Job Creation and Local Economic Development 2023

Bridging the Great Green Divide

Report

Job Creation and Local Economic Development

14 March 2023

2. The green transition in local labour markets

Abstract

This chapter sheds new light on the effect of the green transition on local labour markets. It provides novel estimates for green and polluting employment across OECD regions. It analyses if and why regional labour markets differ in the extent to which their jobs comprise green tasks. It also examines whether green-task jobs differ from non-green task jobs and how that might impact socio-economic divides within local labour markets. Finally, it uses information on recent labour demand to assess in which regions a greater share of new vacancies is green.

In Brief

Around 18% of workers in the OECD work in jobs with a significant proportion of tasks that contribute to environmental objectives (green tasks), ranging from 7% to over 35% depending on the region. Some regions, including many capital regions, have already benefitted from the green transition and have a high and increasing share of green-task jobs and a low share of polluting jobs at risk of disappearing. In other regions, a high share of polluting and green-task jobs coincide, which creates space for job transitions. However, there are also regions with above-average risk of job displacement where the benefits of the green transition have yet to be captured. The demand analysis suggests that few regions with a low share of green-task jobs show signs of catching up.
A region’s ability to benefit from the green transition has, so far, depended on its industrial composition and the skills in the local labour market. Higher shares of scientific, technical and information technology activities in the region correlate with a higher share of green-task jobs. The top regions in terms of the share of green-task jobs also tend to have a higher share of population with tertiary education.
Despite the increasing prevalence of climate change in the public discourse, labour markets have not, on average, become much greener over the last decade. The share of workers in green-task jobs grew from 16% in 2011 to almost 18% in 2021. However, this masks significant differences across regions, with changes ranging from an increase of 10 percentage points to a decrease of 7 percentage points. Regions that invest more in R&D recorded larger growth of green-task jobs. However, recent data on regional labour demand paint a more positive picture. The share of green-task vacancies is higher while the share of polluting vacancies is lower compared to the current employment shares, which may be the first signs of a shift towards a more environmentally friendly labour market.
Green-task jobs exist across sectors and regions, including industries that are not traditionally considered green. While sectors such as “water supply, sewage, waste management and remediation activities” or “electricity, gas, steam and air conditioning supply”, which include many green activities, have a high share of green-task jobs, other sectors such as manufacturing and construction consist of both green-task and polluting jobs.
The green transition may deepen divides within the local labour markets.
- The green transition has a strong gender dimension in the labour market. Women are currently under-represented in green-task jobs, accounting for only 28% of them, suggesting that efforts need to be made to increase female participation in the green transition. On the other hand, men will be the most affected by the disappearance of polluting jobs.
- High-skilled and highly-educated workers are better positioned to benefit from the green transition. Green-task jobs tend to offer higher pay but require more education. While it is possible that in the future green jobs will shift towards medium- and low-skilled occupations (in activities such as waste management, retrofitting or construction, for example), the majority of workers in green-task jobs are currently high-skilled and have completed tertiary education. In contrast, polluting jobs, at risk of disappearing, are held by individuals with lower educational attainment and in medium-skilled occupations.

Net-zero transition: a global challenge with local implications

Tackling climate change and environmental degradation is one of the most formidable tasks the world faces. The United Nations Framework Convention on Climate Change (the “Paris Agreement”) set out the objective of keeping the global temperature increase below 2°C and pursue efforts to keep it to 1.5°C compared to pre-industrial levels. Limiting global warming requires a considerable reduction in the emission of greenhouse gases (GHG), with net GHG emissions falling to zero by 2050. Such emission reductions will demand drastic actions that will affect industrial production, consumption patterns and energy provision across the world. Furthermore, it will likely entail a transformation across every industry, most notably in pollution-intensive sectors such as manufacturing (OECD, 2023[1]).

The shift to a net-zero economy will result in a significant transformation of the labour market as workers move into different occupations or sectors. The greening of the labour market will have three main effects. First, new types of jobs will emerge, creating economic opportunities in occupations that may not yet exist. Second, it will likely result in the loss of some existing jobs, especially among jobs that are highly polluting. Beyond the creation of new jobs or the displacement of old jobs, the green transition will lead to a shift in the skills that are required for many jobs. Therefore, while the public discourse on the green transition has so far focused on its potential risks for workers and firms, the transition can also create new opportunities for local job creation. Reducing the risks and capturing the benefits of the green transition requires a rethinking and updating of education curricula and training courses, which need to enable workers to gain the right set of qualifications and skills demanded by the changing labour market (CEDEFOP, 2019[2]). Hence, risks and opportunities in the labour market in terms of new green jobs and disappearing jobs has direct consequences for policy making in areas such as education, as well as upskilling and retraining opportunities.1

While the green transition is a global megatrend, its labour market impact is inherently local. Both the risks and opportunities for workers are uneven across different places within the same country. Employment risks are often concentrated in specific regions. For instance, regions with a strong reliance on jobs in high-emission sectors are more likely to see jobs losses, due to green policies or market forces. Likewise, economic opportunities and green job creation will not materialise everywhere to the same degree. Therefore, aggregate effects or national data may mask regional disparities in the labour market impact of the green transition (OECD, 2017[3]).

Empirical work on green jobs has so far mainly neglected subnational differences. Most of the existing analysis provides global averages or, at best, examined national data. Studies that offered subnational breakdowns focused on a single country such as the US (Vona, Marin and Consoli, 2019[4]) or the Netherlands (Elliott et al., 2021[5]).

This chapter tries to fill the void of local data and analysis on the labour market and the green transition. This report presents novel analysis of green-task jobs at a regional level that goes beyond studying individual countries. It presents new estimates for the greening of local labour markets in 30 OECD countries. It investigates the geographic differences within countries in terms of green-task jobs as well as employment in polluting jobs that might be at risk of displacement. It also examines in which sectors green-task jobs are concentrated and assesses reasons for their regional variation in labour markets. Finally, it provides a first glimpse of differences between workers in green-task and non-green-task jobs and explores how recent labour demand has developed for green-task jobs since the COVID-19 pandemic.

Green-task jobs: an opportunity for local economic growth?

