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In this paper, we explore what the travel restrictions during the COVID-19 pandemic reveal about the changing geographies of mobilities and the making of homo sacer, the latter constituted through differentiated control of mobilities. Implemented to protect U.S. public health, travel restrictions imposed on travelers from Mainland China during the early days of the pandemic exemplify how sovereign power that declares a state of emergency and creates bare life can be readily applied to groups of people who previously had privileged access to global mobility. In this sense, bare life does not refer to fixed and disadvantaged social categories but is rather contingently and contextually constituted, through the works of hybrid sovereign regimes. At the same time, however, these travelers are not reduced to a state of zero-agency but reside within a liminal space between soft and hard cosmopolitanisms, as they can still deploy agency and cosmopolitan capital to achieve certain degrees of mobility. By examining how Chinese travelers navigated various travel restrictions and the constantly changing policies to travel to the U.S., this paper explores new spaces of exception and forms of bare life, and argues that homo sacer is dynamically, relationally and recursively constructed, both through apparatus of control and the agency of travelers. [ABSTRACT FROM AUTHOR]

Considering the high variability of Covid-19 and unknown sequelae of rebound infections. We may be caught in a long-term struggle against the virus. The evaluation and summary of past anti-epidemic policies highlight profound practical significance for either decision makers or individuals. This paper applies both theoretical and empirical approaches to study the impact of city lockdown and air pollution on Covid-19. First, we apply an extended Susceptible-Infected-Removed (SIR) model to identify the relationship between lockdown, air pollution, and Covid-19 cases. Second, we apply the Differences-in-Differences (DID) model and Poisson Pseudo-Maximum Likelihood (PPML) model to test the impact of lockdown on Covid-19, by sorting out lockdown policies implemented by cities during the epidemic. Third, we empirically analyze the impact of air pollution on Covid-19, based on daily data from 257 cities in China. Finally, we examine the mechanism by which lockdown impacts Covid-19. Newly confirmed Covid-19 cases are reduced by 31.1% nationwide (excluding Wuhan) during the lockdown period. Regions with lower air pollution experience fewer Covid-19 cases. Air pollution aggravates Covid-19, with the pollutants PM2.5, PM10, NO2, CO, and O3 having the greatest impact. The results indicate that an increase in one unit of air quality index (AQI) concentrations is associated with 1.723 more newly confirmed cases. The effects of air pollution on the spread of Covid-19 diminish as the population size increases. Lockdown can inhibit the spread of Covid-19 by reducing air pollution and population movement. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) which emerged in Wuhan, China, in December 2019, is widely controlled now in China. However, the global epidemic is still severe. To study and comment on Hubei's approaches for responding to the disease, the paper considered some factors such as suspected cases (part of them are influenza patients or common pneumonia patients, etc.), quarantine, patient classification (three types), clinically diagnosed cases, and lockdown of Wuhan and Hubei. After that, the paper established an S E L I H R model based on the surveillance data of Hubei published by the Hubei Health Commission from 10 January 2020 to 30 April 2020 and used the fminsearch optimization method to estimate the optimal parameters of the model. We obtained the basic reproduction number R 0 = 3.1571 from 10 to 22 January. R 0 was calculated as 2.0471 from 23 to 27 January. From 28 January to 30 April, R 0 = 1.5014. Through analysis, it is not hard to find that the patients without classification during the period of confirmed cases will result in the cumulative number of cases in Hubei to increase. In addition, regarding the lockdown measures implemented by Hubei during the epidemic, our simulations also show that if the lockdown time of either Hubei or Wuhan is advanced, it will effectively curb the spread of the epidemic. If the lockdown measures are not taken, the total cumulative number of cases will increase substantially. From the results of the study, it can be concluded that the lockdown, patient classification, and the large-scale case screening are essential to slow the spread of COVID-19, which can provide references for other countries or regions. [ABSTRACT FROM AUTHOR]

