Evaluation of water resources carrying capacity in China based on entropy weight TOPSIS model

Abstract

Water resources carrying capacity refers to the ability of the water resources ecosystem to continuously carry the coordination relationship between human society and economy in the normal development process of a country or a region. Its self-sustaining ability, self-regulation and self-development potential often hinder sustainable development in water shortage areas. Research on water resources’ carrying capacity is a meaningful way to support regional water resources security and realize harmonious development of society, economy, and ecological environment. Correct assessment of water carrying capacity and response to government policies will contribute to improved water use and sustainable economic and social development. This study first sorts out the relevant questionnaires of water resources carrying capacity level evaluation, proposes the evaluation indicators of water resources carrying capacity level, collects and standardizes the required data, and calculates the weight of each evaluation index by entropy weight method. Then, it calculates the comprehensive evaluation value of China’s water resources carrying capacity from 2012 to 2022 in the TOPSIS model. The results show that the total afforestation area, total investment in environmental pollution control, and total industrial wastewater discharge are the third most important factors in improving the carrying capacity of water resources. From 2003 to 2010, China’s water resources carrying capacity improved year by year. From 2011 to 2021, China’s water resources carrying capacity remained stable year by year. The continuous adjustment of China’s industrial structure and strengthening environmental pollution control are inevitable measures to improve the carrying capacity of China’s water resources. This study provides a scientific basis for exploring the changing trend of China’s water resources carrying capacity and formulating reasonable optimal allocation of water resources. It also has great significance for promoting China’s water resources’ carrying capacity and sustainable development of the social economy and ecological environment.

Keywords

Entropy weight TOPSIS water resources of China water resources carrying capacity evaluation study

1. Introduction

With the development and progress of society, human demand for ecological resources is also further expanding. In this process, problems such as environmental pollution and resource scarcity have inevitably emerged, which in turn have caused certain obstacles to the development of social science and technology. With the development of society and the deepening of research, the importance of water resources has gradually attracted the attention of scholars among many ecological resources. With the advancement of urbanization, the scale and intensity of social utilization of various resources are also increasing, and regional urbanization development is faced with the limitation of resource “threshold”, and the growing demand for urbanization construction has produced a contradiction with the limited resource constraint. As an irreplaceable strategic resource, water resources can realize different forms of recycling and transformation, but the continuous development, utilization, waste and pollution behaviour of human beings have gradually reduced the number of available water resources, and the constraining effect of water resources shortage on urbanization development is pronounced. The unreasonable development and utilization of water resources and the lack of scientific protection of water resources will eventually sound the alarm on water resources, limit human production and life, and hinder economic development and social progress. Water resources are dynamic water resources produced from local precipitation for various uses of human production and life, which exist in rivers, lakes and underground aquifers and can be renewed year by year, including surface water and groundwater. China is a large country with abundant water resources, but due to its large population, the per capita water resources are only 1/4 of the world average. Moreover, due to the influence of the monsoon climate, the temporal and spatial distribution of water resources is unreasonable, making the water resource shortage in some areas of China more serious.

Due to the destruction of sewage discharge and forest vegetation, the balance between human society and water resources is broken, and flood disasters occur frequently, which brings certain economic losses to society. To balance ecological protection and economic development, more and more scholars have begun to use various indicators and methods to conduct in-depth research on water resources, and water resource carrying capacity is one of the most important symbols. Water resource shortage has become one of the important problems restricting the construction of ecological civilization and sustainable development in China due to the unbalanced distribution of water resources in time and space, the small number of available water resources per capita and the sharp structural contradiction between supply and demand. It has become the focus and hot spot of Chinese government departments and academia to conduct in-depth research on water resources, improve the utilization rate, and resolve the intensifying contradiction between supply and demand of water resources. Water resources carrying capacity, water resources management, water resources utilization and water resources allocation are important components of China’s water resources. Water resources carrying capacity refers to the ability of the water resources ecosystem to continuously carry the social and economic coordination between people and the autonomous ability, self-regulation and self-development potential required for the normal development of a country or a region. Research on water resources carrying capacity is an important way to support regional water resources security and realize harmonious development of society, economy, and ecological environment. A reasonable assessment of the carrying capacity of water resources, response to the state’s call, and implementation of government policies have a positive role in the sustainable development and utilization of China’s water resources and social and economic development.

