3. Skills for a net-zero future: Empowering communities for the green transition

In response to deteriorating environmental conditions and mounting public pressure, in 2015, 196 Parties adopted the Paris Agreement, a legally binding international treaty on climate change with the goal to “limit global warming to well below 2, preferably to 1.5 degrees Celsius, compared to pre-industrial levels” (United Nations Framework Convention on Climate Change, 2015[1]). Since adopting the Paris Agreement, many countries worldwide have implemented policy initiatives to reduce GHG emissions. Furthermore, the economic stimulus packages implemented to sustain economic growth following the coronavirus (COVID-19) pandemic and the energy crisis following Russia’s war against Ukraine have served as a way to accelerate the twin digital and green transition.

Considering the impact of policies aimed at reducing GHG emissions on employment opportunities and skills demands is crucial to ensure long-term environmental sustainability. At the same time, if countries are to achieve ambitious climate targets alongside economic growth and high-quality working conditions, climate policies will need to be accompanied by strong investments in employment, social and skills policies to promote the socio-economic well-being of resident populations. Understanding the labour market impacts of greening policies is thus a key first step in preparing adequate policy responses to mitigate any adverse impacts the transition might have for certain population groups and ensuring that the green transition will be a just transition.

This chapter considers the role of skills policies in building resilience among affected communities based on a technical working paper that analyses the labour market and skills impacts of climate initiatives in the EU (Borgonovi et al., 2023[2]). The following countries were considered in the analysis: Austria, Belgium, Bulgaria, Croatia, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Ireland, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovenia, the Slovak Republic, Spain and Sweden. Although many countries have set climate targets, the EU has also ensured that the targets are binding by translating them into a set of legislative proposals that have been integrated into the Fit for 55 policy package.1

The Fit for 55 policy package sets an intermediate target of reducing net GHG emissions by at least 55% by 2030 compared to 1990 levels.2 The package also indicates that the total emission reduction of 55% should be achieved with differentiations across sectors: emissions in sectors covered by the EU Emission Trading System (ETS) need to be reduced by 61% in 2030 compared to 2005 levels while emissions in other sectors – referred to as Effort Sharing Regulation (ESR) sectors – need to be reduced by 40% by 2030 compared to 2005 levels.

The Recommendation on ensuring a fair transition towards climate neutrality was adopted in 2022 to take into account the distributional implications of the transition. The Recommendation invites EU member states to adopt measures to address the employment and social aspects of climate, energy and environmental policies, encouraging the adoption of actions to support people most affected by the green transition, for instance, by stimulating the creation of quality jobs and facilitating access to safe working conditions protecting health and safety in the context of this green transition (Council of the European Union, 2022[3]). The Recommendation also puts a focus on education and training measures, inviting EU member states to integrate the employment and social aspects of the green transition in the development and implementation of relevant national strategies (Council of the European Union, 2022[3]). Other proposals concentrate on the fairness of tax-benefit and social protection systems and on ensuring access to affordable essential services and housing for people and households most affected by the green transition.

In recent years, a growing number of studies have attempted to estimate the effects climate change mitigation policies might have on labour markets, with the aim of identifying – and eventually preventing – potential mismatches arising from the reallocation of workers from sectors and occupations that are heavy emitters of GHG emissions into sectors and occupations that emit comparatively few GHG emissions – also referred to as “green” in the literature (Biagi, Vona and Bitat, 2021[4]). Such mismatches are likely to arise not only because of the geographical distribution and occupational composition of sectors that may grow or shrink as a result of structural transformations leading to increased environmental sustainability, but also because of the difference in the skill sets required to perform tasks prevalent in economic production processes characterised by high or low levels of GHG emissions.

The degree to which initial education, further education and training, and adult education will have to adapt largely depends on the degree to which the skills required are similar to or different from those that workers possess today. Therefore, minimising reallocation costs for individuals and societies crucially depends on adequately anticipating how similar the skills demanded of workers in a low-emission economy will be compared to the skills workers currently possess. Work-based learning and lifelong learning will play a crucial role, especially in the case of most affected workers in the declining industries. At the same time, work-based learning and lifelong learning will be critical to reduce shortages of workers to fill emerging opportunities in sectors that are rapidly expanding, such as in renewable energy production. Minimising reallocation costs also depends on using such information to inform the design of programmes in education and training systems so that they will effectively develop such skills. This will require potentially wide-ranging changes in school and vocational education and training curricula and, in turn, professional development for teachers and trainers (International Labour Organization, 2017[5]).

A growing body of literature has attempted to quantify the number of jobs aligned with achieving green objectives.3 Although results from different studies are not directly comparable, as they often adopt different estimation methodologies and definitions, they generally indicate that only a small number of workers (between 1.5% and 4%) are employed in jobs directly aligned with the achievement of green objectives, such as those in renewable energy production.

