nuclear decommissioning jobs are retained after plant closures.\u003c/p\u003e\u003cp\u003eIn this study, the extraction jobs are calculated at the continental level by applying fuel-specific employment factors (Ram et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (jobs/MWh) to the annual fuel use of each plant using woody biomass, biogas, waste, oil, natural gas, coal, and lignite. These jobs are then spatially allocated to the NUTS 2 regions of extraction through newly created spatial allocation methods specific to each technology. This allocation method thereby links the change in fuel use of each plant with the change in employment at the regions of extraction. In the current scenario, employment levels for oil, gas, coal and lignite extraction are allocated to each NUTS 2 region proportionally to existing employment in each region. That is, all oil and gas extraction jobs calculated by the model are aggregated and then distributed proportionally to the number of oil and gas extraction workers in each region (Eurostat, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). The same method is conducted separately for coal and lignite mining (Alves Dias et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The employment factors are applied to the installed capacity of the cost-optimal electricity mix in 2035 to find the number of extraction workers per technology. The change in extraction jobs due to a policy scenario is calculated at the continent level and then distributed spatially to each NUTS 2 region with separate methods for each technology. Coal and lignite extraction job changes are mapped using a risk rating (Alves Dias et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with the highest risk regions losing the highest proportion of jobs; peat regions (Alves Dias et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) are accounted for here with the highest risk rating. Oil and gas extraction jobs are mapped using supply cost data (Black et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with the most expensive regions losing the highest fraction of jobs. Woody biomass extraction jobs stay within the same region as the generation unless there are significant wood imports at the country level (see Appendix).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSkills Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe skills data is obtained from the O*NET database (O*NET and U.S. Department of Labor, Employment and Training Administration, 2024) where the individual skills are assigned to groups (Greenspon and Raimi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; O*NET and U.S. Department of Labor, Employment and Training Administration, 2024) (social, content, resource management, complex problem solving, systems, process and technical; see Appendix). Based on similar studies (Greenspon and Raimi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Soydan et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the importance and level of each skill are combined to create a set of skill scores for each job. A list of jobs per supply chain sector group (extraction, construction, operation and maintenance, decommissioning) and technology is first created (see Appendix). The skill scores are weighted for each technology through employment factors from each of these supply chain groups and are normalized to one for each technology (e.g. 3.8% of all skills needed for oil and gas generation jobs are in operations monitoring). As such, the skill scores from each technology are linked to the number of jobs in each region (they are calculated through the combination of employment factors and installed capacity). Skill changes are calculated on a regional basis. Re-skilling scores are the skill requirements for a worker with the opportunity to transfer jobs within the electricity sector and the same NUTS 2 region. They are calculated as the positive difference between the skills of an average job gained in the region and an average job lost.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolicy Scenarios\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, two policy scenarios are compared for 2035: one that is in line with the accepted EU decarbonization policy (the \u0026lsquo;Fit for 55\u0026rsquo; package (European Environment Agency, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)) and a hypothetical EU-wide phaseout of coal for electricity generation. Although other European countries are modelled, the policy constraints are applied only to those in the EU. Given the recent research on the vulnerability of coal mining regions (Alves Dias et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the need for a decline in coal (Diluiso et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; European Court of Auditors, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), coal and lignite are chosen for the technology-focused phaseout. The objective of the EU \u0026lsquo;Fit for 55\u0026rsquo; package (European Environment Agency, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) is to reduce GHG emissions by 55% by 2030 compared to 1990 levels. For 2035, a constraint of an emissions target aligning with the \u0026lsquo;Fit