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This chapter delves into the significant structural changes brought about by digitalisation and the shift towards a green economy, focusing on their impact on the labour market. It explores the effects of robotisation and automation, highlighting the rise of industrial robotics and its implications for various industries. The chapter also examines the emergence of digital platforms and their role in reshaping work organisation and labour relations. Additionally, it discusses the potential for polarisation in the labour market due to digitalisation and the need for upskilling and reskilling. The green transition and its effects on industrial transformation and employment are also analysed, with a focus on the European Green Deal and its policy initiatives. The chapter concludes by presenting a model for assessing the combined impact of digitalisation and green transformation on the labour market, offering insights into the future of work in a sustainable economy.
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Abstract
This chapter explores the dual impact of digitalisation and the green transition on the labour market, highlighting how these transformations reshape work structures, employment relations, and skill demands. It first examines digitalisation through the lenses of automation, robotisation, and digital labour platforms. While these developments boost productivity, they also pose risks such as job displacement and labour market polarisation, especially for low- and medium-skilled workers. Additionally, new forms of platform-based, non-standard work challenge traditional legal definitions and weaken social protections. The green transition is discussed as a major industrial shift influenced by EU policies, particularly the European Green Deal. The chapter outlines how innovation, energy efficiency, and sustainable production alter employment patterns. It identifies key mechanisms of change—such as resource efficiency and process innovation—and evaluates their implications for job creation and transformation. By integrating technological and environmental perspectives, the chapter proposes a framework to assess how these twin transitions interact and shape labour dynamics. It also points to policy measures that can mitigate associated risks and support inclusive, sustainable employment growth.
The past decade has seen unprecedented structural changes, including business automation, digitalisation of systems, and the shift toward the green economy to mitigate climate change. These changes reflect the profound phenomena which take place in the economy and society due to the use of digital technologies (Majchrzak et al. 2016; Agrawal et al. 2022) and popularisation of sustainable economic systems (Razminienė and Tvaronavičienė 2018; Baldassarre et al. 2019). Thus, they indisputably have a significant impact on the labour market, affecting the balance of labour demand and supply, the skills of the labour force required by the market, and the forms of work that emerge in the modern economy. Perception of the links between the phenomena mentioned above and the analysis of their effects on the labour market can help to assess the potential of the long-term sustainable development of the labour market and identify the major risk factors which can be purposefully managed both at the national and the EU levels. This section analyses various aspects of the impact of digitalisation and green transformation on the labour market and presents a model for assessing their combined impact.
2.1 Effects of Digitalisation on the Labour Market
Digitalisation is widely recognised as one of the most significant drivers of structural economic transformation in contemporary societies. Its importance has been emphasized in the World Economic Forum in Davos, Digital Innovation Hubs Day in Stuttgart, Mobile World Congress in Barcelona and many other conferences and debates. Although digitalisation is considered one of the key factors in the green transformation of economic and social life, it will undoubtedly lead to significant structural changes in social life and the labour market.
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The digital transformation of business activities is a response to the rapid development of digital technologies. Digital transformation in business refers to the organisational transformations which are related to the changes in internal business processes, sectoral changes, and transformations of supply chains (Hetmanczyk 2023). In contrast to the traditional approach, where the labour market focused on job functions and characteristics, digitalisation requires competency-based human resource management. In the process of digitalisation, we are moving towards attention to diversity, openness, knowledge, willingness to accept changes, the use of various distribution channels and more efficient organisation of work. Technologies are becoming a source of comparative advantage which can be exploited by technologically literate individuals as an advantage in the labour market, and employers can exploit this advantage to achieve desired business outcomes.
Digitalisation also alters the distribution and quality of jobs, intensifies the need for re-skilling, and reshapes the nature of work relations (Vasilescu et al. 2020). When analysing the impact of digitalisation on the labour market, it is important to answer the questions of how digitalisation changes the distribution of jobs, which jobs are newly created and which are likely to disappear, how work processes are organised and relations between workers and employers are formed, and whether there is a risk of polarisation in the labour market (Buhrer and Hagist 2016).
Based on the literature, the main dimensions through which digitalisation affects the labour market can be summarised as follows (Macias 2018; Vasilescu et al. 2020):
1)
robotisation and automation of work;
2)
work organising through digital platforms;
3)
organising the relations between workers and employers;
4)
polarisation in the labour market.
These aspects are examined in the following subsections. However, while prior research provides a solid foundation, there remains a need to distinguish how these transformations manifest differently across employment categories and national contexts. The analysis that follows addresses this gap by exploring digitalisation in relation to emerging forms of employment regulation and labour market policies, with a particular focus on platform work.
2.1.1 Effects of Robotisation and Automation of Work
This section outlines how automation and robotisation—key drivers of the digital economy—are reshaping employment structures across sectors, with potential long-term implications for platform-based labour models.
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In the next decade, digitalisation may lead to changes in how businesses generate profits: advanced robotics and artificial intelligence solutions will be increasingly used to generate profits. Experts believe the number of robots to increase not only in factories, but also in the environment near people—in workplaces, at home, in streets. 5G technologies will allow digital devices to be connected to a single network. The development of the modern industry and the improvement of the quality of life will require the management of big data and the development of business algorithms (Raczko 2015). Today’s cloud computing solutions will make service delivery processes digital, and at the same time will transform service business models (Raczko 2015).
Industrial robotics is gaining momentum, and the market share of industrial robotics is growing rapidly.
Figure 2.1 indicates that the global market share of industrial robotics was estimated at $18.31 billion in 2022; it is expected to reach $23.44 billion in 2024, and $65.23 billion by 2032, i.e. it is forecast to grow more than 3.5 times in 10 years. Robots are increasingly being used in different industries, most notably in manufacturing. If initially large robotic arms were used to handle heavy objects (e.g. car parts), with the advent technologies, such as visual recognition, machine learning, failure prediction, and collaborative robots (co-bots), the potential of robots has increased significantly.
Fig. 2.1
Dynamics of the industrial robotics market share between 2022 and 2023 (USD billion). Source: “Precedence Research”, 2023
There were 3.9 million operational robots at the global level in 2022 (the World Robotics 2023 Report, presented by the International Federation of Robotics (2023)). The global robot density (the number of industrial robots per 10,000 workers) was 151 in 2022.
Figure 2.2 shows that the leading positions in terms of robot density in 2022 were held by the Republic of Korea (1012 robots per 10,000 employees), Singapore (730 robots per 10,000 employees) and Germany (415 robots per 10,000 employees). The average robot density in Europe is 136, in the EU—208, with Germany being the clear leader with a 5 percent compound annual growth rate (CAGR) increase in robot density since 2017 (Bachmann et al. 2022).
Fig. 2.2
Robot density in the manufacturing industry in 2022. Source: “statist”, 2024
Robots have become an integral part of many industries. The illustration of the top industries, where robots are most widely used, is presented in Fig. 2.3.
Fig. 2.3
Top 10 robot using industries. Source: compiled by the authors with reference to Fairchild (2021)
The automotive industry was the largest user of robots in 2020. About 28 percent of industrial robots are used in this industry (Fairchild 2021). The electrical/electronics industry uses robots because the processes in this industry are characterised by repetitive production and standardization of parts. However, compared to the automotive industry, electronic parts are much smaller than heavy metal car parts. Thus, robots used in electronics tend to be smaller, lighter and faster. The metalworking sector uses robots which can work at high temperatures and rotate at high speeds. In the plastics and chemical industries, they help protect workers from exposure to harmful substances. Although the construction sector is still labour-intensive, robots are increasingly used here for lifting cement blocks and tying reinforcing metal bars. The agricultural sector uses self-driven vehicles, weeding and harvest picking robots, thus reducing high labour costs. The higher operational productivity and lower labour costs generated by robots can also be seen in the warehouse and logistics sector, where box loading, labelling, and sorting processes have been automated. The packaging of food products by robots is already in its commercial stage, and automated storage and retrieval systems (AS/RS) can be fit into the back of many grocery stores. In the pharmaceutical industry, robots place tablets into blister packs and dispense precise amounts of liquid into testing vials (Fairchild 2021).
According to Goos (2018), robotisation and automation are the structural components of business transformation which will lead to changes in the labour market in the near future. “The Future of Jobs Report”, provided by the World Economic Forum (2018), indicates that 28 percent of business companies worldwide expect to increase their work productivity by 2022, and more than a quarter expect that automation will help create new jobs in their companies. Businesses are intending to expand the use of outsourcing contractors for specialised work, also to decentralise operations, and employ workers on flexible terms for remote work. In general, the report points out that the format, location, quality, and duration of new job functions will change significantly. Previous studies suggest that the effects of robotisation and automation on the labour market can be mixed (see Table 2.1).
Table 2.1
The potential effects of industrial automation and robotisation on the labour market: review of some previous findings
Rapid growth of labour productivity, easier work; increasing demand for technology control
Difficulties for the low-skilled labour force to adapt to the new environment; declining demand for manual labour
–
Source: compiled by the authors
After conducting a study in Germany in the period 1994–2014, Dauth et al. (2021) found that automation can be associated with stable employment for incumbent workers because they can learn to perform new types of tasks in their old company, and the new jobs are often of better quality than the old ones. Robots also help reduce workload, thus freeing managers and technical workers from additional tasks. Nevertheless, robotisation can cause the displacement effect in the manufacturing sector, but this effect is likely to be offset by the increase in the number of jobs in the service sector. These findings are slightly different from the results of the German labour market research, presented by Damelang and Otto (2023): having researched the German manufacturing industry, Damelang and Otto (2023) found a considerably lower unemployment risk for high-skilled workers, but did not find any greater robotisation-caused risks for low- and medium-skilled workers. The study by Klenert et al. (2022) also did not find any evidence of robots reducing the share of low-skilled workers in Europe; their study implies that the use of robots is associated with an increase in aggregate employment. These contrasting findings suggest that the employment effects of robotisation cannot be generalised across economies and labour categories. The policy environment, sectoral structure, and adaptability of the workforce are all crucial mediators of automation outcomes.
After conducting a study of the impact of the use of industrial robots on European regional labour markets (in particular, on jobs and employment structures) in the period 1995–2015, Anton et al. (2022) found that the impact of industrial robotisation on employment was negative, but insignificant between 1995 and 2005, and positive between 2005 and 2015. Their research also implies that that robotisation may lead to a slight polarisation in the labour market. Bachmann et al.’s (2022) research, focused on the effects of robotisation on worker flows in 16 European countries in the period 1998–2017, revealed a slight negative effect on job separations and a slight positive effect on finding a job. Their research also found that robotisation has a beneficial effect on the labour transition of young and old workers in the countries with low labour costs.
Having combined country-industry data for robot adoption and occupations, de Vries et al. (2020) found that robotisation tends to reduce the number of manual-task intensive jobs, and this tendency is more pronounced in high-income countries compared to developing economies, i.e. the results of the study imply that the impact of robotisation on the labour market may depend on a country’s level of development: a decrease in jobs is observed in the areas where work functions are associated with intensive routine manual tasks in high-income countries, but this correlation was not found in emerging and transition economies. The authors explain this tendency by invoking the fact that labour costs affect the economic incentives of companies since the higher are the labour costs, the greater is the probability that human labour will be replaced with robotic labour, other conditions being equal. Thus, robotisation is likely to have less significant impact on the economies with low labour costs than the economies with higher labour costs.
Gregory et al.’s (2021) study revealed both positive and negative aspects of the impact of automation and robotisation on the labour market. The authors found that automation and robotisation primarily threaten employment in European countries since robots replace a significant part of the human workforce (a direct effect). The indirect positive effect manifests itself as follows: robotic technologies allow for increased labour productivity, which, in its turn, creates greater value added and generates higher income for the companies and sectors which are most automated and robotized. The effect of higher income spills over into other sectors: the demand for products/services in other sectors is growing, and the number of workplaces is increasing respectively. This results in an indirect positive effect of automation and robotisation on total employment. Similar findings were provided by Bachmann et al. (2022) and Klenert et al. (2022).
Having researched the economic impact of modern industrial robots which are flexible, versatile and autonomous, Graetz and Michaels (2018) found that robots can provide a comparative advantage in performing specific tasks. The authors estimated that the robots increase labour productivity by approximately 0.36 percentage points, and also contribute to the total factor productivity and reduce output prices, thereby reducing the employment share of low-skilled workers, but do not significantly reduce total employment. Koch et al.’s (2021) study of the Spanish manufacturing sector revealed that robots are likely to be installed in ex ante more productive (measured though output and labour productivity) companies; they are expected to increase the output gains by 20–25 percent, reduce labour costs by 5–7 percentage points, and raise net job creation rate by 10 percent within 4 years.
Nevertheless, these results contradict the findings presented by Acemoglu and Restrepo (2020). After researching the potential effects of robots on employment and wages, Acemoglu and Restrepo (2020) noticed that the effects of robots on the economy differ from the effects of types of capital and technology and found that one additional robot per 1000 workers tends to reduce the employment-to-population ratio by approximately 0.2 percentage points, and lessens wages by 0.42 percent. According to Chinoracky and Čorejova (2019), the increasing use of industrial robots will lead to faster labour productivity growth. Some workers will even benefit from it, as the nature of their work will become more pleasant (job functions will be limited to robot maintenance) and they will likely earn higher wages (robot maintenance will require specific skills rare in the labour market, so these skills will be well paid). Meanwhile, other workers will find it difficult to adapt to the new environment, they will face the risk of losing their jobs since the demand for robot maintenance and management will gradually increase, whereas the demand for manual labour will decrease accordingly. The statistical data portal “Statista” (2024) provides the forecasts (based on the estimations by “PwC”, “Frey and Osborne” and OECD), which indicate that the share of jobs at high risk of automation is such developed economies as the USA, the United Kingdom, Germany and Japan, is significant.
According to “Statista” (2024) the share of jobs at high risk of automation can exceed 30 percent (according to the data from different assessment institutions), i.e. nearly a third of the workforce may face the problem of job loss due to automation. While much of the current research and data relate to traditional industries, the observed automation trends are gradually spilling over into platform-mediated work environments, where algorithmic management and autonomous systems increasingly shape task distribution and working conditions.
According to the U.S. Government Accountability Office (2022), workers with lower education and performing routine tasks (e.g. cashiers, case managers, data loggers, accountants) are going to face the greatest risk of losing their jobs. Workers at risk of losing their jobs to automation will need to develop new skills to adapt to changing job demands or to get a new job. The U.S. Government Accountability Office (2022) indicates that the following skills will see the greatest growth in demand over the next 10 years:
a)
soft skills, i.e. communication skills for successful communication with people;
b)
process skills, i.e. the skills which help to quickly acquire knowledge (e.g. active learning, critical thinking);
c)
specific technical expertise skills, i.e. the skills which are required for the management and maintenance of certain equipment (e.g. robot management).