Results from these studies can be used to map the skills needed in the small number of jobs that are well aligned with the achievement of green objectives. However, the transition to net zero will require economy-wide adaptations. Such adaptations will not only reduce employment in sectors that are heavy producers of CO2 emissions and increase employment in carbon-neutral sectors but will also change the allocation of workers across and within sectors in all economic activities. Therefore, it is essential to conduct analyses of the current skills required for jobs directly related to achieving green objectives, along with projections of the skills demanded as employment undergoes structural transformations in the broader economy to meet economy-wide climate targets. Results from past modelling exercises conducted by the OECD suggest that implementing climate change mitigation policies will determine job destruction in fossil-fuel sectors and job creation in renewable energy sectors, which currently employ few workers overall.

This chapter presents a modelling analysis of the impacts of the Fit for 55 policy targets on labour markets, driven by the policy-induced changes in the structure of the economy. It distinguishes impacts by sector for five occupational categories: 1) “managers and professionals” (comprising “managers” and “professionals” in the International Standard Classification of Occupations [ISCO] classification); 2) “technicians and associate professionals” (comprising “technicians and associate professionals” in the ISCO classification); 3) “service and sales workers” (comprising “service and sales workers” in the ISCO classification); 4) “clerical workers” (comprising “clerical support workers” in the ISCO classification); and 5) “blue-collar and farm workers” (comprising “skilled agricultural”; “forestry and fishery workers”; “plant and machine operators, and assemblers”; “elementary occupations”; and “craft and related trades workers” in the ISCO classification). The choice to consider only five occupational groups was driven by model complexity but inevitably hides potentially large differences in trajectories within each group. Therefore, projections presented in this report should be accompanied by granular analyses to reflect geographical, sectoral and occupational differences.

An empirical analysis, based on matching labour market changes and occupations to skills information from positions advertised on line for different sectors, makes it possible to quantify the effects of the policy targets on the demand for skills.4 Given the distribution of workers in 2019 in different EU countries, sectors and occupations, this chapter considers distributional implications and key target groups for the design of upskilling and reskilling interventions to facilitate the reallocation of workers across sectors and occupations that are projected to shrink and sectors and occupation that are projected to expand.

The modelling analysis relies on the OECD ENV-Linkages dynamic global Computable General Equilibrium (CGE) model (Chateau, Dellink and Lanzi, 2014[6]) to quantify the effect of policies on structural change, with a 2030 time horizon. The analysis compares a baseline scenario reflecting current policies, such as the ETS, with a Fit for 55 scenario.5 The empirical analysis uses the job postings database for the 2019-22 period assembled by Lightcast (formerly known as Emsi Burning Glass) (Lightcast, n.d.[7]) to map the distribution of skills across sectors and occupations.

The analysis of changes in sectoral employment relies on the OECD ENV-Linkages model to quantify the overall economy-wide effects of the mitigation policies needed to meet the Fit for 55 emission reduction targets. The main advantage of using a CGE model is that by exploiting its sectoral and regional dimensions, the analysis can consider the interlinkages between the economy’s supply and demand sides, capturing adjustments to new policies in both quantities and prices. CGE models thus capture the changes in the prices of commodities, used as production inputs and for consumers, whether produced domestically or imported, and the shifts in demand and sourcing. Different studies can yield different projections depending on the specific model specifications, which can reflect different assumptions over the way in which policies may impact labour markets and be accompanied by additional policy action (Cedefop, 2021[8]; European Commission, 2023[9]).

The modelling analysis compares two scenarios: 1) a baseline scenario reflecting the implementation of current policies; and 2) a Fit for 55 scenario. The time horizon of the analysis extends to 2030, in line with the Fit for 55 targets. Specifically speaking:

  • The baseline scenario reflects projected socio-economic trends as well as current policies. This scenario incorporates policies that were implemented by 20216 as well as policies that were by then already legislated but not yet implemented.7 This approach is applied to EU countries and the rest of the world. The baseline scenario considers the EU carbon market, the ETS, which is already in place. This corresponds to the European Green Deal pre-Fit for 55 targets.

  • In the Fit for 55 scenario, the EU meets its target to reduce CO2 emissions by 55% in 2030 compared to 1990 levels.8 This economy-wide target is also specified for sectoral groups: an emission reduction of -61% in 2030 compared to 2005 in ETS sectors and -40% in ESR sectors (ESR sectors including all sectors outside of ETS).9 Given that the scenario assumes a global transition, there is very limited potential for carbon leakage, so the Carbon Borden Adjustment Mechanism (European Commission, n.d.[10]) was not included in the analysis.

