Ocean and Coastal Management 209 (2021) 105660

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Ocean and Coastal Management

journal homepage: http://www.elsevier.com/locate/ocecoaman

The effect of COVID-19 pandemic on port performance: Evidence

from China
Lang Xu a, Shumiao Yang a, Jihong Chen b, *, Jia Shi a
a
College of Transport and Communications, Shanghai Maritime University, Shanghai, China
b
College of Management, Shenzhen University, Shenzhen, China

A R T I C L E I N F O A B S T R A C T

Keywords: The COVID-19 outbreak has had a serious effect on the global economy, particularly the volume of port trade
COVID-19 pandemic between imports and exports. We construct a panel regression model with month as time series where panel data
International shipping from 14 major ports in China from January to October 2020 to analyze how the macro economy, the severity of
Port performance
the epidemic, and government control measures affect port operations. Based on the results, we have identified
Panel data
Regression model
the key factors affecting port operations in the context of the pandemic and the managerial insights can help
shipping company, port operator and government to change the strategy to copy with the effect of COVID-19
pandemic.

1. Introduction Australia and Turkey have implemented the quarantine control for ships
entering the port for 14 days, which makes the sailing time of ships
The COVID-19 pandemic is spreading in many parts of the world to longer, the sailing plan disrupted, the containers cannot arrive at the
impact all aspects of human society (Corlett et al., 2020; Chen et al., port to unload on time, and even face the crisis of shipping stoppage.
2020; Wan et al., 2020) where negative influences on port throughput Beyond that, these ports underpin the international trade, most lack
mainly includes closure of shipping line, transportation market disrup­ the ability to cope with epidemic shocks (Corbet et al., 2020). The
tion and increased healthy risk of international goods (Campbell et al., volume of international trade is one of the main factors affecting port
1920). Because of the increased mobility, enhanced connectivity and throughput where mainly simulates the growth of container trans­
increasing efficiency of international trade have been become a portation. For China, impacted by the outbreak of COVID-19, the in­
double-edged sword. Therefore, shipping mobility has allowed more ternational trade has seriously hindered, and the container scale has
goods to transport to more and remote ports, there undoubtedly con­ greatly reduced. From the view of port throughput with designated size,
tributes to the risk of spreading pandemic. In China, the reports of in­ port throughput increased from 2015 to 2019, reaching 261.07 million
fluences on export and import trades in many sectors including TEU in 2019 whereas decreased by 1.3% from January to September in
transportation, have even far-reaching impacts on the agricultural, in­ 2020because the COVID-2019 leads to the hindered international trade.
dustry and living standard. The year-on-year growth of the throughput of China’s ports above
China imports from Japan, South Korea, for example, electrical, designated size is shown in Fig. 1.
electrical, audio and video equipment and accessories, zero upgrades The international trade between export and import volumes during
will affect China’s imports of such products. Next, the outbreaks in the pandemic are distributed unevenly in space and across different
Japan and South Korea affected midstream industries such as metal periods. Hence, two questions need to be further answered: (1) How
products, plastics and chemicals, leading to shortages of raw materials. much the decline in international trade can be explained by the outbreak
In the end, the negative impact of the pandemic will be passed on to of COVID-19? (2) How much do the factors of COVID-19, government
consumer electronics, automobiles and other downstream industries. At and economics contribute to the decline of international trade? To solve
the same time, the government temporarily changed the original export the above-mentioned questions, we build a panel regression model with
and tariff policy, restricting the export of medical supplies, medicines, month as time series where panel data from 14 major ports in China
grain and other products (Simon 2020). For shipping industry, India, from January to October 2020 including the port operation indicators,

* Corresponding author.
E-mail addresses: xulang@shmtu.edu.cn (L. Xu), ysm8068@163.com (S. Yang), cxjh2004@163.com (J. Chen), jiashi0625@163.com (J. Shi).

