Chunnan Qian and Xiaorong TangThis email address is being protected from spambots. You need JavaScript enabled to view it.
School of Economics and Management, Hebei Oriental University, Langfang 065001, China
Received: April 10, 2025 Accepted: July 28, 2025 Publication Date: October 9, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Industry 4.0 technologies, particularly automation and digital transformation, are increasingly central to sustain able innovation in manufacturing. This study examines the role of Industry 4.0 in promoting green innovation within Chinese manufacturing firms. Using firm-level panel data from 2011 to 2019, we develop an econometric model incorporating industry-level I4.0 density and firm-level exposure to automation. Results show that I4.0 adoption significantly enhances green innovation, especially among large firms. Digital transformation and managerial efficiency strengthen this effect, while firms led by IT-savvy CEOs experience further benefits. How ever, regional and sectoral disparities exist: larger firms in technology-intensive and developed regions benefit more, while smaller firms face greater challenges. To address endogeneity, an instrumental variable approach is employed, and robustness is verified through alternative specifications. This study offers methodological contributions through its use of firm-level data and multi-level modeling, and provides strategic guidance for managers and policymakers aiming to achieve environmental goals through automation.
Keywords: Industry 4.0, Automation, Digital Transformation, Green Innovation, Sustainability.
[1] K. Lázár, (2024) “Industrial Robots in the Textile and Clothing Industry" International Journal of Indus trial and Manufacturing Systems Engineering 9: 1 9. DOI: 10.11648/j.ijimse.20240901.11.
[2] J. Rodrigue, D. Sheng, and Y. Tan, (2024) “Exporting, abatement, and firm-level emissions: Evidence from China’s accession to the WTO" Review of Economics and Statistics 106(4): 1064–1082. DOI: 10.1162/rest_ a_01194.
[3] M.Koch,I.Manuylov,andM.Smolka,(2021)“Robots and firms" The Economic Journal 131: 2553–2584. DOI: 10.1093/ej/ueab009.
[4] F. Huneeus and R. Rogerson, (2024) “Heterogeneous paths of industrialization" Review of Economic Studies 91: 1746–1774. DOI: 10.1093/restud/rdad066.
[5] P. Zare, A. Dejamkhooy, S. S. Majidabad, and I. F. Davoudkhani, (2023) “Stochastic MILP model for merging EV charging stations with active distribution system expansion planning by considering uncertainties" Electric Power Components and Systems: 1–31. DOI: 10.1080/15325008.2023.2286616.
[6] D.H.Autor, F. Levy, and R. J. Murnane, (2003) “The skill content of recent technological change: An empirical exploration" The Quarterly journal of economics 118: 1279–1333. DOI: 10.1162/003355303322552801.
[7] A.Berg, E. F. Buffie, and L.-F. Zanna, (2018) “Should we fear the robot revolution?(The correct answer is yes)" Journal of Monetary Economics 97: 117–148. DOI: 10.1016/j.jmoneco.2018.05.014.
[8] R.Shahbazi and A. Aslani, (2013) “Evaluation of social reporting at companies that accepted in Tehran exchange" International Journal of Management Research and Reviews 3: 3059.
[9] C. Ding, J. Ke, M. Levine, and N. Zhou, (2024) “Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale" Nature Communications 15: 5916. DOI: 10.1038/s41467 024-50088-4.
[10] J. S. Shapiro and R. Walker, (2018) “Why is pollution from US manufacturing declining? The roles of environ mental regulation, productivity, and trade" American economic review 108: 3814–3854. DOI: 10.1257/aer. 20151272.
[11] D. Acemoglu, (1998) “Why do new technologies com plement skills? Directed technical change and wage in equality" The quarterly journal of economics 113: 1055–1089. DOI: 10.1162/003355398555838.
[12] P.Zare,A.Dejamkhooy,andI.F.Davoudkhani,(2024) “Efficient expansion planning of modern multi-energy distribution networks with electric vehicle charging stations: Astochastic MILP model" Sustainable Energy, Grids and Networks 38: 101225. DOI: 10.1016/j.segan.2023. 101225.
[13] E. Artuc, P. Bastos, and B. Rijkers, (2023) “Robots, tasks, and trade" Journal of International Economics 145: 103828. DOI: 10.1016/j.jinteco.2023.103828.
[14] S. Nahar, (2024) “Modeling the effects of artificial intelligence (AI)-based innovation on sustainable development goals (SDGs): Applying a system dynamics perspective in a cross-country setting" Technological Forecasting and Social Change 201: 123203. DOI: 10.1016/j. techfore.2023.123203.
[15] F. Liu, R. Wang, and M. Fang, (2024) “Mapping green innovation with machine learning: Evidence from China" Technological Forecasting and Social Change 200: 123107. DOI: 10.1016/j.techfore.2023.123107.
