The influence of the Beijing Winter Olympic games on the demand for winter sports: An empirical analysis based on the Baidu Index.

Background: The 2022 Beijing Winter Olympics is a representative large-scale sporting event, which not only promotes the development of the national and regional economy, society, and culture but also increases the demand of residents for winter sports, thus helping achieve the grand goal of "encouraging 300 million people to participate in winter sports." This research explores the influence of the Beijing Winter Olympics on residents' demand for winter sports in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations in China. Methods: Applying big data mining techniques, the Baidu Index of Winter Olympics-related terms are used to measure residents' interest in the Beijing Winter Olympics, and the ratio of the Baidu Index of five winter sports (ice skating, ice hockey, curling, luge, and skiing) to the number of internet searches is used to capture residents' demand for winter sports. Moreover, we explore the spatial-temporal pattern of the interest in the Winter Olympics and the demand for winter sports and construct an econometric model to test the driving effect of the Winter Olympics empirically. Results: The results show that 1) since 2011, interest in Winter Olympics has been on the rise, and the interest of residents in Beijing-Tianjin-Hebei has been higher than that of the Yangtze River Delta; 2) the demand for skating and skiing, which are two popular winter sports, shows a declining geographical concentration, indicating that the popularity of these two sports is on the increase; 3) the demand for winter sports in the peripheral cities in Beijing-Tianjin-Hebei shows a trend of specialization, while Beijing, Tianjin, and some cities in the Yangtze River Delta present a trend of diversification; and 4) the interest in the Beijing Winter Olympics influences the demand for winter sports positively. Conclusion: This study shows that the increase in interest in the Beijing Winter Olympics boosts residents' demand for winter sports, which implies that hosting Winter Olympics successfully drives winter sports participation in China.

The health benefit of physical exercise on COVID-19 pandemic: Evidence from mainland China.

OBJECTIVES: Our study aims to investigate the health benefit of regular physical exercise participation on a series of COVID-19 outcomes including COVID-19 morbidity, mortality, and cure rate. METHODS: Prefecture-level panel data related to physical exercise and the COVID-19 pandemic in China were collected from January 1 to March 17, 2020, (N = 21379). Multiple linear regression was conducted, and the ordinary least squares technique was used to estimate the coefficient. RESULTS: It was shown that regular sports participation significantly negatively affected COVID-19 morbidity (estimate = -1.1061, p<0.01) and mortality (estimate = -0.3836, p<0.01), and positively affected cure rate (estimate = 0.0448, p<0.01), implying that engaging in physical exercise regularly does have a significant positive effect on COVID-19 outcomes. Then, we explored the heterogeneity of the effect of physical exercise on areas with different risk levels and it was revealed that the effect of physical exercise was more pronounced in high-risk areas in terms of morbidity (estimate = -1.8776, p<0.01 in high-risk areas; estimate = -0.0037, p<0.01 in low-risk areas), mortality (estimate = -0.3982, p<0.01 in high-risk areas; estimate = -0.3492, p<0.01 in low-risk areas), and cure rate (estimate = 0.0807, p<0.01 in high-risk areas; 0.0193 = -0.0037, p<0.05 in low-risk areas). CONCLUSIONS: Our results suggest that regularly engaging in physical exercise before the pandemic has positive health effects, especially in the case of a more severe epidemic. Therefore, we urge readers to actively engage in physical exercise so that we can reduce the risks in the event of a pandemic.

Sports Participation and Anti-Epidemic: Empirical Evidence on the Influence of Regular Physical Activity on the COVID-19 Pandemic in Mainland China.

This study aims to investigate the effects and influencing mechanisms of regular physical activity (RPA) on the COVID-19 pandemic. Daily data from 279 prefecture-level cities in mainland China were collected from 1 January to 17 March 2020. A two-way fixed-effects model was used to identify the causal relationship between physical activity and COVID-19, while also considering factors such as patterns of human behavior and socioeconomic conditions. The instrumental variable (IV) approach was applied to address potential endogeneity issues for a more accurate causal identification, and the mediating effect model was applied to examine the mechanisms of the influence of physical activity on the epidemic. We found that regular physical activity significantly improves individual immunity, which, in turn, leads to a reduction in the probability of being infected with COVID-19. Furthermore, we investigated the heterogeneity of the influence, finding that the negative impact of physical activity on the pandemic is more pronounced in the absence of adequate medical resources, strong awareness of prevention among residents, and fully implemented public health measures. Our results provide empirical evidence for the mechanisms of influence of physical activity on the pandemic. We would suggest that not only should physical activity be actively practiced during the pandemic, but also long-term regular exercise habits should be consciously cultivated to improve the ability of the individual immune system to better cope with sudden outbreaks of emerging infectious diseases.

Are Sports Champions Also Anti-Epidemic Heroes? Quantitative Research on the Influence of Sports Champions' Demonstration Effect on the COVID-19 Epidemic in China.