https://doi.org/10.1016/j.ocecoaman.2021.105660
Received 7 February 2021; Received in revised form 14 April 2021; Accepted 14 April 2021
Available online 21 April 2021
0964-5691/© 2021 Elsevier Ltd. All rights reserved.
L. Xu et al. Ocean and Coastal Management 209 (2021) 105660

macroeconomic indicator, epidemic severity indicator and government et al., 2020; Wan et al., 2019). For example, In the first quarter of 2020,
control indicator were analyzed. On the one hand, the pandemic has a the cargo throughput of China’s ports was approximately 3.073 billion
significant negative effect on import and export throughput, while tons, a year-on-year decrease of 4.6%. The international cruise tourism
government control measures have a certain degree of positive effect on market has entered the stagnation of the entire industry. Take Carnival
exports, while import throughput is not affected. On the other hand, in Corporation as an example. After the COVID-19 outbreak, the com­
the context of the pandemic, the time effect of import throughput is pany’s stock price dropped $50 to a low of $7.80, wiping nearly $24
more obvious, and the traditional peak season for shipping from June to billion off its market value to just $6 billion (Rocklöv et al., 2020;
September is still maintained. However, due to the impact of the Hanaoka et al., 2020).
pandemic, export throughput no longer has a clear difference between In order to explore the impact of finance, transportation and various
low and peak seasons. Based on the results, the managerial insights can industries during the pandemic period, many scholars have conducted
help shipping company, port operator and government to change the research on this. Scholars in the financial field have mainly studied the
strategy to copy with the effect of COVID-19 pandemic. characteristics and reasons of stock volatility under the background of
the pandemic. Al-Awadhi et al. (2020) used a panel data model to
2. Literature review analyze the impact of daily newly confirmed cases on the average daily
return of listed companies. The results show that the daily newly
Beginning in early 2020, the COVID-19 pandemic broke out sud­ confirmed cases are negatively correlated with the stock returns of listed
denly and quickly spread to various countries around the world. As of companies that day. Liu et al. (2020) analyzed the mechanism of the
24:00 on October 31, a total of about 56,4962,000 confirmed cases and impact of the COVID-19 pandemic on the stock market in the short term,
7,479,000 deaths have been reported globally. Many countries imple­ and found that investors’ expectations and emotional fluctuations dur­
menting unparalleled mobility restrictions to control the spread of the ing the pandemic are the main reasons for stock price changes in the
virus (March et al., 2020). For example, many governments have short term. Xiong et al. (2020) conducted a survey of people in eight
adopted restrictive measures to reduce citizens’ social activities and countries including China and Spain and found that negative emotions
suspended the operations of some companies to reduce contact between such as anxiety and depression were relatively high during the
people (Lau et al., 2020). Besides, facts have proved that curbing pop­ COVID-19 pandemic. Michail and Melas (2020) used GARCH regression
ulation movement is one of the effective ways to respond to public and impulse response of the value-at-risk model to capture how the
health emergencies and stop the spread of the epidemic (Chen et al., shipping market responded to COVID-19. Experimental results proved
2020). that COVID-19 had a negative impact on dry bulk and crude oil vessels.
With the implementation of quarantine measures such as work By summarizing the literature on the COVID-19 pandemic, it can be
stoppages, production shutdowns, and even “city closures”, commercial found that the current research objects are mainly concentrated in the
consumption, logistics and transportation have suffered heavy losses. economic or financial fields, and there are few analyses on the maritime
Luo and Tsang, 2020 pointed out that the impact of the COVID-19 industry. In addition, the research goal of most of the literature is to
pandemic has caused global output in 2020 to drop by 1.0% explore the correlation between indicators and the pandemic, without
year-on-year. At the same time, according to the analysis report on the considering the impact of government control measures and the cycle of
impact of the COVID-19 pandemic on the global economy released by the economy itself. Therefore, this paper takes the import and export
the United Nations Conference on Trade and Development (UNTCAD throughput of China’s major ports as the explanatory variable, and se­
2020) in March et al., 2020, the epidemic will become a major threat to lects 4 indicators representing consumption, industry, government
the global economy and may cause the annual growth rate of the global control measures, and the severity of the epidemic as the explanatory
economy to drop to 2.5% in 2020. variables. The panel data model composed of the above monthly data is
As the most efficient, reliable and effective means of transportation, used to study the key factors affecting port throughput in the context of
shipping is especially important in keeping the world supply chain open the pandemic. In addition, by adding time dummy variables to the
during this challenging period (Cleopatra 2020). In particular, the model, we also investigated whether the cycle of the shipping industry
behavior of human activities in the ocean have been radically altered by has changed. The model also reveals the advantages and disadvantages
the COVID-19 pandemic, with port restrictions and changes in con­ of government control measures in the shipping industry. Our research
sumption patterns impacting multiple maritime sectors most notably fills the gaps in the existing literature and helps shipping companies
fisheries, passenger ferries and cruise ships sectors which rely heavily on make strategic decisions.
the movement of people and goods (Bennett et al., 2020; Depellegrin

Fig. 1. Year-on-year growth rate of cargo throughput at ports above designated size in China from January 2018 to December 2020.