Thus, worker training and retraining programs should be directed to help workers to build the skills which are expected to be in-demand for getting a high-quality job.
Overall
The findings presented in this section reveal that the effects of robotisation on employment are complex and context-dependent. Although automation can displace low-skilled labour, its aggregate impact on employment varies across countries and sectors, influenced by wage levels, institutional capacity, and complementary policies such as reskilling programmes. Importantly, this raises questions about whether similar patterns may emerge within the platform economy—where automation is embedded not only in machines but in algorithmic management itself.
2.1.2 Work Organising Through Digital Platforms
Historically, work was geographically and institutionally bounded (Hudson 2001). However, the global expansion of internet connectivity has fundamentally transformed the organisation and coordination of labour. Digital labour platforms emerged in the mid-2000s as a modern phenomenon of the digital economy. In fact, it was part of the evolution of the digital economy, when the shift from online digital services towards institutional and technological drivers occurred between 2000 and 2001. This shift allowed the development of digital platforms as an emerging flexible model of work (Valenduc 2021). Digital labour platforms facilitate new forms of work organisation and restructure traditional labour processes (Rani and Furrer 2021; Collins 2021; Torrent-Sellens et al. 2021; Cruz and Gameiro 2023; Leong et al. 2023, etc.). They are not only technological tools but also social institutions that reshape labour relations, control mechanisms, and income generation models.
Digital platforms operate as intermediaries, linking service users with providers (Berg et al. 2018; ILO 2018c; OECD 2019b; Dunn 2020; Rivera and Lee 2021; Kalamatiev and Murdzev 2022, etc.) (see Fig. 2.4). Working through platforms, workers see various tasks posted by clients to a crowd and can choose to complete these tasks (ILO 2018a). Gawer (2021) notes that digital platforms facilitate direct interaction between two or more economic agents. Basically, it is the interaction within a group participants carried out through technological means (computing and network resources (the Internet, data centres, open standards, and consumer devices) which collect and store digital data) (Evans and Gawer 2016; Constantinides et al. 2018; Jacobides et al. 2018), where either a customer or a platform owner/manager can be treated as an employer, while a product supplier or service provider acts as a worker (Kilhoffer et al. 2017; Graham et al. 2017). These digital infrastructures not only mediate transactions but also embed algorithmic control systems that determine access to work, monitor performance, and enforce discipline. This concept of digital platforms shows that digital platform labour is not based on the traditional concept of ‘firm plus employee’, but uses the model ‘platform plus individual’ (sharing model via crowdsourcing, referred to as C2C) (Zhou 2020). Nevertheless, as noted by Kassi and Lehdonvirta (2016), though being flexible and short-term, digital platform labour (or crowdwork) must be recognised as a model of employment.
Fig. 2.4
Illustration of the principle of operation of digital platforms. Source: compiled by the authors, based on the data by the Council of Europe (2024)
Coordination of work through digital platforms is essential to ensure that participants are aware of their specific tasks and interdependencies (Van Alstyne et al. 2016). Nevertheless, practical coordination of work poses many challenges since the activities of interrelated, but at the same time independent actors are legally autonomous. In contrast to the coordination of employee activities in a traditional organisation based on a hierarchical structure, where actors are bound by the ties of legal employment, digital platforms do not have any formal authority over participating agents (Leong et al. 2023). Moreover, digital platforms are even expected to help workers bypass intermediaries (represented by employers in the traditional employment model) and gain direct access to domestic or international digital labour markets in response to the demand for certain skills (Graham et al. 2017).
European Commission’s (2021) Proposal for a Directive of the European Parliament and of the Council on Improving Working Conditions in Platform Work defines digital platform work as a work performed “by an individual on the basis of a contractual relationship between the digital labour platform and the individual, irrespective of whether a contractual relationship exists between the individual and the recipient of the service” (p. 33). This definition indicates that the relationship between a person who acts through a digital platform as a service provider and the platform itself is contractual, but the relationship between a service provider and a recipient does not have to be formalised. In any case, the contractual nature of the relationship between a digital platform and a worker is one of the central elements of digital platform work, although there is much debate as to whether this contract can be equated to an employment contract. Even if not, it has many characteristics of an employment contract: it accumulates certain skills, knowledge and expertise which are used to accomplish particular tasks (Idowu and Elbanna 2020), a salary is paid for the performance of tasks, which is one of the central elements of the employment relations (Drugau-Constantin 2018; Berg et al. 2018). Considering the fact that digital platform work is recognised as a flexible modern mode of employment, whether temporary or permanent, it can be stated that digitalisation has made digital platforms an integral part of the global labour market.
When analysing the global market of digital platform work, researchers find that the demand for digital platform work is the greatest in the United States (47 percent of the total global demand), and most of the supply is generated by developing countries, notably India, Bangladesh, Pakistan, the Philippines, and Ukraine (Rani and Dhir 2020). According to the estimates provided by the Council of Europe (2024), there were 28.2 million digital platform workers in the European Union in 2022, and this number is expected to grow to 43 million in 2025. This quantitative growth not only indicates market expansion but also underscores the urgency to address regulatory asymmetries between supply and demand regions. These estimations show that the market for digital platform work in Europe has a large growth potential.
Digital labour platforms are penetrating more and more sectors of the economy. According to the ILO (2021a, b), digital platform work is carried out in such key areas as service provision to individual users, work mediation, and exchange facilitation and mediation.
Figure 2.5 illustrates that with the mediation of digital platforms; work can be carried out on the web or in locations. Such remote services as microtasking, competitive programming, medical consulting and various other freelanced work are provided via the web. The main services provided at a customer’s place include taxi, carriers, delivery, various home services and domestic work, care services (care for children, elderly people). Services provided to individual customers may cover news, media, entertainment, advertising, search, reviews, rental goods and assets, communication, application and marketplace, while exchange facilitation and mediation services, typically used by businesses, cover retail and wholesale, manufacturing marketplace and analytics, agriculture marketplace and analytics, financial lending and analytics. This fragmentation of work modalities highlights the hybrid nature of platform labour, which blurs the lines between online and offline service provision, as well as between economic sectors.
Fig. 2.5
Work areas and services on digital platforms. Source: compiled by the authors, based on the report by the ILO (2021a, b)
The Council of Europe (2024) provides a statistics on the distribution of the most popular services available through digital platforms.
The most common services provided through digital platforms in the EU are taxi and transportation services and food and grocery delivery services, home services (such as cleaning and craft) being third. All of them are the services, provided on location, while web-based services occupy a relatively small share in the market of digital platform services in the EU. This predominance of location-based services reflects the current demand structure and implies that, despite the digital nature of platforms, much of the work remains physically embedded and reliant on human presence.
However, according to the statistics, the payment for working through digital platforms is not high. For instance, Berg (2016), De Stefano (2016) and Berg et al. (2018) found that a statistically significant proportion of digital platform workers work for a salary that is lower than the average in their professional category, or is lower than the established minimum monthly salary or an hourly wage rate. According to the ILO (2017), a digital platform worker earned an average of $4.43 per hour in 2017 when calculating paid working time only (part of the time that digital platform workers spend searching for tasks, participating in forums, responding to requests, leaving responses is not paid), and with consideration of the balance between total paid and unpaid hours, the average hourly wage drops to $3.29. The payment for digital platform work is often lower than national minimum rates. For instance, Forde et al. (2017a, b) provide the estimations which show that the median pay for digital platform work in France is 54.1 percent, in the UK—46.8 percent, in Germany—29.3 percent, and in Spain—9.1 percent lower than the national hourly minimum wage. When analysing this situation from an economic point of view, Forde et al. (2017a, b) explain that the relatively low wages for digital platform work are determined by two main reasons: first, shorter paid work time (task search and communication time is not paid); second, the global competition (residents from developed countries compete on digital platforms with cheaper labour force from developing countries, which reduces the average wage level) (the latter argument was confirmed by the results of the study conducted by Forde et al. (2017a, b) not only in France, but also in the UK and Bulgaria). These wage dynamics highlight a fundamental tension within platform work: while it expands opportunities for income generation, it simultaneously weakens the bargaining power of workers, especially in transnational labour markets lacking coordinated wage-setting mechanisms.
One of the main personal motivations why individuals choose to work through digital platforms despite relatively low wages is flexibility and autonomy. For instance, Tusinska (2023) conducted a survey, the results of which revealed that the flexibility and autonomy of working through digital platforms is important for 50 percent of the respondents. The importance of this factor was also confirmed in the studies by Barnes et al. (2015) and Torrent-Sellens et al. (2021). Tomlinson and Corlett’s (2017) study in the United Kingdom revealed an increasing number of mid-professionals who give up their permanent full-time employment and accept a modest decrease in income in exchange for greater flexibility, which is characteristic of digital platform work (flexibility is understood as the ability to freely choose working time, duration, location, means, etc. (Woodcock and Graham 2019), and also the freedom to optimise available resources (e.g. time, technological means, other assets) (Burtch et al. 2018)). The survey, conducted by the European Institute for Gender Equality (EIGE) and the European Foundation for the Improvement of Living and Working Conditions (Eurofound) (2023), shows that work flexibility is the desired work characteristic that does not depend on the age or education of a working person, i.e. work flexibility motivates whether younger or older, lower- or higher-skilled workforce. This suggests that platform work aligns with evolving worker expectations regarding autonomy, indicating a broader shift away from rigid employment models and towards task-based, self-directed economic activity.
As for the skill level required for work through digital platforms, it should be noted that theoretically the skill level of a person should serve as the basis for the nature, scale and complexity of the tasks a person can be hired for (i.e. higher competence should raise the potential of a person to perform more complex and better-paid works). Nevertheless, the concept of “crowdwork” is based on the idea that digital platform work is open to anyone and does not require high qualifications, i.e. the assumption that task performers are interchangeable is followed (Schmidt 2017). The Council of Europe (2024) provides the information which shows that digital platform work generally requires low skills (see Fig. 2.6):
Fig. 2.6
Qualification required for digital platform work in the EU. Source: compiled by the authors, based on the data by the Council of Europe (2024)
Thus, most digital platform workers are over-qualified for the work they do. In this way, the potential of the workforce is not fully utilized (e.g. a qualified engineer can work as a food delivery person), and the requirements formally set on a platform to perform a certain task are not related to a person’s education and qualification, which leads to the so-called “skill mismatch” in the digital labour market (“Cedefop” 2020). This pattern illustrates a structural inefficiency in labour allocation, where accessible platform work can paradoxically result in the underuse of human capital, particularly among the highly educated segments of the workforce.
When analysing the advantages and disadvantages of digital platform work in the economic context, it should be noted that this type of work contributes to simple and faster labour market transactions, it promotes unusual forms of work, such as self-employment and multiple employment (combining platform work with traditional work, work across multiple platforms, etc.) (Torrent-Sellens et al. 2021). Aleksynska et al. (2019) note that digital platform work helps to match labour supply and demand and go beyond the boundaries of local labour markets, thus eliminating their geographical limitation. In addition, the process of matching labour demand and supply is short (a worker is quickly found to perform a specific task). This allows to minimise the frictions inherent in labour markets and reduce transaction costs (De Stefano 2016). By using digital labour platforms, businesses can relatively quickly find workers with the necessary skills and competencies that would be expensive to hire on a permanent basis (Marvit 2014; Aleksynska et al. 2019). It can be observed that these advantages are particularly attractive to employers and platform owners, while the benefits to workers are often limited by precarious conditions, income instability and weak access to social protection.
When analysing digital platform labour from the point of view of labour supply, it can be observed that digital labour platforms help to use the skills of the labour supply in the most efficient way (Graham et al. 2017). According to Sundararajan (2016a, b), digital labour platforms provide innovative income earning opportunities, can expand the market for individuals creating intellectual property, allow service providers to set their own prices, manage customer relationships and create long-term customer relationships based on growing reputation, thus promoting public entrepreneurship.
Summarising
The model of work organising through digital platforms shows that digitalisation is accelerating structural transformations in the labour market and promoting more flexible forms of employment. Alongside the traditional model based on an employment contract, digitally mediated work arrangements—such as service-based contracts, freelancing, and multi-platform work—are increasingly taking root (Eichhorst and Rinne 2017). Digital labour platforms help to match labour supply and demand more efficiently and expand access to cross-border opportunities. However, the analysed data confirm that the market remains segmented: platform work is mostly concentrated in a limited number of service sectors and frequently relies on low-skilled or overqualified workers, raising concerns about skill mismatch. Furthermore, the reviewed evidence highlights that the benefits of platform work are not equally distributed—while platform owners and clients profit from flexibility and reduced transaction costs, workers often face income insecurity, weak social protection, and limited opportunities for career advancement. These asymmetries suggest that without adequate policy responses, platformisation may reinforce rather than reduce labour market inequalities.
2.1.3 Organising the Relations Between Workers and Employers
According to Acquier (2017), not having real estate and devoting the majority of their budgets to the development of search engines and marketing, digital labour platforms are difficult to define in both legal and institutional sense, and the attempt to minimize the external regulation of the relations between workers and employers is a distinct characteristic of digital labour platforms (Lehdonvirta 2016). Forde et al. (2017a, b) argue that the relationship between/among different subjects on digital platforms are regulated by establishing agreements, and various combinations of the agreements between different actors (platform managers, workers, customers, intermediaries) can also be concluded. Since the legal implications of such agreements are not fully understood, the rights and obligations of the parties to an agreement can be interpreted in different ways. This legal uncertainty has direct implications for social protection frameworks and the enforceability of labour rights. It also highlights the need for a renewed regulatory approach capable of capturing the specificities of platform-mediated interactions.
Having analysed the conditions for organising and managing the labour relations on digital platforms, Dieuaide and Azais (2020) argue that the relations between workers and employers on digital platforms are of disruptive nature. Digitalisation is replacing standard employment contracts, and the relations are no longer bilateral, but tripartite—“worker-platform-customer” relations. Under such conditions, labour relations become unclear and undefined, subordination between entities disappears at the organisational level, and the institutional visibility is lost (i.e. a worker and an employer do not see each other). In the legal context, the norms of commercial law gain supremacy over the norms of labour law. Dieuaide and Azais (2020) introduce the term “employment grey zone” which, in their opinion, allows focusing on “intermediate spaces of regulation”, i.e. concentrating on the labour relations which are relatively autonomous and characterised by their own dynamics. This conceptualisation is particularly relevant in the context of platform work, where hybrid organisational arrangements challenge traditional legal classifications and call for nuanced regulatory approaches.
