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Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model

Author

Listed:
  • Donghui Peng
  • Zongzheng Liang
  • Yapeng Ding
  • Liuke Liang
  • Aohui Zhai
  • Yan Zhang
  • Xu Gong

Abstract

With economic progression in China, Yellow River Basin serves as a critical economic belt, which has also been recognized as a cradle of Chinese culture. A watershed is a complex structure of social, economic, and natural factors, and the diversity of its components determines its complexity. Studies on the spatiotemporal distribution characteristics and factors influencing the tourism eco-efficiency at the watershed scale are crucial for the sustainable regional socio-economic development, maintaining a virtuous cycle of various ecosystems, and comprehensively considering the utilization and coordinated development of various elements. Based on tourism eco-efficiency, the coordination degree of regional human–land system and the sustainable development levels can be accurately measured. With the tourism eco-efficiency in the Yellow River Basin from 2009 to 2019, the present study considers 63 cities in the Yellow River Basin as the research area by adopting the super-efficiency slacks-based measure (Super-SBM) model. Methods such as trend surface analysis, spatial autocorrelation analysis, elliptic standard deviation analysis, and hot spot analysis were used to explore their spatiotemporal distribution and evolution characteristics. The geographical and temporal weighted regression (GTWR) model was used to determine the factors influencing the tourism eco-efficiency value. The findings are as follows: ①The level of tourism eco-efficiency in the Yellow River Basin is not high, exhibiting a fluctuating upward trend. ②The tourism eco-efficiency in the Yellow River Basin shows significant spatial interdependence and agglomeration. Furthermore, the track of the center of gravity moves from northeast to southwest. ③ The tourism eco-efficiency in the Yellow River Basin is affected by various factors, with the economic development level having the greatest influence.

Suggested Citation

  • Donghui Peng & Zongzheng Liang & Yapeng Ding & Liuke Liang & Aohui Zhai & Yan Zhang & Xu Gong, 2024. "Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-27, February.
    • Handle: RePEc:plo:pone00:0295186
    DOI: 10.1371/journal.pone.0295186
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    References listed on IDEAS

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    1. Chao Wang & Lele Xu & Menglan Huang & Xiaofeng Su & Riwen Lai & Anxin Xu, 2022. "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," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-20, September.
    2. Zahed Ghaderi & Behnaz Saboori & Mana Khoshkam, 2023. "Revisiting the Environmental Kuznets Curve Hypothesis in the MENA Region: The Roles of International Tourist Arrivals, Energy Consumption and Trade Openness," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    3. W. Liu & W. Meng & X. Li & D. Zhang, 2010. "DEA models with undesirable inputs and outputs," Annals of Operations Research, Springer, vol. 173(1), pages 177-194, January.
    4. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    5. Yuan Zhao & Xinyang Wu, 2023. "The Spatiotemporal Relationship between Tourism Eco-Efficiency and Economic Resilience from Coupling Perspectives in China," Sustainability, MDPI, vol. 15(13), pages 1-17, June.
    6. Hongjie Bao & Xiaoqian Liu & Xiaoyong Xu & Ling Shan & Yongteng Ma & Xiaoshuang Qu & Xiangyu He, 2023. "Spatial-temporal evolution and convergence analysis of agricultural green total factor productivity—evidence from the Yangtze River Delta Region of China," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-20, March.
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