# Assessing the quality of green jobs: An empirical analysis of French data

Mathis Bachelot, Mathilde Guergoat-Larivière

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## To cite this version:

Mathis Bachelot, Mathilde Guergoat-Larivière. Assessing the quality of green jobs: An empirical analysis of French data. International Labour Review, 165 (1), pp.1-20. 10.16995/ilr.23802. hal-05279355v3

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Submitted on 29 Jan 2026

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# Assessing the quality of green jobs: An empirical analysis of French data

Mathis Bachelot, University of Lille, Clersé (UMR 8019), Cnam-CEET, mathis.bachelot@univ-lille.fr [corresponding author]

Mathilde Guergoat-Larivière, University of Lille, Clersé (UMR 8019), Cnam-CEET, mathilde.guergoatlariviere@univ-lille.fr

## Abstract

Our article provides an analysis of green jobs’ quality in France, applying on a large representative database a definition of green jobs that emerged in the last years. Using regressions and classification techniques, we show that green jobs display lower job quality than non-green jobs, especially for low-skilled workers who experience lower wages and socio-economic security. High-skilled workers show either better or not significantly different job quality compared to their non-green counterparts. Our analysis also reveals the heterogeneity of green jobs dispatched in four clusters with various job quality levels, challenging the narrative of ‘good green jobs’.

**Key words:** green jobs, job quality, wages, skills, occupational inequalities

## 1. Introduction

When considering green jobs, most studies focus on quantity, usually assessing their current volume or modelling their expected trends. This is useful, especially when differentiated by sectors, since environmental constraints require a drastic change in our economy that will destroy jobs while creating others. Yet, a widely shared result in the literature is that the most important phenomenon is jobs’ internal transformations (van der Ree, 2019), with effects on work itself. Analysing the quality of green jobs is therefore of paramount importance, not only because their volume is likely to grow but most importantly because many other jobs are expected to become ‘greener’ in the future as well. In this sense, documenting the quality of jobs that directly aim at preserving or restoring the environment can be considered as a gateway to understand whether the green content of a job tends to increase or conversely to lower its quality.

However, the literature on green jobs’ quality remains limited. Besides, most works equate job quality with the skill level (Kozar and Sulich, 2023; Stanef-Puică et al., 2022) and rarely adopt a genuine job quality perspective. While job quality and skills are not two independent spheres, exploring the former needs more than analysing the latter. To do so, we need to broaden the perspective, and one way is to rely on the ‘Job Quality’ approach. Widely used in the literature on European Union (Erhel, Guergoat-Larivière and Mofakhami, 2023; Guergoat-Larivière and Marchand, 2012; Piasna, 2023), this approach usually distinguishes six
dimensions of job quality: (i) wages, (ii) employment conditions and socio-economic security, (iii) working conditions, (iv) skills and career development, (v) working time and work-life balance, (vi) participation and collective representation. Based on this job quality definition, this paper aims to highlight the gaps between green and non-green jobs in these different dimensions, but also to explore the heterogeneity of green jobs' quality.

Instead of using skill level as a proxy for job quality, this article will explore job quality of green jobs both for high-skilled and low-skilled workers. The green content of jobs may indeed relate to very different types of tasks in high- or low-skilled jobs. Among low-skilled green jobs, some can be viewed as 'dirty jobs', such as those in contact with waste – garbage collectors, waste sorting agents, etc. (Corteel and Le Lay, 2011; Gregson et al., 2016). Other ones, involving direct work with nature, such as gardeners, can be considered as 'care jobs' – understood as care for nature, following eco-feminist scholars (Pruvost, 2024). These jobs can thus be devalued, both symbolically and materially, as personal care jobs are. Conversely, the green dimension of high-skilled jobs can be valued, at least by some workers, as it makes their work more meaningful in the context of the ecological crisis (Coutrot and Perez, 2022; Delozière et al., 2021). This higher intrinsic quality of work can however be associated with lower job quality on other – extrinsic – dimensions as it was shown for other jobs (e.g. in the non-profit sector), so that the relationship between the green content of a job and its quality is not straightforward either for high-skilled workers.

By analysing how the green content of jobs affects their quality according to skill level, this article also feeds the debate on 'just transition' and the 'good green jobs' policy-making narrative (e.g. UNEP, 2008). As pointed out by the Intergovernmental Panel on Climate Change (IPCC), people on low incomes are likely to be more affected by the effects of the ecological crisis. We also know that public policies implemented to prevent this crisis can have unequal effects, especially in the short-run and among the working classes. In France, the yellow vests movement started with the announcement of new gasoline taxes and ended in a large social crisis. Given that the ecological transition is a political process dependent on the support of citizens and workers, analysing inequalities in employment and their possible development in the ecological transition is crucial to understand the support for it, particularly among the least qualified workers. Strategically, an ecological transition providing good and attractive jobs would indeed act as an incentive for the needed labour reallocation (Hentzgen et al., 2023).

We will first present the different definitions of green jobs that co-exist and explain why we rely on a specific definition developed in France. In a second part, we will review the literature on green jobs, looking at both quantitative analyses that try to estimate the volume, skill level and wages of green jobs, and qualitative approaches that focus on specific occupations to describe their employment and working conditions. We will then describe the large and representative Labour Force Survey we use to run our empirical analyses as well as the two methods we implement: regressions on the one hand, and factorial analysis and classification techniques on the other hand. Finally, we will present our results in two steps. We will contrast the quality of green jobs compared to non-green jobs to better understand what the green content of a job does to its quality. We will also propose a typology of green jobs to document their heterogeneity in terms of job quality.
## 2. Concepts and literature review

### 2.1. Defining ‘green jobs’

Categories of ‘green jobs’ are not harmonized at international or even European level. A first very large definition was given by the UNEP (2008), as jobs “that contribute substantially to preserving or restoring environmental quality” (p. 3). Two main approaches can then be identified: top-down approaches that count as green all jobs belonging to a ‘green’ sector, and bottom-up approaches that differentiate green and non-green jobs more finely, usually at the job-level based on job titles or other characteristics specific to the occupation (e.g. the task content). If top-down approaches are useful to examine meso-economic trends, they are less appropriate to document the quality of jobs, such as wages, employment contracts, working hours and conditions, that are better grasped at the job-level.

This is why we will rely in this article on a bottom-up approach. However, various bottom-up approaches exist, and different methods have been developed to identify green jobs at the occupational level. The most famous and internationally used is the US O\*NET green jobs classification (Dierdorff et al., 2009, 2011), which has been adapted to many countries (de la Vega et al., 2024; Elliott et al., 2024; Sofroniou and Anderson, 2021; Valero et al., 2021). Created at the end of the 2000s, it distinguished three categories of jobs affected by the ecological transition: the *green new and emerging* (GNE) category, which groups jobs that were new because of the ecological transition; the *green enhanced skills* (GES) one, which identifies already existing jobs whose tasks and skills were evolving internally to adapt to the environmental challenge; and the *green increased demand* (GID) one, which refers to jobs that were expected to grow in volume to support the expansion of the first two categories, but for which the nature of work does not change.

However, the O\*NET approach to green jobs is increasingly criticized (Villani et al., 2025). As green jobs and tasks were identified in 2009/2011, the first limitation relates to the lack of substantial updates since then. The only changes were minor additions of tasks, and the last release of O\*NET to officially include this green classification was in 2019 (version 24.1), after which it was discontinued. More fundamentally, a second limitation is related to the articulation of green jobs and green tasks in O\*NET. In spite of its unique approach linking each job to a set of tasks that are labelled green or not, the green character of a job was not initially defined according to the number nor the intensity of green tasks effectively performed. Green jobs were instead identified on the basis of a review of 60 publications, and then categorized between the three groups according to how well they were already covered in the previous O\*NET nomenclature<fnref n="1" />. Green tasks were identified ex-post only in jobs from the first two groups (GNE and GES), so that GID jobs were arbitrarily excluded despite the fact that some were clearly intensive in green tasks, as pointed out in several recent works (Granata and Posadas, 2024 p. 25; OECD, 2024, p. 80). Finally, the 60 publications reviewed to identify green jobs only covered 12 sectors, excluding jobs in other sectors that can nonetheless

<footnote n="1">¹ GNE jobs were new relative to the previous O\*NET version, GES jobs were evolving relative to it as well (they had “aspects that could merit task list updating and/or alternate title changes”), while GID jobs were considered as correctly identified. This is how the impact of greening on occupations has been approached in O\*NET.</footnote>
contribute to a better environmental protection – thereby importing a shortcoming of the top-
down approach.