Macrostructures used in digital platforms (e.g. the modular technological architecture) are not able to consider the competitive interactions between agreement parties. Thus, although the parties perceive that they have mutual interests, tensions are inevitable (Van Alstyne et al. 2016), and they usually arise between platform owners/managers and workers rather than in more complex web interactions (Leong et al. 2023). Tiwana et al. (2010) and Li and Kettinger (2021) note that the management of digital platforms is usually analysed through the lens of control, authority, and regulation rather than the mutual interests and actions of agreement parties, especially in terms of cooperation when pursuing predetermined goals. Such asymmetry reinforces managerial dominance, despite platforms’ portrayal as neutral facilitators, and illustrates the structural imbalance embedded in digital labour ecosystems.
According to Gawer (2021), despite the use of coordination mechanisms, such factors as the nature, role and choice of an individual side are ignored in the relations between the agents on digital platforms; the major focus falls on the architectural coordination mechanism. The coordination mechanisms, possessed by platform owners, position platforms as market actors, while platform owners remain behind the legal boundaries of traditional business owners, i.e. the economic perspective created by a platform becomes the central element, and the relationships between actors as well as their interdependence are treated as external elements, though they are technologically coordinated at arm's length (Ghazawneh and Henfridsson 2013; Gawer 2014). This structural distancing enables platforms to externalise responsibility while retaining strategic control, thereby complicating accountability mechanisms and weakening protections traditionally afforded through direct employment relationships.
As noted by Stewart and Stanford (2017), Graham et al. (2017), Pesole et al. (2018), Berg et al. (2018) and other researchers, regulating the relations between workers and employers on digital platforms is difficult, primarily because the status of workers is not clearly defined. According to Forde et al. (2017a, b), the problem is that the traditional features, according to which the status of a person is identified, no longer correspond to the new reality, when innovative, technology-based forms of work organising and algorithmic system management are used. Many digital platforms categorise workers as self-employed, freelancers or independent contractors even when the nature of work organising and subordination within a platform are similar to those underlying work organising in traditional employee-employer models (Wood et al. 2016; Graham et al. 2017; Remeikienė et al. 2022). Since digital platforms are considered intermediaries between a customer of the service and its provider rather than an employer, digital platform workers are not categorised as employees, but as self-employed, autonomous workers or independent contractors, who themselves assume all the risks associated with independent work (for instance, the risk of accidents at work, an illness, unemployment, etc.) (Aloisi 2016; Berg et al. 2018; Torrent-Sellens et al. 2021). This framing allows platforms to avoid employer obligations while placing full economic vulnerability on workers, revealing a systemic misalignment between legal categorisation and the functional realities of digital labour. According to Davulis (2020), until the law provides otherwise, platform workers are considered self-employed persons as long as their degree of subordination to an ordering company is not similar or close to subordination of an employee working under an employment contract.
The Council of Europe (2024) provides the data which show that 19 percent of all digital platform workers are likely to be incorrectly classified (see Fig. 2.7).
Fig. 2.7
Classification of the employment status of digital platform workers in the EU. Source: compiled by the authors, based on the data by the Council of Europe (2024)
93 percent of digital platform workers in the EU are categorised as the self-employed. It has been argued that, whether formally recognised as the self-employed or possessing an incorrect employment status, digital platform workers often fall outside of the scope of labour and social protection law, which increases their vulnerability and limits their access to such social protection (Berg et al. 2018; the Council of Europe 2024). This claim is based on the observation that such workers typically do not benefit from the protections afforded by traditional employment contracts, including minimum wages, regulation of working hours, paid holidays, maternity and paternity leave, health and unemployment insurance (Rogers 2015; Risak and Warter 2015; Van Doorn 2017; Rani and Dhir 2020). This reveals a systemic gap in the design of social protection systems, which remain institutionally dependent on binary employment classifications and are ill-equipped to respond to hybrid or ambiguous work forms prevalent in platform-based economies. However, it should be acknowledged that the legal classification of platform workers and their entitlements under social protection law vary across countries. In some jurisdictions, such workers may be covered either as self-employed persons or under a third, intermediate category.
When assessing whether a certain person working on digital platforms can be considered an employee, it should be noted that the EU Directive 2003/88 (the European Parliament and the Council 2003) does not provide the concept of an employee. According to the Court of Justice of the European Union (CJEU), national courts must decide in each case whether the concept of “an employee” is applicable. The decision must be made after assessing the circumstances indicated by all parties involved and based on objective criteria. To determine whether a person carries out activities under the management of another person, a judge must assess whether there is a hierarchical relationship between an employee and an employer (judgments of the CJEU of 10 September 2015, C-47/14, and of 20 November 2018, C-147/17), whether a person can be considered an “independent contractor”, whether this independence is real or imaginary, or perhaps disguised.
If an agreement between the parties allows a person: 1) to use subcontractors or other persons for the services the person has undertaken to provide, 2) to accept or not accept various tasks proposed by a purported employer or unilaterally determine the maximum number of those tasks, 3) to provide services to any third party, including direct competitors of a purported employer, 4) to set working hours within certain parameters and tailor working time to one’s own personal needs, not just the interests of a purported employer, such relationship does not qualify a person as an employee, since it is impossible to establish the existence of a subordinate relationship between the person and a platform (Naumowicz 2021). These legal criteria highlight the inadequacy of traditional tests for employment status when applied to algorithmically mediated work, where managerial control is exercised without direct oversight, and autonomy is often only nominal.
The relationships of platform workers with platform managers and clients are primarily formalised in terms of service documents which workers sign when they start participating in a platform (Berg et al. 2018; Rani and Dhir 2020). Terms of service documents define the obligations, responsibilities and rights of platform workers, managers, and customers, they provide how the work will be evaluated and how it will be paid for. Nevertheless, terms of service are often unilaterally set by platform managers and reflect their commercial interests, while the interests of platform workers, who just sign an agreement without negotiating it (including negotiating wages), are neglected (Rani and Dhir 2020). For this reason, the terms of service signed by platform workers are referred to not as labour contracts, but as ‘adhesion contracts’ (i.e. the contracts written by only one of the contract parties) (Berg et al. 2018; ILO 2021a, b). This shows that platform managers have a significant power-advantage over digital platform workers (Muldoon and Apostolidis 2023). This structural imbalance suggests that formal contractual autonomy often masks underlying power asymmetries, where platform workers operate in a legally permissive environment that limits their ability to contest unfavourable terms. This view is supported by Bucher et al. (2021b) who found that the ‘Amazon MTurk’ platform uses the technologies which allow selecting workers according to established criteria, while no selection mechanisms are available to workers. Goloventchik (2023) emphasizes the asymmetry of the relationship between platform managers/owners and workers actually subordinate to them, and notes that this relationship falls out of the scope of the national labour legislation.
According to Aleksynska et al. (2019), considering the fact that the share of the digital economy in the general economy is gradually increasing, the fact that digital platform workers do not have a clearly defined legal status and often even work informally is an obstacle to the creation of sustainable labour relations and sustainable social protection schemes. Goloventchik (2023) argues that platform workers must be able to exercise their legal and social rights regardless of their employment status. Choudary (2018) notes that the well-being of digital platform workers can be improved by measures that would allow for an increase in the degree of representation of the interests of these workers (for example, it is necessary to create the conditions and encourage the establishment of worker agencies) and would help reduce the control of workers exercised by digital platform managers/owners. This highlights that the absence of legal clarity not only weakens the enforcement of workers’ rights but also hinders the broader policy objective of building resilient and inclusive social protection systems in the digital age. If these measures were implemented, digital platform workers would gain more bargaining power with respect to each transaction.
Some authors (Karanovic et al. 2020; Malgonde et al. 2020; Sandberg et al. 2020, etc.), however, argue that agreement parties on digital platforms do not always blindly obey owners’ power, and their influence and power are growing with the evolution of digital platforms. As knowledge about the working principles of digital platforms is deepening, opportunities for workers to follow logic, operate in a pluralistic environment, make autonomous decisions, and take autonomous actions are increasing. Such dynamics suggest that the distribution of power on platforms may gradually shift, particularly where collective action, platform transparency, or supportive regulation strengthens workers’ strategic position.
Summarising
The attempt to minimise the external regulation of the relations between workers and employers is one of the key characteristics of digital labour platforms. Macrostructures used in digital platforms (e.g. the modular technological architecture) are not able to consider either the competitive interactions between agreement parties or the nature, role, and choice of an individual side. Therefore, the relations between workers and employers on digital platforms are of disruptive nature, they become unclear and undefined, subordination between actors disappears at the organizational level, and institutional visibility is lost. Many digital platforms categorise workers as self-employed persons or workers possess an incorrect employment status, which violates their rights and raises the risk of social vulnerability. Terms of service is the main document formalizing the relationship between workers and employers on digital platforms. But they reflect the commercial interests of platform owners, while the interests of platform workers are neglected. This shows that platform owners have a significant power-advantage over digital platform workers. Nevertheless, with the evolution of digital platforms and deepening knowledge about the principles of their operation, the opportunities for workers to make autonomous decisions are increasing. This evolving balance between managerial control and worker autonomy indicates that digital labour platforms are not static structures, but adaptive systems where power dynamics may shift in response to legal, technological, and social developments.
2.1.4 Polarisation in the Labour Market
Digitalisation leads to the transformations in work processes and operating procedures, i.e. the ways and methods to perform certain tasks and jobs are changing. As noted by Dachs (2017), the use of technologies can increase inequality through the differences in skills and tasks performed by the labour force. Following the ‘task-based approach’ proposed by Autor et al. (2003), Levy and Murnane (2005) note that digitalisation leads to a new division of labour between humans and machines: machines replace human labour in performing routine tasks, but human labour is still irreplaceable in performing non-routine tasks, such as creativity and problem solving. In this way, digitalisation tends to increase polarisation in the labour market since it leads to the division of the workforce into two sharply contrasting groups: manual workers who previously performed routine tasks, whose jobs are likely to be lost due to digitalisation, and educated workers who are creative and able to make decisions, and whose risk of losing a job due to digitalisation is low. This emerging division challenges the cohesion of the labour market and highlights the growing importance of targeted upskilling policies that prioritise adaptability and digital competence across all worker groups.
Having assessed the risk of polarisation in the UK’s labour market, Goos et al. (2010) provide evidence that digitalisation leads to polarisation in the labour market, and the greatest risk of losing a job is for workers with medium skills, who will have to upgrade their qualifications or be retrained to be able to fall into the category of decision-making workers. Having researched the situation in the USA, Frey and Osborne (2013) confirm the risk of polarisation in the labour market posed by digitalisation; they estimate that 47 percent of jobs of various profiles will be lost due to digitalisation. According to Dachs (2017), it can be expected that digitalisation will lead to uneven distribution of costs between lower- and higher-skilled workers because the former are going to face higher risks of displacement in their jobs. This analysis reveals that medium-skilled workers occupy a particularly vulnerable position, as their functions are too complex to be easily automated but too standardised to stand out through creative or strategic competencies. Therefore, greater attention should be paid to upskilling strategies targeted specifically at this group.
Helmrich et al.’s (2017) research in Germany, however, reveals the accelerated structural changes in the labour market due to digitalisation and the trends in employee qualification improvement, which at least partially reduce the risk of polarisation in the labour market. According to the authors, the intensive use of digital work tools (the use of work tools directly determines how workers perform their work) will raise the digital literacy of workers, and thus, will lead to their higher qualifications, which will reduce the problem of polarisation. Weller et al.’s (2020) study confirms these findings and shows that digital technologies as work tools will be used by both groups of workers—the ones performing routine and non-routine tasks. In the first case, workers will make more extensive use of machines and installations as well as measurement devices and diagnostic tools, while in the second case, various types of software will be used more widely. Nevertheless, higher skill requirements will be imposed on workers in both cases. Domini et al. (2021) found that job automation in the French manufacturing sector correlates with a lower separation rate and a higher worker hiring rate. These findings imply that the adverse effects of polarisation can be mitigated through targeted investments in digital skill development and broader access to upskilling pathways. By aligning digital tool use with systematic training, polarisation risks may be reduced not only in high-skill but also in medium-skill sectors.
The above-discussed findings of previous studies indicate that the risk of polarisation in the labour market due to digitalisation vary from country to country. Bachmann et al. (2022) attempted to elucidate the reasons for these differences. Bachmann et al.’s (2022) study, focused on the effects of robotisation on worker flows in 16 European countries in the period 1998–2017, revealed that cross-country differences in the strength of the impact of digitalisation are largely determined by country-specific labour costs: a greater positive impact of digitalisation is noticeable in the countries with low or average labour costs, while it is much milder in the countries with high labour costs, i.e. the results of the research imply that robotisation tends to raise employment in the countries with low or average labour costs. The research also revealed that the groups of workers whose functions are characterised by intensive manual work or routine cognitive tasks are more sensitive. This suggests that labour cost structures not only mediate the speed of automation adoption but also shape the degree of worker vulnerability across skill levels. Policy responses should therefore be calibrated to national wage dynamics and the relative exposure of routine task occupations.
Blix (2017) argues that the effects of digitalisation are not critical in the short run (e.g. they are not as critical as the effects of a fiscal or financial crisis, where GDP can fall dramatically, and many jobs can be lost when economic growth stops or even a recession starts). According to the author, there is still no convincing evidence that the unemployment rate in the OECD countries is decreasing due to digitalisation. The author believes that the modern labour market is characterised by the huge potential for change and the constant creation of new jobs, especially in the service sector. Nevertheless, it is recognised that digitalisation will inevitably lead to tensions in the labour market in the medium to long run. Technological advancement is expected to increase polarisation and is likely to negatively affect middle-level workers, whose income will become volatile and uncertainty about job retention will increase (Goos et al. 2014; Vasilescu et al. 2020). These observations imply that while digitalisation may not immediately destabilise employment figures, it gradually shifts labour market dynamics in ways that weaken the position of mid-skilled workers. In anticipation of these shifts, forward-looking reskilling policies and transitional support measures are critical for maintaining social and economic cohesion.
According to Blix (2017), digitalisation directly and indirectly affects 3 poles of the welfare state:
a)
social protection costs financed by federal transfers (benefits, education, health care);
b)
social inclusion through education and employment;
c)
worker-employer power balance when negotiating working conditions, salary, and resolving conflicts.