To ensure that the Fit for 55 package overall target is reached while also respecting the differentiation between the two sector groups, two separate carbon markets are included in the scenario and in the ENV-Linkages model: the ETS and a market for ESR sectors, covering all sectors of the economy.10 When a policy is introduced in ENV-Linkages, the model adjusts its sectoral production and consumption patterns, including inputs and outputs, until a new equilibrium is reached.11

The Fit for 55 package is more stringent on ETS sectors, which are, on average, more emission intensive. There are also differences in labour intensity across sectors and, specifically, between ETS and ESR sectors. On average, ETS sectors are less labour intensive than ESR sectors. All together, ETS sectors accounted for less than 6% of total employment in the EU in 2019 (and 64% of emissions). In ETS sectors, most people are employed in other energy-intensive industries (e.g. steel, cement, glass, paper), representing around 3% of total employment (and 1% of emissions).

The distribution of workers across the five job categories also varies across sectors (Figure 3.1). ETS sectors rely most on blue-collar and farm workers, followed by technicians and associate professionals, and managers and professionals, with the exception of “air and water transport”, which rely most on clerical workers while also having a high share of managers and professionals. In ESR sectors, a salient difference appears between services sectors and other sectors (i.e. “agriculture”, “construction”, “manufacturing” and “other” sectors). Services sectors rely most on managers and professionals and have a low share of blue-collar and farm workers. Other sectors rely most on blue-collar and farm workers.

Managers and professionals, technicians and associate professionals”, and service and sales workers represent a larger share of total employment in ESR sectors (53% of total employment) than in ETS sectors (31% of total employment). Blue-collar and farm workers represent the largest share of employment in ETS sectors as well as a large share (24%) of employment in the construction sector (ESR sector), which has a relatively high employment level. Clerical workers represent 11% of total employment, with similar shares for ETS (9%) and ESR (11%) sectors. They are the most employed category in transport sectors, which, however, corresponds to a relatively low employment level (1.4% of total employment).

The Fit for 55 scenario achieves significant reductions in CO2 emissions, reducing CO2 emissions to 1.7 Gt (gigatonne) in 2030 from 3.3 Gt in 2019. Emissions are reduced in both EU-ETS (0.8 Gt in 2030, from 1.7 Gt in 2019) and ESR sectors (0.9 Gt in 2030, from 1.5 Gt in 2019), but the reduction in emissions is stronger in ETS sectors, in accordance with the Fit for 55 package targets.

The Fit for 55 scenario results in continued economic growth but also in a small reduction in the gross domestic product (GDP) for the EU (-3% in 2030) compared to the baseline scenario. This decrease is due to the fact that the OECD ENV-Linkages model is conservative on assumptions related to innovation. The modelling approach reflects the technological progress projected to take place in energy production and use, following the International Energy Agency’s World Energy Outlook 2021 (IEA, 2021[11]). However, the model does not explicitly include the possibility of innovation or further development of previously marketed technologies. With additional investments in research and development and assuming that these investments would result in faster technological development and innovation, reaching net-zero emissions by the middle of the century would be less costly and possibly also boost economic growth.

Sectoral production decreases most in sectors regulated by the ETS, especially coal, oil and gas-powered electricity, and air transport (Figure 3.2, Panel A).12 These are some of the most emission-intensive sectors; therefore, reducing sectoral production contributes strongly to abating CO2 emissions. Production losses are more limited in most ESR sectors (Figure 3.2, Panel B), except for mining and fossil fuels extraction and distribution, which are also emission-intensive sectors. Production substantially increases instead in renewable energy (solar, wind and hydro-powered electricity) and nuclear-powered electricity.

The Fit for 55 scenario also reduces the CO2 intensity of key sectors compared to the baseline scenario. For ETS sectors, the largest decreases in CO2 intensity take place in the production of “non-ferrous metals” (-25%), “chemicals” (-19%), “iron and steel” (-17%), as well as “other energy-intensive industries” (-19%) and other energy-intensive industries (“fossil-fuel-powered electricity”: -13%).13 In ESR sectors, CO2 intensity decreases particularly for “land transport” (-41%), “fossil fuels extraction and distribution” (-22%) and the “services sectors”.

The changes in the structure of the economy that follow the implementation of the Fit for 55 scenario lead to a reallocation of employment across sectors, which also results in a different distribution of employment across categories (Figure 3.3). Changes in sectoral employment result from two main interacting effects. First, changes in aggregate employment affect the size of the sectoral employment effects. In the baseline scenario, employment is projected to increase by 3% overall, compared to 2019. However, the contraction in GDP in the Fit for 55 scenario results in a lower increase in employment compared to 2019 (1.3%). This implies a decrease in employment by 2% in the Fit for 55 scenario in 2030 compared to the baseline. Second, the changes in the structure of the economy that follow the implementation of the Fit for 55 scenario lead to a reallocation of employment across sectors that accentuate the changes that already take place in the baseline scenario. Specifically, these include a switch from fuel-based energy towards renewable energy and a structural reallocation towards the service sectors. Together, these effects result in changes in employment by occupational category. In particular, the reorientation of the economy towards more labour-intensive sectors, in which blue-collar and farm workers represent a lower share of employment, is a key driver of the effects by occupational category. Employment decreases for blue-collar and farm workers compared to 2019 (-3%), while it increases for other categories (4-5%).

