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

  • Chen Feng
  • , Yuansheng Wang
  • , Rong Kang
  • , Lei Zhou
  • , Caiquan Bai
  • , Zheming Yan

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Article number686985
JournalFrontiers in Energy Research
Volume9
DOIs
StatePublished - 25 May 2021
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth

Keywords

  • China
  • driving factor
  • renewable energy technology innovation
  • social network analysis
  • spatial association

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