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Spatial Correlation and Influencing Factors of Tourism Eco-Efficiency in the Urban Agglomeration of the Yangtze River Delta Based on Social Network Analysis

Author

Listed:
  • Yuewei Wang

    (School of Business, Liaoning University, Shenyang 110036, China)

  • Lidan An

    (School of Business, Liaoning University, Shenyang 110036, China)

  • Hang Chen

    (School of Tourism Management, Shenyang Normal University, Shenyang 110034, China)

  • Yuyan Zhao

    (School of Management, Shenyang Jianzhu University, Shenyang 110168, China)

Abstract

Tourism eco-efficiency analysis is an effective tool to solve the problem of sustainable tourism development. The tourism eco-efficiency evaluation index system was constructed in the study, and the undesirable output super-slacks-based measure model was used to estimate the tourism eco-efficiency of 26 cities in the Yangtze River Delta. Then, the modified gravity model based on the values of the tourism eco-efficiency analysis of each city was used to construct a spatial correlation network. The structural characteristics of the spatial association networks of tourism eco-efficiency, the interrelationships among different cities, and the roles played by different blocks were explored using a social network analysis. The quadratic assignment procedure (QAP) was applied to analyze the influencing factors that affect the formation of the spatial association network of tourism eco-efficiency. The results show that tourism eco-efficiency has an overall increasing trend, and the gap among cities is decreasing. The structure of the spatial correlation network of tourism eco-efficiency has good connectivity, accessibility, and robustness with the correlations among all of the cities in the network. The spillover effects among the blocks are significant, showing spatial polarization, with the cities such as Shanghai, Suzhou, and Hangzhou occupying the core position of the network. The QAP analysis shows that the spatial correlation network of tourism eco-efficiency is affected by the distance between the cities and the levels of development of the economy and information dissemination. The results of this study innovatively reveal the structural characteristics and influencing factors of the spatial correlation network of tourism eco-efficiency. It could provide valuable insights for the development of corresponding policy measures by government sectors and tourism firms to enhance the sustainability of regional tourism development.

Suggested Citation

  • Yuewei Wang & Lidan An & Hang Chen & Yuyan Zhao, 2022. "Spatial Correlation and Influencing Factors of Tourism Eco-Efficiency in the Urban Agglomeration of the Yangtze River Delta Based on Social Network Analysis," Land, MDPI, vol. 11(11), pages 1-21, November.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:11:p:2089-:d:977937
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    References listed on IDEAS

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    1. Kytzia, Susanne & Walz, Ariane & Wegmann, Mattia, 2011. "How can tourism use land more efficiently? A model-based approach to land-use efficiency for tourist destinations," Tourism Management, Elsevier, vol. 32(3), pages 629-640.
    2. Becken, Susanne & Frampton, Chris & Simmons, David, 2001. "Energy consumption patterns in the accommodation sector--the New Zealand case," Ecological Economics, Elsevier, vol. 39(3), pages 371-386, December.
    3. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    4. Xiaoping Qiu & Yiping Fang & Xueting Yang & Fubiao Zhu, 2017. "Tourism Eco-Efficiency Measurement, Characteristics, and Its Influence Factors in China," Sustainability, MDPI, vol. 9(9), pages 1-19, September.
    5. Song, Malin & Li, Hui, 2019. "Estimating the efficiency of a sustainable Chinese tourism industry using bootstrap technology rectification," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 45-54.
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