Re-Examining Regional Total-Factor Water Efficiency and Its Determinants in China: A Parametric Distance Function Approach
Abstract
:1. Introduction
2. Methods and Materials
2.1. Shephard Water Distance Function
2.2. SFA Estimation Model
2.3. Materials
2.3.1. Variables Selection
- Economic Level (EL). In the literature, economic development level and water use efficiency were found to show a U-shaped relationship. It implies that water efficiency decreases with the economic growth at the low-level stage of economic development, while the opposite situation will appear when the economy grows to a certain degree. This paper adopts per capita GDP (in 2000 constant price) to measure local economic level. Meanwhile, its quadratic form is also taken as an explanatory variable in the model to investigate its nonlinear impact on water efficiency [41].
- Resource Endowment (RE). This paper adopts water resource supply per capital to measure the rich degree of local water resources. Actually, due to the existence of “Resource Curse”, many studies revealed that there is a significantly negative relationship between water efficiency and resource endowment [42].
- Industrial Structure (IS). Agricultural and industrial sectors are the largest water consumers in China. Thus, industrial transformation and upgrading is significant for improving water use efficiency [57]. This paper takes the proportion of value added of the tertiary industry to the total output as the measurement of local industrial structure.
- Import and Export Trade (IET). The import and export demand of products with high water consumption and high pollutant emission would have effects of substitution and promotion on China’s relevant industries, respectively [37]. As such, it may affect the water efficiency to a certain extent. This paper adopts the ratio of imports to exports as the measurement of import and export trade.
- Environmental Regulation (ER). This can help regulate the utilization behavior of water users, restrict the discharge of wastewater and stimulate technological innovation to reduce water use intensity [58]. According to Shen and Liu [59], we use the following indicator to measure local environmental regulation intensity:
- Urbanization Level (UL). The city is a symbol of modern civilization. It has better water supply facilities, water reuse technology and sewage treatment system, which can help improve local water use efficiency [60,61]. This paper adopts the share of urban population as the measurement of local urbanization level.
2.3.2. Data Sources
3. Results and Discussion
3.1. TFWE Estimates
3.2. Spatial-Temporal Evolution
3.3. Influencing Factors
4. Conclusions and Policy Implications
- Due to the large regional differences in economic development level, water resources endowment, and water efficiency performance, the “one size fits all” solution should be avoided and differentiated regional water-saving strategies should therefore be formulated. For example, in terms of the province-level decomposition of national water-saving targets, an alternative scheme could be designed by assigning higher reducing targets to provinces with lower water efficiency scores, while assigning lower targets to those with higher scores.
- In view of the existence of regional differences in location, transport, natural resources and ecological environment conditions, industrial structure exhibiting a positive impact on water efficiency improvement should be adjusted to suit measures to local conditions and give a full play to the superiority. Specifically, Eastern China should further strengthen the growth of the high-tech tertiary industry and orderly guide transfer industry to give a full play of its technology spillover effect. Middle and Central China should vigorously develop ecological agriculture and tourist industry by right of its geographical position and natural resources endowment. Meanwhile, based on resources and environment carrying capacity, local government should actively undertake transferring industries to promote the scientific upgrading of industrial structure.
- Environmental regulation should be implemented on the principle of “common but differentiated responsibilities”. Eastern China should strengthen environmental technological innovation to develop more efficient water saving and sewage treatment technologies. Besides, the government should formulate quantifiable wastewater reduction targets and further set up penalty mechanism to punish enterprises that are not qualified. Central and Western China should eliminate backward production capacity and encourage local enterprises to introduce advanced wastewater treatment technologies. In this regard, proper subsidies could be granted to enterprises which adopt environmental protection technologies and implement active measures of protection.