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L. Xu et al. Ocean and Coastal Management 209 (2021) 105660

3. Data

We collect a set of panel data from 14 major ports in China over the
period January to October 2020 consisting of port throughput, macro­
economic level, pandemic transmit and government control. The port
throughput and the macroeconomic level are provided from the China
Ports Yearbook. In addition, the pandemic transmit and government
control are obtained from the open data in Ministry of Health where the
ports locate. Constrained by the data acquisition, four independent
variables, which can be classified into four categories as shown in
Table 1, are carefully picked as follows:

(a) Industrial added value: IAV is the result of industrial production

activities expressed in monetary form by industrial enterprises
during the reporting period. This ratio helps us to gauge the
health of China’s big industrial companies.
(b) Confirmed case characteristics: We choose the cumulative number
of confirmed cases to measure the severity of COVID-19
pandemic in the different port (Ashraf, 2020).
(c) Stringency index characteristics: Stringency index is published by
Oxford COVID-19 Government Response Tracker (OxCGRT)
database. It records information on social distancing, which is a
lagging indicator on subsequent economic activity. Thus, we use
the first-order lag of stringency index as independent variable
(Michail et al., 2020).
(d) CPI characteristics: CPI is a macroeconomic indicator reflecting
Fig. 2. The ports and regions analyzed in this study.
changes in the price level of good and service generally purchased
by households, which has a high correlation with GDP and can
reflect the region’s macroeconomic development (Shan et al. Table 2
2018). Correlation coefficients of variables.
Although Chinese ports are with designated size, we select those
Cum Stri IAV CPI LIcT LEcT
ports have sufficient data to be representative of located region in the
Cum 1
COVID-19 pandemic as shown in Fig. 2 where consists of the three ports
Stri 0.124 1
(Dalian, Tangshan and Tianjin) belonging to Bohai Sea, three ports IAV 0.332 0.00870 1
(Lianyungang, Qingdao and Rizhao) belonging to Yellow Sea, two ports CPI − 0.130 0.00720 − 0.656 1
(Ningbo-Zhoushan and Shanghai) belonging to Yangtze River Delta, three LIcT 0.136 − 0.0196 0.0847 − 0.294 1
ports (Fuzhou, Quanzhou and Xiamen) belonging to Taiwan Strait, two LEcT 0.0871 − 0.0194 0.0489 − 0.284 0.859 1

ports (Guangzhou and Shenzhen) belonging to Pearl River Delta and one
port (Zhanjiang) belonging to Beibu Gulf. 4. Methodology
In order to study the key factors affecting port operations in the
context of COVID-19, we established a panel regression model. The In this subsection, to investigate the key factors affecting the cargo
cargo throughput is widely accepted as an important indicator of port throughput in the context of the COVID-19 pandemic, we employ a
productivity (Cheung and Yip, 2011). Therefore, in this paper, the panel linear regression model. Further, the logarithm of cumulative
dependent variables are export cargo throughput (EcT) and import confirmed case is considered to ensure the possibility of a non-linear
cargo throughput (IcT), respectively where is usually measured tonnage relationship with explanatory variables because logarithmic trans­
including containerized cargo, bulk cargo and oil. We summary that the formation makes the estimated coefficient is robust for the unit of
correlation coefficient among the variables in Table 2, except for the measurement of the variable (Shan et al., 2014). To further investigate
linear correlation between import and export cargo throughput in the the impact of COVID-19 pandemic on import and export cargo
same month of 2019, there is no multicollinearity problem among other throughput, we first build a mixed effected model as a benchmark to
explanatory variables. However, because they do not appear in the same analyze panel linear regression as follows
equation, the linear relationship between them can be ignored. ( )
Yi,t = α0 + β1 log Cumi,t + β2 Strii,t− 1 + β3 CPIi,t + β4 IAVi,t + α1 LcTi,t + εi,t
(1)

Table 1
Descriptive statistics of variables.
Variable Description Unit Mean SD Min Max

IcT Import cargo throughput Million ton 2230.97 1349.37 450.67 6743.15
EcT Export cargo throughput Million ton 1590.25 1099.42 194.83 5124.41
Cum Cumulative confirmed cases Units 553.97 428.87 32.00 1396.00
Stri Stringency index % 71.01 8.51 58.33 79.80
IAV Industrial added value % − 1.88 7.28 − 21.80 11.50
CPI Consumer Price Index % 102.92 1.37 99.70 106.70
LIcT Import cargo throughput in 2019 Million ton 2130.32 1258.14 655.64 5833.20
LEcT Export cargo throughput in 2019 Million ton 1588.01 1067.31 219.51 5101.98

