A system dynamics optimization model of the industrial structure of Tieling City based on water environmental carrying capacity has been established. This system is divided into the following subsystems: water resources, economics, population, contaminants, and agriculture and husbandry. Three schemes were designed to simulate the model from 2011 to 2020, and these schemes were compared to obtain an optimal social and economic development model in Tieling City. Policy recommendations on industrial structure optimization based on the optimal solution provide scientific decision-making advice to develop a strong and sustainable economy in Tieling.

INTRODUCTION

In the process of economic development, achieving regional sustainable development to coordinate between human activities and the water environment has become pressing. It is widely agreed that increasing water consumption jeopardizes water resources sustainability, and water resources play an important role in the water environmental carrying capacity (WECC). Moreover, Marques considers the principles of water regulation that promote improvements of water resources utilization in developing countries (Marques 2010). In this regard, comprehensive strategies must be developed in the control process of water environment treatment. WECC is based on sustainable development, and it can satisfy a certain standard of water environmental quality, contain pollutants, and sustain population growth and social and economic development (Zhao & Qian 2005a, b; Weng & Nie 2009). WECC describes the degree of coordination between human economic activity and the water environment (Falkenmark & Lundqvist 1998). As the demand quantities and structures for water of primary, secondary, and tertiary industries differ, an unreasonable industrial structure deteriorates the water environment. In turn, the deterioration of the water environment affects healthy regional economic development (Armentrout 1987). It is then critical both practically and strategically to promote the healthy development of an economy through research on optimization of industrial structures based on the water environment. If this can be implemented well in a city, it will have a positive effect on economic and social development. Moreover, the identification of practices in other countries in similar situations suggests that there is room for improvement in water resources utilization (Marques et al. 2013). Currently, there are few quantitative research methods in this area. General methods used for such research are multi-objective optimization, input–output analysis, gray relational analysis, and system dynamics (SD) (Ye et al. 2013). The multi-objective optimization method does not imply the pursuit of a single goal but rather of overall interests. It analyzes problems comprehensively and with strong logical, comprehensive, and systematic results (Zhao & Liu 2008). The input–output analysis method researches the relationship among various sectors comprehensively and is conducted with great integrity (Franke & Kalmbach 2005). Gray relational analysis takes the degree of similarity or dissimilarity between trends of factors to measure the degree of association between factors (Li et al. 2011). The WECC and industrial structures are large and complex systems that involve water resources, economics, the population, and pollutant discharge systems. Therefore, we used the SD method, which is fit for non-linear, high-order, multivariate and long-term forecasting analysis, to predict trends in water resources, population, the economy, and emissions over different schemes. Then, we optimize the industrial structure according to the results, which can provide a reference for future economic development in Tieling City. In addition to this, the division of subsystems in our model is novel, and is more compatible with the actuality of Tieling City.

In addition to the brief introduction above, the paper also includes the following contents: general conditions of Tieling City, an industrial structure optimization model for Tieling City based on the WECC, SD simulation and results on industrial structure optimization of Tieling City, and the conclusion.

GENERAL CONDITIONS OF TIELING CITY

Tieling City is located in the northern part of Liaoning Province, and it includes eight administrative regions. The total population in Tieling City in 2010 was 3.054 million, and the urban population was 1.002 million. The population density of Tieling City reached 235.3 persons per square kilometer in 2010. Total water consumed was 1.097 billion m3, and the gross domestic product (GDP) was 72.213 billion yuan. The ratio among primary, secondary, and tertiary industries was 20:53:27, respectively.

Agriculture and husbandry are the dominant primary industries. In 2010, the output value of animal husbandry was 21.5 billion yuan, and that of agriculture was 13.65 billion yuan. Industry in Tieling has developed quickly, and the industrial output value reached 38.113 billion yuan in 2010. Tieling City has also paid significant attention to the development of the service industry in recent years.

Despite rapid economic development, the level of economic and social development of Tieling City remains relatively low. The total output value is small, and the industrial structure is not properly balanced, as the ratio among the primary, secondary, and tertiary industries of Tieling City was 20:52:28, respectively, in 2010. However, the trend of extensive economic growth has not fundamentally changed, which will result in a waste of resources and in environmental pollution; we aim to optimize industrial structure to a proper degree.

The only major waterway in Tieling City is the Tieling section of the upper reaches of the Liaohe River. Tieling City is characterized by water scarcity and relatively low income. According to the second water resources assessment of Liaoning province in the 21st century, average per-capita water resource of Tieling City (850 cubic meters per person) is lower than half of the national level (2180 cubic meters per person), which has been considered one of the main constraints for economic development. In 2010, total sewage emissions were 74.85 million tons; chemical oxygen demand (COD) emissions were 99,697 tons; and industrial, household, livestock, and non-point source emissions were 2,453 tons, 21,269 tons, 55,915 tons, and 20,060 tons, respectively.

