Yapay Zekanın ECOWAS Enerji Düzenlemelerinin Gelişimine Etkisi
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Enerji düzenlemeleri , Yapay zeka , Bölgesel elektrik otoritesi , Çin YZ düzenlemeleriÖzet
ECOWAS Bölgesel Elektrik Düzenleme Kurumu (ERERA), Batı Afrika'daki sınır ötesi elektrik bağlantılarını denetlemekten sorumlu bölgesel kurumdur. ECOWAS üyesi devletlerin, bölgenin enerji kaynaklarının işbirliği içinde uygulanması ve paylaşılması yoluyla elektrik enterkoneksiyonlarını gerçekleştirme arzusu, Batı Afrika'nın elektrik endüstrisinin büyümesi için uygun kurumsal ve yasal çerçeveyi oluşturmayı amaçlayan bir Enerji Protokolü'nün kabul edilmesiyle kendini göstermiştir. Enerji Protokolü ve Batı Afrika Güç Havuzu (WAPP) Programı kapsamında ECOWAS Üye Devletleri, Ocak 2008'de ECOWAS'ın uzmanlaşmış bir kurumu olarak ECOWAS Bölgesel Elektrik Düzenleme Kurumu'nu (ERERA) kurmuştur. Yapay zekanın enerji sektörüne uygulanması, hem zorlu düzenleyici engeller hem de şimdiye kadar gerçekleşmemiş beklentiler sunmaktadır. YZ, akıllı şebekeleri geliştirirken ve petrol sondajında devrim yaratırken, hesap verebilirlik ve suçlulukla ilgili soruları da gündeme getirmektedir. YZ güdümlü bir geleceğe doğru ilerlerken, yasal, teknolojik ve etik konuların entegre edilmesinde işbirliği şarttır. Bu planı uygulayarak, YZ'nin enerji sektöründeki yıkıcı potansiyelinden yararlanabilir, riskleri azaltabilir ve adil ve sürdürülebilir bir enerji geleceği sağlayabiliriz.
Referanslar
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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