Open Access Journal Article

Dynamic Nonlinear Relationship between Digital Transformation, Green Transformation in Manufacturing Industry and Labor Structure: Evidence from Panel VAR Analysis

by Haiyong Jiang a,* ,  Yinghui Han a ,  Yue Wang b  and  Zhenyu Chen c
a
School of Business, Guilin University of Technology, Guilin, China
b
Ping An Bank Co., Ltd., Jinan Branch, Jinan, China
c
Affairs Service Center of Ecological Environment of Liaoning Province, Shenyang, China
*
Author to whom correspondence should be addressed.
REA  2023, 17; 2(3), 17; https://doi.org/10.58567/rea02030002
Received: 20 June 2023 / Accepted: 23 August 2023 / Published Online: 30 October 2023
View Full-Text
Download PDF

Abstract

The digital transformation of manufacturing industry can promote the development of green transformation and promote the differentiation of workers’ skill structure; On the other hand, it will also hinder the green development due to the huge energy consumption generated by the application of digital technology and facilities. In addition, the green transformation of manufacturing industry will also have differentiated impacts on the employment of labour with different skills due to the innovation of green technology. The existing research has not discussed too much about the interaction among the digital transformation and green transformation in manufacturing industry and labour structure. So, this paper uses the PVAR model to examine the dynamic relationship between digital and green transformation within the industrial sector from the perspective of labour structure, specifically analyzing the impact difference across regions. The results suggest that there is a reciprocal connection between the digitization of manufacturing sector and the labour structure, particularly in the eastern region of China, but the overall interaction between the two remains weak. The interactive between the green transformation of manufacturing industry and the labour structure in the central and western areas has been delayed over periods 1-6. Digital and green manufacturing transformation reinforce each other in central and western regions. However, the digital revolution in the manufacturing industry is hindered by the green transformation in eastern region.

Keywords: Digital transformation of manufacturing industry; Green transformation of manufacturing industry; Labor structure; Dynamic interactive;
Copyright: © 2023 by Jiang, Han, Wang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Funding

Guilin University of Technology Research Initial Fund "Research on the Long-term Mechanism of Ecological Industrialization in Guangxi Ethnic Poor Areas" (GUTQDJJ2018120)

Share and Cite

ACS Style
Jiang, H.; Han, Y.; Wang, Y.; Chen, Z. Dynamic Nonlinear Relationship between Digital Transformation, Green Transformation in Manufacturing Industry and Labor Structure: Evidence from Panel VAR Analysis. Review of Economic Assessment, 2023, 2, 17. https://doi.org/10.58567/rea02030002
AMA Style
Jiang H, Han Y, Wang Y, Chen Z. Dynamic Nonlinear Relationship between Digital Transformation, Green Transformation in Manufacturing Industry and Labor Structure: Evidence from Panel VAR Analysis. Review of Economic Assessment; 2023, 2(3):17. https://doi.org/10.58567/rea02030002
Chicago/Turabian Style
Jiang, Haiyong; Han, Yinghui; Wang, Yue; Chen, Zhenyu 2023. "Dynamic Nonlinear Relationship between Digital Transformation, Green Transformation in Manufacturing Industry and Labor Structure: Evidence from Panel VAR Analysis" Review of Economic Assessment 2, no.3:17. https://doi.org/10.58567/rea02030002
APA style
Jiang, H., Han, Y., Wang, Y., & Chen, Z. (2023). Dynamic Nonlinear Relationship between Digital Transformation, Green Transformation in Manufacturing Industry and Labor Structure: Evidence from Panel VAR Analysis. Review of Economic Assessment, 2(3), 17. https://doi.org/10.58567/rea02030002

