干旱区研究

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2022 , Vol. 39 >Issue 6: 1810 - 1818

DOI: https://doi.org/10.13866/j.azr.2022.06.11

水土资源

甘肃省灰水足迹变化特征及驱动因素

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  • 兰州交通大学环境与市政工程学院，甘肃 兰州 730070
尹明财（1995-），男，硕士研究生，主要研究方向为水文水资源. E-mail: 1179017282@qq.com

收稿日期: 2022-04-20

  修回日期: 2022-08-21

  网络出版日期: 2023-01-17

基金资助

国家自然科学基金重大项目(41690141);国家自然科学基金面上项目(41671029)

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Analysis of various characteristics and driving factors of gray water footprint in Gansu Province

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  • School of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

Received date: 2022-04-20

  Revised date: 2022-08-21

  Online published: 2023-01-17

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摘要

利用STIRPAT模型分析了灰水足迹的驱动因素，研究了甘肃省2011—2020年的灰水足迹变化。结果表明：在这10 a间灰水足迹整体下降，下降了378.53 ×108 m3；最大降幅为81%。其中生活灰水足迹、农业灰水足迹、工业灰水足迹占比为43%、38%、19%。种植业灰水足迹大于畜牧业灰水足迹。灰水足迹强度整体出现下降趋势，说明水资源利用率逐年提高。从水污染程度和剩余灰水足迹来看，2011—2016年甘肃省水污染水平均大于1，水资源污染比较严重。剩余灰水足迹从2017—2020年呈现为负值，说明水质呈现上升的趋势，水环境问题得到改善，水资源持续性增加。从甘肃省灰水足迹的驱动因素来看，城镇化水平、人均GDP、第一、二、三产业产值、灰水足迹强度、社会消费品零售总额均会促进灰水足迹的增加，影响系数分别为0.142、0.126、0.052、0.382、0.132、0.916、0.1。根据影响系数的大小，可以去制定相关的政策，减少甘肃省的灰水足迹，从而减轻水环境压力。

关键词： 甘肃省; 灰水足迹; 驱动因素; STIRPAT模型; 岭回归

本文引用格式

尹明财,朱豪,胡圆昭,李振中,张济世 . 甘肃省灰水足迹变化特征及驱动因素[J]. 干旱区研究, 2022 , 39(6) : 1810 -1818 . DOI: 10.13866/j.azr.2022.06.11

Abstract

This study examines the change in the gray water footprint in Gansu Province from 2011 to 2020 and uses the STIRPAT model to analyze the driving factors of the greywater footprint. The results show that the greywater footprint has been declining over the last ten years. The overall decrease was 378.53 billion m3; the maximum decline was 81%. The life, agricultural, and industrial greywater footprints accounted for 43%, 38%, and 19%, respectively. The graywater footprint of the planting industry is greater than that of animal husbandry. The overall intensity of the greywater footprint shows a downward trend, indicating that water resource utilization has increased yearly. According to the degree of water pollution and residual graywater footprint, the water pollution level in the Gansu Province from 2011 to 2016 was greater than one, and the water pollution is relatively severe. The research shows that the residual ash water footprint was negative from 2017 to 2020, indicating that the water quality shows an upward trend. Water environmental problems have been improved, and water resources continue to increase. From the driving factors of greywater footprint in the Gansu Province, urbanization level; per capita GDP; first, second, and third industrial output value; the intensity of greywater footprint; and total retail sales of social consumer goods will all promote the increase of greywater footprint, and the influencing coefficients are 0.142, 0.126, 0.052, 0.382, 0.132, 0.916, and 0.1, respectively. According to the size of the impact coefficient, relevant policies can be developed to reduce the graywater footprint of the Gansu Province, reducing the pressure on the water environment.

Key words： Gansu Province; gray water footprints; driving factor; STIRPAT model; ridge regression

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