2. State of art

Water resources play a vital role in population growth, economic development and ecological environment and are decisive factors for the sustainable development of a region. However, to a large extent, traditional water resources management cannot avoid the emergence of problems such as rapid population growth, urban ageing, climate change and social and economic constraints. It inhibits the achievement of the United Nations’ sustainable urban water services goal. Therefore, water resources carrying capacity has gradually become a very important research topic of global concern. Over the years, scholars at home and abroad have studied water resources carrying capacity through different evaluation models. As for the research on water resources carrying capacity, Song et al. [1] mainly put forward the concept of carrying capacity, taking Tianjin as an example and selecting Tianjin’s population size and economic scale as the main indexes. The results showed that Tianjin’s current water resource utilization efficiency was low, and the dynamic trend would be partly rational after implementing water resource protection policies in 2010 and 2020. Ait-Aoudia and Berezowska-Azzag [2] evaluated the water resource carrying capacity of Algiers, the capital of Algeria. The results showed that in a country with limited freshwater resources, consideration of water demand and supply is crucial to reducing water supply vulnerability. Mei [3] systematically reviewed the development history of water resources carrying capacity. She believed that future studies on water resource carrying capacity should be combined with studies on optional allocation and ecological water demand to strengthen studies on representative regions. Yang et al. [4] combined the analytical hierarchy process (AHP) and system dynamics (SD) to build a multi-index evaluation system and socioeconomic/water resources SD model of Xi’an’s water resources carrying capacity. The results showed that Xi’an’s water resources carrying capacity would change from “normal” to “poor”, and suggested some practical suggestions.

According to the research of Zhao et al. [5], China’s urban public water consumption intensity, agricultural water consumption intensity, population density, groundwater resources per unit area, industrial wastewater discharge, water-saving investment intensity and soil erosion control had significant impacts on the carrying capacity of water resources. Ren et al. [6] selected appropriate indexes for water resources, society, economy, ecology and other aspects of Guiyang City. They established a comprehensive evaluation index system of Guiyang water resources carrying capacity. The results showed that the comprehensive evaluation value of Guiyang water resources carrying capacity increased by nearly 53% from 2009 to 2018. It showed that water resources can maintain the rapid development of the social economy and still have certain development potential. Liu et al. [7] calculated a comprehensive evaluation index of water resources carrying capacity based on the annual data available in various provinces in China, and it was obvious that China’s water resources carrying capacity did not match population distribution and economic status. Ren et al. [8] established an evaluation index system that comprehensively considered the characteristics of regional water resources, social and economic systems, and the principles of sustainable development and suggested improving regional water resources carrying capacity. Yang et al. [9] proposed an improved evaluation method of regional water resources carrying capacity based on a system dynamics model. Peng et al. [10] believed that the main causes of water resource shortage in karst areas were underdeveloped karst landforms, poor availability of water resources, and difficult utilization. The cloud model was adopted to represent index weights and to carry out comprehensive evaluation calculations, fully considering the randomness and fuzziness of evaluation objects. The results showed that population density, urbanization rate and per capita water consumption were important driving factors affecting the carrying capacity of water resources. Wang et al. [11] adopted a system dynamics model to establish a dynamic feedback system of water resources carrying capacity and simulated five typical scenarios in Guangzhou from 2021 to 2030. The results showed that the water environment protection scenario and water saving scenario significantly improved the carrying capacity of water resources and promoted its coordinated development. The integrated scenario was the best in these two aspects. Wang et al. [12] proposed an improved evaluation method of water resources carrying capacity by combining the improved fuzzy comprehensive evaluation with the system dynamics model. The results showed that if the current development model of Changchun continued to be adopted, the water resources carrying capacity would continue to decline, and the water resources carrying capacity could be improved by changing the production mode of the national economy and adjusting the economic proportion. Dou et al. [13] found that in 2010, the carrying level of water resources in Henan Province was in a moderate load state, and the factors restricting the development of WRCC included available water resources, GDP growth rate, urbanization rate, irrigation water utilization coefficient, industrial water recycling rate and wastewater reuse rate. Wang et al. [14] analyzed the current situation and carrying capacity of water resources in Wuhan urban agglomeration. The results showed that Wuhan was experiencing severe water shortage and overload. Wang et al. [15] proposed a multi-scale eco-carrying capacity index, which compared the traditional ecological footprint with the water footprint generated to correct the former. He et al. [16] combined with evaluating agricultural water resources carrying capacity (AWRCC), established a model for optimizing breeding structure and regulating water quantity and quality. The results showed that the monthly ammonia nitrogen and chemical oxygen demand concentrations met the water quality requirements of less than 1 mg/L and less than 20 mg/L, respectively.