Article Metrics

Article Access Statistics

References

  1. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of political economy 128(6), 2188-2244. https://doi.org/10.1086/705716
  2. Bakry, W., Nghiem, X. H., Farouk, S., & Vo, X. V. (2023). Does it hurt or help? Revisiting the effects of ICT on economic growth and energy consumption: A nonlinear panel ARDL approach. Economic Analysis and Policy 78, 597-617. https://doi.org/10.1016/j.eap.2023.03.026
  3. Ciarli, T., Kenney, M., Massini, S., & Piscitello, L. (2021). Digital technologies, innovation, and skills: Emerging trajectories and challenges. Research Policy 50(7), 104289. https://doi.org/10.1016/j.respol.2021.104289
  4. Cavenaile, L. (2021). Offshoring, computerization, labor market polarization and top income inequality. Journal of Macroeconomics 69,103317. https://doi.org/10.1016/j.jmacro.2021.103317
  5. Chakraborty, P., Chakrabarti, A. S., & Chatterjee, C. (2023). Cross-border environmental regulation and firm labor demand. Journal of Environmental Economics and Management 117, 102753. https://doi.org/10.1016/j.jeem.2022.102753
  6. Chen, Z., Zhu, H., Zhao, W., Cao,B., & Cai,Y. (2022). Dynamic Nonlinear Connectedness between the Financial Inclusion, Economic Growth, and China’s Poverty Alleviation: Evidence from a Panel VAR Analysis. Complexity, 2022. https://doi.org/10.1155/2022/9584126
  7. Du, M., & Zhang, Y.J. (2023). The impact of producer services agglomeration on green economic development: Evidence from 278 Chinese cities. Energy Economics 124, 106769. https://doi.org/10.1016/j.eneco.2023.106769
  8. Domguia, E.N., Pondie, T. M., Ngounou, B. A., & Nkengfack, H. (2022). Does environmental tax kill employment? Evidence from OECD and non-OECD countries. Journal of Cleaner Production 380, part1, 134873. https://doi.org/10.1016/j.jclepro.2022.134873
  9. Ding, J., Wang, J., Liu,B., & Peng, L.(2022a). ‘Guidance’ or ‘Misleading’? The government subsidy and the choice of enterprise innovation strategy. Frontiers in Psychology 13, 1005563. https://doi.org/10.3389/fpsyg.2022.1005563
  10. Ding, J., Liu, B., Wang, J., Qiao, P., & Zhu, Z. (2023). Digitalization of the Business Environment and Innovation Efficiency of Chinese ICT Firms. Journal of Organizational and End User Computing (JOEUC) 35(3), 1-25. https://www.igi-global.com/article/digitalization-of-the-business-environment-and-innovation-efficiency-of-chinese-ict-firms/327365
  11. Ding,J., Liu,B., & Shao,X. (2022b).Spatial effects of industrial synergistic agglomeration and regional green development efficiency: Evidence from China. Energy Economics 112, 106156. https://doi.org/10.1016/j.eneco.2022.106156
  12. Fossen, F. M., & Sorgner, A. (2022). New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data. Technological Forecasting and Social Change 175, 121381. https://doi.org/10.1016/j.techfore.2021.121381
  13. Ferris, A.E., Shadbegian, R.J., & Wolverton, A. (2014). The Effect of Environmental Regulation on Power Sector Employment: Phase I of the Title IV SO2 Trading Program. Journal of the Association of Environmental and Resource Economists 1(4), 521-553. https://doi.org/10.1086/679301
  14. Fallahpour, A., Yazdani, M., Mohammed, A. &Wong, K.Y. (2021). Green sourcing in the era of industry 4.0: towards green and digitalized competitive advantages. Industrial Management and Data Systems 121(9), 1997-2025. https://doi.org/10.1108/IMDS-06-2020-0343
  15. Gallego, A., & Kurer, T. (2022). Automation, Digitalization, and Artificial Intelligence in the Workplace: Implications for Political Behavior. Annual Review of Political Science 25, 463-484. https://doi.org/10.1146/annurev-polisci-051120-104535
  16. Gu,R., Li,C., Yang,Y., & Zhang,J. (2023). The impact of industrial digital transformation on green development efficiency considering the threshold effect of regional collaborative innovation: Evidence from the Beijing-Tianjin-Hebei urban agglomeration in China. Journal of Cleaner Production 420, 138345. https://doi.org/10.1016/j.jclepro.2023.138345
  17. Hutter, C., & Weber, E. (2021). Labour market miracle, productivity debacle: Measuring the effects of skill-biased and skill-neutral technical change. Economic Modelling 102, 105584. https://doi.org/10.1016/j.econmod.2021.105584
  18. Harrigan, J., Reshef, A., & Toubal, F. (2021). The March of the Techies: Job Polarization Within and Between Firms. Research Policy 50(7), 104008. https://doi.org/10.1016/j.respol.2020.104008
  19. Han,X., & Cao,T. (2022). Urbanization level, industrial structure adjustment and spatial effect of urban haze pollution: Evidence from China's Yangtze River Delta urban agglomeration. Atmospheric Pollution Research 13(6), 101427. https://doi.org/10.1016/j.apr.2022.101427
  20. Hao,X., Li,Y., Ren,S., Wu,H., & Hao,Y. (2023).The role of digitalization on green economic growth: Does industrial structure optimization and green innovation matter?. Journal of Environmental Management 325, Part A, 116504. https://doi.org/10.1016/j.jenvman.2022.116504
  21. Isensee, C., Teuteberg, F., Griese, K.M., & Topi,C. (2020). The relationship between organizational culture, sustainability, and digitalization in SMEs: A systematic review. Journal of Cleaner Production 275, 122944. https://doi.org/10.1016/j.jclepro.2020.122944
