Water resources carrying capacity (WRCC) is an important component of the carrying capacity of regional natural resources. It is a restrictive index indicating whether a region can support the coordinated development of its population, economy, and environment. In order to solve the problem that WRCC varies with the supporting capacity of, load on, and capacity for regulation of the water resources, this study establishes the relationship between WRCC and the level of socio-economic development (carrying level) and then develops a WRCC evaluation model by analyzing the connotations, characteristics and influencing factors of WRCC. The model is applied to calculate the population and economic scales supportable by water resources in Jiangsu Province under different carrying levels. Predictions are made of the effects of varying water consumption quotas and the industrial structure so as to explore methods of regulating and controlling WRCC in the region. The evaluation, prediction, and control results provide an important reference for the rational allocation of water resources in Jiangsu Province so as to further improve the regional WRCC and achieve sustainable development of regional water resources. The regional WRCC evaluation model has strong applicability and good application effects.

  • This study establishes the relationship between WRCC and the level of socio-economic development.

  • The model is applied to calculate the population and economic scales supportable by water resources in Jiangsu Province under different carrying levels.

  • Predictions are made of the effects of varying water consumption quotas and the industrial structure so as to explore methods of regulating and controlling WRCC in the region.

Graphical Abstract

Graphical Abstract
Graphical Abstract

As a strategic economic resource and a public social resource, water is the basic natural resource restricting the sustainable development of society and the health of the ecological environment (Zuo & Zhang, 2015; Wang & You, 2016; Yang et al., 2016). Population growth, the advancement of science and technology, and the improvement of living standards are causing water resource problems to become more and more apparent. The demand for water resources is also growing, and the impact of water resources on the sustainable development of the economy is increasing (Xia & Zhu, 2002). Therefore, the study of the carrying capacity of water resources has the potential to solve problems in water resource security in the current developmental stage and ensure that water resources are allocated optimally (Song & Zhan, 2011; Xuebin et al., 2015; Yang et al., 2015).

Development of carrying capacity

The term carrying capacity is originally derived from physical mechanics, in which it refers to the maximum load that an object can bear without incurring damage (Park, 1921; Barnes, 1950; Ehrlich, 1996; Feng et al., 2008). When studying regional systems, this concept is generally borrowed to describe the maximum tolerance of regional systems to external environmental changes. The concept originated from the work of Thomas Robert Malthus, in which he put forward a population theory for how limited resources affect population growth (Malthus, 2011). The other discipline that first borrowed the concept of carrying capacity is community ecology, in which it means the maximum number of individual organisms that can exist under certain environmental conditions (which comprise a combination of ecological factors such as living space, nutrients, and sunlight). Studies on a variety of organisms have shown that populations initially grow slowly, and growth accelerates when environmental conditions are improving, which may lead to excessive growth. After the population reaches a certain value exceeding its carrying capacity, a large number of the population will die due to environmental limitations, leading to a sudden population decline (Winterhalder et al., 1988; Liu et al., 2007). After that, the population will find a new balance. Carrying capacity theory was first applied in animal husbandry (Liu et al., 2007). Due to grassland reclamation and overgrazing, land had begun to degenerate. The concept of carrying capacity was introduced to manage grassland effectively and achieve maximum economic benefit. Related concepts such as grassland carrying capacity and maximum stocking capacity were then successively put forward. Another accompanying concept is the carrying capacity of land. Verhuist, Park et al. developed and applied the concept in the field of human ecology to study how many people can be supported by existing land areas. Relevant associated concepts include regional population carrying capacity, land load capacity, regional capacity, and regional potential. With the development of social economy, various resources are constantly being consumed, and the ecological environment is deteriorating (Park & Burgess, 1970; Vogels et al., 1975). Under such circumstances, the concepts of environmental carrying capacity, resource carrying capacity, ecological carrying capacity, oasis carrying capacity, and water resources carrying capacity have emerged one after another (Feng & Huang, 2008; Winz et al., 2009; Cheng, 2010).

Water resources carrying capacity

Though the term ‘natural resource carrying capacity’ is used widely worldwide, it has no recognized standard definition. It is generally considered that resource carrying capacity refers to the scale of the population that can be supported sustainably by a certain resource within a certain region and to meet a certain material living standard (Xu, 1993; Costanza, 1996; Xia & Zhu, 2002). It has also been defined as the ability of a country or region to support the basic survival and development of its population according to the average quantity and quality of its resources (Wang et al., 2016).

There have been few independent research achievements on the theory of water resources carrying capacity (WRCC). Most are incorporated into the theory of sustainable development. For example, work by Joardar et al. analyzed urban WRCC from the perspective of water supply and incorporated it into urban development planning (Harris & Kennedy, 1999). Rijsberman and others used carrying capacity as a measure of urban water security assurance in a study on urban water resource evaluation and management (Rijsberman & Ven, 2000). Harris focused on agricultural WRCC in agricultural production areas and used it as a measure of regional development potential (Ehrlich, 1996; Haddadin, 2000).

Although Chinese scholars have carried out extensive research on WRCC, the current understanding of the concept is not uniform. As a type of resource, WRCC conforms to the above definition, but the unique characteristics of water resources can be expected to give it corresponding particularity (Long et al., 2004). The term WRCC is also widely used to study the balance between supply and demand for water resources in industries, agriculture, and urban economic development, but it does not yet have a unified definition (Yi et al., 2018). However, its use can be classified into two viewpoints. One is the theory of water resource development capacity or the scale theory of water resource development, and the other is the theory of sustainable development of water resources (Xia et al., 2007).

