前沿与热点

国家数据要素综合试验区数据要素市场政策优化研究*

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  • (1.安徽大学管理学院   安徽合肥   230601)
李雯(1986-),女,安徽大学管理学院讲师,研究方向:信息政策、信息资源管理;汤国傲(1998-),男,安徽大学管理学院硕士研究生,研究方向:信息计量、信息资源管理。

收稿日期: 2025-09-16

  网络出版日期: 2025-12-30

基金资助

*本文系安徽省哲学社会科学规划青年项目“基于源头控制的业务系统原生数据质量优化研究”(项目编号:AHSKQ2021D102)研究成果之一。

Research on Optimization of Data Element Market Policy in the National Data Element Comprehensive Pilot Zone

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Received date: 2025-09-16

  Online published: 2025-12-30

摘要

国家数据要素综合试验区是我国为推动数据要素市场化配置改革,实现数据资源高效流通与价值释放的区域性创新试点,亟需通过优化其各省(市)数据要素市场政策来完善整体制度框架并驱动数字经济高质量发展。文章通过引入政策协同指数考察国家数据要素综合试验区各省级层面数据要素市场政策对中央层面政策的贯彻落实情况,并通过PMC指数探究其优劣点,进而提出国家数据要素综合试验区数据要素市场政策优化建议。结果表明:数据要素市场政策协同广度较好、政策覆盖较全面、政策工具运用充分,但在协同力度、政策效力、政策类型和政策主体方面表现欠佳,建议从加大政策转化深度、提升政策可预期性、增强政策协同效应、避免政策同质化和完善政策体系五个方面对国家数据要素综合试验区数据要素市场政策进行优化。

本文引用格式

李 雯 汤国傲 . 国家数据要素综合试验区数据要素市场政策优化研究*[J]. 图书与情报, 2025 , 45(06) : 66 -78 . DOI: 10.11968/tsyqb.1003-6938.2025072

Abstract

The National Data Element Comprehensive Pilot Zones are regional innovative pilot zones in China aimed at promoting the market-oriented allocation of data elements and achieving efficient circulation and value release of data resources. There is an urgent need to optimize provincial (and municipal) data element market policies to improve the overall institutional framework and drive high-quality digital economic growth. By introducing the Policy Synergy Index (PSI), this paper examines how provincial-level data element market policies within the National Data Element Comprehensive Pilot Zones implement and align with central government policies, and employs the Policy Modeling Consistency (PMC) index model to evaluate their strengths and weaknesses, thereby proposing optimization recommendations for these policies. The findings indicate that the policies exhibit broad synergy, comprehensive coverage, and ample deployment of policy instruments; however, they underperform in synergy intensity, policy efficacy, policy type diversity, and policy subject diversity. Optimization should therefore focus on deepening policy transformation, enhancing policy predictability, strengthening synergistic effects, avoiding policy homogenization, and perfecting the overall policy architecture.
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