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

Hanqing Hu, Xiaofan Yang, Jianling Li (), Jianbo Shen, Jianhua Dai and Yuanyuan Jin
Additional contact information
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

Sustainability, 2023, vol. 15, issue 17, 1-14

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).

Keywords: agricultural surface pollution; Digital Rural Construction; spatial panel; quantile autoregression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/17/13138/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/17/13138/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:17:p:13138-:d:1230363

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().