INDUSTRIAL STRUCTURE OPTIMIZATION MODEL IN TIELING CITY BASED ON THE WECC

SD

SD studies feedback systems and problems (Wang 1995). In SD theory, system behavior is determined using internal feedback mechanisms, and the dynamic relationship of structure, function, and behavior can be improved using an SD model. SD models can also be used as social, economic, and ecological strategic labs for very large complex systems (Wang 2004) to provide good decisions. Since China introduced SD in the 1980s (Zhang & Fang 2008), achievements have been made in the field of natural sciences and social science applications.

System boundary and subsystem division

Water environment (Park & Lee 2002) and industrial structure (Peneder 2003) relate to many factors such as population, economy, resources, and the environment. Coordinated and restrictive relationships among macroeconomic, population, and water environment systems should be considered in studies (Young & Beek 1974; Zhao & Qian 2005a, b). Based on the characteristics of water resources, societal, economic, and ecological environments, the Tieling City system is divided into subsystems used to study its WECC: water resources, economics, population, contaminants, and agriculture.

As the data sources are dependent on administrative area, the spatial boundaries of the population, water resource, economic, pollutant, and agriculture and husbandry subsystems were identified as the administrative region of Tieling City. The system was simulated for 2011–2020 in intervals of 1 year (Zhao & Li 2008).

  1. Water resource subsystem: rational exploitation and water resource use are important prerequisites to sustaining human development (Larch 2007). The basis of a water resource system is total supply and total demand, where auxiliary variables are used as an interface with the economic, population, agriculture, and pollution subsystems to reflect the degree of water use to satisfy industry, agriculture, and daily life.

  2. Economic subsystem: using GDP as a state variable, economic development involves resource use, production, and other activities that have adverse impacts on the ecological environment (Akamatsu 1992). Thus, economic development is a threat to further development of the region to some extent (lack of natural resource and large amounts of waste generation) and to the living environment of people when effects accumulate, especially for developing countries, as rapid development in these countries is usually accompanied by large quantities of natural resource consumption, waste generation and emissions (Zhu & Chai 2010).

  3. Population subsystem: there is a close relationship between the population subsystem and other subsystems. Because of excessive population growth, the water pressure on the environment increases (Tehrania & Makhdoumb 2013), water resources decrease, and the water environment becomes polluted. In contrast, the growth rate of the population is subject to the degree of water shortage and contamination of the water environment (Marelli 2004).

  4. Pollutant subsystem: with the growth of the population and economic improvement, sewage and pollutant emissions from domestic consumption and production are increasing. However, water environmental capacity is limited (Hu & Wei 2008). According to an analysis of water quality in Tieling City, COD is a pollutant that people are generally concerned about (Susilowati et al. 2004). Thus, COD is the object of this study.

  5. Agriculture and husbandry subsystem: this subsystem includes agricultural irrigated areas, irrigation water, the number of cattle and pig breeding stock, water use for animals and husbandry, and COD emissions from agricultural runoff and animal husbandry. Here, we must change the agricultural irrigation quota and area to reflect the variation in agricultural irrigation water.

Establishment of the model flowchart and parameter selection

The relationship between the internal subsystem and other subsystems can be analyzed, if the subsystem and its key variables are ascertained (Jun et al. 2007; Wang & Xue 2010). A flowchart is an abstract reflection of the actual system, and it can illustrate the connected relationship between state variables and rate variables, which is the basis of the equations in the model (Debele et al. 2008; Lane et al. 2014). The flowchart of industrial structure optimization is shown in Figure 1. Meanings of the different acronyms in the SD flowchart are shown in the Appendix (available online at http://www.iwaponline.com/wst/071/099.pdf).

Figure 1

Flowchart of the SD of industrial structure optimization of Tieling City.

Figure 1

Flowchart of the SD of industrial structure optimization of Tieling City.

Using statistical analysis to predict certain parameters in the model, this study used data from the following sources: Tieling Statistical Yearbook (2012), 12th Five-year Plan (2011), Liaoning Provincial Water Resources Bulletin (2009), Discharge Standard of Pollutants (2003), and Technical Specification of Livestock and Poultry Pollution Treatment Project (2009). Parameters were modified continually to be more reasonable and realistic when running the model in the process of establishing the optimal model.

Model testing

The model must be tested before it has practical applications to ensure its validity and reliability. Because of the large quantity of variables, we selected some variables to test the model. The reason we chose these variables was that they are closely linked to the economy, society, contaminant, and water resources. A comparison of simulation and actual values are shown in Table 1. The relative errors of selected variables are within ±6% (within ±10%), which can be considered to be feasible (Burn & Lence 1992; Wang et al. 2009). The simulation results fit well with the actual situation, which verifies the validity of the model. Thus, the basic structure of the model reflects the actual system well.

