Kantil Wavelet Analiz Yöntemiyle Yapay Zekâ-ESG Bağlantısının Değerlendirilmesi

Abstract

Bu çalışma, yapay zekânın ESG performansı üzerindeki etkisini incelemek ve bu etkinin kantil ve zaman-frekans uzayında nasıl gerçekleştiğini araştırmayı amaçlamaktadır. Bu doğrultuda, 2018-2025 dönemi günlük verileri kullanılarak Yapay Zekâ Endeksi’nin (AII) Küresel ESG Endeksi (ESGI) üzerindeki etkisini analiz etmek için Kantil-Kantil Regresyon (QQR), Wavelet Kantil-Kantil Regresyon (WQQR) ve Kantil Nedensellik (QC) yöntemleri uygulanmıştır. Ham veri sonuçları, AII’nın farklı kantil düzeylerinde ESG performansı üzerinde hem pozitif hem de negatif etkilerinin olduğunu ortaya koymaktadır. Kısa, orta ve uzun vadeli dönemlerde ise AII’nın ESGI performansı üzerinde alt ve üst kantillerde pozitif; orta kantil düzeylerinde ise negatif etkiye sahip olduğu görülmektedir. QG sonuçları, ham verilerle birlikte orta ve uzun vadeli dönemlerde yapay zekânın ESG performansını öngörebildiğini göstermektedir. Ancak kısa vadede yapay zekânın ESG performansı üzerinde nedensel bir öngörü gücü bulunmamaktadır. Yapay zekâ teknolojisinin stratejik ve bütüncül entegrasyonu, ESG zorluklarının aşılmasında ve sürdürülebilir kalkınmanın desteklenmesinde önemli bir rol oynamaktadır. Bu çalışma, yatırımcılar ve politika yapıcılar için önemli öngörüler sunmaktadır.

Keywords

• Yapay Zekâ
• ESG Performans
• Kantil-Kantil Regresyon
• Wavelet Analizi

Assessing the AI-ESG Nexus Through a Quantile-Wavelet Analysis

Abstract

This study investigates the impact of artificial intelligence (AI) on environmental, social, and governance (ESG) performance and explores how this relationship evolves across the quantile and time-frequency domains. Using daily data from 2018 to 2025, the analysis applies the Quantile-on-Quantile Regression (QQR), Wavelet Quantile-on-Quantile Regression (WQQR), and Quantile Causality (QC) approaches to examine the influence of the Artificial Intelligence Enabler Index (AII) on the Global ESG Index (ESGI). The results based on raw data reveal that the influence of AI on ESG performance is heterogeneous across quantiles, with both positive and negative effects. In the short, medium, and long term, AII positively affects ESGI at the lower and upper quantiles, while exerting negative effects around the median quantiles. The QC findings, consistent with the raw and wavelet-based analyses, indicate that AI has predictive power for ESG performance over the medium and long horizons, but not in the short run. These results underscore that the strategic and holistic integration of AI technologies can play a pivotal role in overcoming ESG challenges and fostering sustainable development. The study provides novel insights for investors and policymakers aiming to understand the dynamic and multi-scale linkages between AI adoption and ESG performance.

Keywords

• Artificial Intelligence
• ESG Performance
• Quantile-on-Quantile Regression
• Wavelet Analysis

References

1. Acemoglu, D. and Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244. https://doi.org/10.1086/705716
2. Ajagekar, A. and You, F. (2024). Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization. Advances in Applied Energy, 14, 100179. https://doi.org/10.1016/j.adapen.2024.100179
3. Avery, M., Leibbrandt, A. and Vecci, J. (2024). Does artificial intelligence help or hurt gender diversity? Evidence from two field experiments on recruitment in tech (SSRN Paper No. 4764343). https://dx.doi.org/10.2139/ssrn.4764343
4. Balcilar, M., Gupta, R. and Pierdzioch, C. (2016). Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resources Policy, 49, 74-80. https://doi.org/10.1016/j.resourpol.2016.04.004
5. Beier, G., Matthess, M., Shuttleworth, L., Guan, T., Grudzien, D.I.D.O.P., Xue, B., ... & Chen, L. (2022). Implications of Industry 4.0 on industrial employment: A comparative survey from Brazilian, Chinese, and German practitioners. Technology in Society, 70, 102028. https://doi.org/10.1016/j.techsoc.2022.102028
6. Bergner, A.S., Hildebrand, C. and Häubl, G. (2023). Machine talk: How verbal embodiment in conversational AI shapes consumer-brand relationships. Journal of Consumer Research, 50(4), 742-764. Retrieved from https://academic.oup.com/
7. Brambilla, I., César, A., Falcone, G. and Gasparini, L. (2023). The impact of robots in Latin America: Evidence from local labor markets. World Development, 170, 106271. https://doi.org/10.1016/j.worlddev.2023.106271
8. Broock, W.A., Scheinkman, J.A., Dechert, W.D. and LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197-235. https://doi.org/10.1080/07474939608800353

