4.1. Static Water Use Efficiency Analysis
We applied a nondirected Super-SBM-based measurement to analyse the water use efficiency of 34 prefecture-level cities across three eastern provinces between 2003 and 2020 by calculating annual figures for each region. As depicted in
Table 2, out of 612 static efficiency values, 248 values greater than or equal to 1 constituted 40.52% of the sample. These valid measurements provide valuable data for subsequent research.
Through a rigorous study, we obtained a robust understanding of static water use efficiency across the three eastern provinces and calculated the yearly mean static water efficiency for 34 prefecture-level cities [
28,
29] (refer to
Table 3). Among the assessed cities, Panjin exhibited the maximum static water utilization efficiency, in stark contrast to Qiqihar, which demonstrated the lowest efficiency. We established 1 and 0.6 as benchmarks for demarcating high, medium, and low efficiency levels. Broadly, the three northeastern provinces demonstrate commendable water use efficiency, with 91.18% of the cities showcasing either medium or high efficiency levels. Nonetheless, certain areas, including Jilin, Harbin, and Qiqihar, display water utilization efficiencies lower than 0.6, indicating a pressing need for improvements in their water utilization efficiencies.
According to the spatial distribution (
Figure 6), cities utilizing water resources efficiently are scattered geographically, potentially hindering the maximum exploitation of economies of scale. Regions at interprovincial borders, such as Daqing, Liaoyuan, and Baishan, are often home to cities with comparatively high water use efficiency. The northern region exhibits a diverse blend of areas with both high and low water use efficiency, displaying more pronounced regional disparities compared to its southern counterpart. Nonetheless, the southern region demonstrates a relative balance in overall water use efficiency, with a mean efficiency ranging from 0.6 to 1.
Table 2.
Number of 34 prefecture-level cities in the three northeastern provinces with a static efficiency value of water resource utilization efficiency ≥ 1 from 2003 to 2020.
Table 2.
Number of 34 prefecture-level cities in the three northeastern provinces with a static efficiency value of water resource utilization efficiency ≥ 1 from 2003 to 2020.
Year (years) |
2003 |
2004 |
2005 |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
Total |
Number of valid values (in pieces) |
16 |
16 |
11 |
10 |
3 |
6 |
22 |
13 |
12 |
248 |
Year (years) |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
Number of valid values (in pieces) |
13 |
12 |
14 |
18 |
17 |
14 |
18 |
17 |
16 |
Table 3.
The average value of the water resource utilization rate in 34 prefecture-level cities in the three northeastern provinces.
Table 3.
The average value of the water resource utilization rate in 34 prefecture-level cities in the three northeastern provinces.
Region |
Mean |
Ranking |
Region |
Mean |
Ranking |
Panjin |
1.13 |
1 |
Fuxin |
0.77 |
18 |
Heihe |
1.11 |
2 |
Hegang |
0.76 |
19 |
Daqing |
1.09 |
3 |
Suihua |
0.74 |
20 |
Liaoyuan |
1.02 |
4 |
Jiamusi |
0.72 |
21 |
Baishan |
1.02 |
5 |
Jixi |
0.71 |
22 |
Yichun |
0.98 |
6 |
FUshun |
0.70 |
23 |
Baicheng |
0.95 |
7 |
Shenyang |
0.69 |
24 |
Songyuan |
0.92 |
8 |
Dandong |
0.69 |
25 |
Chaoyang |
0.90 |
9 |
Anshan |
0.69 |
26 |
Tonghua |
0.85 |
10 |
Yingkou |
0.68 |
27 |
Dalian |
0.83 |
11 |
Jinzhou |
0.66 |
28 |
Tieling |
0.83 |
12 |
Huludao |
0.63 |
29 |
Benxi |
0.82 |
13 |
Mudanjiang |
0.63 |
30 |
Shuangyashan |
0.82 |
14 |
Changchun |
0.61 |
31 |
Siping |
0.81 |
15 |
Jilin |
0.54 |
32 |
Qitaihe |
0.79 |
16 |
Harbin |
0.52 |
33 |
Liaoyang |
0.77 |
17 |
Qiqihar |
0.44 |
34 |
Figure 6.
Spatial distribution map of the mean value of water resource utilization in 34 prefecture-level cities in the three northeastern provinces from 2003 to 2020.
Figure 6.
Spatial distribution map of the mean value of water resource utilization in 34 prefecture-level cities in the three northeastern provinces from 2003 to 2020.
