Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım

Yıldırım Beyazıt Çiçen

doi:10.33203/mfy.1836226

TR EN

Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım

Abstract

Bu çalışma, yapay zekâ (YZ) temelli teknolojilerin hızla artan enerji talebinin çevresel ve mali etkilerini birlikte değerlendiren özgün bir teorik model geliştirmektedir. Model, YZ altyapısının ekonomik faydalarını, iç ve dış çevresel zararlarını ve kamu bütçesine yansıyan teşvik maliyetlerini tek bir refah fonksiyonu içinde birleştirerek “YZ kaynaklı çevresel–fiskal zaman tutarsızlığı” kavramını formelleştirmektedir. Bulgular, politikacının kısa dönemli YZ faydalarını abartması, enerji teşviklerinin maliyetleri maskelemesi ve uluslararası dışsallıkların sınırlı içselleştirilmesi nedeniyle politik denge enerji düzeyinin toplumsal optimumun üzerinde gerçekleştiğini göstermektedir. Kurumsal kalite arttıkça bu sapma azalmakla birlikte, yapısal parametreler nedeniyle tamamen ortadan kalkmamaktadır. Çalışma, enerji tüketiminin yalnızca teknik bir maliyet unsuru değil; maliye politikası, çevre regülasyonu ve uluslararası iklim iş birliği tarafından şekillenen çok katmanlı bir yönetişim problemi olduğunu ortaya koymaktadır. Son bölümde sürdürülebilir maliye politikaları ve yeşil finans açısından politika önerileri sunulmaktadır.

Keywords

Yapay zekâ, Enerji talebi, Zaman tutarsızlığı, Kurumsal kalite

AI-Driven Environmental–Fiscal Time Inconsistency in Energy Demand: An Institutional Approach

Abstract

This study develops a theoretical model that jointly evaluates the environmental and fiscal implications of the rapidly growing energy demand driven by artificial intelligence (AI) technologies. Integrating the economic benefits of AI, domestic and cross-border environmental damages, and subsidy-related fiscal pressures into a unified welfare framework, the model formalizes the concept of “AI-driven environmental–fiscal time inconsistency”. The results show that political decision-makers systematically choose an energy level above the social optimum due to the overvaluation of short-term AI gains, the masking effect of energy subsidies on true costs, and the limited internalization of international externalities. Although higher institutional quality reduces this deviation, structural parameters prevent its complete elimination. The study demonstrates that AI-related energy consumption is not merely a technical cost issue but a multi-layered governance challenge shaped by fiscal policy, environmental regulation, and global climate cooperation. The paper concludes with policy implications regarding sustainable public finance and green investment frameworks.

Keywords

Artificial intelligence, Energy demand, Time inconsistency, Institutional quality.

References

Akpan, U., & Kama, U. (2024). Does institutional quality really matter for environmental quality?. Energy & Environment, 35(8), 4361-4385.
Alghieth, M. (2025). Sustain AI: A multi-modal deep learning framework for carbon footprint reduction in industrial manufacturing. Sustainability, 17(9), 4734. https://doi.org/10.3390/su17094134
Ali, H. S., Zeqiraj, V., Lin, W. L., Law, S. H., Yusop, Z., Bare, U. A. A., & Chin, L. (2019). Does quality institutions promote environmental quality?. Environmental Science and Pollution Research, 26(11), 10446-10456.
Atta, N., & Sharifi, A. (2024). A systematic literature review of the relationship between the rule of law and environmental sustainability. Sustainable Development, 32(6), 7051-7068.
Barro, R. J., & Gordon, D. B. (1983). Rules, discretion and reputation in a model of monetary policy. Journal of monetary economics, 12(1), 101-121.
Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024). A review of green artificial intelligence: Towards a more sustainable future. Neurocomputing, 599, 128096
Catalano, M., Forni, L., & Pezzolla, E. (2021). Fiscal policies for a sustainable recovery and a green transformation. Washington, DC, USA: World Bank.
Chen, C., Hu, Y., Karuppiah, M., & Kumar, P. M. (2021). Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustainable Energy Technologies and Assessments, 47, 101358.
Chen, Y., Wu, F., & Zhang, D. (2024). Global climate finance architecture: Institutional development. In Climate Finance: Supporting a Sustainable Energy Transition (pp. 51-100). Singapore: Springer
European Parliamentary Research Service (EPRS). (2025). AI and the energy sector (Briefing No. 775859). European Parliament. https://www.europarl.europa.eu/RegData/etudes/BRIE/2025/775859/EPRS_BRI%282025%29775859_EN.pdf