9. Bughin, J., Hazan, E., Lund, S., Dahlstrom, P., Wiesinger, A. and Subramaniam, A. (2018). Skill shift: Automation and the future of the workforce. Retrieved from www.mckinsey.com/featured-insights/future-of-work/skill-shift-automation-andthe-future-of-the-workforce
10. Bui, D.T., Ngo, P.T.T., Pham, T.D., Jaafari, A., Minh, N.Q., Hoa, P.V. and Samui, P. (2019). A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena, 179, 184-196. https://doi.org/10.1016/j.catena.2019.04.009
11. Butt, S., Ramzan, M., Chohan, M.A., Murtaza, N. and Adebayo, T.S. (2025). Analyzing the synergies between green energy, green technology, and environmental sustainability in China: Novel insights from time-varying quantile approaches. Gondwana Research, Advance online publication. https://doi.org/10.1016/j.gr.2025.08.017
12. Camaréna, S. (2020). Artificial intelligence in the design of the transitions to sustainable food systems. Journal of Cleaner Production, 271, 122574. https://doi.org/10.1016/j.jclepro.2020.122574
13. Cann, O. (2018). Machines will do more tasks than humans by 2025. Retrieved from https://www.weforum.org/press/2018/09/machines-will-do-more-tasks-than-humans-by-2025-but-robot-revolution-will-still-create-58-million-net-new-jobs-in-next-five-years/
14. Chen, J., Wang, N., Lin, T., Liu, B. and Hu, J. (2024). Shock or empowerment? Artificial intelligence technology and corporate ESG performance. Economic Analysis and Policy, 83, 1080-1096. https://doi.org/10.1016/j.eap.2024.08.004
15. Chen, J., Wang, Y. and Ren, X. (2022). Asymmetric effects of non-ferrous metal price shocks on clean energy stocks: evidence from a quantile-on-quantile method. Resources Policy, 78, 102796. https://doi.org/10.1016/j.resourpol.2022.102796
16. Chen, R. and Zhang, T. (2025). Artificial intelligence applications implication for ESG performance: Can digital transformation of enterprises promote sustainable development? Chinese Management Studies, 19(3), 676-701. https://doi.org/10.1108/CMS-11-2023-0653
17. Chui, M., Harryson, M., Manyika, J., Roberts, R., Chung, R., van Heteren, A. and Nel, P. (2018). Notes from the AI frontier: applying AI for social good. McKinsey & Company.
18. Clark, D. (2020). Artificial intelligence for learning: How to use AI to support employee development. London: Kogan Page.
19. Cleveland, W.S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of The American Statistical Association, 74(368), 829-836. https://doi.org/10.1080/01621459.1979.10481038
20. Connelly, C.E., Fieseler, C., Černe, M., Giessner, S.R. and Wong, S.I. (2021). Working in the digitized economy: HRM theory & practice. Human Resource Management Review, 31(1), 100762. https://doi.org/10.1016/j.hrmr.2020.100762
21. Damioli, G., Van Roy, V., Vértesy, D. and Vivarelli, M. (2024). Drivers of employment dynamics of AI innovators. Technological Forecasting and Social Change, 201, 123249. https://doi.org/10.1016/j.techfore.2024.123249
22. Davenport, T.H. and Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. Retrieved from https://academichelptoday.com/
23. Dong, M., Wang, G. and Han, X. (2024). Impacts of artificial intelligence on carbon emissions in China: in terms of artificial intelligence type and regional differences. Sustainable Cities and Society, 113, 105682
24. Dou, J., Chen, D. and Zhang, Y. (2025). Towards energy transition: Accessing the significance of artificial intelligence in ESG performance. Energy Economics, 108515. https://doi.org/10.1016/j.eneco.2025.108515
25. Ferràs-Hernández, X. (2018). The future of management in a world of electronic brains. Journal of Management Inquiry, 27(2), 260-263. https://doi.org/10.1177/1056492617724973
26. Friede, G., Busch, T. and Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210-233. https://doi.org/10.1080/20430795.2015.1118917
27. Gençay, R., Selçuk, F. and Whitcher, B.J. (2001). An introduction to wavelets and other filtering methods in finance and economics. New York: Academic Press.
28. Goralski, M.A. and Tan, T.K. (2022). Artificial intelligence and poverty alleviation: Emerging innovations and their implications for management education and sustainable development. The International Journal of Management Education, 20(3), 100662. https://doi.org/10.1016/j.ijme.2022.100662