- As the shortage of water resources per capita is serious, China should reduce the production of water-intensive products and services but turn to international imports from water rich countries, which is a feasible way to alleviate domestic water pressure. In urban water use, urbanization should be continuously accelerated to exert its scale benefit on reducing the cost of public water facilities and popularizing water-saving and pollution-control technologies. Additionally, the government should establish the reasonable water fee system, enhance the water saving propaganda, and promote the application of water saving instruments to regulate household water use behavior.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Category | Variable | Unit |
---|---|---|
Input | Capital Stock | 100 million Yuan |
Labor Employment | 10 thousand persons | |
Water Use | 100 million m3 | |
Desirable Output | GDP | 100 million Yuan |
Undesirable Output | Wastewater Discharge | 10 thousand tons |
Influencing Factor | Economic Level | 10 thousand Yuan/person |
Resource Endowment | 10 thousand m3/person | |
Industrial Structure | - | |
Import and Export Trade | - | |
Environmental Regulation | - | |
Urbanization Level | - |
Variable | N | Min. | Max. | Mean | Std. Dev. |
---|---|---|---|---|---|
K | 480 | 174.40 | 45,455.92 | 6633.52 | 7492.79 |
L | 480 | 275.50 | 6760.40 | 2482.84 | 1661.77 |
W | 480 | 19.18 | 591.30 | 193.08 | 135.66 |
Y | 480 | 11131 | 911,523 | 189,795 | 153,880 |
B | 480 | 263.59 | 14,916.39 | 4097.13 | 3137.91 |
EL | 480 | 0.2601 | 3.0230 | 0.9752 | 0.5411 |
RE | 480 | 0.0159 | 0.2709 | 0.0517 | 0.0438 |
IS | 480 | 0.2830 | 0.7965 | 0.4141 | 0.0788 |
IET | 480 | 0.1242 | 5.8438 | 0.9717 | 0.8481 |
ER | 480 | 0.0014 | 0.0852 | 0.0125 | 0.0115 |
UL | 480 | 0.2320 | 0.8960 | 0.4885 | 0.1559 |
Province | Abbreviation | Area | Min. | Max. | Median | Mean | Std. Dev. | Rank |
---|---|---|---|---|---|---|---|---|
Beijing | BEJ | E | 0.797 | 0.999 | 0.885 | 0.884 | 0.057 | 5 |
Tianjin | TIJ | E | 0.702 | 0.966 | 0.814 | 0.828 | 0.090 | 9 |
Hebei | HEB | E | 0.620 | 0.698 | 0.654 | 0.657 | 0.023 | 16 |
Liaoning | LIN | E | 0.582 | 0.751 | 0.719 | 0.704 | 0.044 | 14 |
Shanghai | SHH | E | 0.811 | 0.995 | 0.885 | 0.893 | 0.049 | 3 |
Jiangsu | JIS | E | 0.407 | 0.527 | 0.463 | 0.460 | 0.039 | 23 |
Zhejiang | ZHJ | E | 0.680 | 0.880 | 0.739 | 0.769 | 0.074 | 11 |
Fujian | FUJ | E | 0.520 | 0.716 | 0.567 | 0.573 | 0.042 | 18 |
Shandong | SHD | E | 0.879 | 0.997 | 0.916 | 0.933 | 0.041 | 1 |
Guangdong | GUD | E | 0.643 | 0.994 | 0.827 | 0.810 | 0.118 | 10 |
Hainan | HAN | E | 0.521 | 0.673 | 0.599 | 0.597 | 0.052 | 17 |
Shanxi | SHX | C | 0.758 | 0.998 | 0.943 | 0.907 | 0.080 | 2 |
