IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i16p7526-d1728633.html
   My bibliography  Save this article

Spatiotemporal Evolution and Driving Factors of Tourism Eco-Efficiency: A Three-Stage Super-Efficiency SBM Approach

Author

Listed:
  • Bing Xie

    (School of Cultural, Tourism and Public Administration, Fujian Normal University, Fuzhou 350117, China)

  • Yanhua Yu

    (School of Cultural, Tourism and Public Administration, Fujian Normal University, Fuzhou 350117, China)

  • Lin Zhang

    (School of Cultural, Tourism and Public Administration, Fujian Normal University, Fuzhou 350117, China)

  • Fazi Zhang

    (School of Geographical Sciences, School of Carbon Neutrality Future Technology, Fujian Normal University, Fuzhou 350117, China)

  • Layan Wei

    (School of Cultural, Tourism and Public Administration, Fujian Normal University, Fuzhou 350117, China)

  • Yuying Lin

    (School of Cultural, Tourism and Public Administration, Fujian Normal University, Fuzhou 350117, China
    Higher Education Key Laboratory for Smart Tourism of Fujian Province, Fuzhou 350117, China)

Abstract

Tourism ecological efficiency (TEE) is a significant indicator of the development level of green and intensive tourism. However, conventional directional and radial TEE measurement approaches overlook critical factors such as intermediate process influences and input–output slack variables, potentially leading to biased estimates. Urban areas are key to coordinating tourism across provinces, so accurately assessing the TEE is vital for sustainable regional tourism. This study uses an improved TEE measurement model to measure the TEE of the Guangdong–Fujian–Zhejiang (GFZ) coastal city clusters from 2010 to 2021. The improved TEE measurement model is a three-stage super-efficiency SBM approach. It then uses standard deviation ellipses and geographic detectors to analyze the TEE’s spatiotemporal characteristics and influencing factors. The findings indicate the following: (1) The three-stage super-efficiency SBM approach improves the accuracy and validity of measurement results by removing external environmental variables. (2) During the study period, the TEE values of the GFZ coastal city clusters were above average (except for Meizhou, where the efficiency improved). Temporally, the TEE values of 75% of the cities showed an increasing trend; spatially, the high-value areas increased significantly, the middle- and low-value areas decreased, and the center of gravity shifted to the north and south. (3) The years 2016–2021 saw an increase in external development factors and the use of external resources. The study’s findings can serve as scientific benchmarks for TEE measurement, as well as the low-carbon and environmentally friendly growth of tourism in urban agglomerations.