In this paper, the complex network of the urban functions in Shenzhen of China under the lockdown of the corona virus disease 2019 (COVID-19) is studied. The location quotient is used to obtain the dominant urban functions of the districts in Shenzhen before and under the lockdown of COVID-19. By using the conditional probability, the interdependencies between the urban functions are proposed to obtain the complex networks of urban functions and their clusters. The relationships between the urban functions, and the overall and cluster characteristics of the urban functions before and under the lockdown of COVID-19 are analyzed based on the complex networks. The mean degree and mean weighted degree of the primary categories of the urban functions are obtained to discuss the classification characteristics of the urban functions before and under the lockdown of COVID-19. Then, the differences and changes of the urban functions before and under the lockdown of COVID-19 are compared, and the corresponding policy implications under the lockdown of COVID-19 are presented. The results show that under the lockdown of COVID-19, the correlation of the urban functions is stronger than that before the lockdown; the common urban functions are more useful and essential, and finance, fine food and medical treatment are important; public service and government departments have the most positive relationship with other urban functions, and finance service has the highest spatial agglomeration distribution trend; and the cluster characteristics of urban functions are more related to people's livelihood, and the urban functions show incomplete and cannot be operated for long term. [ABSTRACT FROM AUTHOR]

This paper examines the impact of Covid‐19 lockdowns on exports by Chinese cities. We use city‐level export data at a monthly frequency from January 2018 through April 2020. Differences‐in‐differences estimates suggest cities in lockdown experienced a ceteris paribus 34 percentage points reduction in the year‐on‐year growth rate of exports. The lockdown impacted the intensive and extensive margin, with higher exit and lower new entry into foreign markets. The drop in exports was smaller in (i) coastal cities; (ii) cities with better‐developed ICT infrastructure; and (iii) cities with a larger share of potential teleworkers. Time‐sensitive and differentiated goods experienced a more pronounced decline in export growth. Global supply chain characteristics matter, with more upstream products and industries that had accumulated larger inventories experiencing a smaller decline in export growth. Also, products that relied more on imported (domestic) intermediates experienced a sharper (flatter) slowdown in export growth. The rapid recovery in cities' exports after lockdowns were lifted suggests the policy was cost‐effective in terms of its effects on trade. [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]

Utilizing national migration data regarding the outbreak of the novel coronavirus (2019-nCoV), this paper employs a difference-in-differences approach to empirically analyze the relationship between human mobility and the transmission of infectious diseases in China. We show that national human mobility restrictions ascribed to the first-level public health emergency response policy effectively reduce both intercity and intracity migration intensities, thus leading to a declining scale of human mobility, which improves the effectiveness in controlling the epidemic. Human mobility restrictions have greater influences on cities with better economic development, denser populations, or larger passenger volumes. Moreover, mobility restriction measures are found to be better implemented in regions with increased public awareness, or with provincial leaders who have healthcare crisis management experience, local administrative experience, or the opportunity to serve a consecutive term. [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]

The COVID-19 spread very quickly around the world following its discovery in China, in December 2019. Lockdowns implemented in China and the Global North to control the propagation of the virus and to save human lives have resulted in a global recession. The transmission of the recessionary effects from the Global North to the Global South is reflected in the decline in sub-Saharan Africa's (SSA) GDP and the associated increase in poverty. The purpose of this paper is to illustrate how the recession induced in China and the Global North by COVID-19 lockdowns may have had indirect effects on SSA mortality that are higher than those directly attributed to the pandemic itself. Our methodology relies on a three-step relationship: (i) the impact of lockdowns on the recession in the North, (ii) the impact of the recession in the North on income in SSA countries, and (iii) the impact of a decline in income on mortality in SSA. We show that COVID-19-induced lockdowns in the Global North, through the severe recessions they induced in the Global South, resulted in the transfer of between 538,000 and 679,000 deaths in one year to SSA, including the deaths of 140,000 to 177,000 children aged 0–5 years. This corresponds to a 6–7% increase in the crude death rate and a 5–6% increase in under-5 mortality. These figures are much higher than the number of deaths directly attributable to COVID-19 in SSA. Thus, policymakers must not lose sight of the indirect excess mortality caused by global economic recession triggered by the pandemic. Our results reveal the need to increase the resilience of SSA countries to exogenous shocks, including COVID-19, which, in addition to increasing poverty, may induce excessive mortality due to the high sensitivity of mortality in SSA countries to economic recession. • Theoretical model of COVID-19 deaths transfer from the Global North to the Global South. • Lockdowns in the Global North generated recession in the Global North. • Spillover effects of the recession from the Global North to sub-Saharan Africa (SSA). • Focus on the impact of income declines on mortality in SSA. • The indirect mortality through recessions in SSA would be higher than the direct mortality. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China's mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies. [ABSTRACT FROM AUTHOR]