[16] E.-Z. Wang, C.-C. Lee, and Y. Li, (2022) “Assessing the impact of industrial robots on manufacturing energy in tensity in 38 countries" Energy Economics 105: 105748. DOI: 10.1016/j.eneco.2021.105748.
[17] D. Antonioli, A. Marzucchi, F. Rentocchini, and S. Vannuccini, (2024) “Robot adoption and product innovation" Research Policy 53: 105002. DOI: 10.1016/j. respol.2024.105002.
[18] E. Gutiérrez and K. Teshima, (2018) “Abatement expenditures, technology choice, and environmental performance: Evidence from firm responses to import competition in Mexico" Journal of Development Economics 133: 264–274. DOI: 10.1016/j.jdeveco.2017.11.004.
[19] E. G. Tsionas, A. G. Assaf, and R. Matousek, (2015) “Dynamic technical and allocative efficiencies in European banking" Journal of Banking & Finance 52: 130–139. DOI: 10.1016/j.jbankfin.2014.11.007.
[20] M. M. Mariani and S. Nambisan, (2021) “Innovation analytics and digital innovation experimentation: the rise of research-driven online review platforms" Technological Forecasting and Social Change 172: 121009. DOI: 10.1016/j.techfore.2021.121009.
[21] Y. L. Ong, B. Hsiao, and N. H. Huan, (2022) “Industry 4.0 and digital green innovation with the mediating role of digital green knowledge creation: an evidence from Vietnam" Journal of Information Systems & Operations Management 16: 196–211.
[22] N. Berente, B. Gu, J. Recker, andR.Santhanam,(2021) “Managing artificial intelligence." MIS quarterly 45: DOI: 10.25300/MISQ/2021/16274.
[23] Y. LeCun, Y. Bengio, and G. Hinton, (2015) “Deep learning" nature 521: 436–444. DOI: 10.1038/nature14539.
[24] M.F. Mubarak andM.Petraite, (2020) “Industry 4.0 technologies, digital trust and technological orientation: What matters in open innovation?" Technological Fore casting and Social Change161: 120332. DOI: 10.1016/ j.techfore.2020.120332.
[25] H. W. H. Jung and A. Subramanian, (2017) “CEO talent, CEO compensation, and product market competition" Journal of Financial Economics 125: 48–71. DOI: 10.1016/j.jfineco.2017.04.005.
[26] J. Pan, H. Bao, J. Cifuentes-Faura, and X. Liu, (2024) “CEO’s IT background and continuous green innovation of enterprises: evidence from China" Sustainability Ac counting, Management and Policy Journal 15: 807–832. DOI: 10.1108/SAMPJ-07-2023-0497.
[27] L. Wang, Y. Lin, W. Jiang, H. Yang, and H. Zhao, (2023) “Does CEO emotion matter? CEO affectivity and corporate social responsibility" Strategic Management Journal 44: 1820–1835. DOI: 10.1002/smj.3474.
[28] S. S. Kamble and A. Gunasekaran, (2023) “Analysing the role of Industry 4.0 technologies and circular economy practices in improving sustainable performance in Indian manufacturing organisations" Production planning & control 34: 887–901. DOI: 10.1080/09537287.2021.1980904.
[29] J. M. Müller, D. Kiel, and K.-I. Voigt, (2018) “What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability" Sustainability 10: 247. DOI: 10.3390/su10010247.
[30] D. Plekhanov, M. Keenan, F. Galindo-Rueda, and D. Ker, (2020) “The digitalisation of science and innovation policy" The Digitalisation of Science, Technology and Innovation: Key Developments and Policies. Paris: OECD Publishing: 182.
[31] D. Acemoglu and P. Restrepo, (2020) “Robots and jobs: Evidence from US labor markets" Journal of political economy 128: 2188–2244. DOI: 10.1086/705716.
[32] P. Goldsmith-Pinkham, I. Sorkin, and H. Swift, (2020) “Bartik instruments: What, when, why, and how" Ameri can Economic Review 110: 2586–2624. DOI: 10.1257/aer.20181047.
[33] A. Schroeder, P. Naik, A. Z. Bigdeli, and T. Baines, (2020) “Digitally enabled advanced services: a socio technical perspective on the role of the internet of things (IoT)" International Journal of Operations & Pro duction Management 40: 1243–1268. DOI: 10.1108/ IJOPM-03-2020-0131.
[34] F. García-Muiña, M. S. Medina-Salgado, R. González Sánchez, I. Huertas-Valdivia, A. M. Ferrari, and D. Settembre-Blundo, (2021) “Industry 4.0-based dynamic Social Organizational Life Cycle Assessment to target the social circular economy in manufacturing" Journal of Cleaner Production 327: 129439. DOI: 10.1016/j. jclepro.2021.129439.
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