Bai Xiwen, Xu Ming, Han Tingting, Yang Dong
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong.
Transp Res Part A Policy Pract. 2022 Oct;164:224-241. doi: 10.1016/j.tra.2022.08.002. Epub 2022 Aug 20.
The recent experience of lockdowns during COVID-19 highlights the prolonged impact a pandemic could have on ports and the shipping industry. This paper uses port call data derived from the Automatic Identification System (AIS) reports from the world's 30 largest container ports to quantify both the immediate and longer-term impact of national COVID-19 lockdown policies on global shipping flows. The analysis uses the Difference-in-Difference (DID) and combined regression discontinuity design (RDD)-DID models to represent the effects of lockdown policies. The combination of RDD and DID models is particularly effective because it can mitigate time trends in the data, e.g., the Chinese New Year effect on Chinese ports. This study further examines the potential shock propagation effects, namely, how lockdown policy in one country (i.e., China) can affect the number of port calls in other countries. We categorize ports in other countries into a high-connectivity (with Chinese ports) group and a low-connectivity group, using a proposed connectivity index with China derived from individual vessel trajectories obtained from the AIS data. The results provide a clearly measurable picture of the kinds of trade shocks and consequent pattern changes in port calls over time caused by responses to lockdown policies of varying levels of stringency. We further document the existence of significant shock propagation effects. As the risk of pandemics rises in the twenty-first century, these results can be used by policy makers to assess the potential impact of different levels of lockdown policy on the maritime industry and trade flows more broadly. Maritime players can also use findings such as these to manage their capacity during lockdowns more effectively and to respond more flexibly to changing demand in seaborne transportation.
新冠疫情期间实施封锁的近期经历凸显了大流行可能对港口和航运业产生的长期影响。本文利用来自全球30个最大集装箱港口的自动识别系统（AIS）报告中的港口停靠数据，来量化各国新冠疫情封锁政策对全球航运流量的直接和长期影响。该分析使用双重差分（DID）模型以及结合了断点回归设计（RDD）的双重差分（DID）模型来体现封锁政策的影响。RDD和DID模型相结合特别有效，因为它可以减轻数据中的时间趋势，例如中国新年对中国港口的影响。本研究进一步考察了潜在的冲击传播效应，即一个国家（即中国）的封锁政策如何影响其他国家的港口停靠次数。我们根据从AIS数据中获取的单艘船舶轨迹得出的与中国的拟议连通性指数，将其他国家的港口分为高连通性（与中国港口）组和低连通性组。研究结果清晰地呈现了不同严格程度的封锁政策应对措施所导致的各类贸易冲击以及随之而来的港口停靠模式随时间的变化情况。我们还证明了显著的冲击传播效应的存在。随着21世纪大流行风险的增加，政策制定者可以利用这些结果更全面地评估不同程度的封锁政策对海运业和贸易流量的潜在影响。海运业参与者也可以利用此类研究结果，在封锁期间更有效地管理运力，并更灵活地应对海上运输不断变化的需求。
