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Rural Resilience Evaluation and Influencing Factor Analysis Based on Geographical Detector Method and Multiscale Geographically Weighted Regression

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  • Huimin Wang

    (College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Institute of Sustainable Building and Energy Conservation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
    Guangdong Lingnan Township Green Building Industrialization Engineering Technology Research Center, Guangzhou 510225, China)

  • Yihuan Xu

    (College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Xiaojian Wei

    (School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China)

Abstract

Resilience evaluation is an important foundation for sustainable rural development. Taking the 57 counties in Guangdong province as examples, this study used the CRITIC method to construct a comprehensive evaluation index system for rural resilience and identified the main influencing factors and their spatial heterogeneity on the basis of the geographical detector method and multiscale geographically weighted regression. The results showed that: (1) Most of the counties in Guangdong province had medium or higher values of comprehensive resilience, and the high-value areas were mainly located in the Pearl River Delta region. (2) The comprehensive resilience and each dimensional resilience measure exhibited significant positive spatial correlations. (3) The geographic detector results showed that the per capita gross regional product and the number of industries above the scale were the main influencing factors for rural resilience, and each influencing factor had an enhanced effect after interaction. (4) The effect of each factor on rural resilience demonstrated spatial heterogeneity. Specifically, the proportion of secondary and tertiary industries showed negative effects in some counties in eastern and northern Guangdong and positive effects in other counties.

Suggested Citation

  • Huimin Wang & Yihuan Xu & Xiaojian Wei, 2023. "Rural Resilience Evaluation and Influencing Factor Analysis Based on Geographical Detector Method and Multiscale Geographically Weighted Regression," Land, MDPI, vol. 12(7), pages 1-18, June.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1270-:d:1176003
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    Cited by:

    1. Yiwei Yang & Yanhui Wang, 2023. "Exploring Rural Resilient Factors Based on Spatial Resilience Theory: A Case Study of Southern Jiangsu," Land, MDPI, vol. 12(9), pages 1-23, August.
    2. Shumei Xiao & Jun Sheng & Guangtao Zhang, 2025. "Rising Tides of Knowledge: Exploring China’s Higher Education Landscape and Human Capital Growth," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 4392-4421, March.

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