Dachs (2017) notes that the power of states to allocate sufficient funds for social protection can reduce the reap of economic benefits from innovation. The author highlights the tendency that only a few companies, mostly large ones, can implement innovations without state support in the modern economy, so the tendency “the winner takes all markets” becomes dominant. The benefit from innovation is not distributed equally to business; it is appropriated by a small number of companies which can gain monopolistic positions, while small businesses are affected negatively. In the absence of equal distribution of benefits, less funds are accumulated by state budgets, so less funds remain for social protection expenses. This reveals a structural tension between innovation-driven economic concentration and the fiscal sustainability of welfare states, suggesting that inclusive digital transformation must also address the redistribution mechanisms that underpin long-term social resilience.
Technological advancement and digitalisation tend to increase polarisation, which affects middle-aged workers whose income is becoming more volatile and social inclusion is decreasing (Goos et al. 2014). The Gini coefficient is a common measure of income inequality. This coefficient tends to rise in many developing countries (Blix 2017). Although trade and globalisation have reduced income inequality in the world, it can still vary from country to country. In the future, the problem of income inequality is likely to be fuelled by the rapidly aging population and new technologies which will compete with human labour in many areas, especially in modern services. With increasing competition between human and technologies, wages are expected to stagnate. This points to the need for proactive policy measures that not only mitigate wage stagnation, but also address generational and sectoral vulnerabilities emerging from this labour–technology competition. Digitalisation-driven changes in the labour market are more gradual than the changes in consumption (the surge in e-commerce and online orders), but the gradualness of the changes depends on the pace at which young people enter the labour market and older people change jobs or retire (OECD 2017a).
Digitalisation in the labour market is likely to reduce the bargaining power of workers. A middle manager will become a function under pressure from robots in many areas of work. This pressure is already visible in the banking and retail sectors (technological progress allows to reduce staff and automate part of the services provided). With a limited attachment to a particular workplace, workers’ awareness of their bargaining power will decrease (Cazes et al. 2019). This dynamic raises structural concerns, as diminished bargaining power may not only exacerbate income inequality but also hinder collective responses to precarity in increasingly fragmented labour markets.
Considering the potential risks of polarisation in the labour market caused by digitalisation, Blix (2017) nevertheless believes that modern welfare states are sufficiently resistant to market changes, so there is no need to create new institutions or safety nets in response to the changes in the labour market driven by digitalisation. With incremental changes, welfare states should have enough time to adapt and reform. However, the reforms can be too slow or not initiated at all. It is often difficult for national political systems to implement reforms when the costs of action are incurred immediately, but the potential economic benefits are gained much later. In addition, when implementing reforms, authorities often face opposition from specific interest groups and employers’ organizations since any transformations usually mean the changes in the levers of power. This indicates a need for more agile and anticipatory governance mechanisms, capable of responding to gradual structural shifts before labour market imbalances become entrenched. Since digitalisation affects some fundamental elements of a welfare state (e.g. employment), the existing institutions need to be reformed in order not to undermine the social contract between society and the state. Welfare states must provide protection to their population in response to disruptive economic/market changes so that innovation is promoted, and its benefits and harms are properly balanced.
Summarising
Although digitalisation-driven changes in the labour market are more gradual than the changes in consumption, technology work competing with human labour may lead to the division of the workforce into two sharply contrasting groups: manual workers who previously performed routine tasks and whose jobs are likely to be lost, and educated workers who are creative and able to make decisions, and whose risk of losing a job due to digitalisation is low. With a limited attachment to a particular workplace, workers’ awareness of their bargaining power is likely to decrease. With increasing competition between human and technologies, wages are expected to stagnate. Cross-country differences in the strength of the effects of digitalisation are largely determined by labour costs specific to a particular country: greater positive effects of digitalisation are observed in the countries with low or average labour costs, while the effects are much milder in the countries with high labour costs. Polarisation in the labour market is reduced by rising worker qualifications, in particular, the wider use of machines, installations, measurement devices and diagnostic tool for routine tasks, and the use of software for non-routine tasks. This suggests that proactive investment in digital upskilling across occupational categories could serve as a key mechanism for buffering labour markets against structural polarisation trends. Since digitalisation affects some fundamental elements of a welfare state (e.g. employment, declining bargaining power of workers), protections must be provided to the population in welfare states in response to the disruptive economic/market changes so that innovation is promoted, and its benefits and harms are properly balanced.
2.2 The Green Transition and Its Effects on Industrial Transformation and Employment
The understanding of conventional economics, i.e. people’s relationship to the natural world, the purpose of economies, the sources of wealth and the function of markets, is changing in the twenty-first century. If the researchers of conventional economics (e.g. Adam Smith and David Ricardo) treated nature as a huge inexhaustible source of economic resources, and the government was considered the main institution for solving environmental problems through the mechanisms of taxes, prices and financial incentives and penalties, sustainable economics treats traditional natural resources as exhaustible, promotes the use of renewable energy sources, reduction in the amount of industrial waste, and considers all economic agents responsible for environmental protection. With different understanding of the major dimensions and agents of the economy, the modern fair and sustainable economy is expected to save the natural environment and provide decent, well-paid jobs. This conceptual shift also reshapes the role of the industry in contemporary economies, positioning industrial transformation not only as an environmental necessity, but also as a social and labour market imperative.
The effects of the green transition on industry are primarily related to the European Green Deal and the Paris Agreement. The European Green Deal, approved in 2020, is a set of policy initiatives by the European Commission with the main objective of making the European Union climate neutral by 2050 (Tamma et al. 2019; Simon 2019). Climate neutrality can be achieved by decarbonising the EU’s energy system and achieving zero greenhouse gas emissions (by 2050, according to the plan) (Simon 2019). The European Green Deal aims to show that countries can develop economically without increasing the use of resources. The main objectives of the Paris Agreement are as follows: to ensure that the increase in the global average temperature is less than 2 °C compared to the temperature of the pre-industrial period, and to make efforts to keep the global average temperature from increasing by more than 1.5 °C. Although the Paris Agreement does not provide for any binding emission limits, it indicates that the policies for reducing carbon emissions must be implemented in the economy as a whole (Kuh 2018). These agreements create a regulatory framework that indirectly influences digital platform activities, particularly in energy-intensive sectors, thereby linking green policy objectives with transformations across employment models, including platform-based forms.
Since the industry is a fundamental driver of the economy, it will have to go through significant changes in the direction of reducing carbon emissions. In the industrial transition, a balance between electrification, energy efficiency and the use of renewable energy should be achieved. In addition, industrial sectors are expected to cooperate to utilize the excess heat (Mathiesen et al. 2023). These industrial transformations will have an impact on employment since the structure of the industry and the rate of its transformation significantly affect the structure and dynamics of employment. While the discussion in this section focuses on industry-level transformation, the implications extend to platform-mediated labour as well. As the demand for green skills rises and sustainability standards reshape market dynamics, platform-based services are also expected to adapt, particularly in areas like logistics, energy efficiency consultancy, and circular economy-related tasks. This subsection analyses how and through which channels the green transition affects the industry as an important source of employment, and what effects can be expected in the labour market.
2.2.1 The Impact of the Green Transformation on the Industry as a Source of Employment
Conventional, or the brown, economy relies on non-sustainable energy resources. It is characterized by high consumption of materials, the reliance on fossil fuels, low energy efficiency and great damage to the environment and climate, which is caused by ignoring the negative consequences of economic production (World Bank 2013). It is said that the current brown economy is experiencing the so-called three “F” crisis, i.e. the crisis of finance, food and fuel (United Nations Environment Program (UNEP) 2008; Atlama and Ozsoy 2011; the World Bank Group 2022). The crisis of finance is related to investment because an unreasonable amount of investment goes to finance cheap credits, financial derivatives and housing bubbles, but this investment does not create decent jobs (Atlama and Ozsoy 2011). The fuel crisis is related to excessive costs on carbon emitting fossil fuels: a significantly larger share of investments and subsidies go to carbon emitting fossil fuels than the use of renewable energy. The recent estimates by the International Institute for Sustainable Development (IISD) (2023) show that G20 countries have reached an all-time high of $1.4 trillion in public funds to support and maintain the fossil fuel industry in 2022. This amount includes $1 trillion subsidies for the fossil fuel sector, $322 investment in this sector by state-owned enterprises, and $50 billion allocations in the form of loans, provided by public financial institutions. The International Monetary Fund (2023) reports $7 trillion subsidies for the fossil fuel industry (the report of 24 August 2023). The differences between the investment in fossil fuel supply and low-emission fuel supply are depicted in Fig. 2.8.
Fig. 2.8
Investment in fuel supply in the period 2019–2023. Source: International Energy Agency (2023)
The figure illustrates that the share of funds allocated for fossil fuel supply is still significantly higher than the share of funds allocated for the supply of low-emission fuels.
Finally, food crisis is related to the dependence of the agricultural sector on subsidies, and when the subsidies are decreasing, the productivity of the agricultural sector is also decreasing. The report by the Institute for European Environmental Policy (2022) suggests that that CO2, temperature changes, ozone concentration, extreme weather events and the increased incidence of pests and diseases have the negative effects on crop yields, yield stability and the quality of crops. The most severe negative effects are observed in Eastern, Western and Southern Europe, where the significant changes in temperature and precipitation have a negative impact on the yields of wheat, barley, maize and sugar beet, leading to the recent yield stagnation. The data provided by the “Eurostat” (2023) indicate that agricultural labour productivity in the EU decreased by 6.6 percent from 2019 to 2022. This decline was caused by a 7.9 percent decrease in the real value of the income generated by economic units (factor income) and a 1.4 percent decrease in the volume of agricultural labour. 19 EU Member States recorded lower agricultural labour productivity in 2023, with the steepest decline observed in Estonia (−57.9 percent), Sweden (−31.7 percent), Ireland (−30.3 percent), Lithuania (−30.2 percent) and Bulgaria (−28.6 percent). This triple crisis—finance, food, and fuel—demonstrates the unsustainability of the brown economy model and highlights the need to redirect capital towards sectors that promote resilience, sustainability, and inclusive employment. Such reallocation is especially relevant for digital platform-based activities, which are increasingly interfacing with sectors influenced by the green transition, including sustainable food delivery chains, renewable energy installation services, and climate-related data collection tasks.
The general result of the aforementioned three crises (finance, food and fuel) is the gross misallocation of capital. This means that too much capital is poured into property, fossil fuels and structured financial assets with embedded derivatives, which no longer stimulates the economy and job creation. In contrast to the brown economy, the green economy is based on the use of renewable energy sources and cleaner technologies, economic activity minimises damage to the environment, and cleaner products and services are provided to the market. This paradigm shift calls for investment patterns that not only support environmental goals but also foster sustainable forms of work. Notably, platform-mediated labour may become a vehicle for delivering green services, such as shared mobility, last-mile delivery via low-emission transport, and remote energy audits, expanding the role of digital platforms in achieving climate-neutral objectives.
The challenges of the European Green Deal are related to the transformation of various sectors of the economy, including construction, biodiversity, energy, transport and food. The main policy areas of the European Green Deal are depicted in Fig. 2.9. These sectoral transitions are not isolated from changes in labour market structures; on the contrary, they increasingly involve platform-mediated services, especially in logistics, energy diagnostics, sustainable mobility, and urban service delivery. The integration of environmental goals into economic policy indirectly reshapes the platform economy by generating demand for digitally mediated green services.
Fig. 2.9
The main policy areas the European Green Deal. Source: compiled by the authors
The main principles which are expected to be followed during the transition to the use of clean energy are prioritising energy efficiency, developing an energy sector based mainly on the use of renewable resources, ensuring affordable energy supply to the population, and creating a fully integrated, interconnected, digitalised EU energy market (European Commission 2019b). The EU Strategy for Energy System Integration, which provides measures for the shift towards the circular economy, will also help the energy transformation. These transformations are already generating new forms of digitally mediated work, such as remote energy audits, virtual consultancy on energy efficiency, and data-driven energy consumption optimisation, often performed via specialised digital platforms. This demonstrates a growing convergence between green and digital transitions.
Sustainable Industry
The foundations for the creation of sustainable industry were laid after the approval of the EU’s Circular Economy Industrial Policy. In March 2020, the EU approved the industrial strategy (European Commission 2020c) aimed at empowering citizens, revitalizing regions and having the best technologies. The key policy aspects in this area include the development of the modern industry, the exploration and development of climate-neutral and circular economy-friendly markets, industrial decarbonisation and the modernization of energy-intensive industries, such as steel and cement production. To reduce the generation of material waste, the Sustainable Products Policy, when the final products will be reused or recycled, will be implemented. These industrial changes increasingly rely on digital monitoring and reporting tools, with emerging opportunities for platform-based labour in areas such as material flow analysis, sustainability reporting, and real-time emissions tracking.
Construction and Renovation
This policy area aims to reform the unsustainable methods of construction and renovation of buildings and reduce the use of non-renewable resources in these processes. In this context, the main focus falls on the application of energy-efficient construction methods (construction of climate-resistant buildings, digitalisation of buildings, compliance with requirements for energy efficiency of buildings). Renovations of council housing will aim to reduce energy bills for the most sensitive groups of society (European Commission 2019a). One of the objectives is to triple the rate of renovation of all buildings in order to reduce pollution (Simon 2019). Applied properly, modern digital technologies can play a significant role in solving environmental problems. Smart urban mobility, precision agriculture, sustainable supply chains, environmental monitoring and disaster forecast are just a few examples of applications of digital technologies (European Investment Bank 2022). This suggests that platform workers involved in building maintenance, energy audits, or remote monitoring services will need to develop digital and green skills simultaneously.
Food Provision ‘Farm to Falk’
This policy aims to create sustainable food supply chains and support food producers—farmers and fishermen (European Commission 2019c). Food must be produced in a climate-friendly manner, but with efficiency so that new processes do not reduce food quality and increase prices. The specific target areas cover reducing the use of chemical pesticides and fertilizers, increasing the availability of healthy food to the population, helping consumers understand health impact assessments of products and sustainable packaging assessments (Spencer 2020). Other objectives include the development of organic agriculture, reduction of losses in the nutritional value of products, creation of a sustainable labelling system for foods, etc. Platform technologies also support this transformation by facilitating short food supply chains, enabling traceability tools, and connecting eco-conscious producers with consumers directly through digital marketplaces.
