key: cord-0923516-fq1z84gm
authors: Li, Yongling; Wang, Jiaoe; Huang, Jie; Chen, Zhuo; Li, Yongling
title: Impact of COVID-19 on domestic air transportation in China
date: 2022-04-29
journal: Transp Policy (Oxf)
DOI: 10.1016/j.tranpol.2022.04.016
sha: ba78a23d672042409e21302a83dbb11c3d64d3ff
doc_id: 923516
cord_uid: fq1z84gm

Assessing the impact of the coronavirus disease 2019 (COVID-19) on air transportation is essential for policymakers and airlines to prevent their widespread shutdown. The panel data observed from January 20, 2020, to April 30, 2020, were used to identify the impact of COVID-19 and the relevant control measures adopted on China's domestic air transportation. Hybrid models within negative binomial models were employed to separate the temporal and spatial effects of COVID-19. Temporal effects show that the number of new confirmed cases and the control measures significantly affect the number of operated flights. Spatial effects show that the network effect of COVID-19 cases in destination cities, lockdown, and adjustment to Level I in the early stages have a negative impact on the operated flights. Adjustment to Level II or Level III both has positive temporal and spatial effects. This indicates that the control measures adopted during the early stage of the pandemic positively impact the restoration of the aviation industry and other industries in the later stage.

The coronavirus disease 2019 (COVID-19) has significantly influenced individuals' lives 19 worldwide since 2020. According to the World Health Organization (WHO), as of December 14, 20 2020, there have been more than 71 million confirmed cases of COVID-19 worldwide, affecting 21 218 countries and regions. Considering the increasing evidence that modern transportation modes 22 such as high-speed rail and air transportation have accelerated the spread of the pandemic (Zhang 23 et al., 2020), urban policies including city lockdown, social distancing, and transportation shutdown 24 have been widely adopted by cities globally. Numerous studies have shown that various control 25 measures can reduce the spread of COVID-19 (Cowling et al., 2020; Leung et al., 2020 ; Wei et al., 26 2021), but the large-scale COVID-19 outbreak and the control measures adopted accordingly have 27 affected the economy and the air transportation industry significantly. For example, the International 28 Air Transport Association (IATA) predicts that global passenger transport revenues between $ 63 29 billion and $ 113 billion in 2020 will be lost due to air travel reduction during the COVID- 19 30 outbreak (IATA, 2020a). In such cases, understanding the correlation between air traffic and 31 COVID-19 is important for air transportation. 32 Despite being one of the hardest-hit countries in early 2020, China has seen a significant 33 decrease in the new confirmed cases of COVID-19 and an increase in the number of flights since 34 March 2020. In June 2020, the domestic passenger volume in China fell 35 .5% compared to the 35 year-ago period, which was lower than the global average of 67.6% (IATA, 2020b). One reason is 36 that China adopted strong control measures in the early stage of the pandemic, which prevented the 37 spread of COVID-19 and provided favorable conditions for the recovery of the aviation industry. 38

After the market-driven consolidation and deregulated competition that began in the 2000s in China, 1 airlines are playing an increasingly important role in adapting to the market (Wang et al., 2016) . 2 Thus, market demand and mandatory policies, such as those pertaining to travel restrictions, affect 3 the supply of flights. The rapid recovery of China's aviation industry under the influence of market 4 demand and control measures provides a good reference for other countries. 5

Currently, most studies have only used historical aviation data to study the changes in air traffic 6 post COVID-19 outbreak; limited studies have observed the quantitative relations between the 7 pandemic and air traffic. Furthermore, although the control measures adopted to combat the 8 outbreak may have had different effects on air traffic, previous studies have not fully examined them. 9 By instigating the research question raised within the Chinese context, this study explores the 10 quantitative relationship between the COVID-19 pandemic, the resulting control measures adopted, 11 and the number of flights operated. 12 The rest of the paper has been organized as follows. Section 2 provides a literature review on 13 the determinants of air travel demand and air traffic volumes. Section 3 presents a brief overview of 14 the COVID-19 outbreak and the relevant control measures adopted in China. Section 4 introduces 15

