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The travel restriction leads to the enormous economic loss to the tourism industry. In this paper, the economic loss and the environmental gains (e.g., water and energy resources) from COVID-19 impact on China's tourism industry are estimated based on environmentally extended multiple regional input-output model. Results indicate that the China's tourism consumption loss triggered 921 billion US$ output loss via supply chain in 2020, which account for 7 % of GDP in 2019. Additionally, the economic loss further ripples tourism water footprint conservation of 10 billion m3 (equal to the annual water consumption of Poland) and tourism energy footprint conservation of 116 million tce (equal to the annual energy consumption of Australia). Findings reveal that regions with large tourism economic losses are not completely consistent with the regions with significant relief of water and energy pressures. Water pressure in Xinjiang (Northwest) and energy pressure in Hainan (South) have been alleviated due to reduced tourism consumption along the Southeast coast. These knock-on effects highlight a deeper internal link between the economy and the environment. The development of tourism in the post-COVID-19 era needs to reduce the direct water and energy footprint of local area by applying advanced water-saving and energy-saving technologies. • China's tourism output loss caused by COVID-19 accounts for 7 % of GDP in 2019. • 10 billion m3 of water saving equals to the annual water consumption of Poland. • 116 million tce of energy saving equals to the annual energy consumption of Australia. • Tourism activities decreasing in East relieve great water use stress in Northwest China. • Decreasing tourism demand in Southeast mitigates energy use stress in South China. [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]

This paper examines how the economies of old industrial cities in Northeast China respond to the on-going COVID-19 pandemic crisis. The notion of resistance in regional economic resilience is used to explore what impact factors shape the response to the early stage of the crisis. The analysis reveals significant differences in terms of regional economic impact between COVID-19 and the 2008 financial crisis. We find that large cities are more vulnerable and exposed to the pandemic at its early stage, state agency plays a crucial role in shaping the economic resistance in most cities. Going beyond the existing 2008 financial crisis-induced account on regional economic resilience, this paper argues that regional resistance amid COVID-19 is not merely shaped by economic structural factors but also influenced by state agency in terms of economic restriction and restoration measures. The study suggests that the nature of COVID-19 as a particular context of crisis itself needs to be taken seriously when exploring the determinants and outcomes of regional economic resilience. • This paper in investigates regional economic resilience under COVID-19 by studying the resistance of Northeast China in the first quarter of 2020. • This paper finds that secondary industry structure, overall variety, innovation, specialization, openness and state agency are the main factors shaping regional resistance. • Local state agency on containing COVID-19 plays a key role in shaping regional resistance and recovery. • The study suggests that the crisis nature of COVID-19 itself needs to be taken seriously when exploring the determinants of regional economic resilience. [ABSTRACT FROM AUTHOR]

Large-scale and diffuse population flow amplifies the localized COVID-19 outbreak into a widespread pandemic. Network analysis provides a new methodology to uncover the topology and evolution of the population flow and understand its influence on the early dynamics of COVID-19 transmission. In this paper, we simulated 42 transmission scenarios to show the distribution of the COVID-19 outbreak across China. We predicted some original (Guangzhou, Shanghai, Shenzhen) had higher total aggregate population outflows than Wuhan, indicating larger spread scopes and faster growth rates of COVID-19 outbreak. We built an importation risk model to identify some major cities (Dongguan and Foshan) with the highest total importation risk values and the highest standard deviations, indicating the core transmission chains (Dongguan-Shenzhen, Foshan-Guangzhou). We built the population flow networks to analyze their Spatio-temporal characteristics and identify the influential sub-groups and spreaders. By removing different influential spreaders, we identified Guangzhou can most influence the network's topological characteristics, and some major cities' degree centrality was significantly decreased. Our findings quantified the effectiveness of travel restrictions on delaying the epidemic growth and limiting the spread scope of COVID-19 in China, which helped better derive the geographical COVID-19 transmission related to population flow networks' structural features. • 42 transmission scenarios were simulated to test contribution of different original epicenters to seeding COVID-19 in China. • An importation risk model was built to test contribution of population flow among multiple epicenters to spreading COVID-19. • A population flow network was built to capture evolution of COVID-19 transmission and identify influential spreaders. • This paper quantified the effectiveness of travel restrictions on reducing the numbers of confirmed COVID-19 cases in China. [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]