At the same time, the economic benefits and the AWRCC were improved. Zhang and Dong [17] showed that the water resources carrying capacity index of 11 cities showed an upward trend from 2011 to 2018. Still, most cities’ water resources carrying capacity level was in the critical bearing or slightly overloaded state. Zhou [18] took Henan Province as an example to evaluate the regional water resources carrying capacity. Practice proved that the comprehensive evaluation method adopted took fuzziness and randomness into account in the evaluation process, and efficient and intuitive results could be obtained through evaluation. Wang et al. [19] established a system dynamic model for the carrying capacity of wetland water resources in Beijing, and the results showed that the water environment of Beijing could not support the long-term economic and social development of Beijing. It can be seen from the existing research literature that the selection of evaluation methods and evaluation indicators of water resources carrying capacity lacks unified standards. Different evaluation methods have defects, and the selection of indicators also has subjective problems. The research methods and the choice of indicators in various regions have not established a complete and unified measurement standard, so subjective and objective evaluation results are inevitably different. Therefore, this study reviews a large number of literature to sort out applicable methods, combined with the study of different regional conditions in China, focusing on water resources-related indicators, and puts forward the calculation process of the water resources carrying capacity evaluation model. The data are obtained through the statistical yearbook, statistical bulletin and official website published by relevant departments in the study area and the field investigation, and the evaluation system is constructed. SPSS26 and Matlab 2022b software determine the index weight using the entropy weight method. The TOPSIS method divides the time dimension of water resources carrying capacity annually, and relevant countermeasures and suggestions are put forward.

3. Modeling and data

3.1 Modeling

This study uses the entropy weight TOPSIS method to measure China’s water resources carrying capacity, based on the entropy weight method to modify the TOPSIS model. The principle is to use the entropy weight method to make the index of each subsystem dimensionless (standardized). The entropy weight method determines the weight of an index according to the amount of information contained in each index. A small index entropy indicates that the index value changes greatly, the information available is large, and the index weight should be large for comprehensive evaluation. The entropy weight method has the characteristics of simple calculation steps, efficient use of index data, and elimination of the influence of subjective factors. Although the traditional entropy weight method can well judge the difference between indicators, it cannot reflect the actual situation and the ideal level when evaluating the same evaluation object. Then, the entropy weight of each evaluation index to the overall index system is calculated.

On this basis, the TOPSIS method is used to analyze and compare the relative proximity between each measurement index and the optimal scheme, and at last, the level of China’s water resources carrying capacity in each province is ranked. TOPSIS is called the “sorting method of approximating ideal solution”, a sort method of approximating ideal solution.

In this study, combining the entropy weight and TOPSIS methods can make the empirical results conform to the real situation. On the other hand, it can also avoid the shortcomings of the analytic hierarchy process, traditional TOPSIS, fuzzy comprehensive evaluation method, etc., which completely rely on the subjective consciousness of experts to determine the weight. It is also conducive to systematically analyzing the gap between the current and ideal state of water resources carrying capacity. It can comprehensively and objectively reflect the dynamic trend of regional water resource carrying capacity.

The specific calculation steps of the entropy weight TOPSIS method are as follows:

Firstly, to eliminate the differences between different measurement indexes and orders of magnitude in the index system and ensure the comparability of each measurement index, the range method is first used to conduct dimensionless (standardized) processing on the original measurement index data collected. The formula is as follows.

For positive indexes, Eq. (1) is adopted.

$\displaystyle R_{ij}=\frac{r_{ij}-\min({r_{ij}})}{\max({r_{ij}})-\min({r_{ij}})}$ (1)

For negative indexes, Eq. (2) is adopted.