These limitations, which apply to US data, are compounded by another problem when
the O*NET categorisation is adapted to other countries. The crosswalk methods indeed suffer
from many shortcomings, mainly aggregation bias due to mismatches between different
occupational nomenclatures. Crosswalks' accuracy has been questioned (Bachelot, 2024) and,
in a report dedicated to European adaptations, Vona (2021) concluded that their quality "is not
sufficient to use [adapted O*NET categories] to measure green employment in Europe" (p. 24).
Hence, for countries other than the US, a national ad hoc definition and measure of green jobs
would be more appropriate.

France has such an approach, developed by an institution called Onemev<fnref n="2" />, which has
been dedicated to identifying and monitoring green jobs since 2010. The definition of green
jobs proposed by the Onemev was renewed in 2020 in accordance with the new French
classification of occupations. It is now defined by a list of more than 140 green occupations,
updated each year and defined at the most detailed level of the French nomenclature (that of
occupational titles). This narrow list covers the ‘core’ of green jobs, i.e. the greenest ones for
which we can assume that they surely favour ecological transition. They are not the largest
category of green jobs, nor are they expected to be in the future, but they are strategically critical
(Fontaine et al., 2023). Besides, this definition is not limited to occupations in the environmental
goods and services sector (EGSS), as is sometimes criticized with overly restrictive definitions
(e.g. the ‘purist approach’ described by Sofroniou and Anderson, 2021).

The French approach developed by Onemev classically defines green jobs as “jobs which, through their purpose and/or the skills they employ, contribute to measuring, preventing, controlling and correcting negative impacts and damage to the environment”. Their identification was based on an automated textual analysis of job titles collected in a large French representative survey (for more details, see section B in Online Appendix). In practice, and although park and garden workers are quite numerous in volume, it includes very diverse occupations, such as waste sorting agents, asbestos removers, air pollution technicians, energy research and development engineers, renewable energy managers, etc. Table A2 and A3 in Online Appendix displays the main occupations of the Onemev green jobs aggregate<fnref n="3" />.

Using the Onemev definition for our empirical analysis has several advantages. First, it
is directly based on French titles of occupations, which avoid translation issues and most
importantly are more likely to reflect the greenness of jobs in this specific national context.
Second, it also avoids crosswalk problems that arise when adapting the O*NET approach and
may jeopardise our identification of green jobs’ characteristics in terms of job quality. In other
words, if jobs which contribute substantially to preserving or restoring environmental quality
are ultimately gathered (due to aggregation bias) with other jobs that have not much to do with
environment, then our measure of green jobs’ quality will definitely be flawed.

<footnote n="2">² National Observatory for Employment and Professions of the Green Economy.</footnote>

<footnote n="3">³ The original list of all green jobs is available on the Insee website: https://www.insee.fr/fr/information/6050093 (accessed in June 2025).</footnote>
As there is no perfect definition of green jobs, we also need here to acknowledge the
limits in using the Onemev definition. The main issue about using this definition is that it is not
transposable to other countries for comparative purposes. However, the conclusions that can be
drawn about the relationship between the greenness of jobs and their quality will still be useful
since this definition ensures that only jobs that contribute substantially to preserving or restoring
environmental quality are included in the aggregate.

The other issue is related to the skill distribution of workers identified empirically with
the Onemev definition. Compared to other approaches, and to O*NET in particular, identifying
green jobs with the Onemev definition leads to a higher share of manual workers, especially
those working directly in contact with nature such as park and garden workers<fnref n="4" />, and a relatively
lower share of high-skilled workers, especially professionals and managers (Bachelot, 2024).
While the over-representation of managers in the O*NET definition has already been
underlined in other analyses (Maldonado et al., 2024), this also seems coherent with more
prospective French sectoral approaches that underline the large need for blue-collar workers in
the ecological transition (Sciberras et al., 2022). Besides, this skill distribution is indicative of
the French green labour market structure and not just a ‘bias’ in the Onemev list of green jobs
since it identifies as many high-skilled job titles as low-skilled ones: of the 143 listed in 2022,
70 indeed refer to managers, directors, engineers, technicians, researchers, consultants, etc. In
order to overcome this difference in composition, we will nonetheless systematically analyse
job quality of green jobs separately for high-skilled and low-skilled jobs.

## 2.2. Literature

The literature on green jobs’ quality divides into two main trends: (i) quantitative analyses based on various definitions of green jobs that offer a global view of green jobs’ average quality; (ii) qualitative analyses focusing on a single sector or job particularly exposed to changes related to the ecological transition and that shed light on the greening of local working practices.

For quantitative analyses, Apostel and Barslund (2024) provide an overview of the literature on developed countries, with Bachelot (2023) also proposing a focus on France. In general, and often based on O*NET categories, the literature on countries other than France finds that wages are higher for green jobs (Bluedorn et al., 2023; Bowen, Kuralbayeva and Tipoe, 2018; OECD, 2024; Valero et al., 2021; Vona, Marin and Consoli, 2019) – although this premium has diminished over time (OECD, 2023; Sato et al., 2023). But quantitative data on non-pecuniary dimensions of job quality are much rarer. In developed countries, including France (SDES, 2023), green jobs appear to be associated with better employment contracts, i.e. more secure and less part-time (Peters, 2014; OECD, 2024; Valero et al., 2021) – whereas the

<footnote n="4">⁴ The Onemev list includes not only jobs that are linked to the current and ongoing ‘ecological transition’ but also what might be called ‘historical’ green jobs, i.e. more established jobs that also have an environmental purpose. On the contrary, these ‘historical’ green jobs are not, by construction, covered by the O*NET approach because they were neither new (they have existed for a long time), nor greening (they were already green), nor necessarily expected to grow (Bachelot, 2023, p. 27). In other words, at the time the O*NET list was adopted, these jobs and their tasks were considered to be already correctly covered by the previous O*NET nomenclature, suggesting that they were not affected by the greening of the ecological transition and thus representative of it.</footnote>
opposite is true in developing countries, where greenness is associated with informality (de la Vega et al., 2024; Mathieu, 2024).

Quantitative analyses on working conditions are rare but an analysis on the French case – based on previous definitions of green and greening jobs – showed that these jobs are more exposed to arduous work (mechanical vibrations, harmful noise, exposure to cancer-causing agents), even when controlling for individual and job characteristics, suggesting a penalty specific to green jobs (Havet et al., 2021). In terms of socio-demographic profiles, a particularly striking feature is the fact that, in every country, green jobs are overwhelmingly held by men (around 80 per cent in France), both in descriptive statistics and once controlled for other factors (Valero et al., 2021).

Regarding the skill level of these jobs and of their holders, the results differ greatly depending on the definition used. As outlined above, due to the different philosophies and construction periods of the O*NET and Onemev definitions, the US one tends to identify higher-skilled occupations. Conversely, the majority of the French perimeter had no or low qualifications, due in particular to a high proportion of blue-collar workers (Babet and Margontier, 2017).

Besides these quantitative analyses, there is a whole strand of a neighbouring ethnographic literature that sheds light on sectors at the heart of the ecological transition, addressing other dimensions of job quality, in particular working conditions. In fact, these works document poor employment and working conditions, for instance in the waste management sector (Corteel and Le Lay, 2011; Gregson et al., 2016; Kirov and van den Berge, 2012) or in organic agriculture (Dumont and Baret, 2017; Guérillot, 2024). Some other studies have also analysed the greening of working practices in jobs or sectors that are not labelled ‘green’ but which are nonetheless being reconfigured by the integration of these considerations (Clos, 2022; Zarka, 2023). By shedding light on the constraints and intensification that can result from environmental regulations or even voluntary standards (Mahlaoui, 2023; Rieucau et al., 2024), this qualitative literature definitively brings important inputs to understand how the greening of working practices may have ambiguous impacts on working conditions.