For the above two viewpoints, the former one starts from the subject body of water resources carrying which is the water resources system. It tries to use a specific quantity, such as using water supply capacity as an indicator of WRCC (Wang et al., 2017). This is a relatively intuitive way. The latter point is from the object body of water resources carrying which starts from the human socio-economic system. It is expected to describe it from the real meaning of WRCC, and use population and socio-economic scale as indicators of WRCC which is more abstract (Wang et al., 2017).

Study area

Jiangsu Province is one of the provincial administrative regions of China. Jiangsu lies between 30°45′–35°20′N and 116°18′–121°57′E. It is located in the downstream area of the Yangtze River Basin and Huai River Basin and occupies a total area of 102,000 km2. The per capita GDP (Gross Domestic Product), comprehensive competitiveness, and Development and Life Index (DLI) of Jiangsu Province rank it first among the provinces in China, and it has become the province with the highest comprehensive development level. Because the region is located in the climatic transition zone between north and south China, the spatial and temporal distribution of precipitation is uneven. The per capita local water resource is 438 m3, only one-fifth of the national per capita average. Water resources in transit are abundant, about 9,490 billion m3 per year, due to its downstream location in the Yangtze and Huai River Basins. However, the amount of useable water resources is unreliable due to inter-annual and annual changes in the incoming water volume and limitations in the diversion engineering capacity. The carrying capacity of this important natural resource for natural and human development is thus limited. Ensuring the sustainable development of Jiangsu's economy and society with the sustainable use of water resources is one of the basic problems that must be solved for the sustainable economic and social development of Jiangsu Province.

This study uses time-series data relating to the GDP, population, amount of water resource utilization and water supply available in Jiangsu. Population and GDP data are acquired directly from the ‘Jiangsu Statistical Yearbook (2003–2018)’, and those for the amount of water resource utilization are from the ‘Jiangsu Province Water Resources Bulletin (2002–2017)’. The water supply capacity was obtained by reference to the ‘Jiangsu Mid–Long Term Supply and Demand Plan, 2015’ (Figure 1).

Fig. 1

Location of the study region (Jiangsu Province, China).

Fig. 1

Location of the study region (Jiangsu Province, China).

Close modal

Water resource carrying level

WRCC evaluation is founded on a certain stated carrying level, that is, the standard of living that must be supported. Different carrying levels can bring about differences in the carrying capacity. Therefore, it is beneficial when studying WRCC at a deeper level to clarify the grading standard being used for the water resource carrying level (Jin et al., 2018).

This paper takes a quantitative approach to the carrying level. Many methods have been put forward for classifying the carrying level required of water resources, such as referring to internationally and domestically recognized standards, dynamic prediction, trend prediction, and mean difference classification (Wang et al., 2013, 2014). The comparison and discrimination of various methods indicates that referring to internationally recognized and Chinese standards for the carrying level is the classification best suited to the current study. This approach can avoid the weakness of strong subjectivity and can maintain consistency with internationally recognized standards while maintaining a close link with the national macro-development strategy (Na et al., 2015; Ren et al., 2016; Jin et al., 2018).

At present, two types of international standard are used to classify carrying level. One takes consumption structure as its reference and is called in economics the Engel coefficient, while the other is based on per capita GDP.

In this paper, the water resource carrying level is divided into four according to the standard of living determined by the Food and Agriculture Organization of the United Nations, which is based on Engel coefficient, the development levels determined by the World Bank, which is based on per capita GDP, and the classification standard determined by China's ‘three-step’ strategic national development goal. The carrying levels used are the basic level, where people are adequately fed and clad, well-off level I, well-off level II, and basic modernization (Table 1).

Table 1

Classification of water resource carrying level.

Reference standardIndexCarrying level in different periods
Food and Agriculture Organization of the United Nations Engel coefficient (%) Basic stage – adequately fed and clad Well-off stage Wealthy stage Most affluent stage 
59–50 50–40 40–30 <30 
World Bank Per capita GDP (dollar) Low-income countries Middle- and lower-income countries Middle- and higher-income countries High-income countries 
<760 761–3,030 3,031–9,630 >9,631 
Chinese development strategy Per capita GDP (dollar) Basic level – adequately fed and clad Well-off level I Well-off level II Basic modernization 
400 800 3,000 9,000 
Reference standardIndexCarrying level in different periods
Food and Agriculture Organization of the United Nations Engel coefficient (%) Basic stage – adequately fed and clad Well-off stage Wealthy stage Most affluent stage 
59–50 50–40 40–30 <30 
World Bank Per capita GDP (dollar) Low-income countries Middle- and lower-income countries Middle- and higher-income countries High-income countries 
<760 761–3,030 3,031–9,630 >9,631 
Chinese development strategy Per capita GDP (dollar) Basic level – adequately fed and clad Well-off level I Well-off level II Basic modernization 
400 800 3,000 9,000 

Methods

Although water resources are limited, the exact amount of water available from the current environment is unknown (Beuhler, 2003). A threshold exists for water resources supplied by any body of water, including rivers, lakes and groundwater, beyond which the ecological environment will enter a degenerative cycle (Ghassemi et al., 1997; Falkenmark & Lundqvist, 1998). In this paper, we calculate a water supply–demand balance index and the WRCC on the basis of the water supply and the volume of water resource demand. Because local water resources are insufficient in Jiangsu Province, it mainly depends on water resources in transit. The amount of water resources flowing through the Yangtze River is large and undeterminable, so the water supply available is used to quantify the water resources. This term refers to the regional water supply capacity, which is the maximum water supply that can be provided in a certain region according to the water inflow conditions, water demand requirements, engineering conditions, application mode, and scheduling rules. Water demand represents the amount of water that is judged necessary to meet the needs of regional development.

