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The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway); (2) a significant impact of age on the switch from public transit to private car and two-wheelers; (3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

The lockdown and social distancing policies to reduce COVID-19 spread and perceived safety threats of COVID-19 significantly affected people’s travel behavior. Greece has been suffering from the COVID-19 pandemic, and people’s mobility behavior has been greatly affected. This study aims at: (1) exploring the variations in individuals’ trip frequencies by mode and purpose before and after the COVID-19 outbreak; (2) understanding the effects of individual differences (i.e., sociodemographic details) and perceptions towards COVID-19 (i.e., the perceived threats of COVID-19) on people’s mobility behavior changes after the outbreak; (3) underlining the individuals’ perceptions of the COVID-19 threat on the willingness of public transportation usage. Overall, 403 responses were collected in late 2020. A series of random parameter Probit modeling results reveal multiple individual and perception factors affecting the changes in mobility behavior in Greece. The results from structural equation modeling indicate that perceived COVID-19 threats affect the attitudes and subjective norms towards people’s intentions to use public transportation. The results from this study provide valuable insights for transportation authorities to develop effective strategies to manage traffic during the spread of disease for a possible future epidemic.

Since the COVID-19 outbreak, online learning has become the norm. Primary school students require parental assistance and supervision due to their lack of digital media capabilities and safety concerns. This study uses the mixed research method to process and analyze the interview data of 10 primary school parents and 564 questionnaire data using topic coding and partial least squares structural equation model (PLS-SEM) to explore the factors influencing primary school parents’ choice of online learning and the mechanism of these factors on choice intention. The results show that online learning continuity and learning attitude development risk are two new variables affecting parents’ cognition of online learning. They have a significant positive impact on performance evaluation and risk perception. Perceived ease of use, performance evaluation, and perceived cost affect parents’ assessment of their children’s satisfaction with online learning during the pandemic. Parents’ satisfaction with online learning positively affects their intention to choose online learning. The results of this study can provide a reference for education departments and schools on how to carry out online teaching with higher quality in case of emergencies.

Public health emergencies such as the COVID-19 outbreak has made all industries to reexamine the way the cities have always grown. Urban planning is no exception. Based on the analysis of the development track and phenomenon of Wuhan City, this paper puts forward the planning thinking of the development of China’s big cities. According to the study, it is urgent to study the suitable size, structure and form of big cities. The consistent practice of high-intensity development needs to stop. Urban development should pay more attention to the space that can not directly generate economic benefits.

Considering the important role of energy in modern society, it is imperative to study the current situation and future development of energy under the influence of COVID-19. This paper identifies the current research hotspots, proposes future research directions accordingly, and summarizes the methodologies via a bibliometric analysis. Five research hotspots include COVID-19 and the changes of energy consumption, COVID-19 and the fluctuation of the energy market, COVID-19 and the development of renewable energy, COVID-19 and climate impacts caused by energy consumption, and COVID-19 and the energy policy. According to the influence mechanism of COVID-19 on each hotspot, the pandemic has exerted short-term influencs on energy consumption, energy price, and air pollution. Meanwhile, the pandemic could have a far-reaching impact on the renewable energy sector, climate, and energy policy. In addition, the main methodologies are reviewed, revealing that regression analysis and scenario analysis are commonly used as the quantitative and qualitative methods, respectively. Moreover, given the nonlinear relations between the pandemic and energy, an artificial neural networks model is used to enhance the prediction efficiency of energy demand and price. Finally, policy implications for obtaining clean, low-carbon, safe, and efficient energy in the context of COVID-19 are proposed.

In emergency medical facilities location, the hierarchical diagnosis and treatment system plays an obvious role in the rational allocation of medical resources and improving the use efficiency of medical resources. However, few studies have investigated the operational mechanism of hierarchical medical systems in uncertain environments. To address this research gap, this paper proposes a hybrid approach for emergency medical facilities’ location-allocation. In the first stage, in order to concentrate on the utilization of medical resources, we choose alternative facility points from the whole facilities through the entropy weight method (EWM). In the second stage, uncertainty sets are used to describe the uncertain number of patients at emergency medical points more accurately. We propose a robust model to configure large base hospitals based on the robust optimization method. Furthermore, the proposed robust models are applied to the emergency management of Huanggang City under COVID-19. The results show that the optimal emergency medical facility location-allocation scheme meets the actual treatment needs. Simultaneously, the disturbance ratio and uncertainty level have a significant impact on the configuration scheme.

