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Using a monthly data between 2008 and 2019, this study investigated how the development of the Internet technologies has shaped the consumer consumption pattern of different types of paper products in China during this period. In detail, this study covers four major types of paper products in the Chinese market: newsprint, printing paper, household paper, and packaging paper. The developments of Internet technologies were decomposed into two aspects: the communication technology (CT) and the software platform technology (PT). Granger causality analysis was employed to determine whether the consumption pattern of paper products was affected by the development of the Internet. Furthermore, impulse-response analyses were utilized to measure how the consumption pattern of each paper product was shaped by the development of each aspect of Internet technologies, i.e., CT or PT. The empirical results suggested that the Internet developments are causing the consumption patterns shifts of all four paper products. Specifically, the consumptions of newsprint and printing paper were driven by both CT and PT. However, the consumptions of packaging paper and household paper were driven by the PT only. Compared to the development of CT, we found that the quick evolution of PT plays a more significant role in shifting people's consumption of paper products in China. The empirical results revealed by this study could provide valuable economics and policy implications to both academia and relevant industries. • Internet development has shifted the paper products consumption pattern in China. • Development of internet boosted the demand for printing paper and household paper. • Development of internet suppressed the demand for newsprint. • The effects of internet technological development are separated into two aspects. • Emerging Internet platforms shifted the consumption of paper products efficiently. [ABSTRACT FROM AUTHOR]

According to a recent World Health Organization estimate, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, which originated in China in 2019, has spread globally, infecting nearly 100 million people worldwide by January 2021. Patients with chronic liver diseases (CLD), particularly cirrhosis, hepatobiliary malignancies, candidates for liver transplantation, and immunosuppressed individuals after liver transplantation appear to be at increased risk of infections in general, which in turn translates into increased mortality. This is also the case for SARS-CoV-2 infection, where patients with cirrhosis, in particular, are at high risk of a severe COVID-19 course. Therefore, vaccination against various pathogens including SARS-CoV-2, administered as early as possible in patients with CLD, is an important protective measure. However, due to impaired immune responses in these patients, the immediate and long-term protective response through immunisation may be incomplete. The current SARS-CoV-2 pandemic has led to the exceptionally fast development of several vaccine candidates. A small number of these SARS-CoV-2 vaccine candidates have already undergone phase III, placebo-controlled, clinical trials in healthy individuals with proof of short-term safety, immunogenicity and efficacy. However, although regulatory agencies in the US and Europe have already approved some of these vaccines for clinical use, information on immunogenicity, duration of protection and long-term safety in patients with CLD, cirrhosis, hepatobiliary cancer and liver transplant recipients has yet to be generated. This review summarises the data on vaccine safety, immunogenicity, and efficacy in this patient population in general and discusses the implications of this knowledge on the introduction of the new SARS-CoV-2 vaccines. [ABSTRACT FROM AUTHOR]

COVID-19 has seriously impacted China's real economy and directly threatened the survival and healthy development of enterprises. Based on the data of Chinese A-share listed enterprises in Shanghai and Shenzhen from 2017 to 2021, this paper empirically examines the impact and mechanism of COVID-19 on enterprises financial distress. The findings are as follows: (1) The impact of COVID-19 significantly increases the risk of enterprises financial distress. (2) There are differences in the effect of COVID-19 on the financial situation of enterprises with different characteristics, with significant negative impacts on enterprises in industries such as mining and manufacturing, as well as smaller enterprises and those located in the central and western regions. (3) Digital transformation and government subsidies can alleviate the negative effects of COVID-19 on enterprises financial distress. The paper provides a basis for enterprises to recover from financial difficulties as soon as possible, enhance competitiveness, and realize sustainable development. [ABSTRACT FROM AUTHOR]

This paper aims to examine the impact of regulatory policies on cryptocurrency market volatility in the context of the COVID-19 pandemic. We find that price volatility, liquidity volatility, and return volatility in the cryptocurrency market increased significantly following regulatory announcements by Chinese policymakers targeting the cryptocurrency industry, and this effect was more pronounced during the COVID-19 pandemic. It indicates that investors are paying a great deal of attention to such events. Yet further findings suggest that as the risk of pandemic transmission increases and stock market returns decline, investor response to regulation decreases. Our findings also suggest that while regulation typically brings market volatility in cryptocurrencies, the release of regulatory policies can instead be beneficial to the stability of the cryptocurrency market when investors are in an highly greedy mood. • Regulatory policies cause market volatility in cryptocurrencies. • COVID-19 transmission risk affects investors' negative reaction to regulation. • Cryptocurrencies are potential safe havens during COVID-19. • Regulatory policies reduce cryptocurrency investor greed. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of COVID-19 on China's coal import trade. First , this paper makes a qualitative analysis of the status quo of China's coal import scale before and after COVID-19. Second, the impact of the pandemic on China's coal import was estimated by panel VAR model based on the confirmed global COVID-19 data and China's coal import data with Johns Hopkins University Center for Systems Science and Engineering database and Wind database from January 2019 to June 2021. The result shows that the pandemic has a significant impact on both the supply and demand of China's coal industry in short term. There is a rebound in coal demand due to manufacturing industries having resumed work and production under the successful pandemic control in China. Finally, this paper analyzes the measures of China's coal import process during the COVID-19 and suggests how to deal with the global public crisis in the future. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been ongoing for more than three years and urgently needs to be addressed. Traditional Chinese medicine (TCM) prescriptions have played an important role in the clinical treatment of patients with COVID-19 in China. However, it is difficult to uncover the potential molecular mechanisms of the active ingredients in these TCM prescriptions. In this paper, we developed a new approach by integrating the experimental assay, virtual screening, and the experimental verification, exploring the rapid discovery of active ingredients from TCM prescriptions. To achieve this goal, 4 TCM prescriptions in clinical use for different indications were selected to find the antiviral active ingredients in TCMs. The 3-chymotrypsin-like protease (3CLpro), an important target for fighting COVID-19, was utilized to determine the inhibitory activity of the TCM prescriptions and single herb. It was found that 10 single herbs had better inhibitory activity than other herbs by using a fluorescence resonance energy transfer (FRET) - based enzymatic assay of SARS-CoV-2 3CLpro. The ingredients contained in 10 herbs were thus virtually screened and the predicted active ingredients were experimentally validated. Thus, such a research strategy firstly removed many single herbs with no inhibitory activity against SARS-CoV-2 3CLpro at the very beginning by FRET-based assay, making our subsequent virtual screening more effective. Finally, 4 active components were found to have stronger inhibitory effects on SARS-CoV-2 3CLpro, and their inhibitory mechanism was subsequently investigated. Among of them, methyl rosmarinate as an allosteric inhibitor of SARS-CoV-2 3CLpro was confirmed and its ability to inhibit viral replication was demonstrated by the SARS-CoV-2 replicon system. To validate the binding mode via docking, the mutation experiment, circular dichroism (CD), enzymatic inhibition and surface plasmon resonance (SPR) assay were performed, demonstrating that methyl rosmarinate bound to the allosteric site of SARS-CoV-2 3CLpro. In conclusion, this paper provides the new ideas for the rapid discovery of active ingredients in TCM prescriptions based on a specific target, and methyl rosmarinate has the potential to be developed as an antiviral therapeutic candidate against SARS-CoV-2 infection. • This paper explores the rapid discovery of the active ingredients from TCMs. • TCM prescriptions were selected to find anti-SARS-CoV-2 3CLpro active ingredients. • Methyl rosmarinate is an allosteric inhibitor of SARS-CoV-2 3CLpro. [ABSTRACT FROM AUTHOR]

