Skip to main navigation menu Skip to main content Skip to site footer
##common.pageHeaderLogo.altText##

Research Article

Vol. 1 No. 1 (2025): International Journal of Multidisciplinary Research

Artificial Intelligence Driving Corporate Sustainable Development

DOI
https://doi.org/10.65231/ijmr.v1i1.27
Submitted
October 27, 2025
Published
2025-10-27

Abstract

Under the dual pressures of global climate challenges and policy regulations, corporate sustainable development has transitioned from a strategic option to a survival imperative. Traditional management models face bottlenecks in efficiency and data management, while artificial intelligence, leveraging its powerful capabilities in data processing, autonomous learning, and multi-objective optimization, is emerging as a core driver for corporate green transformation. This paper systematically reviews the core technical pathways through which AI drives corporate sustainable development, encompassing energy optimization, supply chain management, carbon footprint tracking, and product lifecycle management. It demonstrates the significant effectiveness of AI in enhancing resource efficiency, reducing carbon emissions, and accelerating green innovation. In-depth analysis of industry benchmark cases, such as healthcare, further validates the multi-dimensional value of AI applications, achieving a "efficiency enhancement - resource conservation - social value" synergy. The article also prospects the development trends of AI technology, evolving from specialized to general-purpose intelligence and from an efficiency tool to a strategic core, indicating that enterprises are shifting from technology procurement to ecosystem building and ultimately moving towards a new sustainable development paradigm with AI as the decision-making hub. Artificial intelligence is not only reshaping corporate operations and value chains but also fostering socio-economic systems with lower resource consumption and greater resilience at both micro and macro levels, providing systematic solutions for the synergistic achievement of corporate Environmental, Social, and Governance (ESG) goals.

References

  1. Smith, J. K., & Johnson, A. B. (2023). The role of renewable energy in sustainable urban development.Journal of Environmental Policy, 15(2), 89-105[1]: McKinsey Global Institute, "The Next Normal in Supply Chains"
  2. Hummel K, Jobst D.(2024). An overview of corporate sustainability reporting legislation in the European Union. Accounting in Europe, 21(3): 320-355.
  3. Baoping Ren, Zhaoxuan Qiu, Bei Liu. (2025) Supply chain decarbonisation effects of artificial Intelligence: Evidence from China, Volume 101, 104198. DOI: https://doi.org/10.1016/j.iref.2025.104198
  4. Hummel K, Jobst D.(2024). An overview of corporate sustainability reporting legislation in the European Union. Accounting in Europe, 21(3): 320-355. DOI: https://doi.org/10.1080/17449480.2024.2312145
  5. https://market.us/report/ai-in-esg-and-sustainability-market/
  6. Sangeetha S, Logeshwaran J, Faheem M, et al. (2024). Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems. The Journal of Engineering,2024(2): e12358. DOI: https://doi.org/10.1049/tje2.12358
  7. de Sousa T B, Cantorani J R H, Ramalho de Oliveira M, et al. (2025). Trends in the global research on the relationship between cleaner production and industry 4.0: a bibliometric literature review [J]. Journal of Industrial and Production Engineering, 42(2): 189-213. DOI: https://doi.org/10.1080/21681015.2024.2405100
  8. Wang L, Liu Z, Liu A, et al. (2021). Artificial intelligence in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 114(3): 771-796. DOI: https://doi.org/10.1007/s00170-021-06882-1
  9. https://unfccc.int/process-and-meetings/the-paris-agreement
  10. Rangarajan R, Murugan T K, Govindaraj L, et al. (2025). AI driven automation for enhancing sustainability efforts in CDP report analysis . Scientific Reports, 15(1): 24266. DOI: https://doi.org/10.1038/s41598-025-07584-4
  11. https://tech.cnr.cn/techph/20241118/t20241118_526980032.shtml).
  12. Gu , Y. ., Lin , H. ., Zhao, W. . ., Li, M. . ., & Wang, X. (2025). The Ethical Balance Reconstruction of Green Finance Empowered by Computer Technology. International Journal of Accounting and Economics Studies, 12(6), 580-586. https://doi.org/10.14419/ca6cas51 DOI: https://doi.org/10.14419/ca6cas51
  13. Zeng, R., Wang, X., Wang, Z., & Gu, Y. (2025). Logic and Path of China's Regional Economic Disparities: From Institutional Change, Factor Flow and Technological Innovation. Journal of Global Trends in Social Science, 2(8), 32-39.https://doi.org/10.70731/s98wcs90 DOI: https://doi.org/10.70731/s98wcs90
  14. Xu, S., Cao, Y., Wang, Z., & Tian, Y. (2025, June). Fraud Detection in Online Transactions: Toward Hybrid Supervised–Unsupervised Learning Pipelines. In Proceedings of the 2025 6th International Conference on Electronic Communication and Artificial Intelligence (ICECAI 2025), Chengdu, China (pp. 20-22). DOI: https://doi.org/10.1109/ICECAI66283.2025.11171265
  15. Yi, X. (2025). Real-Time Fair-Exposure Ad Allocation for SMBs and Underserved Creators via Contextual Bandits-with-Knapsacks. DOI: https://doi.org/10.20944/preprints202510.0155.v1
  16. Li, B. (2025, August). High-precision photovoltaic potential prediction using a multi-factor deep residual network. In 2025 6th International Conference on Clean Energy and Electric Power Engineering (ICCEPE) (pp. 300-303). IEEE. DOI: https://doi.org/10.1109/ICCEPE66357.2025.11193280