4.2. Dynamic Water Use Efficiency Analysis
The Super-SBM model fails to represent temporal variations in water use efficiency [
30]. We employed DEAP 2.1 software to assess the dynamic water use efficiency in the three eastern provinces from 2003 to 2020, leveraging the chosen evaluation index system (refer to
Table 4 for more details). Of the 578 calculated dynamic water efficiency scores, 333 exceeded 1, constituting 57.61% of the total sample size. This suggests that nearly half of the cities in the three eastern provinces have enhanced their water use efficiency compared to the preceding year, suggesting a steady improvement in the water utilization capacity of these prefecture-level cities.
We arrange and deconstruct the time series pertaining to the efficiency of dynamic water resources[
31], as illustrated in
Figure 7. The following content presents the results of this analysis:
(1) In general, the average total factor productivity in the three northeastern provinces is 1.012, with an annual growth rate of 1.2%. The consistency between dynamic water resource utilization efficiency and the technological progress efficiency index is strong. To improve the water resource utilization efficiency in northeastern cities, it is necessary to introduce technology, increase research investment, and promote technological progress.
(2) From a temporal perspective, the efficiency of water resource utilization in 2011 increased by 13.6% compared to that in 2010, reaching the highest level in history. In contrast, in 2016, the water resource efficiency decreased by the largest margin of 16.3% compared to that in 2015. The analysis reveals that this decline is primarily attributed to a significant decrease in the index of technological progress efficiency. The impact of technological progress should not be underestimated, as it cannot offset the effects of technological decline. Therefore, emphasis should be placed on the development and advancement of technology.
(3) According to the indicators in
Table 5, the average technical efficiency (EFFCH) for the period of 2003–2020 was 0.998, showing a slight decrease. However, the technological progress rate (TECH) was 1.014, indicating continuous improvement in technology. The technical efficiency index (EFFC) can be decomposed into a pure efficiency change index and a scale efficiency change index (SEC). According to the decomposition index graph shown in
Figure 7(b), the scale efficiency index is more consistent with the technical efficiency index. Therefore, it is worth considering attracting related industries to settle in and enhancing scale effects.
(4) According to the regional analysis, the overall dynamic water resource efficiency of the prefecture-level cities in the three provinces in the eastern region of China is relatively good. Approximately 79.41% of the cities have a water resource efficiency greater than 1. Among the 34 prefecture-level cities, Shenyang city has the highest dynamic water resource efficiency, reaching 1.085, which is 0.04 higher than that of the second-ranked city. The lowest efficiency is observed in Qiqihar city, where 64.71% of the cities surpass the prefecture-level city average of 1.012 in terms of dynamic water resource utilization.
Table 4.
Analysis of the dynamic water resource utilization rate in the three northeastern provinces from 2003 to 2020.
Table 4.
Analysis of the dynamic water resource utilization rate in the three northeastern provinces from 2003 to 2020.
Year(Year) |
2003-2004 |
2004-2005 |
2005-2006 |
2006-2007 |
2007-2008 |
2008-2009 |
2009-2010 |
2010-2011 |
2011-2012 |
Number of effective efficiency values (pcs) |
24 |
23 |
22 |
22 |
17 |
18 |
29 |
29 |
24 |
Year(Year) |
2012-2013 |
2013-2014 |
2014-2015 |
2015-2016 |
2016-2017 |
2017-2018 |
2018-2019 |
2019-2020 |
Total |
Number of effective efficiency values (pcs) |
17 |
19 |
22 |
7 |
16 |
21 |
16 |
7 |
333 |
Table 5.
List of Malmquist Index decomposition items for prefecture-level cities in the three northeastern provinces.
Table 5.
List of Malmquist Index decomposition items for prefecture-level cities in the three northeastern provinces.
Year |
EFFCH |
TECH |
PECH |
SEC |
M index |
2003-2004 |
0.953 |
1.089 |
1.001 |
0.953 |
1.038 |
2004-2005 |
0.98 |
1.062 |
0.976 |
1.004 |
1.04 |
2005-2006 |
1.019 |
1.006 |
1.02 |
1 |
1.025 |
2006-2007 |
0.766 |
1.371 |
0.957 |
0.8 |
1.05 |
2007-2008 |
1.262 |
0.777 |
0.997 |
1.266 |
0.981 |
2008-2009 |
1.025 |
0.963 |
1.045 |
0.981 |
0.987 |
2009-2010 |
0.953 |
1.123 |
0.983 |
0.969 |
1.07 |
2010-2011 |
0.951 |
1.194 |
0.997 |
0.954 |
1.136 |
2011-2012 |
0.942 |
1.099 |
0.997 |
0.945 |
1.035 |
2012-2013 |
1.012 |
0.974 |
1.008 |
1.004 |
0.985 |
2013-2014 |
1.04 |
1.013 |
0.961 |
1.082 |
1.054 |
2014-2015 |
0.988 |
1.087 |
0.977 |
1.011 |
1.074 |
2015-2016 |
1.13 |
0.74 |
1.052 |
1.074 |
0.837 |
2016-2017 |
1.018 |
0.964 |
0.999 |
1.019 |
0.981 |
2017-2018 |
1.034 |
1.002 |
0.992 |
1.043 |
1.037 |
2018-2019 |
0.988 |
1.016 |
0.985 |
1.003 |
1.004 |
2019-2020 |
0.986 |
0.923 |
0.99 |
0.996 |
0.911 |
mean |
0.998 |
1.014 |
0.996 |
1.002 |
1.012 |
Figure 7.