Elimination of Pollution
The “Zero Pollution Action Plan”, which was approved by the European Commission in 2021 (European Commission 2021), aims that there would be no air, water and soil pollution from any sources by 2050 (Schaible 2020). Environmental quality standards must be fully respected, and all industrial activities must be carried out in an environment free of toxic substances. Agricultural and urban industrial water management systems shall be revised to comply with the principles of the pollution elimination policies. To achieve this goal, harmful resources, such as microplastics and chemicals, must be replaced. It is believed that providing food from farms to the population will also help to reduce the level of pollution since less harmful substances will be used in the processes of food production and transportation. In parallel, the implementation of pollution control standards is expected to influence service models in the platform economy, particularly those involved in urban logistics, food delivery, and household services, which increasingly rely on platform-mediated labour.
Sustainable Mobility
Another target area of the European Green Deal policy is the reduction of vehicle emissions. For this purpose, the Sustainable and Smart Mobility Strategy was approved. The use of sustainable and alternative fuels in road, sea and air transport will be promoted, and the emission standards will be set for vehicles with internal combustion engines (“Green Facts” 2022). Sustainable alternative solutions are expected to be available to business and society. The intelligent traffic management systems and programs, as well as the methods of cargo delivery via land or waterways are intended to be developed. The implementation of public transport reforms will aim to reduce traffic congestion and pollution, expand the infrastructure of charging stations for electric cars, and promote the purchase of low-emission vehicles (European Commission 2019d). These transformations in mobility systems are likely to reshape the working environment of ride-hailing, courier, and goods delivery platforms, potentially altering task organisation, emission standards for service providers, and the algorithmic parameters used to assign rides or deliveries.
Biodiversity
The target areas of this European Green Deal policy are the management of forests and marine areas, environmental protection, and solution of the problems of the loss of plant and animal species and ecosystems (“Green Facts” 2022). The negatively affected ecosystems are expected to be restored by applying the methods of organic farming, helping plant pollination processes, restoring free-flowing rivers, reducing pesticides which harm wildlife, and reforesting. The Biodiversity Strategy is one of the essential parts of the European Union’s climate change mitigation strategy. Of the 25 percent of the EU’s total budget for combating climate change, a large part will be allocated for restoring biodiversity and implementing nature-based solutions (European Commission 2020a). The growing demand for environmental monitoring and restoration projects also creates opportunities for platform-based microtasking services, especially those involving geospatial data collection, drone-assisted monitoring, or citizen science platforms.
Sustainable Finance
Sustainable, or green, finance refers to a set of financial regulations, standards, norms and products which aim to achieve environmental goals and, in particular, to facilitate the energy shift towards the use of renewable energy sources. Sustainable finance is expected to enable the EU's financial system to connect with the economy and population, and fund economic actors while sustaining economic growth. This long-term concept began to be implemented with the approval of the United Nations Paris Climate Agreement which stipulates that countries must ensure that financial flows are directed to the reduction of greenhouse gas emissions and the promotion of climate-friendly development (United Nations 2015). Increased investment in green sectors may indirectly stimulate the growth of platform-based labour in sustainability-focused industries, as funding for climate action and environmental entrepreneurship encourages new models of decentralised and flexible service delivery.
The literature analysis revealed that the impact of the green transformation on industry is likely to manifest itself through five major channels: resource and innovation efficiency, transformations in production processes, changes in the demand structure, changes in aggregate income and macroeconomic conditions, and changes in trade and competitiveness (see Fig. 2.10).
Fig. 2.10
Channels of the impact of green transformation on industry. Source: compiled by the authors
The European Green Deal includes the measures to ensure that countries which are dependent on fossil fuels are not left alone in their transition to renewable energy (Geden et al. 2020). National public and private investment should play an important role here. The European Commission is intending to support research and innovation in the areas of transport technologies (in particular, vehicle batteries), clean hydrogen, low-carbon steel production, circular biotechnology and the built environment (European Commission 2019b). A special investment plan—the so-called “InvestEU” plan—is intended to finance the implementation of the European Green Deal policy measures. According to this plan, the estimated amount of investment is nearly €1 trillion. The approximate estimations indicate that around €260 billion of investment will be needed annually to achieve the goals set out in the European Green Deal until 2030 (European Commission 2020b). This shift in innovation priorities is likely to affect labour demand across multiple sectors, including digital platform-mediated services, especially in areas supporting clean technology deployment, resource monitoring, and the maintenance of decentralised energy solutions.
The growth of green innovation is associated not only with the replacement of traditional technologies with green technologies, but also with the growth of innovation overall because green innovation often requires additional complementary innovation (Hasna et al. 2023). According to the European Commission (2022a), eco-innovation reduces business costs, allows expanding growth opportunities and strengthens business reputation among consumers. Hasna et al. (2023) note that the economic benefits of green innovations can be manifested already in the first year with an increase in investments. Economic growth in the long term is stimulated by cheaper energy and more energy efficient production processes. Nosheen et al.’s (2021) study in Eastern and Western European countries, the purpose of which was to assess the cross-sectional dependency and heterogeneity promoted by technological innovation between 2000 and 2017 (the research was based on Westerlund cointegration approach and the fully modified ordinary least square (FMOLS) method), revealed that the use of renewable energy promotes the growth of industries. Therefore, this type of investment should be actively promoted through public-private partnership and the exemptions in taxes for the economic agents that use environmentally friendly technologies. The authors admit that renewable energy innovations are expensive to implement, but they ensure an improved return on green growth. In this context, platform-based services could become enablers of innovation diffusion, for instance, through digital marketplaces for green technologies, on-demand energy audits, or app-mediated maintenance of eco-efficient equipment.
It can be stated that the transformation to a low carbon industry and the development of the green economy are expected to achieve resource and innovation efficiency, thus maintaining not only the industrial status quo, but promoting growth, which, in its turn, stimulates the creation of new jobs. Hasna et al.’s (2023) calculations show that doubling green patent fillings can increase GDP by 1.7 percent in the period of five years. Sun et al.’s (2023) GMM (Generalized Method of Moments) technique-based research of the effects of green innovation on resource efficiency and sustainability in E7 (China, Indonesia, Mexico, Russian Federation, Turkey, Brazil, and India) in the period 2010–2021 revealed that green innovation positively and significantly affects the growth of the green economy in the target countries. The emission-trading schemes, which allow to minimize prices for producers of renewable energy, government spending on R&D and global policy synchronization are considered the effective means to stimulate green innovations (local innovators will have more incentives to develop low-carbon technologies if they know that they can sell these technologies in global markets (the market size effect)) (Hasna et al. 2023). Such developments may also indirectly influence platform-mediated labour markets by expanding demand for specialised services, such as installation and remote diagnostics of low-emission systems, data-driven environmental compliance solutions, or sustainability-focused freelance consultancy. The major sectors with the highest growth in green innovation are presented in Table 2.2.
Table 2.2
Sectors with the highest growth in green innovation
Industry
Sector
Examples of technologies
Change in the number of innovation projects in the period 2015–2022
Infrastructure
Transport
Bus rapid transit, low-emission vehicles and fuels, biogas, hybrid and plug-in electric vehicles
+16
Electricity access
Smart power grids, stoves using renewable energy, off-grid technologies (e.g. wind turbines)
Telecommunication
Wireless communications, cooperative communications, interference alignment, cognitive radio technologies, real time pricing
Climate change
Renewable energy
Smart power grids, renewable energy technologies
+21
Building/Construction energy efficiency
Smart power grids, smart meters, thermal insulation, energy efficient lightning, energy recovering stoves, building envelopes, heat pumps
No data
WASH (provision of water and sanitation)
Water management
Desalinization plants, wastewater treatment plants, non-sewered, off-grid urban green sanitation, wastewater reuse and disposal technologies
+13
Agrifood systems
Agricultural production and processes
Genetically modified crops, mechanical irrigation and farming techniques, higher-yield seeds, drought resistant crops and cultivation practices
−19
Fertilisers, pesticides and other chemicals
Nitrate-based mineral fertilisers with extracted hydrogen, green ammonia, low-carbon fertilisers, pesticides derived from organic sources
Technologies
Zero tillage, vertical farming, drones, fleet management, digital sensors
Health and education
Healthcare
Energy efficiency, waste management, digitalisation of medical records, improvement of electrical infrastructure
+11
Education
LED lighting systems, solar panels, green roofs, rainwater harvesting systems, energy-efficient windows, low-flow faucets
Sources: Hultman et al. (2016), p. 34; UNCTAD (2023); authors’ own elaboration
Table 2.2 indicates that most green innovations are implemented in the renewable energy industry, followed by transport, electricity access and telecommunication innovations. Water provision and sanitation industry as well as health and education sectors remain important green innovation sectors (judging by the increase in the percentage of innovative projects in the period 2015–2022). Meanwhile, the volumes of green innovation in the agrifood industry are declining. This trend suggests that platform work related to green transition may increasingly concentrate in sectors showing high innovation intensity, particularly renewable energy and smart infrastructure, whereas areas like agrifood may offer fewer opportunities unless complementary digital or sustainability solutions are introduced.
According to the United Nations Conference on Trade and Development (UNCTAD) (2023), global investment in industries related to the implementation of sustainable development growth (SDG) increased in 2022. However, the estimations show that the annual investment gap is still significant.
According to UNCTAD (2023) the biggest gaps between targeted and implemented investments are observed in energy, water and sanitation, infrastructure, agrifood and biodiversity industries. This means that the volume of green investments in these sectors will have to grow, so it is likely that more jobs will be created. This investment gap is also likely to shape the geography and nature of platform-mediated work, as emerging employment niches may form around infrastructure maintenance, renewable energy services, and biodiversity monitoring, especially in regions receiving targeted funding.
Transformations in Production Processes
According to Mallik (2023), production processes refer to the preliminary production of tangible products which are later stored in inventory. Production processes have little contact with a user. They are characterised by a longer reaction time to changes and implementation of changes, and they are highly capital intensive. However, their main purpose is to support a company’s competitive priorities and create a competitive advantage. Green transformation and the implementation of digital technologies will allow at least partially mitigating the instabilities inherent in production processes and improving the performance of their main purpose. This transformation is also expected to affect the structure of platform-based work, as the shift towards digitalised and automated production creates demand for new forms of remote supervision, algorithm-based diagnostics, and digital systems maintenance.
The literature (OECD 2019a; Mauksch et al. 2020; Bocker and Silva 2022; Vandeplas et al. 2022; Mallik 2023; Cheba et al. 2023; “EIT Manufacturing” 2023) analysis helped to identify the following potential directions for the changes in production processes:
1)
competence-enhancing and competence destroying technological changes (destruction of the resources and capabilities of traditional mechanical products; efficiency enhancing technologies);
2)
implementation of disruptive technologies (disruptive technologies add attributes to existing technologies, raise production potential);
3)
the use of advanced materials (the use of substitutes (e.g. CVD diamond instead of natural diamond) which will lower costs, add value to products and facilitate recycling);
4)
implementation of forecasting technologies (allows to predict successful technological trends, set standards for an industry).
The study by “EIT Manufacturing” (2023), co-funded by the European Union, proposes that production processes should be transformed through the use of artificial intelligence (AI), the effectiveness of which could be improved by balancing technical knowledge and industry know-how. The innovative ecosystem of production processes, represented by the Internet of Things, artificial intelligence, and digital twins, is expected to develop along the entire value chain. Artificial Intelligence (AI), blockchain technologies and the Industrial Internet of Things (IIoT) will allow to digitalise production processes, thus reducing labour costs, economic input and machine downtime and increasing production speed (OECD 2019a). Machine Learning (ML) methods (a subset of Artificial Intelligence (AI)) will allow the use of computer algorithms in production processes. The algorithms will initially use the available data, but will eventually improve and be able to make decisions automatically, using their experience, i.e. smart technologies will no longer require pre-programming. ML methods in production processes will allow identification of bottlenecks, and will ensure production safety and quality control (Mallik 2023). These technological shifts are likely to create new opportunities for specialised freelance workers, such as algorithm trainers, predictive maintenance operators, and AI-data auditors, many of whom may be contracted through digital labour platforms.
According to “EIT Manufacturing” (2023), the effects of smart technologies on production processes will manifest themselves as a reduction in the resources used, which will allow an increase in production output, an increase in efficiency, an increase in quality by reducing costs, and reimaging employee engagement. Nevertheless, as noted by Mallik (2023), technological changes in production processes will not occur suddenly or according to one specific plan, but will have to be implemented sequentially. In parallel, platform-mediated work is expected to play a role in facilitating this transition, particularly in tasks that involve remote machine diagnostics, virtual commissioning, and just-in-time support services for manufacturing systems. This signals a gradual convergence between industrial modernisation and digital labour markets.
Another source of changes in production processes is business activity in compliance with the principles of environmental protection and climate change awareness. The report by the OECD (2017a, b, c) proposes that the green transformation, which aims for less pollution and more efficient use of resources, will lead to significant structural changes in demand and production processes. By adapting to the principles of the green economy and green growth regulations, business enterprises will use fewer polluting inputs, and their production processes will become less pollution intensive. According to Dai and Yang (2023), significant transformations of production processes in the context of the green transition can be caused by digital empowerment, the impact of which is manifested through scale and technology effects. The authors believe that transformations caused by digital empowerment (for example, digitalisation can empower pollution monitoring technologies) are heterogeneous and can take place in industries with different energy consumption intensities (OECD 2019a; Dai and Yang 2023). Moreover, digital empowerment is expected to provide a positive spillover effect along all industrial chain (i.e. in upstream and downstream enterprises) (Naghshineh and Carvalho 2021; Mallik 2023). To achieve tangible results, it is necessary to align productivity, sustainability and resilience (resilience will be developed through non-linear distributed production) (“EIT Manufacturing” 2023). This emerging industrial ecosystem may increasingly rely on external talent pools accessed through online platforms for environmental auditing, carbon reporting, and lifecycle assessment—functions that require a blend of technical knowledge and digital mobility.
When assessing the impact of the green economy on employment in this context, it is important to note that after the transformation of production processes, workplaces will be maintained, but it will be especially important for the workforce to quickly reorient from the work in traditional production processes to the work in production processes which use different energy and raw materials. It should also be noted that if the traditional “brown” industry (e.g. metallurgical industry) is capital intensive, then the green transformation will stimulate a shift towards more labour-intensive industries. On the other hand, even green sectors will not be able to create many new jobs if they are capital intensive (OECD 2017b). These workforce shifts may also reshape labour platforms, as the demand for technicians, consultants and skilled freelancers with green economy competences is likely to rise. This highlights the importance of equipping platform workers with relevant upskilling opportunities and aligning platform governance with broader green policy goals.
Changes in the Demand Structure
According to the OECD (2019a, b), the substitution of polluting products with non-polluting ones will change the demand structure in the long term, but the total economic output, as well as the aggregate demand, will not decrease. The demand growth is expected for the products delivered by the low-carbon intensity sectors, in particular, electric vehicles and renewable energy.
