Journal of Geographical Sciences >
Water resource utilization efficiency and spatial spillover effects in China
Author: Sun Caizhi (1970-), Professor, specialized in water resources evaluation and management. E-mail: suncaizhi@lnnu.edu.cn
Received date: 2014-03-30
Accepted date: 2014-04-22
Online published: 2014-05-20
Supported by
National Social Science Foundation of China, No.11BJY063.Program for New Century Excellent Talents in University, No.NECT-13-0844
Copyright
Based on provincial panel data of water footprint and grey water footprint, and with the help of data envelopment analysis model considering and without considering the undesirable output, this paper estimates the water resources utilization efficiency in China from 1997 to 2011. The spatial weighting matrix based on economy-spatial distance function is established to discuss spatial autocorrelation of water resources utilization efficiency. With the help of absolute β-convergence model, this paper concludes that there exists β-convergence in the water resources utilization efficiency. Under the conditions of considering and without considering the undesirable output, it takes about 52.6 and 5.6 years respectively to achieve the extent of half of convergence. By mean of the spatial Durbin econometric model, this paper studies spatial spillover effects of the provincial water resources utilization efficiency in China. The results are as follows. 1) With considering and without considering the undesirable output, there is significant spatial correlation in provincial water resource efficiency in China. 2) Under the two cases, the spatial autoregressive coefficients (ρ) are 0.278 and 0.507 respectively, at 1% significance level. There exist the spatial spillover effects of provincial water resources utilization efficiency. 3) With considering the undesirable output, these factors of the education funds, the transportation infrastructure, and the industrial and agricultural water consumption proportion have positive impacts. These factors of foreign direct investment, the industry value-added water consumption per ten thousand yuan, per capita water consumption, and the total precipitation have negative impacts. 4) Without considering the undesirable output, the factor of GDP per laborer has a greater positive significant influence on the water resources utilization efficiency. However the facts of industry value-added water consumption in ten thousand yuan and the transportation infrastructure have no significant influence. 5) Regardless of undesirable output of water resources utilization efficiency, the assessment of the present real water resources utilization in China will be distorted and policy-making will be misled. The water efficiency measure considering environmental factors (such as gray water footprint) is more reasonable.
SUN Caizhi , ZHAO Liangshi , ZOU Wei , ZHENG Defeng . Water resource utilization efficiency and spatial spillover effects in China[J]. Journal of Geographical Sciences, 2014 , 24(5) : 771 -788 . DOI: 10.1007/s11442-014-1119-x
Figure 1 The composition of the water footprints in China |
Table 1 The l water resource utilization efficiency in various provinces of China from 1997 to 2011 |
