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成渝城市群与珠三角城市群创新潜力比较及影响因素分析 被引量:14

Comparison of Innovation Potential between Chengdu-Chongqing City Cluster and Pearl River Delta City Cluster and Analysis of Influencing Factors
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摘要 城市群区域创新一体化对促进区域内部资源优化整合,带动周边城市创新发展具有重大意义。使用随机前沿知识生产模型研究了成渝城市群及珠三角城市群创新潜力,并采用一步法的创新非效率模型分析了影响创新潜力的主要因素。结果表明,随着创新阻力的减少,成渝城市群及珠三角城市群的创新效率在不断提升,且差距逐渐缩小;相比于珠三角城市群,成渝城市群拥有更大的创新潜力。同时,城市群内部各城市之间创新效率差异明显。为进一步提高各城市群的创新效率,应进一步转变经济发展方式,扩大开放,加强政府对创新的扶持力度,优化创新环境,提高工业集聚水平。 Regional innovation integration of urban agglomeration is of far-reaching significance to promote the optimization and integration of regional internal resources and drive the innovative development of surrounding cities. This paper uses stochastic frontier knowledge production model to study the innovation potential of Chengdu-Chongqing city cluster and Pearl River Delta city cluster, and uses one-step innovation non-efficiency model to analyze the main factors influencing innovation potential. The results show that the innovation of the Chengdu-Chongqing urban agglomeration and Pearl River Delta urban agglomeration is improving efficiency with the innovation resistance reducing, and the gap is narrowing, compared to the Pearl River Delta urban agglomeration, Chengdu-Chongqing urban agglomeration has greater innovation potential, at the same time. There are significant differences in innovation efficiency among cities within urban agglomerations. In order to further improve the innovation efficiency of urban agglomerations, we should further transform the mode of economic development and opening wider to the outside, strengthen government support for innovation, optimize the environment of innovation, and improve the level of industrial agglomeration.
作者 文淑惠 陈灿 Wen Shuhui;Chen Can(School of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China)
出处 《科技进步与对策》 CSSCI 北大核心 2019年第9期51-59,共9页 Science & Technology Progress and Policy
基金 国家自然科学基金项目(71463034) 国家社会科学基金项目(SYSX201709) 昆明理工大学管理与经济学院硕博生科研项目预研计划项目(001)
关键词 城市群 创新潜力 随机前沿 创新非效率 City Cluster Innovation Potential Stochastic Frontier Innovation Inefficiency
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