IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0295186.html

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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295186
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295186&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0295186?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    2. Yang Lin & Longzhong Yan & Ying-Ming Wang, 2019. "Performance Evaluation and Investment Analysis for Container Port Sustainable Development in China: An Inverse DEA Approach," Sustainability, MDPI, vol. 11(17), pages 1-13, August.
    3. Taleb, Mushtaq & Khalid, Ruzelan & Ramli, Razamin & Ghasemi, Mohammad Reza & Ignatius, Joshua, 2022. "An integrated bi-objective data envelopment analysis model for measuring returns to scale," European Journal of Operational Research, Elsevier, vol. 296(3), pages 967-979.
    4. Halkos, George & Bampatsou, Christina & Aslanidis, Panagiotis-Stavros, 2025. "Combining policy making tools for green transition: eco-efficiency benchmarking and club convergence," MPRA Paper 123341, University Library of Munich, Germany.
    5. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Hsu, Shih-Hsun & Managi, Shunsuke, 2015. "The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions," Omega, Elsevier, vol. 53(C), pages 30-40.
    6. Xiang Liu & Jia Liu, 2016. "Measurement of Low Carbon Economy Efficiency with a Three-Stage Data Envelopment Analysis: A Comparison of the Largest Twenty CO 2 Emitting Countries," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
    7. Jie Wu & Xiang Lu & Dong Guo & Liang Liang, 2017. "Slacks-Based Efficiency Measurements with Undesirable Outputs in Data Envelopment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1005-1021, July.
    8. Adler, Nicole & Volta, Nicola, 2016. "Accounting for externalities and disposability: A directional economic environmental distance function," European Journal of Operational Research, Elsevier, vol. 250(1), pages 314-327.
    9. Jie Wu & Zhixiang Zhou, 2015. "A mixed-objective integer DEA model," Annals of Operations Research, Springer, vol. 228(1), pages 81-95, May.
    10. George E. Halkos & Jaime Moll Alba & Panagiotis-Stavros C. Aslanidis, 2025. "Correction to: Examining Energy and Carbon Intensity of the European Fashion Industry: A Hybrid DEA Analysis for Circular Economy," Circular Economy and Sustainability, Springer, vol. 5(4), pages 3739-3739, August.
    11. Harald Dyckhoff, 2019. "Multi-criteria production theory: convexity propositions and reasonable axioms," Journal of Business Economics, Springer, vol. 89(6), pages 719-735, August.
    12. Jessica Suarez Campoli & Tatiana Kimura Kodama & Marcelo Seido Nagano & Heloisa Lee Burnquist, 2025. "Advancing Circular Economy: G20 Nations’ Path Towards 12th Sustainable Development Goal," Circular Economy and Sustainability, Springer, vol. 5(1), pages 1-24, February.
    13. Chang, Dong-Shang & Yang, Fu-Chiang, 2011. "Assessing the power generation, pollution control, and overall efficiencies of municipal solid waste incinerators in Taiwan," Energy Policy, Elsevier, vol. 39(2), pages 651-663, February.
    14. Tomasz Żyłowski & Jerzy Kozyra, 2023. "Crop Cultivation Efficiency and GHG Emission: SBM-DEA Model with Undesirable Output Approach," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    15. Hainan Guo & Yang Zhao & Tie Niu & Kwok-Leung Tsui, 2017. "Hong Kong Hospital Authority resource efficiency evaluation: Via a novel DEA-Malmquist model and Tobit regression model," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-24, September.
    16. Chunhua Chen & Chongyu Ma, 2025. "Efficiency of agricultural and pastoral systems in China considering shared factors and undesirable outputs," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 71(8), pages 445-457.
    17. Junqi Wen & Yong Lan & Guoqin Bu, 2024. "Do Innovation and Entrepreneurship Support Policies Promote Urban Green Transformation?—The Mediating Role of Fiscal Technology Expenditure," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
    18. Usman Akbar & József Popp & Hameed Khan & Muhammad Asif Khan & Judit Oláh, 2020. "Energy Efficiency in Transportation along with the Belt and Road Countries," Energies, MDPI, vol. 13(10), pages 1-20, May.
    19. Noor Ramli & Susila Munisamy & Behrouz Arabi, 2013. "Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector," Annals of Operations Research, Springer, vol. 211(1), pages 381-398, December.
    20. Qingxian An & Haoxun Chen & Jie Wu & Liang Liang, 2015. "Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output," Annals of Operations Research, Springer, vol. 235(1), pages 13-35, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0295186. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.