What kind of role do sports champions play in the COVID-19 epidemic? Do they contribute to the mitigation of the epidemic by some pathway? In this paper, we empirically explore the influence and mechanism of the demonstration effect of sports champions upon the COVID-19 epidemic using COVID-19-related dataset of prefecture-level cities in China from 1 January 2020 to 17 March 2020. The two-way fixed effect model of econometrics is applied to estimate the result, the instrumental variable approach is adopted to address potential endogeneity issues, and socio-economic factors including public health measures, residents' self-protection awareness, effective distance from Wuhan are also taken into consideration. The results show that the demonstration effect of champions in major sporting events increases the participation in physical exercise, which in turn reduces the possibility of being infected with the epidemic. An increase of one gold medal results in a 0.93% increase in the sports population, then leads to a 3.58% decrease in the cumulative case growth rate (p < 0.01). Further, we find that the effect is greater in regions with developed economies and abundant sports resources. Interestingly, it is greater in regions with less attention to sports, which again confirms the role of the demonstration effect.

Market integration and green economic growth-recent evidence of China's city-level data from 2004-2018.

Very few studies exist in rationalizing comprehensively the relationship between market integration and green economic growth in China. This paper tries to answer the question whether and how market integration influences regional green economic growth in China. Based on the panel data of 285 city-level regions from year 2004 to 2018 in China, this paper develops explanatory mechanism and discusses the influence theoretically and empirically. To advance the analysis, we construct market integration indicator through relative price variance method based on commodity retail price index and employ the Malmquist-Luenberger (ML) productivity index and DEA-SBM (data envelopment analysis-slacks-based measure) model to evaluate green total factor productivity (GTFP) as indicator for green economic growth. Our empirical findings are: (1) Apparent regional imbalance exists in both the development of market integration and green growth and gaps are expanding from year 2004 to 2018. (2) Market integration promotes regional green growth significantly. (3) Mechanism analysis illustrates that market integration fosters green growth through economies of scale effect, composition effect and spillover effect, respectively. (4) Heterogeneous analysis suggests that the influence from market integration on green growth varies depending on region's difference in traffic situation and in overall development level.

Does lower regional density result in less CO2 emission per capita? : Evidence from prefecture-level administrative regions in China.

Regional density is a useful tool for analyzing regional spatial structure as well as a good starting point for analyzing regional CO2 emissions per capita. This paper empirically analyzes the relationship between regional density and per capita CO2 emissions in China's prefecture-level administrative regions. We improve the CO2 emission measurement method for prefecture-level administrative regions and estimate the per capita CO2 emissions of 252 prefectural-level cities in China from 2003 to 2013. Using panel fixed effect model regression, and taking the terrain roughness index as an instrumental variable to solve endogeneity, we find that the relationship between regional density and per capita CO2 emissions presents in an inverted U-shape, per capita CO2 emissions first increase with the increase of regional density, and after reaching the turning point, it decreases with regional density. In a mechanism test, analyzing the interaction terms between regional density and industrial structure, and regional density and urbanization level respectively. We found that industrial structure and urbanization are important mechanisms for regional density to affect CO2 emissions. In order to reduce per capita CO2 emissions, we propose corresponding policy implications for the regions in different positions of the "U" curve.

The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China.

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

Do prevention and control measures work? Evidence from the outbreak of COVID-19 in China.

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.

Traffic control and social distancing evidence from COVID-19 in China.

We collected COVID-19 epidemiological and epidemic control measures-related data in mainland China during the period January 1 to February 19, 2020, and empirically tested the practical effects of the epidemic control measures implemented in China by applying the econometrics approach. The results show that nationally, both traffic control and social distancing have played an important role in controlling the outbreak of the epidemic, however, neither of the two measures have had a significant effect in low-risk areas. Moreover, the effect of traffic control is more successful than that of social distancing. Both measures complement each other, and their combined effect achieves even better results. These findings confirm the effectiveness of the measures currently in place in China, however, we would like to emphasize that control measures should be more tailored, which implemented according to each specific city's situation, in order to achieve a better epidemic prevention and control.

Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19)

With the rapid development of mobile Internet in China, the information of the epidemic is full-time and holographic, and the role of information diffusion in epidemic control is increasingly prominent. At the same time, the publicity of all kinds of big data also provides the possibility to explore the impact of media information diffusion on disease transmission. This paper explores the mechanism of the influence of information diffusion on the spread of the novel coronavirus, develops a model of the interaction between information diffusion and disease transmission based on the SIR model, and empirically tests the role and mechanism of information diffusion in the spread of COCID-19 by using econometric method. The result shows that there was a significant negative correlation between the information diffusion and the spread of the novel coronavirus; The result of robust test shows that the spread of both epidemic information and protection information hindered the further spread of the epidemic.