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Assessment of environmental improvement measures using a novel integrated model: A case study of the Shenzhen River catchment, China
Journal of Environmental Management 114 (2013) 486e495
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Assessment of environmental improvement measures using a novel integrated model: A case study of the Shenzhen River catchment, China Hua-Peng Qin a, b, *, Qiong Su a, Soon-Thiam Khu b a b
Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, 518055 Shenzhen, China Faculty of Engineering and Physical Sciences, University of Surrey, Civil Engineering (C5), Guildford, Surrey GU2 7XH, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 March 2011 Received in revised form 11 August 2012 Accepted 8 October 2012 Available online 26 November 2012
Integrated water environmental management in a rapidly urbanizing area often requires combining social, economic and engineering measures in order to be effective. However, in reality, these measures are often considered independently by different planners, and decisions are made in a hierarchical manner; this has led to problems in environmental pollution control and also an inability to devise innovative solutions due to technological lock-in. In this paper, we use a novel coupled system dynamics and water environmental model (SyDWEM) to simulate the dynamic interactions between the socioeconomic system, water infrastructure and receiving water in a rapidly urbanizing catchment in Shenzhen, China. The model is then applied to assess the effects of proposed socio-economic or engineering measures on environmental and development indicators in the catchment for 2011e2020. The results indicate that 1) measures to adjust industry structures have a positive effect on both water quantity and quality in the catchment; 2) measures to increase the labor productivity, the water use efficiency, the water transfer quota or the reclaimed wastewater reuse can alleviate the water shortage, but cannot improve water quality in the river; 3) measures to increase the wastewater treatment rate or the pollutant removal rate can improve water quality in the river, but have no effect on water shortage. Based on the effectiveness of the individual measures, a combination of socio-economic and engineering measures is proposed, which can achieve water environmental sustainability in the study area. Thus, we demonstrate that SyDWEM has the capacity to evaluate the effects of both socio-economic and engineering measures; it also provides a tool for integrated decision making by socio-economic and water infrastructure planners. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: System dynamics Water environment model Integrated measures Urbanization catchment
1. Introduction Many catchments in developing countries are undergoing rapid urbanization. At the same time, these catchments are usually confronted with serious water environmental problems (Biswas and Tortajada, 2009; Jones, 2009; Varis and Vakkilainen, 2001; Wang and Li, 2007). One reason for this is the rapid economic growth and population concentration in these countries, which cause a rapid increase in water consumption and wastewater discharge into the catchments. In addition, many labor-intensive industries, such as pulp and paper, chemical products and
* Corresponding author. Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Room 414, E Building, 518055 Shenzhen, China. Tel./fax: þ86 755 26035291. E-mail addresses: [email protected] (H.-P. Qin), [email protected] (S.-T. Khu). 0301-4797/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2012.10.053
by-products, textiles, and agricultural products, have been introduced into these catchments because the industry entry barrier was relatively low when the catchments were in the infancy of urbanization. These industries usually have low labor productivity, high water consumption and high pollution emissions. This imbalanced industry structure aggravates water scarcity and water quality deterioration in rapidly urbanizing areas. Furthermore, urbanization in some developing countries occurs so fast that the water infrastructure development cannot keep pace with the rapid economic growth and cannot provide satisfactory management of drinking water and wastewater (Biswas and Tortajada, 2009). The inadequacy of infrastructure provisions for water supply and wastewater treatment tend to compound the water environmental problems in these catchments. Many water management measures have been proposed to improve the water environment in urbanized catchments. These measures can be categorized as two types: engineering measures and socio-economic measures. Engineering measures include enhancing the water supply, improving the wastewater treatment
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capacity, and wastewater reuse (Cho et al., 2003; Georgieva, 2005; Stanko, 2009). Socio-economic measures, such as industrial structure regulation (Wang et al., 2002), legal measures (Dellapenna and Gupta, 2008), economic incentives and financial policies (Liu, 2002; Massarutto, 2007; Somlyódy and Varis, 2006), aim to improve the efficiency of water consumption and reduce pollutant generation/ emission. Moreover, sustainable catchment management requires integrating socio-economic and engineering measures. However, planning for socio-economic and water infrastructure development are usually carried out independently, with different objectives. A decision-making tool that includes socio-economic and infrastructure aspects is needed to support better communication and interactions between different stakeholders (Liu et al., 2008). The effects of engineering measures on the receiving water body, such as increasing levels of sewage treatment, dredging of contaminated bottom sediments and pumping oxygen into the river, can be evaluated using hydrodynamics and water quality models (Hosoi et al., 1996; Kamal et al., 1999; Muhammetoglu et al., 2005; Ning et al., 2001; Park and Lee, 2002; Xu and Liao, 2005), such as QUAL2E (Broun and Barnwell, 1987), WASP (Ambrose, 1987), and MIKE-11 (DHI, 1993). However, such models do not simulate the interactions between social, economic and environmental issues, and cannot be adequately used to assess the impacts of general social and economic policy measures in the river environment. On the other hand, models are available to evaluate socioeconomic measures. For example, economic inputeoutput models can describe interactions between economic and water environmental systems and have been used to evaluate different socioeconomic measures such as raising the price of water (Yoo and Yang, 1999), water allocation and management policies (Lange et al., 2007; Lenzen and Foran, 2001; Wang et al., 2005), and adjusting the regional industry or trade structure (Ni et al., 2001; Okadera et al., 2006). Cost-benefit analysis is a conventional method to analyze the economic benefits and costs of a policy or project such as technology improvement in the water supply system (Hutton et al., 2007) or in a wastewater treatment plant (EisenHecht and Kramer, 2002). Moreover, system dynamics have been successfully applied to evaluate the efforts of different economic development scenarios on water consumption and pollution discharge control (Guo et al., 2001; Simonovic, 2002), water resource system balance (Xu et al., 2002), and land use management
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(Chen et al., 2004). However, it is difficult for conventional socioeconomic models to simulate the behavior of pollutants in a river, and thus they cannot be used to predict the spatio-temporal variations of water quality in a catchment under different measures. Over the last two decades, a variety of models and frameworks have been developed for integrated water management, such as WaterWare (Fedra and Jamieson, 1996), Hydro Planner (Maheepala et al., 2005), WEAP (Sieber et al., 2007), Elbe-DSS (de Kok et al., 2009), and SAHRA (Liu et al., 2008), by which the effects of socioeconomic and engineering system changes on the water environment can be evaluated. However, socio-economic factors are regarded as external scenarios/constraints (Lautenbach et al., 2009) in these models, and the interactions between different socio-economic components, and between engineering and socioeconomic measures, are usually neglected. A coupled system dynamics and water environmental model (SyDWEM) (Qin et al., 2011) was recently developed in order to better understand how the socio-economic system, water infrastructure and receiving water interact with one another in the rapidly urbanizing catchment. In SyDWEM, the socio-economic system can be considered as an internal sub-module of the whole system, and some socio-economic factors (e.g., industrial structure) can also be regarded as decision variables. Therefore, the model can be used to evaluate the effects of the proposed socio-economic and engineering measures in a catchment or study area. In this paper, we applied the SyDWEM model to the rapidly urbanizing Shenzhen River catchment in China. The objective of the study is to demonstrate SyDWEM capacity to evaluate the effects of socio-economic and engineering measures on environmental improvement, and to devise and evaluate a series of integrated measures to achieve water environment sustainability. 2. Materials and methods 2.1. Study area The Shenzhen River is located in the rapidly urbanizing coastal region of Southeastern China, and forms the administrative border between mainland China and Hong Kong (Fig. 1). The total northern catchment area of the Shenzhen River is 187 km2, and it includes three administrative areas of Shenzhen: Luohu District, Futian
Fig. 1. A general view of the Shenzhen River catchment.
