Coupling research on spatio-temporal differentiation of water resources carrying capacity in mature mining cities

The uneven temporal and spatial distribution of water resources and serious environmental pollution are the basic water situation in China. Although precipitation and total water resources in 2020 have been higher than those in 2019, while the total water consumption has been reduced and water efficiency and water structure are improved, the problem of water resources in China is still quite serious (Ministry of Water Resources of the People's Republic of China 2020). However, with the overexploitation of mineral resources in mining cities, the phenomena of waste of water resources, pollution of water resources and deterioration of water ecology will be exacerbated day by day. The problem of water resources has become one of the most concerning issues in the world in the 21st century. To solve the contradiction between the sustainable development of mining cities and the environmental protection of water resources, formulating a reasonable strategy of joint prevention and governance is particularly important.

The unreasonable exploitation of mineral resources caused heavy metal pollution (Uugwanga & Kgabi 2021), toxic sludge discharge (El Bizri et al. 2016) and water ecosystem damage (Hallgren & Hansson 2021) to the water resources of mining cities. As well as air pollution (Woźniak & Pactwa 2018) and land pollution (Worlanyo & Li 2021) cannot guarantee normal socio-economic development and the quality of residents' life. Therefore, China proposes to build green mines from six aspects: mining environment, resource development mode, comprehensive utilization of resources, energy conservation and emission reduction, scientific and technological innovation, intelligent mines and enterprise management and image (Ministry of Natural Resources of the People's Republic of China 2020). Therefore, a reasonable measurement of the carrying capacity of water resources in mining cities is conducive to promoting the industrial transformation and upgrading of mining cities.

Carrying capacity is a physical concept. Park & Burgess (1921) first introduced the concept of carrying capacity into the field of ecology, which referred to determining the number of people that could be accommodated according to the total amount of resources in the region. Water resources carrying capacity (WRCC) is the specific application of the concept of carrying capacity in the field of water resources. In foreign countries, WRCC was usually applied to ‘water availability’ (Kunu et al. 2021) and ‘water demand and supply’ (Naimi Ait-Aoudia & Berezowska-Azzag 2016). In China, the concept of WRCC was clearly put forward by Shi & Qu (1992) for the first time, and then experienced five stages of germination, initial formation, improvement, development and deepening development (Wang et al. 2017). At present, the concept of WRCC is summarized into three aspects: the first is the maximum scale that can be developed under the rational and optimal allocation of water resources (Xu 1993), the second is the maximum capacity that can maintain the sustainable development of social economy under maintaining the virtuous cycle of water resources (Zhang et al. 2003), and the third is the maximum number of people that the region can accommodate under the condition of efficient utilization of water resources (Wang et al. 2004).

The research focus of WRCC evaluation has two aspects: one is to build a comprehensive evaluation indicator system through the conceptual framework model, and the other is to evaluate and predict the water resources carrying capacity with the help of empirical research methods. Common conceptual framework models can be roughly divided into ‘Driving-Pressure-State-Influence-Response’ DPSIR model (Liu et al. 2020; Ruan & He 2021), ‘Economy-Society- Nature-Environment’ ESNE model (Zhao et al. 2015; Bao et al. 2020) and ‘Quantity-Quality-Area-Current’ QQAC model (Wang et al. 2017). The empirical research methods commonly used by scholars include: AHP-Analytic Hierarchy Process (Deng et al. 2021; Ren et al. 2021; Wang et al. 2022); TOPSIS-Technique for Order Preference by Similarity to an Ideal Solution (Peng & Deng 2020; Deng et al. 2021); EF-Ecological Footprint (Li et al. 2018; Yu et al. 2021); SD-System Dynamics (Yang et al. 2019; Wang et al. 2022); SPA-Set Pair Analysis (Cui et al. 2018) and CME-Cloud Matter Element (Peng & Deng 2020). From the perspective of the research object, it is mainly concentrated in urban agglomeration; basin; eco-economic belt, etc. (Deng et al. 2021; Ruan & He 2021; Zhao et al. 2021).

Although domestic and foreign scholars have made a lot of achievements in the field of water resources, the following points are often ignored in the relevant research of WRCC: (1) the method of assigning the weight of evaluation indicators is relatively single, and the correlation between indicators is not considered. (2) The selected conceptual framework model is relatively common, and the research dimension is not innovative enough. (3) The selected research objects are not representative, and the applicability of the conclusions is not strong. Because the sustainable development of mining cities plays a very important role in China's high-quality economic development, this paper selects mature mining cities in urgent need of transformation and upgrading as the research object.

