中国灰水足迹时空动态演进及驱动因素研究

doi:10.16258/j.cnki.1674-5906.2024.05.008

生态环境学报 ›› 2024, Vol. 33 ›› Issue (5): 745-756.DOI: 10.16258/j.cnki.1674-5906.2024.05.008

• 研究论文【环境科学】 • 上一篇下一篇

中国灰水足迹时空动态演进及驱动因素研究

程鹏¹^,^*(), 孙明东², 宋晓伟¹

1.山西财经大学资源环境学院，山西太原 030006
2.中国环境科学研究院水生态环境研究所，北京 100012

收稿日期:2024-02-04 出版日期:2024-05-18 发布日期:2024-06-27
通讯作者: *
作者简介:程鹏（1989年生），男，副教授，博士，主要研究方向为流域水环境管理。E-mail: pengcheng@sxufe.edu.cn
基金资助:
国家重点基础研究计划项目(2021YFC3101703);国家自然科学基金项目(72104132)

Study on the Spatial and Temporal Dynamic Evolution and Driving Factors of Grey Water Footprint in China

CHENG Peng¹^,^*(), SUN Mingdong², SONG Xiaowei¹

1. College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, P. R. China
2. Institute of Water Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P. R. China

Received:2024-02-04 Online:2024-05-18 Published:2024-06-27

摘要/Abstract

摘要：

灰水足迹可从水量角度评价水环境污染程度，有助于实现水污染对水资源短缺影响的评估。中国是全球灰水足迹最大的国家，对中国灰水足迹进行全面准确核算，分析其时空动态演进特征，并准确识别其驱动因素，对缓解中国水资源短缺具有重要意义。从农业（包括种植业和畜牧业）、工业及生活三方面全面计算2011－2021年中国31个省（市、自治区，不含港澳台）的灰水足迹，采用ArcGIS空间制图、核密度估计和标准差椭圆方法分析灰水足迹时空变化特征和动态演进趋势，采用广义迪氏指数分解法探索中国灰水足迹时空动态演进的驱动因素。结果显示，1）2011－2021年中国灰水足迹及其组成均呈下降趋势，工业灰水足迹下降的比例远大于农业灰水足迹和生活灰水足迹；除西藏、青海和新疆的灰水足迹实现了增长外，其余省域的灰水足迹均呈下降趋势，且各省灰水足迹及其组成的区域差异呈缩小态势。2）河南、山东和四川的灰水足迹一直位于全国前3位，而北京、天津、上海和海南均位于全国末位；灰水足迹标准差椭圆均呈现明显的“东北－西南”分布格局，且重心略微向西移动，中国灰水足迹的总体空间分布变化较小。3）GDP和人口数一直为中国及各省灰水足迹的正向驱动因素，而灰水足迹强度、人均灰水足迹和技术效应一直为灰水足迹的负向驱动因素；GDP对灰水足迹增长的促进作用最大，灰水足迹强度对灰水足迹的降低作用最大。研究结果可为中国及各省域制定针对性水污染管理措施提供科学参考。

关键词: 灰水足迹, 时空动态演进, 核密度估计, 标准差椭圆, 驱动因素, 广义迪氏指数分解法

Abstract:

The grey water footprint enables the assessment of water environmental pollution in terms of water quantity, thereby facilitating the evaluation of the impact of water pollution on water resource scarcity. China possesses the largest grey water footprint globally. It is crucial to mitigate water resource shortages in China, by measuring China’s grey water footprint comprehensively and accurately, analyzing its spatiotemporal dynamic evolution characteristics, and identifying its driving forces precisely. This study comprehensively calculated the grey water footprint of 31 provinces (cities, autonomous regions, excluding Hong Kong, Macao and Taiwan) in China from 2011-2021 across three sectors: agriculture (including planting and animal husbandry), industry and domestic use. ArcGIS spatial mapping, kernel density estimation and standard deviation ellipse methods were used to analyze the temporal and spatial variation characteristics and dynamic evolution trend of grey water footprint. Additionally, the generalized divisia index method was used to explore the driving force of the temporal and spatial dynamic evolution of grey water footprint in China. The results showed that 1) China’s grey water footprint and its composition exhibited a declining trend from 2011 to 2021, with the industrial grey water footprint decreasing at a significantly higher rate compared to the agricultural and domestic sectors. Except for Xizang, Qinghai and Xinjiang, the grey water footprint of the rest of the provinces showed a downward trend, and the regional differences in the grey water footprint and its composition of the provinces showed a narrowing trend. 2) The grey water footprint of Henan, Shandong, and Sichuan ranked top three in China, while Beijing, Tianjin, Shanghai, and Hainan ranked at the lower end. The standard deviation ellipses of grey water footprint showed a clear “northeast southwest” distribution pattern, with the center of gravity slightly moving westward. The overall spatial distribution of grey water footprint in China showed little change. 3) GDP and population were positive driving factors of the grey water footprint in China and various provinces. However, the intensity of grey water footprint, per capita grey water footprint, and technological effects were negative driving factors of these footprints. In addition, GDP exerted the greatest promoting effect on their growth while the intensity of grey water footprint played an essential role in reducing them. The research outcomes could provide valuable scientific insights to formulate targeted measures, aiming at water pollution management across different regions within China.

Key words: grey water footprint, spatiotemporal dynamic evolution, kernel density estimation, standard deviational ellipse, driving forces, generalized divisia index method

中图分类号:

X143

程鹏, 孙明东, 宋晓伟. 中国灰水足迹时空动态演进及驱动因素研究[J]. 生态环境学报, 2024, 33(5): 745-756.