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H.-P. Qin et al. / Journal of Environmental Management 114 (2013) 486e495 GRP-Population module
Water supply module
Net investment
Water transfer quota
Cobb-Douglas production function
Local water resource and development rate
Production efficiency (GRP per labor)
Reclaimed wastewater reuse ratio
Industry structure (GRP fferent industries) proportions of diff different
Sewer and WWTPs module
System Dynamics Water use efficiency (water consumption per unit GRP) Water consumption per capita COD generation per capita and per unit GRP
Model
GRP
Water consumption
Population
Pollutant loads
Water consumptionpollutant load module
Wastewater treatment rate and efficiency
Water Quality Model
Water quality along river
Receiving water module
Fig. 2. Model coupling and data flow.
District, and Buji Town. The Shenzhen River is a typical tide-affected river with a length of 14 km and several tributaries, of which the Liantang, Shawan, Buji, Futian, Huanggang, Xinzhou and Fengtang Rivers drain from the Shenzhen side. The main river drains southwest into the Deep Bay, which joins the Pearl River estuary on its seaward side. Over the past twenty years, this area has undergone rapid urbanization. Between 1990 and 2007, its gross regional product (GRP)1 increased from 9 billion to 103 billion Yuan, the total population increased from 0.81 million to 2.65 million and the percentage of built-up area to developable land increased from 16.7% to 81.3%. However, with rapid urbanization, the catchment suffers from severe water shortages, and the availability of water mainly depends on a large-scale water-transfer project. In 2007, about 88% of the water supply in the area was imported from outside the city boundaries. Furthermore, the water quality in the Shenzhen River has seriously deteriorated. The water body in the middle and lower reaches of the river is malodorous and black. Pollutant concentrations such as the chemical oxygen demand (COD), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP) in the river have a high rate of non-compliance with water quality objectives (Shenzhen Environmental Protection Bureau, 2009). This is because wastewater treatment system development lags far behind economic development in the catchment. Although there are three WWTPs (Luofang, Binhe and Caopu) in the catchment, 26% of the total wastewater was discharged into the Shenzhen River without treatment in 2007. Although investment in pollution control by the local government has increased over the years, the high rate of economic development and population growth has negatively affected the pace of pollution control. Therefore, both engineering measures and social-economic policies should be considered to reduce the pollution load and harmonize water environment protection with population growth and economic development. 2.2. Model description A coupled system dynamics and water environmental model (SyDWEM) has already been developed to study dynamic socio-
1 GRP is identical to GDP in the study area because GDP is widely used in China to measure the economic activity at the country, province, city and town levels (http:// www.stats.gov.cn/). 1.
economic system, water infrastructure and receiving water in a rapidly urbanizing catchment (Qin et al., 2011). In the framework of SyDWEM (Fig. 2), the water-related socio-economic system can be further subdivided into a population/GRP module and a water demand/pollution generation module; the water infrastructure system includes a water supply module, sewer and WWTPs module; and the receiving water module can comprise the river, lake or estuary. The five modules are summarized as follows: 1) Population/GRP module: Population and GRP are considered as intermediate outputs rather than input variables in SyDWEM. Population growth is simulated based on labor demand since the increase in migrant labor caused by economic growth usually dominates the population growth in a rapidly urbanizing area, and economic performance is evaluated by the gross regional product (GRP) using the CobbeDouglas production function for growth forecasting, in which labor force and net investment are important impact factors on GRP growth (Douglas, 1976). The population and the economic models are coupled since they are both related to labor force. Furthermore, the labor force demand is determined by production efficiency (GRP per labor) and industry structure (GRP proportions of different industries) in SyDWEM. Therefore, the main input data are net investment, production efficiency and industry structure for different administrative districts in a catchment. 2) Water demand/pollution generation module: This module estimates the water demand and the pollutants generated for different users in a catchment according to population size, GRP, industrial structure (GRP proportions of different industries), water demand and pollutants per capita, as well as water demand and pollutant load per unit GRP in different industries. 3) Water supply module: This module estimates the potential and total water supply in a whole catchment according to the development rate of local water resources, reclaimed wastewater reuse ratio (percentage of reused water compared to the total treated wastewater) and water transfer quota. 4) Sewer and WWTPs module: This module estimates the total effluent load according to the volumetric wastewater treatment rate, pollutant removal rate and wastewater reuse ratio. 5) Receiving water module: In this module, a water quality model is used to simulate the pollutant transport, transformation and fate in the river system. For a tide-affected river, a 1-D unsteady water quality model is usually applied to describe pollutant behavior. The model is composed of the Saint-Venant equation and a convectionedispersion equation.
