Synergistic relationship or not? Understanding the resilience and efficiency of the tourism economy: evidence from Hainan Province, China.

The COVID-19 pandemic has dealt a serious blow to the global tourism industry, causing a fracturing of and decline in tourism development efficiency and even a stagnation of tourism development in some regions. To solve the contradiction between efficiency and quality, it is necessary to ensure the endogenous power of tourism resilience while pursuing the efficiency of tourism development. This study assumes that Hainan Province follows a tourism development path led by resilience. The improved weighting method, EBM model and Haken model are used to evaluate the level of resilience, the level of efficiency and their co-evolution. The findings indicate that the core tourism cities represented by Sanya and Haikou have a high level in the individual fields of tourism development efficiency and tourism economic resilience but have limited performance in the synergistic relationship between tourism development efficiency and tourism economic resilience. In contrast, the marginal tourism cities represented by Tunchang County and Ledong County have low tourism development efficiency and resilience, but their synergistic development level is high. This result proves that co-evolution plays a dual forward and reverse driving role. Based on the identification of the order parameters, it is concluded that Hainan Province is characterized by a synergistic evolutionary synergy dominated by resilience, which is in line with the trend of social development and the sustainable development of tourism. While reasonably pursuing the tourism economy and development efficiency, we should pay attention to strengthening resilience construction based on multiple aspects, such as tourists, enterprises, organizations, governments and destinations.

Spatiotemporal evolution, pattern of diffusion, and influencing factors of the COVID-19 epidemic in Hainan Province, China.

Within 1 month after the first case occurred in Hainan Province, China, the number of confirmed cases rose to 168, and there was no increase in almost 3 months. As the southernmost province and a famous tourist destination in China, its regular economic exchanges and high-intensity population movements may affect the spread of the epidemic. It is of great theoretical and practical significance to investigate the spatiotemporal evolution, the pattern of diffusion, and factors influencing the coronavirus disease 2019 (COVID-19) epidemic in Hainan Province. Basic and geographic information of confirmed COVID-19 cases was obtained from government websites and other official media. We examined the groups of infection and calculated the diffusion ratio to demonstrate the trend of the epidemic. Map drawing, spatial analysis, and partial least squares regression were used to express the spatiotemporal evolution, the pattern of diffusion, and factors affecting the epidemic. Furthermore, we have made recommendations on the formulation and adaptation of possible future preventive steps. Results show that the COVID-19 epidemic in Hainan Province has substantial spatial heterogeneity but minimal distribution. The tourist city and central city have formed a dual-core pattern for the spread of the epidemic, which could extend to other similar regions. Population density, mobility, and level of urban development have been the major factors of epidemic distribution in the study area.

The application framework of big data technology in the COVID-19 epidemic emergency management in local government-a case study of Hainan Province, China.

BACKGROUND: As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. METHODS: This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management. RESULTS: Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. CONCLUSIONS: This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.