The public discourse on the green transition has so far focused on its potential risks for workers and firms but the transition can also create new opportunities for local job creation. Like other global megatrends such as digitalisation or automation, the green transition will affect the world of work. It will reshape some sectors and jobs, changing job requirements and qualifications for specific occupations. It might also lead to job losses in the sectors most affected by environmental regulation, such as energy-intensive industries (Vona et al., 2018[6]), (Walker, 2011[7]). However, the transition to net-zero may also give rise to new jobs and support local economic growth.

Measuring green-task jobs across local labour markets

The analysis in this report takes a bottom-up approach to defining “green jobs” referred to as green-task jobs. It is based on the tasks different occupations entail and the extent to which those support environmental goals. Two measures are used in this chapter. The main measure is the share of green-task jobs in a regional labour market (Box 2.1). The additional measure is the weighted green intensity of employment in a region (Box 2.2). The former defines green-task jobs as those that have a significant green component (at least 10% green tasks) and enables comparisons of differences across workers in different jobs within regional labour markets. The latter broadly describes the extent to which tasks in a regional labour market contribute to the green transition.

Other definitions of “green jobs” exist and the preferred approach depends on the objectives of the study. For the purpose of this analysis, the bottom-up, task-based approach is preferable as it makes it possible to (i) study the distribution of green-task jobs at the local level, (ii) investigate the characteristics such as education, skills levels, gender, age, etc. of workers in green-task jobs, (iii) examine how workers can transition into new jobs or sectors through retraining or upskilling, and (iv) include in the analysis workers who contribute to net-zero and other environmental objectives but are outside of sectors traditionally considered green. The advantages and disadvantages of the approaches used in the literature to measure “green jobs” are discussed in detail in Chapter 1.

Box 2.1. Measuring the share of green-task and polluting jobs

O*NET classification of green jobs and tasks

Green-task jobs are defined and analysed at the occupation level based on the greenness of their related task content, following the work of (Vona, Marin and Consoli, 2019[4]). It relies on classifications developed by O*NET, which provides a taxonomy of the greenness of all tasks for more than 900 occupations (Dierdorff et al., 2009[8]). Broadly speaking, tasks identified as green contribute to environmental objectives such as preserving the environment and reducing emissions. More details on the methodology of classifying tasks as green and non-green are provided in Chapter 1.

Using the information on the tasks of an occupation, one can compute a greenness score for each occupation. The score of each occupation can range continuously from 0 to 1, with higher values indicating a higher share of green tasks in the occupation. A score of 0 denotes an occupation with no green task. Infographic 2.1 offers a number of illustrative examples of different occupations, including those with a very high greenness score, those with some green tasks, and those with no green tasks. Based on O*NET’s classification, the majority of jobs have no green task. Occupations with no green tasks in O*NET’s classification are not necessarily ‘dirty’, as illustrated by examples below.

Green-task jobs

To examine the geography of jobs with a significant share of green tasks and to examine differences across workers within regional labour markets, a binary measure is constructed which classifies an occupation as being green-task or non-green-task. For this report, green-task jobs consist of those occupations with at least 10% of their tasks considered green.

Polluting jobs

In addition to the share of green-task jobs, a classification of polluting jobs is constructed, building on the classification of (Vona et al., 2018[6]) and the IMF (International Monetary Fund, 2022[9]). Polluting jobs are a subset of non-green-task jobs that are particularly concentrated in high-polluting sectors.

Given that O*NET’s classification of green tasks is not frequently updated, the analysis presented in this report captures the evolution of the share of green-task jobs in the economy over time but does not capture the evolution of the share of green tasks within occupations. Similarly, differences between regions are driven by the differences in occupational composition, given that the task composition of occupations follows O*NET classification and is constant across regions. Moreover, the estimates on green-task jobs are likely to correspond to upper-bound estimates given the aggregation of employment data by occupation. The assumptions and limitations of the analysis are discussed in detail in Chapter 1 and in Annex 2.A Sensitivity Analysis and Robustness Checks.

How green are local labour markets?

The majority of the labour force in OECD regions is employed in non-green-task jobs. Less than a fifth of the workforce in the OECD holds green-task jobs that were either created as a result of the green transition or because their task composition changed. As of 2021, 18% of jobs in the OECD comprised a significant share of green tasks. This headline figure, however, masks large variations (Figure 2.1). Regions in southern Europe - in Greece, Italy, Portugal, and Spain – as well as in North America tend to have a lower share of green-task jobs, while regions in the Baltic and Nordic countries score high on this measure. Many regions in France, United Kingdom, Luxemburg, and Switzerland have a high share of green-task jobs too. The share of green-task jobs ranges from 7% (Western Greece) to 35% (Vilnius Region), which highlights the large differences across regions in the OECD.

Figure 2.1. Share of green-task jobs across regions

The share of green-task jobs differs significantly within countries. On average, the difference between the top and bottom regions is 7 percentage points (Figure 2.2). In Europe, large subnational differences exist in Hungary (14 percentage points), Lithuania, Finland, and France (all 11 percentage points). In the US, this gap reaches 17 percentage points, but this is driven by a significantly greener labour market in the District of Columbia compared to the rest of the country. In 19 out of 25 countries with data for multiple regions, the capital region has the highest share of green-task jobs in the country. Canada, Germany, Italy, Portugal, New Zealand, and Australia are notable exceptions. In the case of Australia, Canada, and the United States (when the District of Columbia is excluded), the smaller regional variance might be partly due to the large size of the regions, which hides sub-regional variation.

Figure 2.2. There is significant dispersion in terms of green-task jobs within countries

A lower share of green-task jobs and a high share of polluting jobs do not always go hand in hand. For example, Greek regions tend to have a low share of green-task jobs as well as a low share of polluting jobs. Regions can also have a high share of green-task and polluting jobs at the same time, as is the case in the Baltic States (Figure 2.3). Regions with a below-average share of polluting jobs range from 7% (Western Greece) to 32% (Stockholm) in terms of the share of green-task jobs, while regions with an above-average share of polluting jobs oscillate around the average score for the share of green-task jobs. Many regions are neither green nor polluting, for example Western Greece, Epirus, and Eastern Macedonia-Trace (Greece), or Newfoundland and Labrador (Canada).