Above all, firstly, this paper constructs a comprehensive evaluation indicator system of water resources carrying capacity based on the state-danger-immunity (SDI) conceptual framework model. Then, with the help of the criteria importance through intercriteria correlation (CRITIC) method, the objective weighting of indicators is carried out, and the WRCC of six mature mining cities in Anhui Province is evaluated combined with the catastrophe progression model. ArcGIS 10.7 software is used to visually analyze the temporal and spatial differentiation of WRCC. Finally, the improved coupling model is used to calculate the coordinated development of mature mining cities. The results can provide theoretical support and practical basis for the sustainable development of mature mining cities.

Anhui Province, located in the east of China, has 10 mining cities (Bao et al. 2020). It is an important energy guarantee base in the Huaihe Eco-Economic Belt (Ruan & He 2021) and the Yangtze River Economic Belt (Liu et al. 2020), with convenient water transportation. According to the types of mining cities, they can be divided into Growing-type, Mature-type, Declining-type and Regenerating-type. This paper focuses on mature mining cities, because their economic development has been limited, and there is an urgent need for industrial transformation and upgrading to change the development status. This paper selects Bozhou, Huainan, Suzhou, Xuancheng, Chizhou and Chuzhou as the research area, as shown in Figure 1.

The water sources of Bozhou, Huainan, Suzhou and Chuzhou come from the Huaihe River, while the water sources of Xuancheng and Chizhou come from the Yangtze River. In terms of industrial production, water supply capacity and per capita resources, the average level of the six mature mining cities is compared with that of Anhui Province or China. The ratio of wastewater discharge to industrial water intake in the mature mining cities is 13.36%, higher than 12.60% in Anhui Province. The comprehensive production capacity of urban water supply in mature mining cities is 260.00 thousand m³/day, less than half of that of Anhui Province (565.60 thousand m³/day). The total population of the mature mining cities is 22.75 million, and the per capita water resources is 913.79 m³, which is slightly higher than that of Anhui Province (848.07 m³), but far less than that of in China (2,077.7 m³). It can be seen that the comprehensive water resources level of the mature mining cities is lower than that of Anhui Province. Therefore, it is necessary to study the WRCC of the mature mining cities.

All the data involved in this paper are from the Statistical Yearbook on the website of Anhui Provincial Bureau of Statistics or National Bureau of Statistics, and the Water Resources Bulletin on the website of Anhui Provincial Department of Water Resources. Considering the availability and scientificity of the indicator data, the basic data of six prefecture-level cities in Anhui Province from 2013 to 2019 are selected as the research data, and some indicator data are obtained through calculation.

Based on the SDI conceptual framework model, and referring to national standards and industrial standards such as code for Design Of Pipeline Structures Of Water Supply And Drainage Engineering (GB 50332-2002), Hygienic Standard For Drinking Water (GB 5749-2006), Groundwater Quality Standard (GB/T 14848-2017), Standard Inspection Method For Urban Water Supply Quality (CJ/T 141-2018), the evaluation indicator system of WRCC is constructed (Zhang et al. 2010), which is divided into 3 subsystems and 15 evaluation indicators, as shown in Table 1. In this conceptual framework, ‘state’ (S) refers to the quality state of water resources to maintain ecological balance, ‘danger’ (D) refers to the danger of production and life on water resources security, ‘immunity’ (I) refers to the immune measures taken to improve the risk of water resources, as shown in Figure 2.

The CRITIC method is an objective weighting method proposed by Diakoulaki et al. (1995), which can be used to comprehensively measure the contrast intensity and conflict characteristics of indicators. A subjective weighting method is difficult to solve the error caused by the fuzziness of the problem and the randomness of the indicators. Objective weighting methods such as the CRITIC method, entropy method and principal component analysis method can well avoid the error caused by subjectivity. Moreover, the CRITIC method can take into account the relationship between indicators, which is more scientific than other objective weighting methods. Now it is often used in various evaluation researches (Wang et al. 2021).

If there are m evaluation objects and n evaluation indicators, means the jth evaluation indicator of the ith object. The specific steps of CRITIC method are as follows:

The catastrophe progression model is to study the qualitative change process of discontinuous change, which is suitable for the research of a water resources ecosystem that is not absolutely stable. The advantage of this model is that it does not involve the specific weight of evaluation indicators, but at the same time, it takes into account the relative importance of evaluation indicators. The common catastrophe types (Zhang et al. 2020) are shown in Table 2. The specific steps of catastrophe progression model are as follows:

Step 1: the catastrophe type is determined and the evaluation indicators are sorted. The catastrophe type is determined according to the number of indicators in each layer, as shown in Figure 3. The indicators are ranked according to their importance, and the objective weight of the indicators is calculated according to the CRITIC method.

