AJSM OPEN ACCESS

Academic Journal of Sociology and Management

ISSN:3005-5040 (print) | ISSN:3005-5059 (online) | Publication Frequency: Bimonthly

Publication

Accepted 2025 July 11 ; Published 2025 July 13

Academic Journal of Sociology and Management, 2025, 3(4), 3005-5040.

Abstract

This study examines the legal application and institutional improvement requirements for Committee on Foreign Investment in the United States (CFIUS) review mechanisms in cross-border lithium battery investments, addressing the critical balance between national security protection and investment facilitation. Through comprehensive analysis of statutory frameworks under the Foreign Investment Risk Review Modernization Act (FIRRMA) of 2018 and the Foreign Investment and National Security Act (FINSA) of 2007, this research identifies systematic challenges in current regulatory approaches to critical energy technology oversight. The investigation analyzes CFIUS case precedents, enforcement actions, and regulatory decisions from 2019-2024, revealing significant jurisdictional ambiguities in emerging technology classifications, procedural inefficiencies impacting investment climate predictability, and enforcement gaps in post-transaction monitoring. Based on examination of CFIUS Annual Reports to Congress, Congressional Research Service (CRS) analyses, and Government Accountability Office (GAO) assessments, this study demonstrates that existing CFIUS frameworks encounter substantial limitations when addressing sophisticated lithium battery technologies, with regulatory uncertainty creating delays averaging 156-203 days for critical technology transactions. The study proposes comprehensive institutional reforms incorporating risk-proportionate assessment protocols, enhanced legal clarity in technology classifications, and streamlined review processes grounded in established CFIUS jurisprudence. Recommendations emphasize implementation of tiered security screening mechanisms aligned with FIRRMA's mandatory filing requirements, conditional approval frameworks consistent with National Defense Authorization Act provisions, and international cooperation strategies for regulatory harmonization. The research contributes theoretical insights into balancing national security imperatives with investment facilitation objectives, providing practical frameworks for modernizing foreign investment review processes in critical technology sectors. These findings inform regulatory policy development and establish foundations for enhanced cross-border investment governance in strategic energy technologies.

Keywords

CFIUS Review Mechanisms , Cross-border Lithium Battery Investment , National Security Regulation , Investment Facilitation Framework .

Metadata

Pages: 7-17

References: 31

Disciplines: Jurisprudence

Subjects: International Law

Cite This Article

APA Style

Li, J. (2025). Legal application and institutional improvement of cfius review mechanisms in cross-border lithium battery investments: a framework analysis for balancing national security and investment facilitation. Academic Journal of Sociology and Management, 3(4), 7-17. https://doi.org/10.70393/616a736d.333034

Acknowledgments

I would like to extend my sincere gratitude to Yue Xi and Yingqi Zhang for their groundbreaking research on measuring time and quality efficiency in human-AI collaborative legal contract review as published in their article titled "Measuring Time and Quality Efficiency in Human-AI Collaborative Legal Contract Review: A Multi-Industry Comparative Analysis" in the Journal of Legal Technology and Innovation (2024). Their insights and methodologies have significantly influenced my understanding of advanced techniques in regulatory efficiency assessment and have provided valuable inspiration for my own research in balancing procedural effectiveness with comprehensive security evaluation in cross-border investment review processes. I would like to express my heartfelt appreciation to Zhuxuanzi Wang, Xu Wang, and Hongbo Wang for their innovative study on temporal graph neural networks for money laundering detection in cross-border transactions, as published in their article titled "Temporal Graph Neural Networks for Money Laundering Detection in Cross-Border Transactions" in the Academia Nexus Journal (2024). Their comprehensive analysis and advanced computational modeling approaches have significantly enhanced my knowledge of cross-border financial transaction monitoring systems and inspired my research in developing sophisticated frameworks for post-transaction oversight mechanisms within CFIUS regulatory processes.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.
da Silva, E. A. (2025). CFIUS Tailored to the US-China Strategic Rivalry: Investment Screening and Regulatory Convergence in the Shadow of the Liberal International Order. In China's Globalisation and the New World Order (pp. 235-258). Singapore: Springer Nature Singapore.

2.
Li, J., & Tang, R. (2023). Superpower Rivalry and the" Modernization" of Foreign Investment Risk Review. U. Ill. L. Rev., 461.

3.
Cheadle, S. C. (2024). The National Security of Inbound & Outbound Investment: Reforming CFIUS and FOCI. Wm. & Mary Bus. L. Rev., 16, 625.

4.
Jalinous, F., & Sensenig, T. (2022). Keeping up with CFIUS: A Practitioner's Report on National Security Trends in the United States. Erasmus L. Rev., 15, 266.

5.
Horn, J. A. (2021). Innovation Meets Regulation: FIRRMA's Significance, the Treasury's Dilemma, and the New Normal for Foreign Investment in the US Venture Capital Ecosystem. Pepp. L. Rev., 48, 829.

6.
Chen, H. (2025). Research on the Convergence of Foreign Investment Security Review Legal Systems in Europe and the United States. Beijing Law Review, 16(2), 749-763.