$\displaystyle R_{ij}=\frac{\max({r_{ij}})-r_{ij}}{\max({r_{ij}})-\min({r_{ij}})}$ (2)

In Eqs (1) and (2), $i$ represents the province and $j$ represents the measure index. $r_{ij}$ and $R_{ij}$ represent the original and standardized horizontal measurement index values of China’s water resources carrying capacity, respectively. $\max({r_{ij}})$ and $\min({r_{ij}})$ represent the maximum and minimum values of each measurement index $r_{ij}$ , respectively. Then, the characteristic proportion or contribution $P_{ij}$ of the $i$ province under the $j^{\text{th}}$ index is calculated, as shown in Eq. (3).

$\displaystyle P_{ij}=\frac{R_{ij}}{\sum\limits_{i=1}^{n}{r_{ij}}}$ (3)

In Eq. (3), $i=1,2,\ldots,n$ , $j=1,2,\ldots,m$ .

The third step is to calculate the information entropy $E_{j}$ of the $j^{\text{th}}$ index of China’s water resources carrying capacity.

$\displaystyle E_{j}=-K\sum\limits_{i=1}^{n}P_{ij}\text{ln}(P_{ij})$ (4)

Among them, $K=\frac{1}{\ln n},0\leqslant E_{j}\leqslant 1$ . Then, continue to calculate the difference coefficient $d_{j}$ , as shown in Eq. (5).

$\displaystyle d_{j}=1-E_{j}$ (5)

Then, the weights of each measurement index are determined, as shown in Eq. (6).

$\displaystyle W_{j}=\frac{d_{j}}{\sum\limits_{j=1}^{m}{d_{j}}}$ (6)

Then, the weighted decision matrix $A$ of each measure index is constructed.

$\displaystyle A=({a_{ij}})_{n\times m}$ (7)

Among them, $a_{ij}=W_{j}\times R_{ij}$ .

According to the weighted decision moment $A$ matrix, the optimal scheme $Z_{j}^{+}$ and the worst scheme $Z_{j}^{-}$ are determined.

$\displaystyle Z_{j}^{+}=({Z_{1}^{+},Z_{2}^{+},Z_{3}^{+},\ldots Z_{n}^{+}})=% \mathop{\max}\limits_{1\leqslant i\leqslant n}\{{Z_{ij}}\}$ (8) $\displaystyle Z_{j}^{-}=({Z_{1}^{-},Z_{2}^{-},Z_{3}^{-},\ldots Z_{n}^{-}})=% \mathop{\max}\limits_{1\leqslant i\leqslant n}\{{Z_{ij}}\}$

Then Euclidean distance formula is used to evaluate the distance $D_{i}^{+}$ and $D_{i}^{-}$ from the standardized vector of the index to the best scheme and the worst scheme respectively.

$\displaystyle D_{i}^{+}=\sqrt{\sum\limits_{j=1}^{m}{({Z_{j}^{+}-a_{ij}})^{2}}}$ (9) $\displaystyle D_{i}^{-}=\sqrt{\sum\limits_{j=1}^{m}{({Z_{j}^{-}-a_{ij}})^{2}}}$

In the ninth step, the relative closeness $S_{i}$ between each measure decision scheme and the ideal scheme is calculated.

$\displaystyle S_{i}=\frac{D_{i}^{-}}{D_{i}^{-}+D_{i}^{+}}$ (10)

Table 1

Evaluation index system of water resources carrying capacity

Serial number	Secondary index name	Unit
1	Total water resources	Billion cubic meters
2	Water resources per capita	Cubic meter per person
3	Total water supply	Billion cubic meters
4	Total water use	Billion cubic meters
5	Per capita water consumption	Cubic meter per person
6	Number of wastewater treatment facilities	Sets
7	Total industrial wastewater discharge	Ten thousand tons
8	Industrial wastewater discharge standard	Ten thousand tons
9	Chemical oxygen demand emissions from industrial wastewater	Ten thousand tons
10	Ammonia nitrogen emissions from industrial wastewater	Ten thousand tons
11	Domestic sewage discharge	Ten thousand tons
12	Chemical oxygen demand emissions in domestic sewage	Ten thousand tons
13	Ammonia nitrogen emissions from domestic sewage	Ten thousand tons
14	Total planted area	Ten thousand hectares
15	Total investment in environmental pollution control	Hundred million RMB
16	Investment in industrial pollution control	Ten thousand RMB

Where the relative proximity $S_{i}$ is between 0 and 1. According to the closeness $S_{i}$ , the measure object can be sorted. The greater the $S_{i}$ value, the closer the decision scheme is to the positive ideal solution, that is, the higher the water resources carrying capacity level. On the contrary, the lower the carrying capacity of water resources. Based on this, the water resources carrying capacity of 30 provinces (municipalities and autonomous regions) can be calculated.

