IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0248361.html
   My bibliography  Save this article

Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. and Singapore

Author

Listed:
  • Fanyu Meng
  • Wenwu Gong
  • Jun Liang
  • Xian Li
  • Yiping Zeng
  • Lili Yang

Abstract

Many countries have been implementing various control measures with different strictness levels to prevent the coronavirus disease 2019 (COVID-19) from spreading. With the great reduction in human mobility and daily activities, considerable impacts have been imposed on the global air transportation industry. This study applies a hybrid SARIMA-based intervention model to measure the differences in the impacts of different control measures implemented in China, the U.S. and Singapore on air passenger and air freight traffic. To explore the effect of time span for the measures to be in force, two scenarios are invented, namely a long-term intervention and a short-term intervention, and predictions are made till the end of 2020 for all three countries under both scenarios. As a result, predictive patterns of the selected metrics for the three countries are rather different. China is predicted to have the mildest economic impact on the air transportation industry in this year in terms of air passenger revenue and air cargo traffic, provided that the control measures were prompt and effective. The U.S. would suffer from a far-reaching impact on the industry if the same control measures are maintained. More uncertainties are found for Singapore, as it is strongly associated with international travel demands. Suggestions are made for the three countries and the rest of the world on how to seek a balance between the strictness of control measures and the potential long-term industrial losses.

