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Garibaldi, Pietro. "Job creation, job destruction and employment reallocation : theory and evidence." Thesis, London School of Economics and Political Science (University of London), 1996. http://etheses.lse.ac.uk/2234/.

Scheidgen, Michael. "On job creation and destruction : theories and evidence." Berlin Logos-Verl, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2844501&prov=M&dokv̲ar=1&doke̲xt=htm.

Dhanah, Darlington. "Small businesses and job creation in South Africa." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27441.

Keck, Jennifer Marguerite. "Making work, federal job creation policy in the 1970s." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1995. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ27793.pdf.

Richards, J. D. W. "The allocation and effects of special employment measures : The case of the temporary employment subsidy and schemes operated by the Department of Industry." Thesis, University of Kent, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.353817.

Byrd, Christopher Merrill. "Public-Private Partnerships for Higher Education Infrastructure: A Multiple-Case Study of Public-Private Partnership Models." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/19287.

Amm, Kathryn Leigh. "Social enterprises, social value and job creation in Cape Town." Thesis, Nelson Mandela Metropolitan University, 2009. http://hdl.handle.net/10948/1260.

Harper, Mary Jane. "Targeted job creation : one federal response to long term unemployment." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26119.

Thomsen, Stephan Lothar. "Evaluating the employment effects of job creation schemes in Germany." Heidelberg : [Mannheim] : Physica-Verlag ; ZEW, Zentrum für Europäische Wirtschaftsforschung, 2007. http://dx.doi.org/10.1007/978-3-7908-1950-2.

Mabusela, Tebogo. "Debt leverage, company growth and job creation : South African manufacturing." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64869.

Fobair, Richard C. "Job creation calculator assessing the potential of energy conservation investments /." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0041254.

Crespo, Cuaresma Jesus, Harald Oberhofer, and Gallina Andronova Vincelette. "Institutional barriers and job creation in Central and Eastern Europe." Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1186/2193-9012-3-3.

Fujita, Shigeru. "Essays on macroeconomic dynamics of job vacancies, job flows, and entreprenerial activities /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3138821.

Ramncwana, Zukiswa. "The role of cooperatives in local economic development and job creation." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/5919.

Nesengani, Thinandavha J. "The impact of expanded public works programme on job creation on the community of Rambuda in Mutale Municipality, Limpopo Province." Thesis, University of Limpopo (Turfloop Campus), 2007. http://hdl.handle.net/10386/850.

Mochusi, Refilwe Solomon. "The effectiveness of youth empowerment wage subsidy on job creation in Makhado Local Municipality." Thesis, University of Limpopo, 2016. http://hdl.handle.net/10386/1722.

Makhosathini, Swazi Sydney. "The expanded public works programme and job creation in East London." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/6444.

Collins, Alan, and Jen Snowball. "Transformation, job creation and subsidies to creative industries: the case of South Africa’s film and television sector." Taylor & Francis Group, 2015. http://hdl.handle.net/10962/67433.

Chama, C. M. "Small scale businesses in Zambia : Their role in employment creation." Thesis, University of Stirling, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234913.

Fort, Carol S. "Developing a national employment policy : Australia 1939-45 /." Title page, contents and abstract only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phf736.pdf.

Blackburn, R. A. "Job generation and employment attributes in small firms : A study of the electrical and electronics engineering industry in Dorset." Thesis, Bournemouth University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377914.

Fung, K. K. "Job creation and destruction in Hong Kong manufacturing industries some empirical evidence /." Click to view the E-thesis via HKUTO, 2002. http://sunzi.lib.hku.hk/hkuto/record/B31954650.

Fung, K. K., and 馮國健. "Job creation and destruction in Hong Kong manufacturing industries: some empirical evidence." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31954650.

Mabece, Victor Nkosabantu. "The role of local economic development in job creation in Dimbaza Township." Thesis, Nelson Mandela Metropolitan University, 2017. http://hdl.handle.net/10948/18069.

Smale, Natasha Kelly. "An analysis of the use of tax incentives to motivate job creation." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/26426.

Thomsen, Stephan L. "Evaluating the employment effects of job creation schemes in Germany with 57 tables." Heidelberg New York Physica-Verl, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2919937&prov=M&dok_var=1&dok_ext=htm.

Leavy-Sperounis, Marianna (Marianna Breakstone). "Manufacturing recovery : a networked approach to green job creation in Massachusetts Gateway cities." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59753.

Ghiassi-Razavi, Hediyih. "The expanded public works programme : a strategy for poverty alleviation and job creation." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/29645.

Maltas, Claire Camilla. "Thy fearful symmetry: Order and disorder in creation in the Book of Job." Thesis, Maltas, Claire Camilla (2016) Thy fearful symmetry: Order and disorder in creation in the Book of Job. PhD thesis, Murdoch University, 2016. https://researchrepository.murdoch.edu.au/id/eprint/34490/.

Salukazana, Temate Lucia. "Poverty alleviation through employment creation in Matatiele, Eastern Cape Province." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/d1021138.

He, Yongjuan. "Optimum population distribution described by dynamic models and controlled by immigration and job creation." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26652.

Erasmus, Johannes Cornelius. "Effective training for job creation in the South African education system / Johannes Cornelius Erasmus." Thesis, Potchefstroom University for Christian Higher Education, 2002. http://hdl.handle.net/10394/8604.

Blagg, Brandon. "Examining the 2013 Kansas state income tax changes and their impact on job creation." Thesis, Kansas State University, 2015. http://hdl.handle.net/2097/18908.

Essop, Shazia. "Tax incentives that support job creation in South Africa - a comparative study amongst BRICS." Diss., University of Pretoria, 2011. http://hdl.handle.net/2263/26832.

Thomo, Sipho Derek. "An investigation of the impact of inward foreign direct investment on skills development and job creation in South Africa." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/26359.

Mthimkhulu, Alfred Mbekezeli. "Small enterprise development in South Africa : an exploration of the constraints and job creation potential." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97117.

Yeoh, Terence Eng Siong. "The Facet Satisfaction Scale: Enhancing the measurement of job satisfaction." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc3899/.

Newman, Keith R. "Small business : its role in job creation, its political support in Canada and an assessment of a government assistance programme in Quebec." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61969.

Hendriks, Jeremy Francisco. "Critical evaluation of possible policy options to reduce unemployment in South Africa." University of the Western Cape, 2016. http://hdl.handle.net/11394/4926.

Hildebrand, Nicole Marie. "The language of creation and the construction of a new concept of theodicy : Job 38-42." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99595.

Taiwo, Olusade. "Evaluation of the effects of micro, small and medium enterprises finance policy on job creation in Nigeria." University of the Western Cape, 2019. http://hdl.handle.net/11394/6956.

Lee, Chan. "Perceived job change toward dimensions of knowledge work among three levels of employees in a Korean bank." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1101953029.

Alecci, Andrea. "The impact of early retirement reform on firms’ employment adjustment." Master's thesis, NSBE - UNL, 2013. http://hdl.handle.net/10362/9696.

Rittau, Yasmin. "Regional labour councils and local employment generation the South Coast Labour Council, 1981-1996 /." Connect to full text, 2001. http://hdl.handle.net/2123/574.

Mabanjwa, Siyabonga. "The use and effectiveness of construction management as a building procurement system in the South African construction industry." Pretoria : [s.n.], 2003. http://upetd.up.ac.za/thesis/available/etd-08272003-104103/.

Ogbonna, Chidiebere, and Victor Akinleye. "Possible Barriers for Expansion and Job Creation : Case Study of Small and Medium Enterprises in Gotland Region, Sweden." Thesis, Högskolan på Gotland, Institutionen för humaniora och samhällsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hgo:diva-907.

Maloma, Ismael. "The role of downstream steel manufacturing co-operatives in job creation and poverty alleviation in Boipatong / Ismael Maloma." Thesis, North-West University, 2005. http://hdl.handle.net/10394/2362.

Sekatane, Mmapula Brendah. "The role of clothing manufacturing co-operatives in job creation and poverty alleviation in Sharpeville / Mmapula Brendah Sekatane." Thesis, North-West University, 2004. http://hdl.handle.net/10394/2381.

Elliott, Greg (Gregory Talcott). "Konbit : bridging social, cultural, and political worlds by accelerating job growth and creation for the illiterate, disconnected workers." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67764.

Sjöholm, Anton. "Entrepreneurial Social Capital and Economic growth : An analysis of local entrepreneurial social capital and job creation in Sweden." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Nationalekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-30552.

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• Published: 18 January 2024

The impact of artificial intelligence on employment: the role of virtual agglomeration

• Yang Shen   ORCID: orcid.org/0000-0002-6781-6915 1 &
• Xiuwu Zhang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  122 ( 2024 ) Cite this article

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• Development studies

Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

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Introduction

Ensuring people’s livelihood requires diligence, but diligence is not scarce. Diversification, technological upgrading, and innovation all contribute to achieving the Sustainable Development Goal of full and productive employment for all (SDGs 8). Since the outbreak of the industrial revolution, human society has undergone four rounds of technological revolution, and each technological change can be regarded as the deepening of automation technology. The conflict and subsequent rebalancing of efficiency and employment are constantly being repeated in the process of replacing people with machines (Liu 2018 ; Morgan 2019 ). When people realize the new wave of human economic and social development that is created by advanced technological innovation, they must also accept the “creative destruction” brought by the iterative renewal of new technologies (Michau 2013 ; Josifidis and Supic 2018 ; Forsythe et al. 2022 ). The questions of where technology will eventually lead humanity, to what extent artificial intelligence will change the relationship between humans and work, and whether advanced productivity will lead to large-scale structural unemployment have been hotly debated. China has entered a new stage of deep integration and development of the “new technology cluster” that is represented by the internet and the real economy. Physical space, cyberspace, and biological space have become fully integrated, and new industries, new models, and new forms of business continue to emerge. In the process of the vigorous development of digital technology, its characteristics in terms of employment, such as strong absorption capacity, flexible form, and diversified job demands are more prominent, and many new occupations have emerged. The new practice of digital survival that is represented by the platform economy, sharing economy, full-time economy, and gig economy, while adapting to, leading to, and innovating the transformation and development of the economy, has also led to significant changes in employment carriers, employment forms, and occupational skill requirements (Dunn 2020 ; Wong et al. 2020 ; Li et al. 2022 ).

Artificial intelligence (AI) is one of the core areas of the fourth industrial revolution, along with the transformation of the mechanical technology, electric power technology, and information technology, and it serves to promote the transformation and upgrading of the digital economy industry. Indeed, the rapid iteration and cross-border integration of general information technology in the era of the digital economy has made a significant contribution to the stabilization of employment and the promotion of growth, but this is due only to the “employment effect” caused by the ongoing development of the times and technological progress in the field of social production. Digital technology will inevitably replace some of the tasks that were once performed by human labour. In recent years, due to the influence of China’s labour market and employment structure, some enterprises have needed help in recruiting workers. Driven by the rapid development of artificial intelligence technology, some enterprises have accelerated the pace of “machine replacement,” resulting in repetitive and standardized jobs being performed by robots. Deep learning and AI enable machines and operating systems to perform more complex tasks, and the employment prospects of enterprise employees face new challenges in the digital age. According to the Future of Jobs 2020 report released by the World Economic Forum, the recession caused by the COVID-19 pandemic and the rapid development of automation technology are changing the job market much faster than expected, and automation and the new division of labour between humans and machines will disrupt 85 million jobs in 15 industries worldwide over the next five years. The demand for skilled jobs, such as data entry, accounting, and administrative services, has been hard hit. Thanks to the wave of industrial upgrading and the vigorous development of digitalization, the recruitment demand for AI, big data, and manufacturing industries in China has maintained high growth year-on-year under the premise of macroenvironmental uncertainty during the period ranging from 2019 to 2022, and the average annual growth rate of new jobs was close to 30%. However, this growth has also aggravated the sense of occupational crisis among white-collar workers. The research shows that the agriculture, forestry, animal husbandry, fishery, mining, manufacturing, and construction industries, which are expected to adopt a high level of intelligence, face a high risk of occupational substitution, and older and less educated workers are faced with a very high risk of substitution (Wang et al. 2022 ). Whether AI, big data, and intelligent manufacturing technology, as brand-new forms of digital productivity, will lead to significant changes in the organic composition of capital and effectively decrease labour employment has yet to reach consensus. As the “pearl at the top of the manufacturing crown,” a robot is an essential carrier of intelligent manufacturing and AI technology as materialized in machinery and equipment, and it is also an important indicator for measuring a country’s high-end manufacturing industry. Due to the large number of manufacturing employees in China, the challenge of “machine substitution” to the labour market is more severe than that in other countries, and the use of AI through robots is poised to exert a substantial impact on the job market (Xie et al. 2022 ). In essence, the primary purpose of the digital transformation of industrial enterprises is to improve quality and efficiency, but the relationship between machines and workers has been distorted in the actual application of digital technology. Industrial companies use robots as an entry point, and the study delves into the impact of AI on the labour market to provide experience and policy suggestions on the best ways of coordinating the relationship between enterprise intelligent transformation and labour participation and to help realize Chinese-style modernization.

As a new general technology, AI technology represents remarkable progress in productivity. Objectively analysing the dual effects of substitution and employment creation in the era of artificial intelligence to actively integrate change and adapt to development is essential to enhancing comprehensive competitiveness and better qualifying workers for current and future work. This research is organized according to a research framework from the published literature (Luo et al. 2023 ). In this study, we used data published by the International Federation of Robotics (IFR) and take the installed density of industrial robots in China as the main indicator of AI. Based on panel data from 30 provinces in China covering the period from 2006–2020, the impact of AI technology on employment in a developing country with a large population size is empirically examined. The issues that need to be solved in this study include the following: The first goal is to examine the impact of AI on China’s labour market from the perspective of the economic behaviour of those enterprises that have adopted the use of industrial robots in production. The realistic question we expect to answer is whether the automated processing of daily tasks has led to unemployment in China during the past fifteen years. The second goal is to answer the question of how AI will continue to affect the employment market by increasing labour productivity, changing the technical composition of capital, and deepening the division of labour. The third goal is to examine how the transformation of industrial organization types in the digital economy era affects employment through digital industrial clusters or virtual clusters. The fourth goal is to test the role of AI in eliminating gender discrimination, especially in regard to whether it can improve the employment opportunities of female employees. Then, whether workers face different employment difficulties in different industry attributes is considered. The final goal is to provide some policy insights into how a developing country can achieve full employment in the face a new technological revolution in the context of a large population and many low-skilled workers.