Figure 2.3. Regions can have a high share of polluting jobs and green-task jobs at the same time

On average, capital regions tend not only to have greener but also less polluting labour markets. For example, in France the share of green-task jobs in the capital region is 30% compared to 22% in the rest of the country (Figure 2.5). The opposite pattern holds for polluting jobs, which account for 10% of employment in Ile-de-France and 16% in the rest of France. Overall, green-task jobs are over-represented in OECD capital regions.2 The opposite is true for polluting jobs, which tend to be under-represented in the capital regions.

The green transition may widen regional economic gaps. Within countries, regions with a higher share of polluting jobs tend to have significantly lower GDP per capita.3 Since polluting jobs face a higher risk of displacement, those regions might experience greater job losses than more affluent regions and a reduction in economic activity, which could lead to further economic divergence across regions in OECD countries.

Box 2.2. How “green” are the green-task jobs in OECD regions?

In addition to studying the share of jobs that involve a significant proportion of green tasks, it is also possible to look at the green intensity of local labour markets. The greenness score for each occupation is calculated as the share of green tasks out of total tasks. It is a continuous measure that ranges from 0 to 1, with higher values indicating a higher share of green tasks in the occupation. The main statistic used in this report is a transformation of this continuous measure into a binary measure, which classifies occupations as “green-task” or “non-green-tasks” jobs (Box 2.1). However, it is also possible to use the greenness score directly to calculate the average share of green tasks out of total tasks carried out by all workers in a local labour market, referred to as green-task intensity of employment. It combines the greenness score for individual occupations with data on the employment share by occupation.

Green-task intensity of employment is necessarily lower than the share of green-task jobs, but it exhibits the same spatial patterns. On average, the green-task intensity of employment is only around 4% in OECD regions. Broadly speaking, this means that, on average, 4% of tasks carried out by workers in OECD regions are green. Regions with a higher share of green-task jobs tend to have a higher green-task intensity of employment.1 Similar to the share of green-task jobs, the green-task intensity of employment differs significantly across regions – from 2% in Western Greece to 7% in Vilnius (Lithuania) – and within countries. Spatial patterns are also similar. In Europe, the regions with the lowest green-task intensity of employment are located in Greece, Italy, and Spain, while many of those regions with greener employment are located in the Baltic States, France, Switzerland, and the United Kingdom. Compared to most of Europe, regions in the US and Canada record a low green-task intensity of employment.

Figure 2.4. Green-task intensity of employment across regions

1. The correlation between green-task intensity of a region and its share of green-task jobs is very high (0.93), which indicates that in regions with a high share of jobs with some green tasks, green-task jobs tend to have a high share of tasks that are green.

Figure 2.5. Capital regions stand out with more green-task and fewer polluting jobs

Innovation, industrial composition, and the education of the workforce are factors associated with the greenness of regional labour markets. Regions that concentrate certain industries such as professional and scientific activities within their country tend to record above average shares of green-task jobs (Figure 2.6). The opposite is true for other specific industries such as agriculture or manufacturing. Regions with relatively low employment in those sectors record higher shares of green-task jobs than the national average.4

Figure 2.6. Share of green-task jobs and industry over/under representation

Where has the share of green-task employment grown the most?

Despite the attention that green policies receive, on average labour markets have not become much greener over the last decade. The share of workers in green-task jobs grew from 16% in 2011 to 18% in 2021 (Figure 2.7). However, that masks significant differences across regions.

Figure 2.7. The share of green-task jobs in the OECD did not change over the last decade

However, there have been more significant changes in the share of green-task jobs at the sub-national level. For example, in Taranaki (New Zealand), the share of green-task jobs grew the most, by 10 percentage points between 2011 and 2021 (Figure 2.8). In contrast, the share of green-task jobs fell by 7 percentage points in the Australian Capital Territory over the same period. Many regions with an already high level of green intensity, recorded a further improvement – notably regions in Nordic countries. Regions in New Zealand stand out as those with the highest percentage point increase in the share of green-task jobs. Across the OECD, most capital regions recorded further increases to already relatively higher shares of green-task jobs.

Figure 2.8. Capital regions have tended to see an increase in green-task jobs

Regions with greater innovation spending have not only greener labour markets, but also recorded significantly larger progress in greening their labour markets (Figure 2.9). The more regions spent on R&D investment (as a % of GDP), the greater the increases they recorded in green-task jobs. Regions with above median growth (marked as “high increase”) in the share of green-task jobs spent almost twice as much on R&D (relative to GDP) as regions that recorded above median reductions in the share of green-task jobs. While it is unclear how such R&D spending is allocated across different activities and fields, policy priorities for the green growth include “unleashing innovation” by supporting “the creation and diffusion of new products, processes and methods” (OECD, 2020[10]).5

Figure 2.9. Green-task jobs and regional innovation

How does the ‘greenness’ of employment differ by industry?

Green-task jobs can be found across all industries, including in industries that were not traditionally considered green. These include water supply, sewage, waste management and remediation activities; electricity, gas, steam, and air conditioning supply, which include economic activities such as, for example waste collection, treatment and disposal activities; materials recovery and remediation activities6, which are traditionally considered green have a high share of green-task jobs and high green-task intensity (Figure 2.10). Professional, scientific and technical activities, which includes scientific research and development and architectural and engineering activities, stand out as an industry with a high share of green-task jobs.

However, other sectors such as construction and mining and quarrying also score high in terms of the share of green-task jobs and green intensity. These industries, while polluting, also employ individuals responsible for the reduction of pollution and mitigation of environmental risks, such as environmental engineers, brownfield redevelopment specialists, solar photovoltaic installers, weatherisation installers and technicians or hazardous materials removal workers. As the green transition gathers momentum, the relative greenness of different sectors might change. For example, while construction employs many polluting jobs, future efforts in activities such as retrofitting or insulation could help create more green-task jobs. Likewise, sectors such as agriculture might experience a rise in green activities through a shift to more sustainable operation models such as organic farming.