The changes in aggregate employment result from the reallocation of employment across sectors. In particular, employment increases significantly in sectors not covered by the ETS, where it grows by 3% between 2019 and 2030 in the Fit for 55 scenario (Figure 3.4). Employment increases in ESR high-employment sectors (6% in total services which include “public services”, “business services” and “other services”, and by 2% in “construction” between 2019 and 2030), except for “fossil fuels extraction and distribution” (-87%), “other manufacturing” sectors (-15%), “transport and electronic equipment” (-8%) and “other services” (e.g. minerals, -1%). Employment grows the most in “renewables and nuclear electricity” (78%) as well as “other services” (10%) and “business services” (9%). Employment decreases the most in fossil-based energy sectors, which the ETS covers. However, employment in these sectors accounts for less than 1% of total employment in the EU in 2030 in the Fit for 55 scenario; this decrease has a limited impact on overall employment. Overall, employment losses and gains will not be equally distributed across different sectors and occupations. In particular, sectors that will be most severely impacted in relative terms in terms of job creation and job destruction will be concentrated in sectors that currently employ relatively few workers and that pay relatively well. In contrast, sectors that currently employ many workers will be less affected by structural transformations. Job destruction will be especially large for blue-collar and farm workers.

With the Fit for 55 package, employment in the EU by 2030 increases in all job categories except for blue-collar and farm workers (Figure 3.5 provides ETS sectors, while Figure 3.6 provides ESR sectors). While employment remains at a similar level for this occupational category between 2019 and 2030 for ESR sectors (-0.1%), it decreases strongly for ETS sectors (-24%). For all other categories, the increase in employment in ESR sectors compensates for the decrease in ETS sectors. Employment increases for service and sales workers, managers and professionals, clerical workers, and technicians and associate professionals as these categories are most employed in sectors with a large share of total employment (services sectors) and/or in sectors in which employment increases the most between 2019 and 2030 (renewables and nuclear electricity).

Projected changes in employment across sectors and occupations will be reflected in changes in the demand for skills. Emerging skill requirements of jobs in different sectors and occupations were measured using information available in European online vacancies from Lightcast, an approach also used in other studies that consider the effect on skill demands of climate change mitigation policies in Europe (Cedefop, 2023[12]).14 The importance of a specific skill in a specific sector-by-occupation category was inferred by considering whether such a skill was more frequently found in job vacancies in that sector-by-occupation category compared to how frequently other skills are found in job vacancies in that sector-by-occupation category and how frequently such a skill is found in vacancies in other sector-by-occupation categories. Skills requirements were then multiplied by employment numbers in different sectors and occupations in 2019 as well as projections in 2030 under the baseline and the Fit for 55 scenarios to identify overall skills demands in different scenarios.

An important caveat of estimates of the skills content of occupations is that estimates have ordinal but not numerical meaning. Therefore, whereas it is possible to describe which skills are projected to increase the most and consider if differences in projected growth under different scenarios for one skill are larger or smaller than those projected for a different skill, it is not possible to say if a skill is projected to grow by a given percentage or, for example, double in demand. However, estimates reflect that if demand is projected to increase or decrease, projected changes can be ranked and grouped into projected growth quartiles. Rank positions and quartiles of growth were used in the following analyses to describe projected changes in skills demand.

Contrary to most empirical work that assumes that the skill requirements of occupations in different countries reflect the skill requirements observed in the United States specified in the context of the O*NET database, in this work, emerging skill requirements contained in job postings for the EU region were used. The use of skills requirements specified in online job vacancies also allows for a better approximation of the emerging skills content of different occupations, given the intention of this work to consider projected changes in skills demands related to structural transformations in production processes to meet ambitious environmental policy targets rather than mapping the distribution of skill requirements in the past in different occupations. To aid comparability with other work, such as the OECD Skills for Jobs database (OECD, 2022[13]), skill requirements expressed in the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy were mapped onto the O*NET classification.

The detailed skills classifiers contained in job vacancies were aggregated into six main skills categories according to the O*NET system: Skills; Knowledge; Abilities; Technology Skills and Tools; Work Activities; and Work Styles (a detailed description of the skills categories is provided in Borgonovi et al. (2023[2])).15 To avoid confusion between individual skills and the broad Skills category, whenever referring to a specific aggregate category of skills, italics are applied. By contrast, the term skills, not italicised, refers to all the categories together and general human capital.

