An analysis of the context factors influencing the diverse response of airports to COVID-19 using panel and group regression

• Published in: Transportation research. Part A, Policy and practice Policy and practice, 2024-01, Vol.179, p.103925, Article 103925
• Main Authors: Chen, Yuting, Fuellhart, Kurt, Grubesic, Tony H., Zhang, Shengrun, Witlox, Frank
• Format: Article
• Language: English
• Subjects: Airport response; COVID-19; Grouped regression; Panel regression
• ISSN: 0965-8564; 1879-2375
• DOI: 10.1016/j.tra.2023.103925

Since 2020, the ongoing impact of the COVID-19 pandemic has led to various levels of recovery across different aviation markets. To better plan for the future, post-pandemic era, air transport policies should consider the potential differential responses of airports to the pandemic based on their geographic network and competitive situations. Much literature provides policy recommendations for planning and development in the post-pandemic era, however fewer studies focus on the short-term recovery trends and diverse responses to the pandemic (that were) made by airports. This paper introduces four impact dimensions faced by airports as a result of the pandemic: airport operations, geographical location, socio-economic environment, and government pandemic policies. Panel and group regression were employed to explore the factors associated with airports’ response to the pandemic in relation to the four dimensions. The seat capacity at the airports was forecast for a non-pandemic scenario using ARIMA models. The residual ratios between the predicted and the actual seat capacity values were the dependent variables capturing the pandemic effect. The results show that, in addition to the direct effect of the pandemic policy, the location of the airport (and other transport modes in its vicinity) had a significant impact on its short-term recovery. Further, the extent of the impact varied by region. The findings of the correlation between different factors and airport responses provide policymakers with more targeted recovery strategies. At the macro-scale, the analysis of multidimensional factors suggests recommendations for airlines and planners for a faster and more efficient recovery of the aviation networks for future disruptions.