The Impact of Market-oriented Transformation of Local Government Platforms on Platform Performance in Eastern China - Artificial Intelligence Perspective
- DOI
- 10.2991/978-94-6239-689-0_13How to use a DOI?
- Keywords
- Financing platform companies; Market-oriented transformation; Local governments; Artificial intelligence
- Abstract
This paper focuses on the effectiveness of market-oriented transformation of local government financing platforms as a core issue in local debt governance. From an AI perspective, it constructs a multivariable statistical model and uses SPSS to analyze the roles of governance mechanism and fiscal dependency as mediating variables, and AI and policy environment as moderating variables. Results indicate that transformation negatively affects asset returns in the short term due to costs from business adjustment, governance improvement and financing marketization. AI investment can lower fiscal dependency and strengthen independent operation for long-term development. Substantive transformation demands differentiated paths, clearer government-enterprise boundaries by resolving principal-agent conflicts and soft budget constraints, and stronger sustainable market-oriented operation via independent audits and third-party performance contracts.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hanke Zhu PY - 2026 DA - 2026/05/28 TI - The Impact of Market-oriented Transformation of Local Government Platforms on Platform Performance in Eastern China - Artificial Intelligence Perspective BT - Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026) PB - Atlantis Press SP - 134 EP - 145 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6239-689-0_13 DO - 10.2991/978-94-6239-689-0_13 ID - Zhu2026 ER -