文献类型：期刊文献

英文题名：Can Digital Rural Construction Improve China's Agricultural Surface Pollution? Autoregressive Modeling Based on Spatial Quartiles

作者：Hu, Hanqing[1];Yang, Xiaofan[1];Li, Jianling[2];Shen, Jianbo[3];Dai, Jianhua[4];Jin, Yuanyuan[5]

第一作者：Hu, Hanqing

通讯作者：Li, JL[1]

机构：[1]Beijing Informat Sci & Technol Univ, Sch Econ & Management, Beijing 100192, Peoples R China;[2]Beijing Union Univ, Business Coll, Beijing 100025, Peoples R China;[3]Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China;[4]China Univ Polit Sci & Law, Business Sch, Beijing 100088, Peoples R China;[5]Beijing Informat Tech Coll, Sch Artificial Intelligence, Beijing 100018, Peoples R China

第一机构：Beijing Informat Sci & Technol Univ, Sch Econ & Management, Beijing 100192, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Business Coll, Beijing 100025, Peoples R China.|[1141721]北京联合大学商务学院;[11417]北京联合大学;

年份：2023

卷号：15

期号：17

外文期刊名：SUSTAINABILITY

收录：;Scopus(收录号：2-s2.0-85170378478);WOS:【SSCI(收录号:WOS:001070082200001)，SCI-EXPANDED(收录号:WOS:001070082200001)】；

基金：& nbsp;This research was funded by Beijing Municipal Social Science Foundation key project grant number 18YJA003.

语种：英文

外文关键词：agricultural surface pollution; Digital Rural Construction; spatial panel; quantile autoregression

摘要：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).