Our analysis is however more in line with the quantitative approaches described above. It enhances them in several ways. To begin with, it is the first to operationalise the original definition of green jobs recently renewed by the French public statistics system, overcoming the limitations of both O*NET adaptations as well as previous French definitions. Together with Havet et al. (2021), it is also the first study that explores the quality of green jobs in France based on a multi-dimensional framework that goes beyond wages or skills alone and relies on controlled regressions for each dimension of job quality. Finally, it uses a large representative French survey, while previous institutional publications (Onemev, Dares, SDES) generally relied on census data, that do not contain much information on job quality.
## 3. Data: French Labour Force Survey to document job quality

We use data from the *Enquête Emploi* (Insee, 2022) which corresponds to the French version of the Labour Force Survey (LFS) defined at the European level. It aims to analyse the labour market and is the only source providing a measure of the concepts of activity, unemployment and employment as defined by the ILO. It includes a large set of variables on labour market situation of individuals aged 15 and over. In this survey, individuals of selected households are interviewed six times, once every three months. The first and last interviews are face-to-face while the four intermediate ones are phone interviews.

Besides the status of activity according to the ILO definition, the survey includes a detailed description of the current main job (status, occupation, working time, type of contract, wages, atypical hours, control over working hours, etc.) according to European classifications. It also includes variables on the level of education and participation in training. Its main limitation to analyse job quality relates to the lack of variables about working conditions or collective representation. We identify a set of job quality variables available in the survey following the main dimensions of job quality mentioned above.

(i) *Wages*: monthly net wage;

(ii) *Employment conditions and socio-economic security*: type of contract (permanent/civil servant, fixed-term/temporary, trainee/apprenticeship), full-time vs. part-time work, and main reason for part-time work (including the category "Did not find full-time work" – called hereafter involuntary part-time work), underemployment;

(iii) *Working conditions*: remote work;

(iv) *Access to training and career perspectives*: participation in training in the last 12 months (either formal or informal);

(v) *Working hours and work-life balance*: number of hours usually worked per week, stability of working hours (more or less the same each week, shift work, variable from one week to the next), control over working hours (full, limited, no control), night and evening work (evening or night work for at least half of working days over the last four weeks), weekend work (at least two Saturdays or at least two Sundays over the last four weeks);

(vi) *Participation and collective representation*: no variable available.

To identify green jobs, we use the ‘green aggregate’ built by Onemev and Insee, that is directly available in the survey. It is a dummy variable that takes one if the worker has a green job, and zero otherwise. Given that most job quality variables are only asked during the first interview, we only consider this first interview for our analysis. Considering the relatively limited number of green jobs, we use data from years 2021 and 2022, which allows a larger sample to be analysed. We also restrict the sample to wage earners because many job quality variables are available and meaningful only for them. Doing so, we do not eliminate many workers in green jobs since only 7.9 per cent of them are in self-employment, compared to about 12.9 per cent for all the working population.
The combined Labour Force Surveys for 2021 and 2022 include almost 800,000 observations. Limitation to the first interview and to individuals in wage employment leads to a sample of 46,835 observations. Among these observations, 783 are in green jobs, representing 1.6 per cent of observations and corresponding to around 400,000 workers. This very limited share of green jobs is in line with estimates from previous analyses based on French data (SDES, 2023), as well as with estimates from similar restrictive approaches in other developed countries (Vona, 2021).

**4. Methods: regressions and typologies to explore the diversity of green jobs**

We aim at comparing the quality of green jobs to other jobs, as well as exploring the diversity of green jobs in terms of job quality. As for other jobs, the ‘average’ quality of a green job may not be meaningful if very different types of jobs co-exist in the ‘green job’ aggregate. To document the job quality of green jobs, our empirical analysis proceeds in two stages.

First, we use controlled regressions (OLS and logistic regressions) to observe whether there is – on average – a premium (or a penalty) for our main job quality variables (wage, stability of contract, working hours, access to training, atypical hours, control over working hours, etc.) in green jobs. In all regressions, we control for gender, age, education, seniority, occupation, type of employer, industry and firm size. In wage regressions, we also include the number of hours worked and the type of contract. To assess whether the green content of a job has similar implications for the job quality of low-skilled and high-skilled workers, we run separate regressions respectively for (i) blue-collar and clerical workers on one hand, and (ii) managers, professionals, technicians and associate professionals on the other hand<fnref n="5" />.

Second, to document heterogeneity in terms of job quality, we focus only on green jobs and build a typology based on their quality. Given the categorical nature of our job quality variables, we first run a Multiple Component Analysis (MCA), followed by a Hierarchical Ascending Classification (HAC). MCA is a method that allows studying the association between qualitative variables. It creates variables called factors that summarize and recompose the information contained in the initial database. Factors are sorted by descending inertia and only first factors are then used to apply classification techniques. Hierarchical Ascending Classification then detects groups of individuals with similar profiles in their answers. Combining MCA and HAC, we identify clusters of green jobs with different levels of job quality and describe them using job quality variables and socio-demographic characteristics of workers (gender, occupation, etc.).

<footnote n="5">⁵ In the rest of the article, we will use the terms ‘low-skilled’ and ‘high-skilled’ to refer to them, although some blue-collar and clerical workers are skilled.</footnote>
# 5. Empirical results

## 5.1. What are green jobs and who are green jobs' holders?

Descriptive statistics on green jobs holders (Table A1 in Online Appendix) show that men are over-represented in green jobs while their structure in age is relatively close to other jobs. In terms of occupation, most workers in green jobs are low-skilled workers, especially skilled blue-collar workers (55.8 vs. 14.8 per cent among all the employees). Conversely, there are almost no clerical, sales and services employees in green jobs (1.9 vs. 29.5 per cent). Professionals and managers are slightly under-represented in green jobs (16.7 vs. 22.7 per cent), and even more so are technicians and associate professionals (18.2 vs. 26.1 per cent). In line with this occupational structure, workers in green jobs have on average lower education: 52.2 per cent of them do not hold the 'Baccalauréat', i.e. the national upper secondary diploma (vs. 33.8 per cent among all the employees).

They are more often employed in the public sector, especially territorial authorities (28.7 vs. 7.2 per cent), and by private individual employers (5.5 vs. 2.4 per cent), but less often in the private sector/NGOs (54.9 vs. 70.5 per cent). They work less frequently in firms of 250 employees or more (19 vs. 25.3 per cent) and more often in firms of 10 to 49 employees (31.5 vs. 27.3 per cent).

They are largely over-represented in manufacturing (20.4 vs. 13.7 per cent), in administrative and support service activities (22.6 vs. 10.2 per cent), and to a lesser extent in public administration, education, health and social work (37.5 vs. 33 per cent) and in other services (8.8 vs. 5.6 per cent). Conversely, green jobs are rare in wholesale and retail, transportation, accommodation and food service activities (4.1 vs. 22.4 per cent).

These first descriptive statistics illustrate and confirm the specificity of the Onemev definition of green jobs. As expected, low-skilled workers are over-represented in green jobs. Looking at the detailed occupations of workers in these green jobs (using French PCS4), we can see that about 45 per cent of them are park and garden workers (see Table A2 in Online Appendix), who are often employed by territorial authorities in France. Technicians as well as engineers and managers in agricultural, aquaculture, forestry and environment protection industry are also represented (11.5 per cent). Workers from the water, energy and waste treatment industry are also present, both skilled and unskilled (14.1 per cent). Finally, there are also engineers and technicians in quality control and safety risk management (9.5 per cent).

## 5.2. Are green jobs better than non-green jobs?

We use controlled regressions with different dimensions of job quality as dependent variables to observe whether having a green job increases or decreases the probability of having good job's characteristics. We do so for each of our job quality variables: wage, permanent contract, full-time work, involuntary part-time work, underemployment, regular hours, control over working hours, regular remote work, access to training, frequent weekend and night work. We
explore differences between high- and low-skilled workers, as defined in section 4, by running all regressions for each sub-group separately<fnref n="6" />.

