Evaluation of Tourism Development Efficiency and Spatial Spillover Effect Based on EBM Model: The Case of Hainan Island, China

Spatiotemporal evolution and mechanisms of tourism efficiency and its decomposition: Evidence from 63 counties in Zhejiang, China

Although efficiency analysis could reflect the state and quality of tourism's economic development, no research has been conducted investigating the spatiotemporal evolution and mechanisms of county-level tourism efficiency. We quantified tourism efficiency and its decomposition in 63 counties of Zhejiang, employing the bootstrap data envelopment analysis (DEA), hot spot analysis, and quantile regression to explore the spatiotemporal evolution and influencing factors of tourism efficiency, and examine its driving and constraining mechanisms. The results uncovered obvious upward trends in the tourism efficiency of Zhejiang's counties, with the mean value increasing from 0.285 to 0.688. Compared with scale efficiency, the influence of technological efficiency on the growth of comprehensive efficiency increased remarkably. Significant differences were evident in the spatial distributions of the identified hot and cold spots of comprehensive efficiency, which were respectively distributed in northern and southern Zhejiang. The distributions of decomposition efficiency were found to be spatially dependent. The driving mechanism of tourism efficiency involve two driving influences and two constraining influences, including economy and resource driving, market and traffic driving, industry and traffic constraining, and market and industry constraining. The findings of this study contribute to understanding of tourism efficiency growth in regional destinations and provide insights for strategic policymaking in various counties of Zhejiang.

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.

Exploring the Ecological Performance of China's Tourism Industry: A Three-Stage Undesirable SBM-DEA Approach with Carbon Footprint

The environmental impact of carbon emissions and the carbon footprint from tourism activities are significant for promoting low-carbon development in the tourism industry. This paper employed a bottom-up approach to estimate the carbon footprint and energy consumption of China's tourism industry. Then, the three-stage undesirable SBM-DEA model was employed to evaluate and decompose the eco-efficiency of China's provincial tourism industry from 2008 to 2017. The results showed that the eco-efficiency of most provinces has experienced a slight increase during the past ten years, while management inefficiency within the tourism industry has been the main restriction of the utilization of tourism resources in most regions. The decomposition and quadrant analysis indicated that scale efficiency was the direct driver of the poor ecological performance in Northeast China, while technical efficiency dominated the tourism eco-efficiency in South-Central China. These two issues have together led to the poor utilization of the rich tourism resources and the natural environment in Southwest China. On the basis of these discussions, differentiated policy implications towards different kinds of regions were provided to promote low-carbon development and to realize the potential of tourism resources in China's tourism industry.

Exploring Tourism Efficiency and Its Drivers to Understand the Backwardness of the Tourism Industry in Gansu, China

Gansu Province is rich in tourism resources, and it is the hometown of the "copper galloping horse", which is the logo of China's tourism. However, the scale and revenues of tourism in Gansu province are still at a low level. This paper first evaluated the tourism efficiency of 14 cities and prefectures of Gansu Province in China from 2011 to 2019 using the super-slack-based measure (Super-SBM) and then investigated the internal driving mechanism of the efficiency change through the Global Malmquist-Luenberger (GML) index and its decomposition, and finally analyzed the external influencing elements of tourist efficiency by the Tobit model. The results revealed that the tourism efficiency of Gansu Province had increased rapidly during the study period, especially after 2016, the rising range increased. From 2011 to 2019, the cumulative changes in GML index, technological change (TC), and efficiency change (EC) of tourism efficiency in Gansu Province were 5.053, 4.145 and 1.160, respectively, indicating that the improvement of tourism efficiency in Gansu province is mainly due to technological progress. The regression results of the Tobit model show that the status of the tourism industry, trade openness, information level, and technological innovation level can significantly promote tourism efficiency in the province. At the same time, upgrading the industrial structure and the improvement of greening coverage inhibit tourism efficiency. However, the impact of the economic development level on the tourism efficiency of Gansu Province is not apparent. According to the research results, this paper puts forward corresponding suggestions to promote the development of tourism in Gansu Province. This study is crucial for hospitality, tourism, and policy sectors to understand the underlying factors and promote the healthy development of the tourism industry in Gansu Province.