Under the influence of COVID-19, the global economic and social development is facing great challenges. With the increase of government financial pressure and the decrease of debt paying ability, the problem of debt risk of local governments in China is attracting wide attention. In order to measure the level of China's local government debt risk under the influence of COVID-19, this paper takes China's Sichuan Province as an example, collects the core indicators data of measuring local government debt risk in 2017-2020 years, and uses AHP-TOPSIS method to make a comprehensive analysis of the local government debt risk situation in different periods before and after COVID-19. It is found that the local government debt risk in Sichuan Province is generally controllable. However, influenced by COVID-19, in 2020, the overall level of local government debt risk in Sichuan province expanded by 22.1% compared with 2019, this is mainly due to the further expansion of debt scale and slower economic growth. This paper suggests that the Chinese government should speed up the construction of comprehensive early warning and supervision system of local government debt risk, and prevent and resolve the debt risk of local government in advance. [ABSTRACT FROM AUTHOR]

This paper studied the impact of COVID-19 on China's capital market and major industry sectors via an improved ICSS algorithm, a time series model with the exogenous variable and a non-parametric conditional probability estimation. Through the empirical analysis, it is found that the epidemic has no significant impact on the return of the stock and bond markets, but it has increased the market volatility and the impact on the stock market volatility is gradual and more obvious. There are significant differences in the significance, direction and duration of the epidemic on different sectors. In addition, the impact of COVID-19 has been gradual in some industries and rapid in others. Different industries show different sensitivities in their response to COVID-19. Based on the analysis of the impact, this paper put forward the corresponding suggestions for investment strategies and macro-control decisions. [ABSTRACT FROM AUTHOR]

This study examined the relationship between the air quality index (AQI), COVID-19, and the oil market using China's provincial data for the first four months of the pandemic (1 January–22 April 2020). The results show that air quality improved significantly during this period. Our income and regional group analyses further clarified that the middle-income group and Eastern and Central provinces, which were most affected by COVID-19, showed a more robust relationship between AQI and COVID-19 cases than other Chinese provinces. The national oil market, which saw a 57% decline in the oil price over the period of 13 January to 22 April, was also found to be insignificantly related to AQI. All in all, our paper implies that an improvement in air quality due to a drastic decline in economic activity. We provide some future research directions in the paper. [ABSTRACT FROM AUTHOR]

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O 3 concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone. [Display omitted] • Emergency response to COVID-19 (ER) had spill-over effect on air quality. • Empirical analysis was made with RDD to evaluate the impact of ER on air quality. • AQI and PM 2.5 , PM 10 , SO 2 and NO 2 concentrations decreased by approximately 10%. • Traffic restrictions and shutdown of industries during ER were important factors. • Socioeconomic resumption and ER termination did not affect air quality. [ABSTRACT FROM AUTHOR]

• Collected papers on estimations of four epidemiological parameters regarding COVID-19. • Quantitatively compared and synthesized preprints and peer-reviewed publications. • Verified the validity of the COVID-19 parameter estimations of the preprints. • Synthesized parameters can effectively capture the patterns of COVID-19. We aimed to explore the collective wisdom of preprints related to COVID-19 by comparing and synthesizing them with results of peer-reviewed publications. PubMed, Google Scholar, medRxiv, bioRxiv, arXiv, and SSRN were searched for papers regarding the estimation of four epidemiological parameters of COVID-19: the basic reproduction number, incubation period, infectious period, and case-fatality-rate. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Four parameters in two groups were synthesized by bootstrapping, and their validities were evaluated by simulated cumulative cases of the susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model. A total of 106 papers were included for analysis. The distributions of four parameters in two literature groups were close, and the timeliness of preprints was better. Synthesized estimates of the basic reproduction number (3.18, 95% CI 2.85–3.53), incubation period (5.44 days, 95% CI 4.98–5.99), infectious period (6.25 days, 95% CI 5.09–7.51), and case-fatality-rate (4.51%, 95% CI 3.41%–6.29%) were obtained. Simulated cumulative cases of the SEIRDC model matched well with the onset cases in China. The validity of the COVID-19 parameter estimations of the preprints was on par with that of peer-reviewed publications, and synthesized results of literatures could reduce the uncertainty and be used for epidemic decision-making. [ABSTRACT FROM AUTHOR]

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3–4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022. [ABSTRACT FROM AUTHOR]

