Feasibility of Artificial Intelligence in the Enforcement of International Environmental Law

Authors

    Mostafa Yousofi Majd Department of Law, Ha.C., Islamic Azad ‎University, Hamedan, Iran.
    Meisam Norouzi * Assistant Professor, Department of Public International Law, Faculty of Humanities, Bu-Ali Sina University, Hamedan, Iran. m.norouzi@basu.ac.ir
    Sobhan Tayebi Department of Law, ST.C., Islamic Azad ‎University, Tehran, Iran.

Keywords:

Artificial intelligence, Technological tools, Enforcement, International law, Environment

Abstract

This study examines the feasibility of artificial intelligence (AI) in the enforcement of international environmental law. Considering that the ultimate objective of international environmental regulations is their effective implementation, the United Nations Environment Programme reported in January 2019 that the enforcement of these laws has been weak and unsuccessful (UNEP, 2019). Therefore, measures must be devised to overcome barriers to the implementation of such regulations. One of these measures is the use of AI, which is analyzed with the aim of improving enforcement conditions and enhancing compliance with international environmental rules, in order to determine whether AI can remedy weaknesses in the implementation of international environmental laws. The research method is descriptive–analytical, and data were collected through a library-based approach. The writing draws upon sources such as international environmental conventions and treaties, books and journals in the field of artificial intelligence, and relevant specialized articles. The gathered data were subsequently analyzed. It is expected that AI—through monitoring compliance with international environmental regulations, identifying and predicting environmental risks, analyzing datasets, and supporting more sustainable decision-making—will facilitate the enforcement of international environmental law. These findings may contribute to improving the implementation of international environmental regulations.

References

Abrokwah-Larbi, K., & Awuku-Larbi, Y. (2024). The impact of artificial intelligence in marketing on the performance of business organiza-tions: Evidence from SMEs in an emerging economy. J. Entrep. Emerg. Econ., 16, 1090-1117. https://doi.org/10.1108/JEEE-07-2022-0207

Allioui, H., Mourdi, Y., Allioui, H., & Mourdi, Y. (2023). Unleashing the potential of AI: Investigating cutting-edge technologies that are transforming businesses. Int. J. Comput. Eng. Data Sci., 3, 1-12.

Asad, M., Aledeinat, M., Majali, T., Almajali, D. A., & Shrafat, F. D. (2024). Mediating role of green innovation and moderating role of resource acquisition with firm age between green entrepreneurial orientation and performance of entrepreneurial firms. Cogent Bus. Manag., 11, 2291850. https://doi.org/10.1080/23311975.2023.2291850

Asif, M. U., Asad, M., Kashif, M., & Abrar ul Haq, M. (2021). Knowledge Exploitation and Knowledge Exploration for Sustainable Performance of Smes. 2021 Third International Sustainability and Resilience Conference: Climate Change, https://doi.org/10.1109/IEEECONF53624.2021.9668135

Bharadiya, J. P. (2023). Machine learning and AI in business intelligence: Trends and opportunities. Int. J. Comput., 48, 123-134.

Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? SSRN Electron. J. https://doi.org/10.2139/ssrn.1819486

Chi, C. G., & Gursoy, D. (2009). Employee satisfaction, customer satisfaction, and financial performance: An empirical examination. Int. J. Hosp. Manag., 28, 245-253. https://doi.org/10.1016/j.ijhm.2008.08.003

Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: A modern data-driven decision theory for big data and analytics. J. Decis. Syst., 31, 337-373. https://doi.org/10.1080/12460125.2021.1894674

Ellahham, S., Ellahham, N., & Simsekler, M. C. E. (2020). Application of Artificial Intelligence in the Health Care Safety Context: Opportuni-ties and Challenges. Am. J. Med. Qual., 35, 341-348. https://doi.org/10.1177/1062860619878515

Gao, L., Li, G., Tsai, F., Gao, C., Zhu, M., & Qu, X. (2023). The impact of artificial intelligence stimuli on customer engagement and value co-creation: The moderating role of customer ability readiness. J. Res. Interact. Mark., 17, 317-333. https://doi.org/10.1108/JRIM-10-2021-0260

Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. J. Behav. Exp. Financ., 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577

Kanaan, O. A., Alsoud, M., Asad, M., TaAmnha, M. A., & Al-Qudah, S. A. (2024). mediated moderated analysis of knowledge management and stakeholder relationships between open innovation and performance of entrepreneurial firms. Uncertain. Supply Chain. Manag., 12, 2383-2398. https://doi.org/10.5267/j.uscm.2024.5.028

Mehrani, A., Alizadeh, H., & Rasouli, A. (2022). Evaluating the Role of Artificial Intelligence Tools in Developing Financial and Marketing Services.

Naradda Gamage, S. K., Ekanayake, E., Abeyrathne, G., Prasanna, R., Jayasundara, J., & Rajapakshe, P. A. (2020). Review of Global Challenges and Survival Strategies of Small and Medium Enterprises (SMEs). Economies, 8, 79. https://doi.org/10.3390/economies8040079

Oyewobi, L. O., Windapo, A., & Rotimi, J. O. B. (2016). Relationship between decision-making style, competitive strategies and organisational performance among construction organisations. J. Entrep. Emerg. Econ., 16(4), 713-738. https://doi.org/10.1108/JEEE-07-2022-0207

Prentice, C., Weaven, S., & Wong, I. A. (2020). Linking AI quality performance and customer engagement: The moderating effect of AI preference. Int. J. Hosp. Manag., 90, 102629. https://doi.org/10.1016/j.ijhm.2020.102629

Siew, L. W., Wai, C. J., & Hoe, L. W. (2017). Data Driven Decision Analysis in Bank Financial Management with Goal Programming Model. Advances in Visual Informatics: 5th International Visual Informatics Conference, IVIC 2017, https://doi.org/10.1007/978-3-319-70010-6_63

Siswanti, I., Riyadh, H. A., Nawangsari, L. C., Mohd Yusoff, Y., & Wibowo, M. W. (2024). The impact of digital transformation for sustainable business: The meditating role of corporate governance and financial performance. Cogent Bus. Manag., 11, 2316954. https://doi.org/10.1080/23311975.2024.2316954

Ta'Amnha, M. A., Al-Qudah, S., Asad, M., Magableh, I. K., & Riyadh, H. A. (2024). Moderating role of technological turbulence between green product innovation, green process innovation and performance of SMEs. Discov. Sustain., 5, 324. https://doi.org/10.1007/s43621-024-00522-w

Tanguturi, R. N. V., & Muley, A. A. (2023). Enhancing Financial Institution Operations Through Data-Driven Decision-Making. J. Namib. Stud. Hist. Polit. Cult., 39, 272-282.

Tolstoy, D., Nordman, E. R., & Vu, U. (2023). The indirect effect of online marketing capabilities on the international performance of e-commerce SMEs. Int. Bus. Rev., 31, 101946. https://doi.org/10.1016/j.ibusrev.2021.101946

Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intell. Syst. Appl., 18, 200235. https://doi.org/10.1016/j.iswa.2023.200235

Downloads

Published

2026-04-01

Submitted

2025-07-01

Revised

2025-11-10

Accepted

2025-11-17

Issue

Section

Articles

How to Cite

Yousofi Majd, M., Norouzi, M., & Tayebi , S. . (2026). Feasibility of Artificial Intelligence in the Enforcement of International Environmental Law. Legal Studies in Digital Age, 1-13. https://jlsda.com/index.php/lsda/article/view/282

Similar Articles

131-140 of 203

You may also start an advanced similarity search for this article.