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L. Xu et al. Ocean and Coastal Management 209 (2021) 105660

where subscript i shows the port index, t is the temporal index, α and β ( )
Yi,t = α0 + β1 log Cumi,t + β2 Strii,t− 1 + β3 CPIi,t + β4 IAVi,t + α1 LcTi,t + μi
are understood as the vector of independent variable. Yi,t is a dependent
+ εi,t
variable representing the import cargo throughput of porti and εi,t is the
idiosyncratic error term. Because each port exists non-individual effect, (3)
to further investigate the individual effect on the import and export
where μi is the unobserved fixed effect for port i. A city’s time-invariant
cargo throughput, we introduce an individual effect item in the bench­
features affect its economy and seaport. For example, the Shanghai port
mark model. Due to the existence of two forms of individual effects, it
serves as a transshipment hub between ocean transport and river
can be divided into random effects model (REM) and fixed effects model
transport, while the Qinhuangdao port mainly serves as an export
(FEM).
gateway for raw materials. There is no good way to quantify those ef­
Random Effects Model is also called the Error component model or
fects, but including specific fixed effect term μi for each port suffices for
generalized least square technique. This model estimates panel data in
our purpose. Additionally, the effect of time factor on cargo throughput
which interference variables may be correlated over time and between
in the context of COVID-19 pandemic, we also employ the time fixed
individuals. In the random effect model, the differences between in­
effect to build the two-tier fixed effect model as follows
tercepts are accommodated by the error terms of each port (Zulfikar
( )
et al., 2018). The regression equation of panel data of random effects Yi,t = α0 + β1 log Cumi,t + β2 Strii,t− 1 + β3 CPIi,t + β4 IAVi,t + α1 LcTi,t + vt + μi
model is as follows
+ εi,t
( )
Yi,t = α0 + β1 log Cumi,t + β2 Strii,t− 1 + β3 CPIi,t + β4 IAVi,t + α1 LcTi,t + δi (4)
+ εi,t For the four approaches, the cluster-robust standard error to estimate
(2) p-value in the regressions is consider to prevent unstable regression
caused by the heteroscedasticity and serial correlation (Petersen 2009).
where δi is the is the individual residual which is the random charac­
teristic of port i. This model assumes that there is a difference of inter­ 5. Empirical analysis
cept for each individual and the intercept is a random variable.
However, the difference between fixed effect model and random effect The regression models of panel data include fixed effects model
model is μi is not correlated with explanatory variable Xi,t ; otherwise, in (FEM), random effects model (REM) and mixed effects model (MEM).
random effect model, the result is almost the opposite. Thus, we can Each of the three models has applicable conditions. Therefore, in order
describe the model as to estimate the coefficients α and β more accurately, based on the panel
data of 14 ports in China from January to October 2020, we construct a
mixed effect model, a random effect model, an individual fixed effect,
and a two-way fixed effect on the import and export throughput

Table 3
Regression results of Import and Export.
Variables Import Export

Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4

Cum 31.330 21.963 − 323.504** − 531.128*** 67.828 59.809 − 279.425* − 459.439***

(0.278) (0.424) (0.016) (0.004) (0.258) (0.251) (0.091) (0.005)
Stri [-1] 1.516 1.166 2.558 24.123 3.127* 3.194* 4.192*** 102.988*
(0.303) (0.440) (0.243) (0.115) (0.050) (0.063) (0.000) (0.061)
IAV 10.607** 13.643** 19.380*** 9.398** 0.874 1.520 1.566 − 3.517
(0.028) (0.050) (0.001) (0.037) (0.849) (0.786) (0.689) (0.123)
CPI 11.244 10.962 − 4.510 20.036 − 50.254 − 72.117 − 89.244*** − 48.376
(0.563) (0.628) (0.665) (0.218) (0.196) (0.142) (0.000) (0.187)
LEcT 1.055*** 1.041*** 0.686*** 0.723*** 1.002*** 0.983*** 0.889*** 0.892***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
month2 308.344 1769.343
(0.316) (0.134)
month3 − 145.606*** 5.341
(0.004) (0.964)
month4 − 111.438*** − 273.866***
(0.004) (0.000)
month5 426.097 1734.237*
(0.116) (0.077)
month6 254.228*** 287.579
(0.001) (0.118)
month7 123.641*** − 24.395***
(0.000) (0.004)
month8 81.439*** 1.249
(0.000) (0.909)
month9 316.256* 1564.352
(0.087) (0.102)
month10 447.191* 1864.273*
(0.091) (0.055)
Constant − 1468.248 − 1322.971 3038.703** 0.000 4552.089 6872.897 10,783.707*** 0.000
(-0.76) (0.564) (0.039) (.) (0.225) (0.152) (0.001) (.)
Observations 126 126 126 126 126 126 126 126
R-squared 0.977 0.617 0.656 0.732 0.960 0.673 0.681 0.689

