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The Structure and Evolution of the Tourism Economic Network of the Tibetan Plateau and Its Driving Factors

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  • Xiaodong Chen

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830000, China
    University of Chinese Academy of Sciences, Beijing 100101, China)

  • Tian Wang

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830000, China
    University of Chinese Academy of Sciences, Beijing 100101, China)

  • Xin Zheng

    (University of Chinese Academy of Sciences, Beijing 100101, China
    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100049, China)

  • Fang Han

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830000, China)

  • Zhaoping Yang

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830000, China)

Abstract

Tourism is one of the core industries driving the economy on the Tibetan Plateau towards green development. Adopting the improved gravity model, social network analysis, and quadratic assignment procedure, this paper explores the structure and evolution characteristics of the tourism economic network on the Tibetan Plateau from 2015 to 2019. The results are as follows: (1) the tourism economic strength increased over time, but it was generally higher in Qinghai Province and lower in Tibet. (2) The network density exhibited an upwards trend overall. (3) The structure of the tourism economic network presented a significant core–periphery distribution pattern. Xining, Haixi, and Lhasa acted as a bridge between the spatial network structure of the tourism economy and had a structural hole advantage. However, some remote cities, such as Ali, Naqu, and Guoluo, had low network centricity. (4) The quadratic assignment procedure regression analysis showed that the A-level attractions and star-rated hotels significantly advanced the formation of the spatial association. This study concludes with recommendations for promoting tourism economic associations for policy-makers.

Suggested Citation

  • Xiaodong Chen & Tian Wang & Xin Zheng & Fang Han & Zhaoping Yang, 2022. "The Structure and Evolution of the Tourism Economic Network of the Tibetan Plateau and Its Driving Factors," Land, MDPI, vol. 11(2), pages 1-18, February.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:2:p:241-:d:743021
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    as
    1. Sylvain Dejean, 2020. "The role of distance and social networks in the geography of crowdfunding: evidence from France," Regional Studies, Taylor & Francis Journals, vol. 54(3), pages 329-339, March.
    2. Feng Wang & Mengnan Gao & Juan Liu & Wenna Fan, 2018. "The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect," Energies, MDPI, vol. 11(10), pages 1-14, October.
    3. Matsumoto, Hidenobu & Domae, Koji, 2019. "Assessment of competitive hub status of cities in Europe and Asia from an international air traffic perspective," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 88-95.
    4. Yin, Ping & Lin, Zhibin & Prideaux, Bruce, 2019. "The impact of high-speed railway on tourism spatial structures between two adjoining metropolitan cities in China: Beijing and Tianjin," Journal of Transport Geography, Elsevier, vol. 80(C).
    5. Kuik, Onno & Branger, Frédéric & Quirion, Philippe, 2019. "Competitive advantage in the renewable energy industry: Evidence from a gravity model," Renewable Energy, Elsevier, vol. 131(C), pages 472-481.
    6. Shengrui Zhang & Linsheng Zhong & Hongrun Ju & Yingjie Wang, 2019. "Land Border Tourism Resources in China: Spatial Patterns and Tourism Management," Sustainability, MDPI, vol. 11(1), pages 1-20, January.
    7. Andres Monzon & Elena Lopez & Emilio Ortega, 2019. "Has HSR improved territorial cohesion in Spain? An accessibility analysis of the first 25 years: 1990–2015," European Planning Studies, Taylor & Francis Journals, vol. 27(3), pages 513-532, March.
    8. Ling-en Wang & Yuxi Zeng & Linsheng Zhong, 2017. "Impact of Climate Change on Tourism on the Qinghai-Tibetan Plateau: Research Based on a Literature Review," Sustainability, MDPI, vol. 9(9), pages 1-14, August.
    9. Pavlovich, Kathryn, 2014. "A rhizomic approach to tourism destination evolution and transformation," Tourism Management, Elsevier, vol. 41(C), pages 1-8.
    10. Lovrić, Marko & Da Re, Riccardo & Vidale, Enrico & Pettenella, Davide & Mavsar, Robert, 2018. "Social network analysis as a tool for the analysis of international trade of wood and non-wood forest products," Forest Policy and Economics, Elsevier, vol. 86(C), pages 45-66.
    11. Liu, Bing & Huang, Songshan (Sam) & Fu, Hui, 2017. "An application of network analysis on tourist attractions: The case of Xinjiang, China," Tourism Management, Elsevier, vol. 58(C), pages 132-141.
    12. Sylvain Dejean, 2019. "The role of distance and social networks in the geography of crowdfunding: evidence from France," Working Papers hal-01645147, HAL.
    13. Kai Wang & Menghan Wang & Chang Gan & Qinchang Chen & Mihai Voda, 2021. "Tourism Economic Network Structural Characteristics of National Parks in the Central Region of China," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
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    Cited by:

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    2. Rui Ding & Jun Fu & Yiling Zhang & Ting Zhang & Jian Yin & Yiming Du & Tao Zhou & Linyu Du, 2022. "Research on the Evolution of the Economic Spatial Pattern of Urban Agglomeration and Its Influencing Factors, Evidence from the Chengdu-Chongqing Urban Agglomeration of China," Sustainability, MDPI, vol. 14(17), pages 1-19, September.
    3. Liang Xu & Zhigao Liu, 2022. "The Evolution of the Spatial Patterns of Startup Firms in the Tibet Autonomous Region, China in the 21st Century," Sustainability, MDPI, vol. 14(15), pages 1-18, August.
    4. Shanshan Shi & Menghao Liu & Jianchao Xi, 2022. "Hierarchical Structure and Organizational Model of County Tourism Network of the Tibetan Plateau," Land, MDPI, vol. 11(11), pages 1-18, October.

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