Mallik (2023) in his analysis of the prospects and challenges of the technology-based manufacturing in the EU indicates that the consumer demand will be stimulated by rapid prototyping (the strategy used for product development), which will allow creating 3-dimensional prototypes of potential products, testing and optimising such product characteristics as size, shape and overall usability. This way a product will be tested in the development process, its prototype will look realistic, and potential users will be able to interact with it. All this will make it possible to better match a product to the needs of consumers and shorten the time of a product’s entry into the market, thus stimulating the demand.
Anti-pollution policies will induce a shift towards the use of renewable energy, while implementation of the principles of circular economy will reduce the efficiency of the use of traditional resources in production processes. Therefore, there will be noticeable changes in the demand for energy and production resources: the demand for fossil fuels will decrease, while the demand for wind, solar, ocean, geothermal, bioenergy and hydropower will increase. Green policies will lead to a reduction of the prices of clean products and services relative to the prices of polluting products and services. Reduced prices will lead to an increase in demand for clean products and services, which, in its turn, will stimulate the growth of the sectors that produce clean products/services and thus an increase in the number of jobs in these sectors (OECD 2019a). The restructuring of demand patterns will also have implications for platform-based commerce and service provision, as digital platforms may become key intermediaries for green products and services. Therefore, ensuring platform transparency and environmental labelling of offerings could be important policy considerations.
Changes in Aggregate Income and Macroeconomic Conditions
According to the OECD (2019a, b), indirect macroeconomic benefits will be obtained through technological and non-technological knowledge spillovers, R&D, which will become the public knowledge, and productivity improvement. The OECD (2019a, b) provides the estimates which show that the marginal social rates of return from R&D and knowledge spillovers can reach from 30 to 50 percent.
The implementation of the principles of the green economy can cause changes in state budgets due to the changes in tax obligations for economic actors: national governments can increase their budget revenues by selling pollution permits, and the reduction of harmful labour market taxation will result in improved well-being of citizens. This means that the aggregate income is likely to increase, which will stimulate the aggregate demand. In response to the growing aggregate demand, business enterprises will increase the volume of production/service provision, and thus the number of jobs will also increase (OECD 2017b). The OECD (2017c) report for the G20 emphasizes that many G20 countries can achieve substantial improvements in their GDP in case they permanently increase the investment in the low-carbon transition. Businesses can also receive support from national governments or targeted funds for green transformation or/and staff training, which will reduce the negative transformational impacts (OECD 2017b).
Nevertheless, as noted by Victor (2022), the impact of green investment on macroeconomic objectives (GDP, unemployment, public debt, private sector (household) debt, income inequality) remains a major concern in the EU and the USA. Having applied the LowGrow SFC simulation model (the model was calibrated to Canada and the United Kingdom) to research the modest and ambitious green transformation scenarios between 2022 and 2072, the author found that the changes induced by the green transformation would lead to an increase in GDP at an average annual rate of 1.5 percent in the modest scenario and 0.5 percent in the ambitious scenario, while GDP in the base case (without the green transformation) would increase by 2.1 percent annually. A slower GDP growth in the conditions of the green transformation under both modest and ambitious scenario would be caused by the diversion of some investment to non-productive capital, and this diversion would be more pronounced in the case of the ambitious scenario. The unemployment rate in all cases fluctuates around 6 percent, which shows that the green transition tends not to lead to any dramatic changes in the labour market. The ratio of government debt to GDP would be very similar under the base and modest scenarios, but it would be significantly higher in the case of the ambitious scenario. This is believed to be due to lower tax revenues and increased government support to households to reduce income inequality. When analysing the indebtedness of the private sector (households), the author found that it would remain stable under the modest scenario, would increase under the base scenario, and would decrease under the ambitious scenario. The latter decrease is explained by the likelihood that consumers would borrow less with the declining levels of consumption. Thus, the results of the study propose that national economies can grow in the conditions of the green transformation, but the growth rates will be slower than in the conditions of the traditional economic environment. The financing of the green transformation will require huge government incentives and direct government investment, so it will be focused not on economic, but on environmental and social benefits as the main goal. In this context, digital labour platforms may play an important role in mediating temporary or project-based green employment, offering flexible access to work opportunities aligned with green transition investments. However, such roles are currently underexamined in macroeconomic modelling, revealing a gap for further research.
Changes in Trade and Competitiveness
Fankhauser et al. (2013) believe that competitiveness in the conditions of the green transition will depend on 3 factors:
1)
the speed at which businesses will be able to transform traditional products and processes into green ones (the rate of green innovation);
2)
the ability of businesses to maintain and increase their market share (comparative advantage);
3)
a favourable starting point (current output).
The greatest increase in competitiveness is expected in the sectors which have the current comparative advantage and implement substantial green innovation, which shows that their transition to low-carbon products and production processes should be comparatively smooth. These sectors are the competitive strength of national economies. For example, if measuring by the Green Innovation Index, the heavy industry sector is considered the competitive strength in Germany, and the light industry sector—in France. The competitive potential in the future is represented by the sectors where the current comparative advantage is low (i.e. they export less than the global average of the sector). By actively implementing green innovation, these sectors will be able to gain an advantage from innovative low-carbon products and processes, and will become the basis of the national economic strength. For example, this is expected from the heavy industry sector in France and partly from the light industry sector in Germany (as measured by the Green Innovation Index) (OECD 2019a).
A special investment plan—the so-called “InvestEU” plan—is intended to finance the implementation of the European Green Deal policy measures. The plan includes the carbon dioxide emission tariffs for the countries which cannot reduce greenhouse gas emissions at the same pace as the EU (Valatsas 2019). A special mechanism called the Carbon Border Adjustment Mechanism (CBAM) is provided for this. Under this mechanism, a carbon tax would be applied to imports of certain goods from third countries. Additional taxes would be levied on the products made in the countries with less stringent environmental requirements than in the EU. This would help ensure that goods imported from third countries are not cheaper than an equivalent product produced in the EU (European Parliament 2021). According to the European Parliament (2021), the abovementioned mechanism should comply with the rules of the World Trade Organization and become part of the EU industrial strategy in the future. The mechanism should cover the entire energy sector and energy-intensive industries by 2030. It is noted that the mechanism is intended only to implement the objectives of combating climate change and reduce the risk of carbon dioxide leakage (emissions) (European Parliament 2021).
The study by “EIT Manufacturing” (2023), co-funded by the European Union, proposes that business competitiveness can be promoted through cooperation with reliable partners to maintain uninterrupted and smoothly functioning trade chains. This strategy will allow businesses to achieve strategic autonomy and maintain global competitiveness. In addition, since the principles of value creation are changing, businesses should move from a competitive to a more collaborative mindset: to exchange knowledge and expertise within a certain branch or local industry by following some pre-agreed criteria and standards. This type of ecosystem will enable more efficient use of valuable resources and information along the entire supply chain, thus creating a competitive advantage.
The green transformation is often seen as regressive due to the high costs to industry. However, according to the European Parliament (2022), if countries manage to reach the objective of 1.5 °C, established in the Paris Agreement, which entered into force on 4 November 2016, the output of energy-intensive industries will be less affected. Industrial competitiveness can be increased by free allocation of emissions trading system (ETS) allowances and the targeted use of carbon revenue.
Nevertheless, the failure to meet climate policy goals would make business less competitive. In jurisdictions where the legal regulation of business will be based on the principles of the green economy, pollution-intensive products will become relatively more expensive than similar products in the jurisdictions which will not consider the principles of the green economy. Therefore, pollution-intensive products produced in green economy jurisdictions will be less competitive, and the export income of the companies which make these products will decrease (OECD 2017a). On the other hand, compliance with environmental standards promotes innovation, which provides companies with a competitive advantage (OECD 2019a). In the context of platform economy, the trade and competitiveness dynamics can also influence platform-mediated services that contribute to green transformation goals. Cross-border freelance and gig workers—especially those involved in sustainability consulting, digital circular economy services, or remote design of green technologies—may become increasingly important for maintaining national competitiveness in low-carbon sectors.
It should be noted that not all green transformations have a positive impact on industrial growth. For example, Nosheen et al.’s (2021) study in Eastern and Western Europe showed that the technological innovations in the areas of transport and production-related climate change technologies tend to have a negative impact on green growth. The report by the OECD (2017a, b, c) suggests that not all business enterprises and industries can reorient so quickly due to the high costs, time and organisational factors, so they will be considered technologically backward from the point of view of the green economy, although their productivity will remain the same when working with old technologies. According to Mathiesen et al. (2023), some scenarios of the European Commission, for instance, the EU 1.5 TECH in “A Clean Planet for all”, do not provide measures to prevent the risks posed by the green transformation to industry, such as overinvestment, blind investment, general pan-industrial investment and unrealistic implementation rates. In addition, excessive protectionism of green innovations on the part of governments (lowering tariffs on low-carbon technologies) can have a negative impact since it will hinder the wider spread of these innovations in the market and there will be a risk of duplication of efforts across countries (Hasna et al. 2023). Also, as noted by Kozluk and Zipperer (2013), Victor (2022), Mallik (2023) and other researchers, the increase in the investment in the green transformation essentially means only the redistribution of investment: investments and innovations are directed to green sectors, but reduced in traditional sectors, but this does not lead to more investment and innovation per se. According to Hasna et al. (2023), the momentum of green innovation in developed countries reached its peak in 2010 (measuring by 10 percent of total patent filings in 2010), but has been slowly decreasing since then. This decrease is determined by various factors, for example, hydraulic fracking, which caused the price of oil to decrease, and technological maturity of some initial technologies (e.g. renewables). Sun et al.’s (2023) research found the negative impact of the workforce on the growth of the green economy. In the case of the latter incompatibility, the authors recommend promoting employment by developing green FDI, activating green financing tools and launching green job creation schemes through SMEs. Li et al.’s (2022) observations of 687 listed companies in China in the period 2016–2020 disclosed that green innovation may significantly reduce the economic efficiency of business enterprises, especially in the cases when innovation achievements are not sufficiently protected, when companies have accumulated limited knowledge and technology. The authors argue that the efficiency of companies in the latter case can be improved by high market competition, upgrading of production processes and freedom of technology selection.
Summarising
The joint effects of finance, food and fuel crises on industry in the conditions of the “brown” economy manifest themselves as the gross misallocation of capital, which no longer stimulates the economy and job creation. In contrast to the “brown” economy, the green economy is expected to promote economic growth in the long term due to cheaper energy, more efficient production processes and socially responsible consumption. The literature analysis revealed that the impact of the green transition on industry is likely to manifest itself through resource and innovation efficiency, transformations in production processes, changes in the demand structure, changes in aggregate income and macroeconomic conditions, and changes in trade and competitiveness. The growth of the green innovation is associated with the growth of innovation overall because the green innovation often requires additional complementary innovation. The biggest gaps between targeted and implemented investments are currently observed in the energy, water and sanitation, infrastructure, agrifood and biodiversity industries. This means that the volume of green investments in these sectors will have to grow, so it is likely that more jobs will be created. The main expected directions of the transformations in production processes are destruction of the resources and capabilities of the traditional mechanical products and the use of efficiency enhancing technologies, implementation of disruptive technologies which increase production potential, the use of advanced materials which lower costs, add value to products and facilitate recycling, and implementation of forecasting technologies. Demand growth is expected for products which are produced in low-carbon intensity sectors, in particular, electric vehicles and renewable energy. The reduced price will lead to an increase in demand for clean products and services, which, in its turn, will stimulate the growth of the sectors which produce these products/services, and thus will increase employment in these sectors. Indirect macroeconomic benefits will be obtained through technological and non-technological knowledge spillovers, R&D, which will become the public knowledge, and productivity improvement. The greatest increase in competitiveness is expected in the sectors which have the current comparative advantage and implement substantial green innovation, which shows that their transition to low-carbon products and production processes should be comparatively smooth. These sectors are the national competitive strength and employment accelerators. In addition, platform-based labour models may support this transition by offering flexible access to highly specialised services, especially in domains related to eco-design, digitalisation of sustainability reporting, and decentralised project work on green infrastructure. This functional flexibility may contribute to industrial resilience in the face of green transformation.
Nevertheless, the existing evidence regarding the positive impact of the green economy on industrial growth is not yet conclusive, and so far the impact of the environmental policy on macroeconomic indicators (in particular, GDP growth) has been relatively small. Researchers note that not all business companies and industries can quickly reorient due to the high costs, time, organisational factors, and some scenarios of the European Commission (for instance, the EU 1.5 TECH in “A Clean Planet for all”) do not provide the measures to prevent the risks posed by the green transformation to industry, such as overinvestment, blind investment, general pan-industrial investment and unrealistic implementation rates. In addition, excessive protectionism of the green innovation on the part of governments (lowering tariffs on low-carbon technologies) can have a negative impact. Therefore, it should be noted that the modeling of business activities for the shift towards the green economy must be carried out in the way so that the transformation would allow to boost economic growth not only through permanent, but also through temporary increases in investment.
2.2.2 The Effects of the Green Transition on the Labour Market
According to the European Parliament (2022), the green transformation policies will significantly transform the labour market, which will face both new opportunities and risks, and certain industries and groups of workers will be disproportionately affected. During the green transition, most occupations will be indirectly affected by energy supply and energy-intensive industries, as well as by changing energy prices and new work practices. This transformation is also expected to reshape the nature of work in platform-mediated environments, especially where on-demand services intersect with green economy goals, such as in sustainable mobility, repair services, and energy efficiency tasks.
The shift from the brown to the green economy is inevitably associated with huge costs, for example, the investment in renewable energy sources, pollution control equipment, pesticide level measurements, waste sorting and recycling systems, etc. On the one hand, this investment helps create new jobs (green jobs) since the operation, monitoring and maintenance of the equipment and systems mentioned in the example require human labour. On the other hand, products and services produced in the green economy are more expensive, and the costs fall on the shoulders of consumers. As a result, the aggregate demand as well as previous production and consumption volumes may decrease, and companies which have not transformed quickly enough may become uncompetitive. There is a risk that the green economy could increase unemployment. This duality of effects underscores the need for policy mechanisms that not only stimulate green investment, but also support labour market adaptability—particularly in flexible employment models such as platform work, where job stability and access to social protection may be insufficient in the face of transition shocks.
