Spatial econometric analysis on influencing factors of water consumption efficiency in urbanizing China

doi:10.1007/s11442-017-1446-9

Abstract

Abstract:

Due to the limitation of total amount of water resources, it is necessary to enhance water consumption efficiency to meet the increasing water demand of urbanizing China. Based on the panel data of 31 provinces in China in 1997-2013, we analyze the influencing factors of water consumption efficiency by spatial econometric models. Results show that, 1) Due to the notable spatial autocorrelation characteristics of water consumption efficiency among different provinces in China, general panel data regression model which previous studies often used may be improper to reveal its influencing factors. However, spatial Durbin model may best estimate their relationship. 2) Water consumption efficiency of a certain province may be influenced not only by its socio-economic and eco-environmental indicators, but also by water consumption efficiency in its neighboring provinces. Moreover, it may be influenced by the neighboring provinces’ socio-economic and eco-environmental indicators. 3) For the macro average case of the 31 provinces in China, if water consumption efficiency in neighboring provinces increased 1%, water consumption efficiency of the local province would increase 0.34%. 4) Among the ten specific indicators we selected, per capita GDP and urbanization level of itself and its neighboring provinces have the most prominent positive effects on water consumption efficiency, and the indirect effects of neighboring provinces are much larger. Therefore, the spatial spillover effects of the economic development level and urbanization level are the primary influencing factors for improving China’s water consumption efficiency. 5) Policy implications indicate that, to improve water consumption efficiency, each province should properly consider potential influences caused by its neighboring provinces, especially needs to enhance the economic cooperation and urbanization interaction with neighboring provinces.

Key words: water consumption efficiency, water resources management, urbanization, spatial spillover effects, spatial Durbin model

Chao BAO, Xiaojie CHEN. Spatial econometric analysis on influencing factors of water consumption efficiency in urbanizing China[J].Journal of Geographical Sciences, 2017, 27(12): 1450-1462.

Figures/Tables 5

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Variable	Description or calculation
Water consumption efficiency	Total water consumption / GDP
Per capita water resources	Total water resources / Total population
Utilization ratio of water resources	Total water consumption / Total water resources
Per capita GDP	GDP / Total population
Urbanization level	Urban population / Total population
Ratio of value added of the tertiary industry in GDP	Value added of the tertiary industry / GDP
Per capita total investment in fixed assets	Total investment in fixed assets / Total population
Per capita social retail sales of consumer goods	Social retail sales of consumer goods / Total population
Urban per capita disposable income	National sample survey data
Rural per capita net income	National sample survey data
Per capita grain yield	Total grain yield / Total population

Variable	Regression coefficient	t
Intercept term	6934.39	5.85(0.0000)***
Per capita water resources	0.02	4.69(0.0000)***
Utilization ratio of water resources	0.86	5.01(0.0000)***
Per capita GDP	257.05	1.37(0.1698)
Urbanization level	-605.77	-2.52(0.0119)**
Ratio of value added of the tertiary industry in GDP	-2.68	-0.63(0.5316)
Per capita total investment in fixed assets	-183.01	-2.62(0.0090)***
Per capita social retail sales of consumer goods	199.55	2.21(0.0273)**
Urban per capita disposable income	-712.97	-3.79(0.0002)***
Rural per capita net income	46.32	0.29(0.7709)
Per capita grain yield	-108.18	-1.41(0.1606)

Item	Time and spatial random effects	Time random and spatial fixed effects	Spatial random and time fixed effects	Time and spatial fixed effects
Spatial lag effect LM test	41.11 (0.000)^***	71.73 (0.000)^***	30.43 (0.000)^***	16.33 (0.000)^***
Spatial lag effect Robustness LM test	13.98 (0.000)^***	33.8 (0.000)***	10.29 (0.001)^***	27.74 (0.000)^***
Spatial error effect LM test	27.38 (0.000)^***	50.19 (0.000)***	20.48 (0.000)^***	6.23 (0.013)^**
Spatial error effect Robustness LM test	0.24 (0.621)	12.26 (0.000)***	0.34 (0.560)	17.65 (0.000)^***

Variable	Regression coefficient		Lag term coefficient
Variable	&#x003b2;	t	&#x003b8;	t
Spatial regression coefficient &#x003bb;	0.34	6.20 (0.0000)^***	-	-
Per capita water resources	0.01	3.35 (0.0008)^***	0.01	0.81 (0.4152)
Utilization ratio of water resources	1.00	6.10 (0.0000)^***	-1.65	-4.26 (0.0000)^***
Per capita GDP	-456.17	-2.48 (0.0132)^**	-2044.96	-5.75 (0.0000)^***
Urbanization level	-186.48	-0.75 (0.0045) ^***	-976.51	-1.94 (0.0520)^*
Ratio of value added of tertiary industry in GDP	-4.23	-1.01 (0.3114)	-24.1	-2.49 (0.0130)^**
Per capita total investment in fixed assets	-115.88	-1.77 (0.0772)^*	19.9	0.15 (0.8793)
Per capita social retail sales of consumer goods	-255.41	-3.05 (0.0023)^***	79.76	0.46 (0.6488)
Urban per capita disposable income	-45.23	-0.17 (0.8640)	467.9	1.20 (0.2311)
Rural per capita net income	-117.76	-0.50 (0.6171)	1479.13	4.31 (0.0000)^***
Per capita grain yield	-106.05	-1.25 (0.2104)	-431.69	-2.61 (0.0090)^***

Variable	Direct effects		Indirect effects
Variable	Coefficient	t	Coefficient	t
Per capita water resources	0.02	3.41 (0.0018)^***	0.02	1.34 (0.1897)
Utilization ratio of water resources	1.23	5.41 (0.0000)^***	-1.87	-3.32 (0.0023)^***
Per capita GDP	-692.34	-1.56 (0.0012)^***	-2701.77	-5.01 (0.0000)^***
Urbanization level	-192.87	-1.08 (0.0028)^***	-1513.65	-2.17 (0.0374)^**
Ratio of value added of the tertiary industry in GDP	-12.12	-0.58 (0.5657)	-32.03	-2.19 (0.0362)^**
Per capita total investment in fixed assets	-24.49	-1.67 (0.1042)	-25.68	-0.13 (0.8938)
Per capita social retail sales of consumer goods	-432.92	-3.1 (0.0041)^***	-242.73	-0.98 (0.3358)
Urban per capita disposable income	-488.69	-0.09 (0.9266)	-656.44	1.28 (0.2092)
Rural per capita net income	448.58	0.02 (0.9863)	2052.82	4.81 (0.0000)^***
Per capita grain yield	6.82	1.83 (0.0768)^*	-679.06	-3.03 (0.0050)^***