Nonlinear dynamics are used to establish a water resources dynamics model for Jiangsu Province to predict the future water resource carrying status and provide a scientific reference for the sustainable development of water resources and of the national economy in Jiangsu Province (Zhang et al., 2008a, 2008b).

Basic concepts

First, this paper determines the water supply available in different periods and calculates the total GDP, industrial structure, and water efficiency level for each period (Jin et al., 2018). WRCC is then calculated from the perspectives of the subject and object of water resources and their mutual relations. The maximum supporting capacity of the carrying subject (water resources) to the carrying object (economic aggregate and population) is determined, and the maximum scale of economic aggregate and population that can be carried by the regional water resources under a certain carrying standard is estimated (Song & Zhan, 2011; Jin et al., 2018).

  • 1.

    Economic scale supported by regional water resources (GDPc)

GDPc is one of the macro-indices of regional WRCC. It is the ratio of regional GDP to the amount of water consumed to produce that GDP. When the amount of water consumed is equal to the regional water supply available, the economic scale of the water resources is the maximum economic scale (Zhang et al., 2007).
  • 2.

    Population size supported by regional water resources (Pc)

Pc is another important macro-index. It is calculated based on the level of social development at a certain developmental stage and the GDP at that stage in the study area. Consumption levels are different at different levels of social development, so Pc is intimately linked to the level of social development.

This modeling calculation mainly consists of calculating the quantity of water available in different periods, calculation and inspection of WRCC, and identification of the carrying status of the regional water resources (Zhang et al., 2007).

Calculation steps

  • 1.

    Calculate the water supply available for production in different periods, that is, the water supply available in different periods with domestic water and water for the ecological environment (outside river systems) removed.

  • 2.

    Calculate and inspect the regional WRCC. The economic scale that can be carried by regional water resources can be obtained by dividing the water supply available for production by the per unit GDP water consumption. The population size that can be carried by regional water resources can be obtained by dividing the total economic output that can be carried by regional water resources by the per capita GDP at a certain carrying level.

    • (1)
      The dynamic prediction model is
      formula
      (1)
      where represents variables, is the annual average growth rate, and t represents time.
    • (2)
      The water supply–demand balance index is calculated with
      formula
      (2)
      where WS is the water supply available for production, and WD is the total water demand for production.
    • When the water supply available is less than the volume of water demanded by the regional socio-economic system, WS < WD and < 0, which indicates that the water supply available does not have the ability to support such a socio-economic scale. However, can be improved by increasing WS by water transfer and decreasing WD through water-saving measures. Conversely, when the water supply available is greater than or equal to the volume of water demanded by the socio-economic system, WSWD, the regional water supply available is able to support a socio-economic system of that scale. The water supply and demand are in good balance.

    • (3)
      The expression of the economic scale supported by regional water resources is
      formula
      (3)
      where is the maximum economic scale supportable by the regional water resources.
    • The ratio of GDP to represents the economic scale supportable by the unit water volume, which is the ratio of the sum of all of the final products to the amount of water used to produce these products in the study area.

    • (4)
      The calculation of the population size supported by regional water resources is
      formula
      (4)
      where is the size of the population available to convert the water supply available into products under a certain level of social development, that is, the maximum population size supportable by the water resources. is the lower limit of per capita GDP for a given level of social development.
    • Since the estimated domestic water consumption is used in the calculation of WRCC mentioned above, the reasonableness of this estimate should be tested after obtaining WRCC. This is done by comparing the domestic water consumption with the domestic water consumption that can be supported at a certain carrying level. If the two are similar, the result is reasonable. Otherwise, the estimated domestic water consumption should be adjusted accordingly by strengthening water management and using more efficient appliances. The calculated bearable population should be compared with the estimated total population. If the two are similar, the result indicates that the calculation meets the requirements. If the two are dissimilar, the estimated domestic water consumption does require adjustment.

  • 3.
    Identify the regional water resource carrying status. The economic scale and the population size that the above-mentioned calculations indicate can be carried are compared with the actual economic aggregates and total populations during the regional study period. We define and as the economic carrying index and the population carrying index, respectively. The carrying index is used to determine whether the carrying capacity of the regional water resources in the research period is in a surplus, critical, or overloaded state.
    formula
    (5)
    formula
    (6)
    where is the actual economic scale in any year, and is the actual population size in any year (Table 2).

Table 2

Division criterion of the carrying index.

Basic typesSub-typesCarrying index
Overload (IHighly overloaded (IA≥2 
Moderately overloaded (IB1.5–2 
Slightly overloaded (IC1–1.5 
Critical (II– 
Surplus (IIIHigh surplus (IIIA<0.5 
Moderate surplus (IIIB0.5–2/3 
Slight surplus (IIIC2/3–1 
Basic typesSub-typesCarrying index
Overload (IHighly overloaded (IA≥2 
Moderately overloaded (IB1.5–2 
Slightly overloaded (IC1–1.5 
Critical (II– 
Surplus (IIIHigh surplus (IIIA<0.5 
Moderate surplus (IIIB0.5–2/3 
Slight surplus (IIIC2/3–1 

Economic and population predictions for Jiangsu Province

Economic predictions

Table 3 shows the dynamic changes in Jiangsu Province's GDP from 2001 to 2017. The data are from the ‘Jiangsu Statistical Yearbook, 2002–2018’. According to the data in the table, the province has an average annual GDP growth rate of 14.65%. The GDP of Jiangsu Province has been in a state of rapid growth in recent years, with an average annual growth rate of 18.8% from 2003 to 2011. Such a high growth rate also brings various social risks. Therefore, Jiangsu Province has also taken corresponding macro-control measures so that it maintained a growth rate of 9.52% from 2012 to 2017. According to the planning goal of the ‘13th Five-Year Plan For National Economic And Social Development in Jiangsu Province, 2017’, the average GDP growth rate during the period covered by the plan will be 10%. Therefore, the medium and long-term GDP in Jiangsu Province is predicted according to the relevant economic and urban plans of Jiangsu Province by taking the GDP of 2017 as the initial value and applying a GDP growth rate of 10% during the ‘13th Five-Year Plan’ period, 9% during the ‘14th Five-Year Plan’ period, and 8% during the ‘15th Five-Year Plan’ period (Table 4).