The remainder of the paper is organized as follows. In Section Literature Review, we summarize the literature on the impact of AI on the labour market and employment and classify it from three perspectives: pessimistic, negative, and neutral. Based on a literature review, we then summarize the marginal contribution of this study. In Section Theoretical mechanism and research hypothesis, we provide a theoretical analysis of AI’s promotion of employment and present the research hypotheses to be tested. In Section Study design and data sources, we describe the data source, variable setting and econometric model. In Section Empirical analysis, we test Hypothesis 1 and conduct a robustness test and the causal identification of the conclusion. In Section Extensibility analysis, we test Hypothesis 2 and Hypothesis 3, as well as testing the heterogeneity of the baseline regression results. The heterogeneity test employee gender and industry attributes increase the relevance of the conclusions. Finally, Section Conclusions and policy implications concludes.

Literature review

The social effect of technological progress has the unique characteristics of the times and progresses through various stages, and there is variation in our understanding of its development and internal mechanism. A classic argument of labour sociology and labour economics is that technological upgrading objectively causes workers to lose their jobs, but the actual historical experience since the industrial revolution tells us that it does not cause large-scale structural unemployment (Zhang 2023a ). While neoclassical liberals such as Adam Smith claimed that technological progress would not lead to unemployment, other scholars such as Sismondi were adamant that it would. David Ricardo endorsed the “Luddite fear” in his book On Machinery, and Marx argued that technological progress can increase labour productivity while also excluding labour participation, thus leaving workers in poverty. The worker being turned ‘into a crippled monstrosity’ by modern machinery. Technology is not used to reduce working hours and improve the quality of work, rather, it is used to extend working hours and speed up work (Spencer 2023 ). According to Schumpeter’s innovation theory, within a unified complex system, the essence of technological innovation forms from the unity of positive and negative feedback and the oneness of opposites such as “revolutionary” and “destructive.” Even a tiny technological impact can cause drastic consequences. The impact of AI on employment is different from the that of previous industrial revolutions, and it is exceptional in that “machines” are no longer straightforward mechanical tools but have assumed more of a “worker” role, just as people who can learn and think tend to do (Boyd and Holton 2018 ). AI-related technologies continue to advance, the industrialization and commercialization process continues to accelerate, and the industry continues to explore the application of AI across multiple fields. Since AI was first proposed at the Dartmouth Conference in 1956, discussions about “AI replacing human labor” and “AI defeating humans” have endlessly emerged. This dynamic has increased in intensity since the emergence of ChatGPT, which has aroused people’s concerns about technology replacing the workforce. Summarizing the literature, we can find three main arguments concerning the relationship between AI and employment:

First, AI has the effect of creating and filling jobs. The intelligent manufacturing industry paradigm characterized by AI technology will assist in forming a high-quality “human‒machine cooperation” employment mode. In an enlightened society, the social state of shared prosperity benefits the lowest class of people precisely because of the advanced productive forces and higher labour efficiency created through the refinement of the division of labour. By improving production efficiency, reducing the sales price of final products, and stimulating social consumption, technological progress exerts both price effects and income effects, which in turn drive related enterprises to expand their production scale, which, in turn, increases the demand for labour (Li et al. 2021 ; Ndubuisi et al. 2021 ; Yang 2022 ; Sharma and Mishra 2023 ; Li et al. 2022 ). People habitually regard robots as competitors for human beings, but this view only represents the materialistic view of traditional machinery. The coexistence of man and machine is not a zero-sum game. When the task evolves from “cooperation for all” to “cooperation between man and machine,” it results in fewer production constraints and maximizes total factor productivity, thus creating more jobs and generating novel collaborative tasks (Balsmeier and Woerter 2019 ; Duan et al. 2023 ). At the same time, materialized AI technology can improve the total factor production efficiency in ways that are suitable for its factor endowment structure and improve the production efficiency between upstream and downstream enterprises in the industrial chain and the value chain. This increase in the efficiency of the entire market will subsequently drive the expansion of the production scale of enterprises and promote reproduction, and its synergy will promote the synchronous growth of the labour demand involving various skills, thus resulting in a creative effect (Liu et al. 2022 ). As an essential force in the fourth industrial revolution, AI inevitably affects the social status of humans and changes the structure of the labour force (Chen 2023 ). AI and machines increase labour productivity by automating routine tasks while expanding employee skills and increasing the value of work. As a result, in a machine-for-machine employment model, low-skilled jobs will disappear, while new and currently unrealized job roles will emerge (Polak 2021 ). We can even argue that digital technology, artificial intelligence, and robot encounters are helping to train skilled robots and raise their relative wages (Yoon 2023 ).

Second, AI has both a destructive effect and a substitution effect on employment. As soon as machines emerged as the means of labour, they immediately began to compete with the workers themselves. As a modern new technology, artificial intelligence is essentially humanly intelligent labour that condenses complex labour. Like the disruptive general-purpose technologies of early industrialization, automation technologies such as AI offer both promise and fear in regard to “machine replacement.” Technological progress leads to an increase in the organic composition of capital and the relative surplus population. The additional capital formed in capital accumulation comes to absorb fewer and fewer workers compared to its quantity. At the same time, old capital, which is periodically reproduced according to the new composition, will begin to increasingly exclude the workers it previously employed, resulting in severe “technological unemployment.” The development of productivity creates more free time, especially in industries such as health care, transportation, and production environment control, which have seen significant benefits from AI. In recent years, however, some industrialized countries have faced the dilemma of declining income from labour and the slow growth of total labour productivity while applying AI on a large scale (Autor 2019 ). Low-skilled and incapacitated workers enjoy a high probability of being replaced by automation (Ramos et al. 2022 ; Jetha et al. 2023 ). It is worth noting that with the in-depth development of digital technologies, such as deep learning and big data analysis, some complex, cognitive, and creative jobs that are currently considered irreplaceable in the traditional view will also be replaced by AI, which indicates that automation technology is not only a substitute for low-skilled labour (Zhao and Zhao 2017 ; Dixon et al. 2021 ; Novella et al. 2023 ; Nikitas et al. 2021 ). Among factors, AI and robotics exert a particularly significant impact on the manufacturing job market, and industry-related jobs will face a severe unemployment problem due to the disruptive effect of AI and robotics (Zhou and Chen 2022 ; Sun and Liu 2023 ). At this stage, most of the world’s economies are facing the deep integration of the digital wave in their national economy, and any work, including high-level tasks, is being affected by digitalization and AI (Gardberg et al. 2020 ). The power of AI models is growing exponentially rather than linearly, and the rapid development and rapid diffusion of technology will undoubtedly have a devastating effect on knowledge workers, as did the industrial revolution (Liu and Peng 2023 ). In particular, the development and improvement of AI-generated content in recent years poses a more significant threat to higher-level workers, such as researchers, data analysts, and product managers, than to physical labourers. White collar workers are facing unprecedented anxiety and unease (Nam 2019 ; Fossen and Sorgner 2022 ; Wang et al. 2023 ). A classic study suggests that AI could replace 47% of the 702 job types in the United States within 20 years (Frey and Osborne 2017 ). Since the 2020 epidemic, digitization has accelerated, and online and digital resources have become a must for enterprises. Many occupations are gradually moving away from humans (Wu and Yang 2022 ; Männasoo et al. 2023 ). It is obvious that the intelligent robot arm on the factory assembly line is poised to allow factory assembly line workers to exit the stage and move into history. Career guides are being replaced by mobile phone navigation software.

Third, the effect of AI on employment is uncertain, and its impact on human work does not fall into a simple “utopian” or “dystopian” scene, but rather leads to a combination of “utopia” and “dystopia” (Kolade and Owoseni 2022 ). The job-creation effects of robotics and the emergence of new jobs that result from technological change coexist at the enterprise level (Ni and Obashi 2021 ). Adopting a suitable AI operation mode can adjust for the misallocation of resources by the market, enterprises, and individuals to labour-intensive tasks, reverse the nondirectional allocation of robots in the labour sector, and promote their reallocation in the manufacturing and service industries. The size of the impact on employment through the whole society is uncertain (Fabo et al. 2017 ; Huang and Rust 2018 ; Berkers et al. 2020 ; Tschang and Almirall 2021 ; Reljic et al. 2021 ). For example, Oschinski and Wyonch ( 2017 ) claimed that those jobs that are easily replaced by AI technology in Canada account for only 1.7% of the total labour market, and they have yet to find evidence that automation technology will cause mass unemployment in the short term. Wang et al. ( 2022 ) posited that the impact of industrial robots on labour demand in the short term is mainly negative, but in the long run, its impact on employment is mainly that of job creation. Kirov and Malamin ( 2022 ) claimed that the pessimism underlying the idea that AI will destroy the jobs and quality of language workers on a large scale is unjustified. Although some jobs will be eliminated as such technology evolves, many more will be created in the long run.

In the view that modern information technology and digital technology increase employment, the literature holds that foreign direct investment (Fokam et al. 2023 ), economic systems (Bouattour et al. 2023 ), labour skills and structure (Yang 2022 ), industrial technological intensity (Graf and Mohamed 2024 ), and the easing of information friction (Jin et al. 2023 ) are important mechanisms. The research on whether AI technology crowds out jobs is voluminous, but the conclusions are inconsistent (Filippi et al. 2023 ). This paper is focused on the influence of AI on the employment scale of the manufacturing industry, examines the job creation effect of technological progress from the perspectives of capital deepening, labour refinement, and labour productivity, and systematically examines the heterogeneous impact of the adoption of industrial robots on employment demand, structure, and different industries. The marginal contributions of this paper are as follows: first, the installation density of industrial robots is used as an indicator to measure AI, and the question of whether AI has had negative effects on employment in the manufacturing sector from the perspective of machine replacement is examined. The second contribution is the analysis of the heterogeneity of AI’s employment creation effect from the perspective of gender and industry attributes and the claim that women and the employees of labour-intensive enterprises are more able to obtain additional work benefits in the digital era. Most importantly, in contrast to the literature, this paper innovatively introduces virtual agglomeration into the path mechanism of the effect of robots on employment and holds that information technologies such as the internet, big data, and the industrial Internet of Things, which rely upon AI, have reshaped the management mode and organizational structure of enterprises. Online and offline integration work together, and information, knowledge, and technology are interconnected. In the past, the job matching mode of one person, one post, and specific individuals has changed into a multiple faceted set of tasks involving one person, many posts, and many types of people. The internet platform spawned by digital technology frees the employment mode of enterprises from being limited to single enterprises and specific gathering areas. Traditional industrial geographical agglomeration has gradually evolved into virtual agglomeration, which geometrically enlarges the agglomeration effect and mechanism and enhances the spillover effect. In the online world, individual practitioners and entrepreneurs can obtain orders, receive training, connect resources and employment needs more widely and efficiently, and they can achieve higher-quality self-employment. Virtual agglomeration has become a new path by which AI affects employment. Another literature contribution is that this study used the linear regression model of the machine learning model in the robustness test part, which verified the employment creation effect of AI from the perspective of positive contribution proportion. In causal identification, this study innovatively uses the industrial feed-in price as a tool variable to analyse the causal path of AI promoting employment.

Theoretical mechanism and research hypothesis

The direct influence of ai on employment.

With advances in machine learning, big data, artificial intelligence, and other technologies, a new generation of intelligent robots that can perform routine, repetitive, and regular production tasks requiring human judgement, problem-solving, and analytical skills has emerged. Robotic process automation technology can learn and imitate the way that workers perform repeated new tasks regarding the collecting of data, running of reports, copying of data, checking of data integrity, reading, processing, and the sending of emails, and it can play an essential role in processing large amounts of data (Alan 2023 ). In the context of an informatics- and technology-oriented economy, companies are asking employees to transition into creative jobs. According to the theory of the combined task framework, the most significant advantage of the productivity effect produced by intelligent technology is creation of new demands, that is, the creation of new tasks (Acemoglu and Restrepo 2018 ). These new task packages update the existing tasks and create new task combinations with more complex technical difficulties. Although intelligent technology is widely used in various industries, it may have a substitution effect on workers and lead to technical unemployment. However, with the rise of a new round of technological innovation and revolution, high efficiency leads to the development and growth of a series of emerging industries and exerts job creation effects. Technological progress has the effect of creating new jobs. That is, such progress creates new jobs that are more in line with the needs of social development and thus increases the demand for labour (Borland and Coelli 2017 ). Therefore, the intelligent development of enterprises will come to replace their initial programmed tasks and produce more complex new tasks, and human workers in nonprogrammed positions, such as technology and knowledge, will have more comparative advantages.

Generally, the “new technology-economy” paradigm that is derived from automation machine and AI technology is affecting the breadth and depth of employment, which is manifested as follows:

It reduces the demand for coded jobs in enterprises while increasing the demand for nonprogrammed complex labour.

The development of digital technology has deepened and refined the division of labour, accelerated the service trend of the manufacturing industry, increased the employment share of the modern service industry and created many emerging jobs.

Advanced productive forces give workers higher autonomy and increased efficiency in their work, improving their job satisfaction and employment quality. As described in Das Kapital, “Although machines actually crowd out and potentially replace a large number of workers, with the development of machines themselves (which is manifested by the increase in the number of the same kind of factories or the expansion of the scale of existing factories), the number of factory workers may eventually be more than the number of handicraft workers in the workshops or handicrafts that they crowd out… It can be seen that the relative reduction and absolute increase of employed workers go hand in hand” (Li and Zhang 2022 ).

Internet information technology reduces the distance between countries in both time and space, promotes the transnational flow of production factors, and deepens the international division of labour. The emergence of AI technology leads to the decline of a country’s traditional industries and departments. Under the new changes to the division of labour, these industries and departments may develop in late-developing countries and serve to increase their employment through international labour export.