In this paper, we coin the term "crypto place" to describe an emerging type of virtual place on the blockchain. Using an encrypted monument that was built to memorialize Dr. Wenliang Li, one of the whistleblowers of China's coronavirus outbreak, we extensively investigate three definitive dimensions of crypto place in terms of decentralized location, immutable locales and transaction-based sense of place. We then reflect upon the complicated social implications of blockchain technology much beyond purely serving as an alternative cryptocurrency, and further examine how place information is stored, disseminated, and incentivized on blockchain. Through this paper, we investigate the relevance of blockchain to geography studies and discuss how it may enrich the concept of place in today's data-intensive and decentralized world. [ABSTRACT FROM AUTHOR]

Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ 2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. [ABSTRACT FROM AUTHOR]

The outbreak and ongoing evolution of the COVID-19 pandemic have dramatically impacted economic development and CO 2 emissions. China is under simultaneous pressure to recover from the outbreak and meet its carbon reduction targets, and the government is endeavouring to stimulate economic recovery through fiscal and monetary policies. This paper uses a computable general equilibrium model to measure the impact on China's economic recovery and carbon emissions by incorporating the pandemic shock and related economic recovery policies of loan prime rate (LPR) and value-added tax (VAT) reduction. The study found that COVID-19 led to a simultaneous shock on China's supply and demand sides in which GDP dropped by 2.62% and carbon emissions fell by 2.53%, compared to the period prior to COVID-19. Although the LPR and VAT reduction effectively mitigated economic loss, the combined LPR and VAT reduction had a more substantial effect on boosting GDP than the single policies. The VAT cut expands production and was used to overcome supply-side shocks, while lowering LPR mitigates the damage of demand-side shocks. Compared to the VAT reduction policy, reduced LPR has smaller carbon emissions per unit of GDP output. Consequently, we recommend that the government concentrate on a combination of policies to navigate pandemic shocks, as the two economic stimulus policies are confirmed to complement one another in terms of strengths and shortcomings. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has radically expanded the role of algorithmic governance in everyday mobility. In China, urban and provincial governments have introduced health codes app as a national contract tracing and quarantine enforcement method to restrict the movements of "risky" individuals through malls, subways, railways, as well as between regions. Yet the health codes have been implemented with uneven efficacy and unexpected consequences. Drawing on glitch politics, we read these unintended consequences as "bugs" emerging from the introduction of platform-based management into everyday life. These bugs mediated individuals' lived experiences of the digital app and the hybrid space constituted by population governance, individual digital navigation, and technology. Drawing on a database of posts scraped from Zhihu, a popular Chinese question-and-answer site, we examine three dimensions of the bug: the algorithmic bug, the territorial bug, and the corporeal bug. This paper sheds light on the significance of end-user experiences in digital infrastructure and contributes to our understanding of the digital geographies of bugs in algorithmic governance and platform urbanism. [ABSTRACT FROM AUTHOR]

• This study applied a logistic growth model with parameters estimated by a non-linear least squares (NLS) method to daily new COVID-19 cases. • The model fitted time-series data exceedingly well for the whole of China, eleven selected Chinese provinces and municipalities, South Korea and Iran. • This study provided key estimates and a 95% confidence interval for parameters K , r, and P 0. • This study found that the growth rates of outbreaks differed between provinces in China and between South Korea and Iran. • As of March 13, 2020, this study's model suggested that countries such as the U.S.A., France, Italy, and Spain were still in the early stages of outbreaks. As the coronavirus disease 2019 (COVID-19) pandemic continues to proliferate globally, this paper shares the findings of modelling the outbreak in China at both provincial and national levels. This paper examines the applicability of the logistic growth model, with implications for the study of the COVID-19 pandemic and other infectious diseases. An NLS (Non-Linear Least Squares) method was employed to estimate the parameters of a differentiated logistic growth function using new daily COVID-19 cases in multiple regions in China and in other selected countries. The estimation was based upon training data from January 20, 2020 to March 13, 2020. A restriction test was subsequently implemented to examine whether a designated parameter was identical among regions or countries, and the diagnosis of residuals was also conducted. The model's goodness of fit was checked using testing data from March 14, 2020 to April 18, 2020. The model presented in this paper fitted time-series data exceedingly well for the whole of China, its eleven selected provinces and municipalities, and two other countries - South Korea and Iran - and provided estimates of key parameters. This study rejected the null hypothesis that the growth rates of outbreaks were the same among ten selected non-Hubei provinces in China, as well as between South Korea and Iran. The study found that the model did not provide reliable estimates for countries that were in the early stages of outbreaks. Furthermore, this study concured that the R 2 values might vary and mislead when compared between different portions of the same non-linear curve. In addition, the study identified the existence of heteroskedasticity and positive serial correlation within residuals in some provinces and countries. The findings suggest that there is potential for this model to contribute to better public health policy in combatting COVID-19. The model does so by providing a simple logistic framework for retrospectively analyzing outbreaks in regions that have already experienced a maximal proliferation in cases. Based upon statistical findings, this study also outlines certain challenges in modelling and their implications for the results. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive system for assessing contaminant emissions, including the dominant contaminant reduction effects, recessive contaminant reduction effects, and contaminant reduction efficiency indexes. The system is used to evaluate the linkage changes in contaminant emissions reduction across different sectors, the ease of regulation, and the returns on emissions reduction efforts. The study's results demonstrate that firstly, COVID-19 initially reduced contaminant emissions across almost all sectors, although this effect waned over time. Secondly, applying conventional regulatory measures to sectors with high reductions in contaminant emissions may not yield the expected results. Thirdly, while some sectors may be more difficult to regulate, they may offer a higher return on investment in terms of emissions reduction. Based on the above results, the paper advocates for timely measures to consolidate environmental gains, prevent rebounding of contaminant emissions, prioritize supervision and management of easily regulated sectors, prioritize upgrading of cleaner production in sectors with high reduction efficiency indices, and increase investment in environmental technology. • Created a new comprehensive system for assessing contaminant emission changes. • Quantified the unique linkage changes of contaminant emissions during COVID-19. • Developed new models to reveal contaminants' regulatory difficulty and return. • The innovative reduction effects of four key contaminant indicators were measured. • Applied WHEM firstly in research across multiple areas of contaminant emissions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has given rise to a major impact on traffic mobility. To implement preventive measures and manage transportation, understanding the transformation of private driving behavior during the pandemic is critical. A data-driven forecasting model is proposed to estimate daily charging demand in the absence of the COVID-19 pandemic by leveraging electric vehicle (EV) charging data from four cities in China. It serves as a benchmark for quantifying the impact of the COVID-19 pandemic on EV charging demand. A vector autoregressive (VAR) model is then used to investigate the dynamic relationship between the changes in charging demand and potential influencing factors. Potential influencing factors are selected from three aspects: public health data, public concern, and the level of industrial activity. The results show that the magnitude of the decline in EV charging demand varied by city during the pandemic. Furthermore, COVID-19 related factors such as daily hospitalizations and national confirmed cases are the primary causes of the decline in charging demand. The research framework of this paper can be generalized to analyze the changes in other driving behaviors during the pandemic. Finally, three policy implications are proposed to assist other countries in dealing with similar events and to stimulate the recovery of the transport system during the post-pandemic period. • A data-driven forecasting model is proposed to quantify the impact of the COVID-19 pandemic on EV charging demand. • This study investigates the dynamic relationship between the changes in charging demand and potential influencing factors. • The magnitude of the decline in EV charging demand varied by city during the pandemic. • COVID-19 related factors are the primary causes of the decline in EV charging demand. • Policy implications are recommended based on the findings of this study. [ABSTRACT FROM AUTHOR]