In particular, because Figure 3.4 reveals that sectors that employ few people, such as “fossil-fuel-powered electricity” and “renewables and nuclear energy” generation, are projected to be highly impacted (some negatively and others positively) by implementing Fit for 55, two sets of analyses were developed. The first compares changes in skills demands considering the change in the absolute number of workers employed in different sectors and occupations in 2030 compared to 2019. The second compares changes in skills demands considering the percentage-point change in the number of workers employed in different sectors and occupations in 2030 compared to 2019.

Because under the Fit for 55 scenario, employment is projected to be lower (1.3% versus 3%) than in the baseline scenario (Figure 3.3), changes in skills categories based on relative and absolute changes in employment presented in the following section reveal a weaker demand for all skills categories in the Fit for 55 scenario than the baseline scenario. The difference between the two scenarios in projections for different skills categories reflects the different demand for skills in each of the six categories in sectors and occupations with different projected growth rates in the two scenarios.

In Figure 3.7, relative changes in the absolute number of workers employed in different sectors between 2019 and 2030 are used to estimate changes in the demand for skills. Relative changes correspond to the projected percentage changes in employment by 2030 under the Fit for 55 and baseline scenarios over 2019 employment levels. The skills categories that are projected to grow the most in demand are Technology Skills and Tools, and Work Activities. These are the skills categories grouping a large number of skills that are used in particular in occupations and sectors that are projected to grow sharply between 2019 and 2030.

Projected changes in the absolute number of workers employed in different sectors between 2019 and 2030 are used to estimate changes in the demand for skills in Figure 3.8. Knowledge and Work Activities are the skills categories that are projected to grow most sharply, whereas Skills and Abilities are projected to grow the least between 2019 and 2030.

Table 3.1 categorises all skills demanded in online vacancies into five groups. The first four groups reflect quartiles of projected skills growth between 2019 and 2030 under the Fit for 55 scenario, with Group 1 being composed of the 25% of skills that are projected to increase the most in demand. Group 4 comprises the 25% of skills that are projected to increase the least in demand. Group 5 comprises all skills that are projected to decline in demand under the same scenario. Projections in demand were obtained by multiplying changes in absolute employment numbers between 2019 and 2030 in the Fit for 55 scenario.

As many as 11 skills out of the 32 skills in the Knowledge category (or around 34%) are projected to be in the group of skills with the strongest estimated demand increase (Group 1). Examples of these skills are: “sales and marketing”, “computers and electronics”, “language”, “economics and accounting”, “customer and personal service”, “administration and management”, “medicine and dentistry”, “production and processing”, “communications and media”, “personnel and human resources”, and “food production”. Most of these skills are essential in the “business services” and “public services” sectors, which employ many workers in EU economies.

A further 11 of the 32 skills in the Knowledge category (or around 34%) are projected to be in Group 2. These are: “law and government”, “chemistry”, “biology”, “public safety and security”, “engineering and technology”, “administrative”, “mathematics”, “building and construction”, “psychology”, “education and training”, and “geography”. Only four skills in the Knowledge category are projected to decline in overall demand or be in the group of skills that are estimated to grow the least in demand.

Many of the skills in the Work Activities category are also estimated to be in the largest increase group. Table 3.1 indicates that 15 of the 41 skills in the Work Activities category are in the largest increase group (around 38% of all skills in the Work Activities category). Examples of Work Activities skills that are estimated to grow the most in demand (Group 1) include: “interacting with computers”; “thinking creatively”; “analysing data and information”; “assisting and caring for others”; “communicating with persons outside an organisation”; “performing general physical activities”; “providing consultation and advice to others”; “guiding, directing, and motivating subordinates”; “performing administrative activities”; “establishing and maintaining interpersonal relationships”; “organising, planning, and prioritising work”; “selling or influencing others”; “monitoring and controlling resources”; “developing objectives and strategies”; and “making decisions and solving problems”.

By contrast, only three skills in the Work Activities category out of 41 (around 10%) are projected to decline in demand. These are: “handling and moving objects”; “repairing and maintaining mechanical equipment”; and “controlling machines and processes”.

Among the 44 skills identified in the Technology Skills and Tools category, 3 (corresponding to 7% of all skills in the Technology Skills and Tools category) belong to the set of skills that are projected to grow the most in demand (Group 1) between 2019 and 2030. These are: “web platform development software”; “operating system software”; and “analytical or scientific software”. As many as 19 skills (corresponding to 43%) belong to the set of skills that are projected to grow the least in demand between 2019 and 2030, and 5 skills are projected to decline in demand (11%). These include: “offset printing presses”; “injection moulding machines”; “computer-aided manufacturing (CAM) software”; “operating lasers”; and “operating milling machines”.

Among the Skills category, two skills, namely programming and time management (corresponding to 12% of skills in the Skills category), are in the group of skills that are projected to increase the most in demand, whereas repairing and equipment maintenance are in the set of skills that are projected to decline in demand.