China's aviation response to the COVID-19 outbreak and domestic flight dynamics. Section 5 16 discusses the methodology and data used as well as empirical results obtained. Finally, we discuss 17 our main findings and conclusions of the study. 18 2. Literature review 19 Air traffic forecasts are the foundation of the aviation industry, which are conducive to long-20 term planning and risk reduction (Akinyemi, 2019 Beijing-Shanghai-Guangzhou generates about one-third of the overall air travel demand. As 42 J o u r n a l P r e -p r o o f Chengdu plays an increasingly important role as a new hub in the western region, this triangular 1 structure is gradually transformed into a diamond structure (Chen, 2017 ). The distance (or time) 2 between departure and destination airports also influences air passenger traffic, which has two 3 conflicting effects: longer route distance discourages social and commercial interactions, but it has 4 a comparative advantage in time-saving compared to other transportation modes (Jorge-Caldeo´n, 5 1997). For instance, Boonekamp et al. (2018) found that the effect of distance on air passengers first 6 appears to be positive and then appears to be negative, with a turning point at 500 km in Europe. In 7

China's case, Wang and Jin, (2007) found that the turning point occurs at 1200 km. Similar results 8

were found in Yang et al. (2018) . Another related variable is airfare, which is often omitted to avoid 9 multicollinearity due to its high correlation with distance or travel time (Grosche et al., 2007 occur over a long-time scale. As a result, these studies use annual passenger flow data to serve long-19

term rather than short-term forecasts. In an emergency that requires rapid strategy adjustment to 20 minimize risks, short-term forecasts may be more practical (Kim & Shin, 2016) . For instance, the 21 outbreak of COVID-19 and the implementation and adjustment of relevant control measures require 22

aviation forecasting systems to improve their adaptability to the ever-changing external environment. 23 In this regard, it is more practical to use daily or monthly data to study the impact of changing 24 policies or control measures on air traffic. 25 Before Covid-19, although infectious diseases such as SARS significantly impacted air traffic, 26 there were few relevant studies. Most studies had only focused on the effect of air travel on the 27 spread of infectious diseases, not the other way around (Findlater & Bogoch, 2018; Grais et al., 28 2003 ). This may be due to its short-term global impact and the limited number of affected countries. 29

However, the outbreak of COVID-19 changed this trend, and several studies have begun to emerge. 30 Most studies use historical aviation data to analyze the change in air traffic after the pandemic and 31

found that the impact of the pandemic on aviation was caused by travel restrictions and the 32 psychological impact ( to the dynamics of the pandemic. Therefore, studying the impact of dynamic changes in pandemic 37 and control measures on aviation is necessary to forecast and improve its adaptability. 38 Since the COVID-19 outbreak, the government has adopted a variety of control measures. First, 2 a nationwide investigation has been carried out on confirmed, suspected, feverish, and close contacts 3 of confirmed patients to control the source of infection. Second, to break the chain of COVID- 19 4 transmission, temperature checks and masks have been mandatory, and "No face-to-face" services 5 have been promoted. Third, a household-based outdoor restriction and closed-off community 6 management have been implemented. Household-based outdoor restriction means that only one 7 person per household is allowed to go out for food every two days. Under closed-off community 8 management, villages, communities, and units in most closed-off areas only retain one entrance and 9

restrict the access of each household. Fourth, hierarchical, classified, dynamic, and accurate 10 prevention and control have been implemented. These measures include public health emergency 11

response and risk regionalization. The strictest lockdown and traffic control were implemented in 12

Hubei Province and Wuhan City, differentiated traffic control was implemented in areas outside 13 Hubei Province, and effective measures were taken to avoid crowd and cross-infection. In addition, 14 the government extended the Spring Festival holiday, canceled or postponed gathering activities, 15 and postponed the opening of various schools. 16 Figure 1 shows how the air traffic volume is affected by the pandemic, government response, 17 and aviation response. These factors mainly affect the air traffic volume based on supply and 18

demand. First, the government response has a twofold impact on air traffic volume: on the one hand, 19 the most stringent policy, such as city lockdown, directly 