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Buji Town Population/ Luohu District GRP module Population/ Futian District GRP module Population/ GRP module
Buji Town Population/ GRP module Luohu District Futian District Water demand / pollutant generation module Wastewater
Water supply module
Wastewater reclamation and reuse
489
Socio-economic system
Untreated wastewater
Luofang WWTP Shawan & Sewer …… WWTP & Sewer Binhe WWTP & Sewer WWTP&Sewer module
Water infrastructure
Treated wastewater Abstraction
Shenzhen River catchment
Water transferred from other catchments
Fig. 3. Conceptual integrated water system model in the Shenzhen River catchment.
For further details of the SyDWEM, please refer to Qin et al. (2011). In SyDWEM, the aforementioned individual water environment models are integrated using the System Dynamics platform. The integrated model can be applied to calculate the GRP, population, water consumption and water quality at the catchment scale. Furthermore, the integrated model can consider the effects of socio-economic factors (e.g., industrial structure, labor productivity, water use efficiency) or engineering factors (e.g., volumetric wastewater treatment rate, WWTP pollutant removal rate, wastewater reuse ratio) on the whole water system. Considering the spatial variation of socio-economic development, the population/GRP module and water demand/pollutant generation module were developed for Futian District, Luohu District and Buji Town, respectively. Fig. 3 shows the relationship between each administrative district, sub-catchment, and WWTP within the Shenzhen River catchment. COD is one of the main pollutants in Shenzhen River and is taken as the representative water quality indicator in this study. The decision variables in the model (e.g., industry structure, reclaimed wastewater ratio, volumetric wastewater treatment rate and pollutant removal rate) can be regarded as input parameters and determined by historical statistics or future development policy/planning. In the case study, GRP per labor force and water consumption per unit GRP for different industries are described by exponential growth model. For further details of model calibration and validation, please refer to Qin et al. (2011).
of Shenzhen (2007e2020),” “Industrial Distribution Studies and Planning of Shenzhen,” “Shenzhen Development Strategy in 2030” and “Water conservation planning of Shenzhen (2005e2020).” Instead of attempting to simulate each policy, the measures are summarized into four groups: M1, M2, M3 and M4. The groupings are based on the intended goals of policy measures rather than their mechanism of implementation. 2.3.1.1. Increasing the GRP proportion of tertiary industries (M1). The industry structures in the study area can be categorized into primary, secondary and tertiary industries. The tertiary industry (this is a Chinese term, also known as the service industry) is characterized by the production of services instead of end products. In the study area, the tertiary industry currently (2009) has a slightly lower labor productivity than the secondary industry (see Table 1). However, it has much higher water use efficiency (around 1.6 times) than the secondary industry. Moreover, the pollutant load from the tertiary industry is assumed to be only related to residential activities. Compared with manufacturing industries, the tertiary industry usually has lower levels of water pollutant emissions. Therefore, it is possible to reduce water consumption and pollutant load by increasing the proportion of the tertiary industry. According to Shenzhen’s socio-economic planning, some tertiary industries such as trade, financial, cultural, and exhibition industries will be encouraged to grow and expand. The GRP proportion of tertiary industry in the catchment has the potential to increase by 0e10% by 2020, while the corresponding GRP proportion of the secondary industry is predicted to decrease.
2.3. Potential measures to improve the water environment In order to improve the water environment in Shenzhen River, local policy makers have proposed many measures over the years. These policies can be categorized into two main groups: socioeconomic measures and engineering measures. 2.3.1. Socio-economic measures Many socio-economic measures have been proposed to improve the water environment according to development planning policies. The relevant planning policies include “Urban Master Planning
Table 1 Labor productivity, water use efficiency and pollutant load of different industries. Type of industry
Labor productivity (103 Yuan per capita)
Water use efficiency 103 Yuan/m3
COD load per GRP t/109 Yuan
Secondary industry - Labor-intensive - Technology-intensive - Capital-intensive Tertiary industry
59 24 59 218 56
0.38 0.15 0.62 0.17 0.59
755 1612 121 24 /
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2.3.1.2. Increasing GRP proportion of certain secondary industries (M2). The secondary industry (also known as the manufacturing industry) generally manufactures finished goods or goods that are suitable for use by other businesses, for export, or for sale to domestic consumers. Within the study area, there are three types of secondary industries: labor-intensive, technology-intensive and capital-intensive. As seen in Table 1, capital-intensive industry has much higher labor productivity and lower COD load per GRP than labor-intensive industry. Therefore, it is possible to decrease the population growth and reduce pollutant loads by increasing the proportion of capital-intensive industry. According to Shenzhen City socio-economic planning guidelines, some capital-intensive (e.g., biotechnology and pharmaceutical industry, new material and new energy industry and fine chemistry industry) will be encouraged and enhanced, and the GRP proportion of capitalintensive industry in the catchment has the potential to increase 0e5% by 2020, while the labor-intensive industry would have a corresponding decrease. 2.3.1.3. Improving labor productivity (M3). Labor productivity can be measured by GRP per capita or GRP per employee (Stephen and Douglas, 2005). The labor productivities of the secondary and tertiary industry in Shenzhen are currently much lower than those of developed countries/areas. For example, in 2009, the labor productivity of the secondary industry in the Shenzhen River catchment was only 40% and 63% of those in Japan and Hong Kong, respectively, whereas the labor productivity of the tertiary industry was only 28% of that in Hong Kong (Census and statistics department of Hong Kong, 2010; Japan Productivity Center, 2010). Some policies for labor productivity improvement have been proposed by socio-economic planners, including increasing the education level of the laborers, increasing the industry entry barrier and promoting trade, financial, cultural and exhibition industries, which have high labor productivity (Development and Reform Commission of Shenzhen, 2009). In this way, the productive efficiency has the potential to improve 0e10% by 2020. 