Step 2: the data are substituted into the formula, following the principle of complementarity. The sorted standardized indicator data are substituted into the normalization formula in turn, and the catastrophe progression of each subsystem in the system layer is calculated. The catastrophe progression of each subsystem can be used as the control variable of the target layer, so as to calculate the catastrophe progression of the target layer. Because each indicator in the evaluation system of water resources carrying capacity follows the principle of complementarity, formula (3) should be used to calculate the catastrophe progression of control variables.

Step 3: grade standard is established and the degree of catastrophe is compared. The membership calculated by the catastrophe progression model is relatively concentrated and the difference is very small. Therefore, all the control variables of the indicator layer are assigned as {0.2, 0.4, 0.6, 0.8, 1}. According to the catastrophe type, the limit value of each evaluation grade is calculated step by step to determine the value range. The catastrophe grade standard of WRCC is shown in Table 3.

The development degree D of water resources is calculated by formula (9). ⁠, ⁠, are calculated by catastrophe progression model and they represent the development level of quality state, security danger and risk immunity respectively. The average development level is calculated by formula (10). C represents the coordination degree of water resources, which is calculated by formula (11). The coupling degree O of water resources is calculated from formula (12). In this paper, the weight of water resources development degree and water resources coordination degree are taken as 0.5, indicating that they are of the same importance.

This paper selected the data of 2013, 2016 and 2019, and calculated the WRCC index and WRCC grade through the catastrophe progression model and catastrophe grade standard, as shown in Table 4. To more intuitively compare the spatial-temporal differentiation degree of WRCC of the six mature mining cities, ArcGIS 10.7 software was used to realize the visual analysis of urban spatial-temporal differentiation, as shown in Figure 4.

From the three-year temporal and spatial changes of each city, it could be seen that the WRCC was at the criticality level and below for all. The WRCC of Suzhou and Huainan had been at the level of severe overload, but the WRCC index of Huainan showed a steady downward trend, from 0.725 in 2013 to 0.689 in 2016, and finally dropped to 0.666 in 2019. Although Suzhou was still at the level of severe overload, the WRCC index remained basically unchanged and had been maintained in the range of 0.815–0.819. The WRCC of Chuzhou and Xuancheng had been at a criticality level, but the WRCC index of Chuzhou and Xuancheng had been declining, only 0.916 and 0.913 respectively in 2019, both of which had the trend of falling to the level of ‘overload’. Chizhou's WRCC had been maintained at the overload level for a long time, and the WRCC index was relatively stable with a small range of change, with a range of only 0.1. Bozhou's WRCC showed a steady development trend, from the severe overload level to the overload level, which was expected to reach the criticality level.

To further analyze the internal change of WRCC in mature mining cities, the WRCC level of each subsystem was also calculated and graded, as shown in Table 5.

The WRCC grade of Bozhou S-subsystem decreased from criticality to overload, and the index increased slightly in 2019. The WRCC grade of the D-subsystem had been maintained at the criticality, but the index had been declining. The WRCC grade of the I-subsystem increased from severe overload to overload, and the index showed a steady growth trend.

The WRCC grade of the S-subsystem in Suzhou was basically stable at the criticality, but the index decreased slightly. The WRCC grade of the D-subsystem remained at overload for a long time, but the index rised steadily and was expected to reach the criticality. The WRCC index of the I-subsystem remained basically unchanged, and the grade was severely overloaded.

The WRCC grade of the S-subsystem in Huainan had been severely overloaded, and the change range of index was small. The WRCC grade of the D-subsystem was consistent with that of the S-subsystem. The index reached the lowest point of 0.094 in 2016 and increased in 2019, but it still failed to return to the grade in 2013. The WRCC of the I-subsystem decreased significantly, the index decreased by 0.24, and the grade decreased from surplus to severe overload.

The WRCC index of the S-subsystem in Chuzhou showed a downward trend, and the grade was reduced from extremely surplus to surplus. The change trend of WRCC index of the D-subsystem was consistent with that of the S-subsystem, and the grade decreased from overload to severe overload. The WRCC index of the I-subsystem changed in a U-type, and the grade had experienced a process of ‘surplus-overload-extremely surplus’.

The WRCC indexes of the S-subsystem and the D-subsystem of Xuancheng were rising steadily. The grade of the former had always been criticality, and the grade of the latter had remained extremely surplus for a long time. The WRCC index of the I-subsystem showed a steady decline trend, and the grade was reduced from overload to severe overload.

