Impacts of the COVID-19 epidemic on carbon emissions from international shipping.

The COVID-19 epidemic made the most countries to take strict lockdown measures, what has seriously caused an unprecedented impact in the shipping industries, whereas these measures have also played a significant impact to control carbon emissions from international shipping. Here, we try to use the threshold generalized autoregressive conditional heteroscedasticity and the exponential generalized autoregressive heteroscedasticity to investigate whether the fluctuations of the control variable on carbon emissions from international shipping are asymmetric or not. On this basis, the GARCH-MIDAS model is introduced to discuss whether the newly confirmed cases are independent of control variables and have an impact on the fluctuation of carbon emissions. From the results, we find that the information contained in the newly confirmed cases cannot be covered when adding the other control variables. In addition, the newly confirmed cases have a negative impact on the volatility of carbon emissions, while the other control variables significantly increase carbon emissions. This study provides a quantitative research method for the analysis of the volatility and impact factors on international shipping carbon emissions, which helps to formulate more reasonable emission reduction measures and promote the low-carbon transformations of the global shipping industry.

Characterization and immunogenicity of SARS-CoV-2 spike proteins with varied glycosylation.

The ongoing coronavirus disease-19 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has drastically changed our way of life and continues to have an unmitigated socioeconomic impact across the globe. Research into potential vaccine design and production is focused on the spike (S) protein of the virus, which is critical for virus entry into host cells. Yet, whether the degree of glycosylation in the S protein is associated with vaccine efficacy remains unclear. Here, we first optimized the expression of the S protein in mammalian cells. While we found no significant discrepancy in purity, homogeneity, or receptor binding ability among S proteins derived from 293F cells (referred to as 293F S-2P), 293S GnTI- cells (defective in N-acetylglucosaminyl transferase I enzyme; 293S S-2P), or TN-5B1-4 insect cells (Bac S-2P), there was significant variation in the glycosylation patterns and thermal stability of the proteins. Compared with the partially glycosylated 293S S-2P or Bac S-2P, the fully glycosylated 293F S-2P exhibited higher binding reactivity to convalescent sera. In addition, 293F S-2P induced higher IgG and neutralizing antibody titres than 293S or Bac S-2P in mice. Furthermore, a prime-boost-boost regimen, using a combined immunization of S-2P proteins with various degrees of glycosylation, elicited a more robust neutralizing antibody response than a single S-2P alone. Collectively, this study provides insight into ways to design a more effective SARS-CoV-2 immunogen.

The influence of COVID-19 epidemic on BDI volatility: An evidence from GARCH-MIDAS model.

In this study, we use the sample data from Jan 22, 2020 to Jan 21, 2022 to investigate the impacts of added infection number on the volatility of BDI. Under this structure, the control variables (freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls) are added to test whether the information contained in the added infection number is covered. In the GARCH-MIDAS model, we divide the volatility of BDI into the long-term and short-term components, then employ in the least squares regression to empirically test the influences of added infection number on the volatility. From the analysis, we find the added infection numbers effectively impact the BDI volatility. In addition, whether the freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls and other variables are considered alone or at the same time, further the added infection number still significantly influences the volatility of BDI. By studying the ability of the confirmed number to explain the volatility of BDI, a new insight is provided for the trend prediction of BDI that the shipping industry can take the epidemic development of various countries as a reference to achieve the purpose of cost or risk control.

Estimating the effect of COVID-19 epidemic on shipping trade: An empirical analysis using panel data.

The unexpected outbreak of COVID-19 epidemic is an unpredictable event in shipping trade. In this paper, we mainly investigate the gaps that occur in the shipping trade between China and different regions during the period February-October 2020 and to provide useful information for operation management of shipping industry. The data include a panel obtained from the National Statistics Institute to analyze the gap where a selected group of shipping trade in three regions are considered: European Union, North America, and Southeast Asia. On this basis, a dynamic panel data model is proposed to estimate the trend. We observe that government prevention and control measures have a negative impact on export trade, while import trade increases accordingly.

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

The COVID-19 outbreak has had a serious effect on the global economy, particularly the volume of port trade between imports and exports. We construct a panel regression model with month as time series where panel data from 14 major ports in China from January to October 2020 to analyze how the macro economy, the severity of the epidemic, and government control measures affect port operations. Based on the results, we have identified 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.