## A wage penalty for green workers

Table 1 shows that green jobs are on average less paid than other jobs: employees in green jobs earn on average 324 net euros less per month than employees in other jobs, when we control for basic individual characteristics. Once we control for occupation, seniority (Model 2) and permanent contract (Model 3), the wage penalty decreases to 295 and then to 250 euros, suggesting that green jobs holders have on average ‘lower’ occupations and are less often on permanent contracts. In Model 4, we also introduce the number of hours worked a week and the wage penalty is reduced to about 155 euros: this is coherent with the slightly lower number of hours worked on average in green jobs (35h vs. 36.6h). Finally, in Model 5 we introduce some characteristics of the firm: industry, size of the firm and type of employer. In this last model, the wage penalty is reduced and not significant anymore, suggesting that firms’ characteristics and sector are important determinants of green jobs’ wage penalty.

<table><caption>Table 1. Wage penalty in green jobs</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 4</th><th>Model 5</th></tr></thead><tbody><tr><td>Green job</td><td>-324.1***<br>(90.2)</td><td>-295.3***<br>(89.5)</td><td>-250.8***<br>(89.5)</td><td>-155.0*<br>(88.7)</td><td>-109.3<br>(89.8)</td></tr><tr><td>Women</td><td>-573.6***<br>(23.3)</td><td>-376.2***<br>(25.2)</td><td>-367.8***<br>(25.2)</td><td>-251.8***<br>(25.2)</td><td>-230.4***<br>(26.0)</td></tr><tr><td>No degree</td><td>-514.6***<br>(40.7)</td><td>-243.4***<br>(42.2)</td><td>-222.0***<br>(42.2)</td><td>-170.2***<br>(41.8)</td><td>-162.4***<br>(42.3)</td></tr><tr><td>Short vocational secondary</td><td>-292.7***<br>(36.3)</td><td>-106.8***<br>(36.7)</td><td>-102.3***<br>(36.6)</td><td>-116.0***<br>(36.3)</td><td>-116.8***<br>(36.6)</td></tr><tr><td>Upper secondary education</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Short cycle tertiary education</td><td>334.9***<br>(39.0)</td><td>85.3**<br>(39.4)</td><td>78.7**<br>(39.4)</td><td>54.7<br>(39.0)</td><td>42.6<br>(39.4)</td></tr><tr><td>Long cycle tertiary education</td><td>1,009.2***<br>(33.5)</td><td>313.4***<br>(38.0)</td><td>311.3***<br>(38.0)</td><td>291.4***<br>(37.6)</td><td>289.7***<br>(38.4)</td></tr><tr><td>Age 15-24</td><td>-788.2***<br>(41.3)</td><td>-453.3***<br>(43.2)</td><td>-329.4***<br>(44.6)</td><td>-282.2***<br>(44.2)</td><td>-275.9***<br>(45.0)</td></tr><tr><td>Age 25-49</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Age 50+</td><td>381.1***<br>(25.8)</td><td>91.4***<br>(29.2)</td><td>102.1***<br>(29.1)</td><td>156.9***<br>(28.9)</td><td>167.7***<br>(29.3)</td></tr><tr><td>Seniority</td><td></td><td>30.4***<br>(3.3)</td><td>19.0***<br>(3.5)</td><td>17.6***<br>(3.4)</td><td>15.7***<br>(3.5)</td></tr><tr><td>Seniority (squared)</td><td></td><td>-0.4***<br>(0.1)</td><td>-0.2*<br>(0.1)</td><td>-0.2*<br>(0.1)</td><td>-0.1<br>(0.1)</td></tr><tr><td>Managers and professionals</td><td></td><td>1,311.1***<br>(47.9)</td><td>1,310.1***<br>(47.8)</td><td>1,151.7***<br>(47.6)</td><td>1,150.7***<br>(49.6)</td></tr><tr><td>Technicians and associate prof.</td><td></td><td>190.4***<br>(42.4)</td><td>196.6***<br>(42.4)</td><td>198.6***<br>(41.9)</td><td>237.4***<br>(43.5)</td></tr></tbody></table>

<footnote n="6">⁶ We checked that our results were robust by running similar models with different Propensity Score Matching options (radius, nearest-neighbour with different calipers). Overall, this does not alter our main findings and interpretations. These models are available on request.</footnote>
<table><tbody><tr><td>Skilled clerical workers</td><td>-52.4<br>(44.9)</td><td>-41.1<br>(44.8)</td><td>-0.8<br>(44.4)</td><td>52.2<br>(46.7)</td></tr><tr><td>Low-skilled clerical workers</td><td>-303.9***<br>(46.5)</td><td>-299.5***<br>(46.4)</td><td>-145.3***<br>(46.2)</td><td>-76.2<br>(49.8)</td></tr><tr><td>Skilled blue-collar workers</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Low-skilled blue-collar workers</td><td>-240.7***<br>(53.7)</td><td>-197.2***<br>(53.8)</td><td>-103.6*<br>(53.3)</td><td>-99.2*<br>(55.7)</td></tr><tr><td>Permanent contract</td><td></td><td>395.6***<br>(36.2)</td><td>256.2***<br>(36.1)</td><td>255.0***<br>(37.2)</td></tr><tr><td>Number of hours per week</td><td></td><td></td><td>39.1***<br>(1.3)</td><td>37.6***<br>(1.3)</td></tr><tr><td>Private employer</td><td></td><td></td><td></td><td>Ref.</td></tr><tr><td>Public employer</td><td></td><td></td><td></td><td>-39.2<br>(35.6)</td></tr><tr><td>Individual employer (home worker)</td><td></td><td></td><td></td><td>88.3<br>(84.9)</td></tr><tr><td>Less than 10 employees</td><td></td><td></td><td></td><td>Ref.</td></tr><tr><td>10+ employees (no detail)</td><td></td><td></td><td></td><td>80.1<br>(60.0)</td></tr><tr><td>10-19 employees</td><td></td><td></td><td></td><td>81.1*<br>(43.9)</td></tr><tr><td>20-49 employees</td><td></td><td></td><td></td><td>107.5***<br>(41.3)</td></tr><tr><td>50-249 employees</td><td></td><td></td><td></td><td>136.5***<br>(38.4)</td></tr><tr><td>250+ employees</td><td></td><td></td><td></td><td>318.2***<br>(39.6)</td></tr><tr><td>Industry (NAF 10)</td><td>excl.</td><td>excl.</td><td>excl.</td><td>excl.</td><td>incl.</td></tr><tr><td>Constant</td><td>2,077.7***<br>(30.0)</td><td>1,685.0***<br>(43.1)</td><td>1,393.3***<br>(50.7)</td><td>35.1<br>(67.1)</td><td>-85.1<br>(75.7)</td></tr><tr><td>Observations</td><td>46,531</td><td>45,958</td><td>45,958</td><td>45,958</td><td>45,497</td></tr><tr><td>R<sup>2</sup></td><td>0.068</td><td>0.104</td><td>0.106</td><td>0.124</td><td>0.125</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.067</td><td>0.104</td><td>0.106</td><td>0.124</td><td>0.124</td></tr><tr><td>Residual Std. Error</td><td>2,489.2</td><td>2,437.4</td><td>2,434.3</td><td>2,410.0</td><td>2,417.8</td></tr><tr><td>F Statistic</td><td>421.3***</td><td>354.8***</td><td>340.9***</td><td>382.2***</td><td>196.8***</td></tr></tbody></table>

Note: Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01

Method: OLS. Source: French Labour Force survey (2021-2022).

In order to feed the debate on 'just transition' and because we know that the French definition of green jobs includes a higher share of blue-collar workers (compared to O*NET), we run the regression separately for low-skilled workers on one hand and for high-skilled workers on the other hand (using the most detailed specification, i.e. Model 5).

As shown in Table 2, the wage penalty remains statistically significant for low-skilled workers, reaching about 156 euros per month<fnref n="7" />. Our result is in line with recent work by the OECD (2024), pointing out that “jobs in [low-skill, green-driven O*NET] occupations are

<footnote n="7">⁷ The wage penalty of low-skilled green workers is confirmed by all the PSM models run as robustness checks. For high-skilled green workers, PSM models confirm the insignificant wage penalty, except in one specification (radius matching with a caliper of 0.01) where it becomes significant (and results for all other job quality variables are similar). Given the limited number of high-skilled green workers, we cannot completely rule out the hypothesis of a wage penalty also applying to them.</footnote>
usually less paid than other low-skill jobs" (p. 96). It also echoes the synthesis by Fontaine et al. (2023) who stress that, although most green jobs are not high-skilled, they do require specific skills, but that these are not or poorly remunerated.

