Asymmetric Shock Transmission Between Artificial Intelligence Stocks and Carbon Markets: A Quantile-on-Quantile Connectedness Approach

Öz

The primary objective of this study is to examine the financial interaction between artificial intelligence (AI) indices and the carbon market and to reveal how shock transmission between the two markets varies according to market conditions. In this regard, the study analyzes the dynamics between two carbon indices, ICE EUA Carbon Futures Excess Return Index (ICEEUA) and S&P Global Carbon Credit Index (GLCARB), and two AI indices, Nasdaq CTA Artificial Intelligence & Robotics Index (NQROBO) and ROBO Global Artificial Intelligence Index (THNQ), using daily data covering the period from February 18, 2022 to June 27, 2025. Findings from the Quantile-on-Quantile Connectedness analysis reveal that the carbon market serves as a net shock transmitter across most quantile combinations; however, this role exhibits significant asymmetry, with transmission intensifying during extreme market conditions. Put differently, in certain periods characterized by heightened technological momentum, the AI indices also generate a meaningful feedback effect toward the carbon market. These interactions intensify in extreme quantile regimes, indicating stronger market integration during periods of stress. The results demonstrate that the financial structure of carbon pricing and the AI sector is becoming increasingly intertwined, and that sustainability policies need to be reconsidered in a manner that appropriately accounts for developments in technology markets.

Anahtar Kelimeler

• Artificial Intelligence
• Carbon Market
• Quantile-on-Quantile Connectedness

Yapay Zekâ ve Karbon Piyasaları Arasındaki Asimetrik Şok Aktarımı: Kantil-Kantil Bağlantılılık Yaklaşımı

Öz

Bu çalışmanın temel amacı, yapay zekâ (YZ) endeksleri ile karbon piyasası arasındaki finansal etkileşimi incelemek ve iki piyasa arasındaki şok geçişkenliğinin piyasa koşullarına göre nasıl değiştiğini ortaya koymaktır. Bu doğrultuda çalışma, 18 Şubat 2022 - 27 Haziran 2025 dönemini kapsayan günlük veriler kullanılarak iki karbon endeksi, ICE EUA Carbon Futures Excess Return Endeksi (ICEEUA) ve S&P Global Carbon Credit Endeksi (GLCARB), ile iki YZ endeksi, Nasdaq CTA Artificial Intelligence & Robotics Endeksi (NQROBO) ve ROBO Global Artificial Intelligence Endeksi (THNQ), arasındaki dinamikleri analiz etmektedir. Kantil-Kantil Bağlantılılık yöntemiyle elde edilen bulgular, karbon piyasasının büyük ölçüde YZ endekslerine doğru birincil şok yayıcı (shock transmitter) olarak hareket ettiğini göstermektedir. Bununla birlikte, teknolojik ivmenin arttığı belirli dönemlerde YZ endekslerinin de karbon piyasasına anlamlı düzeyde geri besleme etkisi oluşturduğu gözlemlenmektedir. Bu etkileşimlerin özellikle uç çeyrek (extreme quantile) rejimlerde belirginleşmesi, iki piyasa arasındaki entegrasyonun stres dönemlerinde daha da güçlendiğini ortaya koymaktadır. Sonuçlar, karbon fiyatlaması ile YZ sektörünün finansal yapılarının giderek daha fazla iç içe geçtiğini ve sürdürülebilirlik politikalarının, teknoloji piyasalarındaki gelişmeleri dikkate alacak şekilde yeniden değerlendirilmesi gerektiğini göstermektedir.

Anahtar Kelimeler

• Yapay Zeka
• Karbon Piyasası
• Kantil-Kantil Bağlantılılık

Kaynakça

1. Adigun, O. A., Falola, B. O., Esebre, S. D., Wada, I., & Tunde, A. (2024). Enhancing carbon markets with fintech innovations: The role of artificial intelligence and blockchain. World Journal of Advanced Research and Reviews, 23(2), 579-586. https://doi.org/10.30574/wjarr.2024.23.2.2387
2. 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
3. Baklaga, L. (2024). Synergizing AI and blockchain: Innovations in decentralized carbon markets for emission reduction through intelligent carbon credit trading. Journal of Computer Science and Technology Studies, 6(2), 111-120. https://doi.org/10.32996/jcsts.2024.6.2.13
4. Balci, N., Gürel, B., & Okur, M. R. (2025). Turning profit into sustainability: Evidence on artificial intelligence, education, and ecological footprint. Sustainable Development. https://doi.org/10.1002/sd.70476
5. Chatziantoniou, I., Gabauer, D., & Stenfors, A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Economics Letters, 204, 109891. https://doi.org/10.1016/j.econlet.2021.109891
6. Chen, Y., & Jin, S. (2023). Artificial intelligence and carbon emissions in manufacturing firms: The moderating role of green innovation. Processes, 11(9), 2705. https://doi.org/10.3390/pr11092705
7. Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66. https://doi.org/10.1016/j.ijforecast.2011.02.006
8. Ding, T., Li, J., Shi, X., Li, X., & Chen, Y. (2023). Is artificial intelligence associated with carbon emissions reduction? Case of China. Resources Policy, 85, 103892. https://doi.org/10.1016/j.resourpol.2023.103892

9. Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root, Econometrica 64(4), 813-836. https://doi.org/10.2307/2171846
10. Gabauer, D., & Stenfors, A. (2024). Quantile-on-quantile connectedness measures: Evidence from the US treasury yield curve. Finance Research Letters, 60, 104852. https://doi.org/10.1016/j.frl.2023.104852
11. Gaur, L., Afaq, A., Arora, G. K., & Khan, N. (2023). Artificial intelligence for carbon emissions using system of systems theory. Ecological Informatics, 76, 102165. https://doi.org/10.1016/j.ecoinf.2023.102165
12. Hamdouni, A. (2025). The role of artificial intelligence in enhancing ESG outcomes: Insights from Saudi Arabia. Journal of Risk and Financial Management, 18(10), 572. https://doi.org/10.3390/jrfm18100572
13. Islam, F. S. (2025). Artificial intelligence-powered carbon market intelligence and blockchain-enabled governance for climate-responsive urban infrastructure in the global south. Journal of Engineering Research and Reports, 27(7), 440-472. https://doi.org/10.9734/jerr/2025/v27i71585
14. Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255-259. https://doi.org/10.1016/0165-1765(80)90024-5
15. Jiang, W., Hu, Y., & Zhao, X. (2025). The impact of artificial intelligence on carbon market in China: Evidence from quantile-on-quantile regression approach. Technological Forecasting and Social Change, 212, 123973. https://doi.org/10.1016/j.techfore.2025.123973
16. Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119-147. https://doi.org/10.1016/0304-4076(95)01753-4
17. Li, X., & Bian, J. (2025). How does artificial intelligence technology affect ESG performance? Evidence from China. Sustainability Accounting, Management and Policy Journal. https://doi.org/10.1108/SAMPJ-03-2024-0272
18. Lim, T. (2024). Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways. Artificial Intelligence Review, 57(4), 76. https://doi.org/10.1007/s10462-024-10708-3
19. Liu, Y., Song, J., Zhou, B., & Liu, J. (2025). Artificial intelligence applications and corporate ESG performance. International Review of Economics & Finance, 104, 104559. https://doi.org/10.1016/j.iref.2025.104559
20. Necula, S. C. (2023). Assessing the potential of artificial intelligence in advancing clean energy technologies in Europe: A systematic review. Energies, 16(22), 7633. https://doi.org/10.3390/en16227633
21. Priya, A. K., Devarajan, B., Alagumalai, A., & Song, H. (2023). Artificial intelligence enabled carbon capture: A review. Science of The Total Environment, 886, 163913. https://doi.org/10.1016/j.scitotenv.2023.163913
22. Qin, M., Hu, W., Qi, X., & Chang, T. (2024). Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy. Energy Economics, 131, 107403. https://doi.org/10.1016/j.eneco.2024.107403
23. Raggad, B., & Bouri, E. (2025). Artificial intelligence and clean/dirty energy markets: Tail-based pairwise connectedness and portfolio implications. Future Business Journal, 11(1), 1-24. https://doi.org/10.1186/s43093-025-00451-8
24. Song, Y., Mi, L., Bian, Z., Tu, W., & He, J. (2025). How does artificial intelligence impact corporate ESG performance? The catching−up effect of digital technological innovation. Journal of Innovation & Knowledge, 10(6), 100843. https://doi.org/10.1016/j.jik.2025.100843
25. Tian, H., Wang, J., & Cai, Y. (2025). Artificial intelligence adoption and corporate ESG performance. Business Strategy and the Environment, 34(7), 8922-8945. https://doi.org/10.1002/bse.70032
26. Tian, L., Li, X., Lee, C. W., & Spulbăr, C. (2024). Investigating the asymmetric impact of artificial intelligence on renewable energy under climate policy uncertainty. Energy Economics, 137, 107809. https://doi.org/10.1016/j.eneco.2024.107809
27. Tiwari, S., Khan, S., Mohammed, K. S., & Bilan, Y. (2024). Connectedness between artificial intelligence, clean energy, and conventional energy markets: Fresh findings from CQ and WLMC techniques. Gondwana Research, 136, 92-103. https://doi.org/10.1016/j.gr.2024.08.013
28. Wang, Q., Zhang, F., Li, R., & Sun, J. (2024). Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness. Journal of Cleaner Production, 447, 141298. https://doi.org/10.1016/j.jclepro.2024.141298
29. Wang, Q., Qi, Y., & Li, R. (2025). Artificial intelligence and corporate sustainability: Shaping the future of ESG in the age of industry 5.0. Sustainable Development, 0, 1-26. https://doi.org/10.1002/sd.70200
30. Wu, J., Liu, T., & Sun, J. (2025). Impact of artificial intelligence on carbon emission efficiency: Evidence from China. Environmental Science and Pollution Research, 32(32), 19450-19461. https://doi.org/10.1007/s11356-023-31139-7
31. Xie, H., & Wu, F. (2025). Artificial intelligence technology and corporate ESG performance: Empirical evidence from Chinese-listed firms. Sustainability, 17(2), 420. https://doi.org/10.3390/su17020420
32. Xu, Y., Shao, X., & Tanasescu, C. (2024). How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers. Energy Economics, 132, 107477. https://doi.org/10.1016/j.eneco.2024.107477
33. Yang, S., Zhu, M. N., & Yu, H. (2024). Are artificial intelligence and blockchain the key to unlocking the box of clean energy?. Energy Economics, 134, 107616. https://doi.org/10.1016/j.eneco.2024.107616
34. Zhang, C., & Yang, J. (2024). Artificial intelligence and corporate ESG performance. International Review of Economics & Finance, 96, 103713. https://doi.org/10.1016/j.iref.2024.103713
35. Zhang, X., Khan, K., Shao, X., Oprean-Stan, C., & Zhang, Q. (2024). The rising role of artificial intelligence in renewable energy development in China. Energy Economics, 132, 107489. https://doi.org/10.1016/j.eneco.2024.107489
36. Zhao, Q., Wang, L., Stan, S. E., & Mirza, N. (2024). Can artificial intelligence help accelerate the transition to renewable energy?. Energy Economics, 134, 107584. https://doi.org/10.1016/j.eneco.2024.107584