3.2 Data

The carrying capacity of water resources is a comprehensive system, which includes many aspects, related influences and interactions among indicators. Water resources carrying capacity covers a comprehensive definition, with many resource, environmental, social and economic factors. Therefore, this study’s evaluation index system of water resources carrying capacity is based on scientific, rational and systematic principles. According to the existing research literature results, this study constructs the index system of water resources’ carrying capacity (Table 1). The study covers 30 provinces in China (limited to data availability, excluding data from Tibet Autonomous Region, Taiwan, Hong Kong and Macau, and the selected sample period is 2012–2022). All indicators are based on yearbook data published by Chinese government agencies such as China Water Statistics Yearbook, China Environmental Statistics Yearbook, China Statistical Yearbook and China Rural Statistics Yearbook.

4. Results

4.1 Descriptive statistics

Table 2
Descriptive statistical results of indexes

Name	Minimum value	Maximum value	Mean value	Standard deviation	Median
Total water resources (billion cubic meters)	23257	32466	27773	2531	27958
Water resources per capita (Cubic meter per person)	1729	2339	2048	161	2060
Total water supply (billion cubic meters)	5320	6183	5920	223	6016
Total water use (billion cubic meters)	5320	6183	5920	223	6016
Per capita water consumption (Cubic meter per person)	412	454	437	13	442
Number of wastewater treatment facilities (sets)	65128	106832	86355	12882	86355
Total industrial wastewater discharge (10,000 tons)	2122527	2768702	2506383	174371	2506383
Industrial wastewater discharge standard (10,000 tons)	1892891	2803780	2402731	255591	2402731
Chemical oxygen demand emissions from industrial wastewater (10,000 tons)	286	555	416	83	416
Ammonia nitrogen emissions from industrial wastewater (10,000 tons)	3	53	21	17	21
Domestic sewage discharge (10,000 tons)	2470115	5762299	4095114	1042822	4095114
Chemical oxygen demand emissions in domestic sewage (10,000 tons)	803	887	841	18	838
Ammonia nitrogen emissions from domestic sewage (10,000 tons)	89	103	98	4	99
Total planted area (10,000 hectares)	360	912	613	144	600
Total investment in environmental pollution control (100 million RMB)	1628	13342	7402	3779	8254
Investment in industrial pollution control (ten thousand RMB)	2218281	9976511	5525777	2002774	5004573

There are no outliers in the 16 index data measuring China’s water resources carrying capacity, which lays a data foundation for further analysis (Table 2).

4.2 Index weight

By analyzing different indicators, the entropy weight method can obtain different weights, which can avoid human subjective interference and make the weight of evaluation more objective and accurate. The entropy of China’s water resources carrying capacity evaluation index is calculated by substituting the data of China’s water resources evaluation index from 2012 to 2022 into the formula.

Table 3
Evaluation index entropy of water resources carrying capacity in China

Item	Information entropy value $e$	Information utility value $d$	Weight coefficient $w$
Total water resources (billion cubic meters)	0.9419	0.0581	5.85%
Water resources per capita (Cubic meter per person)	0.9534	0.0466	4.69%
Total water supply (billion cubic meters)	0.9697	0.0303	3.05%
Total water use (billion cubic meters)	0.9697	0.0303	3.05%
Per capita water consumption (Cubic meter per person)	0.9439	0.0561	5.65%
Number of wastewater treatment facilities (sets)	0.9311	0.0689	6.93%
Total industrial wastewater discharge (10,000 tons)	0.9252	0.0748	7.52%
Industrial wastewater discharge standard (10,000 tons)	0.9529	0.0471	4.73%
Chemical oxygen demand emissions from industrial wastewater (10,000 tons)	0.9281	0.0719	7.23%
Ammonia nitrogen emissions from industrial wastewater (10,000 tons)	0.9328	0.0672	6.76%
Domestic sewage discharge (10,000 tons)	0.9499	0.0501	5.04%
Chemical oxygen demand emissions in domestic sewage (10,000 tons)	0.9286	0.0714	7.18%
Ammonia nitrogen emissions from domestic sewage (10,000 tons)	0.967	0.033	3.32%
Total planted area (10,000 hectares)	0.9153	0.0847	8.52%
Total investment in environmental pollution control (100 million RMB)	0.9176	0.0824	8.29%
Investment in industrial pollution control (ten thousand RMB)	0.9399	0.0601	6.05%