Figure 2.10. Green-task jobs can be found across all industries

Figure 2.11. Manufacturing stands out as the industry with a high share of green-task jobs and a high contribution to the number of green-task jobs in the economy

Within regional labour markets, the greenness of firms differs by their size. Green-task jobs are overrepresented in large firms with at least 250 employees. Large firms account for around 31% of green-task jobs, but only for around 25% of other employment. In contrast, smaller firms provide relatively few green-task jobs compared to their overall share of total employment. These differences across firm size might be related to limited resources in smaller firms, which makes it harder for them to invest in new and greener technology or production processes or provide training that enables workers to acquire green skills.7

Figure 2.12. Green-task jobs are more concentrated in large firms than other jobs

The green transition may deepen divides within local labour markets

The impact of the green transition is different for men and women

Equitable access to new economic opportunities arising from the green transition for both men and women is an important aspect of ensuring a just transition for all. Past labour market transitions have had gender-specific impacts (Brussevich et al., 2018[11]). Therefore, policy makers need to pay particular attention to both the labour market opportunities and risks of the transition to net-zero and how they might differ for men and women.

Evidence from OECD regions shows that the green transition has a strong gender dimension in the labour market. Currently, women are underrepresented with men making up the majority of workers in green-task jobs. Across OECD regions, around 72% of all green-task jobs are held by men, while men and women make up roughly equal shares of non-green-task jobs (Figure 2.13). The disparity in the share of green-task jobs held by women and men differs across regions but, overall, women account for less than half of green-task jobs in all OECD regions (Figure 2.14). The capital regions such as Vilnius Region, Australian Capital Territory, Île-de-France, Stockholm, Warsaw, and Wellington get closest to equal representation of men and women in green-task jobs. Ten regions in Canada are on the other end of the spectrum, with less than 10% of green-task workers being women.

Figure 2.13. Women are significantly under-represented in green-task jobs

Figure 2.14. Share of green-task jobs held by women across regions

While effort needs to be made to increase the share of women in green-task jobs, gender disparities also exist in polluting jobs. On average, men face a higher risk of displacement caused by the green transition, with 83% of polluting jobs held by men. The gender differences in green-task and polluting jobs are mainly driven by differences in the industries, and thus occupations, in which men and women tend to be employed. Women are underrepresented in manufacturing and construction, which are the industries with the highest share of green-task and polluting jobs. Nonetheless, these findings suggest that policies for a just transition need to focus on both broadening the access of women to emerging green-task jobs and to provide targeted support measures. These measures include greater engagement in STEM (science, technology, engineering, and mathematics) early in school, career guidance, and retraining and upskilling opportunities to workers in polluting jobs, which are mainly held by men.

Green job opportunities typically require high skilled and tertiary educated workers

Regional labour markets have experienced a gradual job polarisation over the past decade. Overall, the share of medium-skilled jobs has declined, and in many cases a significant number of medium-skilled jobs disappeared (OECD, 2019[12]). While the share of high-skilled jobs has often compensated for a large part of those displaced jobs, the share low-skilled jobs has also risen in most local labour markets (OECD, 2020[13]). While this development partly reflects higher educational attainment of the labour force, it is also a consequence of megatrends that have been reshaping the world of work. Technological change due to digitalisation and automation has been skills-biased, i.e., has increased the demand for educated workers and complements the skills of more highly educated and skilled workers (Autor, Levy and Murnane, 2003[14]). Thus, it has posed a risk of exacerbating socio-economic inequalities across workers of different skill levels (Acemoglu and Autor, 2011[15]).

Whether the green transition will drive further job polarisation and increase inequalities within regions is still an open question. The social dimension of the green transition’s implications for employment (Cedefop, 2010[16]) as well as productivity or other job characteristics (Fankhauser, Sehlleier and Stern, 2008[17]) is an area that still lacks comprehensive information. However, evidence from past green policies in selected OECD countries shows that they, in fact, were biased towards more skilled workers. In the US, changes to environmental regulations via the US Clean Air Act increased demand for engineering, scientific, and analytical skills (Vona et al., 2018[6]). This shift was also driven by technological and organisational changes in firms. Across European countries, climate policies over the period 1995-2011 were skills-biased, and favoured technical skills and high-skilled occupations at the expense of manual workers and jobs with routine tasks (Marin and Vona, 2019[18]).

So far, mainly workers with higher levels of education appear to have seized green labour market opportunities. Employees in green-task jobs tend to be better educated than those in non-green-task jobs (Figure 2.15). Over 50% of those employed in green-task jobs completed higher education, compared to 34% of those in non-green-task jobs. The opposite is true for those in polluting jobs – they tend to have lower educational attainment, with 22% having completed tertiary education. While polluting jobs represent 12% of employment at the OECD level, the share almost doubles for individuals with lower-secondary education (21%).

Similarly, workers in green-task jobs also have significantly higher skill levels than those in non-green-task jobs across OECD regions. High-skilled occupations such as senior officials and managers, professionals, technicians, and associate professionals currently account for larger proportions of green-task jobs. This pattern is in line with evidence from the US that finds that green occupations are, on average, higher-skill and less routine-intensive than non-green occupations. This, in turns, requires high-level analytical and technical skills linked to technology (Vona et al., 2018[6]). In contrast, individuals with lower educational attainment and in medium-skilled occupations are at higher risk of displacement due to the green transition. In particular, medium-skilled occupations make up the majority of polluting jobs.

Figure 2.15. Distribution of educational attainment in green-task jobs vs. other jobs

Future green jobs might shift towards medium- and low-skilled occupations. The fact that currently the majority of workers in green-task jobs are high skilled or have tertiary education does not mean that this will always be the case, as new jobs emerge due to the green transition. In fact, in Europe, forecasts about the impact the European Green Deal (EGD), a major policy package to address climate change and generate economic growth, suggest an attenuating effect on job polarisation (CEDEFOP, 2021[19]). The forecast indicates a diffusion of the employment benefits of the EGD, with employment growth expected to be higher in skilled manual and elementary occupations than high-skilled occupations. This is linked to larger projected employment increases due to the EGD in specific sectors. A push for recycling and increasing electricity supply is projected to generate new, mostly medium- or low-skilled, jobs in utilities. In construction, the need for energy-efficient buildings could heighten demand for occupations such as heat pump boiler installers, carpenters and joiners, bricklayers, and technicians.

