To discuss the coupling coordination relationship among tourism carbon emissions, economic development and regional innovation it is not only necessary to realize the green development of tourism economy, but also great significance for the tourism industry to take a low-carbon path. Taking the 30 provinces of China for example, this paper calculated the tourism carbon emission efficiency based on the super-efficiency Slacks based measure and Data envelope analyse (SBM-DEA) model from 2007 to 2017, and on this basis, defined a compound system that consists of tourism carbon emissions, tourism economic development and tourism regional innovation. Further, the coupling coordination degree model and dynamic degree model were used to explore its spatiotemporal evolution characteristics of balanced development, and this paper distinguished the core influencing factors by Geodetector model. The results showed that (1) during the study period, the tourism carbon emission efficiency showed a reciprocating trend of first rising and then falling, mainly due to the change of pure technical efficiency. (2) The coupling coordination degree developed towards a good trend, while there were significant differences among provinces, showing a gradient distribution pattern of decreasing from east to west. Additionally, (3) the core driving factors varied over time, however, in general, the influence from high to low were as follows: technological innovation, economic development, urbanization, environmental pollution control, and industrial structure. Finally, some policy recommendations were put forward to further promote the coupling coordination degree.
The sustainable development of rural tourism is a complex system that includes both objective environmental factors and subjective human factors. Based on the three dimensions of “man–machine–environment”, the element event analysis method (EEAM) is introduced to identify and determine the components of the rural tourism composite system. Then, a hierarchical digraph of the rural tourism complex system is constructed by the interpretative structural model (ISM), and the logical structural relations among factors are explored to clarify the action paths. It is found that: (1) through three rounds of soliciting opinions and revising the list of factors, a total of 26 key factors affecting the sustainable development of rural tourism were screened out; (2) the influencing factors are related to each other to form a five-level factor hierarchical structure, which clearly reveals the overall structure of the system and the support dependencies among factors; (3) on the basis of clarifying the path of influencing factors, the targeted countermeasures and suggestions for the sustainable development of rural tourism are proposed for three key paths. This not only provides a certain theoretical basis for sustainable forecasting but also helps decision-makers to take targeted countermeasures.
The optimization of the cooperation network is a key link to accelerate the high-quality development of regional tourism. Taking the Beijing–Tianjin–Hebei region as an example, this paper measures the tourism cooperation intensity with the modified gravity model, on which the original, binary, and Top networks are generated to identify the spatiotemporal evolution characteristics from the multi-dimensional difference–association–agglomeration model, and provide insight into the determinants by the GeoDetector model. The results show that (1) the cooperation network reveals a diffusion trend with Beijing–Tianjin as the main axis chain, and southward expansion, and the overall differences tend to moderate at a slow pace, among which the north is the weak area. (2) The robustness of the cooperation network association structure is enhanced, showing that the outgoing equilibrium is improved, while the cohesion is strengthened and accessibility changes little. Furthermore, the cities show a core–edge distribution pattern in terms of power roles. (3) The cooperation network shows the phenomenon of hierarchical agglomeration gradually with the expansion of network scale, and eventually evolves into two camps: the Beijing–Tianjin cooperation circle and the Shijiazhuang–Xingtai cooperation circle. (4) Tourism cooperation belongs to the locational traffic constraint type, and making up for the shortcomings of rural development is another key to further enhancing regional tourism cooperation. The future optimization of regional tourism cooperation needs to seek multifactorial promotion paths.
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