Table 1

Comparison of simulation and actual values

Year   2001 2002 2003 2004 2005 2006 2007 2008 
Total population (people) Actual value 2,988,644 2,993,315 2,994,397 3,004,299 3,025,535 3,045,359 3,054,479 3,059,310 
Simulation 2,962,525 3,007,194 3,029,484 3,067,306 2,971,171 3,067,062 3,035,238 3,081,367 
Relative error (%) −0.87 0.46 1.17 2.09 −1.79 0.71 −0.63 0.72 
Rural population (people) Actual value 2,075,190 2,071,622 2,069,926 2,054,713 2,070,343 2,071,192 2,075,773 2,079,512 
Simulation 2,049,786 2,078,452 2,106,341 2,089,641 2,019,584 2,096,483 2,059,361 2,103,165 
Relative error (%) −1.22 0.33 1.76 1.69 −2.45 1.22 −0.79 1.14 
Urban population (people) Actual value 913,454 921,693 924,471 949,586 955,192 974,167 978,706 979,798 
Simulation 912,739 928,742 923,143 977,665 951,587 970,579 975,877 978,202 
Relative error (%) −0.08 0.76 −0.14 2.96 −0.38 −0.37 −0.29 −0.16 
Industrial product(106 yuan) Actual value 3,956.7 4,230.2 5,289.5 7,273 9,560.7 13,638.4 17,082.2 23,858.8 
Simulation 3,928.3 4,301.2 5,246.1 7,161.7 9,605.6 12,910.0 17,649.6 24,946.6 
Relative error (%) −0.72 1.67 −0.82 −1.50 0.47 −5.30 3.30 4.50 
GDP (106 yuan) Actual value 13,789.1 15,181.4 17,652.4 21,631.1 26,423.1 32,393.2 40,396.6 51,659.7 
Simulation 13,942.3 15,207.3 17,984.2 20,975.4 26,938.6 31,986.5 39,752.4 52,014.8 
Relative error (%) 1.11 0.17 1.88 −2.10 1.95 −1.26 −1.59 0.68 
Gross primary industrial product (106 yuan) Actual value 4,074.2 4,667.6 5,057.1 6,153.2 6,969.9 7,967.9 9737.8 11,303.0 
Simulation 3,986.4 4,710.5 5,102.2 6,238.5 6,801.3 8,014.2 9564.1 10,986.3 
Relative error (%) −2.10 0.92 0.89 1.38 −2.41 0.58 −1.78 −2.80 
Cattle breeding stock(a) Actual value 318,265 309,176 323,456 351,105 370,997 352,368 420,498 396,983 
Simulation 309,847 315,647 328,976 346,208 373,190 343,846 434,771 414,967 
Relative error (%) −2.64 2.03 1.71 −1.39 0.59 −2.48 3.39 4.53 
Residential and industrial COD emissions (104 tons) Actual value 2.50 2.50 2.80 2.50 3.20 3.20 3.30 2.70 
Simulation 2.55 2.53 2.76 2.73 3.15 3.21 3.35 2.72 
Relative error (%) 2.00 1.20 −1.43 9.20 −1.56 0.31 1.52 0.74 
Industrial COD emissions (104 tons) Actual value 0.80 0.70 0.90 1.30 1.20 1.40 0.90 0.70 
Simulation 0.79 0.68 0.91 1.27 1.19 1.38 0.88 0.72 
Relative error (%) 0.13 −2.85 1.11 −2.3 −0.83 −1.43 −2.22 2.85 
Residential water use (108t) Actual value 0.76 0.65 0.78 0.69 0.65 0.68 0.62 0.77 
Simulation 0.75 0.64 0.77 0.70 0.64 0.67 0.63 0.76 
Relative error (%) −1.32 −1.54 −1.28 1.43 −1.54 −1.47 1.61 −1.29 
Available surface water (108t) Actual value 5.30 5.08 5.46 5.56 5.51 6.71 5.40 4.90 
Simulation 5.18 4.99 5.51 5.58 5.48 6.79 5.60 5.10 
Relative error (%) −2.26 −1.77 0.92 0.36 −0.54 1.19 3.7 4.1 
Available underground water (108t) Actual value 6.93 6.73 7.37 7.11 6.89 5.53 5.24 5.08 
Simulation 6.87 6.64 7.42 7.06 6.77 5.46 5.28 4.98 
Relative error (%) −0.86 −1.33 0.68 −0.70 −1.74 −1.26 0.76 −1.96 
Industrial water use (108t) Actual value 11.59 12.13 12.49 13.12 12.84 13.06 15.42 16.53 
Simulation 11.62 12.32 12.34 12.89 12.76 12.93 15.23 16.78 
Relative error (%) 0.26 1.56 −1.20 −1.75 −0.62 −0.99 −1.23 1.51 
Industrial fresh water use (108t) Actual value 13.14 11.97 10.89 11.70 11.55 0.92 0.87 0.73 
Simulation 13.25 12.13 11.20 11.84 11.76 0.93 0.88 0.72 
Relative error (%) 0.84 1.33 2.85 1.19 1.82 1.08 1.15 −1.37 