29. Guliyev, H., Huseynov, N. and Nuriyev, N. (2023). The relationship between artificial intelligence, big data, and unemployment in G7 countries: New insights from dynamic panel data model. World Development Sustainability, 3, 100107. https://doi.org/10.1016/j.wds.2023.100107
30. Hatemi-J, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43(1), 447-456. https://doi.org/10.1007/s00181-011-0484-x
31. Huang, D.Z. (2021). Environmental, social and governance (ESG) activity and firm performance: A review and consolidation. Accounting & Finance, 61(1), 335-360. https://doi.org/10.1111/acfi.12569
32. Islam, M.M., Shahbaz, M. and Ahmed, F. (2024). Robot race in geopolitically risky environment: Exploring the Nexus between AI-powered tech industrial outputs and energy consumption in Singapore. Technological Forecasting and Social Change, 205, 123523. https://doi.org/10.1016/j.techfore.2024.123523
33. Jiang, Z., Dong, X. and Yoon, S.M. (2025). Impact of oil prices on key energy mineral prices: Fresh evidence from quantile and wavelet approaches. Energy Economics, 145, 108461. https://doi.org/10.1016/j.eneco.2025.108461
34. Kim, S. and Li, Z. (2021). Understanding the impact of ESG practices in corporate finance. Sustainability, 13(7), 3746. https://doi.org/10.3390/su13073746
35. Koenker, R. and Bassett Jr, G. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 33-50. https://doi.org/10.2307/1913643
36. Li, D., Zhang, Z. and Gao, X. (2024a). Does artificial intelligence deter greenwashing? Finance Research Letters, 67, 105954. https://doi.org/10.1016/j.frl.2024.105954
37. Li, M., Waheed, R., Kirikkaleli, D. and Aziz, G. (2024b). Relevance of hybrid artificial intelligence for improving the forecasting accuracy of natural resource prices. Geoscience Frontiers, 15(3), 101670. https://doi.org/10.1016/j.gsf.2023.101670
38. Li, Z. (2023). Artificial fiduciaries (University of Pittsburgh Legal Studies Research Paper No. 2023-31). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4524129#
39. Liu, X., Ma, C. and Ren, Y.S. (2024). How AI powers ESG performance in China's digital frontier? Finance Research Letters, 70, 106324. https://doi.org/10.1016/j.frl.2024.106324
40. Luqman, A., Zhang, Q., Talwar, S., Bhatia, M. and Dhir, A. (2024). Artificial intelligence and corporate carbon neutrality: A qualitative exploration. Business Strategy and the Environment, 33(5), 3986-4003. https://doi.org/10.1002/bse.3689
41. Lv, L., Chen, S., Liu, G.G. and Benckendorff, P. (2024). Enhancing customers’ life satisfaction through AI-powered personalized luxury recommendations in luxury tourism marketing. International Journal of Hospitality Management, 123, 103914. https://doi.org/10.1016/j.ijhm.2024.103914
42. Naqvi, A. (2021), Artificial ıntelligence for asset management and ınvestment: A strategic perspective. Hoboken: John Wiley & Sons.
43. Nguyen, Q.P. and Vo, D.H. (2022). Artificial intelligence and unemployment: An international evidence. Structural Change and Economic Dynamics, 63, 40-55. https://doi.org/10.1016/j.strueco.2022.09.003
44. Pang, L., Zhu, M.N. and Yu, H. (2022). Is green finance really a blessing for green technology and carbon efficiency? Energy Economics, 114, 106272. https://doi.org/10.1016/j.eneco.2022.106272
45. Porter, M.E. and Linde, C.V.D. (1995). Toward a new conception of the environment-competitiveness relationship. Journal of Economic Perspectives, 9(4), 97-118. https://doi.org/10.1257/jep.9.4.97
46. Qin, M., Hu, W., Qi, X. and Chang, T. (2024a). 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
47. Qin, M., Su, C.W., Hao, L.N. and Tao, R. (2020). The stability of US economic policy: Does it really matter for oil price? Energy, 198, 117315. https://doi.org/10.1016/j.energy.2020.117315
48. Qin, M., Su, C.W., Umar, M., Lobonţ, O.R. and Manta, A.G. (2023). Are climate and geopolitics the challenges to sustainable development? Novel evidence from the global supply chain. Economic Analysis and Policy, 77, 748-763. https://doi.org/10.1016/j.eap.2023.01.002
49. Qin, M., Zhu, Y., Xie, X., Shao, X. and Lobonţ, O.R. (2024b). The impact of climate risk on technological progress under the fourth industrial era. Technological Forecasting and Social Change, 202, 123325. https://doi.org/10.1016/j.techfore.2024.123325
50. Qui, M., Qui, L.H., Umar, M., Su, C.W. and Jiao, W. (2020). The inevitable role of El Ni~ no: A fresh insight into the oil market. Economic Research-Ekonomska Istraživanja, 33(1), 1943-1962. https://doi.org/10.1080/1331677X.2020.1768428