“Oral expression” is the only skill in the Abilities category estimated to be in the group of skills projected to increase the most in demand. “Originality” and “fluency of ideas” are in the second group of increased demand. In contrast, “memorisation”, “written comprehension”, and “information ordering” are estimated to be in the group of skills projected to increase the least in demand.

Table 3.2 complements estimates in Table 3.1 by illustrating which skills will contract the most in demand with the implementation of Fit for 55 targets relative to the baseline scenario. Whereas most skills in Table 3.1 are projected to increase between 2019 and 2030, all skills in Table 3.2 are projected to decline in demand because, in virtually all sectors and occupations, Fit for 55 will determine a contraction in employment relative to the baseline scenario.

Most of the skills projected to decline the most in demand as a result of the implementation of Fit for 55 targets refers to operating and maintaining equipment and tools. They include skills such as “controlling machines and processes”; “operating injection moulding machines”; “repairing”; “physics”; “handling and moving objects”; “repairing and maintaining mechanical equipment”; “estimating the quantifiable characteristics of products, events, or information”; “equipment maintenance”; “blow moulding machines”.

By contrast, many of the skills projected to be impacted the least with the contraction of employment due to Fit for 55 are: “mathematical reasoning”; “using video creation and editing software”; “programme testing software”; “network monitoring software”; “persistence”; “management of personnel resources”; “business intelligence and data analysis software”; “transaction security and virus protection software”; “co-ordinating the work and activities of others”; and “presentation software”.

Although the demand for most skills is projected to increase in absolute terms between 2019 and 2030 under the implementation of the Fit for 55 targets (Table 3.1), such an increase is lower than the increase projected under the baseline scenario since overall employment projections are lower in the Fit for 55 than in the baseline scenario. At the same time, the effect of the Fit for 55 implementation is not equal across sectors and occupations, so the contracting effect of Fit for 55 on skills demand varies depending on whether skills are especially used in sectors and occupations that will be most severely impacted by Fit for 55 or not (Table 3.2).

In order to facilitate the successful transition of workers from sectors expected to contract in the next decade to sectors expected to grow, it is necessary to develop training opportunities. This requires identifying both the overall changes in skills demanded across the economy and the extent to which different jobs share similar skill requirements. Additionally, projected employment trends and the relative scale of various employment opportunities need to be considered. Jobs that are growing very rapidly in demand but are small in size might, in fact, offer fewer transition opportunities than jobs that are growing little in demand but represent a large share of the overall labour market.16

Even after having identified the skills that adults should acquire to successfully transition into occupations or sectors that will expand in the medium term, a major challenge is to ensure that education and training systems are designed in a flexible way to enable smooth transitions. This requires adult education and training systems to have a certain degree of flexibility with respect to several dimensions: time (when does learning occur and for how long), place (where does learning occur), mode (which learning style) and content (which skills to learn) (OECD, 2023[16]). However, many adult education and training systems are not yet developed to meet these challenges. Scope for improvement exists in many areas, such as the recognition of prior learning (OECD, 2019[17]), which is also closely linked to occupational entry regulations and, for example, has implications for labour mobility (von Rueden and Bambalaite, 2020[18]); ensuring inclusiveness of learning systems. To date, a large share of workers still does not participate in training (OECD, 2019[17]).

Blue-collar and farm work occupations are projected to shrink in overall demand (Figure 3.3) in the Fit for 55 and baseline scenarios. As a result, many existing blue-collar and farm workers will have to consider transitioning into non-blue-collar and farm work occupations and initial education and training. Therefore, young people should be made aware of shrinking/increasing labour market opportunities while they are in initial education and training, and such information should be reflected in orientation programmes to help them make educational and career decisions that are aligned with labour market needs. At the same time, blue-collar and farm work will not disappear, and there are sectors in which demand is projected to increase. Identifying the degree of similarity in the skills required in different sectors and occupations and their capacity to absorb new workers due to labour market trends can aid both individuals who consider transition opportunities and policy makers in organising effective upskilling and reskilling programmes.

For some blue-collar workers employed in shrinking sectors, transitioning into other blue-collar jobs in growing sectors would entail moving into a sector with relatively high levels of skills similarity (Figure 3.9).17 For example, blue-collar workers employed in Sector 20 (“manufacture of chemicals and chemical products”) and 21 (“manufacture of basic pharmaceutical products and pharmaceutical preparations”) are projected to decrease between 2019 and 2030 in the Fit for 55 scenario (these sectors are shown in the second cluster from the left in Figure 3.9). However, the skill set demanded of blue-collar workers in these sectors is relatively similar to the skill set demanded in Sector 35 (“electricity, gas, steam and air conditioning supply”), Sector 41 (“construction of buildings”) and 42 (“civil engineering”). These sectors are projected to increase between 2019 and 2030 under the Fit for 55 scenario, employ a relatively large share of blue-collar workers and therefore exemplify relatively viable transition opportunities.