Jilin | JIL | C | 0.360 | 0.563 | 0.469 | 0.460 | 0.066 | 22 |
Heilongjiang | HLJ | C | 0.198 | 0.318 | 0.253 | 0.252 | 0.041 | 28 |
Anhui | ANH | C | 0.462 | 0.679 | 0.524 | 0.547 | 0.068 | 19 |
Jiangxi | JIX | C | 0.328 | 0.421 | 0.369 | 0.370 | 0.027 | 26 |
Henan | HEN | C | 0.808 | 0.999 | 0.854 | 0.885 | 0.066 | 4 |
Hubei | HUB | C | 0.457 | 0.545 | 0.491 | 0.492 | 0.025 | 21 |
Hunan | HUN | C | 0.371 | 0.448 | 0.425 | 0.413 | 0.027 | 24 |
Neimenggu | NMG | W | 0.189 | 0.282 | 0.225 | 0.230 | 0.029 | 29 |
Guangxi | GUX | W | 0.246 | 0.453 | 0.322 | 0.337 | 0.067 | 27 |
Chongqing | CHQ | W | 0.741 | 0.996 | 0.875 | 0.884 | 0.088 | 6 |
Sichuan | SIC | W | 0.594 | 0.735 | 0.684 | 0.671 | 0.038 | 15 |
Guizhou | GUZ | W | 0.642 | 0.915 | 0.766 | 0.768 | 0.083 | 12 |
Yunnan | YUN | W | 0.646 | 0.814 | 0.725 | 0.723 | 0.056 | 13 |
Shaanxi | SAX | W | 0.707 | 0.976 | 0.836 | 0.844 | 0.085 | 8 |
Gansu | GAS | W | 0.434 | 0.647 | 0.508 | 0.516 | 0.067 | 20 |
Qinghai | QIH | W | 0.690 | 0.994 | 0.859 | 0.856 | 0.086 | 7 |
Ningxia | NIX | W | 0.245 | 0.473 | 0.404 | 0.382 | 0.070 | 25 |
Xinjiang | XIJ | W | 0.070 | 0.080 | 0.073 | 0.074 | 0.003 | 30 |
Eastern China | E | - | 0.700 | 0.793 | 0.731 | 0.737 | 0.027 | - |
Central China | C | - | 0.490 | 0.601 | 0.545 | 0.541 | 0.034 | - |
Western China | W | - | 0.525 | 0.610 | 0.575 | 0.571 | 0.027 | - |
China | - | - | 0.603 | 0.646 | 0.624 | 0.624 | 0.014 | - |
Model | DEA | SFA |
---|---|---|
SFA | 0.640 ** (0.617 **) | 1 (1) |
DEA | 1 (1) | 0.640 ** (0.617 **) |
Variable | Coefficient | Std. Dev. | t-Ratio |
---|---|---|---|
C | –4.3159 *** | 0.0508 | 9.05 |
–0.3245 | 0.0400 | –1.21 | |
–0.3082 *** | 0.0254 | 12.15 | |
–0.0215 *** | 0.0080 | 2.68 | |
–0.0199 *** | 0.0065 | 3.08 | |
0.1288 ** | 0.0084 | 2.36 | |
–0.0732 ** | 0.0289 | 2.53 | |
–0.0708 * | 0.0434 | 1.63 | |
0.0535 *** | 0.0090 | 13.66 | |
0.7498 *** | 0.0801 | 8.07 | |
log likelihood function | 657.28 | - | - |
LR test of the one-sided error | 1922.96 *** | - | - |
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Zheng, J.; Zhang, H.; Xing, Z. Re-Examining Regional Total-Factor Water Efficiency and Its Determinants in China: A Parametric Distance Function Approach. Water 2018, 10, 1286. https://doi.org/10.3390/w10101286
Zheng J, Zhang H, Xing Z. Re-Examining Regional Total-Factor Water Efficiency and Its Determinants in China: A Parametric Distance Function Approach. Water. 2018; 10(10):1286. https://doi.org/10.3390/w10101286
Chicago/Turabian StyleZheng, Jiao, Hengquan Zhang, and Zhencheng Xing. 2018. "Re-Examining Regional Total-Factor Water Efficiency and Its Determinants in China: A Parametric Distance Function Approach" Water 10, no. 10: 1286. https://doi.org/10.3390/w10101286