Characteristics and Driving Factors of Spatial Association Network of China's Renewable Energy Technology Innovation

被引:10
|
作者
Feng, Chen [1 ]
Wang, Yuansheng [2 ]
Kang, Rong [3 ]
Zhou, Lei [4 ]
Bai, Caiquan [4 ]
Yan, Zheming [5 ]
机构
[1] Shanghai Univ Finance & Econ, Sch Publ Econ & Adm, Shanghai, Peoples R China
[2] Nankai Univ, Sch Finance, Tianjin, Peoples R China
[3] Northwest Univ, Sch Econ & Management, Xian, Peoples R China
[4] Shandong Univ, Ctr Econ Res, Jinan, Peoples R China
[5] Xi An Jiao Tong Univ, Sch Econ & Finance, Xian, Peoples R China
来源
FRONTIERS IN ENERGY RESEARCH | 2021年 / 9卷 / 09期
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
renewable energy technology innovation; spatial association; driving factor; social network analysis; China; RESEARCH-AND-DEVELOPMENT; CARBON EMISSIONS; KNOWLEDGE NETWORKS; TEMPORAL EVOLUTION; EMPIRICAL-EVIDENCE; ECONOMIC-GROWTH; CO2; EMISSIONS; PANEL; POWER; CONSUMPTION;
D O I
10.3389/fenrg.2021.686985
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Based on the provincial panel data of China from 2001 to 2016, this study uses the social network analysis approach to empirically investigate the characteristics and driving factors of the spatial association network of China's interprovincial renewable energy technology innovation. The findings are as following. 1) The spatial association of China's interprovincial renewable energy technology innovation exhibits a typical network structure. Moreover, its network density, network hierarchy and network efficiency are 0.3696, 0.6667 and 0.7833 in 2001 and 0.4084, 0.4764 and 0.7044 in 2016, respectively, implying the spatial association network became more and more complex and the interprovincial association strengthened during the sample period. 2) This spatial association network presents a "core-edge" distribution pattern. The positions and roles of various provinces vary greatly in the spatial association network. Specifically, the developed coastal regions such as Shanghai, Beijing and Tianjin have a degree centrality, closeness centrality and betweenness centrality of above 75, 80 and 10, respectively, indicating that they always play a central role in the network. However, the northeastern regions and the relatively backward central and western regions such as Heilongjiang, Jilin, Xinjiang, Hainan and Hebei only have a degree centrality, closeness centrality and betweenness centrality of below 20, 55 and 0.1, respectively, indicating that they are at a relatively marginal position. 3) The geographical proximity and the expansion of the differences in economic development level and R&D inputs are conducive to the enhancement of the spatial association of China's renewable energy technology innovation.
引用
收藏
页数:14
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