Regions | 1997 | 2001 | 2004 | 2008 | 2011 | Average value |
---|---|---|---|---|---|---|
Beijing | 0.134/0.196 | 0.210/0.316 | 0.287/0.383 | 0.587/0.539 | 1.000/0.759 | 0.414/0.423 |
Tianjin | 0.115/0.165 | 0.182/0.284 | 0.257/0.398 | 0.487/0.639 | 0.885/1.000 | 0.344/0.476 |
Hebei | 0.103/0.146 | 0.145/0.216 | 0.193/0.296 | 0.317/0.455 | 0.511/0.608 | 0.235/0.330 |
Shanxi | 0.064/0.116 | 0.095/0.171 | 0.130/0.247 | 0.208/0.369 | 0.287/0.494 | 0.150/0.272 |
Inner Mongolia | 0.050/0.080 | 0.076/0.117 | 0.114/0.186 | 0.227/0.367 | 0.356/0.534 | 0.152/0.240 |
Liaoning | 0.111/0.190 | 0.163/0.295 | 0.217/0.390 | 0.352/0.606 | 0.581/0.877 | 0.265/0.449 |
Jilin | 0.058/0.112 | 0.087/0.168 | 0.113/0.227 | 0.194/0.376 | 0.290/0.492 | 0.138/0.263 |
Heilongjiang | 0.069/0.111 | 0.099/0.159 | 0.131/0.215 | 0.199/0.314 | 0.272/0.407 | 0.149/0.235 |
Shanghai | 0.176/0.271 | 0.298/0.433 | 0.427/0.549 | 0.714/0.832 | 1.000/1.000 | 0.498/0.605 |
Jiangsu | 0.164/0.206 | 0.258/0.319 | 0.369/0.430 | 0.646/0.665 | 1.000/1.000 | 0.457/0.497 |
Zhejiang | 0.142/0.202 | 0.215/0.296 | 0.306/0.404 | 0.531/0.589 | 0.777/0.840 | 0.381/0.446 |
Anhui | 0.069/0.100 | 0.099/0.141 | 0.129/0.181 | 0.205/0.266 | 0.293/0.358 | 0.153/0.202 |
Fujian | 0.094/0.167 | 0.141/0.231 | 0.184/0.296 | 0.301/0.444 | 0.428/0.574 | 0.221/0.332 |
Jiangxi | 0.045/0.073 | 0.067/0.105 | 0.087/0.142 | 0.135/0.211 | 0.192/0.278 | 0.102/0.157 |
Shandong | 0.136/0.218 | 0.207/0.324 | 0.306/0.475 | 0.597/0.768 | 1.000/1.000 | 0.413/0.539 |
Henan | 0.079/0.112 | 0.105/0.159 | 0.140/0.221 | 0.239/0.350 | 0.352/0.481 | 0.173/0.256 |
Hubei | 0.087/0.126 | 0.129/0.186 | 0.163/0.243 | 0.264/0.365 | 0.386/0.468 | 0.197/0.268 |
Hunan | 0.066/0.088 | 0.093/0.126 | 0.117/0.162 | 0.192/0.256 | 0.291/0.371 | 0.142/0.190 |
Guangdong | 0.183/0.237 | 0.278/0.351 | 0.413/0.484 | 0.699/0.714 | 1.000/1.000 | 0.492/0.537 |
Guangxi | 0.053/0.076 | 0.073/0.105 | 0.099/0.139 | 0.157/0.220 | 0.238/0.316 | 0.118/0.163 |
Hainan | 0.045/0.071 | 0.059/0.098 | 0.072/0.123 | 0.105/0.179 | 0.149/0.251 | 0.082/0.139 |
Chongqing | 0.063/0.125 | 0.092/0.176 | 0.121/0.229 | 0.198/0.348 | 0.344/0.593 | 0.152/0.273 |
Sichuan | 0.093/0.143 | 0.133/0.203 | 0.179/0.272 | 0.295/0.420 | 0.490/0.635 | 0.221/0.315 |
Guizhou | 0.030/0.054 | 0.042/0.072 | 0.053/0.091 | 0.084/0.138 | 0.135/0.233 | 0.064/0.107 |
Yunnan | 0.054/0.081 | 0.071/0.106 | 0.088/0.135 | 0.130/0.190 | 0.181/0.270 | 0.101/0.149 |
Tibet | 0.025/0.031 | 0.037/0.044 | 0.049/0.059 | 0.068/0.079 | 0.087/0.106 | 0.052/0.062 |
Shaanxi | 0.048/0.090 | 0.074/0.141 | 0.100/0.196 | 0.168/0.319 | 0.241/0.458 | 0.120/0.227 |
Gansu | 0.040/0.061 | 0.057/0.087 | 0.073/0.114 | 0.110/0.172 | 0.140/0.231 | 0.081/0.128 |
Qinghai | 0.032/0.043 | 0.046/0.061 | 0.063/0.079 | 0.093/0.120 | 0.133/0.167 | 0.069/0.089 |
Ningxia | 0.028/0.031 | 0.037/0.044 | 0.047/0.060 | 0.067/0.094 | 0.086/0.125 | 0.051/0.068 |
Xinjiang | 0.055/0.065 | 0.210/0.089 | 0.089/0.113 | 0.126/0.160 | 0.156/0.201 | 0.097/0.123 |
Table 2 The Moran’s I of water utilization efficiency in China |
Year | Consider the undesirable output | Year | Without considering the undesirable output | ||||
---|---|---|---|---|---|---|---|
Moran’s I | z-Statistic | P-value | Moran’s I | z-Statistic | P-value | ||