From a long-term perspective, AI will create more jobs through the continuous expansion of the social production scale, the continuous improvement of production efficiency, and the more detailed industrial categories that it engenders. With the accumulation of human capital under the internet era, practitioners are gradually becoming liberated from heavy and dangerous work, and workers’ skills and job adaptability will undergo continuous improvement. The employment creation and compensation effects caused by technological and industrial changes are more significant than the substitution effects (Han et al. 2022 ). Accordingly, the article proposes the following two research hypotheses:

Hypothesis 1 (H1): AI increases employment .

Hypothesis 2 (H2): AI promotes employment by improving labour productivity, deepening capital, and refining the division of labour .

Role of virtual agglomeration

The research on economic geography and “new” economic geography agglomeration theory focuses on industrial agglomeration in the traditional sense. This model is a geographical agglomeration model that depends on spatial proximity from a geographical perspective. Assessing the role of externalities requires a particular geographical scope, as it has both physical and scope limitations. Virtual agglomeration transcends Marshall’s theory of economies of scale, which is not limited to geographical agglomeration from the perspective of natural territory but rather takes on more complex and multidimensional forms (such as virtual clusters, high-tech industrial clusters, and virtual business circles). Under the influence of a new generation of digital technology that is characterized by big data, the Internet of Things, and the industrial internet, the digital, intelligent, and platform transformation trend is prominent in some industries and enterprises, and industrial digitalization and digital industrialization jointly promote industrial upgrading. The innovation of information technology leads to “distance death” (Schultz 1998 ). With the further materialization of digital and networked services of enterprises, the trading mode of digital knowledge and services, such as professional knowledge, information combination, cultural products, and consulting services, has transitioned from offline to digital trade, and the original geographical space gathering mode between enterprises has gradually evolved into a virtual network gathering that places the real-time exchange of data and information as its core (Wang et al. 2018 ). Tan and Xia ( 2022 ) stated that virtual agglomeration geometrically magnifies the social impact of industrial agglomeration mechanisms and agglomeration effects, and enterprises in the same industry and their upstream and downstream affiliated enterprises can realize low-cost long-distance transactions, services, and collaborative production through digital trade, resulting in large-scale zero-distance agglomeration along with neighbourhood-style production, service, circulation, and consumption. First, the knowledge and information underlying the production, design, research and development, organization, and trading of all kinds of enterprises are increasingly being completed by digital technology. The tacit knowledge that used to require face-to-face communication has become codable, transmissible, and reproducible under digital technology. Tacit knowledge has gradually become explicit, and knowledge spillover and technology diffusion have become more pronounced, which further leads to an increase in the demand for unconventional task labour (Zhang and Li 2022 ). Second, the cloud platform causes the labour pool effect of traditional geographical agglomeration to evolve into the labour “conservation land” of virtual agglomeration, and employment is no longer limited to the internal organization or constrained within a particular regional scope. Digital technology allows enterprises to hire “ghost workers” for lower wages to compensate for the possibility of AI’s “last mile.” Information technology and network platforms seek connections with all social nodes, promoting the time and space for work in a way that transcends standardized fixed frameworks. At the same time, joining or quitting work tasks, indirectly increasing the temporary and transitional nature of work and forming a decentralized management organization model of supplementary cooperation, social networks, industry experts, and skilled labour all become more convenient for workers (Wen and Liu 2021 ). With a mobile phone and a computer, labourers worldwide can create value for enterprises or customers, and the forms of labour are becoming more flexible and diverse. Workers can provide digital real-time services to employers far away from their residence, and they can also obtain flexible employment information and improve their digital skills through the leveraging of digital resources, resulting in the odd-job economy, crowdsourcing economy, sharing economy, and other economic forms. Finally, the network virtual space can accommodate almost unlimited enterprises simultaneously. In the commercial background of digital trade, while any enterprise can obtain any intermediate supply in the online market, its final product output can instantly become the intermediate input of other enterprises. Therefore, enterprises’ raw material supply and product sales rely on the whole market. At this time, the market scale effect of intermediate inputs can be infinitely amplified, as it is no longer confined to the limited space of geographical agglomeration (Duan and Zhang 2023 ). Accordingly, the following research hypothesis is proposed:

Hypothesis 3 (H3): AI promotes employment by improving the VA of enterprises .

Study design and data sources

Variable setting, explained variable.

Employment scale (ES). Compared with the agriculture and service industry, the industrial sector accommodates more labour, and robot technology is mainly applied in the industrial sector, which has the greatest demand shock effect on manufacturing jobs. In this paper, we select the number of employees in manufacturing cities and towns as the proxy variable for employment scale.

Core explanatory variable

Artificial intelligence (AI). Emerging technologies endow industrial robots with more complete technical attributes, which increases their ability to act as human beings in many work projects, enabling them to either independently complete production tasks or to assist humans in completing such tasks. This represents an important form of AI technology embedded into machinery and equipment. In this paper, the installation density of industrial robots is selected as the proxy variable for AI. Robot data mainly come from the number of robots installed in various industries at various national levels as published by the International Federation of Robotics (IFR). Because the dataset published by the IFR provides the dataset at the national-industry level and its industry classification standards are significantly different from those in China, the first lessons for this paper are drawn from the practices of Yan et al. ( 2020 ), who matches the 14 manufacturing categories published by the IFR with the subsectors in China’s manufacturing sector, and then uses the mobile share method to merge and sort out the employment numbers of various industries in various provinces. First, the national subsector data provided by the IFR are matched with the second National Economic Census data. Next, the share of employment in different industries to the total employment in the province is used to develop weights and decompose the industry-level robot data into the local “provincial-level industry” level. Finally, the application of robots in various industries at the provincial level is summarized. The Bartik shift-share instrumental variable is now widely used to measure robot installation density at the city (province) level (Wu 2023 ; Yang and Shen, 2023 ; Shen and Yang 2023 ). The calculation process is as follows:

In Eq. ( 1 ), N is a collection of manufacturing industries, Robot it is the robot installation density of province i in year t, \({{{\mathrm{employ}}}}_{{{{\mathrm{ij}}}},{{{\mathrm{t}}}} = 2006}\) is the number of employees in industry j of province i in 2006, \({{{\mathrm{employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) is the total number of employees in province i in 2006, and \({{{\mathrm{Robot}}}}_{{{{\mathrm{jt}}}}}{{{\mathrm{/employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) represents the robot installation density of each year and industry level.

Mediating variables

Labour productivity (LP). According to the definition and measurement method proposed by Marx’s labour theory of value, labour productivity is measured by the balance of the total social product minus the intermediate goods and the amount of labour consumed by the pure production sector. The specific calculation process is \(AL = Y - k/l\) , where Y represents GDP, l represents employment, k represents capital depreciation, and AL represents labour productivity. Capital deepening (CD). The per capita fixed capital stock of industrial enterprises above a designated size is used in this study as a proxy variable for capital deepening. The division of labour refinement (DLR) is refined and measured by the number of employees in producer services. Virtual agglomeration (VA) is mainly a continuation of the location entropy method in the traditional industrial agglomeration measurement idea, and weights are assigned according to the proportion of the number of internet access ports in the country. Because of the dependence of virtual agglomeration on digital technology and network information platforms, the industrial agglomeration degree of each region is first calculated in this paper by using the number of information transmissions, computer services, and software practitioners and then multiplying that number by the internet port weight. The specific expression is \(Agg_{it} = \left( {M_{it}/M_t} \right)/\left( {E_{it}/E_t} \right) \times \left( {Net_{it}/Net_t} \right)\) , where \(M_{it}\) represents the number of information transmissions, computer services and software practitioners in region i in year t, \(M_t\) represents the total number of national employees in this industry, \(E_{it}\) represents the total number of employees in region i, \(E_t\) represents the total number of national employees, \(Net_{it}\) represents the number of internet broadband access ports in region i, and \(Net_t\) represents the total number of internet broadband access ports in the country. VA represents the degree of virtual agglomeration.

Control variables

To avoid endogeneity problems caused by unobserved variables and to obtain more accurate estimation results, seven control variables were also selected. Road accessibility (RA) is measured by the actual road area at the end of the year. Industrial structure (IS) is measured by the proportion of the tertiary industry’s added value and the secondary industry’s added value. The full-time equivalent of R&D personnel is used to measure R&D investment (RD). Wage cost (WC) is calculated using city average salary as a proxy variable; Marketization (MK) is determined using Fan Gang marketization index as a proxy variable; Urbanization (UR) is measured by the proportion of the urban population to the total population at the end of the year; and the proportion of general budget expenditure to GDP is used to measure Macrocontrol (MC).

Econometric model

To investigate the impact of AI on employment, based on the selection and definition of the variables detailed above and by mapping the research ideas to an empirical model, the following linear regression model is constructed:

In Eq. ( 2 ), ES represents the scale of manufacturing employment, AI represents artificial intelligence, and subscripts t, i and m represent time t, individual i and the m th control variable, respectively. \(\mu _i\) , \(\nu _t\) and \(\varepsilon _{it}\) represent the individual effect, time effect and random disturbance terms, respectively. \(\delta _0\) is the constant term, a is the parameter to be fitted, and Control represents a series of control variables. To further test whether there is a mediating effect of mechanism variables in the process of AI affecting employment, only the influence of AI on mechanism variables is tested in the empirical part according to the modelling process and operational suggestions of the intermediary effects as proposed by Jiang ( 2022 ) to overcome the inherent defects of the intermediary effects. On the basis of Eq. ( 2 ), the following econometric model is constructed:

In Eq. ( 3 ), Media represents the mechanism variable. β 1 represents the degree of influence of AI on mechanism variables, and its significance and symbolic direction still need to be emphasized. The meanings of the remaining symbols are consistent with those of Eq. ( 2 ).

Data sources

Following the principle of data availability, the panel data of 30 provinces (municipalities and autonomous regions) in China from 2006 to 2020 (samples from Tibet and Hong Kong, Macao, and Taiwan were excluded due to data availability) were used as statistical investigation samples. The raw data on the installed density of industrial robots and the number of workers in the manufacturing industry come from the International Federation of Robotics and the China Labour Statistics Yearbook. The original data for the remaining indicators came from the China Statistical Yearbook, China Population and Employment Statistical Yearbook, China’s Marketization Index Report by Province (2021), the provincial and municipal Bureau of Statistics, and the global statistical data analysis platform of the Economy Prediction System (EPS). The few missing values are supplemented through linear interpolation. It should be noted that although the IFR has yet to release the number of robots installed at the country-industry level in 2020, it has published the overall growth rate of new robot installations, which is used to calculate the robot stock in 2020 for this study. The descriptive statistical analysis of relevant variables is shown in Table 1 .

Empirical analysis

To reduce the volatility of the data and address the possible heteroscedasticity problem, all the variables are located. The results of the Hausmann test and F test both reject the null hypothesis at the 1% level, indicating that the fixed effect model is the best-fitting model. Table 2 reports the fitting results of the baseline regression.

As shown in Table 2 , the results of the two-way fixed-effect (TWFE) model displayed in Column (5) show that the fitting coefficient of AI on employment is 0.989 and is significant at the 1% level. At the same time, the fitting results of other models show that the impact of AI on employment is significantly positive. The results confirm that the effect of AI on employment is positive and the effect of job creation is greater than the effect of destruction, and these conclusions are robust, thus verifying the employment creation mechanism of technological progress. Research Hypothesis 1 (H1) is supported. The new round of scientific and technological revolution represented by artificial intelligence involves the upgrading of traditional industries, the promotion of major changes in the economy and society, the driving of rapid development of the “unmanned economy,” the spawning a large number of new products, new technologies, new formats, and new models, and the provision of more possibilities for promoting greater and higher quality employment. Classical and neoclassical economics view the market mechanism as a process of automatic correction that can offset the job losses caused by labour-saving technological innovation. Under the premise of the “employment compensation” theory, the new products, new models, and new industrial sectors created by the progress of AI technology can directly promote employment. At the same time, the scale effect caused by advanced productivity results in lower product prices and higher worker incomes, which drives increased demand and economic growth, increasing output growth and employment (Ge and Zhao 2023 ). In conjunction with the empirical results of this paper, we have reason to believe that enterprises adopt the strategy of “machine replacement” to replace procedural and repetitive labour positions in the pursuit of high efficiency and high profits. However, AI improves not only enterprises’ production efficiency but also their production capacity and scale economy. To occupy a favourable share of market competition, enterprises expand the scale of reproduction. At this point, new and more complex tasks continue to emerge, eventually leading companies to hire more labour. At this stage, robot technology and application in developing countries are still in their infancy. Whether regarding the application scenario or the application scope of robots, the automation technology represented by industrial robots has not yet been widely promoted, which increases the time required for the automation technology to completely replace manual tasks, so the destruction effect of automation technology on jobs is not apparent. The fundamental market situation of the low cost of China’s labour market drives enterprises to pay more attention to technology upgrading and efficiency improvement when introducing industrial robots. The implementation of the machine replacement strategy is mainly caused by the labour shortage driven by high work intensity, high risk, simple process repetition, and poor working conditions. The intelligent transformation of enterprises points to more than the simple saving of labour costs (Dixon et al. 2021 ).

Robustness test

The above results show that the effect of AI on job creation is greater than the effect of substitution and the overall promotion of enterprises for the enhancement of employment demand. To verify the robustness of the benchmark results, the following three means of verifying the results are adopted in this study. First, we replace the explained variables. In addition to industrial manufacturing, robots are widely used in service industries, such as medical care, finance, catering, and education. To reflect the dynamic change relationship between the employment share of the manufacturing sector and the employment number of all sectors, the absolute number of manufacturing employees is replaced by the ratio of the manufacturing industry to all employment numbers. The second means is increasing the missing variables. Since many factors affect employment, this paper considers the living cots, human capital, population density, and union power in the basic regression model. The impact of these variables on employment is noticeable; for example, the existence of trade unions improves employee welfare and the working environment but raises the entry barrier for workers in the external market. The new missing variables are the average selling price of commercial and residential buildings, urban population density (person/square kilometre), nominal human capital stock, and the number of grassroots trade union organizations in the China Human Capital Report 2021 issued by Central University of Finance and Economics, which are used as proxy variables. The third means involves the use of linear regression (the gradient descent method) in machine learning regression to calculate the importance of AI to the increase in employment size. The machine learning model has a higher goodness of fit and fitting effect on the predicted data, and its mean square error and mean absolute error are more minor (Wang Y et al. 2022 ).