The global outbreak of the COVID-19 pandemic has slowed down international investment and trade, and partially disrupted the global supply chain. This paper provides an overview of the findings in 80 scholarly articles from recent years, comprehensively summarises and analyzes the research on international capital flows and global value chain（GVC）reconstruction, and deeply reveals the impact of COVID-19 on OFDI, the change of OFDI performance before and after the pandemic and factors that may accelerate GVC reconstruction in the future. We identify research gaps in a combination of OFDI and GVC reconstruction at both the macro and micro level and suggest several future research topics such as political risk and bilateral relations factors, the role of China in OFDI and GVC dynamics, network and OFDI performance, network characteristics of GVC reconstruction, OFDI and GVC reconstruction. [ABSTRACT FROM AUTHOR]

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

This paper studies the formation and the spread of crisis-driven racial animus during the coronavirus pandemic. Exploiting plausibly exogenous variation in the timing of the first COVID-19 diagnosis across US areas, we find that the first local case leads to an immediate increase in local anti-Asian animus, as measured by Google searches and Twitter posts that include a commonly used derogatory racial epithet. This rise in animus specifically targets Asians and mainly comes from users who use the epithet for the first time. These first-time ch-word users are more likely to have expressed animosity against non-Asian minorities in the past, and their interaction with other anti-Asian individuals predicts the timing of their first ch-word tweets. Moreover, online animosity and offline hate incidents against Asians both increase with the salience of the connection between China and COVID-19; while the increase in racial animus is not associated with the local economic impact of the pandemic. Finally, the pandemic-driven racial animus we documented may persist beyond the duration of the pandemic, as most racist tweets do not explicitly mention the virus. [ABSTRACT FROM AUTHOR]

This paper reconsiders transport inequities through the lens of environmental racism. Based on participant observations of 1972 rural-to-urban migrants at 76 worksites and 25 residential communities in five cities in the Yangtze Delta Region, China, we identified two main challenges facing migrants experiencing ethnic discrimination during Covid-19. First, they were more likely to experience housing eviction and, consequently, bear heavier transport burdens when moving. Second, they were more likely to face difficulties when returning to the cities, such as repeated quarantine and displacement, long-time drifting on the highway and transport-related job uncertainty. Although the long-term effects of these policies on migrants' everyday activity-travel behaviour may be limited, their experiences during the early phase of Covid-19 had a significant impact on their Spring Festival homecoming the following year. Regionally targeted transport policies to prevent Covid-19 have fuelled ethnic discrimination by officially classifying people from some provinces as "dangerous". Moreover, transport policies favoured some ethnic groups over others, contributing to environmental racism and exacerbating transport inequity. • Participant observation in 5 cities in the Yangtze Delta Region. • Two main challenges migrants faced during Covid-19: housing instability and transport burdens it engendered, and barriers to returning to the cities. • Experiences during the early phase of the Covid-19 outbreak influence rural-urban migrant workers' Spring Festival homecoming in the long term. • Transport policies implemented in the early phase of the pandemic fuelled environmental racism and exacerbated transport inequity. [ABSTRACT FROM AUTHOR]

• There are significant satisfaction differences in bus traveling before and after COVID-19. • COVID-19 has severely affected satisfaction with bus traveling of low-income individuals. • Low-income individuals' satisfaction with bus traveling is determined by socioeconomic attributes rather than traveling attributes after COVID-19. • Risk concern has a significant positive impact on risk perception, but risk concern and risk perception have only a minor impact on satisfaction before and after COVID-19. • There is a significant relationship between satisfaction levels and modes of traveling alone and traveling with companions by bus. Although all cities in China have promulgated public transportation control measures to choke off the spread of COVID-19, it also has brought severe changes to low-income individuals' bus traveling. However, the study focusing on satisfaction differences in bus traveling before and after COVID-19 is far under-researched, this paper therefore explored satisfaction differences among low-income individuals under socioeconomic attributes, traveling attributes, and psychological attributes by using the data consist of interviews addressed to 930 individuals in Taiyuan, China. Furthermore, the relationship between satisfaction levels and modes of traveling alone and traveling with companions by bus has also been deeply analyzed to reduce single-person driving problem. As a result, many exciting phenomena were discovered: the significant factors affecting low-income individuals' satisfaction occur "shift" on a large scale after COVID-19; risk concern has a significant positive impact on risk perception, but risk concern and risk perception have only a minor impact on satisfaction before and after COVID-19; it was found that there is a significant relationship between satisfaction levels and modes of traveling alone and traveling with companions by bus. Understanding them can be a reference for improving the travel environment between low-income individuals. [ABSTRACT FROM AUTHOR]