Suggested Citation

  • Bing Xie & Yanhua Yu & Lin Zhang & Fazi Zhang & Layan Wei & Yuying Lin, 2025. "Spatiotemporal Evolution and Driving Factors of Tourism Eco-Efficiency: A Three-Stage Super-Efficiency SBM Approach," Sustainability, MDPI, vol. 17(16), pages 1-20, August.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7526-:d:1728633
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/16/7526/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/16/7526/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gjalt Huppes & Masanobu Ishikawa, 2005. "Eco‐efficiency and Its xsTerminology," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 43-46, October.
    2. Gössling, Stefan & Peeters, Paul & Ceron, Jean-Paul & Dubois, Ghislain & Patterson, Trista & Richardson, Robert B., 2005. "The eco-efficiency of tourism," Ecological Economics, Elsevier, vol. 54(4), pages 417-434, September.
    3. Ayres, Robert & Ferrer, Geraldo & Van Leynseele, Tania, 1997. "Eco-efficiency, asset recovery and remanufacturing," European Management Journal, Elsevier, vol. 15(5), pages 557-574, October.
    4. 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.
    5. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    6. Deli Li & Yingjie Zhai & Gang Tian & Richard K. Mendako, 2022. "Tourism Eco-Efficiency and Influence Factors of Chinese Forest Parks under Carbon Peaking and Carbon Neutrality Target," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    7. Yiyang Sun & Guolin Hou, 2021. "Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration," IJERPH, MDPI, vol. 18(5), pages 1-29, March.
    8. Wagner, Martin, 2008. "The carbon Kuznets curve: A cloudy picture emitted by bad econometrics?," Resource and Energy Economics, Elsevier, vol. 30(3), pages 388-408, August.
    9. Song, Malin & Peng, Licheng & Shang, Yuping & Zhao, Xin, 2022. "Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    10. Chaogao An & Polat Muhtar & Zhenquan Xiao, 2022. "Spatiotemporal Evolution of Tourism Eco-Efficiency in Major Tourist Cities in China," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    11. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    12. Laure Latruffe & Kelvin Balcombe & Sophia Davidova & Katarzyna Zawalinska, 2004. "Determinants of technical efficiency of crop and livestock farms in Poland," Applied Economics, Taylor & Francis Journals, vol. 36(12), pages 1255-1263.
    13. 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.
    14. Lamei He & Jianping Zha & Hui Ann Loo, 2020. "How to improve tourism energy efficiency to achieve sustainable tourism: evidence from China," Current Issues in Tourism, Taylor & Francis Journals, vol. 23(1), pages 1-16, January.
    15. Bleischwitz, Raimund, 2003. "Cognitive and institutional perspectives of eco-efficiency," Ecological Economics, Elsevier, vol. 46(3), pages 453-467, October.
    16. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    17. Gössling, Stefan & Scott, Daniel & Hall, C. Michael, 2015. "Inter-market variability in CO2 emission-intensities in tourism: Implications for destination marketing and carbon management," Tourism Management, Elsevier, vol. 46(C), pages 203-212.
    18. Yufeng Cheng & Kai Zhu & Quan Zhou & Youssef El Archi & Moaaz Kabil & Bulcsú Remenyik & Lóránt Dénes Dávid, 2023. "Tourism Ecological Efficiency and Sustainable Development in the Hanjiang River Basin: A Super-Efficiency Slacks-Based Measure Model Study," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    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. Qi Wang & Qunli Tang & Yingting Guo, 2024. "Spatial Interaction Spillover Effect of Tourism Eco-Efficiency and Economic Development," Sustainability, MDPI, vol. 16(18), pages 1-19, September.
    2. Avkiran, Necmi K., 2007. "Stability and integrity tests in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 41(3), pages 224-234, September.
    3. Carla Henriques & Clara Viseu, 2022. "Are ERDFs Devoted to Boosting ICTs in SMEs Inefficient? A Three-Stage SBM Approach," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
    4. Kun Zeng & Xiong Duan & Bin Chen & Lanxi Jia, 2025. "Spatiotemporal Heterogeneity of Eco-Efficiency of Cultivated Land Use and Its Influencing Factors: Evidence from the Yangtze River Economic Belt, China," Sustainability, MDPI, vol. 17(7), pages 1-23, March.
    5. Yufeng Cheng & Kai Zhu & Quan Zhou & Youssef El Archi & Moaaz Kabil & Bulcsú Remenyik & Lóránt Dénes Dávid, 2023. "Tourism Ecological Efficiency and Sustainable Development in the Hanjiang River Basin: A Super-Efficiency Slacks-Based Measure Model Study," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    6. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    7. Chiu, Yung-Ho & Chen, Yu-Chuan, 2009. "The analysis of Taiwanese bank efficiency: Incorporating both external environment risk and internal risk," Economic Modelling, Elsevier, vol. 26(2), pages 456-463, March.
    8. Feng Dong & Ruyin Long & Zhengfu Bian & Xihui Xu & Bolin Yu & Ying Wang, 2017. "Applying a Ruggiero three-stage super-efficiency DEA model to gauge regional carbon emission efficiency: evidence from China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(3), pages 1453-1468, July.
    9. 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.
    10. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    11. Avkiran, Necmi K., 2009. "Removing the impact of environment with units-invariant efficient frontier analysis: An illustrative case study with intertemporal panel data," Omega, Elsevier, vol. 37(3), pages 535-544, June.
    12. Peng, Hongsong & Zhang, Jinhe & Lu, Lin & Tang, Guorong & Yan, Bingjin & Xiao, Xiao & Han, Ya, 2017. "Eco-efficiency and its determinants at a tourism destination: A case study of Huangshan National Park, China," Tourism Management, Elsevier, vol. 60(C), pages 201-211.
    13. Xu Zhang & Huaping Sun & Taohong Wang, 2022. "Impact of Financial Inclusion on the Efficiency of Carbon Emissions: Evidence from 30 Provinces in China," Energies, MDPI, vol. 15(19), pages 1-15, October.
    14. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    15. Jinkai Li & Jingjing Ma & Wei Wei, 2020. "Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    16. Dan Xue & Xianzong Li & Fayyaz Ahmad & Nabila Abid & Zulqarnain Mushtaq, 2022. "Exploring Tourism Efficiency and Its Drivers to Understand the Backwardness of the Tourism Industry in Gansu, China," IJERPH, MDPI, vol. 19(18), pages 1-15, September.
    17. Wei Zhang & Ying Zhan & Ruiyang Yin & Xunbo Yuan, 2022. "The Tourism Eco-Efficiency Measurement and Its Influencing Factors in the Yellow River Basin," Sustainability, MDPI, vol. 14(23), pages 1-14, November.
    18. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    19. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    20. Jia Li & Yahong Zheng & Bing Liu & Yanyi Chen & Zhihang Zhong & Chenyu Dong & Chaoqun Wang, 2024. "The Synergistic Relationship between Low-Carbon Development of Road Freight Transport and Its Economic Efficiency—A Case Study of Wuhan, China," Sustainability, MDPI, vol. 16(7), pages 1-21, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:gam:jsusta:v:17:y:2025:i:16:p:7526-:d:1728633. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.