As seen from the robustness part of Table 3 , the results of Method 1 show that AI exerts a positive impact on the employment share in the manufacturing industry; that is, AI can increase the proportion of employment in the manufacturing industry, the use of AI creates more derivative jobs for the manufacturing industry, and the demand for the labour force of enterprises further increases. The results of method 2 show that after increasing the number of control variables, the influence of robots on employment remains significantly positive, indicating no social phenomenon of “machine replacement.” The results of method 3 show that the weight of AI is 84.3%, indicating that AI can explain most of the increase in the manufacturing employment scale and has a positive promoting effect. The above three methods confirm the robustness of the baseline regression results.

Endogenous problem

Although further control variables are used to alleviate the endogeneity problem caused by missing variables to the greatest extent possible, the bidirectional causal relationship between labour demand and robot installation (for example, enterprises tend to passively adopt the machine replacement strategy in the case of labour shortages and recruitment difficulties) still threatens the accuracy of the statistical inference results in this paper. To eliminate the potential endogeneity problem of the model, the two-stage least squares method (2SLS) was applied. In general, the cost factor that enterprises need to consider when introducing industrial robots is not only the comparative advantage between the efficiency cost of machinery and the costs of equipment and labour wages but also the cost of electricity to maintain the efficient operation of machinery and equipment. Changes in industrial electricity prices indicate that the dynamic conditions between installing robots and hiring workers have changed, and decision-makers need to reweigh the costs and profits of intelligent transformation. Changes in industrial electricity prices can impact the demand for labour by enterprises; this path does not directly affect the labour market but is rather based on the power consumption, work efficiency, and equipment prices of robots. Therefore, industrial electricity prices are exogenous relative to employment, and the demand for robots is correlated.

Electricity production and operation can be divided into power generation, transmission, distribution, and sales. China has realized the integration of exports and distribution, so there are two critical prices in practice: on-grid and sales tariffs (Yu and Liu 2017 ). The government determines the on-grid tariff according to different cost-plus models, and its regulatory policy has roughly proceeded from that of principal and interest repayment, through operating period pricing, to benchmark pricing. The sales price (also known as the catalogue price) is the price of electric energy sold by power grid operators to end users, and its price structure is formed based on the “electric heating price” that was implemented in 1976. There is differentiated pricing between industrial and agricultural electricity. Generally, government departments formulate on-grid tariffs, integrating the interests of power plants, grid enterprises, and end users. As China’s thermal power installed capacity accounts for more than 70% of the installed capacity of generators, the price of coal becomes an essential factor affecting the price of industrial internet access. The pricing strategy for electricity sales is not determined by market-oriented transmission and distribution electricity price, on-grid electricity price, or tax but rather by the goal of “stable growth and ensuring people’s livelihood” (Tang and Yang 2014 ). The externality of the feed-in price is more robust, so the paper chooses the feed-in price as an instrumental variable.

It can be seen from Table 3 that the instrumental variables in the first stage positively affect the robot installation density at the level of 1%. Meanwhile, the results of the validity test of the instrumental variables show that there are no weak instrumental variables or unidentifiable problems with this variable, thus satisfying the principle of correlation and exclusivity. The second-stage results show that robots still positively affect the demand for labour at the 1% level, but the fitting coefficient is smaller than that of the benchmark regression model. In summary, the results of fitting the calculation with the causal inference paradigm still support the conclusion that robots create more jobs and increase the labour demand of enterprises.

Extensibility analysis

Robot adoption and gender bias.

The quantity and quality of labour needed by various industries in the manufacturing sector vary greatly, and labour-intensive and capital-intensive industries have different labour needs. Over the past few decades, the demand for female employees has grown. Female employees obtain more job opportunities and better salaries today (Zhang et al. 2023 ). Female employees may benefit from reducing the content of manual labour jobs, meaning that further study of AI heterogeneity from the perspective of gender bias may be needed. As seen from Table 4 , AI has a significant positive impact on the employment of both male and female practitioners, indicating that AI technology does not have a heterogeneous effect on the dynamic gender structure. By comparing the coefficients of the two (the estimated results for men and those for women), it can be found that robots have a more significant promotion effect on female employees. AI has significantly improved the working environment of front-line workers, reduced the level of labour intensity, enabled people to free themselves of dirty and heavy work tasks, and indirectly improved the job adaptability of female workers. Intellectualization increases the flexibility of the time, place, and manner of work for workers, correspondingly improves the working freedom of female workers, and alleviates the imbalance in the choice between family and career for women to a certain extent (Lu et al. 2023 ). At the same time, women are born with the comparative advantage of cognitive skills that allow them to pay more nuanced attention to work details. By introducing automated technology, companies are increasing the demand for cognitive skills such as mental labour and sentiment analysis, thus increasing the benefits for female workers (Wang and Zhang 2022 ). Flexible employment forms, such as online car hailing, community e-commerce, and online live broadcasting, provide a broader stage for women’s entrepreneurship and employment. According to the “Didi Digital Platform and Female Ecology Research Report”, the number of newly registered female online taxi drivers in China has exceeded 265,000 since 2020, and approximately 60 percent of the heads of the e-commerce platform, Orange Heart, are women.

Industry heterogeneity

Given the significant differences in the combination of factors across the different industries in China’s manufacturing sector, there is also a significant gap in the installation density of robots; even compared to AI density, in industries with different production characteristics, indicating that there may be an opposite employment phenomenon at play. According to the number of employees and their salary level, capital stock, R&D investment, and patent technology, the manufacturing industry is divided into labour-intensive (LI), capital-intensive (CI), and technology-intensive (TI) industries.

As seen from the industry-specific test results displayed in Table 4 , the impact of AI on employment in the three attribute industries is significantly positive, which is consistent with the results of Beier et al. ( 2022 ). In contrast, labour-intensive industries can absorb more workers, and industry practitioners are better able to share digital dividends from these new workers, which is generally in line with expectations (in the labour-intensive case, the regression coefficient of AI on employment is 0.054, which is significantly larger than the regression coefficient of the other two industries). This conclusion shows that enterprises use AI to replace the labour force of procedural and process-based positions in pursuit of cost-effective performance. However, the scale effect generated by improving enterprise production efficiency leads to increased labour demand, namely, productivity and compensation effects. For example, AGV-handling robots are used to replace porters in monotonous and repetitive high-intensity work, thus realizing the uncrewed operation of storage links and the automatic handling of goods, semifinished products, and raw materials in the production process. This reduces the cost of goods storage while improving the efficiency of logistics handling, increasing the capital investment of enterprises in the expansion of market share and extension of the industrial chain.

Mechanism test

To reveal the path mechanism through which AI affects employment, in combination with H2 and H3 and the intermediary effect model constructed with Eq. ( 3 ), the TWFE model was used to fit the results shown in Table 5 .

It can be seen from Table 5 that the fitting coefficients of AI for capital deepening, labour productivity, and division of labour are 0.052, 0.071, and 0.302, respectively, and are all significant at the 1% level, indicating that AI can promote employment through the above three mechanisms, and thus research Hypothesis 2 (H2) is supported. Compared with the workshop and handicraft industry, machine production has driven incomparably broad development in the social division of labour. Intelligent transformation helps to open up the internal and external data chain, improve the combination of production factors, reduce costs and increase efficiency to enable the high-quality development of enterprises. At the macro level, the impact of robotics on social productivity, industrial structure, and product prices affects the labour demand of enterprises. At the micro level, robot technology changes the employment carrier, skill requirements, and employment form of labour and impacts the matching of labour supply and demand. The combination of the price and income effects can drive the impact of technological progress on employment creation. While improving labour productivity, AI technology reduces product production costs. In the case of constant nominal income, the market increases the demand for the product, which in turn drives the expansion of the industrial scale and increases output, resulting in an increase in the demand for labour. At the same time, the emergence of robotics has refined the division of labour. Most importantly, the development of AI technology results in productivity improvements that cannot be matched by pure labour input, which not only enables 24 h automation but also reduces error rates, improves precision, and accelerates production speeds.

Table 5 also shows that the fitting coefficient of AI to virtual agglomeration is 0.141 and significant at the 5% level, indicating that AI and digital technology can promote employment by promoting the agglomeration degree of enterprises in the cloud and network. Research Hypothesis 3 is thus supported. Industrial internet, AI, collaborative robots, and optical fidelity information transmission technology are necessary for the future of the manufacturing industry, and smart factories will become the ultimate direction of manufacturing. Under the intelligent manufacturing model, by leveraging cloud links, industrial robots, and the technological depth needed to achieve autonomous management, the proximity advantage of geographic spatial agglomeration gradually begins to fade. The panconnective features of digital technology break through the situational constraints of work, reshaping the static, linear, and demarcated organizational structure and management modes of the industrial era and increasingly facilitates dynamic, network-based, borderless organizational forms, despite the fact that traditional work tasks can be carried out on a broader network platform employing online office platforms and online meetings. While promoting cost reduction and efficiency increase, such connectivity also creates new occupations that rely on this network to achieve efficient virtual agglomeration. On the other hand, robot technology has also broken the fixed connection between people and jobs, and the previous post matching mode of one person and one specific individual has gradually evolved into an organizational structure involving multiple posts and multiple people, thus providing more diverse and inclusive jobs for different groups.

Conclusions and policy implications

Research conclusion.

The decisive impact of digitization and automation on the functioning of all society’s social subsystems is indisputable. Technological progress alone does not impart any purpose to technology, and its value (consciousness) can only be defined by its application in the social context in which it emerges (Rakowski et al. 2021 ). The recent launch of the intelligent chatbot ChatGPT by the US artificial intelligence company OpenAI, with its powerful word processing capabilities and human-computer interaction, has once again sparked global concerns about its potential impact on employment in related industries. Automation technology represented by intelligent manufacturing profoundly affects the labour supply and demand map and significantly impacts economic and social development. The application of industrial robots is a concrete reflection of the integration of AI technology and industry, and its widespread promotion and popularization in the manufacturing field have resulted in changes in production methods and exerted impacts on the labour market. In this paper, the internal mechanism of AI’s impact on employment is first delineated and then empirical tests based on panel data from 30 provinces (municipalities and autonomous regions, excluding Hong Kong, Macao, Taiwan, and Xizang) in China from 2006 to 2020 are subsequently conducted. As mentioned in relation to the theory of “employment compensation,” the research described in this paper shows that the overall impact of AI on employment is positive, revealing a pronounced job creation effect, and the impact of automation technology on the labour market is mainly positively manifested as “icing on the cake.” Our conclusion is consistent with the literature (Sharma and Mishra 2023 ; Feng et al. 2024 ). This conclusion remains after replacing variables, adding missing variables, and controlling for endogeneity problems. The positive role of AI in promoting employment does not have exert opposite effects resulting from gender and industry differences. However, it brings greater digital welfare to female practitioners and workers in labour-intensive industries while relatively reducing the overall proportion of male practitioners in the manufacturing industry. Mechanism analysis shows that AI drives employment through mechanisms that promote capital deepening, the division of labour, and increased labour productivity. The digital trade derived from digital technology and internet platforms has promoted the transformation of traditional industrial agglomeration into virtual agglomeration, the constructed network flow space system is more prone to the free spillover of knowledge, technology, and creativity, and the agglomeration effect and agglomeration mechanism are amplified by geometric multiples. Industrial virtual agglomeration has become a new mechanism and an essential channel through which AI promotes employment, which helps to enhance labour autonomy, improve job suitability and encourage enterprises to share the welfare of labour among “cultivation areas.”

Policy implications

Technology is neutral, and its key lies in its use. Artificial intelligence technology, as an open new general technology, represents significant progress in productivity and is an essential driving force with the potential to boost economic development. However, it also inevitably poses many potential risks and social problems. This study helps to clarify the argument that technology replaces jobs by revealing the impact of automation technology on China’s labour market at the present stage, and its findings alleviate the social anxiety caused by the fear of machine replacement. According to the above research conclusions, the following valuable implications can be obtained.

Investment in AI research and development should be increased, and the high-end development of domestic robots should be accelerated. The development of AI has not only resulted in the improvement of production efficiency but has also triggered a change in industrial structure and labour structure, and it has also generated new jobs as it has replaced human labour. Currently, the impact of AI on employment in China is positive and helps to stabilize employment. Speeding up the development of the information infrastructure, accelerating the intelligent upgrade of the traditional physical infrastructure, and realizing the inclusive promotion of intelligent infrastructure are necessary to ensure efficient development. 5G technology and the development dividend of the digital economy can be used to increase the level of investment in new infrastructure such as cloud computing, the Internet of Things, blockchain, and the industrial internet and to improve the level of intelligent application across the industry. We need to implement the intelligent transformation of old infrastructure, upgrade traditional old infrastructure to smart new infrastructure, and digitally transform traditional forms of infrastructure such as power, reservoirs, rivers, and urban sewer pipes through the employment of sensors and access algorithms to solve infrastructure problems more intelligently. Second, the diversification and agglomeration of industrial lines are facilitated through the transformation of industrial intelligence and automation. At the same time, it is necessary to speed up the process of industrial intelligence and cultivate the prospects of emerging industries and employment carriers, particularly in regard to the development and growth of emerging producer services. The development of domestic robots should be task-oriented and application-oriented, should adhere to the effective transformation of scientific and technological achievements under the guidance of the development of the service economy. A “1 + 2 + N” collaborative innovation ecosystem should be constructed with a focus on cultivating, incubating, and supporting critical technological innovation in each subindustry of the manufacturing industry, optimizing the layout, and forming a matrix multilevel achievement transformation service. We need to improve the mechanisms used for complementing research and production, such as technology investment and authorization. To move beyond standard robot system development technology, the research and development of bionic perception and knowledge, as well as other cutting-edge technologies need to be developed to overcome the core technology “bottleneck” problem.