Since the first case was reported in Wuhan, China has experienced a rapid increase, then a slow 6 decline in new confirmed cases. On March 12, 2020, the National Health and Welfare Commission 7 stated that the current peak of the pandemic had been controlled. Figure 2 outlines the COVID-19 8 outbreak from January 17, 2020, to March 12, 2020. During this period, the pandemic in China can 9

be broadly divided into five stages. In the first stage (before January 19) , no new cases have been 10 confirmed in other cities except Wuhan. Insufficient understanding of COVID-19 at this stage has 11

prevented the government from imposing effective measures to contain the spread of COVID-19. 12

There was a nationwide outbreak in the second stage (January 19 to January 26). The first confirmed 13 case in other cities occurred on January 19, and the number of cities with confirmed cases rose 14 sharply thereafter. In the third stage (January 27 to the pandemic has dropped to less than ten, and China's pandemic situation is basically under control. 20

In the sixth stage (March 1-March 12), the number of new cases continued to decline. On March 21 12, the National Health and Welfare Commission officially announced that the peak of the pandemic 22

has been controlled. 6 To contain the outbreak, the government responded immediately. According to the National 7

General Plan for Public Emergencies and the National Public Health Emergency Plan, public health 8 emergency is classified into four levels: I, II, III, and IV, with severity reduced from Level I to Level 9 IV. The emergency response measures at all levels of government include measures related to 10 population movements, and the governments can delimit the control area and impose a lockdown 11 within its administrative areas. For example, Hubei implemented the strictest lockdown measures 12 since the outbreak. Governments can also limit or stop crowd gathering activities and suspend work, 13 business, and classes. 14 Hubei took the lead in launching the Level II emergency response to public health emergencies 15 on January 22 and adjusted it to a Level I emergency response on January 24. Since then, other 16 provinces initiated a Level I emergency response and downgraded their response levels as the 17 pandemic gradually came under control ( Figure 3 ). After the lockdown in Wuhan on January 23, the 18 public transportation system in other cities in Hubei Province was suspended. By January 28, all 19 jurisdictions of Hubei Province except the Shennongjia Forest Region-one of the World Natural 20

Heritage Sites with a sparsely populated area-adopted border shutdown measures, and all modes 21 of transportation were suspended. Lockdown time for most cities in Hubei began on January 24 and 22 ended on March 25. As the pandemic eased, some provinces began to lower their response levels to 23 Level II or even Level III in late February. As of March 1, 13 provinces maintained a Level I 24 response, nine provinces were adjusted to a Level II response, and ten were adjusted to Level III. 25 (Fujian adjusted the medium-risk area to the Level II response, and the low-risk area to the Level 26 III response). The 13 first-level response provinces were mainly concentrated in the surrounding 27 areas of Hubei Province. Most northwest and south China provinces were downgraded to a Level 28 III response. 4 To control the pandemic more effectively, the government proposed the concept of risk 5 regionalization on February 25, 2020. The administrative units of risk regionalization are smaller 6 than emergency response measures and therefore easier to manage and control. Based on the 7 pandemic's severity, regions are divided into low-risk, medium-risk, and high-risk areas at the 8 county level. According to the risk regionalization promulgated by the State Council, a high-risk 9

area means that there are disease clusters within 14 days and the cumulative number of confirmed 10 cases exceeds 50. In this case, the government's primary task is to control the pandemic rather than 11