Workers in polluting jobs have particularly low participation rates in training

Continuous education and adult learning are vital tools for managing labour market transformations such as the green transition. The accelerated transformation of the world of work since the start of the COVID-19 pandemic poses new challenges for local labour markets. Therefore, future-proofed local continuous education and adult learning systems are more important than ever (OECD, 2021[20]). As skills needs and job requirements evolve rapidly, targeted retraining and upskilling opportunities that are aligned with local labour market demand are crucial for individual workers, firms, and local economies alike (OECD, 2022[21]). The green transition constitutes a further transformation of jobs and sectors, which means it is more urgent than ever to ensure that workers have access to effective training offers.

Workers in greater need of reskilling are less likely to participate in training. In general, participation rates in training and lifelong learning remain low among workers across OECD regions. On average, 19% of workers took part in training in the four weeks prior to being surveyed. While there is little difference between workers in green-task and non-green-task jobs (19% for both), individuals who work in polluting jobs have significantly lower training rates than other workers (12%) (Figure 2.16). Thus, training rates for workers facing a higher risk of job loss or skills changes due to the green transition have significant room for improvement. Lower training participation rates among workers in polluting jobs is observable across all OECD countries, but the gap is especially alarming in countries with an overall low level of training rates (Figure 2.17).

Figure 2.16. Individuals in polluting jobs are less likely to participate in training despite higher risk of displacement

Figure 2.17. Individuals in polluting jobs are less likely to participate in training

Across regional labour markets, there is a misalignment of training needs and training participation. Polluting jobs face greater changes or risks, as the green transition gathers momentum. Nonetheless, regions that have labour markets that are less green record lower training participation among their workforce (Figure 2.18). Top regions in terms of participation rate, i.e., those with over 15% of population participating in training, have up to 3.9 percentage points more green-task jobs on average than those regions whose participation rate ranges between 8% and 15%, and almost 7 percentage points more green-task jobs than regions with participation rates below 8%.

Figure 2.18. Participation rate and share of green-task jobs

Box 2.3 Green skills: What are the skills most associated with green-task jobs?

Green skills

In addition to providing the set of tasks that make up an occupation, O*NET also provides the skills usually required to carry out each occupation. Given that no skills are equally relevant for a given job, O*NET also provides an importance score for each skill.

Given this information, a regression approach is adopted to identify those skills that are more important in green occupations (an approach first proposed by Vona (2018)). A green skill can then be interpreted as a skill that is not only necessary but also actually important to green-task jobs. In practice, the importance scores of each skill are regressed on the green scores of the respective occupations controlling for the occupations’ primary group. A green skill is then one where the coefficient associated with the green score is significant and positive.

Sixteen skills are identified below as green, categorised into five macro-groupings to simplify presentation, and highlight key features.

List of skills which significantly correlate with green-task jobs

Macro-Groupings	Skill Name	Description
Engineering and technical
	Engineering and Technology	Knowledge of the practical application of engineering science and technology. This includes applying principles, techniques, procedures, and equipment to the design and production of various goods and services.
	Design	Knowledge of design techniques, tools, and principles involved in production of precise technical plans, blueprints, drawings, and models.
	Building and Construction	Knowledge of materials, methods, and the tools involved in the construction or repair of houses, buildings, or other structures such as highways and roads.
	Mechanical	Knowledge of machines and tools, including their design, uses, repair, and maintenance.
	Drafting, Laying Out, and Specifying Technical Devices, Parts, and Equipment	Providing documentation, detailed instructions, drawings, or specifications to tell others about how devices, parts, equipment, or structures are to be fabricated, constructed, assembled, modified, maintained, or used.
	Estimating the Quantifiable Characteristics of Products, Events, or Information	Estimating sizes, distances, and quantities; or determining time, costs, resources, or materials needed to perform a work activity.
	Inspecting Equipment, Structures, or Materials	Inspecting equipment, structures, or materials to identify the cause of errors or other problems or defects.
Operation Management
	Systems Analysis	Determining how a system should work and how changes in conditions, operations, and the environment will affect outcomes.
	Systems Evaluation	Identifying measures or indicators of system performance and the actions needed to improve or correct performance, relative to the goals of the system.
Monitoring
	Law and Government	Knowledge of laws, legal codes, court procedures, precedents, government regulations, executive orders, agency rules, and the democratic political process.
Science
	Physics	Knowledge and prediction of physical principles, laws, their interrelationships, and applications to understanding fluid, material, and atmospheric dynamics, and mechanical, electrical, atomic and sub-atomic structures and processes.
	Chemistry	Knowledge of the chemical composition, structure, and properties of substances and of the chemical processes and transformations that they undergo. This includes uses of chemicals and their interactions, danger signs, production techniques, and disposal methods.
	Biology	Knowledge of plant and animal organisms, their tissues, cells, functions, interdependencies, and interactions with each other and the environment.
	Geography	Knowledge of principles and methods for describing the features of land, sea, and air masses, including their physical characteristics, locations, interrelationships, and distribution of plant, animal, and human life.
	Science	Using scientific rules and methods to solve problems.
General Management		.
	Economics and Accounting	Knowledge of economic and accounting principles and practices, the financial markets, banking and the analysis and reporting of financial data.

Note: Skills as defined by (Vona et al., 2018[6]) encompass what O*NET defines as skills, knowledge and work activities.

Source: OECD calculations based on O*NET data.

Where is the demand for green-task jobs increasing?

This section examines recent labour market demand for green-task jobs. Using Big Data on online vacancies provides a more recent picture of labour market changes and complements the analysis from the preceding sections that looked at the existing shares of workers in green-task jobs.

What is the demand for green-task jobs vs. non-green or polluting jobs?

While most regions have not made much progress in greening their labour force, recent data on regional labour demand paint a more positive picture. Currently, green-task jobs represent 19% of labour demand; while polluting jobs account for 5% of vacancies (see Figure 2.19). Thus, the share of green-task vacancies is almost 2 percentage points higher than the current share of green-task jobs across OECD regions. In contrast, the demand for polluting jobs is over 6 percentage points below their share in terms of employment. A higher share of green vacancies and lower share of polluting vacancies in labour demand compared to the current employment shares may be the first signs of a shift towards a more environmentally friendly labour market.8

Figure 2.19. The majority of vacancies are in jobs that are neither green-task nor polluting, but the demand for non-polluting jobs is higher than current employment levels in non-polluting jobs

Encouragingly, the demand for green-task jobs is rising faster than labour market demand overall. Since the start of the COVID-19 pandemic, the number of green-task vacancies has grown faster than the number of non-green vacancies (Figure 2.20). Green-task vacancies increased by almost 110% (a factor of 2.1) between the last quarters of 2019 and second quarter of 2022, while the number of non-green-task vacancies rose by around 80% (factor 1.8). This faster growth has led to an increase in the share of green-task vacancies from 17% to 19.4%. However, the share of polluting vacancies also increased in that period, from 4% to 5.5%.

