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Evolution, Forecasting, and Driving Mechanisms of the Digital Financial Network: Evidence from China

Author

Listed:
  • Rui Ding

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
    Regional Economic High-Quality Development Research Provincial Innovation Team, Guizhou University of Finance and Economics, Guiyang 550025, China
    These authors contributed equally to this work.)

  • Siwei Shen

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
    These authors contributed equally to this work.)

  • Yuqi Zhu

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Linyu Du

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Shihui Chen

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Juan Liang

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Kexing Wang

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Wenqian Xiao

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Yuxuan Hong

    (College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
    Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China)

Abstract

Digital finance (DF) is the engine driving financial inclusion worldwide, but the current uneven development of DF across regions would hinder this process. Based on cross-sectional data from 288 prefecture-level cities for the representative years 2011, 2014, 2017, and 2020, this paper uses geographic detector methods, social network analysis, and geographical and temporal weighted regression (GTWR) to explore the key drivers of urban DF, revealing and forecasting the DF network structural evolution and its driving mechanism. The results show that (1) economic level, traditional financial level, internet popularity, innovation level, and government intervention are the key drivers of DF development. (2) During the decade, the proportion of high-intensity urban interconnections increased from 3.3% to 12.3%. Most cities are at a low level of intensity, showing a polarization trend. (3) The cities with high betweenness centrality are concentrated in the megacities and the number is stable at 5. The structure of network communities is relatively stable, with the number reduced to 10. Cities with the greatest possibility of connection are located in the Pearl River Delta (PRD) and the Yangtze River Delta (YRD), accounting for 60% of the total. (4) The drivers of DF development present significant spatial heterogeneity over time. The traditional financial level shows a positive and continuous promoting effect, while government intervention plays a negative role.

Suggested Citation

  • Rui Ding & Siwei Shen & Yuqi Zhu & Linyu Du & Shihui Chen & Juan Liang & Kexing Wang & Wenqian Xiao & Yuxuan Hong, 2023. "Evolution, Forecasting, and Driving Mechanisms of the Digital Financial Network: Evidence from China," Sustainability, MDPI, vol. 15(22), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16072-:d:1282623
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    References listed on IDEAS

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