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Spatial Distribution Characteristics and Influencing Factors of Pro-Poor Tourism Villages in China

Author

Listed:
  • Lei Zhu

    (College of Resources and Environment, Anqing Normal University, Anqing 246011, China)

  • Jing Hu

    (College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Jiahui Xu

    (College of Resources and Environment, Anqing Normal University, Anqing 246011, China)

  • Yannan Li

    (College of Resources and Environment, Anqing Normal University, Anqing 246011, China)

  • Mangmang Liang

    (College of Resources and Environment, Anqing Normal University, Anqing 246011, China)

Abstract

This paper aims to contribute to the effectiveness of pro-poor tourism in rural areas. We use 5770 pro-poor tourism villages in China as the research objects; the spatial distribution characteristics of pro-poor tourism villages in China are analyzed using a combination of disequilibrium index, kernel density analysis, and spatial autocorrelation; their influencing factors are detected using a geographical detector and overlay analysis. The study results show the following: (1) The distribution of pro-poor tourism villages is exceptionally uneven in three zones, eight regions, and inter-provincial levels, forming a high-density cluster belt that includes Hebei, Henan, Anhui, and Hubei, and five high-density cluster cores that include southern Gansu, Sichuan–Gansu–Shaanxi border area; Guizhou, Hunan, and Chongqing border area; southern Sichuan; and southwest Guizhou. (2) Regarding spatial correlations, the pro-poor tourism villages in central and western regions are in hot spots, while those in eastern regions are in cold spots. The hot spots gradually increase, while cold spots gradually decrease, and the clustering trend of the distribution of the pro-poor tourism villages is increasingly apparent. (3) Pro-poor tourism villages are affected by social, economic, industrial, and other human factors as well as natural geographical factors such as terrain, precipitation, river, and climate, among which the industrial factors have a more significant impact. Pro-poor tourism villages are concentrated in humid mountainous areas with an altitude of about 1000 m and an annual precipitation of more than 800 mm, and they are mostly distributed in the subtropical monsoon climate zone closer to the river and more suitable climate. (4) It is suggested that pro-poor tourism villages can be divided into four types: the resource underutilization type, mountain environment restriction type, traffic location non-optimization type, and industrial development lagging type, and the sustainable development strategies of different types of pro-poor tourism villages are proposed.

Suggested Citation

  • Lei Zhu & Jing Hu & Jiahui Xu & Yannan Li & Mangmang Liang, 2022. "Spatial Distribution Characteristics and Influencing Factors of Pro-Poor Tourism Villages in China," Sustainability, MDPI, vol. 14(23), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15953-:d:988514
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    6. Xiaogang Feng & Moqing Hu & Sekhar Somenahalli & Xinyuan Bian & Meng Li & Zaihui Zhou & Fengxia Li & Yuan Wang, 2023. "A Study of Spatio-Temporal Differentiation Characteristics and Driving Factors of Shaanxi Province’s Traditional Heritage Villages," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    7. Xinyu Xie & Ying Zhang & Xiaoping Qiu, 2023. "Spatial Distribution Characteristics and Influencing Factors of Rural Governance Demonstration Villages in China," IJERPH, MDPI, vol. 20(5), pages 1-20, March.
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    9. Shiying Li & Yuhong Song & Hua Xu & Yijiao Li & Shaokun Zhou, 2023. "Spatial Distribution Characteristics and Driving Factors for Traditional Villages in Areas of China Based on GWR Modeling and Geodetector: A Case Study of the Awa Mountain Area," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    10. Xi Yuan & Yijiao Li & Yuhong Song & Hongyi Lu & Yi Wang & Beichen Ge & Jing Wang, 2024. "Spatial Distribution Characteristics and Driving Factors of 777 Traditional Villages in Yunnan Province: A Study Based on GWR Model and Geodetector," Land, MDPI, vol. 13(12), pages 1-29, November.

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