for 55\u0026rsquo; policy package of the EU is derived by multiplying the estimated GHG emission intensity of electricity generation required in 2030 (European Environment Agency, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (110 g \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{O}}_{2,eq}\\)\u003c/span\u003e\u003c/span\u003e) with the electricity demand of 2030 (K\u0026auml;ttlitz and Buyuk, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The phaseout target is implemented as generation capacity constraints, with the upper limit for each coal and lignite generation capacity in the EU set to zero. The demand data for the modelled year 2035 is obtained from the \u0026ldquo;National Trends\u0026rdquo; scenario of the TYNDP (K\u0026auml;ttlitz and Buyuk, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and is calculated as an average between 2030 and 2040 data. For comparison, a third, current scenario is added too, using generation and storage capacity from the 2018 grid (Sasse and Trutnevyte, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) while transmission capacity is allowed to increase to accommodate the demand of 2020 (H\u0026ouml;rsch et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Open Power System Data, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is assumed that no new nuclear and fossil fuel generation capacities can be built from the current scenario, except for planned expansions as of 2022 (Sasse and Trutnevyte, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). It is also assumed that all nuclear generation capacities are decommissioned in Belgium and Germany by 2035 (Sasse and Trutnevyte, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Transmission capacity can increase up to four times between 2018 and 2035.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cb\u003eTechnology mixes, emissions, and costs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe two policy scenarios, both with the objective of decarbonization, vary in terms of technology mixes, emissions and system costs. The smaller coal and lignite capacities across Europe under the EU phaseout coincide with larger capacities in hydrogen storage, gas power, open-field solar power, nuclear power, waste power, and hydropower compared to the \u0026lsquo;Fit for 55\u0026rsquo; package. The cumulative European electricity sector emissions reductions are 211.2 MtCO\u003csub\u003e2eq\u003c/sub\u003e (EU: 228.0 MtCO\u003csub\u003e2eq\u003c/sub\u003e) under the \u0026lsquo;Fit for 55\u0026rsquo; scenario and 290.2 MtCO\u003csub\u003e2eq\u003c/sub\u003e (EU: 306.5 MtCO\u003csub\u003e2eq\u003c/sub\u003e) under the EU coal phaseout, representing reductions of 31% and 43% respectively. The additional costs compared to the current scenario are 38.0\u0026nbsp;Billion Euro (EU: 25.0\u0026nbsp;Billion Euro) under the \u0026lsquo;Fit for 55\u0026rsquo; package and 45.4\u0026nbsp;Billion Euro (EU: 33.2\u0026nbsp;Billion Euro) under the EU coal phaseout.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo address inequality in opportunities across European regions, the two policy scenarios are compared in terms of employment changes overall and across NUTS-2 regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As compared to the current scenario, \u0026lsquo;Fit for 55\u0026rsquo; adds a net 236.7 thousand jobs and the coal phaseout adds 175.2 thousand jobs overall. 75% and 69% of these jobs are in the European Union respectively, while others occur in other modelled countries, such as Switzerland, Norway, United Kingdom, and the Balkan countries. More regions have net employment losses under the EU coal phaseout scenario, particularly in the coal and lignite mining regions of Germany, Poland, Czech Republic, Hungary, Romania and Bulgaria. Employment gains are highest in Northern Scotland (driven by offshore wind power) and Southern Spain (driven by onshore wind and open-field solar power). There are higher employment gains in hydrogen and waste in the case of coal phaseout, particularly in Germany. The largest increase in net employment changes is in operation and maintenance (\u0026lsquo;Fit for 55\u0026rsquo;: 192.6 thousand, EU Coal Phaseout: 180.8 thousand), and onshore wind power creates the largest share of these jobs. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), the next largest increase in net employment is in construction (\u0026lsquo;Fit for 55\u0026rsquo;: 94.8 thousand, EU Coal Phaseout: 87.5 thousand), with open-field solar power driving this growth. These changes are followed by those in decommissioning (Fit for 55\u0026rsquo;: 26.3 thousand, EU Coal Phaseout: 23.9 thousand) and extraction (\u0026lsquo;Fit for 55\u0026rsquo;: -46.7 thousand, EU Coal Phaseout: -83.1 thousand).