Year   2001 2002 2003 2004 2005 2006 2007 2008 
Total population (people) Actual value 2,988,644 2,993,315 2,994,397 3,004,299 3,025,535 3,045,359 3,054,479 3,059,310 
Simulation 2,962,525 3,007,194 3,029,484 3,067,306 2,971,171 3,067,062 3,035,238 3,081,367 
Relative error (%) −0.87 0.46 1.17 2.09 −1.79 0.71 −0.63 0.72 
Rural population (people) Actual value 2,075,190 2,071,622 2,069,926 2,054,713 2,070,343 2,071,192 2,075,773 2,079,512 
Simulation 2,049,786 2,078,452 2,106,341 2,089,641 2,019,584 2,096,483 2,059,361 2,103,165 
Relative error (%) −1.22 0.33 1.76 1.69 −2.45 1.22 −0.79 1.14 
Urban population (people) Actual value 913,454 921,693 924,471 949,586 955,192 974,167 978,706 979,798 
Simulation 912,739 928,742 923,143 977,665 951,587 970,579 975,877 978,202 
Relative error (%) −0.08 0.76 −0.14 2.96 −0.38 −0.37 −0.29 −0.16 
Industrial product(106 yuan) Actual value 3,956.7 4,230.2 5,289.5 7,273 9,560.7 13,638.4 17,082.2 23,858.8 
Simulation 3,928.3 4,301.2 5,246.1 7,161.7 9,605.6 12,910.0 17,649.6 24,946.6 
Relative error (%) −0.72 1.67 −0.82 −1.50 0.47 −5.30 3.30 4.50 
GDP (106 yuan) Actual value 13,789.1 15,181.4 17,652.4 21,631.1 26,423.1 32,393.2 40,396.6 51,659.7 
Simulation 13,942.3 15,207.3 17,984.2 20,975.4 26,938.6 31,986.5 39,752.4 52,014.8 
Relative error (%) 1.11 0.17 1.88 −2.10 1.95 −1.26 −1.59 0.68 
Gross primary industrial product (106 yuan) Actual value 4,074.2 4,667.6 5,057.1 6,153.2 6,969.9 7,967.9 9737.8 11,303.0 
Simulation 3,986.4 4,710.5 5,102.2 6,238.5 6,801.3 8,014.2 9564.1 10,986.3 
Relative error (%) −2.10 0.92 0.89 1.38 −2.41 0.58 −1.78 −2.80 
Cattle breeding stock(a) Actual value 318,265 309,176 323,456 351,105 370,997 352,368 420,498 396,983 
Simulation 309,847 315,647 328,976 346,208 373,190 343,846 434,771 414,967 
Relative error (%) −2.64 2.03 1.71 −1.39 0.59 −2.48 3.39 4.53 
Residential and industrial COD emissions (104 tons) Actual value 2.50 2.50 2.80 2.50 3.20 3.20 3.30 2.70 
Simulation 2.55 2.53 2.76 2.73 3.15 3.21 3.35 2.72 
Relative error (%) 2.00 1.20 −1.43 9.20 −1.56 0.31 1.52 0.74 
Industrial COD emissions (104 tons) Actual value 0.80 0.70 0.90 1.30 1.20 1.40 0.90 0.70 
Simulation 0.79 0.68 0.91 1.27 1.19 1.38 0.88 0.72 
Relative error (%) 0.13 −2.85 1.11 −2.3 −0.83 −1.43 −2.22 2.85 
Residential water use (108t) Actual value 0.76 0.65 0.78 0.69 0.65 0.68 0.62 0.77 
Simulation 0.75 0.64 0.77 0.70 0.64 0.67 0.63 0.76 
Relative error (%) −1.32 −1.54 −1.28 1.43 −1.54 −1.47 1.61 −1.29 
Available surface water (108t) Actual value 5.30 5.08 5.46 5.56 5.51 6.71 5.40 4.90 
Simulation 5.18 4.99 5.51 5.58 5.48 6.79 5.60 5.10 
Relative error (%) −2.26 −1.77 0.92 0.36 −0.54 1.19 3.7 4.1 
Available underground water (108t) Actual value 6.93 6.73 7.37 7.11 6.89 5.53 5.24 5.08 
Simulation 6.87 6.64 7.42 7.06 6.77 5.46 5.28 4.98 
Relative error (%) −0.86 −1.33 0.68 −0.70 −1.74 −1.26 0.76 −1.96 
Industrial water use (108t) Actual value 11.59 12.13 12.49 13.12 12.84 13.06 15.42 16.53 
Simulation 11.62 12.32 12.34 12.89 12.76 12.93 15.23 16.78 
Relative error (%) 0.26 1.56 −1.20 −1.75 −0.62 −0.99 −1.23 1.51 
Industrial fresh water use (108t) Actual value 13.14 11.97 10.89 11.70 11.55 0.92 0.87 0.73 
Simulation 13.25 12.13 11.20 11.84 11.76 0.93 0.88 0.72 
Relative error (%) 0.84 1.33 2.85 1.19 1.82 1.08 1.15 −1.37 

