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Research on the spatial association network structure for innovation efficiency of China’s new energy vehicle industry and its influencing factors

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  • Yining Zhang
  • Zhong Wu

Abstract

It is of great significance to study the spatial network of the new energy vehicle (NEV) industry innovation efficiency and its factors to promote the rational allocation of innovative resources and the coordinated development of Chinese NEV industry. First, the Super Efficiency Data Envelope Analysis model is used to measure innovation efficiency in the NEV industry in Chinese provinces, and based on the results, the improved gravity model is applied to construct a spatial correlation network. Then, by applying social network analysis (SNA) to study NEV industry development node spatial correlations, we conclude that there is no overall hierarchical structure. The SNA are applied to examine spatial correlations with respect to NEV industry innovation efficiency in each province, and to analyze the role and position of each province in the spatial correlation network. Finally, the influencing factors of spatial correlation of the innovation efficiency of China’s NEV industry has been discussed. The result shows that the difference in spatial distance and R&D investment has a significant impact on the spatial correlation of the NEV industry.

Suggested Citation

  • Yining Zhang & Zhong Wu, 2021. "Research on the spatial association network structure for innovation efficiency of China’s new energy vehicle industry and its influencing factors," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0255516
    DOI: 10.1371/journal.pone.0255516
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    References listed on IDEAS

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    1. Siran Fang & Xiaoshan Xue & Ge Yin & Hong Fang & Jialin Li & Yongnian Zhang, 2020. "Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    2. Teixeira, Ana Carolina Rodrigues & Sodré, José Ricardo, 2016. "Simulation of the impacts on carbon dioxide emissions from replacement of a conventional Brazilian taxi fleet by electric vehicles," Energy, Elsevier, vol. 115(P3), pages 1617-1622.
    3. Trost, Tobias & Sterner, Michael & Bruckner, Thomas, 2017. "Impact of electric vehicles and synthetic gaseous fuels on final energy consumption and carbon dioxide emissions in Germany based on long-term vehicle fleet modelling," Energy, Elsevier, vol. 141(C), pages 1215-1225.
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    Cited by:

    1. Yameng Wang & Zhe Chen & Xiumei Wang & Mengyang Hou & Feng Wei, 2021. "Research on the Spatial Network Structure and Influencing Factors of the Allocation Efficiency of Agricultural Science and Technology Resources in China," Agriculture, MDPI, vol. 11(11), pages 1-23, November.
    2. Han, Jing & Guo, Ju-E & Cai, Xun & Lv, Cheng & Lev, Benjamin, 2022. "An analysis on strategy evolution of research & development in cooperative innovation network of new energy vehicle within policy transition period," Omega, Elsevier, vol. 112(C).
    3. Dan Su & Xiaoqian Fang & Qing Wu & Yu Cao, 2022. "Exploring the Spatiotemporal Integration Evolution of the Urban Agglomeration through City Networks," Land, MDPI, vol. 11(4), pages 1-18, April.

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