Spatial-temporal variation and driving factors decomposition of agricultural grey water footprint in China

• Published in: Journal of environmental management 2022-09, Vol.318, p.115601-115601, Article 115601
• Main Authors: Kong, Yang, He, Weijun, Zhang, Zhaofang, Shen, Juqin, Yuan, Liang, Gao, Xin, An, Min, Ramsey, Thomas Stephen
• Format: Article
• Language: English
• Subjects: Agricultural water pollution; Factor decomposition analysis; Generalized divisia index method; Grey water footprint; Spatial-temporal analysis; Standard deviational ellipse
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2022.115601

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. •Spatial-temporal variation of agricultural grey water footprint (GWF) in China was analyzed.•Generalized divisia index method was creatively introduced to explore the main driving factors of agricultural GWF.•Areas with higher agricultural GDP generally have higher agricultu