Artificial Intelligence for Sustainable Manufacturing: Governance Models and Supply Chain Resilience in China
Keywords:
artificial intelligence, sustainable manufacturing, governance models, supply chain resilience, ChinaAbstract
The rapid advancement of artificial intelligence (AI) presents transformative opportunities for sustainable manufacturing, particularly in China, where industrial decarbonization and supply chain resilience have become critical priorities under the "dual-carbon" policy framework. However, current research lacks a comprehensive examination of how AI-enabled governance models can simultaneously enhance green manufacturing practices and strengthen supply chain resilience in emerging economies. This study addresses this gap by investigating the interplay between AI adoption, institutional governance, and resilience-building mechanisms within China's manufacturing sector. Employing a mixed-methods approach that combines policy text analysis, case studies of smart factories, and qualitative comparative analysis, the research identifies three predominant governance models: government-led regulatory frameworks, market-driven incentive systems, and technology-enabled collaborative platforms. Key findings indicate that AI-powered dynamic monitoring and decision-support systems substantially reinforce supply chain resilience, with empirical evidence showing a 23-41% improvement in order fulfillment rates among AI-integrated green manufacturers. Furthermore, the study proposes a "smart-ecological co-governance" framework that aligns technological innovation with institutional adaptation. This research contributes to the theoretical discourse on sustainable supply chain management by integrating digital governance theory with principles of industrial ecology. Practically, it offers policymakers actionable insights for promoting AI-driven green transitions, emphasizing the importance of adaptive regulatory sandboxes and cross-industry data-sharing platforms. The findings provide significant implications for developing nations seeking to reconcile economic growth with environmental sustainability through intelligent manufacturing systems.
References
1. R. Agrawal, A. Majumdar, A. Kumar, and S. Luthra, "Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities," Operations Management Research, vol. 16, no. 4, pp. 1720-1741, 2023. doi: 10.1007/s12063-023-00383-y
2. C. Zhang, Y. Zhang, and Z. Huang, “Optimal Reutilization Strategy for a Shipbuilder under the Carbon Quota Policy,” Sustainability, vol. 15, no. 10, p. 8311, 2023.
3. X. Hu and R. Caldentey, “Trust and reciprocity in firms’ capacity sharing,” Manufacturing & Service Operations Management, vol. 25, no. 4, pp. 1436–1450, 2023, doi: 10.1287/msom.2023.1203.
4. R. Autade, "AI-Powered Predictive Maintenance in Industrial IoT," Integrated Journal of Science and Technology, vol. 1, no. 4, 2024.
5. S. Deepan, M. Buradkar, P. Akhila, K. S. Kumar, M. K. Sharma, and M. K. Chakravarthi, "AI-powered predictive maintenance for industrial IoT systems," In 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), May, 2024, pp. 1-6. doi: 10.1109/accai61061.2024.10601983
6. J. Gupta, J. Scholtens, L. Perch, I. Dankelman, J. Seager, F. Sánder, and I. Kempf, "Re-imagining the driver-pressure-state-impact-response framework from an equity and inclusive development perspective," Sustainability Science, vol. 15, no. 2, pp. 503-520, 2020. doi: 10.1007/s11625-019-00708-6
7. R. Huang, S. Zhang, and P. Wang, "Key areas and pathways for carbon emissions reduction in Beijing for the "Dual Carbon" targets," Energy Policy, vol. 164, p. 112873, 2022. doi: 10.2139/ssrn.4007225
8. A. A. Khan, A. A. Laghari, and S. A. Awan, "Machine learning in computer vision: A review," EAI Endorsed Transactions on Scalable Information Systems, vol. 8, no. 32, 2021.
9. C. G. Machado, M. P. Winroth, and E. H. D. Ribeiro da Silva, "Sustainable manufacturing in Industry 4," 0: an emerging research agenda. International Journal of Production Research, vol. 58, no. 5, pp. 1462-1484, 2020.
10. I. D. Mienye, T. G. Swart, and G. Obaido, "Recurrent neural networks: A comprehensive review of architectures, variants, and applications," Information, vol. 15, no. 9, p. 517, 2024. doi: 10.20944/preprints202408.0748.v1
11. J. Oh, M. Hessel, W. M. Czarnecki, Z. Xu, H. P. van Hasselt, S. Singh, and D. Silver, "Discovering reinforcement learning algorithms," Advances in Neural Information Processing Systems, vol. 33, pp. 1060-1070, 2020.
12. N. M. Rezk, M. Purnaprajna, T. Nordström, and Z. Ul-Abdin, "Recurrent neural networks: An embedded computing perspective," IEEE Access, vol. 8, pp. 57967-57996, 2020.
13. A. K. Shakya, G. Pillai, and S. Chakrabarty, "Reinforcement learning algorithms: A brief survey," Expert Systems with Applications, vol. 231, p. 120495, 2023. doi: 10.1016/j.eswa.2023.120495
14. A. Shishodia, R. Sharma, R. Rajesh, and Z. H. Munim, "Supply chain resilience: A review, conceptual framework and future research," The International Journal of Logistics Management, vol. 34, no. 4, pp. 879-908, 2023.
15. V. Vijay Kumar, and K. Shahin, "Artificial intelligence and machine learning for sustainable manufacturing: current trends and future prospects," Intelligent and Sustainable Manufacturing, vol. 2, no. 1, p. 10002, 2025.
16. A. Wieland, and C. F. Durach, "Two perspectives on supply chain resilience," Journal of Business Logistics, vol. 42, no. 3, pp. 315-322, 2021. doi: 10.1111/jbl.12271
17. S. Xu, J. Wang, W. Shou, T. Ngo, A. M. Sadick, and X. Wang, "Computer vision techniques in construction: a critical review," Archives of Computational Methods in Engineering, vol. 28, no. 5, 2021.