According to Atlama and Ozsoy (2011), the effects of green jobs can manifest themselves in two different ways: on the one hand, green jobs reduce unemployment and help solve social problems, such as poverty and inequality; on the other hand, since green jobs are related to raising marginal costs and decreasing sales, these effects can lead to higher unemployment. This two-sided dynamic is particularly relevant in the context of digital platforms, where the creation of new green tasks (e.g. remote energy audits, sustainability consulting) may be offset by job insecurity or lack of upskilling opportunities. The literature analysis allowed to identify the main positive, negative and neutral potential effects of the green transition on the labour market (see Table 2.3).
Table 2.3
The major positive, negative and neutral potential effects of the green transition on the labour market
Direction of the effect
Manifestation
Author(s), year
Positive effects
Creation of green jobs and green collar jobs
The U.S. Department of Energy (2001); Bezdek et al. (2008); UNEP, ILO, ITUC, IOE and WHO (2008); Popp et al. (2020); Vandeplas et al. (2022)
Creation of new jobs in technologically advanced industries
OECD (2019a); Niggli and Rutzer (2021); Vandeplas et al. (2022)
Source: compiled by the authors
The framework for characterising green jobs was provided by the United Nations Environment Program (UNEP) in collaboration with the International Labour Organisation (ILO), the International Trade Union Council (ITUC), the International Organisation of Employers (IOE) and the World Health Organisation (WHO) (2008). In their report, green jobs are defined as “work in agriculture, manufacturing, research and development, administrative and service activities that contributes substantially to preserving and restoring environmental quality”. The report also notes that green jobs include the jobs which help to protect ecosystems and biodiversity, reduce energy, materials and water consumption through high-efficiency strategies, de-carbonize the economy, and minimize and altogether avoid generation of all forms of waste and pollution (2008, p. 3). In addition, the UNEP (2008) emphasises that green jobs need to be decent. i.e. they must offer adequate wages, safe working conditions, job security, reasonable carrier prospects and exercise workers’ rights. This emphasis on decent work is particularly relevant in the platform economy context, where task-based jobs often lack social protection and stability. Ensuring that green platform-based work meets these decency criteria is essential for a truly sustainable labour transformation. Referring to the new 2030 Agenda for Sustainable Development, proposed by the UN General Assembly in September 2015, the ILO (2015) notes that decent work must be based on the four pillars: employment creation, social protection, rights at work and social dialogue, i.e. decent works must be productive and secure, respect labour rights, provide adequate income and social protection, and offer union freedom, social dialogue, collective bargaining and worker participation. These principles are particularly important for guiding labour market reforms in response to both digitalisation and the green transition, as both trends challenge conventional employment models and demand new approaches to ensuring work decency and inclusiveness.
The report, provided by the United Nations Environment Program (UNEP) (2008), concludes that the global transition from the conventional brown to a low-carbon sustainable economy will promote economic development and add a large number of green jobs across many sectors of the innovative green economy. The green economy, i.e. the use of environmentally friendly technologies, products and services not only minimizes pollution and reduces the negative impact of industry on the environment, but also stimulates the creation of green jobs in the major industries, such as construction, transportation, energy, agriculture and forestry. This suggests that the employment potential of the green economy lies not only in emerging sectors but also in the transformation of traditional industries, especially when these sectors embrace technological upgrades aligned with sustainability goals.
One of the main industries growing due to the promotion of the green economy is the renewable energy (wind, solar, photovoltaic and biomass energy) industry. The delivery of products related to this industry, for instance, hydro-small and large geothermal systems, fuel cells, hydrogen, energy conservation and energy efficient products, as well as hybrid vehicles, passive, solar/green, sustainable buildings, energy-smart design and day lighting, is developing rapidly (Bezdek et al. 2008, p. 21). Thus, there is a noticeable correlation between the development of the abovementioned industries and technologies and the growth of the number of jobs, i.e. the expanding use of renewable energy has a positive impact on employment. The pace and scale of this transformation indicate that renewable energy sectors could become central not only to energy policy but also to long-term employment strategies, particularly in regions seeking to replace declining brown economy sectors. With the conduct of air, water, land pollution control, waste management, environmental monitoring, analysis and assessment, as well as environmental research, development and engineering, many new jobs are created in the aforementioned areas (Atlama and Ozsoy 2011). These areas represent an expanding segment of the labour market, which is likely to absorb a growing share of the workforce displaced by traditional sectors, provided that adequate training and upskilling mechanisms are in place.
Popp et al. (2020) found that the number of green jobs in the US increased due to the subsidies provided under the American Recovery and Reinvestment Act (ARRA) of 2009. A significant increase in the number of green jobs is observed in the construction and waste management sectors. This suggests that strategic public investment plays a pivotal role in accelerating the green transition, particularly in labour-intensive sectors, and highlights the importance of coordinated policy support to ensure equitable employment outcomes. Bircan et al. (2023) analysed the “LinkedIn Economic Graph” statistics and found that the demand for workers with “green” skills is so high that a wage premium of 4 percent is paid to these workers. This shows that the demand for the workers with “green” skills exceeds their supply in the labour market. This growing skill premium underscores the urgency of aligning education and training systems with the needs of a green economy to avoid bottlenecks in labour supply and to maximise the employment potential of the green transition. In the region of the European Bank for Reconstruction and Development (EBRD), advertisements for green jobs on the “LinkedIn” platform accounted for 14 percent of total paid job postings at the beginning of 2023 (for comparison, this percentage did not exceed 9 percent in 2018). In developed countries (France, Germany, Italy, the UK, the USA), this number increased from 10 to 21 percent from 2018 to 2023. This growing share of green job postings highlights a structural transformation in labour demand that is not limited to traditional industries. It suggests an expanding role for digital platforms as intermediaries in the green labour market, especially for specialised, location-flexible tasks.
The concept of green jobs is distinguished from the concept of green collar jobs. In general, green collar jobs can be interpreted as traditional blue-collar jobs, but those in green businesses whose products and services directly contribute to improving environmental quality and energy efficiency (Pinderhughes 2007). As noted by Atlama and Ozsoy (2011), these jobs usually require less than a four-year university or college degree, but often are a stepping stone to high-skill professional green jobs and green entrepreneurship. More specifically, green collar jobs are defined as vocational jobs in an ecologically responsible trade (e.g. constructing and maintaining wind farms, practicing organic agriculture, recycling materials, etc.). For instance, the industry of renewable energy offers a wide variety of jobs, and although it can be difficult to find a personal professional niche, persons with different types and degrees of education can improve their qualifications or retrain and get involved in this industry. The analysis provided by the U.S. Department of Energy (2001) proposes that the industry of renewable energy offers many green collar jobs in the areas of communication, sales, marketing, business support (e.g. corporate planning, finance, human resource management, law, information technologies), community outreach. Other jobs are specific to the operation and management of the major renewable energy power sources—wind, solar, bio-, geothermal and hydropower.
Bircan et al. (2023), indicate that a higher proportion of workers moved into green industries than moved out of them in different economies between 2015 and 2023.
“Brown” industries are characterised by a net outflow of the labour force, while “green” industries experience a net inflow. Greater job transitions into “brown” industries are observed in economies outside the EBRD region, whereas EBRD economies see more significant shifts into “green” industries. While this section primarily focuses on traditional industrial labour market changes, platform-mediated work is also starting to contribute to the green transition—particularly in specialised, location-independent roles such as renewable energy consultancy, environmental data analysis, and circular economy services.
According to the OECD (2017a, b, c), the impact of the green economy on the labour market will manifest itself in four following directions:
1)
more jobs will be created in the “green” sectors which will make products/provide services reducing environmental pressure;
2)
the number of jobs will decrease in the sectors/industries characterised by large environmental footprints;
3)
there will be a noticeable net increase in the number of jobs stimulated by the changes in the structure of the economy (the largest increase in the number of jobs is expected in labour-intensive sectors);
4)
there will be a noticeable net decrease in the number of jobs determined by the decrease in business profitability due to extremely high environmental requirements and stringent regulation.
Any of these directions can be both short-term (i.e. related to the transition towards the green economy of a specific business) and long-term (i.e. typical of the green economy in general).
The report by the OECD (2017a, b, c) suggests that environmental policies promote specialisation and the development of “clean” industries, but at the same time mean stringent environmental regulation, the effects of which on industrial productivity may vary across business companies and industries. This means that technologically advanced business companies and industries can expect to grow and create new jobs, but less nimble, larger companies and industries, especially those that have already invested in the most effective operational strategies and technologies, will not be in a hurry to switch to new technologies (for example, because the potential of the old technologies has not been fully utilized, because the transformation may require significant business interruptions which cause losses, because a firm or an industry is essentially not agile, etc.). Therefore, their productivity growth will be very limited from the point of view of sustainability, and no new jobs will be created without productivity growth. Given the accelerated technological changes associated with the green transition, continuous skills development and retraining are critical to prevent labour market mismatches and to enable workers to seize new opportunities in emerging green sectors.
The green transition is expected to cause significant changes in the sectoral composition of employment. For example, the number of jobs in the coal sector is expected to decrease by about 50 percent by 2023. A large decrease in jobs is also expected in other fossil fuel sectors. Chateau et al. (2018) estimated a decrease of approximately 8 percent of jobs in mining and fossil fuels supply and fossil-fuel electricity generation sectors. The ILO (2018) believes that the reduction of the labour force in the gas and petroleum extraction sector may amount to about 11 percent, and in the coal-powered electricity generation sector—about 19 percent by 2023.
The dynamics of the number of jobs in energy-intensive industries should be closely related to the impact of the transition on output in these industries. A large increase in the number of jobs is expected in the electricity supply and construction sectors, and the implementation of climate neutrality measures should increase employment in the industries such as production of renewable energy and building renovation. The services sector, which currently provides the largest number of jobs in the EU, will be affected to a relatively limited extent (European Parliament 2022).
The high-skilled labour force will be more negatively affected than the low- and medium-skilled labour force because a large part of the newly created jobs will be intended for low- and medium-skilled workers; thus, the negative impact of digitalisation on these categories of workers will be offset at least partially. Nevertheless, if carbon revenues are used to reduce labour taxation, aggregate employment will increase, so the negative impact of the green transformation on high-skilled labour force will be partially mitigated (European Parliament 2022).
Many workers will undoubtedly need to be retrained and adapt to innovative production methods, which will require significant investment in human capital to ensure that labour demand matches labour supply in the labour market (European Parliament 2022). Vandeplas et al. (2022) explain that technological progress in the conditions of the green transformation will mean the accelerated obsolescence of technologies, which, in its turn, will result in the obsolescence of human capital, i.e. the obsolescence of some jobs. If the demand for certain skills decreases, workers will be forced to retrain at their own expense, at the expense of the state or a potential new employer. How quickly the skills of the labour force will again meet the requirements of the labour market will depend on the speed with which countries will be able to roll out green technologies. According to Adao et al. (2022), the ICT technology adaptation process showed that skill mismatches tend to persist, especially in local markets, which adapt relatively slowly. The authors argue that the transition to jobs with strong skill specificity will be more difficult. Having applied a three-sector macro-econometric analysis model, Alexandri et al. (2024) researched the impact of the industrial climate change mitigation on the European labour markets. The results of their study show that the climate change mitigation policies will affect the structure of the European labour markets, and will also lead to a small increase in inequality and polarisation in the labour market till 2023. The authors believe that greater manifestations of inequality and polarisation in the labour market will be characteristic of those European countries where industry will be more severely affected by the green transition. However, the uneven regional and sectoral impacts of the green transition underscore the need for targeted social policies to mitigate inequality and support vulnerable groups disproportionately affected by structural shifts in employment.
Nevertheless, as stated by the European Parliament (2022), the assessment of both positive and negative aspects of the green transition implies that the overall impact of the green transition on the labour market should be moderate since the negatively affected industries are expected to constitute a relatively small share of total employment, and the strength of the impact will depend on the use of carbon revenues. Chateau et al.’s (2018) estimations suggest that the aggregate impact of the green transformation on employment should be limited. When analysing the structural changes in the labour market which could be caused by decarbonisation policies by 2035, the authors performed the simulations which revealed that the overall reallocation of jobs (i.e. the sum of destructed and created jobs) will be around 0.3 percent in the OECD countries and around 0.8 percent in non-OECD countries. For comparison, job reallocation rates averaged at 20 percent in the OECD countries between 1995 and 2005. On the other hand, the authors admit that the relatively small job allocation rates obtained during the simulation may be determined by a small share of total employment in energy sectors which will experience the greatest consequences of the green transition, but due to the small share of jobs in total employment, the overall labour market will experience relatively small changes due to employment transformations in the energy sector. Vandeplas et al. (2022) provide the statistical data which indicate that the sectors of electricity production, transport, manufacturing, agriculture and mining, which together produce about 90 percent of all CO2 emissions in the EU, employ only about 25 percent of the total workforce. More than 75 percent of the workforce is employed in the construction, wholesale, retail and other service sectors, but the overall impact of the green transformation on the latter sectors is likely to be less significant because they together produce only about 12 percent of all CO2 emissions in the EU. Alexandri et al. (2024) found that the reduction in the use of fossil fuels in the road transport and electricity supply sectors may even have a modest, but positive impact on GDP and total employment in the European labour markets (in more than half EU Member States, especially those with such industries as mining and quarrying, utilities and manufacturing of coke and refined petroleum products) by 2023.
Arntz et al. (2016) employed the task-based approach in their research of 21 OECD countries and found that only about 9 percent of jobs are at risk of being automated. These findings lead to the conclusion that the green transition poses a greater risk to technological progress than to employment, thus, the effects of the green transition are manageable.
The OECD (2019a, b) argues that the process of the green transformation can shift some jobs to other sub-sectors, but jobs will not be lost. For instance, the expected decrease in the number of jobs in the fossil fuels sector can be offset by the increase in the number of jobs in the renewable energy sector, so the overall effect on employment will be neutral. A similar opinion is held by Vandeplas et al. (2022) who observe that the structural economic changes, brought about by the green transformation, will equalize the proportions of people leaving the labour market and people entering the labour market. Niggli and Rutzer (2021) who conducted an employment study in 19 EU Member States in the period 1992–2010, when environmental requirements were tightened almost three times, found that the impact of this policy on aggregate employment was insignificant because the tightened requirements caused a shift from the sectors characterised by low green potential to the sectors characterised by high green potential. Terzi (2022) notes that the green transition can stimulate the creation of jobs which are less prone to geographical concentration of the population, promote delocalisation and dehumanise repetitive tasks by automation. McCarthy et al. (2018) add that in the circular economy, part of the jobs will be created online, which will at least partially compensate for the reduction of jobs in physical companies.