This study presents an in-depth investigation on the transmission of the novel coronavirus (COVID-19) from the urban perspective. It focuses on the "aftermath" of the outbreak and the spread of the infection among cities. Especially, this study provides insights of the fundamentals of the factors that may affect the spread of the infection in cities, where the marginal effects of some most influential factors to the virus transmission are estimated. It reveals that the distance to epicenter is a very strong influential factor, and is negatively linked with the spread of COVID-19. In addition, subway, wastewater and residential garbage are positively connected with the virus transmission. Moreover, both urban area and population density are negatively associated with the spread of COVID-19 at the early stage of the epidemic. Furthermore, this study also provides high precision estimation of the number of COVID-19 infection in Wuhan city, which is the epicenter of the outbreak in China. Based on the real-world data of cities outside Wuhan on March 2, 2020, the estimated number is 56,944.866 (mean value), which is very close to the officially reported number. The methodology and main conclusions shown in this paper are of general interest, and they can be applied to other countries to help understand the local transmission of COVID-19 as well. • This study presents an in-depth investigation on the transmission of the novel coronavirus (COVID-19) from the urban perspective. • It provides insights of the fundamentals of the factors that may affect the spread of the infection in cities. • It also provides high precision estimation of the number of COVID-19 infection in Wuhan city, which is the epicenter of the outbreak in China. • The methodology and main conclusions shown in this paper are of general interest, and they can be applied elsewhere. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, an exceptional crisis, sparked the introduction of new digital infrastructure to halt the novel coronavirus's spread. This paper explores how such digital infrastructure's impact might reverberate over the long term, by comparing Singapore, Hong Kong, and mainland China's utilization of digital technology in response to the 2003 SARS outbreak, and their responses to the 2020 COVID-19 pandemic. We find that advancements in digital technology since 2003 have boosted governments' surveillance and segregation abilities substantially—most dramatically so in China. Even though some of these new digital interventions are ostensibly designed to be temporary ones to address the needs of the immediate crisis, we argue that the resultant extensions of state power experienced during COVID-19 are likely to have profound long-term effects because they fundamentally affect sociopolitical contexts, institutional capabilities, and digital cultures. We also find that the extent to which governments can extend digital surveillance and segregation abilities during the pandemic is contingent on their respective sociopolitical, institutional, and digital cultural contexts. • COVID-19 pandemic sparked calls for a new digital infrastructure. • We compare 2003 SARS outbreak to COVID-19 pandemic. • New digital technologies increased governments' ability to surveil and segregate. • Their impact on surveillance and segregation will persist post COVID-19. [ABSTRACT FROM AUTHOR]

This paper investigates the imppact of COVID-19 travel restrictions on population flow in the People's Republic of China. We discover an "unreasonable" surge in population flow after the Wuhan travel ban. We further find out that such a sure of population flow is attributed to the "spill-over" effect of the Wuhan travel ban. We utilize a logistic regression model to quantify that the spill-over effect linearly decays with the travel distance to the Pandemic center city. Because of the "spill-over" effect of the travel ban policy, government authorities should design redundancy polity to simultaneously implement a travel ban for the pandemic center city and its neighboring cities to restrain human movement and pandemic transmission. • We discovered the "spill-over" effect of the Wuhan travel ban. • A travel ban policy may stimulate human movement if it's not properly designed • The transportation distance to the COVID-19 pandemic center city negatively linearly determines the "spill-over" effect. • The "spill-over" effects can help city administrators to predicate population flow and make traffic control plans when pandemic outbreaks. [ABSTRACT FROM AUTHOR]

In the face of COVID-19, an emerging infectious disease, in addition to the classic non-pharmaceutical interventions such as isolation, quarantine, social, China also adopted strict mobility restrictions including inter-administrative districts travel restrictions, which severely affect residents' lives and almost completely stopped production activities at cost of a huge economic and social cost. In this paper, we develop the model of Dirk Brockmann and Dirk Helbing (2013) to theoretically explain the impact mechanism of prevention and control measures on the spread of the epidemic. Then, we divide the measures taken in China into two categories: mobility restrictions and other non-pharmacological interventions (O-NPI), and apply econometric approach to empirically test the effects of them. We find that although both of the two measures play a good role in controlling the development of the epidemic, the effect shows significant difference in different regions, and both the two measures had no significant effects in low-risk regions; Further, we prove that measures taken in a low-risk region is mainly against the imported cases, while a high-risk region has to defend against both imported cases and spread from within; The rapid and accurate transmission of information, a higher protection awareness of the public, and a stronger confidence of residents can promote the implementation of the measures. • COVID-19 epidemiological and epidemic control measures -related data of China during the outbreak are collected. • We use real-time data to explore the effect of COVID-19 control measures taken in China. • The public health measures implemented during the COVID-19 outbreak in China are divided into mobility restrictions and O-NPI. • Both of the two kinds of public health measures have no statistically significant effect in the low-risk areas. • We contend that measures should be tailored to the specific local and regional conditions. [ABSTRACT FROM AUTHOR]