The change range of WRCC index of the S-subsystem in Chizhou was small, and the grade had been severely overloaded. The WRCC index of the D-subsystem was relatively stable, the same grade as that of the D-subsystem of Xuancheng, and remained extremely surplus for a long time. The WRCC index of the I-subsystem showed an inverted U-type change, and the grade reached overload in 2016, but it was both severe overloaded in 2013 and 2019.

By calculating the overall WRCC of six cities, the WRCC temporal evolution of mature mining cities in 2013–2019 was analyzed, as shown in Figure 5.

The WRCC index of mature mining cities showed an oblique N-type change, showing an upward trend from 2013 to 2014, and the grade increased from severe overload to overload. It gradually decreased from 2014 to 2015, and the grade remained overloaded. It rose steadily from 2015 to 2017, and the grade increased from overload to criticality, and the index reached the highest value of 0.933. However, from 2017 to 2019, it began to decline gradually, from criticality to overload. The overall change trend of WRCC was ‘severe overload-overload-criticality-overload-severe overload’.

The WRCC temporal evolution analysis of subsystems of the whole mature mining cities is shown in Table 6. The S-subsystem showed an upward trend in 2013–2017, but a downward trend in 2017–2019. There was little difference between WRCC index in 2013 and 2019, and the grades were severe overload. The WRCC grade of the D-subsystem changed from extremely surplus to severe overload, while the grade change of the I-subsystem was completely opposite.

Based on the SDI conceptual framework, the coupling study of six mature mining cities and the overall level from 2013 to 2019 was carried out with the help of catastrophe progression model and coupling degree model, as shown in Table 7.

The development status of WRCC in Bozhou had been in a severe imbalance, but the coupling degree was growing steadily. The coupling degree of WRCC in Suzhou remained basically unchanged, and the development status was stable in a severe imbalance. The WRCC coupling degree of Huainan had decreased slightly, the range was quite small, and the development status had been severely overloaded for a long time. The WRCC coupling degree of Chuzhou showed a steady downward trend, and the development status was reduced from primary imbalance to severe imbalance.

The change trend and development status of the WRCC coupling degree in Xuancheng were similar to Chuzhou. The WRCC coupling degree of Chizhou fluctuated slightly, and the development status remained in a severe imbalance. Finally, the overall WRCC coupling degree of mature mining cities showed an inverted U-type change, and the development status experienced ‘severe imbalance – primary imbalance – severe imbalance’.

The in-depth study of WRCC of mature mining cities can better identify potential ecological risks, help to change the development status of mature mining cities and accelerate the pace of industrial transformation and upgrading. This paper made a comprehensive analysis on the whole and individual WRCC in the mature mining cities. It could be seen from the individual: except that the WRCC grade of Bozhou had been risen from severe overload to overload, the WRCC grades of other cities had all not changed. It could be seen from the whole that the WRCC of mature mining cities showed an oblique N-type fluctuation. In 2013 and 2019, there was little difference between the WRCC and the grades were seriously overloaded. The WRCC in 2017 was the highest grade: criticality.

The WRCC index of Huainan, Chuzhou and Xuancheng showed a steady downward trend. The water resources quality state and water resources security danger in Huainan changed little, but the water resources risk immunity decreased rapidly. This was mainly because the investment in energy conservation & environmental protection had not increased significantly compared with other cities. In 2019, the number of R & D personnel in industrial enterprises was reduced to half of that in 2013. The government's investment was not large enough and did not pay enough attention to the cultivation of scientific and technological talents, which hindered the development of WRCC. Although Chuzhou had reached a extremely surplus level in terms of water resources risk immunity, the water resources quality state and water resources security danger had decreased. Under the condition of halving the total amount of water resources, the water consumption of cities and industrial enterprises and urban sewage discharge had increased rapidly, which greatly limited the balanced development of WRCC. Xuancheng's water resources quality state and water resources security danger were developing steadily, but there were great problems in water resources risk immunity. The number of graduates from colleges and universities had been hovering around 2000. There were few local colleges and universities, resulting in weak supply of educational resources, so that the development momentum of WRCC was insufficient.