(1) The total afforestation area is an important factor affecting the expansion of water resources carrying capacity at present, with a weight of 8.52%, which is much higher than other indicators, indicating that the total afforestation area has a certain improvement effect on water resources carrying capacity. The main reason is that after extensive afforestation, vegetation transpiration puts a large amount of water into the atmosphere as water vapor, and then falls to the ground as precipitation. In addition, vegetation branches, leaves and litter intercept part of precipitation, reduce velocity, delay the water catchment process, and prolong water infiltration time, thus reducing the total surface runoff, increasing surface water infiltration, and increasing underground runoff. At the same time, the carrying capacity of water resources can also optimize and improve the overall ecological environment of afforestation, which also plays a certain role in promoting the development of China’s forestry economic construction. (2) Total investment in environmental pollution control is the second most important factor in improving water resources’ carrying capacity. Especially in recent years, the Chinese government has always attached great importance to environmental pollution control because environmental pollution has become a major obstacle to China’s social development. With the development of China’s economy, the problem of environmental pollution is becoming more and more serious. Therefore, the government has decided to make a huge investment in environmental pollution control to improve people’s living conditions. The total investment in environmental pollution control is mainly concentrated in three aspects: air pollution control, water pollution control and soil pollution control, and these investments have begun to produce results. China still faces the challenge of environmental pollution. Therefore, the government will continue to increase investment in environmental pollution control and take more effective measures to deal with environmental pollution problems. (3) The total amount of industrial wastewater discharge is the third important factor to improve the carrying capacity of water resources. With the continuous strengthening of the treatment of industrial pollution in China, the treatment of domestic industrial wastewater pollution has also achieved remarkable results. The total amount of industrial wastewater discharge is one of the indicators used to measure the level of economic development of a country, and it is also an important reference index used to control the situation of water resources in specific fields (Table 3). To reduce the total amount of industrial wastewater discharge and improve the environment and resources, the Chinese government has adopted many effective policies and measures, such as adopting high standards, comprehensive supervision, local governments taking responsibility as the main body, and industrial construction pollution control measures to achieve source pollution prevention and control and curb the growth of total wastewater discharge. It should strengthen the implementation of standards for wastewater from industrial enterprises, intensify pollution prevention and control and comprehensive utilization of solid waste, and curb the growth of total wastewater discharge. Continuing to strengthen the investment in treating industrial wastewater pollution will become a key development direction of China’s sustainable development and the guidance of the “dual carbon” goal.

4.3 Results of the comprehensive evaluation of water resources carrying capacity

According to Eqs (7)–(10), the distance of China’s water resources carrying capacity from 2012 to 2022 approaching/deviating from the positive and negative ideal solutions is calculated (Table 4), to determine the level of water resources carrying capacity in each year.

Table 4
Water resources carrying capacity in China (2003–2021)

Time	Positive ideal solution distance $D$	Negative ideal solution distance $D-$	Relative proximity $C$	Sort result
2003	0.169	0.172	0.505	9
2004	0.17	0.144	0.459	16
2005	0.17	0.116	0.405	17
2006	0.174	0.112	0.391	19
2007	0.164	0.11	0.401	18
2008	0.138	0.126	0.476	13
2009	0.14	0.131	0.483	12
2010	0.115	0.155	0.575	1
2011	0.146	0.127	0.466	15
2012	0.126	0.139	0.524	5
2013	0.123	0.145	0.541	3
2014	0.13	0.143	0.525	4
2015	0.13	0.142	0.524	6
2016	0.13	0.156	0.545	2
2017	0.14	0.145	0.508	7
2018	0.149	0.147	0.496	10
2019	0.152	0.156	0.507	8
2020	0.165	0.162	0.495	11
2021	0.181	0.158	0.466	14