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegional re-skilling needs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWithin the NUTS 2 regions across Europe, gross job gains and losses in the electricity sector determine the number of workers with opportunities for employment transfer. For these workers, we estimate the necessary re-skilling requirements (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Throughout the results and discussion, workers experiencing employment loss who have the potential to gain employment in the electricity sector within the same NUTS 2 region are referred to as \u0026ldquo;potential employment transfers\u0026rdquo;. If there are more job losses than job gains within the region, then the number of potential employment transfers is equal to the number of employment gains and vice versa. Skill scores are applied to the jobs gained and lost per technology as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The NUTS 2 regional re-skilling needs are the positive difference between the average skill set (skills and scores) gained in the region and the average skill set lost, scaled to the number of potential transfers. Following the workflow in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the re-skilling needs of each region are obtained (see Appendix). As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the example of the EU coal phaseout scenario in Germany, Poland, the Czech Republic, Slovakia and Hungary, coal mining regions show particularly large and localized employment loss. Several coal and lignite mining regions require mostly content and systems re-skilling. The regions in Germany that require a higher share of resource management re-skilling are driven by potential transfers from nuclear power plants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe loss of predominantly fossil fuel employment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) drives a shift in the demanded skills of transitioning workers across Europe. Under both policy scenarios, countries with the largest aggregate of potential same-region employment transfers are Germany (\u0026lsquo;Fit for 55\u0026rsquo;: 21.0 thousand, EU Coal Phaseout: 27.4 thousand), Spain (\u0026lsquo;Fit for 55\u0026rsquo;: 8.9 thousand, EU Coal Phaseout: 7.2 thousand) and Poland (\u0026lsquo;Fit for 55\u0026rsquo;: 8.1 thousand, EU Coal Phaseout: 11.4 thousand). Workers transferring from nuclear and oil and gas technologies (non-extraction) drive the demand for resource management skills in several countries. Coal, lignite, oil and gas extraction workers have low content and systems skills compared to other technologies and have high process and technical skills. Within most countries, potential employment transfers require additional content skills the most to fulfill the requirements of their new employment. Content skills are the foundational skills necessary for applying and gaining more specific skills, such as reading comprehension, active listening, writing, speaking, mathematics and science (O*NET and U.S. Department of Labor, Employment and Training Administration, 2024). This is also reflected across all employment gains at the continental scale, including transfers within the electricity sector, from outside the electricity sector, and newly qualified workers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Critical thinking, reading comprehension, active listening, speaking and writing are amongst the most needed individual skills for employment gains under both policy scenarios. In contrast, technical skills, such as operations monitoring and operations and control, appear amongst the highest skills of lost jobs, mainly driven by losses in fossil fuels extraction and nuclear power.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin each NUTS 2 region, both the positive and negative changes in employment determine the required skills for potential employment transfers creating strong regional variations in re-skilling needs. Employment gains are predominantly in solar and wind power capacities and thus drive demand for similar skills across all NUTS 2 regions in the model (see Appendix). On the other hand, re-skilling needs for workers with the potential to transition are highly diverse across regions and depend on the technologies driving the gains and losses. Brandenburg in Germany (DE40) is the region with the largest number of potential transfers under both policy scenarios (\u0026lsquo;Fit for 55\u0026rsquo;: 2.5 thousand, \u0026lsquo;EU Coal Phaseout\u0026rsquo;: 4.0 thousand). The transfers are driven by the shift from coal and lignite jobs (in extraction and generation) to jobs in onshore wind power, hydrogen and rooftop solar power, while the highest re-skilling needs are in the content group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), such as science, writing, mathematics and reading comprehension. On the contrary, Andalusia (ES61), another region with many potential transfers (1.7 thousand under both policy scenarios) experiences a significant shift from oil, gas and coal employment (non-extraction) to employment in offshore and onshore wind power, and open-field and rooftop solar power. Wind power workers have higher resource management skills than workers in oil, gas and coal non-extraction roles driving the predominant need for skills in this group.