<table><caption>**Table 2. Wage penalty in green jobs: separated estimations for low- and high-skilled workers**</caption><thead><tr><th></th><th>Low-skilled<br>workers</th><th>High-skilled<br>workers</th></tr></thead><tbody><tr><td>Green job</td><td>-156.1***<br>(20.7)</td><td>-185.3<br>(214.4)</td></tr><tr><td>Constant</td><td>448.1***<br>(17.7)</td><td>254.2<br>(170.3)</td></tr><tr><td colspan="3">Controls: gender, age, education, seniority (sq.), occupation, permanent contract, type of employer, number<br>of hours per week, firm size, industry</td></tr><tr><td>Observations</td><td>23,446</td><td>22,051</td></tr><tr><td>R<sup>2</sup></td><td>0.491</td><td>0.072</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.491</td><td>0.071</td></tr><tr><td>Residual Std. Error</td><td>441.9</td><td>3,432.6</td></tr><tr><td>F Statistic</td><td>729.7***</td><td>58.7***</td></tr></tbody></table>

Note: Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01

Method: OLS. Source: French Labour Force survey (2021-2022).

We can put forward three main hypotheses to explain the wage penalty of green workers, all related to some forms of devaluation.

First, this wage penalty may be due to their concentration in specific industries that are on average less unionized. Some recent work conversely showed that 'brown jobs' (i.e. in pollution intensive industries) are more covered by collective agreements and have better union densities (Zwysen, 2024). Hence, green jobs may be characterized by relatively lower bargaining power (OECD, 2024) and consequently lower wages. Though we cannot control for the unionization of the worker or of its firm, we introduce finer industry levels to better reflect the degree of unionization: the wage penalty of low-skilled green workers decreased but remained significant<fnref n="8" />. This means that part of the wage penalty is indeed explained by insertion in less remunerative, potentially less profitable and less unionized sectors. Still, more than two-thirds of the penalty remain explained by the green job variable, suggesting that, even compared to similar jobs within the same narrow industry, green jobs are on average less paid.

Second, the remaining wage penalty may be due to the very nature of tasks performed in these jobs – that we cannot control for – which would be less value-producing. However, recent work on French data (Fana and Giangregorio, 2024) suggests that, at the bottom of the distribution, wages are generally more driven by contractual arrangements (permanent or temporary, number of hours worked) and seniority – that we here control for – than by the content of tasks. Their result can therefore be questioned for green jobs: tasks performed in

<footnote n="8">⁸ In our initial regressions, the industry variable is the 10-category level of the French classification of activities. The wage penalty respectively dropped to 149, 147, 131, 130 and 105 euros for 21, 38, 88, 129 and 732 sectors.</footnote>
these jobs, since they deal with environmental quality whose positive externalities are not well accounted for, may explain part of the wage penalty – and it may be reinforced by our next hypothesis.

Third, this devaluation of green jobs might also stem from their integration of what is identified as ‘dirty work’ or ‘care work’ – if we adopt a large definition of care that includes the ‘care for nature’ (Pruvost, 2024). This seems particularly appropriate for low-skilled workers in the waste processing industry or for gardeners and groundskeeping workers. Investigating labour in New York City parks, Krinsky and Simonet (2012) indeed showed that this type of environmental work was more often done by volunteers or workfare workers and that it was characterized “by a total or partial absence of legal, monetary and/or symbolic recognition of the [worker status]” (p. 2).

<table><caption>Table 3. Having a green job: what relation with different indicators of job quality?</caption><thead><tr><th></th><th>All workers</th><th>Low-skilled workers</th><th>High-skilled workers</th></tr></thead><tbody><tr><td>Permanent (vs. fixed-term/temporary)</td><td>-0.643***<br>(0.112)</td><td>-1.044***<br>(0.133)</td><td>-0.097<br>(0.239)</td></tr><tr><td>Full-time (vs. part-time work)</td><td>-0.609***<br>(0.104)</td><td>-0.964***<br>(0.121)</td><td>0.113<br>(0.238)</td></tr><tr><td>Full-time (vs. other part-time)</td><td>-0.345**<br>(0.128)</td><td>-0.687***<br>(0.165)</td><td>0.056<br>(0.232)</td></tr><tr><td>Involuntary part-time (vs. other part-time)</td><td>0.554***<br>(0.162)</td><td>0.523**<br>(0.211)</td><td>-0.364***<br>(0.036)</td></tr><tr><td>Underemployment</td><td>0.826***<br>(0.143)</td><td>1.068***<br>(0.157)</td><td>-0.393<br>(0.517)</td></tr><tr><td>Training over the last year</td><td>0.048<br>(0.081)</td><td>-0.134<br>(0.109)</td><td>0.183<br>(0.126)</td></tr><tr><td>Remote work</td><td>0.365***<br>(0.137)</td><td>-0.201<br>(0.611)</td><td>0.406***<br>(0.142)</td></tr><tr><td>Regular hours (vs. shift or irregular hours)</td><td>0.552***<br>(0.095)</td><td>0.712***<br>(0.122)</td><td>0.088<br>(0.152)</td></tr><tr><td>Full or limited control over working hours (vs. no control)</td><td>0.560***<br>(0.092)</td><td>0.164<br>(0.121)</td><td>0.829***<br>(0.157)</td></tr><tr><td>Frequent evening or night work</td><td>-1.210***<br>(0.221)</td><td>-0.916***<br>(0.241)</td><td>-1.610**<br>(0.584)</td></tr><tr><td>Frequent weekend work</td><td>-0.649***<br>(0.117)</td><td>-0.181<br>(0.137)</td><td>-1.079***<br>(0.244)</td></tr></tbody></table>

Note: Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01

Method: logistic regressions (binomial or multinomial) with the following controls: gender, age, education, seniority (sq.), occupation, type of employer, firm size, industry.

Source: French Labour Force survey (2021-2022).
***Lower socio-economic security but less atypical, more regular, and better control over working hours***

Apart from wages, we explore differences in other dimensions of job quality between green and non-green jobs. Table 3 presents the coefficients related to the green job dummy in all (controlled) regressions where the dependent variable is alternatively one of the job quality variables listed above<fnref n="9" />.

We observe that workers in green jobs have on average lower socio-economic security. They have less often permanent contracts, are less often in full-time employment and more often in (involuntary) part-time work. Workers in these jobs are also more often underemployed. Besides, while we might expect that green jobs would require more access to training in order to cope with changes in their working practices related to the ecological transition, they actually do not have significantly greater access to it.

Comparing variables on working time and working hours, we observe that workers in green jobs experience less atypical hours: they are less concerned with work at weekends and even less with evening and night work. In terms of working time organization, they show better characteristics: on average, green jobs offer more often stable working hours, and better control over working hours (either limited or total). As mentioned above, indicators on working conditions and collective representation are scarce in the LFS. We have no information on the latter, while the only variable on working conditions concerns remote work, which is on average more frequent for green jobs.

***A stronger segmentation in green jobs?***

Once again, we should distinguish between low-skilled workers on one hand, and high-skilled workers on the other hand. Since the wage penalty is more clear-cut for low-skilled green workers, we can wonder whether they also experience lower levels of job quality on other dimensions. Here, two opposing hypotheses can be advanced: the first, in the vein of the theory of equalizing differences (Cahuc, 2011; Rosen, 1986), is that the wage penalty would be the result of better employment and working conditions. Hence, we would expect good characteristics in other dimensions of job quality to compensate for (and explain) these low wages. The second, which seems more likely, would be the accumulation of disadvantages (Erhel, Donne and Elbaz, 2023), resulting from the interplay of elements presented above – less bargaining power and less valuation of these activities in our capitalist economies.

Our result on lower socio-economic security of green jobs holds only for low-skilled workers and vanishes for high-skilled workers. The hypothesis of stronger segmentation among green jobs is reinforced: low-skilled workers are more concerned with fixed-term/temporary contracts, part-time, involuntary part-time and underemployment.

