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Can Digital Rural Construction Improve China’s Agricultural Surface Pollution? Autoregressive Modeling Based on Spatial Quartiles

Author

Listed:
  • Hanqing Hu

    (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China)

  • Xiaofan Yang

    (School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China)

  • Jianling Li

    (Business College, Beijing Union University, Beijing 100025, China)

  • Jianbo Shen

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Jianhua Dai

    (Business School, China University of Political Science and Law, Beijing 100088, China)

  • Yuanyuan Jin

    (School of Artificial Intelligence, Beijing Information Technical College, Beijing 100018, China)

Abstract

The problem of agricultural surface pollution is becoming increasingly prominent, directly impeding the realization of the goals of “industrial prosperity and ecological livability” in the strategy of rural revitalization. To thoroughly analyze the impact of Digital Rural Construction on agricultural surface pollution and to effectively strengthen the prevention and control measures, the Moran index was used to assess the influence of agricultural surface pollution in 31 provinces and cities across China. The Moran index was employed to conduct global and local spatial autocorrelation analysis of agricultural surface source pollution, and a panel quantile autoregressive model was constructed to explore the effects of Digital Rural Construction on such pollution. The results show the following: (1) agricultural surface pollution in each province and city exhibits spatial spillover effects that are growing stronger; (2) the spatial impact of agricultural surface pollution on neighboring provinces and cities follows an inverted U-shaped pattern at different levels of pollution; (3) the relationship between the degree of agricultural surface pollution and the impact of Digital Rural Construction on it also follows an inverted U-shaped pattern, wherein improvements are observed as the pollution levels deepen. When the level of agricultural surface pollution is located in the quartile point 0.1, the improvement effect of Digital Rural Construction on agricultural surface pollution is small (0.0484), as the quartile point increases, the improvement effect is gradually increased, and it reaches the maximum value at the quartile point 0.5 (0.523), and the coefficient of agricultural surface pollution decreases to the minimum value at the quartile point 0.9 (0.423).

Suggested Citation

  • Hanqing Hu & Xiaofan Yang & Jianling Li & Jianbo Shen & Jianhua Dai & Yuanyuan Jin, 2023. "Can Digital Rural Construction Improve China’s Agricultural Surface Pollution? Autoregressive Modeling Based on Spatial Quartiles," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13138-:d:1230363
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    References listed on IDEAS

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    1. Tae-Hwan Kim & Christophe Muller, 2004. "Two-stage quantile regression when the first stage is based on quantile regression," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 218-231, June.
    2. Schreinemachers, Pepijn & Tipraqsa, Prasnee, 2012. "Agricultural pesticides and land use intensification in high, middle and low income countries," Food Policy, Elsevier, vol. 37(6), pages 616-626.
    3. Yan Mei & Jingyi Miao & Yuhui Lu, 2022. "Digital Villages Construction Accelerates High-Quality Economic Development in Rural China through Promoting Digital Entrepreneurship," Sustainability, MDPI, vol. 14(21), pages 1-29, October.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    Cited by:

    1. Jin Ren & Xinrui Chen & Lefeng Shi & Ping Liu & Zhixiong Tan, 2024. "Digital Village Construction: A Multi-Level Governance Approach to Enhance Agroecological Efficiency," Agriculture, MDPI, vol. 14(3), pages 1-21, March.

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