Table 3

GDP statistics from 2001 to 2017 in Jiangsu Province.

Year200120022003200420052006200720082009
GDP/108 RMB 9,456.8 10,606.9 12,442.9 15,136.8 18,769.3 21,965.6 26,297.8 31,360.6 34,912.0 
Growth rate/% 10.56 12.16 17.31 21.65 24.00 17.03 19.72 19.25 11.32 
Year 2010 2011 2012 2013 2014 2015 2016 2017  
GDP/108 RMB 41,971.3 49,801.6 54,888.8 60,712.8 66,150.6 71,289.5 77,388.3 85,900.9  
Growth rate/% 20.22 18.66 10.22 10.61 8.96 7.77 8.55 11.00  
Year200120022003200420052006200720082009
GDP/108 RMB 9,456.8 10,606.9 12,442.9 15,136.8 18,769.3 21,965.6 26,297.8 31,360.6 34,912.0 
Growth rate/% 10.56 12.16 17.31 21.65 24.00 17.03 19.72 19.25 11.32 
Year 2010 2011 2012 2013 2014 2015 2016 2017  
GDP/108 RMB 41,971.3 49,801.6 54,888.8 60,712.8 66,150.6 71,289.5 77,388.3 85,900.9  
Growth rate/% 20.22 18.66 10.22 10.61 8.96 7.77 8.55 11.00  
Table 4

Medium and long-term predictions for GDP in Jiangsu Province.

Year2018201920202021202220232024
GDP/108 RMB 94,491.0 103,940.1 114,334.2 124,624.2 135,840.4 148,066.0 161,392.0 
Year 2025 2026 2027 2028 2029 2030  
GDP/108 RMB 175,917.3 189,990.64 205,189.9 221,605.1 239,333.5 258,480.2  
Year2018201920202021202220232024
GDP/108 RMB 94,491.0 103,940.1 114,334.2 124,624.2 135,840.4 148,066.0 161,392.0 
Year 2025 2026 2027 2028 2029 2030  
GDP/108 RMB 175,917.3 189,990.64 205,189.9 221,605.1 239,333.5 258,480.2  

Population predictions

The average annual growth rate of the population of the province is calculated to be 0.54% on the basis of the ‘Jiangsu Statistical Yearbook’. Taking the population of 2001 as the initial value, the dynamics model was used to predict medium and long-term increase in the population of Jiangsu Province (Table 5). Comparison with the actual population allowed the average annual error to be calculated to be −0.814%, −0.185% in the past 3 years. By revising the population growth rate and combining this with the ‘13th Five-Year Plan for Population Development in Jiangsu Province’, the population in 2017 is taken as the initial value (8029.3 × 104), and an average annual growth rate of 0.5% is applied to predict the medium and long-term population numbers in Jiangsu Province (Table 6).

Table 5

Population statistics from 2001 to 2017 for Jiangsu Province.

Year200120022003200420052006200720082009
Actual population/104 persons 7,358.5 7,405.5 7,457.7 7,523.0 7,588.2 7,655.7 7,723.1 7,762.5 7,810.3 
Growth rate/% 0.427 0.638 0.705 0.875 0.868 0.888 0.881 0.510 0.616 
Forecast population/104 persons 7,366.8 7,406.6 7,446.6 7,486.8 7,527.2 7,567.9 7,608.7 7,649.8 7,691.1 
Relative error/% 0.113 0.015 −0.149 −0.481 −0.804 −1.147 −1.481 −1.451 −1.526 
Year 2010 2011 2012 2013 2014 2015 2016 2017  
Actual population/104 persons 7,869.3 7,898.8 7,920.0 7,939.5 7,960.1 7,976.3 7,998.6 8,029.3  
Growth Rate/% 0.756 0.374 0.268 0.246 0.259 0.204 0.280 0.384  
Forecast population/104 persons 7,732.6 7,774.4 7,816.4 7,858.6 7,901.0 7,943.7 7,986.6 8,029.7  
Relative error/% −1.737 −1.575 −1.308 −1.019 −0.742 −0.409 −0.150 0.005  
Year200120022003200420052006200720082009
Actual population/104 persons 7,358.5 7,405.5 7,457.7 7,523.0 7,588.2 7,655.7 7,723.1 7,762.5 7,810.3 
Growth rate/% 0.427 0.638 0.705 0.875 0.868 0.888 0.881 0.510 0.616 
Forecast population/104 persons 7,366.8 7,406.6 7,446.6 7,486.8 7,527.2 7,567.9 7,608.7 7,649.8 7,691.1 
Relative error/% 0.113 0.015 −0.149 −0.481 −0.804 −1.147 −1.481 −1.451 −1.526 
Year 2010 2011 2012 2013 2014 2015 2016 2017  
Actual population/104 persons 7,869.3 7,898.8 7,920.0 7,939.5 7,960.1 7,976.3 7,998.6 8,029.3  
Growth Rate/% 0.756 0.374 0.268 0.246 0.259 0.204 0.280 0.384  
Forecast population/104 persons 7,732.6 7,774.4 7,816.4 7,858.6 7,901.0 7,943.7 7,986.6 8,029.7  
Relative error/% −1.737 −1.575 −1.308 −1.019 −0.742 −0.409 −0.150 0.005  
Table 6

Medium and long-term predictions for population in Jiangsu Province.