As an essential sub-network of the global liner shipping network, China's international liner shipping network was the earliest to be affected by the COVID-19 and also had a significant impact on the global shipping network. This paper uses Automatic Identification System (AIS) data to analyze the impact of COVID-19 on the typical route networks and major ports of China's international liner shipping. On this basis, the changes in network efficiency and connectivity under the failure of important nodes is simulated. The research finds that, during the epidemic period, the scale of China's international liner shipping network increased, with more routes gathering at fewer hub ports. Still, the overall connectivity and connection strength declined. Meanwhile, the epidemic caused fluctuations in container volume and the mismatch of ship cargo capacity supply, in which China-U.S. routes was the most prominent. From the view of node, the competitiveness of China's mainland ports was significantly promoted during the epidemic. In addition, ports such as Busan, Singapore, and Hong Kong substantially impacted China's international liner shipping network. The current study might be helpful for the industry management departments and related companies to prepare contingency plans, thus enhancing the resilience of the logistics chain and ensuring the stability of the global supply chain. [ABSTRACT FROM AUTHOR]

The Corona Virus Disease 2019 (COVID-19) outbreak took a heavy toll on the global tourism industry in 2020, and affected the value realization of coastal recreational ecosystem service. From the micro perspective, this paper combines travel cost method with contingent behaviour method to obtain residents' actual behaviour and contingent behaviour data, and discusses the impact of the outbreak of COVID-19 on the value realization of coastal recreational resources from the perspective of the change in residents' recreational behaviour in Qingdao, China. Residents are observed to significantly reduce their outdoor activities in response to the COVID-19. The number of visits to the beach decreases by 25.2% when there is an outbreak, and decreases by 0.064% for every 1% increase in the number of confirmed cases, which is used to represent the severity of the epidemic. The asymmetries effects of epidemic situation on residents' recreational behaviour show that the improvements lead to larger and more significant impacts than the deteriorations. The disappearance of the pandemic crisis will provide considerable welfare for the citizens in Qingdao, which reaches to 1.9323 billion CNY/year. If the number of confirmed cases deteriorates to 900, the environmental welfare loss will be 0.3366 billion CNY/year. Additionally, we test the effects of residents' cognitive variables, and find that risk perception can strengthen the negative impacts of COVID-19 cases. Furthermore, the deteriorations in the environmental attributes are found to have stronger impacts on the number of visits than the improvements. This paper provides empirical-support results about the change of coastal recreational value through the evaluation of recreational behaviours in the post-epidemic period, which will give important implications for government's marine ecosystem restoration and coastal management work. [ABSTRACT FROM AUTHOR]

This paper examines the daily practices of caring for COVID-19 patients in Wuhan, China, in early 2020 and the challenges that nurses faced. The paper shows that the affective contagion, especially among patients, introduced unexpected challenges for nurses in caring for COVID-19-infected patients. Nurses had to contend with the challenges of treating both physical and psychological problems in patients simultaneously. As a result, it was necessary for nurses to adapt to the different rhythm of COVID-19 wards to address these challenges and do so through taking on a range of general and specific nursing tasks and playing a diverse range of roles on the wards, from garbage collector to "psychological counselor." Thus, the paper brings attention to the experiences and demands of providing nursing care in an emergency pandemic context, in particular the necessity of responding to both the physical and the psychological needs of patients. These insights could better prepare health services in China and elsewhere in the world for responding effectively to potential future pandemics. • The affective contagion introduced unexpected challenges for nurses in COVID-19 wards. • Nurses adapt to the different working rhythm in COVID-19 wards than in "normal" wards. • A hierarchy of necessity emerged in the context of caring for COVID-19 patients. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on the entire cruise industry. This research aims to provide an understanding of the impacts of COVID-19 on the cruise industry from various stakeholders and recommend corresponding post-COVID recovery strategies for building a sustainable cruise industry. By conducting 22 semi-structured interviews in Shanghai, China and analysing the interview data using content analysis, this research finds five aspects of the impacts that are worth discussing, namely social, health and well-being, regulatory, operational, and financial aspects. Key findings include the impacts of different stakeholders' opinions, the problems existing in the current cruise industry, and the potential for future improvement. Recommendations and recovery strategies are proposed to mitigate the negative impacts. This research not only explores the impact of COVID-19 on cruise tourism and fosters recommendations in the most fast-developing region (China) but also facilitates researchers and policymakers to understand the effects of the pandemic and proposes future risk mitigation strategies. • This paper investigated the impacts of COVID-19 on the cruise industry. • This paper examined the impacts from five aspects i.e., social, health and well-being, regulatory, operational, and financial aspects. • Strategies were proposed to mitigate the negative impacts. • The findings contribute to the literature by exploring the impact of COVID-19 on cruise tourism in the most fast-developing region. • Recovery strategies were proposed that can facilitate researchers and policymakers to build a more sustainable future. [ABSTRACT FROM AUTHOR]

Using daily data on the coronavirus disease 2019 (COVID-19) cases from China and the rest of the world, this paper investigates the corresponding effects on the global economic activity. The empirical results based on a structural vector autoregression model using crude oil prices (COP) and the Baltic Exchange Dry Index (BDI) are consistent with increases in COVID-19 cases acting as negative demand shocks in the global economic activity (reflected as reductions in COP) and negative supply shocks in the global transportation of commodities (reflected as increases in BDI). The historical decomposition results further suggest that the effects of COVID-19 cases on COP and BDI have been mostly observed in the early COVID-19 period. [ABSTRACT FROM AUTHOR]