\u003c/p\u003e\u003cp\u003eNuclear power represents a significant share of gross job losses across the continent (\u0026lsquo;Fit for 55: -15.7 thousand, EU Coal Phaseout: -14.3 thousand), along with coal and lignite extraction (\u0026lsquo;Fit for 55: -59.8 thousand, EU Coal Phaseout: -113.0 thousand), non-extraction coal and lignite (\u0026lsquo;Fit for 55: -19.1 thousand, EU Coal Phaseout: -34.4 thousand), non-extraction oil and gas (\u0026lsquo;Fit for 55: -9.3 thousand, EU Coal Phaseout: -7.6 thousand), and oil and gas extraction (\u0026lsquo;Fit for 55: -16.9 thousand, EU Coal Phaseout: -3.7 thousand). However, unlike these technologies, nuclear power provides net job gains (\u0026lsquo;Fit for 55: 8.9 thousand, EU Coal Phaseout: 10.3 thousand) overall from capacity expansion in some regions. The technologies that create the largest number of gross employment gains are onshore wind power (\u0026lsquo;Fit for 55\u0026rsquo;: 110.3 thousand, EU Coal Phaseout: 105.4 thousand), open-field solar power (\u0026lsquo;Fit for 55\u0026rsquo;: 90.0 thousand, EU Coal Phaseout: 98.2 thousand) and offshore wind power (\u0026lsquo;Fit for 55\u0026rsquo;: 44.7 thousand, EU Coal Phaseout: 27.8 thousand). Considering a nuclear worker transferring to an onshore wind plant, the most required skills are in the resource management group. A coal and lignite extraction worker following this transition would require skills the most in the content group with the skill gaps being quantitatively larger. This example is relevant for several regions where coal and lignite extraction workers have the potential to transfer to onshore wind power in Germany, Estonia, Spain, Finland, Italy, Lithuania, Latvia, Poland, Romania, Sweden, Slovakia, and the UK. The regions where nuclear power workers have the potential to transfer to onshore wind power are in Belgium, Germany, and Spain. This analysis of specific types of workers in transition highlights the technology-specific re-skilling needs of workers in employment transition.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis analysis of the European electricity system under two policy scenarios (the EU \u0026lsquo;Fit for 55\u0026rsquo; package of legislation and a hypothetical EU-wide coal phaseout) shows the tradeoff between costs, employment and GHG emissions. The EU coal phaseout results in higher system costs, more employment losses, more regional disparities in unemployment, and yet boosts GHG emissions reductions by 37% compared to the \u0026lsquo;Fit for 55\u0026rsquo; package. Under the EU coal phaseout, the additional costs of the electricity system are 19% higher compared to the \u0026lsquo;Fit for 55\u0026rsquo; package. A previous estimate for electricity sector employment creation driven by the \u0026lsquo;Fit for 55\u0026rsquo; package estimated 17% increase by 2030 (Fragkiadakis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is comparable with the 22% increase by 2035 from our analysis aligned with the same policy. Net employment creation is higher under the implemented \u0026lsquo;Fit for 55\u0026rsquo; policy plan compared to the hypothetical EU coal phaseout, but the associated GHG emissions reductions are significantly higher under the EU coal phaseout. Coal phaseout also has additional benefits, such as reduction in local air pollution and improved health (Pehle et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In combination with previous retrospective work that suggests that a timely coal phaseout in the past would have led to lower costs, less environmental damage and a faster recovery in regions (Oei et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this study provides more insight into such ambitions at the European level.\u003c/p\u003e\u003cp\u003eThe phaseout of coal and lignite for electricity generation would exacerbate the need for re-skilling coal and lignite workers who could transition to technologies of high growth (especially solar and wind power) across the continent. For coal and lignite extraction workers, there is an increased need for systems and content skills (as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) but not social, technical or process skills in which these workers are rich. This aligns with prior findings that fossil fuel extraction workers surpass the required level of technical skills in areas of employment growth (Greenspon and Raimi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, re-skilling needs for electricity sector workers who transfer jobs within the same NUTS 2 region vary greatly based on the nature of their regional energy transition. In contrast, the skill needs of employment gains, i.e. of workers who gain their first employment in the electricity sector and those transferring from other sectors, are homogeneous across European regions as they are driven by technologies requiring similar skills (solar and wind power). This study hence is useful as it identifies regional employment gains, losses and potential transfers, including the most in-demand skills for both new jobs and potential employment transfers (re-skilling). The methods can be used to identify regions that are at risk of adverse effects of the energy transition and the re-skilling support needed to ensure that these regions are not disadvantaged. This information can support the design of re-skilling programs (e.g. through the Just Transition Mechanism (European Commission, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)), ensuring regional equality.