Year2018201920202021202220232024
Forecast population/104 persons 8,069.4 8,109.8 8,150.3 8,191.1 8,232.0 8,273.2 8,314.6 
Year 2025 2026 2027 2028 2029 2030  
Forecast population/104 persons 8,356.1 8,397.9 8,439.9 8,482.1 8,524.5 8,567.2  
Year2018201920202021202220232024
Forecast population/104 persons 8,069.4 8,109.8 8,150.3 8,191.1 8,232.0 8,273.2 8,314.6 
Year 2025 2026 2027 2028 2029 2030  
Forecast population/104 persons 8,356.1 8,397.9 8,439.9 8,482.1 8,524.5 8,567.2  

Evaluation of the current WRCC

According to the above division of living standards, the per capita GDP of Jiangsu Province is taken as the index to determine the year when the province reached the four carrying levels of a basic level of living, where people are adequately fed and clad, well-off level I, well-off level II, and basic modernization. According to the exchange rate of the US dollar to the RMB in 2000, the per capita GDP reflecting the four carrying levels is 3,312 yuan, 6,422 yuan, 24,840 yuan, and 74,520 yuan, respectively. According to yearly GDP from 1978 to 2017, the per capita GDP was converted to the 2000-price GDP. It was found that the four carrying levels were reached in 1993, 1995, 2006, and 2013, respectively.

In this model, WRCC is calculated based on the minimum standard of living (GDP per capita) at each developmental stage. As shown by Table 7 and Figure 2, IWSD exhibited a downward trend from 2003 to 2011 and an upward trend from 2011 to 2017 in Jiangsu Province. It dipped below zero from 2004 to 2012, indicating that the water supply available in these years did not have enough carrying capacity to support the contemporaneous socio-economic system. IWSD values were greater than zero from 2013 to 2017, indicating that the water supply available has had the carrying capacity to support the socio-economic system in recent years. This can be attributed to an increase in water conservation. According to Table 6 and Figure 3, there is little difference between the actual GDP and the economic scale that can be supported (GDPc) in Jiangsu Province. As can be seen from Table 6 and Figure 4, the actual population is lower than the population that can be supported (Pc) in Jiangsu Province. exhibited an upward trend from 2003 to 2011, rising from 0.84 to 1.13, indicating that the capacity of the water resources to support the economic scale was gradually reducing during that period; this was a period of rapid economic development in China. From 2011 to 2016, was in a downward trend, from 1.13 to 0.9, indicating that the capacity of water resources to support the economy has been gradually increased in recent years. had always been in a surplus state, indicating a state of sustainable development. Although Pc increased during some developmental stages, the overall trend was downward from 2002 to 2017. From 2002 to 2005, development was at well-off level I, and dropped from 0.43 to 0.28, which is high surplus (IIIA). From 2006 to 2012, it was in well-off level II, and dropped from 0.94 to 0.4, which is slight surplus (IIIC) transitioning to high surplus (IIIA). 2013 to 2017 was in the basic modernization stage, and dropped from 0.98 to 0.65, which is slight surplus (IIIC) transitioning to moderate surplus (IIIB).

Table 7

WRCC from 2002 to 2017 in Jiangsu Province.

YearGDPc (108 RMB)Pc (104)IWSD
2002 11,371.4 17,166.9 0.07 0.93 0.43 
2003 14,872.8 22,452.9 0.16 0.84 0.33 
2004 14,611.1 22,057.8 − 0.04 1.04 0.34 
2005 18,000.9 27,175.2 − 0.04 1.04 0.28 
2006 20,132.4 8,104.8 − 0.09 1.09 0.94 
2007 23,860.3 9,605.6 − 0.10 1.10 0.80 
2008 28,229.8 11,364.7 − 0.11 1.11 0.68 
2009 31,433.6 12,654.4 − 0.11 1.11 0.62 
2010 37,567.6 15,123.8 − 0.12 1.12 0.52 
2011 44,236.9 17,808.7 − 0.13 1.13 0.44 
2012 49,131.4 19,779.2 − 0.12 1.12 0.40 
2013 60,615.7 8,134.2 0.00 1.00 0.98 
2014 68,688.4 9,217.4 0.04 0.96 0.86 
2015 77,432.6 10,390.9 0.08 0.92 0.77 
2016 85,575.0 11,483.5 0.10 0.90 0.70 
2017 92,198.4 12,372.3 0.07 0.93 0.65 
YearGDPc (108 RMB)Pc (104)IWSD
2002 11,371.4 17,166.9 0.07 0.93 0.43 
2003 14,872.8 22,452.9 0.16 0.84 0.33 
2004 14,611.1 22,057.8 − 0.04 1.04 0.34 
2005 18,000.9 27,175.2 − 0.04 1.04 0.28 
2006 20,132.4 8,104.8 − 0.09 1.09 0.94 
2007 23,860.3 9,605.6 − 0.10 1.10 0.80 
2008 28,229.8 11,364.7 − 0.11 1.11 0.68 
2009 31,433.6 12,654.4 − 0.11 1.11 0.62 
2010 37,567.6 15,123.8 − 0.12 1.12 0.52 
2011 44,236.9 17,808.7 − 0.13 1.13 0.44 
2012 49,131.4 19,779.2 − 0.12 1.12 0.40 
2013 60,615.7 8,134.2 0.00 1.00 0.98 
2014 68,688.4 9,217.4 0.04 0.96 0.86 
2015 77,432.6 10,390.9 0.08 0.92 0.77 
2016 85,575.0 11,483.5 0.10 0.90 0.70 
2017 92,198.4 12,372.3 0.07 0.93 0.65 
Fig. 2

IWSD values in Jiangsu Province.