The pandemic caused by coronavirus disease 2019(COVID-19) continues to disrupt the global supply chain system, bringing new risks and challenges. The uncertainty created by COVID-19 makes it is difficult for various industries to deal with the pandemic. Since the pandemic, the supply chain's resilience has been discussed and examined in some studies. However, most existing works start from a single industry perspective or pay more attention to the disturbance caused by changes in the production side. Supply chain networks of different industries, mainly transport networks, are relatively limited under the epidemic's impact. In this paper, from the perspective of highway freight transport, a comprehensive competitiveness evaluation framework was proposed to reveal and the disruption and resilience of the supply chain under the outbreak based on nine indexes with five dimensions, including efficiency, capacity, activity, connectivity, and negotiability. Based on the availability of the data(Large-scale truck trajectory), we sorted out seven categories of Chinese industries(related to highway transport) and divided them into four categories respectively: (a) Slight disruption and worse resilience; (b) Slight disruption and remarkable resilience; (c) Serious disruption and worse resilience; (d) Serious disruption and remarkable resilience. The measurement results of supply chain network performance show that the industries (cold-chain, general products, and other industries) dominated by "Efficiency - Negotiability - Connectivity" are slightly disrupted (about 33%), forming a spatial diffusion with Wuhan(the city where the pandemic first broke out) as the disrupted center, spreading outward in a circle structure. Simultaneously, five urban agglomerations surrounding it have been impacted. By contrast, due to the strict isolation measures, the industries (building materials, construction, engineering, and high-value products industry) more vulnerable to be disrupted seriously (about 82%) tend to be the pattern of "Capacity - Activity". However, a large-scale centralized disruption was observed in the Triangle of Central China urban agglomeration was presented, resulting in almost stagnation of industry development. Meanwhile, as the future of the pandemic remains uncertain, the supply chain represented by the engineering industry, construction industry, etc are deserved to be paid more attention in line with they are prone to large-scale centralized damage due to the disruption of a single city node. • Disruption and resilience of multi-industry supply chain under COVID-19 epidemic. • Multi-dimensional comprehensive competitiveness evaluation framework for supply chain performance. • Supply chain destruction and repair performance of seven industries in China under COVID-19 epidemic. • Characteristic differences of supply chain between different industries from the perspective of highway freight transport. [ABSTRACT FROM AUTHOR]

Mobility, group awareness, and temperature are considered as the important factors that may impact the increase in confirmed cases of the COVID-19[1]. This paper aims to verify the above factors on the COVID-19 and show the possible confounding factors of each research variable in reality. Based on this, we collected data about the epidemic from January 20, 2020 to February 24, 2021, including the relevant data of 31 provinces and regions in China. Plus, we use the directed acyclic graph (DAG)[2] to show the causal relationship between the above influencing factors and the confirmed daily epidemic cases, and the confounding is estimated based on DAG. The effective adjustment set of factors are used to perform the regression of the total causal effect among the explanatory variables and the confirmed cases of the epidemic using negative binomial regression. Through the comprehensive causal analysis of the decisive factors for the COVID-19, we provide strong evidence for population mobility, group awareness and the impact of weather on the epidemic, and estimates the possible confounding factors in all aspects of society. Incorporating the above factors, we provide suggestions for future decisions on the prevention of large-scale epidemics. [ABSTRACT FROM AUTHOR]

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic. • M-ULS is introduced to break through the plight of city logistics. • A set of effective macro-micro incentive policies are established to promote the implementation of M-ULS. • The delivery time, transportation cost and government subsidies play different roles in the market penetration of M-ULS. • The benefits of M-ULS have been analyzed. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the COVID-19 pandemic impacts on the interconnectedness between the Chinese stock market and major financial and commodity markets—gold, silver, Bitcoin, WTI, S&P 500, and Euro STOXX 50—and analyze the portfolio design implications. Using daily data from 2018 to 2021, we first apply the wavelet power spectrum (WPS) to visualize volatility shifts. In contrast to previous research, we empirically identify the precise COVID-19 outbreak dates for each market using the Perron (1997) breakpoint test. Finally, we employ the bivariate DCC-GARCH model to analyze the connectedness between markets. The findings reveal that the COVID-19 pandemic caused volatility shifts of different intensities for all of the studied markets. Moreover, each return series exhibits one break date, which is specific to each market and corresponds to a distinct COVID-19-related event. Correlations, hedge ratios, and optimal portfolio weights changed significantly after the COVID-19 outbreak. There is evidence of contagion effects between the Chinese stock market and S&P 500, Euro STOXX 50, gold, and silver. Interestingly, the latter two assets lost their safe haven property with SSE. However, WTI and Bitcoin act as safe havens against SSE risks. [ABSTRACT FROM AUTHOR]

• There is significant variation in people's compliance with lockdown measures introduced by governments to curb the spread of the COVID-19. • Much of the variation can be explained by different cultural traits. • Individualism, which emphasises personal freedom, makes government intervention harder, whereas collectivism, which emphasises the wellbeing of the group, makes collective action easier. • We conclude that cultural factors play a critical role in successful policy implementation. In the first wave of the pandemic, places where geographic mobility declined more rapidly saw fewer cases of COVID-19. And yet, there is significant variation in people's compliance with the lockdown measures introduced by governments in order to curb the spread of the virus. In this paper, we show that much of this variation can be explained by different cultural traits. Specifically, we advance the hypothesis that individualism, which puts greater value on personal freedom, makes government intervention harder, whereas collectivism, which emphasises the wellbeing of the group, makes collective action easier. We find support for these ideas across 111 countries, but also when exploiting within country variation in the two largest economies in the world: China and the United States. Across a host of specifications, people were less abiding by the lockdown rules in places with greater prevalence of individualistic cultural traits. We conclude that cultural factors play a critical role in successful policy implementation. [ABSTRACT FROM AUTHOR]

Using a daily foreign and institution flows data, this paper studies how institutional and foreign investors respond to the COVID-19 pandemic events in China. The results indicate that during the COVID-19 crisis foreign investors play a market stabilization role showing significant negative feedback trading, whereas institution investors do not stabilize the market. And compared to the pre-COVID-19 period, foreign investors even exhibit stronger negative feedback trading. Further analyses confirm that foreign investors' negative feedback is mainly driven by their response to negative returns. Moreover, both institutional and foreign investors' trading show stronger forecastability of future returns during the pandemic period. And the negative returns after foreigners' selling and positive returns after institutional buying are much stronger during the crisis period. [ABSTRACT FROM AUTHOR]