1997 | 0.0647 | 2.8350 | 0.0023 | 1997 | 0.0686 | 2.9467 | 0.0016 |
1998 | 0.0647 | 2.8355 | 0.0023 | 1998 | 0.0713 | 3.0254 | 0.0012 |
1999 | 0.0660 | 2.8740 | 0.0020 | 1999 | 0.0719 | 3.0426 | 0.0012 |
2000 | 0.0663 | 2.8831 | 0.0020 | 2000 | 0.0735 | 3.0902 | 0.0010 |
2001 | 0.0677 | 2.9211 | 0.0017 | 2001 | 0.0752 | 3.1393 | 0.0008 |
2002 | 0.0637 | 2.8075 | 0.0025 | 2002 | 0.0730 | 3.0746 | 0.0011 |
2003 | 0.0632 | 2.7931 | 0.0026 | 2003 | 0.0768 | 3.1855 | 0.0007 |
2004 | 0.0616 | 2.7451 | 0.0030 | 2004 | 0.0771 | 3.1934 | 0.0007 |
2005 | 0.0660 | 2.8745 | 0.0020 | 2005 | 0.0819 | 3.3312 | 0.0004 |
2006 | 0.0636 | 2.8047 | 0.0025 | 2006 | 0.0788 | 3.2415 | 0.0006 |
2007 | 0.0650 | 2.8429 | 0.0022 | 2007 | 0.0800 | 3.2777 | 0.0005 |
2008 | 0.0655 | 2.8586 | 0.0021 | 2008 | 0.0807 | 3.2969 | 0.0005 |
2009 | 0.0674 | 2.9134 | 0.0018 | 2009 | 0.0802 | 3.2815 | 0.0005 |
2010 | 0.0623 | 2.7669 | 0.0028 | 2010 | 0.0740 | 3.1034 | 0.0010 |
2011 | 0.0725 | 3.0606 | 0.0011 | 2011 | 0.0659 | 2.8702 | 0.0021 |
Figure 2 LISA map of water utilization efficiency in China |
Table 3 Model regression results |
Regression result | Considering the undesirable output | Regression result | Without considering the undesirable output | ||
---|---|---|---|---|---|
Regression coefficient | t-Statistic | Regression coefficient | t-Statistic | ||
α | 0.083** | 2.125 | α | -0.074* | -1.740 |
b | 0.013 | -0.691 | b | 0.109*** | -4.131 |
Convergence rate β | 0.013 | Convergence rate β | 0.115 | ||
R2 | 0.552 | R2 | 0.424 | ||
Likelihood ratio | 905.059 | Likelihood ratio | 913.031 |
Note: *** refers to 1% significant level; ** to 5% significant level; * to 10% significant level. |
Table 4 Model regression results |
Variable | Considering the undesirable output | Variable | Without considering the undesirable output | ||
---|---|---|---|---|---|
Regression coefficient | t-Statistic | Regression coefficient | t-Statistic | ||
Foreign direct investment (108 yuan) | 0.017* | 1.684 | Foreign direct investment (108 yuan) | 0.003 | 0.632 |
GDP per laborer (108 yuan) | 0.449*** | 9.011 | GDP per laborer (108 yuan) | 0.685*** | 34.565 |
Transportation infrastructure (km) | -0.021 | -1.053 | Transportation infrastructure (km) | -0.021*** | -2.661 |
Per capita water consumption (m3) | -0.156*** | -3.504 | Per capita water consumption (m3) | -0.178*** | -10.005 |
Per ten thousand yuan industry value-added water consumption (m3) | -0.045** | -1.974 | Per ten thousand yuan industry value-added water consumption (m3) | -0.044*** | -4.790 |
Per acre farmland irrigation water consumption (m3) | 0.154*** | 4.359 | Per acre farmland irrigation water consumption (m3) | 0.013 | 0.955 |
Education funds (104 yuan) | -0.140*** | -5.063 | Education funds (104 yuan) | -0.001 | -0.003 |
Industrial water consumption proportion (%) | -0.007 | -0.256 | Industrial water consumption proportion (%) | 0.024*** | 2.199 |
Agricultural water consumption proportion (%) | -0.181*** | -7.496 | Agricultural water consumption proportion (%) | -0.089*** | -9.247 |
Marketization degree (%) | 0.225*** | 4.291 | Marketization degree (%) | 0.060*** | 2.855 |
Total precipitation (108 m) | -0.038** | -2.148 | Total precipitation (108 m) | 0.002 | 0.219 |