51. Ramzan, M., Hossain, M.R., Eskandari, H., Razi, U. and Adeboya, T.S. (2025). Winding down Spain's looming energy poverty amid green energy transition: Evidence from novel multivariate quantile-on-quantile (M-QQR) regression. Energy Economics, 145, 108436. https://doi.org/10.1016/j.eneco.2025.108436
52. Saba, C. S. and Ngepah, N. (2024). The impact of artificial intelligence (AI) on employment and economic growth in BRICS: Does the moderating role of governance matter? Research in Globalization, 8, 100213. https://doi.org/10.1016/j.resglo.2024.100213
53. Shahbaz, M., Zakaria, M., Shahzad, S.J.H. and Mahalik, M.K. (2018). The energy consumption and economic growth nexus in top ten energy-consuming countries: Fresh evidence from using the quantile-on-quantile approach. Energy Economics, 71, 282-301. https://doi.org/10.1016/j.eneco.2018.02.023
54. Shahzad, S.J.H., Shahbaz, M., Ferrer, R. and Kumar, R.R. (2017). Tourism-led growth hypothesis in the top ten tourist destinations: New evidence using the quantile-on-quantile approach. Tourism Management, 60, 223-232. https://doi.org/10.1016/j.tourman.2016.12.006
55. Sim, N. and Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1-8. https://doi.org/10.1016/j.jbankfin.2015.01.013
56. Stone, M. (1977). An asymptotic equivalence of choice of model by cross‐validation and Akaike's criterion. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 44-47. https://doi.org/10.1111/j.2517-6161.1977.tb01603.x
57. Su, C.W., Pang, L., Umar, M. and Lobonţ, O.R. (2022). Will gold always shine amid world uncertainty? Emerging Markets Finance and Trade, 58(12), 3425-3438. https://doi.org/10.1080/1540496X.2022.2050462
58. Su, C.W., Wang, Y., Qin, M. and Lobonţ, O.R. (2023). Do precious metals hedge against global supply chain uncertainty? Borsa Istanbul Review, 23(5), 1026-1036. https://doi.org/10.1016/j.bir.2023.05.004
59. Su, C.W., Yang, S., Peculea, A.D., Biţoiu, T.I. and Qin, M. (2024). Energy imports in turbulent eras: Evidence from China. Energy, 306, 132586. https://doi.org/10.1016/j.energy.2024.132586
60. Su, C.W., Yang, S., Peculea, A.D., Biţoiu, T.I. and Qin, M. (2024). Energy imports in turbulent eras: Evidence from China. Energy, 306, 132586. https://doi.org/10.1016/j.energy.2024.132586
61. Szramowiat-Sala, K., Penkala, R., Horák, J., Krpec, K., Hopan, F., Ryšavý, J., ... & Górecki, J. (2024). AI-based data mining approach to control the environmental impact of conventional energy technologies. Journal of Cleaner Production, 472, 143473. https://doi.org/10.1016/j.jclepro.2024.143473
62. Tseng, C. J. and Lin, S. Y. (2024). Role of artificial intelligence in carbon cost reduction of firms. Journal of Cleaner Production, 447, 141413. https://doi.org/10.1016/j.jclepro.2024.141413
63. Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 233. https://doi.org/10.1038/s41467-019-14108-y
64. Wang, J., Wang, W., Liu, Y. and Wu, H. (2023). Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China. Technology in Society, 72, 102208. https://doi.org/10.1016/j.techsoc.2023.102208
65. Wang, L., Ruan, H., Hong, Y. and Luo, K. (2023). Detecting the hidden asymmetric relationship between crude oil and the US dollar: A novel neural Granger causality method. Research in International Business and Finance, 64, 101899. https://doi.org/10.1016/j.ribaf.2023.101899
66. Wang, Q. (2025). Generative AI-assisted evaluation of ESG practices and information delays in ESG ratings. Finance Research Letters, 74, 106757. https://doi.org/10.1016/j.frl.2025.106757.
67. Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J. and Wang, J. (2020). Tackling environmental challenges in pollution controls using artificial intelligence: A review. Science of the Total Environment, 699, 134279.
68. Zhang, C. and 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.
69. Zhang, D. (2024). The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence. Energy Economics, 133, 107562. https://doi.org/10.1016/j.eneco.2024.107562.
70. Zhang, X. and Zhu, H. (2023). The impact of industrial intelligence on carbon emissions: Evidence from the three largest economies. Sustainability, 15(7), 6316. https://doi.org/10.3390/su15076316.
71. Zhao, J. and Gómez Fariñas, B. (2023). Artificial intelligence and sustainable decisions. European Business Organization Law Review, 24(1), 1-39. https://doi.org/10.1007/s40804-022-00262-2