This paper examines the impact of female analysts' coverage on firm's philanthropic activities amidst the outbreak of the COVID-19 pandemic in China. Using a hand-collected dataset of corporate philanthropy, the paper provides robust evidence that firms covered by female analysts are more likely to contribute actively to the well-being of societies by increasing corporate donation. This positive relationship is more pronounced if the company is privately controlled or covered by female analysts with more working experience, or located in more infectious provinces. Overall, our findings call for more female analyst recruitment, yielding benefits of pressuring firms to engage in philanthropy. • Firms covered by more female analysts provide more donation during the pandemic. • This positive impact is more pronounced if analysts have more working experience. • The positive relationship is more pronounced if a firm is privately controlled. • The positive impact is more pronounced if a firm is in more infectious province. • Firm site visit is the channel that female analysts affect corporate donation. [ABSTRACT FROM AUTHOR]

Travel activities and travel behaviors have been greatly affected by the outbreak of Covid-19. Facing the change of individuals' travel choices, policymakers have to make an appropriate response to mitigate negative consequences. This paper aims to explore how the COVID-19 would impact travel mode choice and the intention of car purchase. The data was collected from a large-scale survey conducted in June 2020 after the highest point. Random utility maximization (RUM), random regret minimization (RRM) and generalized regret minimization (GRRM) are employed to examine the effects of various factors on mode choice behaviors. The estimation results reveal that regret aversion psychology doesn't have a dominant proportion of decision choices, even if the congested condition of the mass mobility plays a significant role in the consideration of decision-making. Combined with the statistical results from the official departments, we concluded that public transport displays a great propensity on the long trip, and meanwhile, the industry of ride-hailing services has shocked sharply. In terms of the intention of traffic tool purchase, carless people prefer to buy electric two-wheel vehicles rather than automobiles. The research findings and the contribution to policy implications give assistance to authority in understanding citizens' travel mode preferences under the impact of COVID-19. [ABSTRACT FROM AUTHOR]

In this paper, blunt slogans used in China's health campaign against coronavirus are closely examined and the public's conflicting comments on them are analyzed. These slogans, due to their extreme effectiveness in making the public comply with the health preventive measures suggested by the government, are called Yinghe or "hardcore" slogans by the Chinese people. Containing harsh and taboo language, they convey threats of death and disease, insults or negative evaluation, and harsh demands. Despite their impolite nature, "hardcore" slogans have received significantly more positive judgment than negative judgment, especially when they made their debut in rural areas in Henan, an agricultural province in China. Criticism towards these blunt slogans then gradually increased after their initial appearance. Plausible factors contributing to the change of judgment are analyzed. The public's conflicting judgment regarding the impoliteness of the slogans may be related to the public's different positioning across time and space. This study shows that impoliteness is a practice situated not only in discourse, genres, and institutions, but also in social, cultural, and political contexts. More attention should be paid to impoliteness in special social configurations (e.g., rural areas) and social emergencies, which not only contextualize a discourse event but also define it. • Impoliteness is situated culturally, politically, temporarily, and spatially. • Impoliteness can be a strategic and appropriate reaction to social emergencies. • Distinction between rural and urban settings is relevant to impoliteness studies. [ABSTRACT FROM AUTHOR]

• Chinese demand for melons has transformed the rural economy of northern Myanmar. • Mid-chain brokers play key governance roles in these China-centric value chains. • This suggests distinctive new forms of value chain structure serving China. • Value chain structures create new sources of regional dependence and vulnerability. • These were brought into sharp focus when trade collapsed with COVID-19. Mainland Southeast Asia has become an increasingly important source for Chinese imports of fresh fruit. But how have these activities contributed to patterns of regional development in sites of production? This paper uses insights from China-centric melon production in Myanmar to generate new insights into this question. We argue that the expansion of this trade has created a structural form of strategic coupling in which benefits have flowed to a relatively small number of participants. Whereas these outcomes have some similarities to fresh fruit and vegetable export chains elsewhere in the world, what makes this case special is their creation through a form of value chain governance quite unlike the retailer-led arrangements typically associated with export fresh fruit and vegetable production. In the Myanmar-China melons trade, brokers, not retailers, exercise lead firm functions. These arrangements have generated distinctive and highly uneven distributions of risk and reward that have inhibited value chain upgrading and created substantial environmental despoliation. The vulnerabilities associated with these processes were brought into sharp relief with the COVID-19 pandemic of 2020, which led to local farmers bearing huge costs and debts when the border trade was abruptly curtailed. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 epidemic spreads with strong transmission power in every part of China. Analyses of the trend is highly need when the Chinese government makes plans and policies on epidemic control. This paper provides an estimation process on the trend of COVID-19 outbreak using the provincial-level data of the confirmed cases. On the basis of the previous studies, we introduce an effective and practical method to compute accurate basic reproduction numbers (R 0 s) in each province-level division of China. The statistical results show a non-stop downward trend of the R 0 s in China, and confirm that China has made significant progress on the epidemic control by lowering the provincial R 0 s from 10 or above to 3.21 or less. In the inferential analysis, we introduce an effective AR(n) model for the trend forecasting. The inferential results imply that the nationwide epidemic risk will fall to a safe level by the end of April in China, which matches the actual situation. The results provide more accurate method and information about COVID-19. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of COVID-19 pandemic on Chinese systemic risk using a dynamic network model. We select 104 listed financial institutions to construct a dynamic tail-risk network during the epidemic. The tail-risk network is estimated by the Factor-Adjusted Regularized Model (FARM) selection approach. To analyze the impact of COVID-19 pandemic on systemic risk, we identify the role of financial institutions in the dynamic tail-risk network. Results show that COVID-19 pandemic increases the number of risk-drivers and decreases the number of risk-takers, meaning that it enhances systemic financial risk in China. Our evidence provides an effective method to regulate systemic risk in the financial market and is helpful to offer rescue policies once systemic risk events occur. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 in 2020 has a severe impact on all countries. This paper first studies the impact of the epidemic on the macro-economy in China, which is one of the countries with early epidemic situation, in the rapid development stage of the epidemic situation, and the specific performance in macro-economy aspects. On this basis, study the development trend and macroeconomic performance of the United States as one of the most serious countries, and the relationship between the epidemic development and macro-economy in the United States. As for the differences in epidemic control measures between China and the US, China's indicators are significant in terms of sentiment factors, but the impact of COVID-19 on the sentiment factors in the US is different. The Chinese government has not adopted unlimited easing and maintained consistent policies. At present, the economic impact of COVID-19 on the US is far greater than that of China. [ABSTRACT FROM AUTHOR]