It is suggested that government departments improve the social security system and stabilize employment through multiple channels. The first channel is the evaluation and monitoring of the potential destruction of the low-end labour force by AI, enabled through the cooperation of the government and enterprises, to build relevant information platforms, improve the transparency of the labour market information, and reasonably anticipate structural unemployment. Big data should be fully leveraged, a sound national employment information monitoring platform should be built, real-time monitoring of the dynamic changes in employment in critical regions, fundamental groups, and key positions should be implemented, employment status information should be released, and employment early warning, forecasting, and prediction should be provided. Second, the backstop role of public service, including human resources departments and social security departments at all levels, should improve the relevant social security system in a timely manner. A mixed-guarantee model can be adopted for the potential unemployed and laws and regulations to protect the legitimate rights and interests of entrepreneurs and temporary employees should be improved. We can gradually expand the coverage of unemployment insurance and basic living allowances. For the extremely poor, unemployed or extreme labour shortage groups, public welfare jobs or special subsidies can be used to stabilize their basic lifestyles. The second is to understand the working conditions of the bottom workers at the grassroots level in greater depth, strengthen the statistical investigation and professional evaluation of AI technology and related jobs, provide skills training, employment assistance, and unemployment subsidies for workers who are unemployed due to the use of AI, and encourage unemployed groups to participate in vocational skills training to improve their applicable skillsets. Workers should be encouraged to use their fragmented time to participate in the gig and sharing economies and achieve flexible employment according to dominant conditions. Finally, a focus should be established on the impact of AI on the changing demand for jobs in specific industries, especially transportation equipment manufacturing and communications equipment, computers, and other electronic equipment manufacturing.

It is suggested that education departments promote the reform of the education and training system and deepen the coordinated development of industry-university research. Big data, the Internet of Things, and AI, as new digital production factors, have penetrated daily economic activities, driving industrial changes and changes in the supply and demand dynamics of the job market. Heterogeneity analysis results confirmed that AI imparts a high level of digital welfare for women and workers in labour-intensive industrial enterprises, but to stimulate the spillover of technology dividends in the whole society, it is necessary to dynamically optimize human capital and improve the adaptability of man-machine collaborative work; otherwise, the disruptive effect of intelligent technology on low-end, routine and programmable work will be obscured. AI has a creativity promoting effect on irregular, creative, and stylized technical positions. Hence, the contradiction between supply and demand in the labour market and the slow transformation of the labour skill structure requires attention. The relevant administrative departments of the state should take the lead in increasing investment in basic research and forming a scientific research division system in which enterprises increase their levels of investment in experimental development and multiple subjects participate in R&D. Relevant departments should clarify the urgent need for talent in the digital economy era, deepen the reform of the education system as a guide, encourage all kinds of colleges and universities to add related majors around AI and big data analysis, accelerate the research on the skill needs of new careers and jobs, and establish a lifelong learning and employment training system that meets the needs of the innovative economy and intelligent society. We need to strengthen the training of innovative, technical, and professional technical personnel, focus on cultivating interdisciplinary talent and AI-related professionals to improve worker adaptability to new industries and technologies, deepen the adjustment of the educational structure, increase the skills and knowledge of perceptual, creative, and social abilities of the workforce, and cultivate the skills needed to perform complex jobs in the future that are difficult to replace by AI. The lifelong education and training system should be improved, and enterprise employees should be encouraged to participate in vocational skills training and cultural knowledge learning through activities such as vocational and technical schools, enterprise universities, and personnel exchanges.

Research limitations

The study used panel data from 30 provinces in China from 2006 to 2020 to examine the impact of AI on employment using econometric models. Therefore, the conclusions obtained in this study are only applicable to the economic reality in China during the sample period. There are three shortcomings in this study. First, only the effect and mechanism of AI in promoting employment from a macro level are investigated in this study, which is limited by the large data particles and small sample data that are factors that reduce the reliability and validity of statistical inference. The digital economy has grown rapidly in the wake of the COVID-19 pandemic, and the related industrial structures and job types have been affected by sudden public events. An examination of the impact of AI on employment based on nearly three years of micro-data (particularly the data obtained from field research) is urgent. When conducting empirical analysis, combining case studies of enterprises that are undergoing digital transformation is very helpful. Second, although the two-way fixed effect model and instrumental variable method can reveal conclusions regarding causality to a certain extent, these conclusions are not causal inference in the strict sense. Due to the lack of good policy pilots regarding industrial robots and digital parks, the topic cannot be thoroughly evaluated for determining policy and calculating resident welfare. In future research, researchers can look for policies and systems such as big data pilot zones, intelligent industrial parks, and digital economy demonstration zones to perform policy evaluations through quasinatural experiments. The use of difference in differences (DID), regression discontinuity (RD), and synthetic control method (SCM) to perform regression is beneficial. In addition, the diffusion effect caused by introducing and installing industrial robots leads to the flow of labour between regions, resulting in a potential spatial spillover effect. Although the spatial econometric model is used above, it is mainly used as a robustness test, and the direct effect is considered. This paper has yet to discuss the spatial effect from the perspective of the spatial spillover effect. Last, it is important to note that the digital infrastructure, workforce, and industrial structure differ from country to country. The study focused on a sample of data from China, making the findings only partially applicable to other countries. Therefore, the sample size of countries should be expanded in future studies, and the possible heterogeneity of AI should be explored and compared by classifying different countries according to their stage of development.

Data availability

The data generated during and/or analyzed during the current study are provided in Supplementary File “database”.

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Acknowledgements

This work was financially supported by the Natural Science Foundation of Fujian Province (Grant No. 2022J01320).

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YS: Data analysis, Writing – original draft, Software, Methodology, Formal analysis; XZ: Data collection; Supervision, Project administration, Writing – review & editing, Funding acquisition. All authors substantially contributed to the article and accepted the published version of the manuscript.

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Shen, Y., Zhang, X. The impact of artificial intelligence on employment: the role of virtual agglomeration. Humanit Soc Sci Commun 11 , 122 (2024). https://doi.org/10.1057/s41599-024-02647-9

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Received : 23 August 2023

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Published : 18 January 2024

DOI : https://doi.org/10.1057/s41599-024-02647-9

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Article Contents

I. introduction, ii. literature review, iii. case studies, iv. findings, discussion, and policy implications.

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Job creation and deep decarbonization

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Kelly Sims Gallagher, Soyoung Oh, Job creation and deep decarbonization, Oxford Review of Economic Policy , Volume 39, Issue 4, Winter 2023, Pages 765–778, https://doi.org/10.1093/oxrep/grad038

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This paper explores whether economic viability is the key to achieve deep decarbonization or net zero emissions. The hypothesis tested is that popular support for decarbonization policies is conditional upon most people’s belief that their economic well-being will improve, or at least not suffer with these policies. While GDP growth is the typical metric for economic health, a more useful socio-economic indicator for gauging the political viability of climate policies may be job creation. Specifically, the paper reviews the existing evidence about whether climate policies are more successful in achieving deep decarbonization in the long run if policy-makers include job creation as well as emissions reductions when designing and implementing climate policies, because, to date, climate policy-makers have often focused on emissions reductions as the primary criterion for policy choice. While empirical evidence remains thin, we find that job creation in low-carbon industries appears to lead to greater political support for the climate policies that contribute to decarbonization, but employment factors are not always the most salient factor in a voter’s decision. We also find empirical evidence that clean energy deployment policies, such as feed-in tariffs, have led to significant net gains in employment in the countries that have been studied. The review points to several policy implications, including the need to assess competitive advantage, develop plans, design and execute industrial policy, and develop a low-carbon workforce.

This paper explores whether economic viability is the key to achieve deep decarbonization or net zero emissions. The hypothesis tested is that popular support for decarbonization policies is conditional upon most people’s belief that their economic well-being will improve, or at least not suffer with these policies. While GDP growth is the typical metric for economic performance, a more useful socio-economic indicator for gauging the political viability of climate policies may be job creation. This paper specifically reviews existing evidence about whether climate policies are more successful in achieving deep decarbonization in the long run if policy-makers include job creation as well as emissions reductions when designing and implementing climate policies, because, to date, climate policy-makers have often focused on emissions reductions as the primary criterion for policy choice.

There are several types of industries that can be considered ‘low-carbon’ industries. Clean energy industries, such as renewable energy, energy efficiency, nuclear, and energy storage industries, will be needed to allow for the transition from fossil fuels to zero-carbon fuels. This ‘low-carbon’ category could include carbon capture and storage from fossil fuels as well as direct air capture and storage if all CO 2 is captured and permanently stored. Industries that don’t need to use fossil fuels are also highly desirable as an economic diversification strategy because they can contribute to the economy and create jobs without increasing greenhouse gas emissions. Any industry that can be electrified would fit into this category (assuming the electricity is produced in a carbon-free manner). Finally, industries that provide goods and services that foster adaptation and resilience are also necessary given that climate change is already upon us. These industries must also be able to utilize non-fossil fuels.

This paper investigates, through a review of existing scholarly political economy literature as well as specific literature on ‘green’ jobs or job creation and decarbonization and two comparative case studies, whether climate policies will be more successful in achieving deep decarbonization in the long run if they are designed to achieve both emissions reductions and job creation. Other criteria for policy choice, of course, remain highly desirable, including cost-effectiveness, equity, and the co-benefit of contribution to climate resilience.

We first review the existing literature on the relationship between job creation and climate policy in democratic countries. We next analyse two sets of case studies, again drawing upon existing literature: Europe’s Green New Deal versus the US Inflation Reduction Act and the development of the solar PV industry in China versus the United States. Based on the literature and the two case studies, findings, a discussion, and policy implications are presented.

To begin, we identify a classic chicken and egg dilemma: do climate policies lead to job creation (and thus political support) or does a government need political support to establish and implement climate policies in the first place? If the latter, how can political support be mobilized to allow for the design and implementation of new climate policies? In this section, we explore what is known about job creation and climate policy, drawing upon empirical studies from around the world.

(i) Job creation and politics

Job creation and job loss influences voting behaviour in general, but only as part of a multi-dimensional process where employment is one of numerous factors that are important to the individual voter. According to Kim et al . (2003) , there are two schools of thought about voting behaviour in the United States: (i) voters either reward or punish the incumbent based on performance ( Key and Munger, 1959 ) or (ii) voters are motivated by partisan or ideological priorities ( Downs, 1957 ).

Previous literature suggests that economic situations matter to citizens’ voting behaviour in two ways: personal economic experience (‘pocketbook’) voting and neighbours’ economic experience (‘sociotropic’) voting ( Wu and Huber, 2021 ). Although there is a general agreement that voters rely more on ‘sociotropic’ than ‘pocketbook’ in determining which candidate to vote for, some studies suggest that personal experiences are also important, especially during the first year of the government’s term ( Healy et al ., 2017 ).

In the United States, unemployment at the local level used to lead to increased support for Democratic presidential candidates ( Kiewiet, 1981 ), but more recent research indicates that this support is mediated by perceptions of the health of the national economy, which can drive down support for the incumbent, no matter whether they are a Democrat or Republican ( Park and Reeves, 2020 ). Similarly, Bisgaard et al . (2016) showed that exposure to unemployment in citizens’ immediate residential environments can influence how voters perceive the national economy, although, in their study, the effect of left–right orientation appears more important ( Bisgaard et al ., 2016 ).

In Western Europe, unemployment leads voters to vote for left-leaning candidates, but when high unemployment occurs, all incumbents are affected ( Dassonneville and Lewis-Beck, 2013 ). It is a similar pattern as in the United States that incumbents are negatively affected by high unemployment regardless of their political parties. A study of Polish elections in 1997 and 2001 found that ‘job flows significantly affected voting’ ( Smeets and Warzynski, 2006 ). Similarly, in Sweden, voting behaviour is affected by personal economic situations ( Healy et al ., 2017 ) and a reduction in unemployment is associated with an increase in support for the incumbent national government in Sweden ( Elinder, 2010 ). In the context of South Korea, however, Lee and Repkine (2022) found that unemployment did not have a pronounced influence in shaping voter choice compared to its Western counterparts.

(ii) Job creation and political support for climate policies

Scholarly literature on job creation and political support for climate policy around the world is in its infancy but indicates early empirical support for the need to incorporate consideration of employment into climate policy design.

A study comparing countries in Europe with the United States found that the energy intensity of an individual’s employment correlates with political support for global climate cooperation. The authors found that the more carbon-intensive the employment sector, the weaker the support for climate cooperation ( Bechtel et al ., 2014 ).

In Europe, Tvinnereim and Ivarsflaten (2016) similarly find that support for, and opposition to, climate mitigation policies are associated with individuals’ economic interests. Individuals employed in the oil and gas sector in Norway are less likely to believe climate change is a serious threat, for example ( Tvinnereim and Austgulen, 2014 ). A study in Sweden of public attitudes towards wind power found that lower-income survey respondents had more positive attitudes towards wind energy than higher-income respondents, perhaps because of anticipated local employment prospects associated with new wind energy construction ( Söderholm et al ., 2007 ).

In the United States, even if not directly employed, residents in areas with extractive activities (e.g. mining, gas production) showed less support for renewable energy policies than individuals living outside such places. The authors showed that belief in anthropogenic global warming is the strongest predictor of renewable energy policy support. Similarly, Mayer (2022) finds that, in the United States, the number of coal jobs created does not predict support for the coal industry, suggesting that support for the coal industry has more to do with factors like the cultural salience of coal. Mayer (2022) notes that nostalgia and political partisanship 1 are positively associated with support for coal in western Colorado. Even though net economy-wide benefits in health and labour market exist, localized contextual effects and politico-economic factors amplify the persistence of the ‘job-killing’ argument ( Vona, 2019 ). Due to these hyper-local factors, public perception and support for climate policies could be significantly reduced in the presence of negative shocks, especially job losses ascribed to climate policies ( Vona, 2019 ).