Lag foreign direct investment (108 yuan) | -0.359*** | -6.304 | Lag foreign direct investment (108 yuan) | -0.069*** | -3.066 |
Lag GDP per laborer (108 yuan) | -0.269 | -1.548 | Lag GDP per laborer (108 yuan) | -0.266*** | -2.886 |
Lag transportation infrastructure (km) | 0.247*** | 4.013 | Lag transportation infrastructure (km) | 0.030 | 1.222 |
Lag per capita water consumption (m3) | -0.223 | -1.639 | Lag per capita water consumption (m3) | -0.102* | -1.695 |
Lag per ten thousand yuan industry value-added water consumption (m3) | -0.027 | -0.543 | Lag per ten thousand yuan industry value-added water consumption (m3) | 0.046** | 2.346 |
Lag per acre farmland irrigation water consumption (m3) | 0.238 | 1.442 | Lag per acre farmland irrigation water consumption (m3) | 0.179*** | 2.669 |
Lag education funds (104 yuan) | 0.517*** | 10.979 | Lag education funds (104 yuan) | 0.103*** | 5.353 |
Lag industrial water consumption proportion (%) | 0.524*** | 7.102 | Lag industrial water consumption proportion (%) | 0.171*** | 5.563 |
Lag agricultural water Lag consumption proportion (%) | 0.641*** | 8.041 | Lag agricultural water Lag consumption proportion (%) | 0.213*** | 6.701 |
Lag marketization degree (%) | -0.059 | -0.410 | Lag marketization degree (%) | 0.020 | 0.354 |
Lag total precipitation (108 m) | -0.214*** | -2.932 | Lag total precipitation (108 m) | -0.116*** | -3.903 |
ρ | 0.278*** | 3.198 | ρ | 0.507*** | 7.492 |
R2 | 0.994 | R2 | 0.998 | ||
Likelihood ratio | 651.753 | Likelihood ratio | 1076.924 |
Note: *** refers to 1% significant level; ** to 5% significant level; * to 10% significant level. |
Table 5 Total effect, direct effect and indirect effect of explanative variables |
Considering the undesirable output | Without considering the undesirable output | ||||
---|---|---|---|---|---|
Total effects | Regression coefficient | t-Statistic | Total effects | Regression coefficient | t-Statistic |
Foreign direct investment (108 yuan) | -0.475*** | -4.786 | Foreign direct investment (108 yuan) | -0.138** | -2.678 |
GDP per laborer (108 yuan) | 0.243 | 1.042 | GDP per laborer (108 yuan) | 0.848*** | 6.277 |
Transportation infrastructure (km) | 0.312*** | 3.289 | Transportation infrastructure (km) | 0.018 | 0.349 |
Per capita water consumption (m3) | -0.518** | -2.646 | Per capita water consumption (m3) | -0.571*** | -4.713 |
Per ten thousand yuan industry value-added water consumption (m3) | -0.104* | -1.708 | Per ten thousand yuan industry value-added water consumption (m3) | 0.005 | 0.148 |
Per acre farmland irrigation water consumption (m3) | 0.541** | 2.251 | Per acre farmland irrigation water consumption (m3) | 0.391*** | 2.746 |
Education funds (104 yuan) | 0.525*** | 6.006 | Education funds (104 yuan) | 0.212*** | 4.483 |
Industrial water consumption proportion (%) | 0.717*** | 6.389 | Industrial water consumption proportion (%) | 0.400*** | 5.899 |
Agricultural water consumption proportion (%) | 0.641*** | 4.442 | Agricultural water consumption proportion (%) | 0.258*** | 3.472 |
Marketization degree (%) | 0.225 | 1.179 | Marketization degree (%) | 0.165 | 1.461 |
Total precipitation (108 m) | -0.349*** | -3.068 | Total precipitation (108 m) | -0.234*** | -3.506 |
Direct effects | Regression coefficient | t-Statistic | Direct effects | Regression coefficient | t-Statistic |