SD SIMULATION AND RESULTS OF INDUSTRIAL STRUCTURE OPTIMIZATION OF TIELING CITY

Simulation setting for industrial structure optimization of Tieling City

Three schemes were designed for a dynamic simulation prediction according to system composition to further study the dynamic evolution process of various policies from 2011 to 2020.

Scheme 1 does not use any measures to change the development model, maintaining the current development trend of the parameters.

Scheme 2 uses throttling measures and pollutant emissions reduction techniques while ensuring yields to keep the irrigation quota at 12,000 t/ha; it cuts the water use quota for livestock by 20% and the urban per-capita water consumption to 42 tons annually. Measures in agriculture include constructing vigorous farmland water conservancies, using sprinkler and drip-irrigation methods to reduce irrigation water consumption (Li 2013), and reducing fertilizer use. Measures in animal husbandry include strengthening standard, scaled, and intensive livestock and poultry breeding, promoting the construction of standard farms, and building new ones. Furthermore, rural per-capita water consumption is reduced to 14 tons annually, and water use per 10,000 yuan of industrial production is reduced to 200 m3. Moreover, the rate of industrial sewage recovered after treatment is improved to 35%. Thus, overall COD emissions are reduced by 9% annually. Measures in industry include strengthening water-saving technologies and developing water-saving equipment, accelerating clean production, and implementing recycling.

The comprehensive program of Scheme 3 includes reducing the irrigation quota to 10,000 t/ha, cutting the water use quota for livestock by 20%, cutting annual urban and rural per-capita water consumption to 40 tons and 11 tons, respectively, reducing water use per 10,000 yuan of industrial production to 150 m3, improving the rate of industrial sewage recovered after treatment to 45%, and increasing the recycling rate of industrial water to 97%. Thus, overall COD emissions are reduced by 9% annually. The measures of Scheme 3 are similar to Scheme 2, but they different in the magnitude of execution. In addition, the annual rate of GDP growth is controlled to within 10%, where the proportion of primary industry is maintained and that of tertiary industry is increased to 35%. Finally, the urbanization rate is increased to 35%.

Analysis results

Table 2 presents the simulation results. Along the current development trend, the difference between the supply and demand of water resources becomes increasingly obvious. Concurrently, the WECC in Tieling City continuously decreases, whereas the shortage of water resources continuously increases. According to the amount of water demand, agriculture requires the most water, followed by industry and livestock and finally residential use. Therefore, we should increase the intensity of water saving in agriculture, animal husbandry, and industry. Although residential water consumption is low, saving water is still appropriate (Burn & McBean 1985; Guo & Tang 1995). Finally, because COD emissions are mainly caused by agricultural runoff and livestock, these areas should be paid attention.

Table 2

Simulation of industrial structure optimization for Tieling City under different schemes

Indicator Scheme 1
 
Scheme 2
 
Scheme 3
 
2011 2015 2020 2011 2015 2020 2011 2015 2020 
Water demand (108t) 9.44 10.64 11.63 9.39 9.62 9.90 9.26 8.55 7.69 
Agricultural water use (108t) 7.34 8.11 8.45 7.34 7.37 7.39 7.22 6.75 6.17 
Residential water use (108t) 0.74 0.81 0.84 0.78 0.77 0.74 0.74 0.69 0.64 
Animal husbandry water use (108t) 0.47 0.51 0.55 0.38 0.40 0.44 0.41 0.40 0.44 
Industrial fresh water use (108t) 0.89 1.21 1.79 0.89 1.08 1.33 0.89 0.71 0.44 
Available water (108t) 9.47 10.07 10.16 9.47 10.07 10.16 9.47 10.07 10.16 
COD emissions (104t) 9.72 10.19 10.74 9.09 6.41 3.83 9.09 6.39 3.79 
Agricultural COD emissions (104t) 2.14 2.03 1.88 1.84 1.31 0.76 1.84 1.31 0.76 
Residential COD emissions (104t) 2.03 2.00 1.71 1.93 1.31 0.81 1.93 1.31 0.81 
Animal husbandry COD emissions (104t) 5.28 5.77 6.51 5.09 3.64 2.16 5.09 3.64 2.16 
Industrial COD emissions (104t) 0.27 0.39 0.64 0.23 0.15 0.10 0.23 0.13 0.06 
GDP (108 yuan) 873.80 1,534.10 3,170.10 873.80 1,534.10 3,170.10 887.80 1,377.10 2,194.50 
Gross primary industrial product (108 yuan) 172.10 294.20 624.30 172.10 294.20 624.30 172.10 264.20 432.30 
Gross secondary industrial product (108 yuan) 460.50 808.70 1,642.60 460.50 808.70 1,642.60 413.70 680.50 1,062.20 
Gross tertiary industrial product (108 yuan) 241.20 431.20 903.20 241.20 431.20 903.20 460.50 676.40 994.10 
Total population (104 people) 304.90 306.40 309.80 304.90 306.40 309.80 304.90 306.40 309.80 
Urban population (104 people) 100.90 102.30 102.20 100.90 102.30 102.20 101.30 104.50 108.40 
Indicator Scheme 1
 