Fig. 2

IWSD values in Jiangsu Province.

Close modal
Fig. 3

GDP values in Jiangsu Province.

Fig. 3

GDP values in Jiangsu Province.

Close modal
Fig. 4

Population values in Jiangsu Province.

Fig. 4

Population values in Jiangsu Province.

Close modal

By comparing the change trend of China's water resources carrying index IWSD, it can be found that the data for 2011–2017 was obtained. The change trend of IWSD from 2011 to 2017 is basically the same as that of Jiangsu Province, and both are gradually increasing. China's quantity and change trend are basically the same as that of Jiangsu Province. However, China's is much higher than that of Jiangsu Province. This is because Jiangsu's economic development is at the forefront of the country, so it can carry more people. It also shows the overpopulation of China. Through the comparison between China and Jiangsu Province, it can be found that the WRCC evaluation model established in this paper is reasonable and extendable (Table 8).

Table 8

WRCC from 2011 to 2017 in China.

YearGDPc (108 RMB)Pc (104)IWSD
2011 490,268.8 197,370.7 0.01 0.99 0.68 
2012 551,682.1 222,094.2 0.03 0.97 0.61 
2013 604,178.3 81,076.0 0.03 0.97 1.69 
2014 666,945.8 89,498.9 0.03 0.97 1.54 
2015 711,592.1 95,490.1 0.04 0.96 1.45 
2016 790,872.2 106,128.9 0.06 0.94 1.31 
2017 893,472.8 119,897.1 0.07 0.93 1.17 
YearGDPc (108 RMB)Pc (104)IWSD
2011 490,268.8 197,370.7 0.01 0.99 0.68 
2012 551,682.1 222,094.2 0.03 0.97 0.61 
2013 604,178.3 81,076.0 0.03 0.97 1.69 
2014 666,945.8 89,498.9 0.03 0.97 1.54 
2015 711,592.1 95,490.1 0.04 0.96 1.45 
2016 790,872.2 106,128.9 0.06 0.94 1.31 
2017 893,472.8 119,897.1 0.07 0.93 1.17 

WRCC prediction results

In terms of living standards and economic strength, Jiangsu Province has always been at the forefront of China's development and has essentially achieved modernization. At present, the living standard in Jiangsu Province has reached the lowest income level of a moderately developed country. The two papers, ‘The main economic indicators of the comprehensive modernization and their realization path’ and ‘The income standard of moderately developed countries by 2050 calculated from the long-term growth trend of all countries in the world’, predicted the range in income standards for moderately developed countries in the future through trend analysis of the income standards of moderately developed countries in the Conference Board world economic database. From this, it can be concluded that the GDPP of moderately developed countries will be 16.03 × 104 yuan in 2020 and 19.54 × 104 yuan in 2030. The above data are converted according to the exchange rate of the US dollar against the RMB in 2012.

Developmental scenario settings

  • 1.

    Scenario A

In terms of economy, Table 4 shows that the GDP is forecast to increase from 94491 × 108 yuan to 258480 × 108 yuan from 2018 to 2030, an increase of 1.73 times. According to the planning target of the ‘13th Five-Year Plan For National Economic And Social Development in Jiangsu Province, 2017’ and the ‘Bulletin Of National Economic And Social Development in Jiangsu Province, 2002–2017’, the proportions of primary, secondary and tertiary industry will be adjusted from 0.05:0.45:0.5 to 0.05:0.359:0.591 over that period. Primary industry will maintain a contribution of 5%, that of tertiary industry will increase by 1.2% every year, from 50 to 59.1%, and secondary industry will be correspondingly reduced.

In terms of water, the average annual water consumption of primary industry is 284.1 × 104m3, which is a decline at the rate of 0.8%. Water consumption by secondary industry will increase at a rate of 3% from 2017. Tertiary industry will increase at a rate of 4% from 2017, and the water consumption by the ecological environment (outside river systems) will increase at a rate of 4% from 2017. According to ‘Long-Term Water Planning in Jiangsu Province, 2015’, per capita daily domestic water consumption will be 139.8 L in 2020 and 152.3 L in 2030.

The data regarding various indicators under different carrying levels in Jiangsu Province are shown in Table 9.

  • 2.

    Scenario B

Table 9

Data for various indicators under different carrying levels in Jiangsu Province.