Three months into the Covid-19 crisis, lockdown has become a global response to the pandemic. Why have so many countries resorted to lockdown? How is it being implemented in different places? Why have some places had more success with lockdowns and others not? What does the effectiveness of lockdowns tell us about the local institutions entrusted with enforcing them? This paper compares how lockdown orders have been implemented in China, Italy, and the U.S. The analysis points to two major factors that have shaped the enforcement: tensions between national and local governments, and the strength of local territorial institutions. [ABSTRACT FROM AUTHOR]

The migration determinants of graduates are of particular importance for the analysis of regional activities and thus influence city resilience in areas after the COVID-19 pandemic. By constructing a unique dataset from an online recruiting platform , we introduce a new measure of mobility intentions. We also apply the stacked difference-in-differences model to tackle the limitation of staggered treatments with a non-absorbing state brought about by lockdown policies. By comparing intention changes in cities with lockdown regulations to those without such regulations, we conclude that lockdown does influences the decisions of graduates and alter the attractiveness of cities mainly through panic job searching and recruiting digitalization. In a big picture, lockdown causally reduces graduates' mobility intentions by 0.7 %, but this intention increases by 1.2 % after lockdown is lifted. Lockdown policies also affect non-lockdown cities, particularly in cities that are less competitive and those that are in close geographical proximity. We further estimate the effects of lockdown duration and intensity on graduates' mobility intentions and find that full-scale lockdown increases intentions by 2.5 %, whereas partial lockdown increases intentions by only 0.9 %. This study fills the gap between the mobility literature and the COVID-19 literature by establishing the clean lockdown effects on graduate mobility intentions. • Lockdowns change the attractiveness of no locked-down cities to graduate migrants • New graduates cope with the anxiety from lockdowns by engaging in panic job searching • Graduates boost online job searches to offset lost offline recruitment during lockdowns • A data-driven analytical paper uses the large-scale human behavior data to quantify migration intention [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO 2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO 2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO 2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO 2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO 2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO 2 emissions declines into firm climate action. • COVID-19 has negative effects on economy, but positive effects on CO 2 emissions. • A new model is used to measure economic and CO 2 emission effects during COVID-19. • Hubei had the biggest economic and CO 2 emission effects on China due to COVID-19. • Economic and CO 2 emission effects of COVID-19 are combined to select key sectors. [ABSTRACT FROM AUTHOR]

Due to COVID-19, Macao established a prolonged tourism lockdown for over half a year. With no COVID-19 cases for 3 months, the lockdown and resilient measures provided the ability to recover, becoming the first city globally to reopen travel borders to all of China. Using the case of Macao during a real-time pandemic crisis we develop a conceptual framework on economic resilience and tourism recovery. We propose for the first-time, destination vulnerabilities as a consequence of recovery and resilient actions. Contrary to government direction that Macao diversify beyond gaming revenues after COVID-19, our study affirms this is unlikely, as casino companies focus on recuperating revenue losses. Our theoretical assertions have wider implications as tourism cities globally look at COVID-19 exit strategies. The short-term resilient endeavors taken now by cities may have longer-term consequences on their 'reset' milieu. With the challenges of vaccination rollout, a COVID-19 exit will be prolonged further with continued economic and tourism recovery challenges for cities, giving greater significant the study's theoretical assertions. • Due to COVID-19, Macao became one of the first cities outside Mainland China to go into lockdown. • The paper examines Macao's resilience throughout the half-year lockdown, with substantial government subsidies provided. • After over 3 months COVID-19 free in Macao, the city reopened to all of China, signaling the city's recovery phase. • With the 'how to' recover largely absent from the literature this paper developed a tourism resilience and recovery model. • The paper showed consequences to recovery measures, an important reference to global cities looking to exit COVID-19. [ABSTRACT FROM AUTHOR]

