热带地理 ›› 2020, Vol. 40 ›› Issue (2): 323-334.doi: 10.13284/j.cnki.rddl.003210

• 论文 • 上一篇    下一篇

中国城市创新能力及其影响因素的空间分异——基于GWR模型的实证

陈依曼, 李立勋(), 符天蓝   

  1. 中山大学 地理科学与规划学院,广州 510275
  • 收稿日期:2019-10-02 修回日期:2020-01-21 出版日期:2020-03-10 发布日期:2020-05-15
  • 通讯作者: 李立勋 E-mail:eesllx@mail.sysu.edu.cn
  • 作者简介:陈依曼(1996—),女,广东潮州人,硕士研究生,主要研究方向为城市地理、经济地理、城市与区域规划,(E-mail) chenym56@mail2.sysu.edu.cn。
  • 基金资助:
    中国博士后科学基金面上项目(2019M653145);高校基本科研业务费-青年教师培育项目(19lgpy40)

Spatial Heterogeneity in Chinese Urban Innovation Capabilities and Its Determinants: Approach Based on the Geographically Weighted Regression Model

Chen Yiman, Li Lixun(), Fu Tianlan   

  1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Received:2019-10-02 Revised:2020-01-21 Online:2020-03-10 Published:2020-05-15
  • Contact: Li Lixun E-mail:eesllx@mail.sysu.edu.cn

摘要:

基于城市发明专利授权数,利用克里金插值法、空间自相关方法分析了中国城市创新能力的空间分异特征;并运用地理加权回归(GWR)模型探讨了289个地级及以上城市创新能力影响因素的空间异质性,揭示主导因素的空间分异。结果表明:1)中国城市创新能力呈现由东南向西部递减的趋势,创新能力最强的城市主要集中在东南部沿海地区,而以西藏各市及青海、新疆部分城市为主的西部城市创新能力最弱,空间集聚性显著;创新能力高-高集聚的地区为京津冀、长三角、珠三角地区,低-低集聚的地区为西部及东北地区。2)影响因素方面,各自变量的显著性水平较好,显著区域占比由大到小依次为财政投入、人才要素、经济基础、经济外向度、金融环境、信息化水平,除财政投入对城市创新能力具有显著的正相关性之外,其他因素皆同时存在正负相关效应;各自变量指标回归系数区间范围较大,具有明显的空间分异特征。3)主导因素方面,东南部城市创新能力受经济基础影响较大,而东北以及西部城市受金融环境影响较大。中国城市创新能力影响因素存在空间分异,政府及相关部门在制定创新政策时需要考虑不同城市的特点,采取针对性措施,促进中国城市创新健康协调发展。

关键词: 城市创新能力, 空间分异, 地理加权回归, 中国

Abstract:

Innovation is the primary driving force of development and provides strategic support for building a modern economic system. Its role in promoting regional development has been studied and acknowledged by academics. Since the Chinese economic reform, the country’s scientific and technological level and capacity for innovation has significantly improved, and the overall national strength has steadily increased. Presently, innovation plays a critical role in China's economic transformation from a stage of high-speed growth to a stage of high-quality development. Based on urban invention patents, this study analyzes the spatial characteristics of the innovation capabilities of Chinese cities by using the kriging interpolation method and a spatial autocorrelation model. The Geographical Weighted Regression (GWR) model was used to explore the spatial heterogeneity in the factors influencing the innovation ability of 289 cities, and to reveal the spatial differences in the dominant factors. The results indicate that the innovation ability of Chinese cities shows a decreasing trend from the southeast to the west, and the cities with the highest innovation capacity are concentrated in the southeastern coastal areas. The cities in the west, mainly in Tibet, have the lowest innovation capacity. The spatial agglomeration is significant. In terms of the determinants, the significance level of each variable was good. The proportion of significant regions in all cities in order from large to small is financial input, talent element, economic foundation,economy extraversion, financial environment, and information level. In addition to the significant positive correlation between financial input and urban innovation capacity, there are positive and negative correlations among the other determinants. Moreover, the range of the index’s regression coefficient is large and has an obvious spatial differentiation. The talent element is positively correlated with the innovation ability of most cities. The influence of the economic foundation and information level on urban innovation ability is positive for economically developed areas and negative for underdeveloped areas. Economic extraversion is positively correlated with the innovation ability of the eastern coastal areas and the central and western regions, but negatively correlated with that of most cities in northeast and north China. The financial environment, represented by residents' savings, has a positive correlation effect on the innovation output of the northeast and western regions, while it has the opposite effect in the Yangtze river delta and other regions. The input of innovation elements in China is still a leading determinant in improving the country’s innovation ability. For southeastern Chinese cities with a higher level of economic development, more attention should be paid to improving their economic level and promoting the overall improvement of their innovation ability. For cities in the northeast and west, where the economic level is relatively inferior, financial support plays a significant role in the development of their innovation activities. Thus, attention should be paid to the cultivation of the financial market in these areas. The research shows that there are spatial differences in the factors influencing urban innovation capabilities in China, and the characteristics of different cities should be taken into account when formulating innovation policies to make policies more targeted and facilitate the healthy and coordinated development of national urban innovation.

Key words: urban innovation capabilities, spatial differentiation, Geographic Weighted Regression, China

中图分类号: 

  • F299.2