甘肃省灰水足迹变化特征及驱动因素

doi:10.13866/j.azr.2022.06.11

摘要/Abstract

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 $×$ 10⁸m³；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

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

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.

YIN Mingcai,ZHU Hao,HU Yuanzhao,LI Zhenzhong,ZHANG Jishi. Analysis of various characteristics and driving factors of gray water footprint in Gansu Province[J]. Arid Zone Research, 2022, 39(6): 1810-1818.

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年份	污水排放量/10⁴t		污染物排放量/10⁴t			水资源量/10⁸m³	GDP/10⁸元
年份	生活污水	工业污水	生活COD	工业COD	总氮	水资源量/10⁸m³	GDP/10⁸元
2011	59477	10218	10	0.45	3.6	410.88	9016.7
2012	59718	10409	11.23	0.87	1.28	325.88	8718.3
2013	59478	10539	11.35	0.96	1.37	354.89	8104.1
2014	54003	10426	11.31	1.39	1.35	280.79	7336.7
2015	53283	13022	13.82	1.74	1.59	209.56	6907.9
2016	48275	18760	14.66	8.36	5.13	198.81	6556.6
2017	46209	19742	14.41	8.86	5.12	230.84	6518.4
2018	44769	20171	14.54	9.05	4.74	303.209	6014.5
2019	43589	19188	14.99	9.3	4.75	300.69	5393.1
2020	39491	19720	15.23	9.61	5.19	272.14	4816.9
均值	50829.20	15219.50	13.15	5.07	3.41	288.77	6938.32

项目	牛粪	猪粪	羊粪	家禽粪	牛猪尿
COD	6.16	5.58	5.50	8.59	50
BOD	4.87	6.14	6.70	6.78	50
NH₃-N	2.22	3.04	4.10	4.15	50
TP	5.50	5.25	5.20	8.42	50
TN	5.68	5.34	5.30	8.47	50

变量名称	计算方法	符号
灰水足迹/10⁸ m³	各年灰水足迹	Y
人口/10⁴人	各年人口数量	X₁
城镇化水平/%	城镇人口占总人口比例	X₂
人均GDP/(元·人^－1)	各年人均 GDP	X₃
第一产业产值/10⁸元	提供生产物资材料的产业	X₄
第二产业产值/10⁸元	加工、制造产业	X₅
第三产业产值/10⁸元	商业、餐饮业、金融保险业等非物质生产部门	X₆
灰水足迹强度/﹝m³·(10⁴元)^-1﹞	水资源效率变化状况	X₇
社会消费品零售总额/10⁴元	指城乡居民用于生活消费的商品金额	X₈

模型	非标准化系数		标准化系数	t	Sig	共线性
模型	弹性系数B	标准误差	试用版	t	Sig	容差	VIF
变量	-14.697	42.908		-0.343	0.765
lnX₂	-3.412	5.912	-0.092	-0.577	0.622	0.006	161.101
lnX₃	4.427	1.880	2.100	2.354	0.143	0.000	5092.938
lnX₄	-.580	0.699	-0.494	-0.830	0.494	0.000	2265.950
lnX₅	1.901	0.540	0.504	3.519	0.072	0.008	131.477
lnX₆	6.131	1.518	3.811	4.039	0.056	0.000	5696.821
lnX₇	5.417	0.953	2.450	5.682	0.030	0.001	1190.048
lnX₈	-0.081	0.027	-0.253	-3.016	0.095	0.022	44.982

模型汇总
样本量	R²	调整R²	模型误差RMSE
10	0.999	0.994	0.02