The standard model, provided by the European Parliament (2022) (the JRC-GEM-E3 model is based on the presumption that wages are fully flexible, allocation in industries is free, business companies cannot incorporate the opportunity costs, generated by free allocation, and cannot optimise their market share, the power, buildings and road transport sectors are characterised by auctioning, and unemployment rate is close to the baseline level), suggests that the negative effects of the green transition on employment should reach about −0.26 percent (i.e. negatively affect about 494,000 jobs) by 2023. However, after using carbon revenues to reduce labour taxation, the positive impact on employment should reach about 0.06 percent (i.e. affect about 110,000 jobs). If carbon revenue is directed to support energy efficient investment and reduce VAT, consumption and GDP growth will be stimulated, which, in their turn, are likely to increase employment by 0.20 percent (i.e. to contribute to creating nearly 412,000 jobs). Finally, if carbon revenue is used to reduce labour taxation for low-skilled labour force, net wages for lower-skilled workers will increase and labour costs for business will decrease, which is likely to increase employment by 0.45 percent (European Parliament 2022).
According to Vandeplas et al. (2022), the major labour market intervention measures, which can mitigate the shock caused by the green transition, can be categorized as pre-transition and transition measures. The pre-transition measures include identifying which groups of workers will be most affected, conducting an audit of their skills, reviewing the current labour regulations, setting up institutions and partnerships to manage the labour market transition, and providing early job search assistance. The transition (during and after) measures include providing temporary income support, implementing active labour market policies and providing access to vital social services, such as healthcare, childcare, social services. The above-mentioned measures are believed to cushion the loss of income for workers who have lost their jobs due to the changes in the economic structure caused by the green transformation, and to help them reintegrate into the labour market. It should be emphasized that, given the uneven regional and sectoral impacts of the green transition, targeted and flexible social policies will be necessary to support workers and communities disproportionately affected by these changes, ensuring an inclusive and just labour market transition.
Summarising
The green transformation policies will significantly transform the labour market which will face both new opportunities and risks. The literature analysis revealed that the impact of the green transition on the labour market can be assessed as positive, negative and neutral. The positive effect is expected to manifest itself through the creation of green and green collar jobs in transforming industries, especially construction, transportation, energy, agriculture and forestry, the creation of new jobs in technologically advanced industries (for instance, waste management, environmental monitoring, analysis and assessment, environmental research, development and engineering), net increase in the number of jobs due to the changes in the structure of the economy, the increase in the share of low- and medium-skilled labour force in total employment, the creation of jobs which are less prone to geographical concentration of the population, and creation of jobs online. The negative impact of the green transformation on the labour market is likely to manifest itself through rising marginal costs and decreasing sales, which can lead to a reduction of jobs, especially in industries characterised by large environmental footprints, limited productivity growth in less agile companies that have not yet exploited the potential of their old investments, the decrease in the share of high-skilled labour force in total employment, the rapid obsolescence of technologies and large additional investments in retraining of human capital, and slightly higher inequality and polarisation in labour markets. Nevertheless, after assessing the aspects of both positive and negative effects, the overall impact of the green transition on the labour market is believed to be moderate since the negatively affected industries are expected to form a relatively small share of total employment, the limited impact on the service sector will not cause significant changes in employment in this sector, only 9 percent of jobs are expected to be automated, and many jobs may not be lost, but shifted across sectors. How quickly the current skills of workers can be adapted to the demand for new skills in the labour market will depend to a large extent on the speed with which countries develop green technologies. The experience of ICT implementation has shown that greater mismatches between labour supply and demand can be expected in local labour markets which are characterised by slower reorientation in response to global and national changes.
2.3 The Joint Impact of Digitalisation and Green Transformation on the Labour Market in the Context of the European Green Deal
Digital transformations in Europe are taking place in parallel with the implementation of the Green Deal policy. Although at first glance digitalisation and the Green Deal may appear to be separate things, they are in fact closely interrelated since the success of neither is possible without the other. This interdependence underscores the necessity of integrated policy approaches that simultaneously foster digital innovation and green investments, ensuring that neither transformation hampers but rather accelerates the other’s progress. Moreover, both of them are extremely important for the future of Europe (European Commission 2022b). Digitalisation can help reduce carbon emissions, people can join video conferences instead of traveling to meetings, monitor how much energy they use at home, and even make farming more sustainable. The dual role of digitalisation as both an enabler of sustainability and a sector with its own carbon footprint calls for balanced strategies that maximise environmental benefits while minimising digital sector emissions.
As noted by Wolf et al. (2021), the Green Deal is much more than climate neutrality. It also covers the measures for reducing regional and social exclusion, job creation and price stability since the changes in the climate strategy are only possible after moving the economy in the direction of digitalisation. The European Union is exploring the potential of integrating digital technologies into the industry and economy without deviating from the objectives of the Green Deal. This underinvestment highlights a strategic gap that needs urgent attention to leverage digital technologies for climate goals effectively.
The Communication ‘Shaping Europe’s Digital Future’ by the European Commission (2020d) provides the vision of the role of digitalisation in achieving climate and sustainable development goals. The Communication emphasises that positive social changes are expected due to the synergy between digitalisation and sustainability. The European Commission (2020d) highlights the need for more sustainable, energy- and resource-saving digital infrastructures and technologies and introduces Digital Product Passports. A Digital Product Passport should enable sharing the key product-related information which is essential for sustainability and circularity of a product. It is expected to provide new business opportunities through circular value retention and optimisation, and at the same time provide greater transparency and accountability in monitoring the resources used for production (European Commission 2023). Also, it is a significant proposal for digitalisation in the area of production. In addition, the European Commission expects to implement digital solutions which could help reduce greenhouse gas emissions and the use of pesticides in agriculture, and promote the use of transport technologies which can reduce the sector’s impact on the environment (Institute for European Environmental Policy 2021).
The interrelations between the European Green Deal and digitalisation were also revealed by many scientific studies. For instance, Fetting (2020) believes that digitalisation is a key enabler of the Green Deal policies which will contribute to expanding sustainable and labour-intensive economic activities. Digitalisation can help make the industry more mobile, improve the availability of information on the new standards for construction, energy efficiency in buildings and insulation. The food production industry could digitalise labelling the products which were made with consideration of the principles of the Green Deal. The transportation sector could associate digitalisation with intelligent traffic management systems, development of multimodal transport (when goods are transported under one contract, but with the use of at least two different modes of transport), reduction of pollution, and traffic congestion in cities.
According to Wolf et al. (2021), although the information and communication technology (IT) sector itself is characterised by carbon dioxide emissions, digitalisation can help to significantly reduce emissions by saving energy in buildings, the transport sector, industry, households, agriculture, etc. With the exception of some specialised niches, the IT sector in the EU is lagging behind its main global competitors. The investment in R&D activities in the IT sector amounted to only about 0.2 percent of GDP in 2019, and the investment financed by federal budgets was less than 0.05 percent of GDP (Wolf et al. 2021).
Gailhofer et al. (2021) found that digital technologies can help fight against the negative environmental effects since digital technologies are capable of accumulating huge amounts of data, the analysis of which can help to make informed decisions. Data analysis performed by artificial intelligence can help to more accurately forecast the potential impact on the environment, and intelligent systems allow monitoring of ongoing processes. Automatic mechanisms and technologies allow to optimise the security of the operations performed. With artificial intelligence monitoring energy consumption, it is easier to integrate renewable energy sources into common energy networks. In the agricultural sector, digital technologies will make it possible to use water more efficiently and reduce the negative impact of pesticides and fertilizers on the environment, They will help to better plan transport and infrastructure systems, increase the efficiency of engines, optimise the charging process of electric cars, control and manage railway systems, and coordinate various modes of transport in the transport sector. Digital technologies will help to inspect, sort, separate and disassemble materials for reuse in circular processes in the manufacturing sector.
Bertoncelj (2022) argues that the Internet of Things and other interconnected cyber-physical systems can completely take control of production and eliminate the need for human intelligence to monitor/intervene task execution. The author expects the greatest efficiency in waste reduction and recycling systems, the transport and logistics sectors. Smart technologies are believed to have the potential to exponentially increase market value and scale.
Marvin et al. (2022) researched the effects of digitalisation and artificial intelligence on sustainable food systems and found that creating a sustainable food system, as envisioned in the European Green Deal strategy, requires a strategic approach which would cover all economic, environmental (climate, ecosystems) and social aspects related to the production and consumption of sufficient and healthy food. The researchers conclude that artificial intelligence and digitalisation have great potential to change the roles and interactions of economic actors throughout the value chain when products travel from farmers to consumers, and thus accelerate the shift towards a sustainable food system. They emphasise that data availability and processing are the critical factors at all stages of the value chain. Such technological advancements should be accompanied by policies to manage workforce transitions, ensuring reskilling and protection against displacement.
Thus, both the European Commission’s communication “Shaping Europe’s Digital Future” (2020d) and previous scientific studies show that the European Green Deal can help create value for consumers through more sustainable production processes and final products/services. Increasing sustainability is expected to bring greater economic and social benefits to society. When implementing the Green Deal strategy, the main factors which are necessary for transforming traditional business models in the modern economy are digitalisation and green transformation.
The purpose of digitalisation is to create a significant competitive advantage that customers/consumers are willing to pay for (“Innolytics” 2022). Thus, digitalisation helps create the value which is based on the development of customer/user benefits through digital technologies. Development of digital business models is an important task for businesses when dealing with the challenges of digital transformation. Merely extending an existing analog business model with a certain digital component (for example, ordering goods online from a brick-and-mortar retailer) is a preliminary stage in the implementation of a digital business model, but it is not yet a digital business model. Digital business models are characterised by different features, but the main ones are as follows:
The use of digital technologies. Creating value added would not be possible without the use of digital technologies. For example, companies, such as ‘Amazon’, ‘Uber’ or ‘Airbnb’, could not operate without Internet technologies.
Digital business innovations. Digital business models are based on providing services that are new to the market.
Customer attraction and distribution of products/services are based on digital channels. Companies which develop and promote digital business models often reach potential customers through digital technologies. Sales are characterised by such trends as sales automation and early onboarding.
Customers are willing and agree to pay for digital services. Digital business models help create unique customer value which can be monetised.
Consumer willingness to pay and independent value creation are the most prominent features of a digital business model. However, it should be noted that the mere provision of digital services (e.g. the ability for consumers to monitor the amount of electricity consumed through a special app can be treated as a digital offer to consumers, but it is not yet a digital business model (“Innolytics” 2022).
The concept of green business transformation refers to innovative and environmentally friendly ways of doing business (“Force technology” 2014). A greater focus on green business models can ensure that a business is ready for potential legal requirements, customer demands, energy supply, limited resources, waste management systems, etc.
Green business models typically cover one or more of the following elements: product and material recycling, exchange economies, and replacing unwanted or toxic materials with more environmentally friendly ones (“Force technology” 2014).
Green business models are associated with product orientation (e.g. clean technology companies which use renewable wind and/or solar energy, make resource-efficient products), service orientation (e.g. environmental consulting), and process-oriented initiatives in the value chain (e.g. environmental ISO standards, corporate social responsibility, “green” reports, etc.). Adopting these innovative business models requires both organisational agility and supportive regulatory frameworks to thrive within evolving market and environmental demands.
The previous subsections revealed the specific aspects of the effects of digitalisation and green transformation on the labour market. On the basis of the literature analysis, the model of the joint impact of these two factors on the labour market in the context of the European Green Deal can be developed.
Figure 2.11 shows that digitalisation-driven automation and robotisation can directly reduce employment because smart devices replace the human workforce. Nevertheless, since technologically advanced sectors create more added value, demand for labour in those sectors is likely to increase, which will have a positive impact on total employment. As the management of automated and robotic systems requires higher human resource expertise, skilled labour will be better paid. Digital platform work creates a potentially growing digital labour market which is part of the total labour market, so digital platform work tends to increase total employment. Nevertheless, due to the shorter actual paid working time compared to the paid working time under traditional employment contracts, as well as the high competition on digital platforms (competition not only in domestic, but also in international markets, when lower-paid workers from developing economies compete with higher-paid workers from advanced economies), average wages are decreasing. Digital platform work does not require a highly qualified workforce. Digital organising of the worker-employer relations leads to the disruptive nature of this relation, as well as uncertainty and unclear subordination between parties, which violates workers’ rights and increases the risk of their social vulnerability. Polarisation between low-/medium- and high-skilled labour force in the labour market reduces workers’ bargaining rights and can lead to wage stagnation, which in turn increases workers’ social risk and income inequality.
Fig. 2.11
The model of the joint impact of digitalisation and green transformation on the labour market in the context of the European Green Deal. Source: compiled by the authors. *Positive effects are represented by green arrows, negative effects—by red arrows
Resource and innovation efficiency promoted by the green transformation is expected to stimulate economic growth in the long term, so it should lead to higher total employment rate. Production, focused on resource and technology efficiency, is likely to increase production potential, lower production costs and add value to products, which should also stimulate economic growth, the aggregate income, and thus employment. Greater demand for clean products/services will stimulate the growth of the sectors which produce/provide these products/service; the green sectors will be characterised by a higher comparative advantage, which will allow to expect an increase in employment in these sectors. The negative impact of green transformation on the labour market is likely to manifest itself through rising marginal costs of investment and high costs for retraining workers, which can lead to reduction of jobs, especially in the industries characterised by large environmental footprints and small businesses which do not have funds for large investment.
After comparing the most important directions of the impact of digitalisation and green transformation on the labour market, it can be stated that digitalisation directly (positively or negatively) affects the major elements of the labour market—the structure of the market, wages, worker qualifications and social protection. Meanwhile, the impact of green transformation is indirect: it manifests itself through the changes in the structure of the economy, influence on macroeconomic indicators (GDP reflecting economic growth, aggregate income growth, competitiveness, etc.). Thus, the impact of digitalisation on the labour market is felt in the short run, while the impact of the green transformation is felt in the long run.
The report by the World Economic Forum (2023) suggests that big data analytics, climate change, environmental management technologies, encryption and cybersecurity will be the biggest drivers of job growth, while agricultural technologies, digital platforms and applications, e-commerce, digital commerce and artificial intelligence will cause the most significant disruptions to the labour market in the next five years. Employers expect that the structural decline of the labour market will amount to 23 percent in the next five years.
Nevertheless
After assessing various aspects of the impact of digitalisation and ther green transformation on the labour market, it can be stated that the overall impact should be moderate in the long run since the most negatively affected industries are expected to constitute a relatively small share of total employment, and many jobs may not be lost, but shifted across sectors. Complex impacts of digitalisation and green transition require continuous research and policy monitoring. The biggest questions arise regarding the changes in the workers’ qualifications, their social protection and wage dynamics since the results of previous studies on these issues are very contradictory.
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