Figure 2.20. Over the last two years, the demand for green-task jobs has grown faster than for other jobs

The demand for green-task jobs evolved differently across OECD regions over the last two years. It rose in roughly two-thirds of regions (68%), though the change ranged from a 7-percentage point decrease in Basilicata, Italy to a 11-percentage point increase in Southern Denmark (Figure 2.21). The French region of Auvergne-Rhone-Alpes and the region Copenhagen in Denmark stand out as those with an already high share of green-task jobs in employment and a fast increase in demand for green-task jobs. Southern Denmark and Central Jutland have experienced the fastest “greening” of labour demand and may be on track to join the group of regions with the highest share of green-task jobs in the OECD. However, other regions with a low share of green-task employment, such as Asturias, Sardinia and Basilicata, are at risk of falling further behind with the demand for green-task jobs faltering over the last two years in these regions.

Figure 2.21. Few regions with a low share of green-task jobs show signs of catching up

What are the industries with the highest demand for green-task jobs?

Manufacturing, construction, professional, scientific and technical activities, and wholesale and retail trade are the four industries where green-task jobs are concentrated, as described in the previous section. Three of these industries are also the leading industries in terms of the demand for green-task jobs. In Europe, green vacancies are concentrated in manufacturing (21%), professional, scientific and technical activities (18%), and in administrative and support service activities (17%) (Figure 2.22). These three industries together account for 56% of all green vacancies in Europe and for 54% of green vacancies added in the last two years. Therefore, manufacturing and scientific and technical activities are expected to remain the main contributors of green-task jobs in the economy.

Figure 2.22. Distribution of green-task jobs and vacancies across industries

The overall trends in terms of demand for green-task jobs across industries are mixed. Fifteen out of 19 industries registered an increase in the share of green-task vacancies. Notably, in the four industries with the highest share of green-task jobs, the demand became “greener” over the last two years (see Figure 2.23). The change in demand for polluting jobs as a share of total demand was mixed. The largest increase was registered in manufacturing and professional, scientific and technical activities. Therefore, the positive contribution in terms of green vacancies of the first two industries is, at least partly, offset by the increase in demand for polluting jobs in these industries.

Figure 2.23. The demand in three out of four industries that contribute the highest share of green-task jobs to the economy has become “greener”

Box 2.4. Manufacturing will play a critical role in the green transition

Manufacturing is likely to play a critical role in the green transition. Manufacturing stands out as the industry with the highest contribution of green-task jobs in the economy, a high share of green-task jobs, and a high demand for green-task jobs as a percentage of total labour demand in the industry. Moreover, manufacturing accounts for a high share of polluting jobs in the economy and a high share of polluting vacancies. Therefore, manufacturing is the industry where new jobs that contribute to the reduction of greenhouse gas emissions are likely to be created. At the same time, manufacturing is also the industry that will be significantly affected by the displacement of workers who are currently employed in polluting jobs. A successful shift in employment from polluting to neutral and greener jobs in manufacturing will be critical for the success of the green transition. However, even within manufacturing there are significant differences in the contribution of the industrial groups to the greening of the economy (see Figure 2.24). Policies targeted at the manufacturing sector should therefore take into account the diversity within the manufacturing sector.

Figure 2.24. There are significant differences in demand for green-task jobs within the manufacturing industry

The aggregate green-task and pollution scores for manufacturing mask significant differences across regions. For example, in Stockholm, South East England, Helsinki-Uusimaa, Ile-de-France and Oslo and Akershus more than half of jobs in manufacturing are green-task jobs, while less than 10% of jobs are in Epirus, Western Macedonia and Corsica. Regions with a higher share of green-task jobs in manufacturing tend to have a lower share of polluting jobs in this industry (Figure 2.25). While this is partly mechanical, this result demonstrates that the aggregate share of green-task and polluting jobs in manufacturing (30% and 50%, respectively) is to a large extent driven by green-task and polluting jobs being present within manufacturing but in different regions. This observation implies that there might be scope for regions to support a transition within manufacturing from a polluting to a green employment structure. However, further analysis is needed at the more detailed sub-sector level.

Figure 2.25. There is significant dispersion across regions in the 'greenness' of the manufacturing sector

Do green-task jobs (skills) come with a wage premium?

Financially, green-task jobs appear to be more attractive than other jobs. Based on data for online vacancies, green-task jobs come on average with higher wages than both polluting and non-green-task jobs.9 Across the OECD, the wage premium of green-task jobs over non-green-task jobs is 20% and 12% compared to polluting jobs (Figure 2.26). These results are qualitatively the same as the wage analysis conducted using employment data from labour force surveys. The wage premium is present in almost all OECD countries. The higher wages in green-task jobs are partly a result of the fact that they, on average, require higher levels of education and experience (see analysis below). It might also reflect a lack of supply of workers with the right set of skills, qualifications and experience needed to fill green vacancies.

Green skills shortages are a major obstacle to local economic growth, firms’ investments, and delivering the green transition. As documented in a recent report, “growth in the demand for workers with green skills has outpaced the growth in the supply of green talent” (Linkedin, 2022[22]). In Europe, more than four-fifths of companies face skills shortages, especially for green and digital skills, and almost 70% of local authorities report a skills shortage that prevents projects on climate change from being implemented (EIB, 2023[23]). Those skills shortages are particularly acute in engineering and the digital economy.