By contrast, employment in renewable energy industries appears to be associated with greater political support for climate mitigation policies. Allan et al . (2021) argue that governments can gain political support for renewable energy technologies when they promise job creation and domestic technological progress in clean energy. Michaelowa (2005) traced the growth of political support for wind subsidies in Germany and found that very high political support for renewable energy policies was created when workers from shipbuilding industries found new employment in the emerging wind industry because the diversification of the economy enabled them to find new jobs. Political coalitions between farmers, shipbuilding workers, and wind companies led to strong local support for wind projects in coastal regions. The German study indicates that renewable energy policies led to the creation of political support from labour after new jobs were created (‘the chicken’).

In a study of popular support for wind projects along the east coast of the United States, the authors find that the prospect of job creation can significantly increase support for wind projects, but interestingly that employment is not one of the top three reasons for public support for wind projects ( Firestone et al ., 2012 ). Instead, the top three reasons for supporting new deployment of wind energy at the time of this 2012 study were energy independence, reduced electricity rates, and a positive impact on environment. Stokes and Warshaw (2017) find that policy design and framing (e.g. effects of renewable portfolio standards bills on residential electricity costs, jobs, and pollution) influence public support for renewable energy technologies across the United States. In the case of renewable portfolio standards (RPS), the anticipated employment effect was an important factor in building initial political support in the US states ( Stokes, 2015 ). Through setting two different treatments, Stokes and Warshaw (2017) empirically assessed the job creation question and found that there is a significant drop in public support for an RPS if respondents were not informed that an RPS generates jobs in their state. A recent study similarly shows that a proposed policy mix of social and economic reforms such as providing a job guarantee, setting a minimum wage, retraining fossil fuel workers, or providing unionized clean energy jobs increases public support for climate mitigation policies in the United States ( Bergquist et al ., 2020 ). These studies indicate that prospectively analysing and talking about likely job benefits creates political support for new policies (‘the egg’).

In emerging economy countries, policy-makers face more pressure to create employment opportunities for the young demographics and to make up for the job losses in the existing carbon-intensive industries ( Narassimhan, 2021 ). Through conducting case studies of green industrial policies in India, China, Ethiopia, and South Africa, Narassimhan (2021) found that creating jobs and working with dominant actors in their political economy help governments to pursue a green transition.

These studies indicate early support for both the ‘egg’ and the ‘chicken’ in that including the prospect of new jobs in a proposed climate policy mix leads to increased popular support. One of the studies also shows that labour support for climate policies increases after new jobs in low-carbon industries are created, indicating that a positive feedback cycle can be created whereby a policy that is sold to voters as a job-creating policy then actually creates new jobs in low-carbon industries and, in turn, creates even more political support for climate policies in the future.

(iii) Do climate policies actually create jobs?

Turning to whether or not climate policies actually create jobs, there are few ex post empirical studies of clean energy support policies and employment impacts around the world, but more ex ante studies that estimate the potential for job creation.

Prospective studies

The estimates of job creation can vary depending on the categorization and assumptions in modelling, but there is a general agreement in prospective studies that there will be global job growth by 2050 if the low-carbon energy transition is pursued, even accounting for job losses in high-carbon industries. The Stern Review (2007) calculated that more than 25 million jobs in low-carbon technology would be created in sectors worldwide by 2050 if investments are made in low-carbon technologies ( Stern, 2007 ). A more recent study concludes that under a ‘well-below 2°C’ scenario, energy sector jobs are expected to grow to 26 million by 2050 in 50 countries, in which fossil fuel extraction jobs would decrease and green jobs will make up for the losses ( Pai et al ., 2021 ).

The International Labour Organization (ILO) finds that approximately 24 million ‘green-jobs’ 2 could be created by 2030 under a scenario to achieve the 2°C goal throughout the world economy ( ILO, 2018 ). In particular, energy efficiency and electric vehicles would be key drivers of generating green jobs. While the creation of ‘green jobs’ would be enough to offset fossil fuel industry job losses, the scenario does not consider adjustment and transition costs (e.g. skills mismatches and rigidities in the labour market) and it assumes that aggressive decarbonization activities will be taken to meet the 2°C goal ( ILO, 2018 ). Under a more stringent 1.5°C scenario, the International Renewable Energy Agency (IRENA) forecasts that about 139 million jobs would be created worldwide in the energy sector, including 38.2 million renewable energy jobs and 74.2 million jobs in energy transition-related sectors by 2030 ( IRENA, 2022 a ). This study includes renewable energy, energy efficiency, electric vehicles, power systems/flexibility, and hydrogen. By 2030, the Business and Sustainable Development Commission estimates that achieving global sustainability goals would yield 380 million new jobs in environment-related economic areas, including food and agriculture, cities, energy, and materials ( European Commission, 2021 a ).

With a global dataset of jobs related to 11 energy technologies in both developed and developing countries, Pai et al . (2021) found that job losses from fossil fuel extraction would be compensated by gains in solar and wind jobs in 2050, especially in the manufacturing sector. Another study also estimates a net employment effect when $1 billion of subsidies in fossil fuel industries is shifted into public investment in renewable energy ( Garrett-Peltier, 2017 ).

In the EU, the clean energy transition is forecast to lead to job creation in electricity, solar PVs, wind, and biofuels, which would represent 1.3 per cent of the EU workforce by 2050 ( Fragkos and Paroussos, 2018 ). Through the implementation of the European Green New Deal, the European Centre for the Development of Vocational Training estimated that approximately 2.5 million additional jobs across the EU economy would be created by 2030 compared to the baseline scenario, even when accounting for jobs losses in certain sectors ( Cedefop, 2021 ). In particular, the largest employment gains are expected in utilities, electricity supply, manufacturing of appliances/electrical equipment, and construction sectors ( Cedefop, 2021 ).

In sum, there is a wide range of ex ante estimates of global job creation related to clean energy, ranging from 25 million to 380 million new jobs during the coming decades. Most of the studies only examine the energy sector, and not the spillover effects that result from the value chains created or lost, nor the impacts of structural change in the economy, such as a shift to lighter or service-based industries and away from heavier or dirtier industries.

Empirical studies of actual job creation

Different types of models are used to estimate the employment impact of climate policies, including the input–output (IO) models and analytical process models (bottom-up model) ( Wei et al ., 2010 ; Hondo and Moriizumi, 2017 ). While the analytical process model calculates the direct employment impacts, the IO model can compute both direct and indirect 3 employment impacts and quantify the life-cycle employment opportunities ( Hondo and Moriizumi, 2017 ).

Jobs can be created during the investment and construction phase of new clean energy infrastructure, but these tend to be temporary in nature, and then a smaller number of longer-term jobs are created for operating and maintaining the infrastructure. Of course, assuming that there is continuous new investment and construction, those jobs are retained though they may be discontinuous due to geographic dislocation. The supply chains for various technologies also can lead to job creation or loss through backward and forward linkages in the economy.

Previous literature suggests that the short-term effect would be concentrated in directly related industries like renewable energy, and the medium- to long-term employment effects of climate policy can be found along the value chains of affected industries ( Rosiek, 2014 ). The types and duration of jobs also differ depending on stages and specific sectors. In the investment cycle for renewable energy including solar PV, wind, and geothermal, construction, installation, and manufacturing (CIM) jobs occur and a smaller number of operations and maintenance (O&M) jobs are created and sustained over the lifetime of a project ( Kim and Mohommad, 2022 ). Jobs created in the CIM stage are of shorter duration and are usually temporary with stronger employment effects, whereas far fewer O&M jobs prove durable over the entire lifetime with modest employment effects ( Wei et al ., 2010 ; Ejdemo and Söderholm, 2015 ). For instance, in Japan, solar PV creates 2.10 person-years per GWh in the construction stage, but only 0.63 in the O&M phase ( Hondo and Moriizumi, 2017 ).

Further, there are differences in labour intensity between manufacturing jobs and deployment-related jobs and along the supply chains of low-carbon energy technologies, which affect estimates of employment impacts. Empirical studies show that deployment-related jobs that are labour-intensive, such as residential rooftop solar installation, generate significant job opportunities ( Blyth et al ., 2014 ), while the evidence on the quality of jobs created is less clear ( Martinez-Fernandez et al ., 2010 ).

Various factors influence the degree of employment impacts and the quality of jobs created. For instance, in Greece, wind-power construction resulted in 8.8 man-years per MW during manufacturing and construction and 7.5 man-years per MW during O&M. The degree of job creation varies between countries partially because the number of jobs depends on the labour intensity of the country ( Tourkolias and Mirasgedis, 2011 ). Narassimhan (2021) found that developing countries such as Ethiopia see only short-term jobs created for renewable energy deployment due to their lack of local know-how and the difficulty associated with importing raw materials and equipment for green manufacturing. Similarly, South Africa created only short-term low-skilled jobs in renewable energy deployment and operations, instead of higher-value-added jobs in manufacturing or project development in the technological value chain ( Narassimhan, 2021 ).

Climate policies have different employment impacts depending on the regional/national contexts and policy types. To gauge the employment effects of policies, government effectiveness would be an important factor in promptly implementing policies aimed at reskilling or other active market programmes that support an effective reallocation of workers across industries and facilitate an economy’s smooth transition ( Montt et al ., 2018 ).

In North America, a 2013 study of state-level climate policies in the United States finds that local clean energy policies have a statistically positive impact on green job creation ( Yi, 2013 ). For example, a gas-fired plant averages around 1 job-year/installed MW while solar PV projects create over 20 job-years per installed MW largely due to the higher labour intensity during the installation phase ( Blyth et al ., 2014 ). A study of British Columbia’s revenue-neutral carbon tax (where carbon-tax revenues were redistributed to offset corporate and individual income taxes and to provide lump-sum transfers to low-income residents) found that the carbon tax generated small but statistically significant annual increases in employment between 2007 and 2013 ( Yamazaki, 2017 ). A more recent (unpublished) paper finds significant short-term unemployment impacts from the British Columbia tax but also finds that most displaced workers find new jobs very quickly, although their new wages may be lower ( Yip, 2020 ). In this latter study, employment falls in fossil fuel-intensive and trade-sensitive industries, but it rises in clean service industries, especially in health care services.

In Europe, different policies have mixed employment outcomes. Fragkos and Paroussos (2018) find that the net impact of renewable energy expansion on EU jobs is also positive as renewable technologies have higher labour intensity and domestic job content relative to fossil fuels. A study of the UK carbon tax shows no negative or positive impact on employment was found ( Martin et al ., 2014 ). In Germany, feed-in-tariff (FiT) policies helped to create renewable energy employment in both absolute and relative terms between 2000 and 2018 ( O’Sullivan and Edler, 2020 ).

In Latin America, a study in Brazil on the wind industry found that for every MW installed, 13.5 person-years worth of jobs were created in the first year and that 24.5 person-years are created over the lifetime of the wind farm ( Simas and Pacca, 2014 ).

When comparing the gross number of jobs created per unit of electricity between fossil fuel and renewable power generation, Blyth et al . (2014) find that coal- and gas-fired power generation do not appear to be significantly more job-intensive than renewable energy and energy efficiency. The average for fossil fuels is about 0.15 jobs/GWh (coal: 0.15, gas: 0.12), while the average for all renewable energy is 0.65 jobs/Gwh. Further, the authors show that the average net job creation across all renewable energy technologies is positive even when considering its impacts on displacing traditional fossil fuel-based jobs.

In summary, most of the climate policies assessed to date have been policies designed to deploy clean energy in advanced industrialized countries, including renewable portfolio standards, tax credits, and feed-in tariffs. For the clean energy deployment policies in these countries, the empirical evidence indicates that these policies do generate a net gain in new jobs, although the quality of the jobs is unclear. The evidence is less clear regarding the job impacts of carbon taxes. One UK study found no effect, and two studies on the British Columbia carbon tax found mixed effects, likely due to the very different designs of these two tax policies. One study of developing country effects found only short-term employment gains in low-skilled jobs emerged from renewable energy projects.

(iv) Correlation between green industrialization policy and political support for climate policy

In research that explores the connection between green industrialization policy and political support for climate policy, Meckling et al . (2015) argue that green industrialization policies can lead to political support for more direct forms of climate change policy, such as carbon pricing policies. Their argument rests less on employment effects and more on the strengthening of the ‘winning coalitions’ or lobbying power of newly-created green industries. Through a review of 117 articles, Hess (2019) finds that coalition compositions are changing along with their reframed goals to align renewable energy development with job creation. The reframing allows coalitions to expand by attracting a wider range of civil society organizations ( Hess, 2019 ). However, as the same job framing is being used in fossil fuel sectors, the ‘energy-transition coalitions’ could face resistance in effectively building public support. In Chile, coalitions of environmental organizations and social movements were crucial for advancing renewable energy policy and transforming the energy sector ( Madariaga and Allain, 2020 ).

Theoretically, industrial policy for low-carbon industries is more likely to create jobs if the policies are designed with job creation as a condition of government support. Amsden (2001) noted that such conditional policies are ‘reciprocal control’ mechanisms, where the government provides a subsidy or protection from external competition in exchange for the achievement of performance against desired criteria. Such support for industries is gradually withdrawn after the public goals are achieved. Performance in job creation could be a condition of support for low-carbon industries.

The sequencing of climate policies can improve the political support for more ambitious policies as noted by Pahle et al . (2022) who write that sequencing ‘implies policy pathways in which policies change over time, and wherein each stage is conducive to achieving the subsequent, more stringent one’. And further that ‘intentional, strategic sequencing mandates the anticipation of barriers and how to overcome them as a core policy design principle’. The authors note that job creation was important to help sustain Germany’s renewable energy policy when opposition to renewables from utilities increased.

In summary, a review of the available literature indicates that political support for decarbonization policies is generally higher when people believe that jobs will be created, or at least not lost. Additional political motivations for climate policies are also salient for voters, including energy security, the cost of electricity and fuels, and other environmental benefits, such as reducing conventional air or water pollution. The energy security or ‘energy independence’ motivation rings especially true in the wake of the Russian invasion of Ukraine, which roiled energy markets and caused temporary price spikes. In addition, emerging literature indicates that sequencing of policies may be necessary to build political support to enact more stringent climate laws or promulgate tough climate regulations. The logic is that green industrialization policies lead to the establishment of green industries, which are in turn self-interested in advocating for themselves in ‘winning coalitions’ on behalf of more stringent climate policies since they already have a form of first-mover advantage. These green industries create jobs along the way, which reinforces the political desirability of more stringent climate policies.