to resume production and living activities. In high-risk areas, regional traffic is controlled and 12 external traffic links are cut off. A medium-risk area is defined as areas with new confirmed cases 13 within 14 days and the cumulative confirmed cases do not exceed 50, or the cumulative confirmed 14 cases exceed 50 and no disease clusters occur within 14 days. The government is committed to 15 orderly resuming production and living activities. In medium-risk areas, individual travel may be 16 limited to a certain extent; A low-risk area means that there are no confirmed cases within their 17 administrative areas or no new confirmed cases within 14 days (Lai et al., 2020). In these areas, 18 production and living activities should be fully restored. The low-risk area adopts strict import 19 prevention, and individual travel is not strictly controlled. Since this study used the city as the basic 20 unit, the county-level risk area cannot be used in our study. preventing and controlling civil aviation, airlines, and airports. In the ensuing days, the CAAC 4 issued a set of ticket refund policies for various groups and regions, including passengers to Wuhan 5 or other cities, student passengers, medical professionals, and passengers affected by entry 6

restrictions. Since a small number of medical staff and passengers affected by entry restrictions have 7 a limited impact on domestic flights, they will not be discussed in detail in this study. Three types 8 of refund policies can be summarized: 1) Free refund policy to Wuhan (January 21). On January 21, 9

the CAAC requested all airlines to refund tickets free of charge for flights involving Wuhan (tickets 10 purchased before January 31, 2020, and traveling from January 1, 2020, to March 29, 2020); 2) Free 11

refund policy to all cities (January 28). On January 24, the CAAC required all airlines to refund 12 tickets free of charge purchased before January 24, 2020. After four days, the CAAC extended the 13 ticket purchase date to January 28, 2020; 3) Free refund policy for students (February 11). To cope 14

with the ticket refunds caused by delays in school start dates, the CAAC required that students who 15 had purchased tickets before February 11 and traveled between February 11 and March 31 could 16 refund or reschedule their tickets for free. This policy, coupled with the delay in school start dates, 17

increased canceled flights. 18 10,000 flights were canceled. In general, cities with larger average scheduled flights tend to have 10 higher cancelation rates. Although major cities still play the role of transportation hubs, flights in 11 these cities have been severely affected by the pandemic. The cancelation rate of some main routes 12 has reached between 75% and 90%, including Shanghai-Qingdao, Shanghai-Beijing, Shanghai-13

Guangzhou, Shanghai-Shenzhen, Shanghai-Chongqing, Beijing-Shenzhen, and Beijing-Chengdu, 14 forming a diamond structure. However, the impact on the thin routes cannot be underestimated. 15 Approximately 1,600 pairs of cities have a cancellation rate of 100%, with an average of 3 scheduled 16 flights, indicating that non-closely connected routes have been disconnected. The origin cities with 17 the largest number of cancelations of the thin routes are Chongqing, Xi'an, Wuhan, Zhengzhou, 18 Yinchuan, Changsha, Shanghai, and Urumqi. (https://www.variflight.com/en/) includes flight data from January 01, 2020, to December 31, 2020. 14 However, we chose the study period from January 20, 2020, to April 30, 2020, due to the following 15 reasons. First, the human-to-human transmission was confirmed on 20 January 2020, thus beginning 16 to have an impact on aviation. Second, at the end of April, the flight operated had recovered to 70% 17 and maintained a stable trend, and the epidemic has been basically under control. Since all of our 18 independent variables are city-level, we combined airport-level flights into city-level flights. After 19 data processing, a total of 16,731 observations with 169 cities were retained. 20

As our dependent variable-the number of operated flights-is a non-negative count variable, 21

Poisson regression, and negative binomial regression are our primary choices (Aguiléra & Proulhac, 1 2015). We examined whether the mean value of the dependent variable changes for each 2 independent variable and found that in all cases, the conditional variance of the dependent variable 3 is much greater than its conditional mean. It shows the existence of over-dispersion, and thus the 4 negative binomial regression is more appropriate (Colin & Pravin, 2013). 5

