管理评论

管理评论 ›› 2024, Vol. 36 ›› Issue (12): 3-14.

• 数据要素管理 •    下一篇

基于PMC指数模型的数据要素政策量化评价及优化路径

张兮1, 石焱文1, 杜万里2, 孙可欣1, 王旭燕3, 张特1, 骈宇彤1, 王秋人1   

  1. 1. 天津大学管理与经济学部, 天津 300072;
    2. 国家发展和改革委员会创新驱动发展中心(数字经济研究发展中心), 北京 100038;
    3. 天津财经大学商学院, 天津 300222
  • 收稿日期:2023-09-30 出版日期:2024-12-28 发布日期:2025-01-02
  • 作者简介:张兮,天津大学管理与经济学部教授,博士生导师,博士;石焱文,天津大学管理与经济学部博士研究生;杜万里,国家发展和改革委员会创新驱动发展中心(数字经济研究发展中心)数据要素制度法规处处长,高级工程师;孙可欣,天津大学管理与经济学部博士研究生;王旭燕,天津财经大学商学院讲师,博士;张特,天津大学管理与经济学部博士研究生;骈宇彤,天津大学管理与经济学部博士研究生;王秋人,天津大学管理与经济学部博士研究生。
  • 基金资助:
    国家自然科学基金应急管理项目(72241432)。

Quantitative Assessment and Optimization Paths of Data Factor Policies Based on the PMC Index Model

Zhang Xi1, Shi Yanwen1, Du Wanli2, Sun Kexin1, Wang Xuyan3, Zhang Te1, Pian Yutong1, Wang Qiuren1   

  1. 1. College of Management and Economics, Tianjin University, Tianjin 300072;
    2. Center for Innovation-Driven Development, National Development and Reform Commission, Beijing 100038;
    3. School of Business, Tianjin University of Finance and Economics, Tianjin 300222
  • Received:2023-09-30 Online:2024-12-28 Published:2025-01-02

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摘要： 我国数据要素市场尚处于初期发展阶段，科学化政策制定对市场稳定发展至关重要。本文采用文本内容分析法，对截至2023年底国家层面的28项数据要素政策文本进行挖掘，并利用PMC指数模型对这些政策进行量化评价。结果显示，5项政策达到完美水平，16项为优秀，3项为良好，在政策的多元性、全面性、平衡性和可持续性等方面仍存在提升空间。本文提出以下建议：第一，针对以往政策内容中对部分主体维度覆盖不足的问题，建议建立更具包容性的合作机制，确保各方在数据要素市场建设中发挥积极作用；第二，针对以往政策内容中技术工具和服务场景维度得分较低的问题，建议后续数据要素市场政策内容中进一步强化技术工具与服务场景支持；第三，针对以往政策内容中风险管控维度得分较高，价值创造维度得分较低的问题，建议后续政策在保障安全可控的基础上，进一步聚焦效率提升和价值创造；第四，针对政策时效中长期规划维度得分较低的问题，建议在后续政策中进一步加强政策的可持续性。

关键词: 数据要素, 政策评价, PMC指数模型, 数据要素市场

Abstract: China’s data factor market is still in its early stage of development, making scientifically-based policy formulation crucial for its stable growth. This paper employs content analysis to examine 28 national-level data factor policies as of the end of 2023 and utilizes the PMC index model for quantitative evaluation. The results indicate that 5 policies achieved a perfect level, 16 were rated excellent, and 3 were acceptable. There remains room for improvement in the diversity, comprehensiveness, balance, and sustainability of these policies. This paper proposes the following recommendations: First, to address the insufficient coverage of certain stakeholders in previous policies, a more inclusive cooperation mechanism should be established to ensure active participation from all parties in developing the data factor market. Second, to enhance the support for technological tools and service scenarios, which were rated lower in previous policies, future policies should further strengthen these areas. Third, given the high scores in risk management and lower scores in value creation, future policies should focus on improving efficiency and value creation while ensuring security and control. Fourth, to address the lower scores in long-term planning within policy timeliness, the sustainability of future policies should be enhanced.

Key words: data factor, quantitative evaluation, PMC index model, data factor market