Note: Standard errors in parentheses, where ***p < 0.01, **p < 0.05, *p < 0.1. Figures in brackets (⋅) indicate lagged values, where (− n) is the nth month before the
day examined.

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respectively. The models are compared and tested to select the optimal of confirmed cases. The estimated coefficient is − 531.128 and is sta­
model to analyze the key factors affecting port operations under the tistically significant at p <0.001 level. This means that the severity of the
background of the COVID-19 pandemic. pandemic has largely affected the throughput of imported goods at
The regression results of imports are shown in Table 3. After com­ various ports, which may be directly caused by the company’s shut­
parison, it is found that for import throughput, the regression co­ down. Similarly, Table 3 shows that the export cargo throughput is
efficients of the individual fixed effects and two-way fixed effects models negatively correlated with the cumulative number of confirmed cases,
are more significant. The R2 of the MEM is the closest to 1, which shows with a regression coefficient of − 279.425, but the p-value of 0.091 is
that the MEM has the best fit. Furthermore, we use the F statistic of only statistically significant at the 90% confidence level, indicating that
Chowtest to judge the pros and cons of the MEM and the FEM. The null the pandemic has limited impact on export cargo throughput. The
hypothesis H0 is all ui − u = 0, that is, there is no individual effect. export of medical and health equipment (including masks, protective
According to the F test statistics, the p value can be obtained. The clothing, etc.) may alleviate the losses caused by the decrease in the
calculation result is to reject the null hypothesis, indicating that the export volume of other products. In addition, under the influence of the
establishment of a fixed-effect model is more reasonable than a mixed- pandemic, new demand for home office and home entertainment has
effect model. In addition, we can see that the R2 of the two-way fixed recovered strongly, which has also driven the export of furniture and
effects model is greater than the individual fixed effects, and the home entertainment facilities.
regression coefficients of the time dummy variables in the two-way fixed Moreover, we also find that the coefficients of the dummy variables
effects model is significant. Therefore, the two-way fixed effects model is in March and April are negatively correlated with import throughput,
the optimal model. but they are positively correlated after May. We know that the pandemic
The regression results of exports are also shown in Table 3. We can in China was severe before May and had a greater impact on the ship­
see that the regression results of the individual fixed-effects model are ping industry. After May, thanks to government control and the efforts of
the most significant, while the R2 of the MEM is the closest to 1, indi­ medical personnel, the pandemic gradually eased and the shipping in­
cating that the explanatory variables in the mixed-effects model have dustry began to resume normal operations. What is interesting is that
the strongest correlation with the explained variables. As before, we time does not have a greater impact on exported goods. This may be
again use the F test of Chow’s statistics to determine whether to choose a related to China’s large export of anti-pandemic materials, but the
mixed-effects model or a fixed-effects model, and the calculation results specific reasons are worthy of our further study.
also reject the null hypothesis. Therefore, when export cargo throughput Another point worth noting is that the control variable, the port’s
is the explained variable, the individual fixed effects model is more import and export cargo throughput in the same month in 2019, has a
suitable. very significant positive impact on the dependent variable in both
Industrial added value and the government’s strict index of pre­ models. Therefore, it can be considered that the port’s initial scale has a
vention and control have the same impact on the throughput of im­ certain impact on the throughput of imported goods, but this is not the
ported goods and export goods. As shown in column 4 and row 5 of focus of our study.
Table 3, import cargo throughput is positively correlated with Industrial In general, the cumulative number of confirmed cases has had a
added value above designated size and the regression coefficient of IAV significant negative impact on the throughput of imported and exported
is 9.398, which means that for every 9.398% increase in industrial added goods, but the throughput of imported goods has been more severely
value, the port’s import cargo throughput will increase by 1%. And the p affected by the pandemic. The government’s strict prevention and
value of the IAV regression parameter is less than 5%, indicating that it is control index only has a significant positive effect on exports. In terms of
statistically significant at the 95% confidence level. With the recovery macroeconomics, the value-added of industries above designated size is
and development of the primary industry, the demand for raw materials significantly positively correlated with the throughput of imported
by enterprises has gradually increased. The import of a large amount of goods, while CPI is significantly negatively correlated with the export
raw materials such as iron ore and rubber has promoted the increase in cargo throughput. The time effect of import throughput is more signif­
the port’s import throughput. According to Table 3, column 7 and row 5, icant, but the change of export throughput over time is not obvious. The
for export cargo throughput, although the industrial added value has a shipping industry gradually resumed normal operations after May. The
positive impact on it, the regression results are not statistically results of the model basically agree with the actual situation.
significant.
In addition, combining the analysis of row 3, we find the govern­ 6. Conclusion
ment’s strict prevention and control index is positively correlated with
both import and export throughput, but the regression result for import In this paper, we collected the relevant panel data of 14 ports in
cargo throughput is not significant, while the regression result for ex­ China from January 2020 to October 2020, mainly including epidemic
ports is very significant. The results of FEM model show that the stricter indicators and city-level economic data, to evaluate the impact of
the government’s prevention and control measure, the more goods various indicators on the operation of Chinese ports under the back­
exported. Thus, we have every reason to believe that the prevention and ground of COVID-19 pandemic. Specifically, we employ the port cargo
control measures have effectively controlled the domestic pandemic and throughput to represent the port operations, import and export goods
help the gradual return of the shipping industry to normal. The CPI and put in the same period in 2019 the port cargo throughput as control
index has opposite effects on the throughput of imported cargo and the variables, respectively, using the mixed effects model, random effects
throughput of exported cargo. As shown in row 7, CPI is negatively model and individual fixed effects model to analyze the import and
correlated with port export cargo throughput, the regression coefficient export throughput. Through relevant theories and empirical analysis,
is − 89.244, and p < 0.01. The rise in CPI leads to inflation, currency the main conclusions are as follows:
devaluation, and a decline in the competitiveness of exported goods, First of all, the severity of the epidemic has a significant negative
which has led to a decline in exported goods. The CPI is positively effect on both import and export cargo throughputs, further the impact
correlated with import throughput, with a regression coefficient of of the pandemic on import is greater than export. This may be related to
20.036, but the regression results are not significant. large-scale shutdown of factories during the pandemic and the inclusion
Compared with other factors, the cumulative number of confirmed of a certain amount of anti-pandemic materials in the export goods. In
cases has the greatest impact on the throughput of imported and order to prevent the pandemic from causing a more serious impact on
exported cargo. As shown in the fourth column of Table 3, the import the ship’s transportation process, port and shipping companies should
cargo throughput is negatively correlated with the cumulative number conduct pandemic investigation and registration for boarding personnel,
and set up fixed cabins as isolation cabins. In addition, the government