7.
Hochen, R. (2021). When Your Apps Threaten National Security-A Review of the Tiktok and Wechat Bans and Government Actions under IEEPA and FIRRMA. Brook. J. Corp. Fin. & Com. L., 16, 193.

8.
Wang, V. (2022). A new CFIUS: Refining the committee's multimember structure with for-cause protections. Geo. Wash. L. Rev., 90, 1316.

9.
Adarov, A., & Ghodsi, M. (2023). Heterogeneous effects of nontariff measures on cross‐border investments: Bilateral firm‐level analysis. Review of International Economics, 31(1), 158-179.

10.
Cash, M. (2022). Reversing CFIUS: Analyzing the International and Constitutional Implications of the Revised National Critical Capabilities Defense Act. Duke J. Comp. & Int'l L., 33, 289.

11.
Maula, M. V., & Lukkarinen, A. (2022). Attention across borders: Investor attention as a driver of cross‐border equity crowdfunding investments. Strategic Entrepreneurship Journal, 16(4), 699-734.

12.
Gregori, W. D., & Nardo, M. (2021). The effect of restrictive measures on cross‐border investment in the European Union. The World Economy, 44(7), 1914-1943.

13.
Zhang, S., Zhu, C., & Xin, J. (2024). CloudScale: A Lightweight AI Framework for Predictive Supply Chain Risk Management in Small and Medium Manufacturing Enterprises. Spectrum of Research, 4(2).

14.
Zhang, S., Mo, T., & Zhang, Z. (2024). LightPersML: A Lightweight Machine Learning Pipeline Architecture for Real-Time Personalization in Resource-Constrained E-commerce Businesses. Journal of Advanced Computing Systems, 4(8), 44-56.

15.
Chen, Y., Ni, C., & Wang, H. (2024). AdaptiveGenBackend A Scalable Architecture for Low-Latency Generative AI Video Processing in Content Creation Platforms. Annals of Applied Sciences, 5(1).

16.
Ju, C., Jiang, X., Wu, J., & Ni, C. (2024). AI-Driven Vulnerability Assessment and Early Warning Mechanism for Semiconductor Supply Chain Resilience. Annals of Applied Sciences, 5(1).

17.
Li, M., Liu, W., & Chen, C. (2024). Adaptive Financial Literacy Enhancement through Cloud-Based AI Content Delivery: Effectiveness and Engagement Metrics. Annals of Applied Sciences, 5(1).

18.
Wu, Z., Wang, S., Ni, C., & Wu, J. (2024). Adaptive Traffic Signal Timing Optimization Using Deep Reinforcement Learning in Urban Networks. Artificial Intelligence and Machine Learning Review, 5(4), 55-68.

19.
Chen, J., & Lv, Z. (2025, April). Graph Neural Networks for Critical Path Prediction and Optimization in High-Performance ASIC Design: A ML-Driven Physical Implementation Approach. In Global Conference on Advanced Science and Technology (Vol. 1, No. 1, pp. 23-30).

20.
Ma, D., Shu, M., & Zhang, H. (2025). Feature Selection Optimization for Employee Retention Prediction: A Machine Learning Approach for Human Resource Management.

21.
Li, M., Ma, D., & Zhang, Y. (2025). Improving Database Anomaly Detection Efficiency Through Sample Difficulty Estimation.

22.
Yu, K., Chen, Y., Trinh, T. K., & Bi, W. (2025). Real-Time Detection of Anomalous Trading Patterns in Financial Markets Using Generative Adversarial Networks.

23.
McNichols, H., Zhang, M., & Lan, A. (2023, June). Algebra error classification with large language models. In International Conference on Artificial Intelligence in Education (pp. 365-376). Cham: Springer Nature Switzerland.

24.
Zhang, M., Heffernan, N., & Lan, A. (2023). Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. arXiv preprint arXiv:2306.00791.

25.
Zhang, M., Baral, S., Heffernan, N., & Lan, A. (2022). Automatic short math answer grading via in-context meta-learning. arXiv preprint arXiv:2205.15219.

26.
Wang, Z., Zhang, M., Baraniuk, R. G., & Lan, A. S. (2021, December). Scientific formula retrieval via tree embeddings. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 1493-1503). IEEE.

27.
Zhang, M., Wang, Z., Baraniuk, R., & Lan, A. (2021). Math operation embeddings for open-ended solution analysis and feedback. arXiv preprint arXiv:2104.12047.

28.
Qi, D., Arfin, J., Zhang, M., Mathew, T., Pless, R., & Juba, B. (2018, March). Anomaly explanation using metadata. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1916-1924). IEEE.

29.
Zhang, M., Mathew, T., & Juba, B. (2017, February). An improved algorithm for learning to perform exception-tolerant abduction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).

30.
Zhang, S., Feng, Z., & Dong, B. (2024). LAMDA: Low-Latency Anomaly Detection Architecture for Real-Time Cross-Market Financial Decision Support. Academia Nexus Journal, 3(2).

31.
Wang, Z., Wang, X., & Wang, H. (2024). Temporal Graph Neural Networks for Money Laundering Detection in Cross-Border Transactions. Academia Nexus Journal, 3(2).

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