2.3.1.4. Improving water use efficiency (M4). The water use efficiency of economic activities can be measured by GRP per unit water consumption. In 2009, the water use efficiency in the study area was 0.417 $ 103 Yuan/m3, which is only one third of that in Hong Kong or Singapore. There are various potential means to improve water use efficiency, including the following measures: a. Upgrading water recycling technology: Industrial water recycling rate refers to the ratio of recycled water to total water consumption of an industry. The water recycling rate is as high as 90% in some developed countries (e.g., Japan). However, the recycling rate is only 39% in Shenzhen due to the obsolete technologies in use. According to the “Catalogue Guideline for Major Industry Clean Production Technology” issued by the National Development and Reform Commission (NDRC), obsolete technologies should be replaced by advanced technologies in the next 10 years. By upgrading industrial technology, the recycling rate is expected to increase to 50% by 2020. b. Decreasing pipeline leakage: Water loss due to network leakage is less than 5% in developed countries. However, it is estimated that nearly 10% of water leaked from the water supply pipeline in the study area in 2004 (Shenzhen Municipal Water Affairs Bureau, 2007). Thus, there is a plan to decrease the network leakage rate to 5% by 2020 by renovating urban water supply pipelines. c. Promoting water-saving appliances: Local statistical data indicate that only 50% of public utilities were equipped with
water-saving devices; and 10e30% of total water consumption was wasted due to irrational use of water devices in 2004 (Shenzhen Municipal Water Affairs Bureau, 2007). Watersaving devices may reduce 20e30% of the water use, and water consumption may be reduced to 10% by the year 2020 by promoting water-saving devices and practices. d. Water tariff adjustment: The industrial water price in Shenzhen is 2.25 Yuan/m3 (current price), which is only half of that in Hong Kong. Based on an econometric analysis of the relationship between residential water demand, water price and income (Liu and Lv, 2006), it is estimated that a water price increase of 10% would result in a water demand decrease of approximately 4.1%. Therefore, it is possible to promote water conservation by increasing the water price or implementing a cascade water tariff. If the aforementioned measures are considered, water use efficiency has the potential to improve by up to 20% by the year 2020. 2.3.2. Engineering measures Many engineering measures have been proposed to improve the water environment according to water supply system planning (2006e2020), and Shenzhen municipal wastewater system planning (2002e2020) (Shenzhen Urban Planning and Land Resources Bureau, 2003). The measures can be classified into four groups (M5eM8): 2.3.2.1. Increasing the water transfer quota (M5). Currently, the water supply in Shenzhen is mostly transferred from the upstream parts of Dongjiang River, and the transfer quota is based on an agreement with the Bureau of Dongjiang River basin management. The current water-transfer capability servicing for the Shenzhen River catchment is 1.39 million m3/day. Considering the environmental/ecological flow requirements of Dongjiang River, the maximum quota of water transfer for the Shenzhen River catchment is 1.53 million m3/day. Therefore, the amount of water transfer has the potential to increase by up to 10%. 2.3.2.2. Improving the volumetric wastewater treatment rate (M6). There are three WWTPs in the study area: Binhe, Luofang and Caopu. The wastewater treatment rate was 78% in 2009. According to Shenzhen municipal wastewater system planning, three new WWTPs will be built gradually in the next 10 years (Fig. 1). Although the future wastewater treatment capacity will reach 1.5 million m3/d in 2020, the volumetric wastewater treatment rate is not expected to exceed 95%. The existing separated drainage system built in the mature district of the Shenzhen River catchment has low efficiency due to misconnections between the sewer and storm water pipes, causing some wastewater discharge into the river through storm water pipes. Such misconnections are not expected to be fully rectified in the short term. Therefore, the wastewater treatment rate in the study area has the potential to increase to 85%e95%. 2.3.2.3. Improving the pollutant removal rate (M7). Although existing WWTPs are equipped with secondary treatment, the expected COD pollutant removal rate is only around 80%. According to Shenzhen municipal wastewater system planning guidelines, the existing and new WWTPs will be equipped with tertiary treatment. Therefore, it is possible to improve the COD removal efficiency to 90% by 2020. 2.3.2.4. Wastewater reuse (M8). Currently, the wastewater reuse ratio (percentage of reused water compared to the total treated wastewater) in the Shenzhen River catchment is very low (only 5%
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in 2009). This is because, in the study area, water demand for agriculture accounts for a very small percentage of total water demand, and very few industries in Shenzhen require large amounts of low-quality water. Therefore, municipal water, landscaping and new domestic housing become the potential users of reclaimed wastewater. Considering the potential constraints on wastewater reuse, such as water quality, price of the treated wastewater and user acceptance of reclaimed water, it is expected that the reclaimed wastewater reuse ratio will increase by at most 15% (Shenzhen Urban Planning and Land Resources Bureau, 2003). In summary, eight potential measures (M1 e M8) have been proposed according to local socio-economic planning or water infrastructure planning for water environmental management in the Shenzhen River catchment (Table 2).
2.4. Evaluation criteria In this study, the GRP growth rate, rate of population increase, water shortage index (WSI, defined as the ratio of water demand to potential water supply) and the maximum COD along the river are selected as status indicators of the water environment in an urbanizing catchment. More specifically, the objectives of the proposed measures are to: 1) balance water supply and demand (WSI <1) in the future; 2) meet the requirement of Class V of Environmental Quality Standard for Surface Water in China (COD<40 mg/L); 3) slow down the population growth; and 4) keep GRP growth as stable as possible.
3. Results and discussion 3.1. Results of the baseline simulation The SyDWEM model is used to simulate the population increase and economic growth for the next 10 years (2011e2020) and their impact on water demand and water quality in the Shenzhen River. A base case scenario was proposed based on the assumption that current growth trends will be maintained (0% increase for each possible measure in Table 2). The simulation results indicate that the GRP will increase by 98% (from 141 billion Yuan in 2011 to 279 billion Yuan in 2020), while the population will increase by 10% (from 2.77 million in 2011 to 3.06 million in 2020). In the year 2011, the water shortage index is expected to be 0.98 and the water resource is expected to balance. However, water supply cannot keep up with water demand starting in 2012, and the WSI will increase to 1.33 in 2020 (Fig. 4). The results also indicate that the water quality in the Shenzhen River will worsen in the planning horizon due to pollution load increases. The maximum COD along the river is expected to occur at the confluence of the Buji and Shenzhen Rivers, because most of the untreated wastewater in the catchment discharges into Shenzhen
Fig. 4. GRP, Pop, WSI and COD from 2011 to 2020 (base case).