Malmquist index and its decomposition for the three northeastern provinces.
Figure 7.
Malmquist index and its decomposition for the three northeastern provinces.
Based on the average values of pure technical efficiency and scale efficiency of 34 prefecture-level cities, the 34 prefecture-level cities in the three northeastern provinces were divided into four regions (
Figure 8). Furthermore, targeted recommendations were proposed based on the characteristics of each region.
In Zone I, both technical efficiency and scale efficiency are greater than the average technical efficiency of prefecture-level cities as a whole. Water resource utilization is advantageous in the three provinces of the east, including 14 cities such as Shenyang, Liaoyang, and Heihe. These prefecture-level cities have good conditions and relatively high levels of water resource utilization. It is necessary for them to maintain their advantages, keep pace with the pace of technological iteration and updates, and ensure that their advantages continue to be exerted.
In Zone II, the scale efficiency is greater than the average level of 34 prefecture-level cities, but the technical efficiency is lower than the provincial average. It includes four cities: Anshan, Changchun, Fushun, and Yichun. For these cities, the key to improving water resource utilization lies in enhancing their own technological strength and improving their operational and management systems.
In Zone III, both technical efficiency and scale efficiency are relatively low. Compared with other cities, they are at a relatively backwards level. The five cities included Daqing, Jiamusi, Mudanjiang, Harbin, and Qiqihar. It is difficult for them to catch up with the preceding cities, so they need to reflect on whether their water resource planning and municipal expenditures are reasonable and formulate a reasonable development model. At the same time, they should also pay attention to improving their own technology, learn from other cities or advanced cities in terms of water resource utilization in China, and adopt new technologies to promote the improvement of water resource utilization.
In Zone IV, the technical efficiency is relatively high, but the scale efficiency is lower than the average level of the three northeastern provinces. This means that these cities may have problems with water supply and drainage in some areas. It is recommended to consider increasing investment in these areas, promoting regional rectification, rationalizing pricing and financial support, and improving the scale of water resource utilization.
Figure 8.
The 34 prefecture-level cities in the three northeastern provinces are divided into four categories based on PTE and SE.
Figure 8.
The 34 prefecture-level cities in the three northeastern provinces are divided into four categories based on PTE and SE.
4.3. Spatial Autocorrelation Analysis of Water Resource Utilization Efficiency
The spatial clustering of static water resource efficiency in the Northeast Region was explored using Geoda software (
Table 5). Overall, the static water resource efficiency in the three provinces of the Northeast Region did not exhibit significant spatial clustering every year. This study examined the spatial significance of the water resource utilization rates in the three provinces from 2003 to 2020 (Table 6) and revealed that the significance of recent spatial clustering decreased compared to that in previous years.
At a confidence level of 95%, the local spatial autocorrelation of the three provinces in the Northeast Region was observed, and the changes in spatial clustering were examined at 5-year intervals. It was found that there was an improvement in the spatial clustering of water resource utilization, transitioning from low-low clustering to high-low clustering and high-high clustering areas. This indicates that the scale effect of cities is gradually taking effect. Some cities have recognized the importance of water resource utilization, continuously improving water resource utilization rates, and generating regional effects that drive the improvement of water resource utilization efficiency in surrounding areas.
Table 5.
List of significant years in the global Moran index of the three northeastern provinces.
Table 5.
List of significant years in the global Moran index of the three northeastern provinces.
Years Index
|
2004 |
2007 |
2011 |
Moran's I |
0.223 |
0.196 |
0.153 |
Z Score |
2.161 |
2.074 |
1.584 |
P Value |
0.027** |
0.037** |
0.069* |
Note: * Distinguish significance, * * * P<0.001, indicating very significant ** P<0.05 is more significant, * P<0.1 is more significant |
Figure 9.
Local spatial autocorrelation analysis of the three northeastern provinces in 2005 (A), 2010 (B), 2015 (C), and 2020 (D).
Figure 9.
Local spatial autocorrelation analysis of the three northeastern provinces in 2005 (A), 2010 (B), 2015 (C), and 2020 (D).