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Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity

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  • Jing Zhang

    (Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    The Rural Development Academy, Zhejiang University, Hangzhou 310058, China
    Shandong (Linyi) Institute of Modern Agriculture, Zhejiang University, Linyi 276000, China
    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China)

  • Bingbing Huang

    (Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    Shandong (Linyi) Institute of Modern Agriculture, Zhejiang University, Linyi 276000, China)

  • Xinming Chen

    (Territorial Consolidation Center in Zhejiang Province, Department of Natural Resources of Zhejiang Province, Hangzhou 310007, China)

  • Congmou Zhu

    (Department of Land Resources Management, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Muye Gan

    (Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    The Rural Development Academy, Zhejiang University, Hangzhou 310058, China)

Abstract

Evaluating and revealing the spatial differentiations of quality of rural life (QRL) is the basis for formulating rural revitalization planning to promote rural transformation and achieve common prosperity. Taking the Lin’an District of Hangzhou city in China, an economically developed mountainous area, as an example, this study explored the connotation of QRL from the perspective of common prosperity and constructed a QRL evaluation framework involving living, employment, consumption, and leisure aspects. Then, based on multi-sourced data of 270 administrative villages as the assessment unit, we revealed the spatial patterns of QRL and proposed optimization paths to improving QRL. The results showed that (1) differences in the spatial distribution of quality of rural living, employment, consumption, and leisure of Lin’an District were significant, presenting stepped, block clustering, irregularity, and scattered patterns, respectively. (2) The overall QRL was mainly at a low level, clustered spatially, distributed in a strip pattern, and with obvious road directionality. (3) Based on the evaluation results of QRL, we divided the 270 administrative villages into six types of improvement: livability, employment, consumption, leisure, and balanced and lagged development types. This study could provide a scientific cognitive basis for the improvement of QRL and a useful reference for rural revitalization in China.

Suggested Citation

  • Jing Zhang & Bingbing Huang & Xinming Chen & Congmou Zhu & Muye Gan, 2022. "Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity," IJERPH, MDPI, vol. 19(21), pages 1-21, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14166-:d:957661
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    1. Mei Zhang & Xinliang Wang, 2023. "Measurement of Common Prosperity of Chinese Rural Households Using Graded Response Models: Evidence from Zhejiang Province," IJERPH, MDPI, vol. 20(5), pages 1-16, March.

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