张玲玲,黄从利,周非特,徐祥,刘龙.构建面向重大科技基础设施的区域战略性新兴产业筛选体系:结构框架与实践思考[J].中国科学院院刊,2024,(3):436-446.
构建面向重大科技基础设施的区域战略性新兴产业筛选体系:结构框架与实践思考
Build regional strategic emerging industry selection system for major scientific and technological infrastructure: Structural framework and practical consideration
构建面向重大科技基础设施的区域战略性新兴产业筛选体系:结构框架与实践思考
Build regional strategic emerging industry selection system for major scientific and technological infrastructure: Structural framework and practical consideration
作者
张玲玲1,3,4*
中国科学院大学 经济与管理学院 北京 100190;中国科学院大数据挖掘与知识管理重点实验室 北京 100190;1 中国科学院大学 经济与管理学院 北京 100190
ZHANG Lingling1,3,4*
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China;Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China;1 School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
黄从利1,2
中国科学院大学 经济与管理学院 北京 100190;中国科学院工程热物理研究所 北京 100190
HUANG Congli1,2
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China;Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
周非特1
中国科学院大学 经济与管理学院 北京 100190
ZHOU Feite1
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
徐祥2
中国科学院工程热物理研究所 北京 100190
XU Xiang2
Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
刘龙1
中国科学院大学 经济与管理学院 北京 100190
LIU Long1
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
中国科学院大学 经济与管理学院 北京 100190;中国科学院大数据挖掘与知识管理重点实验室 北京 100190;1 中国科学院大学 经济与管理学院 北京 100190
ZHANG Lingling1,3,4*
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China;Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China;1 School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
黄从利1,2
中国科学院大学 经济与管理学院 北京 100190;中国科学院工程热物理研究所 北京 100190
HUANG Congli1,2
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China;Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
周非特1
中国科学院大学 经济与管理学院 北京 100190
ZHOU Feite1
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
徐祥2
中国科学院工程热物理研究所 北京 100190
XU Xiang2
Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
刘龙1
中国科学院大学 经济与管理学院 北京 100190
LIU Long1
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
中文关键词
重大科技基础设施;战略性新兴产业;产业筛选
英文关键词
major scientific and technological infrastructure;strategic emerging industry;industry selection
中文摘要
在新一轮科技革命和产业变革的形势下,考虑区域资源和产业基础等多方面要素,面向十四五产业发展规划及社会实际需求,筛选适合区域重点发展的战略性新兴产业,充分发挥重大科技基础设施的科技创新能力,已经成为政府产业规划的重要任务。文章从产业经济学和管理学视角出发,借鉴多个产业筛选经典理论,构建了一套基于波特钻石模型的重大科技基础设施区域战略性新兴产业筛选体系,包含区域资源禀赋、产业升级潜力、技术支撑能力、社会需求条件、政府干预力度和其他相关机会六大维度,以期为政府和相关部门提供一定的指导性建议和产业筛选工具。
英文摘要
Under the circumstances of a new wave of scientific and technological revolution and industrial transformation, it has become imperative for the government’s industrial planning to effectively integrate regional resources with scientific and technological innovation capabilities, identify suitable regional strategic emerging industries, and establish clear directions for industrial development. Drawing on various classical theories of industrial selection from the perspectives of industrial economics and management, this research comprehensively considers six dimensions: technical support capacity of facilities, potential for industrial upgrading, regional resource endowment, social demands, government, and opportunities. Based on the Porter Diamond Model, a robust system is developed to select leading industries under major science and technology infrastructures. This research hopefully provides valuable guidance suggestions as well as industry selection tools for governmental authorities and relevant agencies.
DOI10.16418/j.issn.1000-3045.20240129005