城镇化进程中人口结构变动对用水量的影响

doi:10.18402/resci.2018.04.12

摘要/Abstract

摘要：

本文在城镇化背景下,利用系统GMM模型和面板门槛模型分析评价中国31个省份及东中西部地区2000—2016年人口结构变动与用水量之间的关系,结果表明：①中国人口结构中人口老龄化对用水量的弹性系数最大（0.042）;非人口结构因素中上期用水量对本期的弹性系数最大（0.978）,其次是万元GDP用水量（0.020）和人口规模（0.018）,产业结构调整显著抑制用水量增加（-0.041）,其他变量回归结果不显著;②东部用水量受人口结构变动影响大于其他因素,高端消费、人口城镇化和农业劳动人口每提高1个百分点分别导致用水量分别提高12.1、6.5和3.6个百分点;中西部人口结构变动暂时不能显著影响用水量;③人口年龄结构和产业结构在各自作用下对用水量存在双重门槛效应,弹性系数分别是0.174、0.150、0.139和-0.012、-0.008、-0.020;2016年仅西藏未跨越人口年龄结构的第一门槛值,其他省份均跨越第二门槛值;北京和上海2016年已跨越产业结构的第二门槛值,天津、山西等11个省份处于门槛的第二个阶段,其他18个省份未跨越第一门槛值。因此,需要缓解人口结构变动对用水量的促进作用,适当延长退休年龄,合理控制城镇流动人口,引导居民形成节水消费和理念,推广节水型公共设施及家庭设备,合理配置各产业间人力资源,提高第三产业比重,加快技术创新及技术溢出,实现经济高质量发展和区域协调发展。

关键词: 人口结构变动, 用水量, 城镇化进程, 系统GMM, 门槛效应, 中国

Abstract:

We used the dynamical system GMM and panel threshold model to analyze the relationship between population structure change and water consumption in 31 provinces of China from 2000 to 2016. We found that the elasticity coefficient of aging population to water consumption is the largest (0.042). Among non-population structure factors, the elasticity coefficient of the water consumption of previous period to this period is the largest (0.978), followed by the water consumption by million CNY of GDP (0.020) and population scale (0.018). Industrial restructuring significantly inhibits water increases (-0.041); other regression results were not significant. Water use in the east is more affected by changes in population structure than other factors. With one percentage point increase in high-end consumption, population urbanization and rural labor force, water consumption respectively increased by 12.1, 6.5 and 3.6 percentage points respectively. Population structure changes in central and western regions did not significantly affect water consumption. The age and industrial structures have a threshold effect on water consumption, and the elastic coefficients are 0.174, 0.150, 0.139 and -0.012, -0.008 and -0.020, respectively. In 2016, only Tibet did not cross the first threshold of the population age structure, while other provinces crossed the second threshold. Beijing and Shanghai have crossed the second threshold of industrial structure in 2016, with 11 provinces in the second phase of the threshold, while the other 18 provinces have not crossed the first threshold. It is necessary to reduce the promoting effect of population structural change on water consumption, appropriately extend the retirement age and rationally control urban floating population. Other measures such as guiding residents to form water-saving ideas and achieving coordinated regional development are also needed.

Key words: population structure change, water consumption, urbanization, system GMM, threshold effect, China

金巍, 章恒全, 张洪波, 孔伟, 毛广雄, 张陈俊, 严翔. 城镇化进程中人口结构变动对用水量的影响[J]. 资源科学, 2018, 40(4): 784-796.

Wei JIN, Hengquan ZHANG, Hongbo ZHANG, Wei KONG, Guangxiong MAO, Chenjun ZHANG, Xiang YAN. The influence of population structural change on water consumption in urbanization[J]. Resources Science, 2018, 40(4): 784-796.