Scheme 2
 
Scheme 3
 
2011 2015 2020 2011 2015 2020 2011 2015 2020 
Water demand (108t) 9.44 10.64 11.63 9.39 9.62 9.90 9.26 8.55 7.69 
Agricultural water use (108t) 7.34 8.11 8.45 7.34 7.37 7.39 7.22 6.75 6.17 
Residential water use (108t) 0.74 0.81 0.84 0.78 0.77 0.74 0.74 0.69 0.64 
Animal husbandry water use (108t) 0.47 0.51 0.55 0.38 0.40 0.44 0.41 0.40 0.44 
Industrial fresh water use (108t) 0.89 1.21 1.79 0.89 1.08 1.33 0.89 0.71 0.44 
Available water (108t) 9.47 10.07 10.16 9.47 10.07 10.16 9.47 10.07 10.16 
COD emissions (104t) 9.72 10.19 10.74 9.09 6.41 3.83 9.09 6.39 3.79 
Agricultural COD emissions (104t) 2.14 2.03 1.88 1.84 1.31 0.76 1.84 1.31 0.76 
Residential COD emissions (104t) 2.03 2.00 1.71 1.93 1.31 0.81 1.93 1.31 0.81 
Animal husbandry COD emissions (104t) 5.28 5.77 6.51 5.09 3.64 2.16 5.09 3.64 2.16 
Industrial COD emissions (104t) 0.27 0.39 0.64 0.23 0.15 0.10 0.23 0.13 0.06 
GDP (108 yuan) 873.80 1,534.10 3,170.10 873.80 1,534.10 3,170.10 887.80 1,377.10 2,194.50 
Gross primary industrial product (108 yuan) 172.10 294.20 624.30 172.10 294.20 624.30 172.10 264.20 432.30 
Gross secondary industrial product (108 yuan) 460.50 808.70 1,642.60 460.50 808.70 1,642.60 413.70 680.50 1,062.20 
Gross tertiary industrial product (108 yuan) 241.20 431.20 903.20 241.20 431.20 903.20 460.50 676.40 994.10 
Total population (104 people) 304.90 306.40 309.80 304.90 306.40 309.80 304.90 306.40 309.80 
Urban population (104 people) 100.90 102.30 102.20 100.90 102.30 102.20 101.30 104.50 108.40 

After taking throttling and emission reduction measures, the total water consumption by 2020 is reduced from 11.63 to 9.90 billion tons: a reduction of 15%. Total COD emissions are reduced from 107,400 to 38,300 tons. Thus, WECC pressure will be significantly reduced in comparison with Scheme 1.

With a 15% economic growth rate in Tieling City, rapid development of the economy will increase the consumption of water resources and create substantial pollution (Rui & Wang 1993). The economic growth rate in Tieling City should be controlled while guaranteeing economic development.

In Scheme 3, industrial fresh water consumption by 2020 will decrease after the economic development rate is controlled. A reduction in water use per 10,000 yuan of industrial production and an increase in the recycling rate of industrial water will occur simultaneously when the rate of secondary industry is reduced. Because two impact factors of industrial water consumption are reduced, industrial fresh water use is reduced after improving the industrial water recycling rate. The proportion of primary, secondary, and tertiary industry changes from 20:52:28 to 20:45:35, which indicates an increasingly rational industry structure.

According to the simulation results and analysis, the WECC is effectively increased. A comparison between the optimized and non-optimized cases is shown in Table 3. The predicted GDP growth rate changes from 15 to 9%, which indicates that China focuses on the quality of the economy, rather than its speed and quantity. In 2020, the proportion of primary, secondary, and tertiary industry will change from 20:52:28 to 20:45:35; this is the aim that Tieling City pursued and it is also is favorable to coordinating the development of the water environment and the economy. There will be a small increase in the percentage of the urban population compared to the total population in 2020. If the measures of Scheme 3 in the model can be implemented well, they will play a significant role in the development of the environment and the economy.

