Business School, Hohai University, Nanjing 211100, China.
College of Economics and Management, China Three Gorges University, Yichang 443002, China.
J Environ Manage. 2022 Sep 15;318:115601. doi: 10.1016/j.jenvman.2022.115601. Epub 2022 Jul 2.
The management of agricultural water pollution is crucial to alleviate the water crisis and promote regional sustainable development. Therefore, it is necessary to clarify the spatial-temporal variation characteristics of the agricultural grey water footprint (GWF) and accurately identify its main influencing factors, aiming at formulating differentiated regional management strategies. Based on this, the agricultural GWFs of 31 provincial regions in China from 2011 to 2019 were firstly calculated, and then the spatial-temporal variation characteristics of agricultural GWF were analyzed using the ArcGIS software and Standard Deviational Ellipse (SDE) method. Finally, the Generalized Divisia Index Method (GDIM) was creatively introduced to decompose the factors of agricultural GWF change and their respective contributions at the national and provincial levels. The main results are as follow: (1) Agricultural GWF in China decreased on the whole and showed significant provincial differences. Among them, the agricultural GWF of Henan Province was the largest while that of Shanghai City was the smallest. Compared with 2011, most provinces saw a decrease in agricultural GWF in 2019 while Yunnan, Tibet, Qinghai, Ningxia and Xinjiang Provinces achieved growth. (2) Areas with higher agricultural GDP generally had higher agricultural GWF. The spatial distribution of agricultural GWF and breeding GWF generally tended to be consistent, with the lower value in northwest and southeast of China and higher value in the northeast and southwest of China. Meanwhile, the mean center of SDE of agricultural GWF was located in Henan Province from 2011 to 2018, and shifted to Shaanxi Province in 2019, showing a slight northwest shift. (3) Agricultural GWF intensity and agricultural GDP had the largest restraining effect and driving effect on agricultural GWF growth, respectively. Additionally, China has achieved decoupling between agricultural GWF and agricultural GDP, reflecting that the patterns of agricultural production and consumption have become more sustainable. The findings of this study can provide important decision-making insights for agricultural water pollution management and industry adjustment.
农业水污染管理对于缓解水危机和促进区域可持续发展至关重要。因此，有必要阐明农业灰色水足迹（GWF）的时空变化特征，并准确识别其主要影响因素，旨在制定有区别的区域管理策略。基于此，本文首先计算了 2011 年至 2019 年中国 31 个省级行政区的农业 GWF，然后利用 ArcGIS 软件和标准离差椭圆（SDE）方法分析了农业 GWF 的时空变化特征。最后，创造性地引入广义Divisia 指数法（GDIM）来分解全国和省级农业 GWF 变化的因素及其各自的贡献。主要结果如下：（1）中国农业 GWF 整体呈下降趋势，且具有显著的省级差异。其中，河南省农业 GWF 最大，上海市农业 GWF 最小。与 2011 年相比，2019 年大多数省份的农业 GWF 呈下降趋势，而云南、西藏、青海、宁夏和新疆则有所增长。（2）农业 GDP 较高的地区一般农业 GWF 也较高。农业 GWF 和养殖 GWF 的空间分布总体上趋于一致，中国西北部和东南部的数值较低，东北部和西南部的数值较高。同时，2011 年至 2018 年农业 GWF 的 SDE 均值中心位于河南省，2019 年转移到陕西省，呈略微向西北方向移动。（3）农业 GWF 强度和农业 GDP 对农业 GWF 增长的抑制作用和驱动作用最大。此外，中国农业 GWF 与农业 GDP 已实现脱钩，这反映出农业生产和消费模式变得更加可持续。本研究的结果可为农业水污染管理和产业调整提供重要的决策见解。