Results on other dimensions of job quality are however different. All green jobs (low- and high-skilled) are better off in terms of atypical hours: both are less concerned by frequent evening and night work, while high-skilled workers are also less exposed to weekend work. Yet, only high-skilled green jobs are characterized by more control over working hours and

<footnote n="9">⁹ Tables with all coefficients for each regression are available on request.</footnote>
remote work, with the two probably going together. Conversely, another positive result holds
only for low-skilled workers: they exhibit more stable working hours (vs. irregular hours or
shift work) than their non-green counterparts. These results are in line, for instance, with the
waste collection operators studied by Hetzel (2024), which are described as ‘good bad jobs’
notably for this reason, in comparison with the other opportunities available to the male
fractions of urban working classes. The over-representation of the State and territorial
authorities as employers of workers in green jobs, especially low-skilled ones, can partly
explain these relatively good features in terms of limited atypical hours and control over
working hours. Jobs in the public sector generally present good characteristics in terms of
working hours. This result for low-skilled workers also echoes recent work on job quality
(Erhel, Donne and Elbaz, 2023) that showed that one of the very rare occupations where low
wages seem partly ‘compensated’ by other positive dimensions of job quality (and working
hours in particular) are civil service administrative employees without national upper secondary
diploma.

This analysis shows that job quality dimensions of green jobs vary across occupations. Some forms of reinforced segmentation among green jobs may be at play, with low-skilled workers having lower socio-economic security while such a difference does not hold for higher-skilled jobs. In order to deepen our understanding of differentiated segments of green jobs, we now build a typology of green jobs in terms of job quality.

5.3. Are all green jobs similar in terms of job quality? Building a typology

To go further than these average (or skill-split) comparisons between green and non-green jobs, we build a typology of green jobs in terms of job quality. Given the qualitative nature of most of our job quality variables, we run an MCA based on the sample of workers holding a green job<fnref n="10" />. We restrict the analysis to the first five axes that are identified as the only ones carrying real information: they explain 53.26 per cent of the total inertia in the data and further axes do not bring additional useful information<fnref n="11" />. Using these five factors generated by the MCA, we run an HAC to produce the job quality typology.

Four groups are identified, both because this partition presents the higher relative loss of inertia (see dendrogram on Graph A1 in Online Appendix), and because it is relevant for analytical purposes. Graph 1 shows both the plot of categories and of observations among clusters on the first two axes. The first axis is characterized by variables on wages and socio-economic security while the second axis is mainly characterized by variables about working time organization and working conditions.

<footnote n="10">¹⁰ We include all the job quality variables of Table 3, as well as the monthly net wage split into five groups.</footnote>

<footnote n="11">¹¹ To determine how many axes to keep, we rely on the *explor* package (Barnier, 2023). It is based on a ‘parallel analysis’ that runs MCAs on 10,000 randomly generated datasets with the same number of individuals and variables as our original dataset – each variable following a normal distribution N (0,1). For each axis, it ranks the simulated eigenvalues in ascending order and identifies the 95th percentile value, which serves as the five per cent significance threshold. It then only retains axes for which the observed eigenvalue is greater than their simulated threshold: in our case, this holds for the first five axes.</footnote>
<figure><img src="image_3.png" /><figcaption>Graph 1. Plot of observations and categories (first two axes)</figcaption></figure>

Note: Dots represent individuals and triangles represent categories of variables. To avoid visual overload, only categories whose squared cosines are greater than 0.3 are displayed.

Source: French Labour Force survey (2021-2022).

The first cluster, accounting for 22.4 per cent of employees in green jobs, can be called "Good green jobs". It is located on the upper left-hand side of the graph and groups workers with high levels of job quality. It is characterized by high earnings (2,000-2,999 and 3,000+ euros), stable employment (either permanent contract or civil servant) and full-time employment. Workers in this group have more frequent access to training than other groups. Control over working hours and remote work is also more developed. Women, high educated workers and managers and professionals are over-represented in these jobs. We also find more workers employed by the State (58 per cent of them belong to this group) or within large firms (50-249 and 250+).

The second cluster, which we call "Green stable average quality jobs", is the largest one as it represents 60.6 per cent of green employees. At the bottom of the graph, it groups workers with stable full-time jobs but with lower earnings and less control over working hours than the first group. They earn monthly net wages between 1,000 and 1,999 euros (especially 1,500-1,999 euros). They also have stable contracts (either permanent contract or civil servant) and are in full-time jobs. Consequently, under-employment is rare in this group. Men are significantly over-represented (88 vs. 82 per cent). They are often employed within the public administration sector and their employer is more often territorial authorities (i.e. local administration, for which 80 per cent of green jobs belong to this cluster). In terms of education and skills, they mainly have secondary education and are more frequently blue-collar workers (as well as unskilled clerical workers).

The last two clusters are smaller and composed of workers with lower levels of job quality, compared to the first two groups. They are on the right-hand side of the graph, which is characterized in particular by low earnings.
The third group (10.1 per cent of green jobs) is composed of what we call “Green odd jobs”. As its title suggests, and in contrast to the fourth group, these green workers are very often on ‘voluntary’ part-time or student contracts (trainees and apprenticeships). Their monthly net wages are very low (less than 1,000 euros), and their working hours are more frequently irregular – even though they have more control over it. Remote work is not developed at all. Employers are more often small firms (1-9 employees), including individual employers (70 per cent of them belong to this group). In this group, we mainly find skilled blue-collar workers with no education (possibly not yet). In view of these characteristics, the poorer quality of these jobs may be more transitory than for the last group. Yet, this does not make it desirable or sustainable, especially if it appears that there is a predominance of green jobs in this type of low-quality jobs (compared to the rest of the economy), and thus that the ecological transition increasingly relies on this type of objectively degraded jobs.

The fourth cluster, grouping the remaining 7 per cent of green jobs, gathers what are “Precarious green jobs”. It is mainly composed of underemployed workers in involuntary part-time work, which are on fixed-term/temporary employment and earn less than 1,000 euros per month. They have no control over their working hours, and they do not benefit from training or remote work. As in the previous group, workers with no education and skilled blue-collar workers are over-represented. They are also more often employed in the ‘public administration, education, human health and social work’ sector (52 per cent of this group).

This typology illustrates, beyond the average results of section 5.2, the strong heterogeneity of green jobs in terms of job quality. This challenges the ‘good green jobs’ narrative, in the sense that a significant proportion of green jobs display a very poor job quality, especially with respect to socio-economic security.

Lastly, although the groups might seem, at first sight, to reflect a division in terms of
skill level and occupations, a careful analysis suggests that this is not enough to explain job
quality. Indeed, several occupations are broadly distributed across two or even three of the
clusters (see Table A3 in Online Appendix). This is the case for park and garden occupations
that account for a large part of our (low-skilled) sample – some are even found in the first “Good
green jobs” cluster, albeit in a much smaller proportion.

This clearly shows that there is no ‘occupational determinism’ in terms of job quality,
but on the contrary calls for attention to institutional embeddedness. For instance, the role of
employer and sector appears to be important, with some being over-represented in low quality
jobs, such as individual employers in the third group, or the broad public administration sector
in the fourth. In this respect, the public sector offers a direct policy lever for improving job
quality, especially as it is able to offer good jobs to other segments of the green workforce, as
shown by its strong presence as an employer in the first and seconds clusters.

Conclusion

While green jobs’ analyses are generally limited to estimating their volume, our analysis
provides detailed insights on their quality. Based on a statistical category of green jobs recently
introduced in France, it appears that these jobs have a relatively lower job quality than non-green jobs, especially for low-skilled workers.

Although all definitions of green jobs can be subject to discussion, this definition has two main advantages: it is limited to the core of green jobs, ruling out as much as possible ‘greenwashing’ issues, and it avoids many of the limitations that the literature increasingly raises about the US O*NET classification of green jobs and tasks (old and out of date, contestable assignment of green tasks, crosswalk aggregation bias, etc.). Besides, it has been created and directly operationalized in large French representative surveys so that it can be seized and discussed by researchers. Our article offers the first comprehensive analysis of green jobs’ quality based on this definition, which can be seen as a gateway to understanding how the green content of a job affects its quality.

While green jobs are characterized by lower job quality than non-green jobs, we show differences in job quality across skill levels. Low-skilled (blue-collar and clerical) workers in green jobs have lower socio-economic security (wages, contracts, involuntary part-time) than their non-green counterparts, even though they have slightly better conditions in terms of working hours (more stable and less atypical). For high-skilled workers in green jobs, the situation is different: they show either better (on atypical hours, control over working hours and involuntary part-time) or not significantly different (contracts, wages) job quality compared to their non-green counterparts. The green content of a job thus does not seem to have the same impact on high- and low-skilled workers, which could call into question the support of the latter to the ecological transition and jeopardize the opportunity of a ‘just transition’.