\u003c/p\u003e\u003cp\u003eThis work links a European electricity sector model with an employment and skill study to identify the employment and skill-related barriers for workers transitioning between electricity sector jobs under two policy scenarios. The novelty of this work also lies in the quantification of employment impacts in the whole supply chain at a NUTS 2 regional scale. In addition, the study distinguishes the skill requirements of transitioning workers from those associated with overall employment gains in the power sector. Future work can extend the scope beyond the electricity sector and outside of Europe to allow the integration of employment data from other steps in the supply chain, such as manufacturing, or to investigate potential job transfers from the electricity sector to others. A majority of fossil fuel workers who transition to green jobs will do so without relocating (Lim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, spillover effects across NUTS 2 regions could be added to the study to account for employment mobility between regions (including data on commuting distance (Wu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and precise plant locations). Uncertainties and long-term changes in employment factors could be included as employment factors are expected to decrease over time (Fragkos and Paroussos, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, only two policy scenarios for 2035 have been analyzed here and future work could extend the analysis to a larger suite of policies and their implementation scenarios.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received funding from the Swiss State Secretariat for Education, Research, and Innovation SEFRI for the project IAM COMPACT \u0026ldquo;Expanding Integrated Assessment Modelling: Comprehensive and Comprehensible Science for Sustainable, Co-Created Climate Action\u0026rdquo; (Project no. 101056306) and for the project PRISMA \u0026ldquo;Net-zero pathway research through integrated assessment model advancements\u0026rdquo; (Project no. 101081604). Development of the EXPANSE model was supported by the partnership between the University of Geneva and Services Industriels de Gen\u0026egrave;ve. The computations were performed at the University of Geneva using Bamboo High Performance Computing service. The authors bear sole responsibility for the conclusions and results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlves Dias, P., Kanellopoulos, K., Mandras, G., Medarac. H, Nijs, W., Ruiz, P., Somers, J., Tarvydas, D., Kapetaki, Z., 2021. Recent trends in EU coal, peat and oil shale regions. 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Chang. 13, 1203\u0026ndash;1212. https://doi.org/10.1038/s41558-023-01802-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Geneva","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"electricity system model, employment, skills, just transition, regional analysis","lastPublishedDoi":"10.21203/rs.3.rs-7232815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7232815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSupporting fossil fuel workers through low-carbon transition and ensuring a sufficient workforce for renewable energy technologies is of utmost importance. To boost climate action while maintaining a just transition in the electricity sector, we assess the regional employment implications in 2035 of two policy ambitions, the ‘Fit for 55’ package of the European Union (EU) and a hypothetical EU-wide coal phaseout. We account for changes in employment and associated skills from electricity generation, storage and transmission, covering the whole supply chain from resource extraction to decommissioning. We then distinguish employment transfers from employment gains to identify skill needs at a NUTS 2 regional scale. We find that net employment creation is higher and more evenly distributed under the ‘Fit for 55’ policy plan compared to the hypothetical EU coal phaseout. Under both policy scenarios, the skill requirements for the workers in transition (many of whom are transferring from fossil fuel technologies to low-carbon technologies) depend on the specific region, while the skillsets needed for employment gains are similar across all European regions as they are driven by employment growth in technologies that require similar skills (wind and solar power). This information can support the design of re-skilling programs, helping to ensure that regions are not left behind in the energy transition.\u003c/p\u003e","manuscriptTitle":"Regional electricity sector employment and skills under different European policy scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 07:57:45","doi":"10.21203/rs.3.rs-7232815/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62090a75-c19a-4aca-8206-825f7c30c180","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52226058,"name":"Environmental Policy"},{"id":52226059,"name":"Energy Engineering"}],"tags":[],"updatedAt":"2025-07-29T07:57:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 07:57:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7232815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7232815","identity":"rs-7232815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}