Suggested Citation

  • Fanyu Meng & Wenwu Gong & Jun Liang & Xian Li & Yiping Zeng & Lili Yang, 2021. "Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. and Singapore," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-19, March.
  • Handle: RePEc:plo:pone00:0248361
    DOI: 10.1371/journal.pone.0248361
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248361
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248361&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0248361?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    2. Tsui, Wai Hong Kan & Ozer Balli, Hatice & Gilbey, Andrew & Gow, Hamish, 2014. "Forecasting of Hong Kong airport's passenger throughput," Tourism Management, Elsevier, vol. 42(C), pages 62-76.
    3. Lu, Zudi, 1996. "A note on geometric ergodicity of autoregressive conditional heteroscedasticity (ARCH) model," Statistics & Probability Letters, Elsevier, vol. 30(4), pages 305-311, November.
    4. Neumeyer, Natalie & Omelka, Marek & Hudecová, Šárka, 2019. "A copula approach for dependence modeling in multivariate nonparametric time series," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 139-162.
    5. Minha Lee & Jun Zhao & Qianqian Sun & Yixuan Pan & Weiyi Zhou & Chenfeng Xiong & Lei Zhang, 2020. "Human mobility trends during the early stage of the COVID-19 pandemic in the United States," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-15, November.
    6. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, March.
    7. Jennifer Min & Christine Lim & Hsien-Hung Kung, 2011. "Intervention analysis of SARS on Japanese tourism demand for Taiwan," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(1), pages 91-102, January.
    8. Gelhausen, Marc C. & Berster, Peter & Wilken, Dieter, 2018. "A new direct demand model of long-term forecasting air passengers and air transport movements at German airports," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 140-152.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    10. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    11. Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
    12. Hörmann, Siegfried & Horváth, Lajos & Reeder, Ron, 2013. "A Functional Version Of The Arch Model," Econometric Theory, Cambridge University Press, vol. 29(2), pages 267-288, April.
    13. Chang, Yu-Chun & Lee, Wei-Hao & Hsu, Chia-Jui, 2020. "Identifying competitive position for ten Asian aviation hubs," Transport Policy, Elsevier, vol. 87(C), pages 51-66.
    14. Milton Friedman, 1962. "Introduction to "The Interpolation of Time Series by Related Series"," NBER Chapters, in: The Interpolation of Time Series by Related Series, pages 1-3, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Babatunde A. OKUNEYE & Oluwatosin O. OGUNYOMI-OLUYOMI, 2022. "The Role of Digitalization in the Airline Industry Performance AMID COVID-19: Evidence from Emirate Airline Balanced Scorecard Performence," Business & Management Compass, University of Economics Varna, issue 1-2, pages 365-379.
    2. Zhang, Junyi & Zhang, Runsen & Ding, Hongxiang & Li, Shuangjin & Liu, Rui & Ma, Shuang & Zhai, Baoxin & Kashima, Saori & Hayashi, Yoshitsugu, 2021. "Effects of transport-related COVID-19 policy measures: A case study of six developed countries," Transport Policy, Elsevier, vol. 110(C), pages 37-57.
    3. Junsik Park & Gurjoong Kim, 2021. "Risk of COVID-19 Infection in Public Transportation: The Development of a Model," IJERPH, MDPI, vol. 18(23), pages 1-16, December.
    4. Kotcharin, Suntichai & Maneenop, Sakkakom & Jaroenjitrkam, Anutchanat, 2023. "The impact of government policy responses on airline stock return during the COVID-19 crisis," Research in Transportation Economics, Elsevier, vol. 99(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    2. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    3. Fullerton, Thomas M. & Jiménez, Alan A. & Walke, Adam G., 2015. "An econometric analysis of retail gasoline prices in a border metropolitan economy," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 450-461.
    4. Jonathan Eaton & Samuel Kortum & Brent Neiman & John Romalis, 2016. "Trade and the Global Recession," American Economic Review, American Economic Association, vol. 106(11), pages 3401-3438, November.
    5. Dimitrios Panagiotou & Athanassios Stavrakoudis, 2023. "Price dependence among the major EU extra virgin olive oil markets: a time scale analysis," Review of Agricultural, Food and Environmental Studies, Springer, vol. 104(1), pages 1-26, March.
    6. Barnett, William A. & Su, Liting, 2017. "Data sources for the credit-card augmented Divisia monetary aggregates," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 899-910.
    7. Zadrozny, Peter A., 2016. "Extended Yule–Walker identification of VARMA models with single- or mixed-frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 438-446.
    8. de Haen, H. & von Braun, J., 1977. "Regionale Veränderungen des Arbeitseinsatzes in der Landwirtschaft – Demographische Analyse und arbeitsmarktpolitische Schlussfolgerungen," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 14.
    9. Alvaredo, Facundo & Atkinson, Anthony B. & Morelli, Salvatore, 2018. "Top wealth shares in the UK over more than a century," Journal of Public Economics, Elsevier, vol. 162(C), pages 26-47.
    10. Thomas M. FULLERTON & Miguel MARTINEZ & Wm. Doyle SMITH & Adam WALKE, 2015. "Inflationary Dynamics in Guatemala," Journal of Economics and Political Economy, KSP Journals, vol. 2(4), pages 436-444, December.
    11. Sang T. Truong & Humberto Barreto, 2023. "Teaching Income Inequality with Data-Driven Visualization," The American Economist, Sage Publications, vol. 68(1), pages 140-155, March.
    12. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201316, University of Hawaii at Manoa, Department of Economics.
    13. Yu Jin & Wallace E. Huffman, 2016. "Measuring public agricultural research and extension and estimating their impacts on agricultural productivity: new insights from U.S. evidence," Agricultural Economics, International Association of Agricultural Economists, vol. 47(1), pages 15-31, January.
    14. Chen, Jieh-Haur & Wei, Hsi-Hsien & Chen, Chih-Lin & Wei, Hsin-Yi & Chen, Yi-Ping & Ye, Zhongnan, 2020. "A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification," Journal of Air Transport Management, Elsevier, vol. 82(C).
    15. De Vita, G. & Endresen, K. & Hunt, L.C., 2006. "An empirical analysis of energy demand in Namibia," Energy Policy, Elsevier, vol. 34(18), pages 3447-3463, December.
    16. T. M. Fullerton & A. G. Walke, 2013. "Public transportation demand in a border metropolitan economy," Applied Economics, Taylor & Francis Journals, vol. 45(27), pages 3922-3931, September.
    17. Cerovecki, Clément & Francq, Christian & Hörmann, Siegfried & Zakoïan, Jean-Michel, 2019. "Functional GARCH models: The quasi-likelihood approach and its applications," Journal of Econometrics, Elsevier, vol. 209(2), pages 353-375.
    18. Bernardí Cabred & Jose Pavía, 1999. "EstimatingJ (>1) quarterly time series in fulfilling annual and quarterly constraints," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(3), pages 339-349, August.
    19. Eric Ghysels & J. Isaac Miller, 2015. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 797-816, November.
    20. Fullerton, Thomas M. Jr & Walke, Adam G., 2012. "Border Zone Mass Transit Demand in Brownsville and Laredo," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 51(2).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0248361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.