地理学报 ›› 2018, Vol. 73 ›› Issue (4): 738-754.doi: 10.11821/dlxb201804011

• 区域发展 • 上一篇    下一篇

中国城市创新技术转移格局与影响因素

段德忠1,2,杜德斌1(),谌颖2,管明明1   

  1. 1. 华东师范大学全球创新与发展研究院,上海 200062
    2. 路易斯安那州立大学地理与人类学系,美国 巴吞鲁日 70820
  • 收稿日期:2017-08-19 出版日期:2018-04-20 发布日期:2018-04-10
  • 基金资助:
    国家自然科学基金项目(41471108, 41501141, 41601149);华东师范大学优秀博士学位论文培育资助项目(YB2016004)

Technology transfer in China's city system: Process, pattern and influencing factors

DUAN Dezhong1,2,DU Debin1(),CHEN Ying2,GUAN Mingming1   

  1. 1. Institute for Global Innovation & Development, East China Normal University, Shanghai 200062, China;
    2. Department of Geography & Anthropology, Louisiana State University, Baton Rouge 70820, USA;
  • Received:2017-08-19 Online:2018-04-20 Published:2018-04-10
  • Supported by:
    [Foundation: National Natural Science Foundation of China, No.41471108, No.41501141, No.41601149; Outstanding Doctoral Dissertation Cultivation Plan of Action of East China Normal University, No.YB2016004]

摘要:

以国家知识产权局专利检索及分析平台中历年专利转让记录为数据源,采用大数据挖掘技术、地理信息编码技术、空间自相关模型和多元线性回归模型,并从集聚和扩散两个方面构建城市创新技术转移能力评价指标体系及评估模型,对2001-2015年中国城市技术转移的时空格局、集聚模式及影响因素进行了研究。结果发现:① 2001-2015年,随着城市创新技术转移能力的不断上升,且在参与创新技术转移的城市数量不断增加情境下,中国城市创新技术转移能力的两极分化及强集聚特征持续发育;② 中国城市创新技术转移格局经历着空间不断极化的历程,由京津冀、长三角和珠三角主导的三极格局逐渐凸显;③ 中国城市创新技术集散体系不断完善,从全球至地方的中国创新技术集散体系已初步形成;④ 中国城市创新技术转移呈现出显著的空间关联与集聚效应,4种类型基本呈“抱团”分布,城市创新技术转移的地理邻近性显著;⑤ 多元线性回归模型发现,城市创新技术的需求能力和供给能力决定其转移能力,第三产业产值规模和专利申请量对城市创新技术转移能力影响较大。另外,研发人员数量也是影响城市技术转移能力的重要因素,但是相关性较低,而城市第一产值规模对城市创新技术转移能力具有显著的阻抗作用。(注:①考虑到专利技术从申请至授权以及转移的期限较长,因此本文城市吸收、转出的专利速度主要基于1年转移量、2年转移量和5年转移量来综合评定。)

关键词: 技术转移, 时空格局, 集聚模式, 影响因素, 中国

Abstract:

Based on the records of patent transfer from the patent retrieval and analysis platform in the State Intellectual Property Office of China, this research built an assessment index and model for technology transfer in China's city system in terms of agglomeration and dispersion, using big data mining technology, geo-coding technology, spatial autocorrelation model and multiple linear regression model. Then we studied the spatial-temporal pattern, agglomeration model and influencing factors of technology transfer in China's city system from 2001 to 2015, and obtained the following results. Firstly, with the increasing capability of city's technology transfer and the growing number of cities involved in transferring technology, the polarization and strong agglomeration of technology transfer in China's city system have been intensified. Secondly, technology transfer in China's city system has experienced a process of constant spatial polarization, the three-pole pattern led by the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region has been gradually prominent. Thirdly, technology transfer system from global to local scale in China's city system has initially taken shape. Beijing, Shanghai and Shenzhen have become the three global centers of China in technology transfer. Fourthly, technology transfer in China's city system has produced an obvious spatial correlation and agglomeration effect. The four types are mainly in the cluster, and the geographical proximity of technology transfer in China's city system is significant. Last but not least, the influencing factors of technology transfer in China's city system were also verified by multiple linear regression model. We found that the demand and supply capacity respectively represented by the scale of tertiary industry and the number of patent applications has a great influence on the growth of technology transfer capability. In addition, the number of R & D employees is an important factor, but its correlation is low. The findings further confirm that the scale of primary industry has a significant impedance effect on city's technology transfer capability.

Key words: technology transfer, space-time pattern, agglomeration model, influencing factor, China