  22. Jongwanich, J., Kohpaiboon, A., & Obashi, A. (2022). Technological advancement, import penetration and labour markets: Evidence from Thailand. World Development 151, 105746. https://doi.org/10.1016/j.worlddev.2021.105746
  23. Knuth,S.(2018).“Breakthroughs ”for a green economy? Financialization and clean energy transition. Energy Research & Social Science 41, 220-229. https://doi.org/10.1016/j.erss.2018.04.024
  24. Laddha, Y., Tiwari, A., Kasperowicz, R., Bilan, Y., & Streimikiene, D. (2022). Impact of Information Communication Technology on labor productivity: A panel and cross-sectional analysis. Technology in Society 68, 101878. https://doi.org/10.1016/j.techsoc.2022.101878
  25. Liu, Y., & Zhang, X. (2022). Does labor mobility follow the inter-regional transfer of labor-intensive manufacturing? The spatial choices of China's migrant workers. Habitat International 124, 102559. https://doi.org/10.1016/j.habitatint.2022.102559
  26. Liao, T., Liu, G., & Liu, Y., & Lu, R. (2023). Environmental regulation and corporate employment revisited: New quasi-natural experimental evidence from China's new environmental protection law. Energy Economics https://doi.org/10.1016/j.eneco.2023.106802
  27. Lange, S., Pohl, J., & Santarius,T.(2020).Digitalization and Energy Consumption. Does ICT Reduce Energy Demand?. Ecological Economics 176, 106760. https://doi.org/10.1016/j.ecolecon.2020.106760
  28. Liu, B., Ding,C.J., Hu,J.,Su,Y., & Qin,C.(2023).Carbon trading and regional carbon productivity. Journal of Cleaner Production 420, 138395. https://doi.org/10.1016/j.jclepro.2023.138395
  29. Michaels, G., Natraj, A., & Reenen, J.V. (2014). Has ICT polarized skill demand? Evidence from eleven countries over twenty-five years. Review of Economics and Statistics 96(1), 60-77. https://doi.org/10.1162/REST_a_00366
  30. Mondejar, M. E., Avtar, R., Diaz, H. L. B.,Dubey, R. K., Esteban, J., Gómez-Morales, A., Hallam, B., Mbungu, N. T., Okolo, C. C., Prasad, K. A., She, Q.H., & Garcia-Segura, S. (2021). Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Science of The Total Environment 794, 148539. https://doi.org/10.1016/j.scitotenv.2021.148539
  31. Magacho, G., Espagne, E., Godin, A., Mantes, A., & Yilmaz, D. (2023). Macroeconomic exposure of developing economies to low-carbon transition. World Development 167, 106231. https://doi.org/10.1016/j.worlddev.2023.106231
  32. Mao, W., Wang,W., & Sun,H.(2019).Driving patterns of industrial green transformation: A multiple regions case learning from China. Science of The Total Environment 697, 134134. https://doi.org/10.1016/j.scitotenv.2019.134134
  33. Raff, Z., & Earnhart,D. (2022). Employment and environmental protection: The role of regulatory stringency. Journal of Environmental Management 321, 115896. https://doi.org/10.1016/j.jenvman.2022.115896
  34. Sharma, R., Jabbour, A.B.L.D.S., Jain, V. & Shishodia, A. (2022). The role of digital technologies to unleash a green recovery: pathways and pitfalls to achieve the European Green Deal. Journal of Enterprise Information Management 35(1), 266-294. https://doi.org/10.1108/JEIM-07-2021-0293
  35. Santos, A.M., Barbero,J., Salotti,S., & Conte,A.(2023). Job creation and destruction in the digital age: Assessing heterogeneous effects across European Union countries. Economic Modelling 126, 106405. https://doi.org/10.1016/j.econmod.2023.106405
  36. Santarius, T., Dencik, L., Diez, T., Ferreboeuf, H., Jankowski, P., Hankey, S., Hilbeck, A., Hilty, L. M., Höjer, M., Kleine, D., Lange, S., Pohl, J., Reisch, L., Ryghaug, M., Schwanen, T., & Staab,P. (2023). Digitalization and Sustainability: A Call for a Digital Green Deal. Environmental Science and Policy 147, 11-14. https://doi.org/10.1016/j.envsci.2023.04.020
  37. Sarkis, J., Kouhizadeh, M., & Zhu, Q.S. (2021). Digitalization and the greening of supply chains. Industrial Management and Data Systems 121(1), 65-85. https://doi.org/10.1108/IMDS-08-2020-0450
  38. Sheng,X.,& Liu, Y. (2023).Research on the impact of carbon finance on the green transformation of China's marine industry. Journal of Cleaner Production 418, 138143. https://doi.org/10.1016/j.jclepro.2023.138143
  39. Tzeremes, P., Dogan, E., & Alavijeh, N.K. (2023). Analyzing the nexus between energy transition, environment and ICT: A step towards COP26 targets. Journal of Environmental Management 326, PartA, 116598. https://doi.org/10.1016/j.jenvman.2022.116598
  40. Tong, H., Wang, Y., & Xu, J. (2020). Green transformation in China: Structures of endowment, investment, and employment. Structural Change and Economic Dynamics 54, 173-185. https://doi.org/10.1016/j.strueco.2020.04.005
  41. Usabiaga, C., Núñez, F., Arendt, L., Gałecka-Burdziak,E., &Pater, R. (2022).Skill requirements and labour polarisation: An association analysis based on Polish online job offers. Economic Modelling 115, 105963. https://doi.org/10.1016/j.econmod.2022.105963
  42. Zhu, T., Zhang, X., & Liu, X. (2022). Can University Scientific Research Activities Promote High-Quality Economic Development? Empirical evidence from provincial panel data. Review of Economic Assessment 1(1), 34–50. https://doi.org/10.58567/rea01010003