Year2020
2030
IndexWater consumption quotaIndustrial added valueWater consumptionWater consumption quotaIndustrial added valueWater consumption
(m3/104 RMB)(108 RMB)(108m3)(m3/104 RMB)(108 RMB)(108m3)
Primary industry 485.2 5,716.7 277.4 198.1 12,924.0 256.0 
Secondary industry 27.8 49,838.5 138.8 20.1 92,851.4 186.5 
Tertiary industry 3.3 58,779.0 19.7 1.9 152,704.8 29.1 
Ecological environment water (108m32.36 4.23 
Per capita daily domestic water (L) 139.8 152.3 
Year2020
2030
IndexWater consumption quotaIndustrial added valueWater consumptionWater consumption quotaIndustrial added valueWater consumption
(m3/104 RMB)(108 RMB)(108m3)(m3/104 RMB)(108 RMB)(108m3)
Primary industry 485.2 5,716.7 277.4 198.1 12,924.0 256.0 
Secondary industry 27.8 49,838.5 138.8 20.1 92,851.4 186.5 
Tertiary industry 3.3 58,779.0 19.7 1.9 152,704.8 29.1 
Ecological environment water (108m32.36 4.23 
Per capita daily domestic water (L) 139.8 152.3 
The statistical results plotted in Figure 5 show that water consumption per ten thousand yuan of GDP reduced from 2002 to 2017, falling from 451.4 m3 in 2002 to 54.2 m3 in 2017. In the past 16 years, water consumption per ten thousand yuan of GDP has fallen to only one-eighth of the 2002 value. Exponential fitting of the statistical results gives the function y = 3E + 127e^-0.144x for water consumption per ten thousand yuan of GDP with time. The fit is good since 2005, indicating that it can be used as a prediction formula for water consumption per ten thousand yuan of GDP for a period of time into the future. Application of this formula gives a water consumption per ten thousand yuan of GDP of 38.7 m3 in 2020. According to the long-term planning and economic forecast results for Jiangsu Province, the water consumption per ten thousand yuan of GDP will be 20.4 m3 in 2030.

Fig. 5

Water consumption per ten thousand yuan of GDP in Jiangsu Province.

Fig. 5

Water consumption per ten thousand yuan of GDP in Jiangsu Province.

Close modal

Prediction results

It can be seen from Table 10 that the medium and long-term IWSD is greater than zero in Jiangsu Province under both scenario A and scenario B, which indicates that the social and economic system is sustainable by the water resources. The economic scale and population size are both sustainable. However, a comparison of the results for 2020 and 2030 indicates that IWSD is decreasing and is increasing, indicating that the load-bearing status is becoming worse in these two aspects. However, is decreasing, indicating that the carrying status of the population is improving.

Table 10

Calculated results for variables relevant to WRCC in Jiangsu Province.

ScenarioYearWater demandWater supplyIWSDGDPcPcCIeCIp
104104108104
Scenario A 2020 435.8 616.1 0.293 161,618.8 10,082.4 0.707 0.808 
2030 471.6 621.5 0.241 340,639.3 17,432.6 0.759 0.491 
Scenario B 2020 399.0 616.1 0.352 176,537.6 11,013.0 0.648 0.740 
2030 475.7 621.5 0.235 337,708.7 17,282.7 0.765 0.496 
ScenarioYearWater demandWater supplyIWSDGDPcPcCIeCIp
104104108104
Scenario A 2020 435.8 616.1 0.293 161,618.8 10,082.4 0.707 0.808 
2030 471.6 621.5 0.241 340,639.3 17,432.6 0.759 0.491 
Scenario B 2020 399.0 616.1 0.352 176,537.6 11,013.0 0.648 0.740 
2030 475.7 621.5 0.235 337,708.7 17,282.7 0.765 0.496 

Scenario A is based on the planned change in the industrial structure in Jiangsu Province and the change trends in the water consumption by various levels of the industry. According to the predicted results, the water supply available for production will be 616.1 × 108 m3 in 2020 and 621.5 × 108 m3 in 2030, a water supply capacity increase of 5.4 × 108 m3. The water demand for production will be 435.8 × 108 m3 in 2020 and 471.6 × 108 m3 in 2030, a water demand increase of 35.8 × 108 m3.

Scenario B is based on the predicted water consumption per ten thousand yuan of GDP in Jiangsu Province, combined with the planning target in ‘Long-Term Water Planning in Jiangsu Province, 2015’. According to the predictions, the water supply available for production is the same as in scenario A, but the water demand for production is 399 × 108 m3 in 2020 and 475.7 × 108 m3 in 2030, an increase of 76.7 × 108 m3. The fluctuation of the total water demand is slightly larger than under scenario A.

It can be seen from the above results that WRCC is good in Jiangsu Province at present, but water resources may become a problem in the future. IWSD is getting smaller and smaller. Water resources are becoming scarce with the development of the economy. Therefore, the author suggests that three measures be taken. First, macro-control should be strengthened to appropriately control the growth rate of GDP and reduce per unit GDP and per capita domestic water consumption. Second, the economic structure should be adjusted to establish an economic system that is compatible with WRCC. Third, revenue should be increased and expenditure reduced to reduce pollution and waste. Specific measures include increasing the intensity of sewage treatment, increasing the utilization rate of unconventional water, increasing external water transfer, building a new water supply project, collecting and using rainwater, etc.

Regulation is a combination of adjustment and control (Song & Zhan, 2011; Jin et al., 2018). WRCC regulation aims at improving water resources carrying capacity, from making changes to the current state to the promotion of future ability, through measures regulating both support and load. Its connotations include enhancing carrying capacity and making it more sustainable (Gao & Liu, 1997). The key technologies for the regulation of WRCC include the regulation of water resource availability at the subjective level of WRCC and the regulation of water consumption per unit of GDP at the objective level of WRCC. The regulation of water resource availability for production involves the regulation of water resource availability as a whole and of ecological water demand outside river systems (Li & Jin, 2009; Zhu et al., 2010). The regulation of integrated water consumption per unit of GDP includes the regulation of the industrial structure and water quota. When regional WRCC evaluation results show that an area is in an overloaded or critically overloaded condition, decision-makers achieve the goal of strengthening the WRCC and the sustainable carrying capacity of water resources by instituting regulatory measures that improve the supporting force of the bearing subject and alleviate the load from the carried object (Sun et al., 2014).

In order to further improve WRCC and realize sustainable support from regional water resources, this paper preliminarily discusses carrying capacity regulation by taking the plan leading to water resource capacity scenario A in 2020 as its research object and considering two factors, the water consumption quota and industrial structure.

