ABSTRACT
Coronavirus disease (COVID-19) has disrupted health, economy, and society globally. Thus, many countries, including China, have adopted lockdowns to prevent the epidemic, which has limited human activities while affecting air quality. These affects have received attention from academics, but very few studies have focused on western China, with a lack of comparative studies across lockdown periods. Accordingly, this study examines the effects of lockdowns on air quality and pollution, using the hourly and daily air monitoring data collected from Lanzhou, a large city in Northwest China. The results indicate an overall improvement in air quality during the three lockdowns compared to the average air quality in the recent years, as well as reduced PM2.5, PM10, SO2, NO2, and CO concentrations with different rates and increased O3 concentration. During lockdowns, Lanzhou's "morning peak" of air pollution was alleviated, while the spatial characteristics remained unchanged. Further, ordered multi-classification logistic regression models to explore the mechanisms by which socioeconomic backgrounds and epidemic circumstances influence air quality revealed that the increment in population density significantly aggravated air pollution, while the presence of new cases in Lanzhou, and medium- and high-risk areas in the given district or county both increase the likelihood of air quality improvement in different degrees. These findings contribute to the understanding of the impact of lockdown on air quality, and propose policy suggestions to control air pollution and achieve green development in the post-epidemic era.
ABSTRACT
Ice and snow tourism has yielded considerable socioeconomic benefits, especially for the countries and regions that are rich in ice and snow resources. Boosted by the Beijing Winter Olympics, China's ice and snow tourism economy is likely to usher in a period of high-quality development. This study utilized Hailuogou Glacier Forest Park, a relatively well-developed glacier tourism area in China, as the study area. We used data on the regional GDP of Ganzi Prefecture and the comprehensive tourism revenue of Hailuogou scenic area from the period of 1990-2021 to quantitatively analyze the reciprocal feedback relationships between the regional economic growth of Ganzi Prefecture and the development of Hailuogou glacier tourism. The analyses were performed through the construction of a VAR model and through conducting robustness tests using the instrumental variable method. The results show that there is a long-term equilibrium cointegration relationship between regional economic growth in Ganzi Prefecture and tourism development in the Hailuogou scenic area, with each 1% increase in the former driving a corresponding growth of 2.01% in the latter. There is an asymmetric reciprocal feedback relationship between the economic growth of Ganzi Prefecture and the development of tourism in the Hailuogou scenic area. Economic growth in Ganzi Prefecture significantly contributes to the development of glacier tourism in Hailuogou over a longer period of time, with a contribution rate of 49%;meanwhile, the development of tourism in the Hailuogou scenic area has a limited effect on the quality and acceleration of economic development in Ganzi Prefecture, with a contribution rate of 16%. Based on the above results, relevant policy recommendations are proposed to provide a theoretical basis and decision-making reference for the high-quality development of the Hailuogou scenic area in the post-COVID-19 and post-Winter Olympics era.
ABSTRACT
The implementation of China’s Beautiful Village Initiative was an extraordinary achievement and aroused extensive public attention. However, existing research mostly focuses on the construction and seldom on public attention towards the Beautiful Village Initiative. For this reason, this paper investigated the spatiotemporal characteristics of public attention based on the Baidu index using time-constrained clustering and the spatial autocorrelation test. Our results showed that the evolutionary process can be divided into three stages: very little national attention (2011–2012), injection of a strong impetus (2013–2015), and rooted in the people’s minds (2016–2020). Spatially, provincial public attention demonstrated obvious spatial differentiation and stable spatial autocorrelation, with Low–Low clusters in Northwest China and High–High Clusters in East, Central, and North China. Spatial econometric models were further utilized to quantify the effects of socioeconomic factors on public attention. The results of the SEM model proved the existence of spatial spillover effects and indicated that the urbanization rate, population density, education level, and network popularity rate all positively affected public attention. The relationship between Beautiful Village construction and public attention was uncoordinated and, in most provinces, advances in public attention were ahead of the construction level. Our findings contribute to the understanding of public attention towards the Beautiful Village Initiative, and policy suggestions we proposed would be applied to increasing public awareness and participation.