Understanding the processes and mechanisms of the spatial spread of epidemics is essential for making reasonable judgments on the development trends of epidemics and for adopting effective containment measures. Using multi-agent network technology and big data on population migration, this paper constructed a city-based epidemic and mobility model (CEMM) to stimulate the spatiotemporal of COVID-19. Compared with traditional models, this model is characterized by an urban network perspective and emphasizes the important role of intercity population mobility and high-speed transportation networks. The results show that the model could simulate the inter-city spread of COVID-19 at the early stage in China with high precision. Through scenario simulation, the paper quantitatively evaluated the effect of control measures "city lockdown" and "decreasing population mobility" on containing the spatial spread of the COVID-19 epidemic. According to the simulation, the total number of infectious cases in China would have climbed to 138,824 on February 2020, or 4.46 times the real number, if neither of the measures had been implemented. Overall, the containment effect of the lockdown of cities in Hubei was greater than that of decreasing intercity population mobility, and the effect of city lockdowns was more sensitive to timing relative to decreasing population mobility. • A city-based epidemic and mobility model is proposed for simulating the spatial spread of an epidemic between cities. • The effect of "city lockdowns" and "decreasing population mobility" measures were evaluated under ten scenarios. • The effect of decreasing returns to scale and distance decay of population mobility on the epidemic spread were found. [ABSTRACT FROM AUTHOR]

The Corona Virus Disease 2019 (COVID-19) appeared in Wuhan, China, at the end of 2019, spreading from there across China and within weeks across the whole world. In order to control the rapid spread of the virus, the Chinese government implemented a national lockdown policy. It restricted human mobility and non-essential economic activities, which, as a side effect, resulted in the reduction of the emission of pollutants and thus the improvement of the air quality in many cities in China. In this paper, we report on a study on the changes in air quality in the Guanzhong Basin during the COVID-19 lockdown period. We compared the concentrations of PM2.5, PM10, SO2, NO2, CO and O3 obtained from ground-based monitoring stations before and after the COVID-19 outbreak. The analysis confirmed that the air quality in the Guanzhong Basin was significantly improved after the COVID-19 outbreak. During the emergency response period with the strictest restrictions (Level-1), the concentrations of PM2.5, PM10, SO2, NO2 and CO were lower by 37%, 30%, 29%, 52% and 33%, respectively, compared with those before the COVID-19 outbreak. In contrast, O3 concentrations increased substantially. The changes in the pollutant concentrations varied between cities during the period of the COVID-19 pandemic. The highest O3 concentration changes were observed in Xi'an, Weinan and Xianyang city; the SO2 concentration decreased substantially in Tongchuan city; the air quality had improved the most in Baoji City. Next, to complement the sparsely distributed air quality ground-based monitoring stations, the geographic and temporally weighted regression (GTWR) model, combined with satellite observations of the aerosol optical depth (AOD) and meteorological factors was used to estimate the spatial and temporal distributions of PM2.5 and PM10 concentrations with a resolution of 6 km × 6 km before and after the COVID-19 outbreak. The model was validated by a comparison with ground-based observations from the air quality monitoring network in five cities in the Guanzhong Basin with excellent statistical metrics. For PM2.5 and PM10 the correlation coefficients R2 were 0.86 and 0.80, the root mean squared errors (RMSE) were 11.03 µg/m3 and 14.87 µg/m3 and the biases were 0.19 µg/m3 and −0.27 µg/m3, which led to the conclusion that the GTWR model could be used to estimate the PM concentrations in locations where monitoring data were not available. Overall, the PM concentrations in the Guanzhong Basin decreased substantially during the lockdown period, with a strong initial decrease and a slower one thereafter, although the spatial distributions remained similar. [ABSTRACT FROM AUTHOR]