(i) Europe’s Green Deal versus US Inflation Reduction Act

In the past few years, unprecedented progress has been made in advancing policies that would establish green industries and jobs. Two of the most salient examples are the European Union’s European Green Deal (EGD) in 2020 and the US Inflation Reduction Act (IRA) in 2022. As both the EGD and the IRA were adopted relatively recently, they have not been fully implemented yet to materialize their employment benefits. However, both the US and the EU intend through their policies to increase the number of green jobs and subsequently gain more political support for climate policy in the coming years through the implementation of the EGD and IRA.

In the United States, President Joe Biden has committed to creating ‘high-quality’ jobs in clean energy technologies ( The White House, 2022 ). According to the Department of Labor and the Department of Commerce, the federal agencies consider eight principles that constitute a ‘good job’. Some of the principles include recruitment and hiring, diversity, equity, inclusion, and accessibility, and pay and benefits ( US Department of Labor, 2022 ). Currently, jobs in low-carbon energy industries in the United States made up approximately 40 per cent of total energy jobs in 2021 ( DOE, 2022 ). The Inflation Reduction Act of 2022 is estimated to have the potential of both reducing emissions and creating about 1.4–1.5 million additional jobs by 2030 ( Mahajan et al ., 2022 ). A more optimistic estimate shows that the IRA will create over 9 million jobs by 2032, driven mostly by electricity programmes including tax credits on renewable energy production and nuclear production ( Pollin et al ., 2022 ). This latter study aggregated direct, indirect, and induced jobs to calculate the total job creation but did not consider the job losses in fossil fuel sectors. Through investing in large-scale clean power generation and manufacturing industries through grants and tax credits, the IRA is expected to create green jobs across different sectors.

New opportunities arising from the IRA, such as the expanded tax credits, will encourage businesses to pay prevailing wages to their workers and hire registered apprentices. Further, the IRA could also help the government to gain political support from states whose representatives are sceptical of climate change ( Ewing, 2022 ). For instance, half of the states that are anticipated to receive investments from the IRA are Republican ‘red’ states like Tennessee. By accelerating investments in key manufacturing industries like electric vehicles, local economies in Republican states could shift voters’ attitudes towards green industry and climate policy. According to a preliminary study, as of December 2022, 6,850 new manufacturing jobs had already been created in just a few months following the passage of the IRA, and 80 per cent of all utility-scale wind or solar farms and battery projects currently in advanced development are in Republican-leaning states ( American Clean Power, 2022 ).

With the goal of becoming the first ‘climate neutral’ continent by 2050, the European Green Deal plans to allocate approximately €1.8 trillion of climate-related finance between 2021 and 2027. When initially proposed in 2019, the European Council noted that ‘the transition to climate neutrality will bring significant opportunities, such as the potential for economic growth, for new business models and markets, for new jobs and technological development’. The European Commission noted that the EU’s green economy is relatively small, but its growth rate outperformed the EU economy between 2000 and 2017 ( European Commission, 2020 b ), taking up to 2.2 per cent of its GDP in 2017.

So far, the number of green jobs in the EU has not grown dramatically, increasing from 3.1 million in 2000 to 4.2 million in 2017 ( European Commission, 2020 b ), but these figures do not take into account the policy effect of the EGD. The increase in green jobs between 2000 and 2017 was primarily from the renewable and energy efficiency sectors. The EGD, however, could be a tipping point for Europe to rapidly expand employment in green industries by 3.7 per cent ( Cedefop, 2021 ). The EGD is anticipated to add more jobs than the baseline scenario, which is approximately 2.5 million additional jobs in the EU ( Cedefop, 2021 ). In particular, under the Circular Economy Action Plan, water supply and waste management are likely to benefit most from the EGD as the reframed waste management and sustainable water (re)use facilities will create more jobs. While jobs in fossil fuel sectors such as coal mining are expected to decrease by about 286,000 jobs by 2030, job gains across different sectors including electricity, manufacturing, and construction are expected to more than make up for the losses. Unfortunately, this study did not examine the geographic variation in the impact of the EGD across the EU ( Cedefop, 2021 ). Depending on the level of technology readiness and the level of natural endowment, the employment effects would vary. For instance, a recent study shows that to support the concept of a circular bioeconomy in the Nordic hinterlands across different countries, regional-level revolving funds can be used to ensure employment effects ( Andersen et al ., 2022 ).

Further, the EGD’s Just Transition Mechanism aims to mobilize €55 billion by 2027 to facilitate employment opportunities in new sectors and offer re-skilling opportunities ( European Commission, 2020 a ). A recent study shows that the European Green Deal could spur the EU industrial sector and transform areas where coal and lignite are still mined and used in thermal power plants ( Kougias et al ., 2021 ).

The EU’s European Green Deal and the US Inflation Reduction Act both send strong government signals for pursuing the green transition, which should attract private-sector investment. While the EGD broadly supports low-carbon technologies and the European Commission set up the Innovation Fund with a budget of €1.5 billion to finance ‘breakthrough’ technologies ( European Commission, 2021 b ), the IRA puts a stronger focus on targeted subsidies and tax credits for domestic production of low-carbon energy technologies. The IRA has sparked tension between Washington and Brussels, which led the EU to simplify state aid rules to accelerate climate finance ( Goldthau and Neuhoff, 2022 ). Nevertheless, the EU maintains its position to support a rule-based policy framework that can spur domestic and foreign investment in the EU. Although it is difficult to gauge which precise implications the two initiatives will have on global trade dynamics in the long term due to uncertainties in technological development, additional political and diplomatic discourse will be needed about how to create mutually beneficial and positive economic impacts from the green transition, which could lead to garnering public support on the deployment of renewable energy in both countries.

(ii) China versus the United States in solar PV manufacturing and deployment

While the United States invented much of the solar PV technology used commercially around the world today, its inconsistent and volatile deployment policies during the 1990s and early 2000s caused it to cede the global marketplace to competitor countries. US manufacturing of solar PV declined, many American firms went out of business, and, consequently, the sector did not experience growth or steadiness in employment during this period. Conversely, China’s solar PV industry largely utilized existing technologies licensed by firms in other countries. The Chinese solar PV sector grew dramatically, particularly after China entered the World Trade Organization (WTO) in 2000 and could leverage global markets to export its solar PV modules to Germany, Spain, and the United States, among other countries ( Gallagher, 2014 ).

Employment in the solar PV industry in the United States remained essentially flat during the past decade (see Figure 1 ), at approximately 240,000 jobs compared with a growth in Chinese solar industry employment from 1.6 million in 2013 to 2.3 million in 2020. Normalized for total energy jobs to account for the larger population in China, China still had twice as many solar jobs in 2020 compared with the United States (IRENA and ILO, 2021 ). In 2021, China accounted for 62.5 per cent of global solar PV jobs, while the United States accounted for 5.2 per cent (IRENA and ILO, 2021 ).

Direct and indirect solar PV jobs in thousands (L) and solar PV deployment (GW) in China and the United States, 2013–21.

Source : IRENA (2014, 2015, 2016, 2017, 2018, 2019, 2020), Renewable Energy and Jobs Annual Review ; IRENA and ILO (2021 , 2022 ), Renewable Energy and Jobs Annual Review ; IRENA (2022 b ).

Concurrent with the growth in employment, Chinese manufacturing capacity and domestic deployment of solar cells, modules, and wafers likewise grew dramatically during the first two decades of the twenty-first century. China has cumulatively deployed 306 GW of solar energy as of 2021, while the United States has deployed 94 GW ( IRENA, 2022 b ).

In manufacturing, eight of the of the world’s top ten solar manufacturers today are Chinese firms. The Chinese-manufactured share of global PV shipments grew from 1 per cent in 2004 to 69 per cent in 2021 ( Feldman et al ., 2022 ). LONGi Green Energy (the world’s largest module producer in 2020), Tongwei Solar (the world’s largest cell producer in 2020), and JA Solar are the main Chinese players. As of 2021, Tongwei Solar and JA Solar employed about 13,000 and 29,368 people, respectively, which add up to about 2 per cent of the total solar energy jobs in China. Meanwhile, the US-manufactured percentage of global PV shipments declined from around 13 per cent in 2004 to 1.2 per cent in 2021 ( Feldman et al ., 2022 ). Between 2011 and 2021, US solar energy manufacturers experienced a steep decline—the US wafer production ended in 2015, and cell production closed in 2021. As a result, 60 per cent of China’s solar energy employees are in the manufacturing sector in 2021, while the majority of US solar energy jobs are in the construction industry (52 per cent). The sheer number of solar energy jobs in China is ten times higher than that of solar energy jobs in the US.

The gap in solar energy employment between the United States and China widened during the past 10 years, even though the US has continuously increased its solar energy deployment. One of the major differences between these two countries is different political commitments and consistency for solar deployment and manufacturing. For solar energy deployment across the United States, Steward et al . (2014) show that governmental actions (e.g. implementation of standards, policy related to the valuation of excess electricity, and long-term government support for a solar PV market) explain 70 per cent of the variation among US states in new PV capacity.

Clearly, in solar manufacturing, the industries and their job creations are strongly affected by governmental policies. IRENA and ILO (2021) note that Chinese firms have benefited from government support (e.g. low-cost credit, free land, tax exemptions, direct cash payments, and export credits) of large production capacities, yielding economies of scale. Dlouhy (2021) notes that the ‘inconsistent, piecemeal policy of the US’ was incapable of competing with ‘China-styled industrial strategy’, which aims at dominating the solar manufacturing sector. The increasing number of utility-scale projects also employ less than residential/commercial projects ( SEIA, 2021 ). Yi and Liu (2015) quantified provincial clean energy policies and their correlations with green jobs at the city level in China, finding that the green economy with solar cells is concentrated in certain Chinese provinces. The authors also show that there are significant variations in clean energy policy actions and green jobs across different regions in China. As installation jobs experience an increase in labour productivity, the role of end-to-end solar energy supply chains would become more critical in facilitating sustainable employment in solar energy.

Nevertheless, the United States is well positioned to reallocate some of its fossil-fuel workforce into solar energy jobs, although geographic dislocation would affect the mobility of this workforce. Louie and Pearce (2016) find that 43 per cent of coal-fired power plant workers in the United States could be transitioned to the PV sector without additional training, with 35 per cent needing to be retrained. Similarly, Bowen et al . (2018) pointed out that even states with a large natural resource industry have a substantial potential for greening as natural resource workers have similar skills as those required for green jobs. The US Department of Energy estimates that, by 2035, investments and installations could create between 500,000 to 1.5 million solar jobs in the United States ( DOE, 2021 ).

Some states like Texas and Colorado have experienced growth in clean energy jobs even while maintaining employment in fossil-fuel industries according to the US Department of Energy ( Coplon-Newfield et al ., 2022 ). In Colorado, solar electricity generation accounts for the largest percentage of jobs in the electricity sector (8,147 jobs), far more than coal (2,467 jobs) or natural gas (834 jobs). However, when adding in jobs related to fuels mining and extraction, there are 13,086 oil jobs and 7,800 natural gas jobs in Colorado as of 2022. Interestingly, energy efficiency jobs exceed supply-side jobs with 34,205 workers in the energy efficiency industry in Colorado. In Texas, energy jobs account for more than 11 per cent of total energy jobs in the United States. Within the electricity sector, wind and solar account for 63 per cent of total employment (38,930 clean energy jobs). Fossil fuel extraction jobs remain by far the largest source of employment there, however, with 244,914 people employed in oil and gas extraction.

The relative decline in the competitiveness of the US (and European) solar manufacturers caused them to successfully lobby the US government to impose tariffs on Chinese manufactured modules. The initial tariffs were imposed in 2012 ( Swanson and Plumer, 2022 ). In December 2022, the Biden Administration’s Commerce Department announced a preliminary decision to expand the solar tariffs, effective June 2024, after determining that certain Chinese firms including BYD, Trina Solar, Canadian Solar, and LONGi Energy had circumvented the previous tariffs by shifting final manufacturing to several Southeast Asian countries, including Malaysia, Cambodia, Thailand, and Vietnam ( Groom, 2022 ). The new tariffs provide 2 years additional ‘infant industry’ trade protection to US manufacturers as they ramp up production. The 2022 Inflation Reduction Act provides $37 billion in incentives to American clean-tech manufacturers (including solar manufacturers), so they will receive both significant subsidization and protection during this 2-year period. Meanwhile, the European Union signalled opposition to the IRA’s make-in-America provisions, and concerns are rising that it could lead to either a ‘subsidy war’ or a new trade war between the United States and EU ( Sorkin et al ., 2022 ).

China filed a case against the US solar tariffs at the World Trade Organization in 2018, and the United States agreed to undertake consultations with China, along with the European Union and Thailand. Subsequently, China requested the establishment of a Panel, which rejected all of China’s claims. China has stated that it intends to appeal as of September 2021 ( WTO, 2022 ).

The American tariffs on Chinese manufacturers may have benefited US solar PV module manufacturers, but they hurt American consumers and US solar employment because the US solar installation industry has experienced delays and higher prices for solar equipment and modules as a result of the tariffs ( Swanson and Plumer, 2022 ). Solar importers in the United States have struggled to import adequate quantities of solar panels and have been unable to meet consumer demand for new solar installations. These challenges were compounded by the US government blocking imports of any panels or polysilicon that originated from Xinjiang Province in China due to concerns about human rights violations there ( Swanson and Plumer, 2021 ).

Politically, the power of the solar PV industry has grown in the United States, culminating in the passage of the Inflation Reduction Act in 2022. The main trade association, the Solar Energy Industry Association (SEIA), has grown from an initial five members in 1974 to nearly 1,000 members as of 2022. It claims to have successfully advocated for the establishment and subsequent extensions of the solar Investment Tax Credit ( SEIA, 2022 ). A parallel association exists in China called the China Renewable Industry Association (CREIA), which was established in 2000 with the support of the UN Development Program, the Global Environment Facility (GEF), and the Chinese government. It claims to serve as a ‘bridge between regulatory authorities, research institutes, and industry professionals’, as well as a facilitator of project development and networks ( CREIA, 2020 ).

