Industrial Policy for Semiconductors in the AI Hardware Era: Text Based Measurement and Cross Country Evidence

Kai Ye

doi:10.70393/6a6374616d.333730

Authors

Kai Ye Hezhou University

DOI:

https://doi.org/10.70393/6a6374616d.333730

ARK:

https://n2t.net/ark:/40704/JCTAM.v3n1a06

Disciplines:

Natural Language Processing

Subjects:

Text Mining

References:

27

Keywords:

Semiconductor Industrial Policy, AI Hardware, Text Mining, Subsidy Quantification, International Spillovers

Abstract

As demand for AI hardware increases, semiconductor industry policies, through subsidies, tax credits, and targeted financing, are expanding, leading to uneven distribution of policy support documents and strategic frameworks, as well as long implementation delays. This paper proposes a replicable measurement process to identify semiconductor-related interventions in global trade alerts through iterative validation. Each indicator is categorized according to value chain objectives and policy tools, providing a dataset and evaluation framework for semiconductor research.

Author Biography

Kai Ye, Hezhou University

Hezhou University, 542899, China.

References

[1] Goldberg, P. K., Juhász, R., Lane, N. J., Forte, G. L., & Thurk, J. (2024). Industrial policy in the global semiconductor sector (No. w32651). National Bureau of Economic Research.

[2] Huang, S. (2025). LSTM-Based Deep Learning Models for Long-Term Inventory Forecasting in Retail Operations. Journal of Computer Technology and Applied Mathematics, 2(6), 21-25.Li, K., Chen, X., Song, T., Zhang, H., Zhang, W., & Shan, Q. (2024). GPTDrawer: Enhancing Visual Synthesis through ChatGPT. arXiv preprint arXiv:2412.10429.

[3] Bal, R., & Gill, I. S. (2020). Policy approaches to artificial intelligence based technologies in China, European Union and the United States.

[4] Wang, H., Li, Q., & Liu, Y. (2023). Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint. Stat, 12(1), e514.

[5] Wang, H., Sun, W., & Liu, Y. (2022). Prioritizing autism risk genes using personalized graphical models estimated from single-cell rna-seq data. Journal of the American Statistical Association, 117(537), 38-51.

[6] Nazir, M., Rasheed, M. Q., Yu, X. H., & Ahmed, Z. (2025). Can Computer Technology, Semiconductors, and Artificial Intelligence Shape a Sustainable Future? Evidence From Leading Semiconductor‐Producing Countries. Sustainable Development.

[7] Rizi, A. D., Roy, A., Noor, R., Kang, H., Varshney, N., Jacob, K., ... & Asadizanjani, N. (2023). From talent shortage to workforce excellence in the chips act era: Harnessing industry 4.0 paradigms for a sustainable future in domestic chip production. arXiv preprint arXiv:2308.00215.

[8] Janjeva, A., Baek, S., & Sellars, A. (2024). China’s Quest for Semiconductor Self-Sufficiency.

[9] Chen, Y. (2025). Artificial Intelligence in Economic Applications: Stock Trading, Market Analysis, and Risk Management. Journal of Economic Theory and Business Management, 2(5), 7-14.

[10] Yin, M. (2025). Predictive Maintenance of Semiconductor Equipment Using Stacking Classifiers and Explainable AI: A Synthetic Data Approach for Fault Detection and Severity Classification. Journal of Industrial Engineering and Applied Science, 3(6), 36-46.

[11] Yin, M. (2025). Data Quality Control in Semiconductor Manufacturing through Automated ETL Processes and Class Imbalance Handling Techniques. Journal of Industrial Engineering and Applied Science, 3(6), 13-22.

[12] Yin, M. (2025). A Data-Driven Approach for Real-Time Bottleneck Detection and Optimization in Semiconductor Manufacturing Using Active Period Method and Visualization. Academic Journal of Natural Science, 2(4), 19-26.

[13] Awad, H., & Bayoumi, E. H. (2025). Next-generation smart inverters: bridging AI, cybersecurity, and policy gaps for sustainable energy transition. Technologies, 13(4), 136.

[14] Sun, Y., & Ortiz, J. (2024). An ai-based system utilizing iot-enabled ambient sensors and llms for complex activity tracking. arXiv preprint arXiv:2407.02606.

[15] Pasricha, S., & Wolf, M. (2023). Ethical design of computers: From semiconductors to IoT and artificial intelligence. IEEE Design & Test, 41(1), 7-16.

[16] Yin, M. (2025). Defect Prediction and Optimization in Semiconductor Manufacturing Using Explainable AutoML. Academic Journal of Natural Science, 2(4), 1-10.

[17] Chen, Y. (2025). A Comparative Study of Machine Learning Models for Credit Card Fraud Detection. Academic Journal of Natural Science, 2(4), 11-18.

[18] Zhu, H., Luo, Y., Liu, Q., Fan, H., Song, T., Yu, C. W., & Du, B. (2019). Multistep flow prediction on car-sharing systems: A multi-graph convolutional neural network with attention mechanism. International Journal of Software Engineering and Knowledge Engineering, 29(11n12), 1727–1740.

[19] Fu, Q. (2025). Implementation Standards and Industry-Specific Adaptation of SAP-Lean Production Integration in Manufacturing: A Multi-Case Validation with Quantitative Performance Optimization. Frontiers in Management Science, 4(5), 67-7

[20] Chen, Y. (2025). Daily Asset Pricing Based on Deep Learning: Integrating No-Arbitrage Constraints and Market Dynamics. Journal of Computer Technology and Applied Mathematics, 2(6), 1-10.

[21] Pang, F. (2020, November). Research on Incentive Mechanism of Teamwork Based on Unfairness Aversion Preference Model. In 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) (pp. 944-948). IEEE.

[22] Wang, J., Tse, T. K., Li, S., & Fung, J. C. (2023). A model of the sea–land transition of the mean wind profile in the tropical cyclone boundary layer considering climate changes. International Journal of Disaster Risk Science, 14(3), 413-427.

[23] Menaldo, V. The Progenitors of the Information Age and the Birth of AI: Introducing the “Political Economy of the Fourth Industrial Revolution”.

[24] Chen T. Innovative Approaches in Art Vocational Education: Exploring Industry-Academia Collaboration and Internationalization. Journal of Advanced Research in Education. 2025;4(5):11-7.

[25] Chen, Y. (2025). Leveraging LSTM Networks for Vehicle Stability Prediction: A Comparative Analysis with Traditional Models under Dynamic Load Conditions. Computing and Interdisciplinary Science, 1(2), 15-22.

[26] Wang, J., Cao, S., Tim, K. T., Li, S., Fung, J. C., & Li, Y. (2025). A novel life-cycle analysis framework to assess the performances of tall buildings considering the climate change. Engineering Structures, 323, 119258.

[27] Aboy, M., Minssen, T., & Kop, M. (2022). Mapping the patent landscape of quantum technologies: patenting trends, innovation and policy implications. IIC-International Review of Intellectual Property and Competition Law, 53(6), 853-882.