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Dynamic Evolution of Venture Capital Network in Clean Energy Industries Based on STERGM

Author

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  • Chen Zhang

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

  • Xinghua Dang

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

  • Tao Peng

    (International School of Business & Finance, Sun Yat-sen University, Zhuhai 519082, China)

  • Chaokai Xue

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

Abstract

This paper provides a detailed description of venture capital (VC) investments in clean energy industries in China over the period 2006–2017 and explores the evolution of clean energy industry VC networks through network formation and network dissolution. Results from the separable temporal exponential-family random graph model (STERGM) show that the factors vary in their relative importance for clean energy industry VC network formation and dissolution. Specifically, governmental venture capital (GVC) and geographic proximity have strong impacts on the formation of networks but not on their dissolution. Reputation and structural embeddedness promote the formation of networks and inhibit their dissolution, and cognitive proximity is found to cause network formation while facilitating network dissolution. The results provide practical and theoretical guidance for the network development of VC firms investing in clean energy industries.

Suggested Citation

  • Chen Zhang & Xinghua Dang & Tao Peng & Chaokai Xue, 2019. "Dynamic Evolution of Venture Capital Network in Clean Energy Industries Based on STERGM," Sustainability, MDPI, vol. 11(22), pages 1-25, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6313-:d:285620
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