Although Figure 3.9 suggests that some blue-collar workers would have opportunities to transition into sectors with similar skills profiles, for other workers, skills requirements in sectors projected to increase in demand and that account for a large number of the blue-collar workforce differ substantively from the skill set required in their current jobs. For example, the highest degree of similarity in demand of blue-collar workers employed in Sector 5 (“mining of coal and lignite”) is the skill set demanded of blue-collar workers in Sector 33 (“repair and installation of machinery and equipment”) (these sectors are shown in the fifth cluster from the left in Figure 3.9). However, both sectors are projected to decline between 2019 and 2030. There are no other sectors in the same cluster with a skill set that is relatively similar and that is projected to grow, thus providing more viable transition opportunities.

Blue-collar workers are the only category of workers that are projected to experience an absolute contraction in employment opportunities. Figure 3.9 sheds light on the overall share of employment of blue-collar and farm workers in different clusters of sectors with similar skills requirements in 2019 and indicates if employment is projected to increase or decrease between 2019 and 2030. However, it does not indicate the extent to which such an increase/decrease will result in an overall expansion of employment opportunities – a necessary condition of within-cluster transitions – or a net loss or gain of employment.

Figure 3.10 illustrates absolute employment losses and gains for each of the sectors in the first cluster (from the left) presented in Figure 3.9, one of the largest clusters in terms of overall employment in 2019 for blue-collar workers. Within this cluster, a positive net employment gain across EU countries of around 315 000 people is projected between 2019 and 2030. This means that for blue-collar and farm workers in this cluster, transition opportunities are available that require a relatively small difference in skills requirements, because projected employment creation in this cluster exceeds projected employment destruction in their original sector.

By contrast, in Figure 3.10, which illustrates absolute employment losses and gains for the second cluster presented in Figure 3.9, total employment gains are smaller than total employment losses. Overall, approximately 1 300 000 jobs are projected to be lost within this cluster between 2019 and 2030. Therefore, blue-collar and farm workers in sectors in this cluster will not enjoy sufficient transition opportunities involving redeployment in jobs with similar skills requirements and will have to consider transitions into sectors with higher dissimilarity in skills requirements in other clusters, a likely indication of longer and more intense training requirements.

However, workers in certain sectors that are projected to shrink in employment following the Fit for 55 will also need to transition into new roles. Borgonovi et al. (2023[2]) illustrate opportunities for managers and professionals, technicians and associate professionals, clerical workers and sales and service workers.

The COVID-19 pandemic gave new impetus to the implementation of climate change mitigation policies worldwide. In particular, in the aftermath of the pandemic, EU governments recognised the short-, medium- and long-term potential threat to public health posed by environmental degradation. Moreover, given the severity of the economic crisis induced by lockdowns, many countries adopted stimulus packages to promote economic growth. In many countries, such investments were tied to achieving reductions in GHG emissions and ambitious structural investments in digital infrastructures. Because past waves of structural transformation led to job losses and long-term vulnerability for some groups of workers, it is crucial to ensure that efforts to promote environmental sustainability will also aim to ensure that the green transition will be a just and inclusive transition, leading to improvements in working conditions and minimising widespread job losses and contractual instability. In order to enhance societal-level resilience and mitigate the risk of environmental disasters resulting from GHG emissions, it is crucial to complement efforts to reduce emissions in production with investments in resilient labour markets. This can be achieved through effective skills policies that facilitate environmentally-driven structural transformations.

Projected changes in employment illustrated in the chapter reflect the specific scenario implemented. However, there is no single pathway towards the green transition and towards meeting the Fit for 55 policy targets. For instance, the EU could rely more on the transition towards a circular economy, scaling up policies such as taxes on primary raw materials, extended producer responsibility, recycled content standards or subsidies for the use of secondary materials as well as R&D towards recycling and better product design for recyclability. A previous modelling analysis shows that the implementation of a material fiscal reform aimed at increasing the relative price of primary materials as compared to secondary materials, would substantially increase the circularity of the economy, while also reducing the environmental impacts of materials use, including greenhouse gas emissions (Bibas, Chateau and Lanzi, 2021[19]). Such a policy package would result in a reallocation of jobs with increases in sectors such as services and waste management and decreases in other sectors such as extraction and mining. Overall, the policy package would result in a small increase in employment (Mavroeidi and Chateau, 2020[20]).