River via Buji River. The maximum COD will rise from 69.5 mg/L in 2011 to 79.5 mg/L in 2020 (Fig. 5). 3.2. Sensitivity of individual measures The model was applied to evaluate the sensitivity of the water system to each measure while keeping the values of the other measures at 2010 levels. As seen in Fig. 6(a), the GRP and population will slightly increase with the increase of the tertiary industry percentage (M1). Furthermore, because the tertiary industry has a slightly lower labor productivity than the secondary industry in the catchment, labor productivity will slightly decrease under M1 (Table 3), and the population will have a relatively higher increase rate than the GRP. However, water shortages will be alleviated by improvements in water use efficiency, and water quality will improve due to the decrease in COD load per GRP under M1 (Table 3). If the maximum possible increase (10%) of the tertiary industry percentage is implemented, the water shortage index and COD of the river in 2020 will be decreased by 3% and 10%, respectively. As seen in Fig. 6(b), the GRP and population will decrease with the increase in capital-intensive industry (M2). Furthermore, because the capital-intensive industry has higher labor productivity than the labor-intensive industry, the labor productivity will increase under M2 (Table 3), and the population will decrease more than the GRP. In addition, M2 will improve water use efficiency, decrease the COD load per GRP and thus alleviate the water shortage and improve water quality. If the maximum possible increase (5%) of
Table 2 Measures and their possible adjustment range in 2020. Measures/decision variables
The maximum possible % increase relative to base case
M1/GRP proportion of tertiary industry M2/GRP proportion of capital-intensive industry M3/labor productivity M4/water use efficiency M5/water transfer quota M6/volumetric wastewater treatment rate M7/COD removal rate of WWTP M8/Reclaimed wastewater reuse ratio
10% 5% 10% 20% 10% 10% 10% 15%
Fig. 5. Average COD in a tidal period along the river (base case).
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Fig. 6. Sensitivity of the water environment to the individual measures (2020).
Table 3 Labor productivity, water use efficiency and COD load per GRP in 2020. Measure
Base case Individual measure
Integrated measure
M1 M2 M3 M4 M5e8 M9 M10 M11
Labor productivity 103 Yuan
Water use efficiency 103 Yuan/m3
COD load per GRP ton/billion Yuan
119 116 136 131 119 119 136 119 129
0.546 0.585 0.549 0.546 0.650 0.546 0.708 0.546 0.658
267 196 239 265 267 267 144 267 159
the capital-intensive industry percentage is implemented, the water shortage index and COD of the river in 2020 compared to the base case will decrease by 4% and 6%, respectively. As seen in Fig. 6(c), the GRP will slightly decrease but population will significantly decrease with the improvement in labor productivity (M3). Furthermore, M3 will alleviate the water shortage and slightly improve water quality due to the decrease in population. If the maximum possible increase (10%) of labor productivity is implemented, the water shortage index and COD of the river in 2020 compared to base case will decrease by 5% and 2%, respectively. As seen in Fig. 6(d), improvements in water use efficiency (M4) have no impact on the GRP and population growth. Furthermore, although M4 will alleviate water shortages, it will slightly decrease
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Fig. 7. Prediction of socio-economic and water environmental indicators in 2020.
water quality in the river. This is because the average WWTP effluent flow discharged into the Shenzhen River is around three times the river natural base flow (e.g., COD is around 40 mg/L when the COD removal rate of WWTP is 80%), and the water quality of WWTP effluent is much better than the water quality of the river (e.g., COD is 86 mg/L in 2020 under M4). Thus, the WWTP effluent can dilute the pollutants in the river. With an increase in water use efficiency, the WWTP effluent will decrease and the water quality of the river will worsen. If the maximum possible increase (20%) of water use efficiency is implemented, the water shortage index and the COD of the river in 2020 compared to the base case will decrease by 10% and increase by 8%, respectively. In the study, the four proposed engineering measures (M5eM8) were assumed to have no impact on population and GRP growth. In addition, M5 (increase of water transfer quota) has a significant effect on the water resource balance but no effect on the water quality of the river (Fig. 6(e)). If the maximum possible increase (10%) of the water transfer quota is implemented, the water shortage index in 2020 compared to the base case will decrease by 8%. Both M6 (improvement of wastewater treatment rate) and M7 (improvement of pollutant removal rate) can greatly improve water quality but have no contribution to water shortage alleviation (Fig. 6(feg)). If the maximum possible increase in wastewater collection/treatment is implemented, the COD of the river in 2020 under M6 and M7 compared to the base case will decrease by 30% and 23%, respectively. Furthermore, M8 can greatly reduce the water shortage index (Fig. 6(h)). However, the water quality will become a little worse because less WWTP effluent will discharge into the river with increased reclaimed wastewater reuse. If the maximum possible increase (15%) of reclaimed wastewater reuse is implemented, the water shortage index and the COD of the river in 2020 compared to the base case will decrease by 14% and increase by 3%, respectively. To summarize, M1 and M2 have positive effects on both water quantity and water quality in the catchment. M3, M4, M5 and M8 can alleviate the water shortage, but cannot improve water quality
in the river. On the other hand, M6 and M7 can improve the water quality, but have no effect on the water shortage. It should be noted that the status indicators in the study have an almost linear sensitivity to the decision variables with relative variations less than 10% in sensitivity analysis. However, water shortage index shows a slight, but clear nonlinear response to water use efficiency with relative variation ranging from 0% to 20% (Fig. 6(d)). Furthermore, although each individual measure has a certain effect on water resources or the water quality in the Shenzhen River catchment, the goals of water quantity and quality improvement cannot be achieved by the individual measures (M1e8 in Fig. 7); instead, an integrated plan formed by combining the individual measures is needed. 