Navigating the Dual Edges of Tax Havens and Offshore Financial Centers in Global Finance Authors Yuzhe Yu
Vol. 29 (2026): 2026 3rd International Conference on Global Economics, Education and the Arts (GEEA 2026)
Received: 2026-08-01
Accepted: 2026-08-01
Published: 2026-08-01
Abstract
This paper comprehensively examines the dual role of tax havens and offshore financial centers (THOFCs) within the contemporary global financial system, particularly in the context of increasing economic globalization and escalating regulatory complexity. We argue that while these specialized jurisdictions significantly facilitate cross-border capital flows, substantially reduce transaction and financing costs, and provide critical institutional advantages for multinational enterprises, they simultaneously present profound challenges to global economic governance. Specifically, THOFCs frequently enable aggressive corporate tax avoidance, sophisticated regulatory arbitrage, and pervasive financial opacity. These detrimental practices can severely undermine the fiscal capacity of sovereign states and threaten broader systemic financial stability. Drawing on diverse multidisciplinary perspectives from financial services, economic geography, industrial transformation, and individual taxation, this analysis synthesizes the complex mechanisms through which THOFCs operate. Furthermore, the theoretical framework is supported by robust empirical evidence, including the high concentration of outward foreign direct investment (OFDI) and the prevalence of capital round-tripping practices. Ultimately, the study concludes that THOFCs function paradoxically as both vital efficiency-enhancing mechanisms for global capital and primary sources of international governance challenges. To effectively mitigate these inherent vulnerabilities, policymakers must prioritize stronger international coordination, enhance cross-border transparency, and implement robust regulatory frameworks designed to carefully balance the economic benefits of capital mobility against the systemic risks of financial deregulation.
Keywords
References
[1] Z. Zhao, S. H. Zhang, Z. Y. Xu, K. Bellisario, N. H. Dai, H. Omrani, and B. C. Pijanowski, "Automated bird acoustic event detection and robust species classification," Ecological Informatics, vol. 39, pp. 99–108, 2017.
[2] Y. Fu, C. Yu, Y. Zhang, D. Lv, Y. Yin, J. Lu, and D. Lv, "Classification of birdsong spectrograms based on DR-ACGAN and dynamic convolution," Ecological Informatics, vol. 77, p. 102250, 2023.
[3] J. Xie, Y. Zhong, J. Zhang, S. Liu, C. Ding, and A. Triantafyllopoulos, "A review of automatic recognition technology for bird vocalizations in the deep learning era," Ecological Informatics, vol. 73, p. 101927, 2023.
[4] C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, pp. 1–27, 2011.
[5] S. Fraixedas, A. Lindén, M. Piha, M. Cabeza, R. Gregory, and A. Lehikoinen, "A state-of-the-art review on birds as indicators of biodiversity: Advances, challenges, and future directions," Ecological Indicators, vol. 118, p. 106728, 2020.
[6] E. Akbal, S. Dogan, and T. Tuncer, "An automated multispecies bioacoustics sound classification method based on a nonlinear pattern: Twine-pat," Ecological Informatics, vol. 68, p. 101529, 2022.
[7] J. Xie and M. Zhu, "Sliding-window based scale-frequency map for bird sound classification using 2D-and 3D-CNN," Expert Systems with Applications, vol. 207, p. 118054, 2022.
[8] P. P. Shinde and S. Shah, "A review of machine learning and deep learning applications," in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Aug. 2018, pp. 1–6.
[9] Q. Hou, D. Zhou, and J. Feng, "Coordinate attention for efficient mobile network design," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 13713–13722.
[10] E. Şaşmaz and F. B. Tek, "Animal sound classification using a convolutional neural network," in 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sep. 2018, pp. 625–629.
[11] Y. Qiao, K. Qian, and Z. Zhao, "Learning higher representations from bioacoustics: A sequence-to-sequence deep learning approach for bird sound classification," in International Conference on Neural Information Processing, Cham: Springer International Publishing, Nov. 2020, pp. 130–138.
[12] P. Magron, R. Badeau, and B. David, "Model-based STFT phase recovery for audio source separation," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 6, pp. 1095–1105, 2018.
[13] L. Chen, X. Chu, X. Zhang, and J. Sun, "Simple baselines for image restoration," in European Conference on Computer Vision, Cham: Springer Nature Switzerland, Oct. 2022, pp. 17–33.
[14] A. Howard, M. Sandler, G. Chu, L. C. Chen, B. Chen, M. Tan, ... and H. Adam, "Searching for mobilenetv3," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1314–1324.
[15] S. Kahl, C. M. Wood, M. Eibl, and H. Klinck, "BirdNET: A deep learning solution for avian diversity monitoring," Ecological Informatics, vol. 61, p. 101236, 2021.
[16] F. Yang, Y. Jiang, and Y. Xu, "Design of bird sound recognition model based on lightweight," IEEE Access, vol. 10, pp. 85189–85198, 2022.
Copyright and License
Published in2026-08-01 16:20:11
DOI https://doi.org/10.70088/scb0ba25
Creative Commons
Copyright: © 2026 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Copyright
Copyright © The Author(s), 2026. Published by GEEA 2026
Journal Information
- Vol. 29 (2026): 2026 3rd International Conference on Global Economics, Education and the Arts (GEEA 2026)
- 2026-08-01
- ISSN: (Print) 3078-770X/ (Online) 3078-7718
- Journal Homepage