Gonguet, M. F., Wendling, M. C. P., Sakrak, O. A., & Battersby, B. (2021). Climate-Sensitive Management of Public Finances—" Green PFM”. International Monetary Fund.
Greenpeace (2023). Invisible emissions: Tracking greenhouse gas emissions from the electronics supply chain. Greenpeace Invisible Emissions Report. https://www.greenpeace.org/static/planet4-eastasia-stateless/2023/04/620390b7-greenpeace_energy_consumption_report.pdf
Helm, D., Hepburn, C., & Mash, R. (2003). Time-inconsistent environmental policy and optimal delegation. Oxford University Environmental Change Institute Working Paper. University of Oxford.
Hosseinkhani, N. T. (2025). Artificial Intelligence and Large Language Models in Energy Systems and Climate Strategies: Economic Pathways to Cost-Effective Emissions Reduction and Sustainable Growth. SSRN 5385513.
Hu, H., Chen, D., Chang, C. P., & Chu, Y. (2021). The political economy of environmental consequences: A review of the empirical literature. Journal of Economic Surveys, 35(1), 250-306.
International Energy Agency (IEA). (2025). Energy and AI. https://iea.blob.core.windows.net/assets/601eaec9-ba91-4623-819b-4ded331ec9e8/EnergyandAI.pdf
Iqbal, A., Zhang, W., & Jahangir, S. (2025). Building a sustainable future: The nexus between artificial intelligence, renewable energy, green human capital, geopolitical risk, and carbon emissions through the moderating role of institutional quality. Sustainability, 17(3), 990. https://doi.org/10.3390/su17030990
Khallaf, D. A. N., & Alqerafi, D. N. M. (2024). Using AI to help reduce the effect of global warming. 1927 48(1), 1927-1947.
Kretsos, L., Tabaghdehi, S. A. H., & Braganza, A. (2024). The Political Challenge of AI in Modern Society: From National AI Strategy to the Algorithmic Elections. In Business Strategies and Ethical Challenges in the Digital Ecosystem (pp. 319-331). Emerald Publishing Limited.
Ligozat, A. L., Lefèvre, J., Bugeau, A., & Combaz, J. (2022). Unraveling the hidden environmental impacts of AI solutions for environment life cycle assessment of AI solutions. Sustainability, 14(9), 5172.
Liu, M., Zhang, L., Chen, J. et al. Large language models for building energy applications: Opportunities and challenges. Build. Simul. 18, 225–234. https://doi.org/10.1007/s12273-025-1235-9
Lu, Y., & Liao, Z. (2025). The influence of AI application on carbon emission intensity of industrial enterprises in China. Scientific Reports, 15(1), 12585. https://doi.org/10.1038/s41598-025-97110-3
Marsiliani, L., & Renstrom, T. I. (2000). Time inconsistency in environmental policy: Tax earmarking as a commitment solution. The Economic Journal, 110(462), 123-138.
Midttun, A. (1999). The weakness of strong governance and the strength of soft regulation: Environmental governance in post‐modern form. Innovation: The European Journal of Social Science Research, 12(2), 235-250.
Nordhaus, W. (2015). Climate clubs: Overcoming free-riding in international climate policy. American Economic Review, 105(4), 1339-1370.
Omri, A., Hamza, F., & Slimani, S. (2025). The role of green finance in driving artificial intelligence and renewable energy for sustainable development. Sustainable Development, 33(5), 6844-6870.
Organisation for Economic Co-operation and Development. (2025). Governing for the Green Transition. OECD Publishing. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/04/governing-for-the-green-transition_0608c8e1/5b0aa7d0-en.pdf
Pınar, A. (2024). Technological unemployment and the AI revolution: An investigation on macroeconomic consequences. Econharran, 8(14), 15-26.
Pimenow, S., Pimenowa, O., & Prus, P. (2024). Challenges of artificial intelligence development in the context of energy consumption and impact on climate change. Energies, 17(23), 5965.
Rasheed, M., Zhao, Y., Ahmed, Z., Haseeb, A., & Saud, S. (2024). Information communication technology, economic growth, natural resources, and renewable energy production: Evaluating the asymmetric and symmetric impacts of artificial intelligence in robotics and innovative economies. Journal of Cleaner Production. 447, 141466. https://doi.org/10.1016/j.jclepro.2024.141466
Rodrik, D. (2025). Shared Prosperity in a Fractured World: A New Economics for the Middle Class, the Global Poor, and Our Climate. Princeton, NJ: Princeton University Press; 2025.
Shah, S. A., Ye, X., Wang, B., & Wu, X. (2024). Dynamic linkages among carbon emissions, artificial intelligence, economic policy uncertainty, and renewable energy consumption: Evidence from East Asia and Pacific countries. Energies, 17(16), 4011. https://doi.org/10.3390/en17164011
Stern, N., & Valero, A. (2021). Innovation, growth and the transition to net-zero emissions. Research Policy, 50(9), 104293.
Şenyapar, H. N., & Bayindir, R. (2025). The Energy Hunger Paradox of Artificial Intelligence: End of Clean Energy or Magic Wand for Sustainability?. Sustainability, 17(7), 2887.
United Nations Environment Programme Copenhagen Climate Centre (UNEP-CCC). (2025). Business Models and Finance to Enhance Energy Efficiency in AI and Data Centres in Emerging Economies, Copenhagen, Denmark. https://unepccc.org/wp-content/uploads/2025/10/business-models-and-finance-ai-data-centres-policy-brief.pdf
Yan, H., Qamruzzaman, M., & Kor, S. (2023). Nexus between green investment, fiscal policy, environmental tax, energy price, natural resources, and clean energy—a step towards sustainable development by fostering clean energy inclusion. Sustainability, 15(18), 13591.