When the World Health Organization (WHO) analyzed the culprits of global warming, it was found that in developed countries with relatively few high-polluting heavy industries, the medical sector is one of the sources of high-density carbon emissions. Therefore, the medical industry has a noticeable impact on the environment. Amid the current COVID-19 epidemic, this study adopts the theory of planned behavior (TPB), widely used in decision-making science. We selected a regional teaching hospital with 339 employees in Taiwan to obtain valid questionnaire data. We explore the comparative analysis of different intra-organizational stakeholders’ “attitudes,” “subjective norms,” and “perceived behavioral control” on the hospital’s behavioral intention to promote green healthcare. The results show that the TPB model has reliable explanatory power. All three factors have a positive and significant effect on promoting green hospital behavior. Among them, perceived behavioral control was the most notable. A comparative analysis of the differences among stakeholders in the research model shows that “medical administrators” and “nursing staff” have a higher proportion of significant influence effects in various hypotheses, highlighting the critical roles of these two groups in promoting green hospitals. This research policy suggests that the cross-departmental staff in the hospital put forward green innovation ideas, strengthen internal environmental education and management, establish a good incentive system for front-line nursing staff, and implement the sustainable development strategy of the hospital.

The COVID-19 pandemic affects all of society and hinders day-to-day activities from a straightforward perspective. The pandemic has an influential impact on almost everything and the characteristics of the pandemic remain unclear. This ultimately leads to ineffective strategic planning to manage the pandemic. This study aims to elucidate the typical pandemic characteristics in line with various temporal phases and its associated measures that proved effective in controlling the pandemic. Besides, an insight into diverse country's approaches towards pandemic and their consequences is provided in brief. Understanding the role of technologies in supporting humanity gives new perspectives to effectively manage the pandemic. Such role of technologies is expressed from the viewpoint of seamless connectivity, rapid communication, mobility, technological influence in healthcare, digitalization influence, surveillance and security, Artificial Intelligence (AI), and Internet of Things (IoT). Furthermore, some insightful scenarios are framed where the full-fledged implementation of technologies is assumed, and the reflected pandemic impacts in such scenarios are analyzed. The framed scenarios revolve around the digitalized energy sector, an enhanced supply chain system with effective customer-retailer relationships to support the city during the pandemic scenario, and an advanced tracking system for containing virus spread. The study is further extended to frame revitalization strategies to highlight the expertise where significant attention needs to be provided in the post-pandemic period as well as to nurture sustainable development. Finally, the current pandemic scenario is analyzed in terms of occurred changes and is mapped into SWOT factors. Using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution based Multi-Criteria Decision Analysis, these SWOT factors are analyzed to determine where prioritized efforts are needed to focus so as to traverse towards sustainable cities. The results indicate that the enhanced crisis management ability and situational need to restructure the economic model emerges to be the most-significant SWOT factor that can ultimately support humanity for making the cities sustainable.

The COVID-19 pandemic caused severe economic contraction and paralyzed industrial activity. Despite a growing body of literature on the impacts of COVID-19 mitigation measures, scant evidence currently exists on the impacts of lockdowns on the economic and industrial activities of developing countries. Our study provides an empirical assessment of lockdown measures using 298,354 data points on daily electricity consumption in 396 sub-industries. To infer causal relationships, we employ difference-in-differences models that compare cities with and without lockdown policies and provide quantitative evidence on whether the long-term gain of lockdowns outweighs the short-term loss. The results show that lockdown policies led to a significant short-term drop in electricity consumption of 15.2% relative to the control group. However, the electricity loss under the no-lockdown scenario is 2.6 times larger than that under the strict lockdown scenario within 4 months of the outbreak. Discrepancies in the impacts among industries are identified, and even within the same industry, lockdowns have heterogeneous effects. The impact of lockdowns on small and medium-sized enterprises in developing countries is seriously underestimated, raising concerns about the distributional impact of subsidy measures. This study serves as a crucial reference for the government when facing public health emergencies and shocks to support better policies.