As can be seen from Table 4, the change trend of China’s water resources carrying capacity can be divided into two stages. The first stage was from 2003 to 2010, during which China’s water resources carrying capacity improved year by year. The main reason for this is that the current series of major economic development strategies proposed by the Chinese government have made reasonable arrangements for China’s industrial layout, promoting the harmonious unity of economic and social development and water resources and environment, promoting green development, sustainable development, and circular development. We will carry out dual control actions on the total amount and intensity of water resources consumption, increase water-saving efforts, and rationally allocate water resources to ensure a balanced supply and demand of water resources to meet the production water demand of enterprises to the maximum extent. In particular, we should increase the integration and innovation of water-saving technology, product research and development, big data and artificial intelligence. Government departments, colleges and universities or relevant scientific research units can carry out scientific and technological research and development products and technologies, such as accurate measurement of water, water-saving living utensils, industrial water recycling and reuse technology, unconventional water resources utilization, rapid leak repair technology, etc., vigorously promote the transformation of water-saving scientific and technological achievements, and publicize and popularize scientific and technological achievements through multi-angles and multi-platforms. Only by carrying out scientific and technological product exchange exhibitions can we better promote the marketization of water-saving technological achievements. The second stage is from 2011 to 2021, which is the stable maintenance of China’s water resources carrying capacity year by year. Since China’s economic growth has been very rapid since 2011, there are big differences in development among different regions in China, and the development of water resources in most regions has an obvious spatial spillover effect. The Chinese government fully considers the actual situation of different regions and makes measures according to local conditions when formulating policies on water resources management. For example, the two provinces of Hunan and Jiangxi are rich in water resources and high carrying capacity but also introduce high-quality water sources from the region to areas with relatively scarce water resources, such as Hubei Province, to achieve cross-regional water transfer. At the same time, a sound science, technology and production resource-sharing platform for water resources protection should be established to build a community of economic development and resource protection in urban agglomeration. It should give full play to the spatial spillover effect of areas with high water resources, and drive the coordinated development of the economy and water resources carrying capacity of neighboring provinces.

5. Conclusions

Water resource shortage has become one of the important problems restricting the construction of ecological civilization and sustainable development in China due to the unbalanced distribution of water resources in time and space, the small number of available water resources per capita and the sharp structural contradiction between supply and demand. The study of water resource carrying capacity is significant for promoting water resource use efficiency in China. A reasonable assessment of the carrying capacity of water resources, response to the state’s call, and implementation of government policies have a positive role in the sustainable development and utilization of China’s water resources as well as social and economic development. This study puts forward the evaluation index of water resources carrying capacity level. After collecting and standardizing the required data, the weight of each evaluation index is calculated by the entropy weight method. Then the comprehensive evaluation value of water resources carrying capacity in China from 2012 to 2022 is calculated by the TOPSIS model. The conclusions are as follows. (1) Total afforestation area, total investment in environmental pollution control, and total industrial wastewater discharge are the third most important factors in improving water resources carrying capacity. (2) China’s water resources carrying capacity improved year by year from 2003 to 2010; from 2011 to 2021, China’s water resources carrying capacity remained stable year by year. (3) Continuous adjustment of China’s industrial structure and strengthening environmental pollution control are inevitable measures to improve China’s water resources carrying capacity. It is suggested that further research should be carried out in the future on the partial causes of the change in water resources’ carrying capacity and the obstacles to water resources’ carrying capacity.

Footnotes

Acknowledgments

This study was supported by the China Postdoctoral Science Foundation (No. 2022M712660), the Youth Project of Zhejiang Natural Science Foundation (No. LQ21G020001), and the Soft Science Research Project of Ningbo (No. 2022R012).

Declarations of interest

None.

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Evaluation of water resources carrying capacity in China based on entropy weight TOPSIS model

Abstract

Keywords

1. Introduction

2. State of art

3. Modeling and data

3.1 Modeling

4. Results

4.1 Descriptive statistics

Table 2 Descriptive statistical results of indexes

Table 3 Evaluation index entropy of water resources carrying capacity in China

Table 4 Water resources carrying capacity in China (2003–2021)

Footnotes

Acknowledgments

Declarations of interest

References

Table 2
Descriptive statistical results of indexes

Table 3
Evaluation index entropy of water resources carrying capacity in China

Table 4
Water resources carrying capacity in China (2003–2021)