The Impact of AI on ECOWAS Energy Regulation Development
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Keywords:
Energy regulations , Artificial intelligence, Regional electricity authority , Chinese AI regulationsAbstract
The ECOWAS Regional Electrical Regulatory Authority (ERERA) is the regional body responsible for overseeing cross-border electrical interconnections in West Africa. The desire of ECOWAS member states to realize electricity interconnections through the cooperative implementation and sharing of the region's energy resources is manifested in adopting an Energy Protocol, which aims to establish the proper institutional and legal framework for the growth of West Africa's electricity industry. Within the Energy Protocol and the West African Power Pool (WAPP) Program scope, the Member States of ECOWAS formed the ECOWAS Regional Electricity Regulatory Authority (ERERA) in January 2008 as a specialized institution of ECOWAS. Applying AI to the energy sector presents both challenging regulatory barriers and hitherto unrealized prospects. While AI enhances smart grids and revolutionizes oil drilling, it also raises questions about accountability and culpability. As we move toward an AI-driven future, collaboration in integrating legal, technological, and ethical matters is essential. By implementing this plan, we can leverage AI's disruptive potential in the energy sector, reduce risks, and ensure a fair and sustainable energy future.
References
Adigun, M. (2024). Legal remedies for energy injustice in the ECOWAS sub-region: the role of the ECOWAS Court. Journal of Energy & Natural Resources Law, 1-18.
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.
Akinyemi, O., Efobi, U., Osabuohien, E., & Alege, P. (2019). Regional integration and energy sustainability in Africa: Exploring the challenges and prospects for ECOWAS. African Development Review, 31(4), 517-528.
Baş, B., & Demirtaş, I. (2022). A View of Energy Cooperatives from the Framework of Energy Justice. Journal of Recycling Economy & Sustainability Policy, 1(1), 18-26.
Calzada, I. (2022). Citizens’ data privacy in china: The state of the art of the personal information protection law (PIPL). Smart Cities, 5(3), 1129-1150.
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., & Xie, X. (2024). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3), 1-45.
Cheng, J., & Zeng, J. (2023). Shaping AI’s future? China in global AI governance. Journal of Contemporary China, 32(143), 794-810.
Clarke, R. (2019). Regulatory alternatives for AI. Computer Law & Security Review, 35(4), 398-409.
Cui, S., & Qi, P. (2021). The legal construction of personal information protection and privacy under the Chinese Civil Code. Computer Law & Security Review, 41, 105560.
Dhabliya, D. (2024). Smart Grids Optimization for Energy Trading with AI Solutions. Acta Energetica, 2, 71-81.
Dixon, R. B. (2023). A principled governance for emerging AI regimes: lessons from China, the European Union, and the United States. AI and Ethics, 3(3), 793-810.
Fan, S., Ai, Q., & Piao, L. (2018). Bargaining-based cooperative energy trading for distribution company and demand response. Applied energy, 226, 469-482.
Feng, Y. (2019). The future of China’s personal data protection law: challenges and prospects. Asia Pacific Law Review, 27(1), 62-82.
Gatete, C., & Dikko, H. (2024). Policy Framework and Regulations to Promote Clean Energy and Renewable Energy Transition in ECOWAS Countries. Energy Regulation in Africa: Dynamics, Challenges, and Opportunities , 131-150.
Hine, E., & Floridi, L. (2024). Artificial intelligence with American values and Chinese characteristics: a comparative analysis of American and Chinese governmental AI policies. AI & SOCIETY, 39(1), 257-278.
Homoki, P., & Ződi, Z. (2024). Large language models and their possible uses in law. Hungarian Journal of Legal Studies.
Kőkuti, T. (2023). Artificial Intelligence in a Transforming Labour Market – New Skills are Needed? Journal of Recycling Economy & Sustainability Policy, 2(1), 21-27.
Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2023). Auditing large language models: a three-layered approach. AI and Ethics, 1-31.
Niet, I. (2022). Between vision and practice: lack of alignment between AI strategies and energy regulations in the Dutch electricity sector. Discover Artificial Intelligence, 2(1), 24.
Ogwezzy, M. (2017). An Appraisal of ECOWAS Regional Energy Investment Initiatives. Nigerian Law Journal, 20, 486.
Pan, X., Ai, B., Li, C., Pan, X., & Yan, Y. (2019). Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China. Technological Forecasting and Social Change, 144, 428-435.
Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V., & Floridi, L. (2021). The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation. Springer International Publishing.
Sheehan, M. (2023). China’s AI regulations and how they get made. Horizons: Journal of International Relations and Sustainable Development, 24, 108-125.
Shimizu, H., & Nakayama, K. (2020). Artificial intelligence in oncology. Cancer science, 111(5), 1452-1460.
Smuha, N. A. (2021). From a ‘race to AI’to a ‘race to AI regulation’: regulatory competition for artificial intelligence. Law, Innovation and Technology, 57-84.
Stuurman, K., & Lachaud, E. (2022). Regulating AI - A label to complete the proposed Act on Artificial Intelligence. Computer Law & Security Review, 44, 105657.
Tallberg, J., Lundgren, M., & Geith, J. (2024). AI regulation in the European Union: examining non-state actor preferences. Business and Politics, 26(2), 218-239.
Veale, M., & Zuiderveen Borgesius, F. (2021). Demystifying the Draft EU Artificial Intelligence Act—Analysing the good, the bad, and the unclear elements of the proposed approach. Computer Law Review International, 22(4), 97-112.
Wang, Y., Qian, W., Zhou, H., Chen, J., & Tan, K. (2023). Exploring new frontiers of deep learning in legal practice: A case study of large language models. International Journal of Computer Science and Information Technology, 1(1), 131-138.
Wesseh Jr, P. K., & Lin, B. (2016). Output and substitution elasticities of energy and implications for renewable energy expansion in the ECOWAS region. Energy Policy, 89, 125-137.
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