We have adopted a between-within model (also known as a hybrid model), whose major 6 advantage is that it can decompose the relationship between COVID-19 and flights into temporal 7 effects (within-effects) and spatial effects (between-effects) ( = 0 + 1 ( − ̅ ) + 2 ̅ + 3 + + (1) 12 13 where the dependent variables are the number of operated flights of the city i on day t. The 14 coefficient 1 represent the temporal effects (within effects), while the coefficient 2 represent the 15 between effect (spatial effects). 3 is the coefficient of the time-invariant variable. 16 We used COVID-19, control measures, and geo-economic elements as our independent 17

variables. Several lines of evidence suggest that COVID-19 has resulted in a severe loss of global 18 passenger transport revenue (Gössling et al., 2020; IATA, 2020a) . Although the government 19 proposed the concept of risk regionalization at the end of February, the county-level risk areas were 20 not taken as the independent variable because this study took cities as the basic unit. by their destination cities. Here, _ 14 only considers the direct impacts from the 41 destination cities of the origin city, and therefore it is a rough estimate of the true network effect. 1 Since severe emergency response levels can restrict population movement, we assume this 2 variable negatively affects operated flights. When the relationship between emergency response 3 level and the number of flights was decomposed into within effect and between effect, we found 4 that categorical variables will lose part of the information. Therefore, we divided this variable into 5 three dummy variables-Level I, Level II, and Level III. 6 Regarding municipal control measures, city lockdown is one important measure that should be 7

considered. The severest lockdown measure, border shutdown measures in Hubei Province, was 8

introduced. All modes of transportation were suspended during the lockdown. Shiyan, Yichang, 9

Enshi, Wuhan, and Xiangyang were on lockdown from January 25 to March 35, January 26 to March 10 35, January 26 to March 35, January 24 to April 8, and January 26 to March 35 respectively. 11 We included the free refund policy in the model to reflect the aviation industry responses. We 12 included all three free refund policies in the preliminary analysis. However, due to multicollinearity, 13 we finally deleted the free refund policy variable for Wuhan (after January 21) and all cities (after 14 January 24 and January 28). In the final model, we only used the free refund policy for students 15 (February 11) as an independent variable. 16 In terms of geo-economic and service-related variables, population size, GDP per capita, and 17

hup airports were selected as our independent variables. We performed a logarithmic transformation 18 of population size and GDP per capita since these variables were highly skewed to the left. We used 19

2017 data for population size and GDP per capita, as the China City Statistical Yearbook has been 20 updated to 2018 (data for 2017). We took the cities with the top ten hub airports as an independent 21 variable and named it Hubtop10, which includes Beijing, Shanghai (Pudong and Hongqiao), 22 Guangzhou Note: The adjustment time of the response level of each province is shown in Figure 3 . 

4 Table 2 shows the results of the between-within model for all periods and different stages. 5

According to the characteristics of each stage, we re-divide the six stages into two stages. Since the 6 first stage of COVID- 19 had not yet started to affect aviation, we started with the second stage. 7 Figures 2 and 4 show that the period from the second stage to the fourth stage was the most severe 8 stage of the epidemic and also the period with the greatest impact on the number of operated flights. 9

Accordingly, we combined the second, third, and fourth stages. The fifth and the sixth stages were 10 combined as the epidemic was largely under control and flight volumes began to recover. In addition, 11 we examined the stages beyond the sixth stage to examine the impact of relevant variables on 12 aviation once the epidemic was largely under control. Since the number of operated flights recovered 13

to 70% by the end of April, our study period was up to the end of April. We did the Wald test for 14 lnalpha equal to 1 (it corresponds to the test for alpha equal to 0) and found that all alphas of the 15 three models are significantly different from 0, which means that using a negative binomial can 16 better estimate. 17 With respect to within-effects, COVID-19 at both origin and destination negatively impacts the 18 operated flights. 