Figure 2.26. Green-task jobs pay higher wages than other jobs

More detailed analysis of the green-task job wage premium across US regions offers further insights on observable factors that contribute to the wage premium. On average, the green wage premium (compared to non-green-task jobs) amounts to 25% in US regions. Higher levels of education and greater requirements in terms of work experience jointly explain more than half (13.6 percentage points) of this wage premium. The fact that green-task jobs are concentrated in specific, more high-skilled occupations explains around 6 percentage points of the wage premium. Thus, skills, education and experience explain around 20% higher wages for green-task jobs (see Figure 2.27). Once the general type of occupation (2-digit) is taken into account, further differences in occupations (3-digit) do not explain the wage difference. The wage premium decreases further when industry of employment is taken into account. However, the fact that green-task jobs pay more because they are concentrated in different industries than other jobs can be interpreted as an opportunity for workers to shift between industries and benefit from a higher salary.

Figure 2.27. Wage premium associated with green-task jobs is, to a large extent, explained by the differences in the level of education, experience and skills of workers

Conclusion

The impact of the green transition differs across regions. Large regional differences are observed both in the share of green-task jobs and in the share of polluting employment. The “greenness” of regional labour markets is associated with its industrial composition and the level of education of the workforce, but more research is needed to better understand what helps regions capture opportunities that are emerging with the advent of the green transition. Despite the prominence of climate change in the public discourse, on average, labour markets have not become much greener over the last decade, but that too differs across places. Regions with larger investments in innovation recorded faster increases in the share of green-task jobs in greening. Encouragingly, there are positive signs that momentum for the green transition is growing. The demand for green-task jobs has been growing in recent years and, since the start of the pandemic, has outpaced demand for non-green task jobs by around 20%.

The green transition could deepen social divides within regions. There is a strong gender dimension to the impact of the green transition on the labour market, with women under-represented in green-task jobs and men over-represented in polluting jobs that are at risk of disappearing. These differences are to a large extent driven by the composition of occupations held by men and women. Workers in green-task jobs also differ from other workers in terms of their skills-level and educational attainment. So far, most green-task jobs, which come with a significant wage premium, are held by high-skilled and highly educated workers. In contrast, polluting jobs are dominated by medium-skilled workers with lower educational attainment. While future jobs created by the green transition may be in medium-skilled and low-skilled occupations in activities such as construction, retrofitting, insulation, or waste management, these effects are yet to fully materialise. Monitoring the characteristics of workers in green-task and polluting jobs can help design and target up- and re-skilling initiatives to avoid further social divergence.

The green transition will affect industries differently, and while some industries can transition from polluting to green employment, the reallocation of workers across industries will likely be needed. On aggregate, green-task and polluting jobs often coincide within an industry. However, more detailed analysis reveals that, for example, in manufacturing, this effect is driven by the industry being dominated by green-task jobs in some regions and by polluting jobs in others. More research at sub-industry level would help policy makers ascertain whether this phenomenon could constitute an opportunity for regions to transform their manufacturing and other sectors from polluting to green employment. In other sectors, such as mining, the green transition is likely to reduce employment, thus creating a need for transition-oriented re-skilling of workers. The analysis presented in this chapter focuses on jobs with tasks directly related to reducing greenhouse gas or improving environmental sustainability. Future research could shed light on the supply chain effects of the green transition.

More research is needed to understand the shortages and mismatches in terms of green-task jobs in local labour markets, the scope for transitions between occupations, and the way skills demand evolves in response to the green transition. New jobs will be created as a result of the green transition, many existing jobs will require new skills, and some jobs will disappear. The labour force will need to re- and up-skill to adapt. However, training rates remain low in many OECD regions and workers in polluting jobs, at risk of displacement, tend to train less. In addition to increasing participation in training, efforts could be made to improve the effectiveness of training and its alignment with labour market needs. Forecasts at the local level can help direct students and job seekers towards occupations that are in high demand. Studying the ‘skills proximity’ of occupations, in combination with labour market demand at the local level, could reveal the potential new employment opportunities of workers in polluting jobs and identify appropriate training. It could also shed light on the potential for workers in non-green-tasks and polluting jobs to re-train into green-task occupations. Finally, better tools to measure how skills required in each occupation evolve in response to the green transition and other megatrends could also help adapt the training content to labour market needs. The following chapters – Chapters 3 and 4 – provide recommendations for how national and local actors alike can learn from past major transitions and ensure that local economies can adapt, equip their workforce with the right set of skills, and deliver a just and inclusive transition.

References

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[14] Autor, D., F. Levy and R. Murnane (2003), “The Skill Content of Recent Technical Change: An Empirical Exploration”, Quarterly Journal of Economics, Vol. 118, pp. 1279-1334, https://www.jstor.org/stable/25053940.

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[1] OECD (2023), Regional Industrial Transitions to Climate Neutrality, OECD Regional Development Studies, OECD Publishing, Paris, https://doi.org/10.1787/35247cc7-en.

[21] OECD (2022), Future-Proofing Adult Learning in Berlin, Germany, OECD Reviews on Local Job Creation, OECD Publishing, Paris, https://doi.org/10.1787/fdf38f60-en.

[20] OECD (2021), Future-Proofing Adult Learning in London, United Kingdom, OECD Reviews on Local Job Creation, OECD Publishing, Paris, https://doi.org/10.1787/c546014a-en.

[13] OECD (2020), Job Creation and Local Economic Development 2020: Rebuilding Better, OECD Publishing, Paris, https://doi.org/10.1787/b02b2f39-en.

[10] OECD (2020), “Making the green recovery work for jobs, income and growth”, OECD Policy Responses to Coronavirus (COVID-19), OECD Publishing, Paris, https://doi.org/10.1787/a505f3e7-en.

[12] OECD (2019), Under Pressure: The Squeezed Middle Class, OECD Publishing, Paris, https://doi.org/10.1787/689afed1-en.

[3] OECD (2017), Employment Implications of Green Growth: Linking jobs, growth, and green policies.

[25] Valero, A. et al. (2021), Are ‘green’ jobs good jobs?, https://www.google.com/search?client=firefox-b-d&q=Are+%E2%80%98green%E2%80%99+jobs+good+jobs%3F.

[4] Vona, F., G. Marin and D. Consoli (2019), “Measures, drivers and effects of green employment: evidence from US local labor markets, 2006–2014”, Journal of Economic Geography, Vol. 19, pp. 1021–1048, https://doi.org/10.1093/jeg/lby038.

