中国绿色科技创新效率空间关联网络结构特征及影响因素  

Structural Characteristics and Influencing Factors of Spatial Correlation Network of Green Science and Technology Innovation Efficiency in China

作　　者：孙中瑞 樊杰 孙勇[1,2] 刘汉初 SUN Zhongrui;FAN Jie;SUN Yong;LIU Hanchu(Institute of Science and Development,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[1]中国科学院科技战略咨询研究院,中国北京100190 [2]中国科学院大学,中国北京100049 [3]中国科学院地理科学与资源研究所,中国北京100101

出　　处：《经济地理》2022年第3期33-43,共11页Economic Geography

基　　金：国家自然科学基金重点项目(41630644)。

摘　　要：采用超效率SBM模型对中国绿色科技创新效率进行测度,并基于引力模型构建空间关联网络,进而对网络结构特征及网络影响因素进行分析。结果表明:①中国绿色科技创新效率总体呈波动增长态势,东部地区明显高于中、西部地区,近年来东部地区效率增长相对平稳,中部地区增长较大,而西部地区下降较为明显。②中国已形成一个结构稳定的绿色科技创新效率空间关联网络,网络关系数、网络密度波动增长,网络效率不断下降,但仍具有较强的小世界性。③网络核心—边缘结构明显,上海、北京、浙江处于中心位置,中介作用显著,新疆、宁夏、内蒙古等偏远省份处于边缘位置。④四大板块内部联系稀疏,板块间联系紧密,溢出效应较强。⑤相邻矩阵、经济发展水平差异矩阵、开放程度差异矩阵显著正向影响空间关联,而地理距离、知识基础差异矩阵显著负向影响空间关联关系的建立。The super efficiency SBM model is used to measure the efficiency of China’s green science and technology innovation,and the spatial correlation network is constructed based on the gravity model, and then the network structure characteristics and network influencing factors are analyzed. The results show that: 1) The overall efficiency of green scientific and technological innovation in China shows a fluctuating growth trend,and the efficiency growth in the eastern region is significantly higher than that in the central and western regions. In recent years, the efficiency growth in the eastern region is relatively stable, the growth in the central region is large, and the decline in the western region is more obvious. 2) China has formed a structurally stable spatial correlation network of green scientific and technological innovation efficiency. The number of network relationships and network density show the characteristic of fluctuating increase, and the network efficiency continues to decline, but it still has a strong small world. 3) The core-edge structure of the network is obvious. Shanghai, Beijing and Zhejiang are in the center and play a significant intermediary role.Remote provinces such as Xinjiang, Ningxia and Inner Mongolia are in the edge. 4) The internal links of the four plates are sparse, the links between plates are close, and the spillover effect is strong. 5) Adjacency matrix, economic development level difference matrix and openness difference matrix have a significant positive impact on spatial correlation, while geographical distance and knowledge base difference matrix have a significant negative impact on the establishment of spatial correlation.

关 键 词：绿色科技 创新效率 网络结构 社会网络分析 知识生产函数 块模分析 创新发展 

分 类 号：F129.9[经济管理—世界经济]