Skills policies, which comprise education and training policies targeted at both young people and adults, can play an essential role in achieving the twin objectives of greening the economy and ensuring that the benefits of new investments do not lead to new forms of vulnerability and deprivation. Skills policies can facilitate the reallocation of workers away from sectors that will shrink because they are responsible for a large share of CO2 emissions, such as mining of coal and lignite, into sectors that will expand because they can sustain the production of energy without emitting large quantities of CO2, such as wind and solar energy production or in sectors that will expand because of the new demands induced by the demographic transition (to care for and support rapidly ageing populations) (OECD/ILO, 2022[21]) or the digital transition (to work alongside digital tools and applications performing tasks that will not be automatable) (Lassébie and Quintini, 2022[22]). They are therefore important both because they can facilitate the provision of an adequate supply of workers in sectors that need to develop if CO2 emission reductions are to be met while maintaining current levels of overall consumption and because they can ensure workers who previously worked in sectors that will decline or disappear will be able to find employment in other parts of the economy. At the same time, skills policies are only part of a broader set of policies that can ensure that the transition is just and inclusive. These include social policies, active labour market policies, and local economic development policies. Achieving green objectives while maintaining strong labour markets and broader social well-being is possible but requires the participation of key actors, including governments as well as social partners.

Results presented in this chapter should be evaluated alongside results from other studies that map how the green transition will change the tasks workers will be required to perform in existing jobs to reduce GHG emissions or in new jobs that will emerge to promote the green transition. In particular, changes in the task content of occupations will change the bundle of skills individual workers and/or teams of workers will need to possess to successfully carry out their jobs, with important implications for the development and implementation of education and training programmes.


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← 1. The Fit for 55 package is described in a series of legislations from the European Parliament and the Council (2009[26]; 2009[25]; 2018[27]; 2003[29]; 2008[28]). Following the Fit for 55 package (EU Commission, 2021[24]), EU member states need to reach more ambitious targets than those stated in the Nationally Determined Contributions (NDCs). Furthermore, in June 2022, the Fit for 55 package was revised to include more sectors and more stringent targets for 2030 (European Council and Council of the European Union, 2022[30]).

← 2. Net emissions include emissions and removals from land-use change and forestry (LULUCF).

← 3. A review of this literature is available in Borgonovi et al. (2023[2]).

← 4. In line with information provided in the context of the OECD Skills for Jobs database, the term “skills” is used both as a generic indicator for human capital as well as a term indicating a specific set of proficiencies in manipulating data and things (OECD, 2017[23]). As a generic indicator of human capital, the term skills refers to the broad set of cognitive abilities, physical abilities, socio-emotional abilities and metacognitive abilities (e.g. information-processing skills, dexterity, teamwork, self-organisation) as well as to abilities in performing specific jobs or tasks (e.g. accounting or hair colouring) (OECD, 2017[23]). At the same time, in the context of official classifications of the different sets of skills individuals possess, the term “Skills”, capitalised, is used to refer to a particular category of competences.

← 5. A detailed description of the model is available in Borgonovi et al. (2023[2]).

← 6. The cut-off date for the baseline scenario policies derives from IEA’s World Energy Outlook 2021.

← 7. Some jurisdictions enacted climate policies after the publication of the World Energy Outlook 2021, such as the Inflation Reduction Act in the United States. These climate policies have not been included in the baseline.

← 8. While this analysis focuses on emission reductions, the Fit for 55 package also includes other targets, such as achieving a 40% share of renewable energy in total energy consumption and an emission reduction of 55% for new cars and of 50% for new vans. Furthermore, this analysis applies the targets to CO2 emissions.

← 9. ETS sectors include: fossil-fuel-powered electricity, other energy-intensive industries (e.g. steel, cement, glass, paper), air transport, maritime transport (added in new Fit for 55 package). ESR sectors include: road transport, buildings, agriculture, waste, small industries.

← 10. The resulting levels of the carbon prices needed to achieve the targets are provided in Borgonovi et al. (2023[2]).

← 11. When interpreting the results, it is important to keep in mind that in a CGE model like ENV-Linkages the labour market is cleared so that labour demand equals labour supply. Therefore, while the policy simulations result in a reallocation of sectoral employment, the labour market will overall remain in equilibrium. In a CGE model, employment can increase or decrease following changes in production. However, the labour market will remain in equilibrium so that it is not possible to evaluate unemployment. While CGE models have the advantage of being able to assess the economy-wide impacts of policies, the fact that they cannot evaluate unemployment is a shortcoming.

← 12. The new Fit for 55 package is more ambitious for EU-ETS sectors: -61% between 2005 and 2030 levels, vs -43% for the former target.

← 13. In these sectors, a decrease in emission intensity implies that CO2 emissions decrease more than production.

← 14. A detailed description of the methodology is available in Borgonovi et al. (2023[2]).

← 15. A detailed description of the different skills categories is available in Borgonovi et al. (2023[2]).

← 16. A final consideration pertains to wages.

← 17. A detailed description of the methodology used to develop skills similarity is available in Borgonovi et al. (2023[2]).

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