科研管理

科研管理 ›› 2025, Vol. 46 ›› Issue (2): 53-63.DOI: 10.19571/j.cnki.1000-2995.2025.02.006

• 论文 • 上一篇    下一篇

城市知识创新网络对新质生产力形成的影响研究

周建平1,2，徐维祥1,2，郭加新1,2   

  1. 1.浙江工业大学经济学院，浙江 杭州310023；
    2.浙江工业大学现代化产业体系研究院，浙江 杭州310023
  • 收稿日期:2024-03-31 修回日期:2024-12-12 出版日期:2025-02-20 发布日期:2025-02-11
  • 通讯作者: 徐维祥
  • 基金资助:
    国家社会科学基金重大项目：“新时代乡村振兴与新型城镇化的战略耦合及协同治理研究”(18ZDA045, 2018—2024)；浙江省社科联研究课题：“网络视角下数实融合创新对生产力布局优化的影响效应与机制”(2025B079, 2024—2026)；教育部人文社科基金项目：“共同富裕目标下平台经济促进要素跨区域适配的实现机制、组织模式及政策设计研究”(22YJC790073, 2022—2025)；教育部人文社科基金项目：“专精特新中小企业技术创新网络的动态演化、驱动机理及其创新绩效影响机制研究”(23YJA790069, 2023—2026)。

Research on the impact of urban knowledge innovation network on the formation of new quality productive forces

Zhou Jianping1,2, Xu Weixiang1,2, Guo Jiaxin1,2   

  1. 1. School of Economics, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China; 
    2. Institute for Industrial System Modernization, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China
  • Received:2024-03-31 Revised:2024-12-12 Online:2025-02-20 Published:2025-02-11
  • Contact: Weixiang Xu

摘要：       城市知识创新网络对新质生产力的形成发挥着基础支撑作用。本文运用2011至2021年285个城市的数据，在“要素—结构—功能”的系统论框架下测度了新质生产力发展水平，并应用双重机器学习方法检验了知识创新网络对新质生产力的具体影响。主要发现：（1）知识创新网络能够正向推动新质生产力的发展，尤其在提升生产力要素特质与功能取向方面展现出较强的促进作用。（2）进一步分析发现，知识产权保护、信息壁垒消除以及数据要素供应等因素，在知识创新网络影响新质生产力的过程中发挥了正向调节作用。（3）异质性检验表明，知识创新网络对新质生产力的影响呈现出明显的空间异质性和层级异质性。在东部地区及城市群地区，城市知识创新网络的促进作用更为突出。而且知识创新网络的赋能效应呈现出由“先行区”至“滞后区”递减的梯度特征。本文丰富了新质生产力影响因素和城市知识创新网络外部效应的相关研究，结论为城市基于知识创新推动生产力新质态的涌现提供了政策启示。

关键词: 知识创新网络, 新质生产力, 调节效应, 异质性检验, 双重机器学习

Abstract:    The urban knowledge innovation network plays a fundamental supporting role in the formation of new quality productive forces. This paper utilized data from 285 cities spanning from 2011 to 2021, and within the "elements-structure-function" framework of systems theory, measures the development level of new quality productive forces. Additionally, it employed double machine learning methods to examine the specific impact of the knowledge innovation network on new quality productive forces. The main findings are as follows: (1) The knowledge innovation network positively promotes the development of new quality productive forces, particularly playing an active role in enhancing the elemental characteristics and functional orientation of productivity. (2) Further analysis reveals that factors such as intellectual property protection, elimination of information barriers, and supply of data elements play a positive moderating role in the impact of knowledge innovation networks on new quality productivity. (3) Heterogeneity testing shows that the impact of knowledge innovation networks on new quality productivity exhibits significant spatial and hierarchical heterogeneity. In the eastern regions and urban agglomerations, the promoting effect of the urban knowledge innovation network is more pronounced. Moreover, the impact of the knowledge innovation network shows a gradient characteristic, decreasing from "leading areas" to "lagging areas". This paper has enriched the research on the influencing factors of new quality productive forces and the external effects of urban knowledge innovation networks. The conclusions will provide some policy implications for cities to promote the emergence of new quality productive forces through knowledge innovation.

Key words: knowledge innovation network, new quality productive forces, moderating effect, heterogeneity test, double machine learning