Beyond these average and sub-group results, our typology also documents the heterogeneity of green jobs’ quality. While most green jobs are characterized by an average-to-good job quality, a non-negligible proportion of them combine poor conditions on several dimensions. Once again, this result questions the often-held narrative of ‘good green jobs’. A further analysis even revealed that, compared to the rest of the (non-green) labour market, green jobs are slightly under-represented among high-quality jobs (cluster 1) and over-represented in the lowest quality ones (cluster 4)<fnref n="12" />.

This must be a concern since higher job quality in green jobs could facilitate the ecological transition and the needed labour reallocation. Our results point to different directions in terms of public policies. On the one hand, since the wage penalty in green jobs is partly explained by their insertion in particular firms and sectors, it calls for sector-specific policies aimed at improving wages and more broadly job quality, such as conditional subsidies or the fostering of negotiations at branch level. On the other hand, and as for many low-skilled jobs (Devetter and Valentin, 2020), given that another part of the wage penalty seems to depend on the task content and the underlying division of labour, a possible lever for improvement could be to encourage large training provisions as well as negotiations over greater task diversification within the firm. The State could also better act as a role model since it employs a significant share of green workers, especially in territorial authorities, including some low-quality jobs.

<footnote n="12">¹² We did this by running a similar classification on the whole LFS sample (not only green jobs) and then testing for equality of proportions between the clusters.</footnote>
In terms of research, additional work on the relationship between the ecological transition and the quality of jobs is still needed, both qualitative and quantitative. In the French case, the forthcoming availability of the Working Conditions Survey will make it possible to enrich the analysis on this dimension of job quality that is crucial but could not be approached with the Labour Force Survey.

## Acknowledgments

We thank the editor, the two reviewers as well as Thomas Amossé, Michaël Orand, Nathalie Moncel and Henri Cottel for their suggestions. This work is part of the ETEWI project (ANR-23-CE26-0012) and is supported by the Lille University Data Platform (PUDL) and the Hauts-de-France Region. Access to confidential data was made through Secure Data Access Center (Ref. 10.34724/CASD).

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# Online Appendix

## Section A. Additional tables and graph

<table><caption>Table A1. Green jobs holders' characteristics</caption><thead><tr><th></th><th>Green jobs</th><th>All employees</th></tr></thead><tbody><tr><td><strong>Sex</strong></td><td></td><td></td></tr><tr><td>Woman</td><td>17.9</td><td>50.8</td></tr><tr><td>Man</td><td>82.1</td><td>49.2</td></tr><tr><td><strong>Age</strong></td><td></td><td></td></tr><tr><td>15-24 years old</td><td>9.3</td><td>10.1</td></tr><tr><td>25-49 years old</td><td>63.3</td><td>59.3</td></tr><tr><td>50 years old and more</td><td>27.4</td><td>30.6</td></tr><tr><td><strong>Socio-professional category</strong></td><td></td><td></td></tr><tr><td>Managers and higher intellectual professionals</td><td>16.7</td><td>22.7</td></tr><tr><td>Middle-level occupations, technicians and associate professionals</td><td>18.2</td><td>26.1</td></tr><tr><td>Skilled clerical, sales and services employees</td><td>1.8</td><td>16.6</td></tr><tr><td>Unskilled clerical, sales and services employees</td><td>0.1</td><td>12.9</td></tr><tr><td>Skilled industrial and blue-collar workers</td><td>55.8</td><td>14.8</td></tr><tr><td>Unskilled industrial and blue-collar workers</td><td>7.4</td><td>6.9</td></tr><tr><td><strong>Diploma level</strong></td><td></td><td></td></tr><tr><td>Long tertiary diploma (more than Bac+2)</td><td>25.5</td><td>29.7</td></tr><tr><td>Short tertiary diploma (Bac +2)</td><td>7.0</td><td>15.3</td></tr><tr><td>Upper secondary diploma (Baccalauréat or equivalent)</td><td>15.4</td><td>21.3</td></tr><tr><td>Short vocational secondary diploma (CAP, BEP or equivalent)</td><td>30.5</td><td>20.8</td></tr><tr><td>No diploma (or lower secondary diploma at most)</td><td>21.7</td><td>13.0</td></tr><tr><td><strong>Sector of activity</strong></td><td></td><td></td></tr><tr><td>Agriculture, forestry and fishing</td><td>1.2</td><td>1.2</td></tr><tr><td>Manufacturing, mining and other industries</td><td>20.4</td><td>13.7</td></tr><tr><td>Construction</td><td>3.6</td><td>5.8</td></tr><tr><td>Wholesale and retail, transportation, accommodation and food service</td><td>4.1</td><td>22.4</td></tr><tr><td>Information and communication</td><td>0.4</td><td>3.5</td></tr><tr><td>Financial and insurance activities</td><td>0.8</td><td>3.6</td></tr><tr><td>Real estate activities</td><td>0.7</td><td>1.1</td></tr><tr><td>Professional, scientific and technical activities, and administrative and support service activities</td><td>22.6</td><td>10.2</td></tr><tr><td>Public administration, education, human health and social work activities</td><td>37.5</td><td>33.0</td></tr><tr><td>Other services activities</td><td>8.8</td><td>5.6</td></tr><tr><td><strong>Type of employer</strong></td><td></td><td></td></tr><tr><td>Private company or NGO</td><td>54.9</td><td>70.5</td></tr><tr><td>Public company</td><td>4.6</td><td>4.4</td></tr><tr><td>State or public administrative establishment</td><td>5.5</td><td>10.7</td></tr><tr><td>Territorial authority</td><td>28.7</td><td>7.2</td></tr><tr><td>Hospital or public medico-social establishment</td><td>0.7</td><td>4.4</td></tr><tr><td>Social security body</td><td>0</td><td>0.2</td></tr><tr><td>Individual employer</td><td>5.5</td><td>2.4</td></tr><tr><td><strong>Firm size</strong></td><td></td><td></td></tr><tr><td>Between 1 and 9 people</td><td>18.8</td><td>18.0</td></tr><tr><td>More than 10 people but don't know the range</td><td>6.7</td><td>5.3</td></tr><tr><td>Between 10 and 19 people</td><td>13.5</td><td>11.8</td></tr><tr><td>Between 20 and 49 people</td><td>18.0</td><td>15.5</td></tr><tr><td>Between 50 and 249 people</td><td>24.0</td><td>24.1</td></tr><tr><td>250 or more</td><td>19.0</td><td>25.3</td></tr><tr><td><strong>Weighted share of total employees</strong></td><td><strong>1.6</strong></td><td><strong>100</strong></td></tr><tr><td><strong>N</strong></td><td><strong>783</strong></td><td><strong>46,825</strong></td></tr></tbody></table>

Source: French Labour Force survey (2021-2022). Weighted data.
<table><caption>Table A2. List of top ten detailed occupations (French nomenclature) in green jobs</caption><thead><tr><td>PCS4 code</td><td>Original title in French</td><td>Title translated in English</td><td>%</td></tr></thead><tbody><tr><td>63A1</td><td>Ouvrier(e)s des parcs et jardins</td><td>Park and garden workers</td><td>45.4</td></tr><tr><td>47A1</td><td>Technicien(ne)s de l'agriculture, de l'aquaculture, des forêts et de la protection de l'environnement</td><td>Agricultural, aquaculture, forestry and environmental protection technicians</td><td>7.8</td></tr><tr><td>68D2</td><td>Ouvrier(e)s peu qualifié(e)s de l'assainissement et du traitement des déchets</td><td>Low-skilled sanitation and waste treatment workers</td><td>7.4</td></tr><tr><td>62C4</td><td>Ouvrier(e)s qualifié(e)s de l'eau, de l'énergie et du traitement des déchets</td><td>Skilled water, energy and waste treatment workers</td><td>6.7</td></tr><tr><td>38F3</td><td>Ingénieur(e)s et cadres du contrôle-qualité et de la prévention des risques</td><td>Quality control and risk prevention engineers and managers</td><td>4.8</td></tr><tr><td>47D2</td><td>Technicien(ne)s du contrôle-qualité et de la prévention des risques</td><td>Quality control and risk prevention technicians</td><td>4.7</td></tr><tr><td>38B1</td><td>Ingénieur(e)s et cadres techniques de l'agriculture, de l'aquaculture, des forêts et de la protection de l'environnement</td><td>Engineers and technical managers in agriculture, aquaculture, forestry and environmental protection</td><td>3.7</td></tr><tr><td>64B2</td><td>Chauffeur(se)s livreur(se)s, conducteur(ice)s de la collecte des déchets</td><td>Delivery drivers, waste collection drivers</td><td>3.7</td></tr><tr><td>33B1</td><td>Ingénieur(e)s et cadres techniques de la fonction publique</td><td>Civil service engineers and technical managers</td><td>3.4</td></tr><tr><td>47C3</td><td>Technicien(ne)s de fabrication industrielle</td><td>Industrial manufacturing technicians</td><td>3.4</td></tr><tr><td colspan="2">Total</td><td></td><td>91</td></tr></tbody></table>