3.3. Impact of integrated measures on the water environment Three integrated measures (M9, M10 and M11) are proposed. M9 is an integrated socio-economic measure that assumes: 1) all four socio-economic measures (M1e4) are followed; 2) the maximum possible increase in each socio-economic measure is implemented; and 3) no water engineering measures are carried out. M10 is an integrated engineering measure that assumes: 1) all four water engineering measures (M5e8) are followed; 2) the maximum possible increase in each water engineering measure is implemented; and 3) no socio-economic measures are carried out. M11 is an integrated socio-economic and engineering measure that assumes that both socio-economic and water engineering measures are implemented (Table 4). Compared with the base case, M9 slightly decreases the GRP but significantly decreases the population (Fig. 7). This is because M9 would increase the labor productivity from 0.119 million Yuan/ capita to 0.136 million Yuan/capita (Table 3). Furthermore, M9 would alleviate the water shortage by improving the water use efficiency and decreasing the population. In addition, M9 would improve the water quality by decreasing the COD load per GRP and decreasing the population. However, the simulation results indicate that the water shortage index and COD can only decrease to 1.2 and
Table 4 Percentage increase of the decision variables in the integrated measures. Measure
Tertiary industry
Capital-intensive industry
Labor productivity
Water use efficiency
Water transfer quota
Wastewater treatment rate
COD removal rate
Reclaimed wastewater reuse
M9 M10 M11
10% 0% 10%
5% 0% 5%
10% 0% 5%
20% 0% 10%
0% 10% 10%
0% 10% 10%
0% 10% 10%
0% 15% 10%
494
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69 mg/L, respectively, such that the integrated socio-economic measure cannot fully satisfy the requirements of water environment improvement. It should be noted that due to the nonlinearity of the system, the effect of an integrated measure is different from the sum of the individual effects of the each measure when they are implemented independently. For example, M1, M2, M3 and M4 can reduce the water shortage index by 0.04, 0.05, 0.06 and 0.13, respectively, while their combination (M9) can only reduce the water shortage index by 0.14 (Fig. 7). Although the integrated water engineering measure (M10) has no impact on population or GRP growth, it can greatly reduce the water shortage index by increasing water transfer and wastewater reuse (Fig. 7). M9 can also greatly improve water quality by improving the wastewater treatment capacity. However, the simulation results indicate that the water shortage index and COD can only decrease to 1.1 and 51 mg/L, respectively, and thus the integrated water engineering measure cannot fully satisfy the requirements of water environment improvement. Based on the simulation results, socio-economic and water infrastructure planners can consult each other and make trade-offs among various individual/combination measures in order to propose an integrated measure including both socio-economic and water engineering methods. According to the effectiveness of the individual measures, one possible integrated measure (M11) is proposed here (Table 4): 1) improve the percentage of tertiary industry (M1) and percentage of capital-intensive industry (M2) to their possible maximum values (10% and 5%, respectively), since they have positive effects on both water quantity and quality; 2) increase the quota of water transfer (M5) to the possible maximum value (increase quota 10% by 2010), since the quota has been approved by Dongjiang River Catchment Management Bureau; and 3) make trade-offs among other individual measures with the objectives of water resource balance and water quality improvement; the values of model input parameters in these measures can be estimated by trial and error. The simulation results indicate that the GRP and population in Shenzhen River catchment will increase to 275 billion Yuan and 2.72 million, respectively, under M11. Water demand and supply will keep pace in 2020 due to the population decrease, increase in water use efficiency, increase in water transfer and increase in wastewater reuse. Furthermore, water quality will improve gradually and the COD at all sections will meet the Class V standard in 2020 due to the decrease of COD load per GRP and increase of wastewater treatment capacity in the catchment under M11. Therefore, by carrying out the integrated engineering and socioeconomic measures, the objectives of environmental improvement can be fully achieved.
4. Conclusions We use an integrated model (SyDWEM) to describe the dynamic socio-economic system, water infrastructure and receiving water in the rapidly urbanizing Shenzhen River catchment in China. The model is further applied to evaluate the effects of eight proposed socio-economic or water engineering measures on the GRP and population growth, water demand/supply balance and water quality in the catchment for the next 10 years (2011e2020). The results indicate that: 1) All of the measures have little effect on GRP growth; an increase in the percentage of capital-intensive industry (M2) and labor productivity improvement (M3) can significantly decrease population, while other measures have little effect on population growth.
2) An increase in the percentage of tertiary industry (M1) and in the percentage of capital-intensive industry (M2) will have a positive effect on both water quantity and quality in the catchment; and labor productivity improvement (M3), water use efficiency improvement (M4), increase of the water transfer quota (M5) and increase of reclaimed wastewater reuse (M8) can alleviate the water shortage, but cannot improve water quality in the river. However, an increase in the wastewater treatment rate (M6) and the pollutant removal rate (M7) can improve water quality in the river, but has no effect on water shortage. Moreover, individual measures do not meet the goals of water quantity and quality improvement in the catchment. 3) Neither the integrated socio-economic measure (M9) nor the integrated engineering measure (M10) can fully achieve the requirements of water environment improvement. 4) Based on the simulation results of SyDWEM, socio-economic and water infrastructure planners can consult each other and make trade-offs among various individual/combination measures. According to the effectiveness of the individual measures, we propose a possible integrated socio-economic and engineering measure that can achieve water environment sustainability in the Shenzhen River catchment. Therefore, SyDWEM has the capacity to evaluate the effects of both socio-economic and engineering measures on GRP and population growth, the water resource balance and water quality in the river; it also provides a tool for integrating the decision-making of socio-economic and water infrastructure planners.
Acknowledgments The research leading to these results received funding from the European Community’s Seventh Framework Programme under grant agreement n PIIF-GA-2008-220448.