CHENG Peng, SUN Mingdong, SONG Xiaowei. Study on the Spatial and Temporal Dynamic Evolution and Driving Factors of Grey Water Footprint in China[J]. Ecology and Environment, 2024, 33(5): 745-756.

图/表 13

表1 灰水足迹及其分解变量的指标意义

Table1 The index meaning of grey water footprint and its decomposition variables

指标	具体含义
Z	灰水足迹: 衡量用水污染的程度
X₁	经济规模效应: 为GDP, 代表生产力水平
X₂	灰水足迹强度效应: 为GWF与GDP的比率, 表明水污染控制的技术水平
X₃	自然禀赋效应: 为用水量, 代表水资源的消耗
X₄	技术效应: 为灰水足迹与用水量的比率, 衡量中国生产生活中每单位用水的水污染
X₅	人口规模效应: 为人口数, 代表常驻居民的规模
X₆	人均灰水排放效应: 为灰水足迹与人口数的比率, 衡量人均水污染程度
X₇	经济效率效应: 为GDP与人口数的比率, 代表人均生产力
X₈	用水强度效应: 为用水量与GDP的比率, 代表节水的技术水平

表2 畜禽养殖COD的产污系数和入河系数

Table 2 Excretion and delivery coefficients of COD load for livestock

系数	猪		牛		羊	家禽
系数	粪便	尿	粪便	尿	粪便	粪便
粪便排放系数/ (kg·d⁻¹)	2.00	3.30	20.00	10.00	2.60	0.125
粪便中COD质量分数/(g∙kg⁻¹)	52.00	9.00	31.00	6.00	4.63	45.65
入河系数/%	5.50	8.59	6.16	5.58	5.50	8.59

图1 2011－2021年中国灰水足迹及其组成的时间变化趋势

Figure 1 The temporal variation trend of grey water footprint and its components in China from 2011 to 2021

图2 2011－2021年中国各省域（港澳台除外）灰水足迹的时间变化趋势

Figure 2 Temporal change trend of grey water footprint of provinces in China (excluding Hong Kong, Macao and Taiwan) from 2011 to 2021

图3 中国灰水足迹及其组成的核密度估计

Figure 3 Kernel density estimation of grey water footprint and its composition in China

图4 2011－2021年中国灰水足迹的空间分布该图基于中华人民共和国自然资源部标准地图服务网站下载的审图号为GS（2023）2763号的标准地图制作，底图无修改

Figure 4 Spatial distribution of grey water footprint in China from 2011 to 2021.

图5 2011－2021年中国省域灰水足迹标准差椭圆及其重心迁移变化

Figure 5 The standard deviation ellipse of grey water footprint and its shift of center of gravity in China from 2011 to 2021

表3 中国省域灰水足迹标准差椭圆参数

Table 3 Elliptic parameter of standard deviation of grey water footprint in provinces of China

年份	长半轴/km	短半轴/km	旋转角/(°)	重心经度/(°)	重心纬度/(°)	重心地理位置
2011	1153.94	936.28	33.57	112.349	33.570	河南省南阳市镇平县
2013	1154.22	948.52	40.59	112.219	33.584	河南省南阳市内乡县
2015	1158.72	970.27	42.01	112.031	33.640	河南省南阳市内乡县
2017	1168.32	997.30	45.48	111.815	33.732	河南省南阳市内乡县
2019	1168.81	1010.03	47.82	111.649	33.629	河南省南阳市西峡县
2021	1165.12	1022.38	49.95	111.477	33.635	河南省南阳市西峡县

图6 2011－2021年中国逐年灰水足迹各驱动因素贡献值

Figure 6 Contribution value of each driving factor of grey water footprint in China from 2011 to 2021

图7 2011－2021年中国灰水足迹各驱动因素总贡献值

Figure 7 Total contribution value of each driving factor of China’s grey water footprint during 2011-2021

图8 2011－2021年期间中国各省域（港澳台除外）灰水足迹各驱动因素总贡献值

Figure 8 Total contribution value of each driving factor of grey water footprint in China’s provinces (excluding Hong Kong, Macao and Taiwan) during 2011-2021

表4 2011－2021年中国各省域（港澳台除外）灰水足迹及组成平均排名

Table 4 The mean ranking of grey water footprint and composition of provinces in China (excluding Hong Kong, Macao and Taiwan) from 2011 to 2021

省份	灰水足迹	农业灰水足迹	工业灰水足迹	生活灰水足迹
北京	31	30	30	27
天津	29	29	28	28
河北	8	5	6	13
山西	24	26	22	19
内蒙古	12	10	16	23
辽宁	15	15	14	16
吉林	17	16	23	22
黑龙江	13	14	17	14
上海	30	31	27	24
江苏	5	8	1	3
浙江	21	25	3	12
安徽	11	12	18	5
福建	18	21	15	11
江西	16	18	11	10
山东	2	2	7	8
河南	1	1	8	7
湖北	7	6	13	6
湖南	6	7	9	4
广东	4	9	2	1
广西	10	11	5	9
海南	28	27	29	26
重庆	23	22	25	21
四川	3	3	12	2
贵州	19	17	24	17
云南	9	4	10	15
西藏	25	23	31	31
陕西	20	19	19	18
甘肃	22	20	21	25
青海	26	24	26	29
宁夏	27	28	20	30
新疆	14	13	4	20

图9 2011－2021年中国灰水足迹各驱动因素总体变化率

Figure 9 Overall change rate of each driving factor of China’s grey water footprint in 2011-2021

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中国灰水足迹时空动态演进及驱动因素研究

Study on the Spatial and Temporal Dynamic Evolution and Driving Factors of Grey Water Footprint in China

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