Araştırma Makalesi

Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness

Cilt: 8 Sayı: 2 30 Ekim 2026
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Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness

Öz

This study examines the dynamic relationships among geopolitical risk, artificial intelligence (AI), and clean energy markets. The Geopolitical Risk Index (GPR) is used to represent geopolitical risk; the STOXX Global Artificial Intelligence Index, Indxx Global Robotics & Artificial Intelligence Index, and Nasdaq CTA Artificial Intelligence & Robotics Index represent AI markets; and the FTSE Environmental Opportunities Renewable & Alternative Energy Index, NASDAQ Clean Edge Green Energy Index, and S&P Global Clean Energy Index represent clean energy markets. Using daily data from December 18, 2017 to May 1, 2026, the study employs Wavelet Quantile-on-Quantile Regression (WQQR), Quantile-on-Quantile Connectedness (QQCON), and Frequency Quantile Connectedness (FQCON) approaches. The findings reveal that the effects of geopolitical risk on AI and clean energy markets are nonlinear, heterogeneous, and dependent on market regimes and time horizons. Moreover, AI and clean energy markets exhibit asymmetric connectedness, while their net transmitter and receiver roles vary across quantiles and over time. These findings provide important implications for investors, portfolio managers, and policymakers regarding risk management, portfolio diversification, and the coordination of green and digital transformation policies.

Anahtar Kelimeler

Destekleyen Kurum

This study received no financial support from any institution or organization.

Etik Beyan

This study which does not require ethics committee approval and/or legal/specific permission complies with the Research and Publication Ethics.

Kaynakça

  1. Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation, and work. NBER Working Paper Series, No. 24196. Available at: http://www.nber.org/papers/w24196
  2. 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. https://doi.org/10.1016/j.jclepro.2021.125834
  3. Ahmed, W. M. A., & Sleem, M. A. E. (2025). On the dynamic interdependence between risk factors and clean energy stock prices. Resources Policy, 105, 105595. https://doi.org/10.1016/j.resourpol.2025.105595
  4. Akadiri, S. S., & Özkan, O. (2026). Navigating uncertainty: The impact of ESG factors on clean energy markets and investment dynamics. Corporate Social Responsibility and Environmental Management, 33(1), 1270-1285. https://doi.org/10.1002/csr.70227
  5. Al-Maadid, A., Ali, M. S. B., & Mohammed, K. S. (2026). Driving the energy transition in the GCC: The role of artificial intelligence, technology and energy prices. Energy Strategy Reviews, 63, 102029. https://doi.org/10.1016/j.esr.2025.102029
  6. Almeida, D., Ferreira, P., Dionísio, A., & Aslam, F. (2025). Exploring the connection between geopolitical risks and energy markets. Energy Economics, 141, 108113. https://doi.org/10.1016/j.eneco.2024.108113
  7. Ando, T., Greenwood-Nimmo, M., & Shin, Y. (2022). Quantile connectedness: Modeling tail behavior in the topology of financial networks. Management Science, 68(4), 2401-2431. https://doi.org/10.1287/mnsc.2021.3984
  8. Asl, M., Ben Jabeur, S., Nammouri, H., & Bel Hadj Miled, K. (2024). Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy. Energy Economics, 140, 108017. https://doi.org/10.1016/j.eneco.2024.108017

Ayrıntılar

Birincil Dil

İngilizce

Konular

Finansal Piyasalar ve Kurumlar

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

30 Ekim 2026

Gönderilme Tarihi

31 Ağustos 2026

Kabul Tarihi

29 Ekim 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 8 Sayı: 2

Kaynak Göster

APA
Erben Yavuz, A. (2026). Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness. International Journal of Business and Economic Studies, 8(2). https://doi.org/10.54821/bes.2029368
AMA
1.Erben Yavuz A. Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness. BES JOURNAL. 2026;8(2). doi:10.54821/bes.2029368
Chicago
Erben Yavuz, Asuman. 2026. “Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness”. International Journal of Business and Economic Studies 8 (2). https://doi.org/10.54821/bes.2029368.
EndNote
Erben Yavuz A (01 Ekim 2026) Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness. International Journal of Business and Economic Studies 8 2
IEEE
[1]A. Erben Yavuz, “Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness”, BES JOURNAL, c. 8, sy 2, Eki. 2026, doi: 10.54821/bes.2029368.
ISNAD
Erben Yavuz, Asuman. “Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness”. International Journal of Business and Economic Studies 8/2 (01 Ekim 2026). https://doi.org/10.54821/bes.2029368.
JAMA
1.Erben Yavuz A. Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness. BES JOURNAL. 2026;8. doi:10.54821/bes.2029368.
MLA
Erben Yavuz, Asuman. “Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness”. International Journal of Business and Economic Studies, c. 8, sy 2, Ekim 2026, doi:10.54821/bes.2029368.
Vancouver
1.Asuman Erben Yavuz. Geopolitical risk, artificial intelligence, and clean energy markets: Nonlinear effects and dynamic connectedness. BES JOURNAL. 01 Ekim 2026;8(2). doi:10.54821/bes.2029368

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