Table 3

Comparison of optimized and non-optimized cases for industrial structure optimization

Indicator Non-optimized
 
Optimized
 
2011 2015 2020 2011 2015 2020 
GDP growth rate (%) 14.0 14.5 15.0 12.7 10.2 9.0 
Proportion of gross primary industrial product (%) 19.7 19.2 19.7 19.7 19.2 19.7 
Proportion of gross secondary industrial product (%) 52.7 52.7 51.8 52.7 49.1 45.3 
Proportion of gross tertiary industrial product (%) 27.6 28.1 28.5 27.6 31.7 35.0 
Population growth rate (%) −0.073 0.2014 0.2314 −0.073 0.2014 0.2314 
Percentage of urban population to total population (%) 33 33 33 33 34 35 
Indicator Non-optimized
 
Optimized
 
2011 2015 2020 2011 2015 2020 
GDP growth rate (%) 14.0 14.5 15.0 12.7 10.2 9.0 
Proportion of gross primary industrial product (%) 19.7 19.2 19.7 19.7 19.2 19.7 
Proportion of gross secondary industrial product (%) 52.7 52.7 51.8 52.7 49.1 45.3 
Proportion of gross tertiary industrial product (%) 27.6 28.1 28.5 27.6 31.7 35.0 
Population growth rate (%) −0.073 0.2014 0.2314 −0.073 0.2014 0.2314 
Percentage of urban population to total population (%) 33 33 33 33 34 35 

CONCLUSION

This study established an SD model of the WECC of Tieling City using SD. The model is shown to be reasonable and effective after testing, and it can be used to simulate the WECC trends of Tieling City.

We found that throttling reduction and transformation of industrial developmental policy are important ways to improve the WECC.

Measures to ensure that the objectives are implemented well in agriculture and animal husbandry include: constructing vigorous farmland water conservancies; using sprinkler and drip-irrigation methods to reduce irrigation water consumption; reducing fertilizer use to reduce pollution emissions; strengthening standard, scaled, and intensive livestock and poultry breeding; and promoting the construction of standard farms. In industry, measures include: strengthening water-saving technologies and developing water-saving equipment; accelerating clean production; and implementing recycling. In the tertiary industry, the main sources of pollutants are from the catering and accommodation industry. The COD content of sewage from the catering industry is extremely high and it must be addressed to meet the standards. In addition, the development of industry standards in the catering, accommodation, bathing, and other tertiary industries should be accelerated, and the rate of reused water should be increased. For domestic consumption, the construction of water treatment facilities should be hastened, and the rate of sewage treatment should be improved to reduce non-treated sewage discharge.

Industrial development policy should focus on developing tertiary industries (particularly services) to increase the proportion of output value. In addition, the growth rate of secondary industries should be controlled and that of primary industries should be maintained.

ACKNOWLEDGEMENTS

This research was supported by the National Natural Science Foundation of China (grant no. 71373003) and the National Science and Technology Major Project on Water Pollution Control and Management of China (grant no. 2012ZX07505-001).