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Assessment of environmental improvement measures using a novel integrated model: A case study of the Shenzhen River catchment, China Hua-Peng Qin a, b, *, Qiong Su a, Soon-Thiam Khu b a b
Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, 518055 Shenzhen, China Faculty of Engineering and Physical Sciences, University of Surrey, Civil Engineering (C5), Guildford, Surrey GU2 7XH, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 March 2011 Received in revised form 11 August 2012 Accepted 8 October 2012 Available online 26 November 2012
Integrated water environmental management in a rapidly urbanizing area often requires combining social, economic and engineering measures in order to be effective. However, in reality, these measures are often considered independently by different planners, and decisions are made in a hierarchical manner; this has led to problems in environmental pollution control and also an inability to devise innovative solutions due to technological lock-in. In this paper, we use a novel coupled system dynamics and water environmental model (SyDWEM) to simulate the dynamic interactions between the socioeconomic system, water infrastructure and receiving water in a rapidly urbanizing catchment in Shenzhen, China. The model is then applied to assess the effects of proposed socio-economic or engineering measures on environmental and development indicators in the catchment for 2011e2020. The results indicate that 1) measures to adjust industry structures have a positive effect on both water quantity and quality in the catchment; 2) measures to increase the labor productivity, the water use efficiency, the water transfer quota or the reclaimed wastewater reuse can alleviate the water shortage, but cannot improve water quality in the river; 3) measures to increase the wastewater treatment rate or the pollutant removal rate can improve water quality in the river, but have no effect on water shortage. Based on the effectiveness of the individual measures, a combination of socio-economic and engineering measures is proposed, which can achieve water environmental sustainability in the study area. Thus, we demonstrate that SyDWEM has the capacity to evaluate the effects of both socio-economic and engineering measures; it also provides a tool for integrated decision making by socio-economic and water infrastructure planners. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: System dynamics Water environment model Integrated measures Urbanization catchment
1. Introduction Many catchments in developing countries are undergoing rapid urbanization. At the same time, these catchments are usually confronted with serious water environmental problems (Biswas and Tortajada, 2009; Jones, 2009; Varis and Vakkilainen, 2001; Wang and Li, 2007). One reason for this is the rapid economic growth and population concentration in these countries, which cause a rapid increase in water consumption and wastewater discharge into the catchments. In addition, many labor-intensive industries, such as pulp and paper, chemical products and
* Corresponding author. Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Room 414, E Building, 518055 Shenzhen, China. Tel./fax: þ86 755 26035291. E-mail addresses: [email protected] (H.-P. Qin), [email protected] (S.-T. Khu). 0301-4797/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2012.10.053
by-products, textiles, and agricultural products, have been introduced into these catchments because the industry entry barrier was relatively low when the catchments were in the infancy of urbanization. These industries usually have low labor productivity, high water consumption and high pollution emissions. This imbalanced industry structure aggravates water scarcity and water quality deterioration in rapidly urbanizing areas. Furthermore, urbanization in some developing countries occurs so fast that the water infrastructure development cannot keep pace with the rapid economic growth and cannot provide satisfactory management of drinking water and wastewater (Biswas and Tortajada, 2009). The inadequacy of infrastructure provisions for water supply and wastewater treatment tend to compound the water environmental problems in these catchments. Many water management measures have been proposed to improve the water environment in urbanized catchments. These measures can be categorized as two types: engineering measures and socio-economic measures. Engineering measures include enhancing the water supply, improving the wastewater treatment
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capacity, and wastewater reuse (Cho et al., 2003; Georgieva, 2005; Stanko, 2009). Socio-economic measures, such as industrial structure regulation (Wang et al., 2002), legal measures (Dellapenna and Gupta, 2008), economic incentives and financial policies (Liu, 2002; Massarutto, 2007; Somlyódy and Varis, 2006), aim to improve the efficiency of water consumption and reduce pollutant generation/ emission. Moreover, sustainable catchment management requires integrating socio-economic and engineering measures. However, planning for socio-economic and water infrastructure development are usually carried out independently, with different objectives. A decision-making tool that includes socio-economic and infrastructure aspects is needed to support better communication and interactions between different stakeholders (Liu et al., 2008). The effects of engineering measures on the receiving water body, such as increasing levels of sewage treatment, dredging of contaminated bottom sediments and pumping oxygen into the river, can be evaluated using hydrodynamics and water quality models (Hosoi et al., 1996; Kamal et al., 1999; Muhammetoglu et al., 2005; Ning et al., 2001; Park and Lee, 2002; Xu and Liao, 2005), such as QUAL2E (Broun and Barnwell, 1987), WASP (Ambrose, 1987), and MIKE-11 (DHI, 1993). However, such models do not simulate the interactions between social, economic and environmental issues, and cannot be adequately used to assess the impacts of general social and economic policy measures in the river environment. On the other hand, models are available to evaluate socioeconomic measures. For example, economic inputeoutput models can describe interactions between economic and water environmental systems and have been used to evaluate different socioeconomic measures such as raising the price of water (Yoo and Yang, 1999), water allocation and management policies (Lange et al., 2007; Lenzen and Foran, 2001; Wang et al., 2005), and adjusting the regional industry or trade structure (Ni et al., 2001; Okadera et al., 2006). Cost-benefit analysis is a conventional method to analyze the economic benefits and costs of a policy or project such as technology improvement in the water supply system (Hutton et al., 2007) or in a wastewater treatment plant (EisenHecht and Kramer, 2002). Moreover, system dynamics have been successfully applied to evaluate the efforts of different economic development scenarios on water consumption and pollution discharge control (Guo et al., 2001; Simonovic, 2002), water resource system balance (Xu et al., 2002), and land use management
487
(Chen et al., 2004). However, it is difficult for conventional socioeconomic models to simulate the behavior of pollutants in a river, and thus they cannot be used to predict the spatio-temporal variations of water quality in a catchment under different measures. Over the last two decades, a variety of models and frameworks have been developed for integrated water management, such as WaterWare (Fedra and Jamieson, 1996), Hydro Planner (Maheepala et al., 2005), WEAP (Sieber et al., 2007), Elbe-DSS (de Kok et al., 2009), and SAHRA (Liu et al., 2008), by which the effects of socioeconomic and engineering system changes on the water environment can be evaluated. However, socio-economic factors are regarded as external scenarios/constraints (Lautenbach et al., 2009) in these models, and the interactions between different socio-economic components, and between engineering and socioeconomic measures, are usually neglected. A coupled system dynamics and water environmental model (SyDWEM) (Qin et al., 2011) was recently developed in order to better understand how the socio-economic system, water infrastructure and receiving water interact with one another in the rapidly urbanizing catchment. In SyDWEM, the socio-economic system can be considered as an internal sub-module of the whole system, and some socio-economic factors (e.g., industrial structure) can also be regarded as decision variables. Therefore, the model can be used to evaluate the effects of the proposed socio-economic and engineering measures in a catchment or study area. In this paper, we applied the SyDWEM model to the rapidly urbanizing Shenzhen River catchment in China. The objective of the study is to demonstrate SyDWEM capacity to evaluate the effects of socio-economic and engineering measures on environmental improvement, and to devise and evaluate a series of integrated measures to achieve water environment sustainability. 2. Materials and methods 2.1. Study area The Shenzhen River is located in the rapidly urbanizing coastal region of Southeastern China, and forms the administrative border between mainland China and Hong Kong (Fig. 1). The total northern catchment area of the Shenzhen River is 187 km2, and it includes three administrative areas of Shenzhen: Luohu District, Futian
Fig. 1. A general view of the Shenzhen River catchment.