The WRCC indexes of Suzhou and Chizhou were relatively stable. Suzhou's water resources quality state and water resources risk immunity decreased slightly, but water resources security danger increased significantly. The reason was that the R and D personnel resources of industrial enterprises were short for a long time. On the premise of the decline of total water resources and precipitation, the urban water consumption, urban sewage discharge and fertilizer use were reasonably controlled. Therefore, WRCC remained relatively balanced as a whole. Chizhou's water resources security danger remained basically unchanged, and the quality state of water resources was complementary to the risk immunity of water resources. Chizhou conserved the water source quality through afforestation, effective irrigation and other measures to make up for the shortage of R & D personnel resources in industrial enterprises. Bozhou's WRCC index had a good development trend of rising to the criticality level. Bozhou's water resources quality state had been hovering on the edge of overload and criticality, water resources security danger had been at a criticality level, and water resources risk immunity had increased greatly, which was expected to break through the criticality level. In terms of investment in energy conservation and environmental protection and water conservation of industrial enterprises, it was far more than the other five mature mining cities.

The overall water resources quality state of mature mining cities changed little, but the changes in water resources security danger and water resources risk immunity formed a great contrast. The WRCC grade of water resources security danger changed from extremely surplus to severe overload. The WRCC grade of water resources risk immunity changed from severe overload to extremely surplus. Although the urban water consumption and sewage discharge were increasing day by day, the investment in energy conservation and environmental protection, the intensity of ecological water replenishment and the construction length of sewage pipelines were also significantly increasing.

A comprehensive comparison of the coordinated development status in the mature mining cities showed that Bozhou, Suzhou, Huainan and Chizhou had been in a severe imbalance. Bozhou was on the rise. Suzhou, Huainan and Chizhou were relatively stable with a small range of change. Chuzhou and Xuancheng both changed from primary imbalance to severe imbalance, and both showed a downward trend. The overall coordinated development status of the mature mining cities rised from severe imbalance to primary imbalance, and then droped to severe imbalance. The coupling degrees between 2013 and 2019 were not much different.

Based on the improved catastrophe progression model and coupling degree model, this paper scientifically calculated the WRCC of mature mining cities, which made up for some vacancies in the field of water resources evaluation. At the same time, it could put forward targeted suggestions for the mature mining cities and promote the transformation and upgrading of such cities. In the next research, we should consider selecting a more appropriate research cycle, a more representative evaluation indicator system and research objects.

To scientifically calculate the WRCC and solve the ecological problems related to the sustainable development of mining cities, this research takes the mature mining cities in Anhui Province as the research object, constructs a comprehensive evaluation indicator system based on the SDI conceptual framework, and calculates the WRCC from 2013 to 2019 by combining the CRITIC method and the catastrophe progression model. The coupling degree model is used to test the coordinated development status of the city. The conclusions can be summarized as follows:

• (1)

  From the individual perspective of WRCC: except that the WRCC grade of Bozhou rose to overload, the WRCC grade of other cities remained unchanged. The WRCC index of Huainan, Chuzhou and Xuancheng showed a steady downward trend, and the WRCC index of Suzhou and Chizhou was relatively stable. The WRCC index of Bozhou had a good development trend, rising to the critical balance.

• (2)

  From the overall perspective of WRCC: mature mining cities showed an oblique N-type fluctuation. The WRCC of 2013 and 2019 were both at severe overload level, reaching the criticality level of the highest value in 2017. The change range of water resources quality state was small, and the changes of water resources security danger and water resources risk immunity were completely opposite.

• (3)

  Coordinated development status of WRCC: Bozhou, Suzhou, Huainan and Chizhou have been in severe imbalance, and Chuzhou and Xuancheng have changed from primary imbalance to severe imbalance. The overall coordinated development status of the mature mining cities has been from severe imbalance to primary imbalance, and then to severe imbalance.

In summary, the current coordinated development of the mature mining cities is not conducive to the realization of high-quality sustainable development. Therefore the following suggestions are put forward:

• (1)

  Mining areas should increase greening on the basis of repairing the ore appearance, and select resource mining technology according to local conditions.

• (2)

  Enterprises should monitor the changes of ecological environment in mining areas in real time, realize land reclamation, and build an emergency response mechanism at the same time.

• (3)

  Mining cities should improve the comprehensive utilization efficiency of water resources and improve the discharge standards of domestic sewage and industrial wastewater.

• (4)

  Relevant departments should increase investment in scientific and technological innovation, and create a collaborative innovation system to realize intelligent mining.

• (5)

  The government should improve the level of higher education and implement the talent introduction policy to attract high-level talents to settle down.

• National Natural Science Foundation of China (51574010)

• Postgraduate Innovation Fund Project of AUST (2021CX1013)

• Anhui Philosophy and Social Science Planning Project (AHSKY2019D026)

• Key Research Project of Anhui Social Science Innovation and Development (2019CX110)

• Scientific Research Education Demonstration Project of AUST (KYX202123).

None declared.

All relevant data are included in the paper.