The impact of high-speed rail on SO2 emissions-based on spatial difference-in-differences analysis.

SO2 emissions have brought serious hidden danger to human health and environmental quality, thus hindering sustainable economic development. The development of high-speed rail indirectly has an important impact on SO2 emissions through its economic effects. Controlling SO2 emissions from the source has increasingly become the focus of many scholars, and it is very important to assess the environmental effects of high-speed rail on SO2 emissions reduction. We use the panel data of 285 cities in China from 2007 to 2017, and adopt the spatial Difference-in-Differences model to study the impact of the opening of high-speed rail on SO2 emissions. We also introduce an improved spatial DID model that distinguishes neighboring treatment groups and neighboring control groups to test the spatial spillover effect of high-speed rail on neighboring heterogeneous samples. We find that the opening of high-speed rail significantly reduces the city's SO2 emissions through the internal accumulation effect of technological innovation and industrial structure optimization and the urban external interaction mechanism of the cross-regional flow of production factors. Moreover, the spatial spillover effect of the opening of high-speed rail on neighboring cities is significantly positive, especially the spatial spillover effect of HSR on SO2 emissions from neighboring cities without HSR. In addition, heterogeneity analysis shows that the effect varies with the different cities' tiers and income levels. These findings are conducive to accurately assessing the environmental effects of high-speed rail, and provide important policy references for achieving sustainable development and reducing SO2 emissions.

High-speed rail and city's carbon productivity in China: a spatial difference-in-differences approach.

Improving carbon productivity is an important measure to promote low-carbon development. Since high-speed rail (HSR) has both economic and environmental effects, it is particularly important to clarify the relationship between HSR development and carbon productivity. In this paper, 285 cities in China from 2007 to 2017 are used as a research sample, and the relationship between the opening of HSR and the city's carbon productivity is studied using the spatial difference-in-difference method (SDID). The result shows that due to the intermediary effect of technological innovation and industrial structure, the opening of HSR significantly increases urban carbon productivity. At the same time, this influence has a significant positive spatial spillover effect. On average, when a city opens HSR, the local carbon productivity increases by 5.18%, and the carbon productivity of its neighboring cities increases by 13.52%. Overall, the positive effect of HSR on carbon productivity is more pronounced in the middle and western regions. However, the spatial spillover effect in the eastern region is significantly negative. These findings help to accurately assess the social benefits of HSR network expansion and provide important decision-making references for climate governance in the HSR era.

The influence of neighborhood quality on tourism in China: Using Baidu Street View pictures and deep learning techniques.

Previous studies have investigated the determinants of urban tourism development from the various attributes of neighborhood quality. However, traditional methods to assess neighborhood quality are often subjective, costly, and only on a small scale. To fill this research gap, this study applies the recent development in big data of street view images, deep learning algorithms, and image processing technology to assess quantitatively four attributes of neighborhood quality, namely street facilities, architectural landscape, green or ecological environment, and scene visibility. The paper collects more than 7.8 million Baidu SVPs of 232 prefecture-level cities in China and applies deep learning techniques to recognize these images. This paper then tries to examine the influence of neighborhood quality on regional tourism development. Empirical results show that both levels of street facilities and greenery environment promote tourism. However, the construction intensity of the landscape has an inhibitory influence on the development of tourism. The threshold test shows that the intensity of the influence varies with the city's overall economic level. These conclusions are of great significance for the development of China's urban construction and tourism economy, and also provide a useful reference for policymakers. The methodological procedure is reduplicative and can be applied to other challenging cases.

Special Economic Zone, Carbon Emissions and the Mechanism Role of Green Technology Vertical Spillover: Evidence from Chinese Cities.

Although the special economic zones (SEZs) are considered the backbone of rapid economic development in China, it is unclear whether they contribute to green economic development. From the perspective of the localized industrial chains formed as a result of the SEZ policy, this paper aims to analyze and explain how the development of SEZs influences carbon emissions in Chinese cities by promoting green technologies' vertical spillover along the industrial chain. Based on the panel data of 264 prefecture-level cities from 2011 to 2016 and a relatively new and mostly disaggregated city-level MRIO (multi-region input-output) table in China, this paper constructs green technology vertical spillover as a mechanism variable and discusses the influence theoretically and empirically. The results show that the development of SEZs can reduce a city's carbon emissions. More specifically, for every 10 m2 increase in the size of the SEZ area, the carbon dioxide emission can be reduced by 0.882 g per m2 of the city area. Moreover, mechanism analysis shows that the development of SEZs promotes green technology vertical spillover inside the city, through which the SEZs reduce the city's carbon emissions. The mediation effect occupies 21.96% of the total effect. Furthermore, the impact of the development of SEZs on carbon emissions has regional heterogeneity due to the city's industry structure, green technology stocks, and the zones' administrative hierarchies. The finding of this study could provide several important implications for regional green development, especially in China.

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

With the rapid development of the Mobile Internet in China, epidemic information is real-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. We explored the mechanism of the influence of information diffusion on the transmission of COVID-19, developed a model of the interaction between information diffusion and disease transmission based on the Susceptible-Infected-Recovered (SIR) model, and conducted an empirical test by using econometric methods. The benchmark result showed that there was a significant negative correlation between the information diffusion and the transmission of COVID-19. The result of robust test showed that the diffusion of both epidemic information and protection information hindered the further transmission of the epidemic. Heterogeneity test results showed that the effect of epidemic information on the suppression of COVID-19 is more significant in cities with weak epidemic control capabilities and higher Internet development levels.

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.

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.