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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.

COVID-19, caused by a member of the coronavirus family of viruses, has spread to most countries around the world since it was first recorded in humans in China in late 2019. Closing universities and cancelling all face-to-face activities have become a COVID-19 inevitable reality in many parts of the world. Its impact on university programs, particularly to maintain academic standards and quality assurance procedures, has become significantly more challenging and complex. New ways of working digitally, to minimize disruption to daily operations, have also led to enormous anxiety and uncertainty within the student population, and meeting students’ expectations has also become significantly more difficult. This paper reviews actions taken by universities to safeguard high academic standards and quality assurance procedures during this time and appraise the challenges and impacts on students’ academic performance.

Recently, robots have been widely adopted in the hospitality and tourism industry. Efficient robots can help hoteliers and tourism suppliers with their repetitive or manual labor. Due to the coronavirus disease (COVID-19) pandemic, there is an increasing number of publications on robotic applications in hospitality and tourism. However, a comprehensive literature review of this realm remains lacking. Therefore, to provide a holistic view of the existing literature on robotic applications in hospitality and tourism, this study reviewed 86 extant robotic application-related articles by conducting descriptive analysis and content analysis. The findings of this study showed that most of the existing relevant studies were conducted from the perspective of consumers in the hospitality context. Potential future research directions for academics are identified herein. Practical implications on robotic adoption are also provided for industry practitioners.

The accessibility of hospital facilities is of great importance not only for maintaining social stability, but also for protecting the basic human right to health care. Traditional accessibility research often lacks consideration of the dynamic changes in transport costs and does not reflect the actual travel time of urban residents, which is critical to time-sensitive hospital services. To avoid these defects, this study considered the city of Kaifeng, China, as an empirical case, and directly acquired travel time data for two travel modes to the hospital in different time periods through web mapping API (Application Program Interface). Further, based on travel time calculations, we compared five baseline indicators. For the last indicator, we used the optimal weighted accessibility model to measure hospital accessibility for each residential area. The study discovered significant differences in the frequency and spatial distribution of hospital accessibility using public transit and self-driving modes of transportation. In addition, there is an imbalance between accessibility travel times in the study area and the number of arrivals at hospitals. In particular, different modes of transportation and different travel periods also have a certain impact on accessibility of medical treatment. The research results shed new light on the accessibility of urban public facilities and provide a scientific basis with which local governments can optimize the spatial structure of hospital resources.

Few studies on population-specific health effects of extreme temperature on cardiovascular diseases (CVDs) deaths have been conducted in the subtropical and tropical climates of China. We examined the association between extreme temperature and CVD across four cities in China. We performed a two-stage analysis; we generated city-specific estimates using a distributed lag non-linear model (DLNM) and estimated the overall effects by conducting a meta-analysis. Heat thresholds of 29 °C, 29 °C, 29 °C, and 30 °C and cold thresholds of 6 °C, 10 °C, 14 °C, and 15 °C were observed in Hefei, Changsha, Nanning, and Haikou, respectively. The lag periods for heat-related CVD mortality were observed only for 0–2 days, while those of cold-related CVD mortality were observed for 10–15 days. The meta-analysis showed that a 1 °C increase above the city-specific heat threshold was associated with average overall CVD mortality increases of 4.6% (3.0%–6.2%), 6.4% (3.4%–9.4%), and 0.2% (−4.8%–5.2%) for all ages, ≥65 years, and <65 years over a lag period of 0–2 days, respectively. Similarly, a 1 °C decrease below the city-specific cold threshold was associated with average overall CVD mortality increases of 4.2% (3.0%–5.4%), 4.9% (3.5%–6.3%), and 3.1% (1.7%–4.5%), for all ages, ≥65 years, and <65 years over a lag period of 0–15 days, respectively. This work will help to take appropriate measures to reduce temperature-mortality risk in different populations in the subtropical and tropical climates of China.

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.

This study explores the impacts of environmental pollution and the digital economy on the new energy industry with panel data on 30 Chinese provinces from 2005 to 2020. Mean group regression was performed, and fully modified OLS and dynamic OLS were conducted to check the robustness of the results. The authors reached two conclusions: (1) environmental pollution exerts significant negative impacts on the new energy industry. For every 1% increase in environmental pollution, the level of development of the new energy industry drops by 0.1658%. In other words, environmental pollution levels down the new energy industry. (2) The digital economy produces significant positive impacts on the new energy industry. For every 1% increase in the digital economy, the level of development of the new energy industry rises by 0.4262%. That is, the digital economy levels up the new energy industry. Our policy recommendations place equal stress on both the prevention and the control of environmental pollution, strengthening digital infrastructure, enhancing the government’s digital governance and service capabilities, protecting consumer rights, and replacing conventional energy with new energy.