Note: To get a broader view of the jobs covered, we present them at a higher level of aggregation than that of the occupational titles used for identification (for some of them, see Table A3). Translated with DeepL.com

Source: French Labour Force survey (2021-2022). Weighted data.
<table><caption>Table A3. List of top ten occupation titles within each cluster of green jobs</caption><thead><tr><th>Cluster 1 – Good green jobs</th><th>%</th></tr></thead><tbody><tr><td>Quality, safety and environment (QSE) manager – Responsable qualité sécurité environnement (QSE)</td><td>11.3</td></tr><tr><td>Environmental engineer (environmental protection) – Ingénieur(e) environnement (protection de l'environnement)</td><td>7.3</td></tr><tr><td>Energy research and development (R&D) engineer – Ingénieur(e) recherche et développement (R&D) de l'énergie</td><td>6.8</td></tr><tr><td>Environment project officer – Chargé(e) de mission environnement</td><td>5.7</td></tr><tr><td>Agricultural and environmental engineer – Ingénieur(e) de l'agriculture et de l'environnement (IAE)</td><td>4.2</td></tr><tr><td>Environmental inspector – Inspecteur(ice) environnement</td><td>3.0</td></tr><tr><td>Environmental consultant – Chargé(e) d'études en environnement</td><td>2.9</td></tr><tr><td>Industrial risk prevention engineer – Ingénieur(e) prévention des risques industriels</td><td>2.9</td></tr><tr><td>Environmental manager (industry) – Responsable environnement (industrie)</td><td>2.7</td></tr><tr><td>Geotechnical engineer – Ingénieur(e) géotechnique</td><td>2.6</td></tr></tbody></table>

<table><thead><tr><th>Cluster 2 – Green stable average jobs</th><th>%</th></tr></thead><tbody><tr><td>Landscaper – Paysagiste</td><td>18.2</td></tr><tr><td>Green spaces maintenance agent – Agent d'entretien des espaces verts</td><td>13.7</td></tr><tr><td>Gardener – Jardinier(e)</td><td>6.0</td></tr><tr><td>Municipal gardener – Jardinier(e) municipal(e)</td><td>5.0</td></tr><tr><td>Green spaces employee – Employé(e) des espaces verts</td><td>4.8</td></tr><tr><td>Refuse truck driver – Chauffeur de bennes à ordures</td><td>3.7</td></tr><tr><td>Refuse collector – Ripeur(se) éboueur(se)</td><td>3.5</td></tr><tr><td>Road cleaning agent – Agent de nettoyage de la voirie</td><td>3.4</td></tr><tr><td>Waste sorting agent – Agent de tri des déchets</td><td>2.8</td></tr><tr><td>Environmental technical officer – Agent technique de l'environnement (ATE)</td><td>2.0</td></tr></tbody></table>

<table><thead><tr><th>Cluster 3 – Green odd jobs</th><th>%</th></tr></thead><tbody><tr><td>Gardener – Jardinier(e)</td><td>30.4</td></tr><tr><td>Landscaper – Paysagiste</td><td>22.6</td></tr><tr><td>Green spaces maintenance agent – Agent d'entretien des espaces verts</td><td>14.0</td></tr><tr><td>Landscape gardener – Jardinier(e) paysagiste</td><td>6.2</td></tr><tr><td>Quality, safety and environment (QSE) manager – Responsable qualité sécurité environnement (QSE)</td><td>6.2</td></tr><tr><td>Green space worker – Ouvrier(e) des espaces verts</td><td>5.1</td></tr><tr><td>Green spaces employee – Employé(e) des espaces verts</td><td>2.3</td></tr><tr><td>Gardener employee – Employé(e) jardinier</td><td>2.2</td></tr><tr><td>Environmental coordinator – Animateur(ice) environnement</td><td>1.9</td></tr><tr><td>Environmental technician (environmental protection) – Technicien(ne) environnement (protection de l'environnement)</td><td>1.8</td></tr></tbody></table>

<table><thead><tr><th>Cluster 4 – Precarious green jobs</th><th>%</th></tr></thead><tbody><tr><td>Green spaces maintenance agent – Agent d'entretien des espaces verts</td><td>30.3</td></tr><tr><td>Gardener – Jardinier(e)</td><td>16.7</td></tr><tr><td>Waste sorting agent – Agent de tri des déchets</td><td>8.3</td></tr><tr><td>Municipal gardener – Jardinier(e) municipal(e)</td><td>6.1</td></tr><tr><td>Green space worker – Ouvrier(e) des espaces verts</td><td>5.4</td></tr><tr><td>Green spaces employee – Employé(e) des espaces verts</td><td>4.3</td></tr><tr><td>Environmental advisor (environmental protection) – Conseiller(e) environnement (protection de l'environnement)</td><td>4.1</td></tr><tr><td>Gardener employee – Employé(e) jardinier</td><td>3.6</td></tr><tr><td>Waste centre operator – Agent d'exploitation de déchetterie</td><td>3.5</td></tr><tr><td>Waste collection operator – Équipier(e) de collecte de déchets</td><td>3.5</td></tr></tbody></table>

Note: Translated with DeepL.com

Source: French Labour Force survey (2021-2022). Weighted data.
## Graph A1. HAC dendrogram

<figure><img src="image_4.png" /></figure>

Method: Dendrogram calculated from the square of the Φ² distances using Ward's method. The tree is cut according to the partition with the higher relative loss of inertia.

## Section B. Identifying green jobs in France: the original approach adopted by the French statistical system

As operational definitions of green jobs need to rely on existing nomenclatures, the 2020 renewal of the French classification of occupations was an opportunity to discuss the definition of new aggregates that would be included in French representative databases, in particular that of green jobs.

The work on green jobs' identification in the new classification was jointly carried out by different public actors: Insee (French national statistic department), SDES (department of statistics of the Ministry of Ecological Transition), Céreq (Center for studies and research on qualifications), Onemev, etc. The definition of green jobs was based on an automated textual analysis of job titles collected in a large French representative survey on the professional integration of young people called *Génération*, using a lexicon of green keywords. It also relied on the identification of these green keywords in the 'Operational Directory of Professions and Jobs' (ROME) used by the French Employment Agency.

Finally, as pointed in the presentation report, “particular attention was paid to the discrepancies that can exist between the intention to ‘measure, prevent, control and correct negative impacts and damage to the environment’, the identification of a job title based on a key word, and the actual effect on the environment. Although the possibility of green washing
cannot be completely ruled out, the proposed list makes it possible to identify ‘green’
occupations much more precisely” (Amossé et al., 2019, p. 79).

It identifies job titles associated with various fields or activities with an environmental focus, including: (i) the study of environment and sustainable development; climate and climate change; natural environments (air, water, land); biodiversity, ecosystems, nature, fauna and flora; (ii) the improvement of energy efficiency (buildings, transport) and the development of renewable energies; hygiene, health, safety and environmental prevention (including agriculture, industry and tourism); the protection and preservation of natural heritage; (iii) the management of industrial, environmental and natural risks; of waste and recycling; pollution measurement and decontamination; wastewater treatment and sanitation; and urban cleanliness.