The current COVID pandemic is happening while the long-term effects of coronavirus infection remain poorly understood. The present article meta-analyzed mental health outcomes (anxiety, depression, etc.) from a previous coronavirus outbreak in China (2002). CNKI, Wanfang, PubMed/Medline, Scopus, Web of Science, Baidu Scholar, and Google Scholar were searched up to early June 2020 for articles in English or Chinese reporting mental illness symptoms of SARS patients. Main outcome measures include SCL-90, SAS, SDS, and IES-R scales. 29 papers met the inclusion criteria. The longest follow-up time included in the analysis was 46 months. The systematic meta-analysis indicated that mental health problems were most serious before or at hospital discharge and declined significantly during the first 12 months after hospital discharge. Nevertheless, average symptom levels remained above healthy norms even at 12 months and continued to improve, albeit slowly, thereafter. The adverse mental health impact of being hospitalized with coronavirus infection long outlasts the physical illness. Mental health issues were the most serious for coronavirus infected patients before (including) hospital discharge and improved continuously during the first 12 months after hospital discharge. If COVID-19 infected patients follow a similar course of mental health development, most patients should recover to normal after 12 months of hospital discharge. [ABSTRACT FROM AUTHOR]

Within half a year, COVID-19 spreads to most countries in the world, as well as posed a great threat to the public health of human beings. The implementation of non-pharmaceutical intervention (NPI), including travel ban, proved to be an effective way for controlling the epidemic spreading, e.g., the ban of inter-city transportation stops transporting virus through passengers between cities. However, travel ban could significantly impact many industries, e.g. tourism and logistics, thus jeopardizing the regional economy. This paper focus on assisting the national or regional government to make dynamic decisions on restricting and recovering intercity multi-modal travel services. Our model can characterize impacts of inter-city traffic on the spread of the COVID-19, as well as on the regional economy. By applying a reinforcement learning approach, we develop an online optimization model to identify the modal-specific travel banning strategy that can balance the epidemic control as well as the negative impacts on regional economy. The numerical study based on a network of multiple cities in China shows that the proposed approach can generate better strategies compared with some existing methods. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the U.S and China equity markets on global equity markets during the COVID-19 pandemic. Specifically, we compare the contagion of global equity markets, their volatility persistence, and the volatility spillover effects from the U.S and China to other countries in pre-pandemic and during-pandemic periods. We find evidence of significant contagion effects from the U.S and China stock markets during the COVID-19 pandemic period. Further, we observe that the nature of stock index volatility does not change and there is weak evidence for volatility spillover effects during the pandemic period. [ABSTRACT FROM AUTHOR]

Value added tax (VAT) reduction is an important tool for cultivating new driving forces for economic development, and it has had a notable impact on local fiscal pressure. Taking the 2018–2019 VAT rate reduction in China as an example, this paper uses a CGE model to estimate the impact of the VAT reduction policy on local fiscal pressure in China in light of the COVID-19 pandemic. The results show that local fiscal pressure increased from 0.342 to 0.435, an increase of 27.08%. The study provides policy implications on optimizing tax structure and alleviating local fiscal pressure. [ABSTRACT FROM AUTHOR]

In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3–6% improvement in accuracy. [ABSTRACT FROM AUTHOR]

This paper constructs a new risk evaluation index system for overseas energy investment, while considering the country's response ability to major risk events using COVID-19 as experience. The entropy weight method and the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method are used to conduct overall and sub-dimensional risk assessment for 52 countries. The results show that: (1) among the seven risk dimensions considered, the response ability to major risk events dimension has the highest weight, accounting for 16.47 %. This indicates that Chinese energy corporations cannot ignore the risk posed by major risk events, such as COVID-19, in their overseas investments. (2) Among the 52 countries investigated, investment risk is significantly lower in developed countries than in developing countries, mainly because of the better performance of the former across the four dimensions of economics foundation, governing ability, social development, and investment environment. (3) The COVID-19 pandemic has impacted the landscape of China's overseas energy investment risk. Due to the different performance of each country in other dimensions, the risk rankings of the majority of developing countries decreased when considering their COVID-19 response, while that of most developed countries increased. • Assesses the risk of China's overseas energy investments. • An overseas energy investment risk evaluation system considering the response ability to major risk events is constructed. • The response ability to major risk events impacts the landscape of China's overseas energy investment risk. • Investment risk is lower in developed countries than in developing countries. [ABSTRACT FROM AUTHOR]

• A user persona oriented toward the enterprise is constructed to characterize power consumption in the context of COVID-19. • Abstract labels are extracted from enterprise power profiles based on industrial, regional, periodic power profiles. • Enterprises with similar electricity consumption characteristics or the same label are grouped to allow recommendations for customized electricity products, policy, and services. The COVID-19 epidemic has led to devastating consequences worldwide due to strict social distancing measures, travel bans, city lockdowns, and other activity restrictions. Numerous lessons have been accumulated by various industries or sectors in the process of coping with COVID-19. In our paper, the actual electricity consumption of 1.145 million enterprises in a province of China is investigated in the normal period, the breakout period, and the recovery period. An artificial intelligence based enterprise profiling model is established to characterize power consumption profile geographically, temporally, and industrially. The recovery rate of enterprise power consumption is proposed as a key indicator representing the status of production resumption. Under lockdown measures, enterprise in different regions or sectors exhibits diversified responses in power consumption, the unsupervised learning model and the correlated coefficient extract the abstract characteristics and labels from various enterprise power profile. Ultimately, enterprises with similar electricity consumption characteristics are grouped and labeled to provide customized power services. Accurate enterprise profiling can effectively aid in facing with the challenges of insufficient orders, tight capital chains, and rising costs caused by the epidemic. [ABSTRACT FROM AUTHOR]