[6] Vona, F. et al. (2018), “Environmental Regulation, Green Skills: An Empirical Exploration.”, Journal of the Association of Environmental and Resource Economists, Vol. 5/4, pp. 713-753, https://doi.org/10.1086/698859.

[7] Walker, W. (2011), “Environmental Regulation and Labor Reallocation: Evidence from the Clean Air Act”, American Economic Review, Vol. 101/3, https://doi.org/10.1257/aer.101.3.442.

Annex 2.A. Sensitivity Analysis and Robustness Checks

Comparing labour market estimates based on occupations across countries entails two challenges. First, different occupational classification systems vary in their level of detail. Second, employment data by occupation based on labour force surveys or from national statistical institutions also differ by level of detail.

Detail of occupational data

The level of detail of occupational classifications or available employment data differs across countries, which may undermine international comparability as less detailed data could lead to an overestimation of the green intensity of employment or the share of green jobs (see (JRC, 2021[24])).

To ensure comparability across countries, occupational data for countries is aggregated with more detailed information to a level that is comparable across countries. Therefore, for the United States and Canada, information is aggregated to a lower number of digits in order to make it more comparable to European data, which is only available at three digits for ISCO-08. The digits of aggregation are chosen in such a way that the number of unique occupations is similar across countries preserving the greatest level of detail possible. Based on the examples of the United States and Canada, choosing a lower digit of occupational detail does not lead to significant changes in the estimates (see below).

Aggregation of employment and occupational data

Aggregating green scores is necessary for two reasons. First, crosswalks are not always one-to-one matches. For example, an occupation at 3-digit ISCO level may correspond to two occupations at 6-digit US SOC level. Second, data is available at a less detailed level than the O*NET classification of green occupations.

When aggregation is necessary, the green intensity of an aggregated occupation is the simple average of the green intensities of the original occupations. Determining if an aggregated occupation is green or not green is not as straightforward. In the original O*NET classification, occupations are classified as green if they contain any green tasks. However, at an aggregate level, when an occupation corresponds to two or more occupations, with some but not all of them being green, there is no obvious way to classify.

The simplest approach is to consider an occupation at a higher level of aggregation as green if any of the corresponding occupations in the O*NET classification are green. This would be in line with the approach of considering an occupation green if at least one task it relates to is green. Unfortunately, this method generates an upwards bias in the estimation as a small number of green tasks are passed on to aggregated occupations. To mitigate the impact of this upward bias, a threshold approach is adopted where occupations must have a minimum level of green intensity for them to be green. The thresholds are chosen in such a way that they minimise the gap between the share of green jobs across multiple digits without being so large that detail is lost by removing jobs with some significant green intensity.

Using data from the US and Canada as examples, where the data is highly granular, sheds light on this estimation bias. The objective is to aggregate US detailed data into fewer digits and measure the green scores at different levels of aggregation. By comparing the results of the greenness measures for different aggregation levels (digits), coarser employment data, i.e., aggregation, only results in a small upward bias of greenness measures.

For green intensity of employment, less detailed employment data does not have a strong effect. Additionally, the analysis reveals that using a 10% threshold yields a share of green jobs that is not significantly affected by the aggregation from 6-digit level (lowest aggregation level available in the data for the US) to 5 or 3 digits. Aggregating from 6 to 5 or 3 digits generates a difference of 1% and 1.6% in green intensity of employment respectively on average. Therefore, we define a job as green if its green intensity is at least 10%.

Annex Figure 2.A.1. Robustness check: green intensity in the US

Annex Figure 2.A.2. Robustness check: the share of green-task jobs, US

Annex Figure 2.A.3. Robustness check: the share of green-task jobs, Canada

Annex 2.B. Additional tables and figures

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
VARIABLES	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8

green_th10	0.256***	0.257***	0.252***	0.170***	0.116***	0.0481***	0.0524***	0.0281***
	(0.000580)	(0.000580)	(0.000568)	(0.000485)	(0.000463)	(0.000528)	(0.000566)	(0.000569)
Constant	10.68***	10.71***	10.62***	10.35***	10.24***	10.39***	10.46***	10.52***
	(0.000230)	(0.000410)	(0.00228)	(0.00193)	(0.00183)	(0.00183)	(0.00224)	(0.00601)

Observations	5,510,884	5,510,884	5,510,884	5,510,884	5,510,884	5,510,884	5,510,884	5,510,884
R-squared	0.034	0.036	0.075	0.343	0.412	0.462	0.486	0.504
Year		YES	YES	YES	YES	YES	YES	YES
Region			YES	YES	YES	YES	YES	YES
Education				YES	YES	YES	YES	YES
Experience					YES	YES	YES	YES
Occupation 2d						YES	YES	YES
Occupation 3d							YES	YES
Industry 3d								YES

Note: From 2019 to 2021. Data for the US. See Box 2.1 for the definition of green-task jobs.

Source: OECD calculations based on Lightcast.

Notes

← 1. While job transitions are a normal feature of modern labour markets, the feasibility of transitions to green jobs hinges on the proximity of available jobs and those that might be displaced by the greening of labour markets.

← 2. Over-/under-representation of green-task jobs in the capital region is measured as the difference between the share of all green-task jobs in the economy that are located in the capital region and the share of all jobs in the economy that are located in the capital region.

← 3. Based on regression results that take into account country fixed effects.

← 4. Several sectors have substantial future greening potential. For example, greater adoption of activities such as organic farming, retrofitting, or insulation could result in a rising share of green-task jobs in agriculture and construction.

← 5. There is limited evidence of the effects of R&D on green job creation. Focussing on clean innovation within Dutch firms, (Elliott et al., 2021[5]) use matched employer-employee data and find that over the 2006–2010 period, firms that pursued eco-innovation had 12 more green employees than non-innovating firms. However, the effects are driven by a replacement of non-green workers rather than net job creation.

← 6. See https://ec.europa.eu/competition/mergers/cases/index/nace_all.html.

← 7. The methodology for identifying green-task jobs assumes the same greenness of tasks in the same occupation regardless of whether a job is in a large or small firm.

← 8. Vacancy data collected by Lightcast may not be representative of all vacancies in the economy. Therefore, care should be taken when comparing the vacancy and employment data.

← 9. Wage data is available only for a subset of vacancies, which may introduce bias to the analysis.

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