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Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

Author

Listed:
  • Jun Zhang

    (School of Economics and Management, Wuhan University, Wuhan 430072, China)

  • Shenghao Zhao

    (School of Economics and Management, Wuhan University, Wuhan 430072, China)

  • Chaonan Peng

    (School of Economics and Management, Wuhan University, Wuhan 430072, China)

  • Xianming Gong

    (School of Economics and Management, Wuhan University, Wuhan 430072, China)

Abstract

The impact of COVID-19 on traffic volume makes it essential to study the spatial heterogeneity and impact mechanisms of the recovery of road traffic volume to promote the sustainability of related industries. As the research method, this study used a principal component analysis to evaluate the recovery of road traffic volume in China quantitatively, and further conducted an empirical study using a spatial autocorrelation index and a dynamic spatial panel model. The results show that income has a negative impact on the recovery of road traffic volume, while climate suitability has a positive impact. Economic development and COVID-19 can play moderating and mediating effects, respectively. From the aspect of spatial heterogeneity, the recovery of road traffic volume has a positive spatial spillover effect on the surrounding provinces, while the spread of COVID-19 has a negative short-term indirect spatial spillover effect. Corresponding practical insights are provided for the stakeholders based on the above findings. The results of this study will contribute to the development of effective policies to facilitate the recovery of road traffic volume from the impact of COVID-19 and the revitalization of the transportation industry.

Suggested Citation

  • Jun Zhang & Shenghao Zhao & Chaonan Peng & Xianming Gong, 2022. "Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14297-:d:960481
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