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

Yang Kong; Weijun He; Zhaofang Zhang; Juqin Shen; Liang Yuan; Xin Gao; Min An; Thomas Stephen Ramsey

doi:10.1016/j.jenvman.2022.115601

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

J Environ Manage. 2022 Sep 15:318:115601. doi: 10.1016/j.jenvman.2022.115601. Epub 2022 Jul 2.

Authors

Yang Kong¹, Weijun He², Zhaofang Zhang³, Juqin Shen⁴, Liang Yuan⁵, Xin Gao⁶, Min An⁷, Thomas Stephen Ramsey⁸

Affiliations

¹ Business School, Hohai University, Nanjing 211100, China. Electronic address: yangkong@hhu.edu.cn.
² College of Economics and Management, China Three Gorges University, Yichang 443002, China. Electronic address: weijunhe@ctgu.edu.cn.
³ Center of International Cooperation Service, Ministry of Agriculture and Rural Affairs, Beijing 100125, China. Electronic address: zhangzf@agri.gov.cn.
⁴ College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China. Electronic address: jqshen@hhu.edu.cn.
⁵ College of Economics and Management, China Three Gorges University, Yichang 443002, China. Electronic address: liangyuan@ctgu.edu.cn.
⁶ Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China. Electronic address: gxtz1987@sjtu.edu.cn.
⁷ College of Economics and Management, China Three Gorges University, Yichang 443002, China. Electronic address: anmin@ctgu.edu.cn.
⁸ College of Economics and Management, China Three Gorges University, Yichang 443002, China. Electronic address: tramsey@ctgu.edu.cn.

PMID: 35949098
DOI: 10.1016/j.jenvman.2022.115601

Abstract

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.

Keywords: Agricultural water pollution; Factor decomposition analysis; Generalized divisia index method; Grey water footprint; Spatial-temporal analysis; Standard deviational ellipse.

MeSH terms

Agriculture*
China
Cities
Water Pollution
Water*

Substances

Water