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<p>Emergency logistics is an important safeguard to ensure the safety of human beings, properties, and economic activities under major disasters and emergencies. This study proposed a vulnerability assessment framework for emergency logistics systems (ELS) from three dimensions: exposure, sensitivity, and adaptability, in which the principal component analysis and comprehensive index evaluation methods are adopted. Furthermore, a geographically weighted regression model (GWR) was established to explore the driving mechanism of vulnerability change of ELS. By taking provincial-level data in the mainland of China from 2010 to 2022, this empirical study found that ELS vulnerability showed strong fluctuation, in which the overall trend was upward during 2010–2018, but downward during 2019–2022. Meanwhile, ELS vulnerability was generally low in southeast China and high in northwest China, and strength of this spatial clustering showed a fluctuating upward trend. Four driving factors affected the spatiotemporal change of vulnerability—ecological threat level mainly positively drove the change, while natural environment protection level, logistics transportation capacity, and logistics fixed asset investment were negatively correlated. This study provides management implications for improving regional emergency logistics systems according to their spatiotemporal variation and local conditions.</p>

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Investigating Spatial-Temporal Change of Emergency Logistics System Vulnerability—Taking China as an Example

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

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  • Hongmei Shan,
  • Jialu Shi,
  • Yiyi An,
  • Xinni Hu,
  • Jing Shi

摘要

Emergency logistics is an important safeguard to ensure the safety of human beings, properties, and economic activities under major disasters and emergencies. This study proposed a vulnerability assessment framework for emergency logistics systems (ELS) from three dimensions: exposure, sensitivity, and adaptability, in which the principal component analysis and comprehensive index evaluation methods are adopted. Furthermore, a geographically weighted regression model (GWR) was established to explore the driving mechanism of vulnerability change of ELS. By taking provincial-level data in the mainland of China from 2010 to 2022, this empirical study found that ELS vulnerability showed strong fluctuation, in which the overall trend was upward during 2010–2018, but downward during 2019–2022. Meanwhile, ELS vulnerability was generally low in southeast China and high in northwest China, and strength of this spatial clustering showed a fluctuating upward trend. Four driving factors affected the spatiotemporal change of vulnerability—ecological threat level mainly positively drove the change, while natural environment protection level, logistics transportation capacity, and logistics fixed asset investment were negatively correlated. This study provides management implications for improving regional emergency logistics systems according to their spatiotemporal variation and local conditions.