Foreign direct investment (108 yuan) | 0.012 | 1.178 | Foreign direct investment (108 yuan) | -0.001 | -0.033 |
GDP per laborer (108 yuan) | 0.449*** | 9.07 | GDP per laborer (108 yuan) | 0.688*** | 34.042 |
Transportation infrastructure (km) | -0.018 | -0.908 | Transportation infrastructure (km) | -0.021** | -2.519 |
Per capita water consumption (m3) | -0.159*** | -3.674 | Per capita water consumption (m3) | -0.185*** | -9.806 |
Per ten thousand yuan industry value-added water consumption (m3) | -0.046* | -2.018 | Per ten thousand yuan industry value-added water consumption (m3) | -0.043*** | -4.716 |
Per acre farmland irrigation water consumption (m3) | 0.157*** | 4.382 | Per acre farmland irrigation water consumption (m3) | 0.020 | 1.368 |
Education funds (104 yuan) | -0.133*** | -4.628 | Education funds (104 yuan) | 0.004 | 0.360 |
Industrial water consumption proportion (%) | -0.001 | -0.001 | Industrial water consumption proportion (%) | 0.031** | 2.704 |
Agricultural water consumption proportion (%) | -0.173*** | -6.906 | Agricultural water consumption proportion (%) | -0.082*** | -8.797 |
Marketization degree (%) | 0.225*** | 4.42 | Marketization degree (%) | 0.061*** | 2.913 |
Total precipitation (108 m) | -0.041** | -2.253 | Total precipitation (108 m) | -0.003 | -0.413 |
Indirect effects | Regression coefficient | t-Statistic | Indirect effects | Regression coefficient | t-Statistic |
Foreign direct investment (108 yuan) | -0.487*** | -5.103 | Foreign direct investment (108 yuan) | -0.138*** | -2.795 |
GDP per laborer (108 yuan) | -0.205 | -0.898 | GDP per laborer (108 yuan) | 0.160 | 1.218 |
Transportation infrastructure (km) | 0.33*** | 3.491 | Transportation infrastructure (km) | 0.038 | 0.776 |
Total effects | Regression coefficient | t-Statistic | Total effects | Regression coefficient | t-Statistic |
Per capita water consumption (m3) | -0.359* | -1.925 | Per capita water consumption (m3) | -0.386*** | -3.379 |
Per ten thousand yuan industry value-added water consumption (m3) | -0.058 | -0.885 | Per ten thousand yuan industry value-added water consumption (m3) | 0.048 | 1.287 |
Per acre farmland irrigation water consumption (m3) | 0.385 | 1.654 | Per acre farmland irrigation water consumption (m3) | 0.372** | 2.714 |
Education funds (104 yuan) | 0.658*** | 8.17 | Education funds (104 yuan) | 0.208*** | 4.821 |
Industrial water consumption proportion (%) | 0.717*** | 6.759 | Industrial water consumption proportion (%) | 0.369*** | 5.760 |
Agricultural water consumption proportion (%) | 0.814*** | 5.918 | Agricultural water consumption proportion (%) | 0.340*** | 4.791 |
Marketization degree (%) | -0.001 | -0.005 | Marketization degree (%) | 0.103 | 0.929 |
Total precipitation (108 m) | -0.308*** | -2.851 | Total precipitation (108 m) | -0.231*** | -3.625 |
Note: *** refers to 1% significant level; ** to 5% significant level; * to 10% significant level. |
The authors have declared that no competing interests exist.
1 |
|
2 |
|
3 |
|
4 |
|
5 |
|
6 |
|
7 |
|
8 |
|
9 |
|
10 |
|
11 |
|
12 |
|
13 |
|
14 |
|
15 |
|
16 |
|
17 |
|
18 |
|
19 |
|
20 |
|
21 |
|
22 |
|
23 |
|
24 |
|
25 |
|
26 |
|
27 |
|
28 |
|
29 |
|
30 |
|
31 |
|
32 |
|
33 |
|
34 |
|
35 |
|
/
〈 | 〉 |