地理研究 ›› 2018, Vol. 37 ›› Issue (6): 1223-1237.doi: 10.11821/dlyj201806012

• 研究论文 • 上一篇    下一篇

中国省域科技创新模式及其时空演变

李哲(), 申玉铭(), 曾春水   

  1. 首都师范大学资源环境与旅游学院,北京 100048
  • 收稿日期:2017-12-05 修回日期:2018-03-03 出版日期:2018-06-10 发布日期:2018-07-06
  • 作者简介:

    作者简介:李哲(1993- ),女,黑龙江大庆人,博士,研究方向为产业发展与空间布局。E-mail: lz80701@163.com

  • 基金资助:
    国家自然科学基金项目(41471107)

Science and technology innovation patterns and their spatial and temporal evolution of provinces in China

Zhe LI(), Yuming SHEN(), Chunshui ZENG   

  1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
  • Received:2017-12-05 Revised:2018-03-03 Online:2018-06-10 Published:2018-07-06
  • About author:

    Author: Shi Zhenqin (1988-), PhD, specialized in regional development and land space management in mountain areas. E-mail: kevinszq@163.com

    *Corresponding author: Deng Wei (1957-), Professor, specialized in mountain environment and regional development.

    E-mail: dengwei@imde.ac.cn

摘要:

中国的经济增长模式正在由要素驱动型向创新驱动型转变,适宜的科技创新模式是促进中国经济社会协调可持续发展的关键。基于熵值法构建综合指标评价体系,运用Theil指数对中国科技创新投入与产出水平的时空演变进行分析,并结合K-means聚类分析法将其分别归为相同的5个等级。根据不同的投入—产出组合,得出投入产出协调型、投入领先型与产出领先型3个科技创新类型及其子类。最后,利用多阶段DEA模型,从科技创新投入的角度,将科技创新模式划分为混合驱动型、创新平台驱动型、人力与资本驱动型和人力驱动型四类;从科技创新产出的角度,划分为经济创新导向型、知识与经济创新导向型和知识创新导向型三类模式。对比发现,中国科技创新模式与科技创新类型的时空演变呈现高度的相关性。在此基础上,为中国未来区域科技创新的发展提出若干建议。

关键词: 科技创新模式, 时空演变, 熵值法, K-means聚类分析, 多阶段DEA模型, 省域尺度

Abstract:

Given the fact that China's economic growth pattern is transforming from the previous factor-driven type into innovation-driven one, the more innovative a country is, the stronger competition advantage it will obtain and maintain around the world. Likewise, appropriate science and technology innovation pattern tends to be the key to promoting China's economic and social development in a balanced and sustainable manner. In this paper, entropy technology is introduced for the purpose of building a comprehensive index evaluation system. Based on weighted values generated by the evaluation system, this paper analyzes the temporal and spatial evolution of provincial science and technology innovation input and output level in 2004, 2009 and 2014 via Theil index. Then, by exercising K-means clustering analysis, 31 provincial weighted values of science and technology innovation input are divided into 5 grades from high to low, so are the weighted values of science and technology innovation output. On this account, there are totally 11 input-output combinations of science and technology innovation in China which can be classified into 3 types, namely, input-output coordination, input leading and output leading. Among them, there are 3 subtypes of input-output coordination which are high-, medium- and low-level input-output coordination in regard of input and output grades, while the latter one can be divided into 2 subtypes which involve high- and low-level output leading in comparison of input and output grades. This paper further studies the temporal and spatial evolution of all the types. The results show that: (1) input-output coordination maintains the dominant position in China on the move; (2) Innovation platform construction imposes the most significant influence on science and technology innovation type transformation in eastern China. By means of multi-stage DEA model, this paper identifies four science and technology innovation patterns in terms of science and technology innovation input, namely mixed driven, innovation platform driven, labor-capital driven and labor driven pattern, and three science and technology innovation patterns in terms of science and technology innovation output, which involve economy, knowledge-economy and knowledge innovation oriented pattern. It is obvious that science and technology innovation types are associated with the patterns in temporal and spatial evolution. What's more, this paper makes some suggestions to regional science and technology innovation development of China in the future.

Key words: science and technology innovation patterns, temporal and spatial evolution, entropy technology, K-means clustering analysis, multi-stage DEA model, provincial scale