REFERENCES

REFERENCES
Akamatsu
K.
1992
Historical pattern of economic growth in developing countries
.
Hydro Science
23
,
13
25
.
Armentrout
W.
1987
Assessment of low flow in streams in northeastern Wyoming
.
USGS Water Resources Investigation Report
, pp.
533
538
.
Bureau of Statistics of Tieling City
2012
Tieling Statistical Yearbook 2000–2012
(in Chinese), Tieling City, China
.
Burn
D.
Lence
B.
1992
Comparison of optimization formulations for waste-load allocations
.
Journal of Environmental Engineering
118
(
4
),
597
612
.
Burn
D.
McBean
E.
1985
Optimization modeling of water quality in an uncertain environment
.
Water Resources Research
21
(
7
),
934
940
.
Falkenmark
M.
Lundqvist
J.
1998
Towards water security: Political determination and human adaptation
.
Natural Resources Forum
21
(
1
),
37
51
.
Franke
R.
Kalmbach
P.
2005
Structural change in the manufacturing sector and its impact on business-related services: an input–output study for Germany
.
Structural Change and Economic Dynamics
12
(
3
),
160
188
.
Guo
H. C.
Tang
J. W.
1995
Study on water environment and sustainable development of society and economy (in Chinese)
.
Journal of Environmental Science
15
(
3
),
363
369
.
Hu
T. J.
Wei
R.
2008
Optimization of the Industrial Structure Subjected to the Water Resources
(in Chinese). Vol.
6
,
Beijing Jiaotong University
,
Beijing
, pp.
34
35
.
Larch
M.
2007
The multinationalization of the water carrying capacity
.
Journal of Policy Modeling
45
(
29
),
397
416
.
Li
X.
2013
A ten-year study on the strategy of the relationship between economic development and environment quality of old industry base in Liaoning province (in Chinese)
.
Environmental Science
6
(
21
),
170
171
.
Li
Y.
Liu
P.
Wang
G. D.
2011
Simplification of indicator system for evaluating water environmental carrying capacity based on grey correlation analysis (in Chinese)
.
Journal of Shenyang Jianzhu University
27
(
1
),
135
139
.
Liaoning Provincial Department of Water Resources
2013
Liaoning Provincial Water Resources Bulletin
Marques
R.
2010
Regulation of Water and Wastewater Services. International Comparison
.
IWA Publishing
,
London
.
Ministry of Environmental Protection of the People's Republic of China
2003
Discharge Standard of Pollutants
Ministry of Environmental Protection of the People's Republic of China
2009
Technical Specification of Livestock and Poultry Pollution Treatment Project
Park
S. S.
Lee
Y. S.
2002
A water quality modeling study of the Nakdong River, Korea
.
Ecological Modelling
152
(
l
),
65
75
.
Peneder
M.
2003
Industrial structure and aggregate growth
.
Structural Change and Economic Dynamics
14
(
4
),
427
448
.
Rui
M. J.
Wang
F. H.
1993
Industrial Economy
(in Chinese). Vol.
3
,
Shanghai Science and Technology Press
,
Shanghai
, pp.
30
40
.
Susilowati
Y.
Mengko
T. R.
Rais
J.
Leksono
B. E.
2004
Water quality modeling for environmental information system
. In:
Proceedings of the 2004 IEEE Asia Pacific Conference on Circuit and System, Institute of Electrical & Electronics Engineering
,
Taiwan, Vol
.
12
, pp.
929
932
.
The People's Government of Liaoning Province
2011
12th Five-year Plan
.
Wang
Q. F.
1995
System Dynamics
(in Chinese). Vol. 9,
Tsinghua University Press
,
Beijing
, pp.
20
30
.
Wang
Q. F.
2004
The History, The Present Situation and Development Prospects of The System Dynamics
(in Chinese). Report of Chinese management science and engineering development
,
Shanghai, Harbin Institute of Technology, Harbin, China
, pp.
91
100
.
Wang
J. L.
Xue
Q. X.
2010
Relationship Study on Harbin's Economy and Water Environment Pollution
(in Chinese). Vol. 6,
Harbin Institute of Technology
,
Harbin, China
, pp.
45
76
.
Wang
J.
Li
X. L.
Li
F. Y.
Bao
X. H.
2009
Simulation and prediction of water environmental carrying capacity in Liaoning Province based on system dynamics (in Chinese)
.
Chinese Journal of Application Ecology
20
(
9
),
2233
2240
.
Weng
M. H.
Nie
Q. Y.
2009
Study on water environment bearing capacity of Liaocheng City (in Chinese)
.
Water Conservation
25
(
3
),
41
44
.
Ye
L. H.
Zhou
F.
Guo
H. C.
2013
System optimization control of Qinhe River Basin based on the water environmental carrying capacity (in Chinese)
.
Geographical Research
32
(
6
),
1007
1016
.
Young
P.
Beek
B.
1974
Modeling and control of water quality river system
.
Environmental Monitoring and Assessment
10
(
5
),
455
468
.
Zhang
L. B.
Fang
Z. G.
2008
Several problems in system dynamics and its application (in Chinese)
.
Journal of Nanjing University of Aeronautics & Astronautics
10
(
3
),
43
47
.
Zhao
W.
Liu
S. J.
2008
Simulation on the water environmental carrying capacity of Liaohe River Basin (in Chinese)
.
Journal of the Graduate School of the Chinese Academy of Sciences
52
(
6
),
738
747
.
Zhao
Y. H.
Qian
J. P.
2005a
The Current Water Environment in Hebei Province and Study on Water Environmental Carrying Capacity
(in Chinese). Vol. 6,
Hebei Normal University
,
Shi Jiazhuang
, pp.
34
35
.
Zhao
Y. H.
Qian
J. P.
2005b
The present situation of water environment and the study on water environment carrying capacity of Hebei province (in Chinese)
.
Environmental Sciences
16
(
8
),
38
41
.
Zhao
Y. L.
Li
W. C.
2008
The simulative experimental research on the law of the industry structure's evolution based on system dynamics (in Chinese)
.
Journal of Systems Science
16
(
4
),
51
58
.
Zhu
Y. Y.
Chai
L.
2010
Study on dynamic changes in water environment carrying capacity based on system dynamics (in Chinese)
.
Water Conservancy Science and Technology and Economy
16
(
9
),
1039
1041
.

Supplementary data