Research on Spatial Pattern Dynamic Evolution Algorithm and Optimization Model Construction and Driving Mechanism of Provincial Tourism Eco-Efficiency in China under the Background of Cloud Computing

Abstract

Based on the research of spatial pattern dynamic evolution algorithm and optimization model construction and driving mechanism of provincial tourism eco-efficiency in China under the background of cloud computing, this paper takes 30 provinces in mainland China (excluding Tibet, Hong Kong, Macao, and Taiwan) as the research object and scientifically constructs the measurement index system of tourism eco-efficiency. The Super-SBM-Undesirable model is used to measure the tourism eco-efficiency of each province from 2004 to 2017, and the algorithm and model are optimized. This paper explores the spatial evolution trajectory and path of tourism eco-efficiency by using the barycentric standard deviation ellipse method and constructs a dynamic panel model to identify the factors affecting the evolution trajectory and their driving mechanisms by using the SYS-GMM method. The results show that China’s tourism eco-efficiency is at a high level and the eastern region is higher than the central and western regions. From the moving track of the center of gravity, the center of gravity of China’s tourism eco-efficiency is located in Henan province, which has experienced a process of moving from southeast to northwest. From the standard deviation ellipse, the spatial distribution direction of China’s tourism eco-efficiency presents a “northeast-southwest” pattern, and there is a further strengthening trend of deviation. There is a significant positive correlation between tourism eco-efficiency and tourism industrial structure upgrading, tourism industrial structure rationalization, tourism technology level, and tourism human capital, as well as a significant negative correlation between tourism eco-efficiency and tourism economic development level, environmental regulation intensity, and the degree of opening to the outside world, while the relationship between urbanization and tourism eco-efficiency is relatively vague.

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

“…The coefficient of industrial structure (IS) had passed the 1% significance level test; interestingly, the sign was negative, indicating that upgrading of industrial structure will inhibit tourism efficiency in Gansu Province, which is contrary to the research of Lu et al [ 27 ]. The reason may be that, as a resource-based province in Western China, the development of tourism in Gansu Province was still driven by fixed-asset investment, which was closely related to construction and manufacturing.…”

“…The influencing factors of tourism efficiency have also attracted much attention. Scholars have analyzed the impact of economic development level [ 26 ], tourism industrial structure, agglomeration of the tourism industry [ 27 ], greening coverage [ 17 ], opening up [ 28 , 29 ], and information technology [ 30 ] on Tourism efficiency. Li et al [ 31 ] considered that there is a positive relationship between tourism and economical, but not all, circumstances.…”

Spatial Network Structure of China’s Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis

“…In addition, some scholars have also investigated the factors driving the evolution of tourism eco-efficiency. The main factors include the level of tourism economy [7], technological innovation [15], industrial structure of tourism [16] and environmental regulation [17].…”

“…What's more, exploring the spatial network structure of tourism eco-efficiency can provide reference for the cooperation and communication of sustainable tourism development. Some scholars have explored the spatiotemporal evolution characteristics regarding tourism eco-efficiency by adopting exploratory spatial data analysis (ESDA) based on the attribute data [15][16][17]19,20]. However, scholars failed to devote to enough attentions to the spatial network structure of tourism eco-efficiency based on the relational data.…”