The above review of the scholarly literature and the two case studies lead us to emerging findings and policy implications about the complex relationships between job creation and climate change policy.

While empirical evidence remains inadequate for a definitive finding, the scholarly literature supports the proposition that job creation in low-carbon industries leads to greater political support for climate change policies that contribute to decarbonization. When people are employed in low-carbon industries, they are more likely to support climate change policies. As clean energy industries have grown, they have organized themselves into trade associations that, in turn, lobby for policies that will support further growth in their industries. Employment factors are not always the most salient factor in a voter’s decision, however.

All the existing studies reviewed indicate that clean energy deployment policies result in net growth in job creation. In other words, the many ex ante studies predicting job creation as a result of clean energy policies have been proven largely correct in ex post studies, even if the precise numbers of jobs created have varied. Moreover, job creation is larger than job losses, which means that there is a net gain in employment as a result of climate policies.

Even though climate policies appear to create net employment benefits, it is important to explore the nuances because jobs that are created and lost may not be of the same quality, wages may differ, and there may be geographic dislocation. We did not attempt to explore these important issues in this paper, and further research in this regard is needed.

There are numerous policy tools that can be used to create low-carbon jobs while also contributing to emissions reductions, which are likely to then lead to new and additional political support for more ambitious climate change policies, but the policy mixes must be specific and intentional to address both goals. Countries have used substantially different combinations of policy tools in their quest to develop competitive low-carbon industries and their consequent employment, including carbon pricing, feed-in-tariffs, tax credits, financial subsidies, and various forms of industrial policy, some of which have taken protectionist forms.

Some believe that the next industrial ‘revolution’ is already under way and that it is centred around green industries. Innovation policies that are designed systematically to both ‘push and pull’ clean energy or low-carbon technologies into the marketplace could create many new jobs. Yet, innovation policies that tilt strongly ‘supply side’ or ‘demand side’ are less likely to work in isolation. For example, government investments in clean energy research and development will support faculty and graduate students at universities and researchers in private firms that win government grants but are unlikely to lead to substantial new job creation unless there are complementary industrial and market-formation policies. China successfully leveraged market-formation policies in Germany, Spain, and the United States and concurrently established companion industrial policies to support the development of a solar PV industry using already-invented solar PV technologies ( Gallagher, 2014 ). In so doing, China was able to generate remarkable growth in solar PV manufacturing employment during the past 15 years. The United States was less successful in taking a systemic policy approach to push and pull solar PV into the market, and as a result, employment growth in the American solar PV industry has been less robust.

There is also a path dependence to a green economy. Mealy and Teytelboym (2022) find that the ‘accumulation of green productive capabilities is path-dependent: the more green production capabilities a country has, the easier it is to diversify into additional new green products’, and this will have employment consequences, which are not thoroughly explored in this article. Still, the authors identify ‘green adjacent industries’ that firms in one industry can stretch into with existing capabilities, knowledge, and skills, and so it is likely that there will be increasing returns to investments in a green economy.

Likewise, climate policies aimed purely at the deployment of low-carbon technologies may not create substantially more new jobs in the country unless conditions are placed upon the projects, such as local-content requirements, because technology can be sourced from manufacturers in other countries. Such protectionist measures may hurt domestic consumers, however, so policy-makers will need to strike the right balance between fostering growth in employment (which can boost purchasing power) and protecting consumers from the higher prices that can result from autarkic trade and investment policies. One way to balance these varying objectives is to design subsidies and accompanying protectionist policies to phase down as market penetration increases, and thereby open the targeted sectors up to global competition as the industries gain strength. The more stable jobs gained in green manufacturing and services will boost political support for more ambitious climate policies, and the stronger firms can then pursue exports and boost revenues.

Nuanced, sector-specific policies to address global supply chains in low-carbon industries are also necessary because supply chain bottlenecks can hinder manufacturers, cause prices to rise, and cause job losses. Obviously, striking the right balance between promoting domestic industries and maintaining a diplomatic relationship with other countries can be challenging. The new Inflation Reduction Act imposes local content requirements in many of its provisions, and this has triggered a backlash from Europe, East Asia, and many developing countries due to fears that investors and manufacturing firms will relocate to the United States, causing job losses in home countries. Job losses in any country could prove counter-productive to the public interest because unemployment is likely to reduce political support for climate policies. Global reductions in greenhouse-gas emissions are necessary to achieve a stable climate in the future.

Achieving job growth during decarbonization also requires thinking beyond achievement of net employment gains from low-carbon energy industries compared with fossil energy industries. The government can support industries that do not consume much energy to achieve a compositional shift in the economy away from carbon-intensive industries to carbon ‘lite’ industries ( Grossman and Krueger, 1995 ). If the labour intensity of green jobs decreases over time, policy-makers should consider the overall economic efficiency of low-carbon energy technologies through spurring innovation and structural economic adjustment for growth. While we are not suggesting that there is any evidence of a limit to the potential for green jobs, economic policy-makers should consider the broader composition of their economy and ensure diversification into any industry that does not emit greenhouse gases (e.g. firms that require only electricity that can be carbon free) and that may be ripe for job creation. Policy-makers should also consider the sequencing of employment transitions, specifically creating new and less carbon-intensive jobs before destroying the jobs in traditional and higher-carbon industries.

Industrial policy requires planning because resources are inevitably limited. No country will be competitive in all low-carbon industries so choices will need to be made about where to concentrate policy effort and fiscal resources. Each country must determine its own sources of competitive advantage and develop its own plan for exploiting them. These plans should inform policy-making for each industry. Programmes to address labour shortages and skills gaps in low-carbon energy sectors should also be considered in sectors like wind energy and the EV sector (e.g. maintainence and repair, battery producers), which require specific skill sets.

Finally, it’s impossible to imagine deep decarbonization without the workforce that brings it about. Workforce development policies are thus also necessary and could include provision of scholarships or zero or low-interest loans for students who commit to work in low-carbon or climate-resilient industries. Such programmes will be needed for both vocational and university programmes. Scholarships for retraining workers from higher-carbon traditional and legacy industries will also be needed, coupled with provision of living stipends during their training period. The US Inflation Reduction Act contains few provisions to develop this workforce, but the European Green Deal has specifically allocated its budget to reskill workers as part of the Just Transition Mechanism.

We thank Pontus Braunerhjelm for organizing the workshop in Sweden in June 2022 coincident with the Stockholm +50 celebration where this paper was originally presented, and all the participants in that workshop for their constructive comments. We also thank the reviewers for their further suggestions.

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Political partisanship is defined as ‘whether a person identifies as a Republican or Democrat’ and nostalgia for coal is situated within a broader phenomenon of claims of interspatial equity, often from rural areas. For capturing nostalgia, respondents were asked about the extent to which their local economy was better in the past if there were more local jobs in the past, and if their local area was generally better in the past ( Mayer, 2022 ).

‘Green jobs’ are ‘decent jobs that contribute to preserving or restoring the environment, be they in traditional sectors such as manufacturing and construction, or in new, emerging green sectors such as renewable energy and energy efficiency’ ( ILO, 2016 ). Green jobs can be related to reducing the consumption of energy and raw materials, minimizing waste and pollution, protecting ecosystems, and adapting to climate change itself ( ILO, 2018 ). The challenge of defining ‘green jobs’ exists as there can be different levels of how ‘green’ a specific job should be in practice ( Office for National Statistics, 2021 ).

‘Direct employment’ refers to jobs created in the execution of projects (e.g. design, manufacturing, construction, installation, O&M). ‘Indirect employment’ refers to upstream and downstream jobs linked to the supply chain ( Kim and Mohommad, 2022 ).

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Factors Affecting Performance and effectiveness of job opportunities creation, in case of Sidama National Regional State, Ethiopia

The objective of this study was to assess the factors that affect the performances and e f f e ct i ve ne ss of job opportunity creation. The Micro and Small Enterprises Sectors are important to the economy of nations' by creating employment opportunities, production of goods and services and other value-added activities with dual objective of enhancing growth and alleviating poverty. Primary data were collected through interview, group discussion and structured questionnaire from the samples of members of business enterprises by multi-stage stratified random sampling method among engaged in respondents. Data were analyzed using descriptive and inferential statistics with the aid of STATA Software. As a result, most of MSEs government support of facilitating credit, access for product market and training show a significant positive influence on the performance. In addition to this strong data management system, priority to strategic business initiatives and inclusiveness of job opportunity creation (JOC), positive attitude and commitment of owners, higher education level and industry experience, a good business management and customer relationship show positive influence on performance of JOC. According to this study, the major factors that affect the enterprises negatively were; have no a good selling places, lack of enough working capitals, high cost of raw materials, lack of assets for collaterals to credit institutions, and lack of financial capacity to participate in bidding with private sectors. Hence, Sidama National Regional State (SNRS) and concerned offices should solve these problems of youth.

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Micro and Small enterprises (MSEs) are important to economic growth and significantly essential to generate employment. Therefore, the future of the Ethiopian economy i n the urban area depends to a great degree on the success of MSEs as in many other developing countries. Ethiopian MSEs are facing a series of external and internal factors that have significant adverse effects on their growth and additionally there are challenges for them to make a greater contribution to the economy. The purpose of this research is to explicitly research and consider aspects that are hampering the performance (growth) o f MSEs in Sodo and Boditi towns of Wolaita Zone. The study was utilized causal research design to achieve the research objectives. The target population under study was 384 The study was conducted between February to June 2015. In this study, Primary and secondary data types and sources was used. The instruments to collect primary data were questionnaires and interviews. Sample size of 195 MSEs were selected to participate in this study, to determine the required sample size, stratified random sampling technique was utilized. The data collected was analyzed quantitatively and qualitatively. The major MSEs performance affecting factors were financial, marketing, working premise, infrastructural, government support and legal and internal management factors and this identified factors explained the variance of 77.7% of MSEs performance affecting factors and 22.3% of variance was did not explained by this study variables. Among six identified factors except public infrastructural factor all other factors were statistically significant and among significant factors financial constraint contribute very high impact (56.1%) on MSEs performance when other factors held constant and followed by marketing and management problems constrain

Journal ijmr.net.in(UGC Approved)

This research aims to investigate factors affecting the performance of Micro & Small Enterprises (MSEs) with a special emphasis on Business management aspect. To achieve the objectives of this study, questionnaires were analyzed using statistical package for social science (SPSS) software. Descriptive and inferential analyses were done on the information collected through questionnaire from a sample of 140 operators of MSEs. The respondents were selected using stratified sampling technique. Besides, the qualitative interview questions from Woreda coordinators were analyzed using descriptive narrations through concurrent triangulation strategy. The empirical study considered five major factors which seem to affect performance of MSEs which are: inadequate finance, poor management practices, lack of relevant and timely information, marketing problems, and government policies and regulation problems including bureaucratic bottlenecks system.The findings indicate that, there exists a linear relationship between independent variables and dependent variable. Moreover, the selected independent variables may significantly explain the variations in the dependent variable at 5% level of significance. It is also found from this study thatthe main sources of startup and finance for expansion or funds for most MSEs are personal savings followed by support from family and friends/relatives. Overall study showed that managerial skill and experience, marketing skill, information availability, finance and government policies and regulations contribute a great deal for the performance of MSEs. Based on the findings, recommendations were forwardedto make MSEs competitive and profitable. Furthermore, government bodies should work to increasing the capacity and skill of the operators through continuous trainings, experience sharing from successful enterprises, and provision of advice and consultancy from experts. Further research directions are also forwarded in the study.

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Micro and Small Enterprises (MSEs) play an important economic role and recognized as an important vehicles of employment creation, income generation and poverty alleviation. The purpose of this article reviews was to reviews the contribution of MSEs and it factors constraining the growth of micro and small business (MSEs) growth in Ethiopia. The reviews were based on the secondary data that are published on the micro and small business in Ethiopia. The several authors argue that employment generation by the small business may be high in quantitative term but very low in quality. Technological up-gradation would enable the small firms to create quality employment improving remuneration, duration and skill. This structural shift may reduce the rate of employment generation in the short run but would ensure high-income employment generation in the long run. Furthermore, It provides employment opportunities, encourages and sustains self-reliance, provides technical inventions and innovations promotes competition in the market which acts as a check in the activities of monopolists, utilize waste product from big firm for further production. Therefore, this article reviews shows that poor government regulation, evaluation, follow, working premises, initial investment, high collateral requirement from financial institutions, lack of clear job description among members, lack of training, poor linkage of MSEs to the market, were some infrastructural, managerial, financial, legal, and marketing challenges as factors affecting the growth of MSEs in Ethiopia. Therefore, the concerned office should support the MSEs to tackle factors hindering its growth. MSEs should tie to the market and members should develop culture of working together for the growth of SMEs.

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This thesis consists of four essays on the determinants, the dynamics and the policy implications of simultaneous job creation and destruction in labour markets. Firstly, it proposes and solves a stochastic search model with endogenous job separation and it shows that the amplitude and time variation of job reallocation depend crucially upon the arrival rate of exogenous firing permissions. Tighter firing restrictions, albeit not directly relevant for differences in average unemployment rates, dramatically reduce the relative volatility of job creation and destruction. A parameterization of the model can rationalise cross-country differences in the cyclical behaviour of job creation and destruction. Secondly, it brings together aggregate data on job reallocation and labour market policy for nine OECD countries. It shows that long term unemployment and job reallocation are negatively correlated and that job reallocation is lower in countries that offer limited benefit for a limited period of time. Thirdly, it studies the role of time-consuming search in generating the size distribution of firms and the dynamics of firm-level turnover. It solves a dynamic matching model where the joint distribution of wages and employment results from interacting idiosyncratic shocks, firm-level asymmetries in job creation and destruction and time-consuming search on the part of workers. Theoretical results offer a structural interpretation of existing empirical evidence on firm-size wage differentials and point out novel empirical implications. Finally, it measures the relation between job flows and establishment size applying econometric techniques best suited for analysing the dynamics of large cross-sections. Using a balanced panel from the Mexican Manufacturing sector it finds no evidence of small establishments converging toward the mean, thus no evidence of convergence.

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