Details

Primary Language

Turkish

Subjects

Economic Theory (Other)

Journal Section

Research Article

Authors

Yıldırım Beyazıt Çiçen ^*
0000-0002-3425-280X
Türkiye

Publication Date

December 31, 2025

Submission Date

December 4, 2025

Acceptance Date

December 28, 2025

Published in Issue

Year 2025 Number: Özel Sayı 3

DOI

https://doi.org/10.33203/mfy.1836226

IZ

https://izlik.org/JA53GF36FZ

APA

Çiçen, Y. B. (2025). Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım. Maliye Ve Finans Yazıları, Özel Sayı 3, 128-153. https://doi.org/10.33203/mfy.1836226

AMA

1.Çiçen YB. Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım. Maliye ve Finans Yazıları. 2025;(Özel Sayı 3):128-153. doi:10.33203/mfy.1836226

Chicago

Çiçen, Yıldırım Beyazıt. 2025. “Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım”. Maliye Ve Finans Yazıları, no. Özel Sayı 3: 128-53. https://doi.org/10.33203/mfy.1836226.

EndNote

Çiçen YB (December 1, 2025) Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım. Maliye ve Finans Yazıları Özel Sayı 3 128–153.

IEEE

[1]Y. B. Çiçen, “Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım”, Maliye ve Finans Yazıları, no. Özel Sayı 3, pp. 128–153, Dec. 2025, doi: 10.33203/mfy.1836226.

ISNAD

Çiçen, Yıldırım Beyazıt. “Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım”. Maliye ve Finans Yazıları. Özel Sayı 3 (December 1, 2025): 128-153. https://doi.org/10.33203/mfy.1836226.

JAMA

1.Çiçen YB. Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım. Maliye ve Finans Yazıları. 2025;:128–153.

MLA

Çiçen, Yıldırım Beyazıt. “Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım”. Maliye Ve Finans Yazıları, no. Özel Sayı 3, Dec. 2025, pp. 128-53, doi:10.33203/mfy.1836226.

Vancouver

1.Yıldırım Beyazıt Çiçen. Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım. Maliye ve Finans Yazıları. 2025 Dec. 1;(Özel Sayı 3):128-53. doi:10.33203/mfy.1836226

The scope of the Journal of Finance Letters consists of studies in the fields of economics, public finance, finance, and banking.

Yapay Zekâ Enerji Talebinde Çevresel-Fiskal Zaman Tutarsızlığı: Kurumsal Bir Yaklaşım

Abstract

Keywords

AI-Driven Environmental–Fiscal Time Inconsistency in Energy Demand: An Institutional Approach

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite