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Evaluation Index System of Rural Ecological Revitalization in China: A National Empirical Study Based on the Driver-Pressure-State-Impact-Response Framework

Author

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  • Guang Han

    (College of Humanities & Social Development, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
    China Resources & Environment and Development Academy, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
    Institute of Regional Agricultural Research, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
    The authors contributed equally to this work.)

  • Zehao Wei

    (College of Humanities & Social Development, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
    The authors contributed equally to this work.)

  • Huawei Zheng

    (School of Marxism Academy, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China)

  • Liqun Zhu

    (College of Humanities & Social Development, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China
    Institute of Regional Agricultural Research, Nanjing Agricultural University, No. 1 Weigang, Nanjing 210095, China)

Abstract

Rural ecological revitalization (RER) is one of the five goals of China’s rural revitalization strategy. However, there is a lack of an effective index system to evaluate RER levels, which hinders the implementation of this national policy and reduces the effectiveness and efficiency of public resource input. Using the driver-pressure-state-impact-response (DPSIR) framework, this study developed an evaluation framework consisting of 5 subsystems, 12 secondary indicators, and 33 tertiary indicators. Using the entropy-weighted TOPSIS method, we analyzed a set of 30 provinces’ data and empirically determined the weights of each indicator. We found that the response subsystem had the largest weight (0.338), followed by the state (0.271), impact (0.148), pressure (0.130), and driver (0.113). We then evaluated the RER level in each province and found that five provinces had high RER levels, 16 provinces had moderate RER levels, and nine provinces had low RER levels. Using Moran’s I, we examined spatial autocorrelation of provincial RER levels at global and local dimensions. We found significant positive global autocorrelations across all subsystems, indicating that geological aggregation exists in all RER subsystems. The local autocorrelation results showed that low–low and high–high patterns were the dominant local autocorrelation patterns. According to the findings, we discussed the possible implications of this RER evaluation index system and provided policy recommendations for strengthening RER in different regions across the country.

Suggested Citation

  • Guang Han & Zehao Wei & Huawei Zheng & Liqun Zhu, 2024. "Evaluation Index System of Rural Ecological Revitalization in China: A National Empirical Study Based on the Driver-Pressure-State-Impact-Response Framework," Land, MDPI, vol. 13(8), pages 1-26, August.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:8:p:1270-:d:1454733
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    References listed on IDEAS

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