The regulation of industrial structure is reflected in the adjustment of the GDP ratio in different industries. In view of the decline in secondary industry and increase in tertiary industry in Jiangsu Province in recent years, this paper will adjust the initial industrial structure (primary: secondary: tertiary industry) from 0.05:0.44:0.51 to 0.05:0.39:0.56.

The regulation of water quotas is implemented through a reduction in the water quotas for primary, secondary, and tertiary industry and domestic water use through measures such as water conservation and efficient use of water resources. The optimal allocation of water resources can be achieved by optimizing the industrial structure, reducing high water-consuming industries, and giving priority to the development of water-saving enterprises, etc. In this paper, the water quota for the primary industry will be reduced from 485.2 m3/ × 104 yuan to 436.7 m3/ × 104 yuan, that for the secondary industry will be reduced from 27.8 m3/ × 104 yuan to 25.1 m3/ × 104 yuan, and that for the tertiary industry will be reduced from 3.3 m3/ × 104 yuan to 3 m3/ × 104 yuan. The domestic water quota is reduced from 139.8 L/(person·d) to 125.82 L/(person·d).

Calculations are made for different load control schemes based on the above adjustments. Scheme 1 only considers the adjustment of the industrial structure. Scheme 2 only considers the regulation of the water quota. Scheme 3 comprehensively considers the regulation of the water quota and industrial structure. The results of these controls are shown in Table 11.

Table 11

Results of variables relevant to WRCC regulation in Jiangsu Province.

YearSchemeWater demandWater supplyIWSDGDPcPcCIeCIp
104104108104
2020 Scheme 1 423.0 616.08 0.313 166,531.1 10,388.8 0.687 0.785 
Scheme 2 392.3 620.24 0.368 180,789.0 11,278.3 0.632 0.723 
Scheme 3 380.7 620.24 0.386 186,283.9 11,621.0 0.614 0.701 
YearSchemeWater demandWater supplyIWSDGDPcPcCIeCIp
104104108104
2020 Scheme 1 423.0 616.08 0.313 166,531.1 10,388.8 0.687 0.785 
Scheme 2 392.3 620.24 0.368 180,789.0 11,278.3 0.632 0.723 
Scheme 3 380.7 620.24 0.386 186,283.9 11,621.0 0.614 0.701 

It can be seen from the table that all of the regulation schemes cause the water resources to have better economic and population carrying capacity than under the initial scheme. From the perspective of single control measures, the adjustment of the water quota in scheme 2 has a stronger effect than the adjustment of the industrial structure in scheme 1. The use of both measures has the best effect.

The WRCC regulation system is the key subsystem for improving WRCC. It can be assessed from the perspective of subject or object. This paper makes a preliminary discussion from the perspective of the object. In future research, it will be necessary to comprehensively consider the role of the subjective factor, with the goal of developing a WRCC regulation scheme that couples subject and object.

Based on existing work on regional WRCC evaluation, this paper proposes the concept of a regional WRCC system consisting of three subsystems: the supporting capacity of, load on, and capacity for regulation of regional water resources. Through the integration of resource availability, economic and social development, water resources development and utilization, water resource management and other aspects of regional WRCC, this paper analyzes the main factors affecting WRCC and focuses on solving the technical obstacles to extracting significant relationship factors between WRCC and a long series, multi-factor, high volume data set. The study proposes an evaluation method for regional WRCC under different carrying levels.

The paper scientifically analyzes the mechanisms and factors that impact WRCC. A WRCC evaluation model is put forward for Jiangsu Province under different loading standards that is based on the concepts of IWSD and CI and takes the perspectives of the subject and object of WRCC. Analysis and verification against real-world data indicate that the method is realistic and easy to calculate and provides satisfactory experimental results. This method can be extended to WRCC evaluation in other regions.

Through research on the regulation of WRCC, we find that WRCC can be increased by changing the trend in industrial structure development and reducing water consumption quotas. Sustainable development can also be achieved in the foreseeable future by establishing and implementing water resource mitigation and development programs such as water transfer projects, water conservation, wastewater reuse, and seawater desalination.

Regional WRCC is a complex subject. This paper has made further explorations on the basis of existing research results. With future research, we aim to further improve the carrying-level-based regional WRCC evaluation model by incorporating system risk theory to provide new ideas to regional WRCC evaluation research. The key system for improving WRCC in the future is the subsystem of water resource carrying regulation. Further research will have the goal of developing a complete WRCC evaluation system based on supporting capacity, load, and regulation capacity.

This work was financially supported by the National Key Research and Development Program of China [Grant numbers: 2018YFC0407206, 2018YFC04065056, 2016YFC0401305].

The Supplementary Material for this paper is available online at http://tj.jiangsu.gov.cn/col/col4091/index.html (Jiangsu Statistical Yearbook, 2002–2017), http://jswater.jiangsu.gov.cn/col/col51453/index.html (Jiangsu Province Water Resources Bulletin, 2002–2017), https://1drv.ms/w/s!AuQ0zMqwfDDdkS0cZT_e8rPMq7c7?e=jjyTLB (Jiangsu Mid-Long Term Supply and Demand Plan, 2015), http://www.jiangsu.gov.cn/art/2017/5/22/art_46484_2557495.html (13th Five-Year Plan For National Economic And Social Development in Jiangsu Province, 2017), http://www.jiangsu.gov.cn/art/2018/2/22/art_34151_7492227.html (Bulletin Of National Economic And Social Development in Jiangsu Province, 2002–2017).

All relevant data are included in the paper or its Supplementary Information.

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