Tourism eco-efficiency is an important indicator that has often been applied to measure the quality of green tourism development. This paper takes the 31 provinces of China as examples to analyze regional tourism eco-efficiency. By constructing multiple input and output indicator systems for regional tourism, we estimated the eco-efficiency of 31 provinces in 1997–2016 using an undesirable output model of a slack-based model (undesirable-SBM) for data envelopment analysis (DEA). Then, we analyzed the spatial–temporal evolutionary trends and patterns of the eco-efficiency over 20 years by using the Hot Spot Model and Spatial Center of Gravity Model. Finally, we explored the driving forces internal and external to the tourism eco-economic system using the Panel Tobit Regression Model and Geodetector Model, respectively. The results show that: In the last 20 years, the tourism eco-efficiency of provinces in China declined, though tourism has experienced rapid but extensive development. The western regions of China, which have better eco-environmental conditions, and the southeastern coastal regions, which have higher levels of economic development, have higher tourism eco-efficiency. Regions with lower tourism eco-efficiency show diffusion trends, while regions with higher tourism eco-efficiency are characterized by a lack of obvious space spillover effects. Technology is the core driving force of regional tourism eco-efficiency, while traffic conditions and social civilization levels are key external influence factors leading to improvement of tourism eco-efficiency. The research results reveal the great significance of laws for sustainable green tourism development with different economic levels in the different regions. Our work could provide a reference for similar countries and regions in the world with the rapid growth of tourism or obvious spatial differentiation in socioeconomic development.

Abstract:Tourism is an important sustainable industry in the economy that optimizes the industrial structure. Thus, as a core part of this market, tourism enterprises perform a key role in the effective operation of this industry. This paper applies data envelopment analysis (DEA) and Malmquist index (MI) models to calculate the efficiency of Chinese tourism enterprises between 2005 and 2014. Results showed that: (1) The efficiency and the total factor productivity change index (TFPC) of tourism enterprises remained low, and both have decreased. (2) The efficiency of regional tourism enterprises across China cloud be characterized as high in the east region, low in the central region, and high in both northeast and western regions. (3) The efficiency levels of the cities of Beijing and Shanghai were ahead of the country over the period of this study, while Chongqing, Tibet, Qinghai, and Ningxia all possess a number of obvious advantages in the western region. (4) Centers of overall tourism enterprise efficiency mainly moved in a southeast-to-northwest direction over the period of this research. (5) The spatial autocorrelation of tourism enterprise efficiencies is also assessed in this study, and the results show that the comprehensive efficiency (CE) of tourism enterprises in southeastern coastal regions of China tended to a certain spatial agglomeration effect, while the correlation between the central region and northern China was not significant. (6) The Geodetector model is applied to analyze the key factors driving the spatial differentiation of tourism enterprise efficiencies, and the results show that the degree of opening to the outside world, potential human capital, and traffic conditions were the most important factors driving spatial differentiation in the efficiency of tourism enterprises.

Under the dual background of climate change and post-epidemic economic recovery, the study of the eco-efficiency of tourism destinations in the process of urbanization is critical to promoting the green and healthy development of tourism. This paper selects tourism destinations in 30 provinces of China in 2000–2019 as the research object, calculates the economic efficiency and eco-efficiency of China’s tourism destinations by constructing the Super-SBM (Slacks-Based Measure) model and visualizes the spatial distribution pattern and evolution trend of economic efficiency and eco-efficiency of China’s tourism destinations through spatial hotspot and center of gravity analysis. The coupling model is used to find the coupling relationship between the efficiency of China’s tourism destinations (economic efficiency and eco-efficiency) and urbanization. Finally, Tobit panel regression is used to find out how urbanization affects the eco-efficiency of tourism destinations. The results show that: (1) the eco-efficiency of tourism destinations in China is higher than the economic efficiency, as well as showing a downward trend. (2) The economic efficiency of tourism destinations in western China has increased while the eco-efficiency has declined. (3) China’s tourism destinations are undergoing the process of transformation and restructuring, and have not yet reached the decoupling standard. (4) On the whole, the improvement in urbanization is conducive to promoting the economic and environmentally sustainable development of tourism destinations. The main driving indicators are the living standards for urban residents, urban resources and environment, the industrial structure, and the role of the government. This study attempts to find a balance between the economic benefits and ecological impacts of tourism destinations and alleviate the real demand for the rapid economic recovery of tourism destinations in the post-epidemic era and the tension between human activities and the ecological environment. The research results are expected to provide a path for the healthy development of tourism destinations in the process of China’s new urbanization and provide a reference for tourism destinations in developing countries similar to China.

At present, COVID-19 is seriously affecting the economic development of the hotel industry, and at the same time, the world is vigorously calling for “carbon emission mitigation”. Under these two factors, tourist hotels are in urgent need of effective tools to balance economic and social contributions with ecological and environmental impacts. Therefore, this paper takes Chinese tourist hotels as the research object and constructs a research framework for Chinese tourist hotels by constructing a Super-SBM Non-Oriented model. We measured the economic efficiency and eco-efficiency of Chinese tourist hotels from 2000 to 2019; explored spatial-temporal evolution patterns of their income, carbon emissions, eco-efficiency, and economic efficiency through spatial hotspot analysis and center of gravity analysis; and identified the spatial agglomeration characteristics of such hotels through the econometric panel Tobit model to identify the different driving factors inside and outside the tourist hotel system. The following results were obtained: (1) the eco-efficiency of China’s tourist hotels is higher than the economic efficiency, which is in line with the overall Kuznets curve theory, but the income and carbon emissions have not yet been decoupled; (2) most of China’s tourist hotels are crudely developed with much room for improving the economic efficiency, and most of the provincial and regional tourist hotels are at a low-income level, but the carbon emissions are still on the increase; and (3) income, labor, carbon emissions, waste emissions, and water consumption are the internal drivers of China’s tourist hotels, while industrial structure, urbanization rate, energy efficiency, and information technology are the external drivers of China’s tourist hotels. The research results provide a clear path for the reduction in carbon emissions and the improvement of the eco-efficiency of Chinese tourist hotels. Under the backdrop of global climate change and the post-COVID-19 era, the research framework and conclusions provide references for countries with new economies similar to China and countries that need to quickly restore the hotel industry.

In the age of rapid development of social media, most researches focus on the tourists’ motive for sharing, content preference, and the impact of content shared on potential tourists, while few researches pay attention to the impact of tourism sharing on the sharer itself. With grounded theory, this paper analyzes the sharing preferences of tourists at different tourism experience levels and the impact of sharing on the sharer’s own tourism experience and next destination choice. The research shows the following: (1) sharing tourism experience on social media will positively regulate the sharer’s tourism experience, thus positively promoting the tourist’s satisfaction for this trip and expectation for next trip. (2) The sharing preferences and focuses of tourists at different tourism experience levels are different, which constitutes the hierarchy model of tourism sharing. (3) The destination choice preferences of tourists at all sharing levels are related to the sharing levels at which the tourists are. The paper also verifies the fact that Gaffman’s dramaturgical theory onstage and backstage are not completely independent but have significant effect on each other.