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变量名		变量代码	指标说明	数据来源
人口结构变量	人口城乡结构	lnUP	采用各省年末城镇常住人口比重表示	2001—2017年《中国人口和就业统计年鉴》^[36]、《中国农村统计年鉴》^[37]、《中国统计年鉴》^[38]及各省份统计年鉴
	人口年龄结构	lnAP	采用各省65岁以上老龄人口比重表示
	人口就业结构	lnRP	采用第一产业人口比重,即农林牧渔从业人口表示
	人口教育结构	lnEYP	采用城乡居民受教育程度^[35],* 表示
	人口消费结构	lnEGE	采用各省城乡恩格尔系数表示
非人口结构变量	人口规模	lnP	借鉴李超等^[33]对人口自然结构的定义,同时考虑到人口规模变化是驱动水资源消耗增加的直接因素,将人口规模作为解释变量进行回归,采用各省年末人口数表示
	气候环境	lnPW	气候环境能够直接决定地区水资源是否丰沛,故采用地区水资源禀赋表示气候环境
	社会经济发展	lnPGDP	人均GDP表示地区社会经济发展程度表示
	产业结构	lnIS	采用第三产业产值占比表示产业结构变动表示
	技术进步	lnWGDP	技术进步对用水量的影响直接体现在经济增长过程中水资源产生的价值,用万元GDP用水量衡量技术进步程度表示

自变量	OLS			固定效应FE			系统GMM
自变量	Coef.		T Statistics		Coef.	T Statistics	Coef.	T Statistics
lnW_i_,_t_-1	0.539***	18.030		0.549***	25.600		0.978***	92.890
lnP	0.458***	15.040		0.450***	20.980		0.018*	1.680
lnUP	0.056***	3.790		0.058***	3.560		0.027	1.390
lnAP	0.111***	10.770		0.122***	10.560		0.042***	2.690
lnRP	0.035***	4.480		0.038***	5.350		-0.007	-0.900
lnEYP	-0.163***	-5.560		-0.171***	-6.660		-0.053	-1.540
lnEGE	-0.038***	-3.220		-0.072***	-4.450		-0.020	-0.770
lnPW	0.004**	2.030		0.005***	2.750		0.001	0.750
lnPGDP	0.423***	14.750		0.409***	19.110		0.001	0.140
lnIS	-0.069***	-4.450		-0.043***	-2.910		-0.041**	-2.150
lnWGDP	0.458***	15.380		0.450***	20.980		0.020*	1.910
_cons	-7.989***	-14.100		-7.802***	-19.960		—
A-R²	0.998			0.998			—
AR(1)	—			—			0.000
AR(2)	—			—			0.278
Sargan test	—			—			0.911

自变量	lnW_i_,_t_-1	lnP	lnUP	lnAP	lnRP	lnEYP	lnEGE	lnPW	lnPGDP	lnIS	lnWGDP
Coef.	0.987***	0.010	0.030	0.043**	-0.004	-0.045	-0.025	0.001	0.014	-0.029	0.012*
T Statistics	132.820	1.410	1.560	2.890	-0.490	-1.400	-1.560	0.620	1.080	-1.420	1.790
AR(1)	0.000
AR(2)	0.261
Sargan test	0.981

自变量	东部地区			中部地区			西部地区
自变量	Coef.		T Statistics		Coef.	T Statistics	Coef.	T Statistics
lnW_i_,_t_-1	0.998***	51.260		0.957***	19.990		0.982***	41.370
lnP	-0.014	-0.860		-0.015	-0.280		0.025	0.890
lnUP	0.065**	1.970		-0.090	-1.330		0.061	1.010
lnAP	-0.016	-0.620		0.076	0.720		0.039	0.800
lnRP	0.036**	2.510		-0.035	-0.630		0.026	0.590
lnEYP	-0.133	-1.340		-0.050	-0.220		-0.107	-0.980
lnEGE	-0.121*	-1.720		-0.000	-0.000		-0.117	-1.450
lnPW	-0.009*	-1.660		-0.027*	-1.700		0.009**	2.060
lnPGDP	0.042**	1.970		0.078*	1.560		-0.004	-0.180
lnIS	-0.010	-0.230		-0.064	-0.980		-0.014**	-0.270
lnWGDP	0.034	1.330		0.080	1.450		0.028	1.010
AR(1)	0.000			0.000			0.000
AR(2)	0.488			0.147			0.864
Sargan test	0.969			0.357			0.619

门槛依赖变量及门槛顺序	lnAP				lnIS
门槛依赖变量及门槛顺序	单一		双重	三重	单一	双重	三重
F值	18.621***	7.250***	6.168*		8.194**	9.302**	4.534
P值	0.003	0.007	0.083		0.050	0.043	0.333
1%临界值	13.936	5.975	12.671		11.584	13.405	16.935
5%临界值	7.927	3.957	7.531		8.460	9.794	12.977
10%临界值	5.615	2.593	5.343		6.180	7.779	10.570