Research on the evolution of spatial network structure of tourism eco-efficiency and its influencing factors in China’s provinces based on carbon emission accounting

Abstract

In the context of global warming, although the coordinated development of tourism has led to regional economic growth, the high energy consumption-driven effects of such development have also led to environmental degradation. This research combines the undesired output of the Super-SBM model and social network analysis methods to determine the eco-efficiency of provincial tourism in China from 2010–2019 and analyzes its spatial correlation characteristics as well as its influencing factors. The aim of the project is to improve China’s regional tourism eco-efficiency and promote cross-regional tourism correlation. The results show that (1) the mean value of provincial tourism eco-efficiency in China is maintained at 0.405~0.612, with an overall fluctuating upward trend. The tourism eco-efficiency of eastern China is higher than that of central, western and northeastern China, but the latter three regions have not formed a stable spatial distribution pattern. (2) The spatial network of provincial tourism eco-efficiency in China is multithreaded, dense and diversified. Throughout the network, affiliations are becoming closer, and network structure robustness is gradually improving, although the “hierarchical” spatial network structure remains. In individual networks, Jiangsu, Guangdong and Shandong provinces in eastern China have higher centrality degrees, closeness centrality and betweenness centrality than other provinces, which means they are dominant in the network. Hainan Province, also located in eastern China, has not yet built a "bridge" for tourism factor circulation. In the core-periphery model, the core-periphery areas of China’s provincial tourism eco-efficiency are distributed in clusters, and the number of "core members" has increased. (3) The economic development level, information technology development level, and tourism technology level collectively drive the development and evolution of China’s provincial tourism eco-efficiency spatial network.

Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China

“…In this context, the optimal synergistic combination of each tourism factor and regional economy in spatial location defines the urban tourism development process [43]. However, the conventional tourism gravitational model uses only total tourism revenue, number of A-class scenic spots, total tourist arrivals, and distance as formula variables [44][45]. Consequently, it fails to provide a comprehensive tourism efficiency analysis of cities from the input-output perspective of each factor, and the measurement results do not fully reflect the interaction relationship between tourism efficiency development and economic development.…”

“…Consequently, it fails to provide a comprehensive tourism efficiency analysis of cities from the input-output perspective of each factor, and the measurement results do not fully reflect the interaction relationship between tourism efficiency development and economic development. To address this limitation, the present study constructs a modified tourism efficiency gravity model and its matrix network by selecting the GDP of each city as well as tourism efficiency as variables based on Wang et al's (2017) [45] approach. The formula for the model is as follows: (10) where the tourism efficiency gravitational strength, denoted by Y ij , is determined by the tourism efficiency values, E i and E j , of city points i and j, respectively.…”

“…Unlike traditional parametric tests, QAP does not require the assumption of mutual independence among independent variables, making it more robust. It can also be applied to regression analysis of matrices and examine interaction effects between multiple networks [45]. The method has been shown to be robust and reliable by several scholars [46][47], avoiding issues of multiple collinearities of independent variables in causality analysis.…”

“…To further examine the interaction between the aviation network and the tourism efficiency network, we referred to previous studies on network interaction [45,48] and converted the sub-indicators of tourism efficiency and aviation network dominance into a matrix of difference relationships. This involves calculating the difference between city pairs of each sub-indicator based on the route relationship, and constructing a "network-net" mechanism of action.…”

Evolution of Industrial Ecology and Analysis of Influencing Factors: The Yellow River Basin in China

“…At the same time, the joint action of market and government has formed many sites practicing IE, including industrial parks, which prompted the flow of technology, capital, and policies to such industries and integrated ecological systems. Therefore, the influence and mechanisms of action of IE, including technological innovation and spillovers [41], financial level [42], environmental regulation [33,43], regional resource endowment [10,44], local fiscal expenditure [45], and population layout [46], have also received more attention from scholars.…”