With respect to between-effects (spatial effects), in the early stages, COVID-19 status in the 1 origin city was positively correlated with operated flights. It is because, in the early stages, cities 2 heavily affected by the epidemic tend to be those with a higher number of operated flights, so there 3 is a positive correlation between the two variables. It should be noted that this is a comparison 4 between cities, and for one city, the severity of COVID-19 will cause a decrease in the operated 5 flights (time effects). However, this significant positive correlation disappeared after Stage 5, as 6 almost all cities were affected by the outbreak after Stage 4 ( Figure 2 ). Unlike departure cities, the 7 network effect of COVID-19 cases in destination cities has a negative impact on the operated flights. 8

This result suggests that cities with a large number of flight connections to hard-hit cities tend to 9 have fewer flights than those with the opposite situation. Regarding the emergency level, cities that 10 implement Level I earlier have fewer flights than cities that implement level I later (Model 2). In 11

Stages 5 and 6, cities that adjust to Level II or Level III earlier have more flights than cities that 12 adjust to Level II or Level III later (Model 3). After Stage 6, the emergency level no longer has 13 spatial effects. As expected, cities with lockdowns have fewer flights than cities without lockdowns. 14 After Stage 6, since most cities in Hubei were unlocked on March 25, the spatial effect of this 15 variable disappeared. Since the free refund policy for students applies to all cities, there is no 16 difference between them, resulting in automatic omission during the calculation process. 17 In accordance with previous studies ( population size and GDP per capita ranged from 0.558 to 1.019 and from 1.099 to 1.246, respectively. 21

This study investigated the extent to which the COVID-19 outbreak and the relevant control 23 measures adopted affect the aviation industry. Although there is abundant evidence that the aviation 24 industry has been drastically affected by the COVID-19 outbreak, few studies have investigated the 25 quantitative relationship between the pandemic and the number of flights. This study filled this gap 26 by examining the impact of COVID-19 on China's domestic air transportation from January 17, 27 2020, to April 30, 2020. We adopted a between-within model to separate the temporal effects and 28 spatial effects. 29

This study has shown that COVID-19 and the relevant control measures adopted have a 30 significant negative effect on the number of operated flights, which is mainly reflected in the 31 temporal dimension. In general, imposing lockdown measures is related to a 97% reduction in the 32 number of operated flights. Moreover, adjustment to the Level I responses in the early stages will 33 result in a reduction in the number of operated flights, while adjustments from Level I responses to 34 Level II or Level III responses in the later stages (Stage 5-6) will result in an increase in the number 35 of operated flights. 36 In terms of spatial effects, COVID-19 status in the origin city in the early stage was positively 37 correlated with operated flights since cities heavily affected by the epidemic tend to be those with a 38

higher number of operated flights. In contrast, the network effect of COVID-19 cases in destination 39 cities has a negative impact on the operated flights. The number of operated flights in cities under a 40 lockdown is significantly less than that observed in cities that are not under a lockdown. In terms of 41 emergency responses, in the early stages, cities that implement Level I earlier have fewer flights 42 than cities that implement level I later. At a later stage, cities that adjust to Level II or Level III 1 earlier have more flights than cities that adjust to Level II or Level III later. After Stage 6, the 2 response level has no spatial effects. 3 This study shows that stringent control measures will result in a decrease in the number of 4 operated flights. However, effective control measures can contain the spread of the pandemic, which 5 is of great significance to the rapid recovery of the aviation industry. When the outbreak is contained 6

to a certain extent, that is when the response level is downgraded from Level I to Level II or Level 7 III, the negative impact of the response level on operated flights is weakened. This indicates that the 8 control measures adopted during the early stage of the pandemic positively impact the restoration 9

of the aviation industry and other industries in the later stage. In addition, compared with provincial 10 control measures, smaller control management units and differentiated control measures are more 11

conducive to restoring the number of operated flights. 12

We are grateful to the anonymous reviewers and the guest editors for their thoughtful and 14 constructive comments, which helped improve both the exposition and technical quality of the paper. 15 The research was financially supported by the Major Program of National Fund of Philosophy and 16

Social Science of China (Grants 20&ZD099) and the National Natural Science Foundation of China 17 (Grants 42071147). 18

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Formal analysis, Methodology, Writing-original draft. Jiaoe Wang: Conceptualization, Investigation, Writing-review&editing, Funding acquisition, Project administration. Jie Huang: Writing-review&editing. Zhuo Chen: Formal analysis, Methodology, Writing-review&editing