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should do a good job of supervision to prevent ship transportation from Acknowledgments

becoming a carrier of spreading the virus.
Secondly, the strict index of government prevention has a significant The authors gratefully acknowledge supports from the National
positive impact on the export cargo throughput, indicating that gov­ Natural Science Foundation of China (Grant No. 51879156, 71704103,
ernment prevention plays a certain role in promoting the recovery of 71974123 and 51409157), the Program for Professor of Special
shipping industry. However, for import, the government’s strict control Appointment (Eastern Scholar) at Shanghai Institutions of Higher
index does not seem to have much impact, and the reason behind this is Learning, Innovation Program of Shanghai Municipal Education Com­
worth further study. In addition to the prevention and control of the mission (Grant No. 2017-01-07-00-10-E00016), and the High-level
pandemic, differentiating the severity of the impact of the pandemic on talent project funding plan of the transportation industry supported by
various sectors of the shipping industry, the government can provide the Ministry of Transport of the People’s Republic of China (Grant No.
targeted policy support. For the waterway passenger transport industry 2019-012). However, the authors are solely responsible for all the views
that has been hit hardest, the government can provide financial sub­ and analyses in this paper.
sidies to prevent the industry from regressing. For the freight industry,
financial concessions such as tax cuts and interest-free loans can enable References
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Declaration of competing interest the spatial structure of the China’ s global shipping network. Journal of Transport
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The authors declare that there is no conflict of interests regarding the of COVID-19 pandemic on mental health in the general population: a systematic
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