488
H.-P. Qin et al. / Journal of Environmental Management 114 (2013) 486e495 GRP-Population module
Water supply module
Net investment
Water transfer quota
Cobb-Douglas production function
Local water resource and development rate
Production efficiency (GRP per labor)
Reclaimed wastewater reuse ratio
Industry structure (GRP fferent industries) proportions of diff different
Sewer and WWTPs module
System Dynamics Water use efficiency (water consumption per unit GRP) Water consumption per capita COD generation per capita and per unit GRP
Model
GRP
Water consumption
Population
Pollutant loads
Water consumptionpollutant load module
Wastewater treatment rate and efficiency
Water Quality Model
Water quality along river
Receiving water module
Fig. 2. Model coupling and data flow.
District, and Buji Town. The Shenzhen River is a typical tide-affected river with a length of 14 km and several tributaries, of which the Liantang, Shawan, Buji, Futian, Huanggang, Xinzhou and Fengtang Rivers drain from the Shenzhen side. The main river drains southwest into the Deep Bay, which joins the Pearl River estuary on its seaward side. Over the past twenty years, this area has undergone rapid urbanization. Between 1990 and 2007, its gross regional product (GRP)1 increased from 9 billion to 103 billion Yuan, the total population increased from 0.81 million to 2.65 million and the percentage of built-up area to developable land increased from 16.7% to 81.3%. However, with rapid urbanization, the catchment suffers from severe water shortages, and the availability of water mainly depends on a large-scale water-transfer project. In 2007, about 88% of the water supply in the area was imported from outside the city boundaries. Furthermore, the water quality in the Shenzhen River has seriously deteriorated. The water body in the middle and lower reaches of the river is malodorous and black. Pollutant concentrations such as the chemical oxygen demand (COD), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP) in the river have a high rate of non-compliance with water quality objectives (Shenzhen Environmental Protection Bureau, 2009). This is because wastewater treatment system development lags far behind economic development in the catchment. Although there are three WWTPs (Luofang, Binhe and Caopu) in the catchment, 26% of the total wastewater was discharged into the Shenzhen River without treatment in 2007. Although investment in pollution control by the local government has increased over the years, the high rate of economic development and population growth has negatively affected the pace of pollution control. Therefore, both engineering measures and social-economic policies should be considered to reduce the pollution load and harmonize water environment protection with population growth and economic development. 2.2. Model description A coupled system dynamics and water environmental model (SyDWEM) has already been developed to study dynamic socio-
1 GRP is identical to GDP in the study area because GDP is widely used in China to measure the economic activity at the country, province, city and town levels (http:// www.stats.gov.cn/). 1.
economic system, water infrastructure and receiving water in a rapidly urbanizing catchment (Qin et al., 2011). In the framework of SyDWEM (Fig. 2), the water-related socio-economic system can be further subdivided into a population/GRP module and a water demand/pollution generation module; the water infrastructure system includes a water supply module, sewer and WWTPs module; and the receiving water module can comprise the river, lake or estuary. The five modules are summarized as follows: 1) Population/GRP module: Population and GRP are considered as intermediate outputs rather than input variables in SyDWEM. Population growth is simulated based on labor demand since the increase in migrant labor caused by economic growth usually dominates the population growth in a rapidly urbanizing area, and economic performance is evaluated by the gross regional product (GRP) using the CobbeDouglas production function for growth forecasting, in which labor force and net investment are important impact factors on GRP growth (Douglas, 1976). The population and the economic models are coupled since they are both related to labor force. Furthermore, the labor force demand is determined by production efficiency (GRP per labor) and industry structure (GRP proportions of different industries) in SyDWEM. Therefore, the main input data are net investment, production efficiency and industry structure for different administrative districts in a catchment. 2) Water demand/pollution generation module: This module estimates the water demand and the pollutants generated for different users in a catchment according to population size, GRP, industrial structure (GRP proportions of different industries), water demand and pollutants per capita, as well as water demand and pollutant load per unit GRP in different industries. 3) Water supply module: This module estimates the potential and total water supply in a whole catchment according to the development rate of local water resources, reclaimed wastewater reuse ratio (percentage of reused water compared to the total treated wastewater) and water transfer quota. 4) Sewer and WWTPs module: This module estimates the total effluent load according to the volumetric wastewater treatment rate, pollutant removal rate and wastewater reuse ratio. 5) Receiving water module: In this module, a water quality model is used to simulate the pollutant transport, transformation and fate in the river system. For a tide-affected river, a 1-D unsteady water quality model is usually applied to describe pollutant behavior. The model is composed of the Saint-Venant equation and a convectionedispersion equation.
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Buji Town Population/ Luohu District GRP module Population/ Futian District GRP module Population/ GRP module
Buji Town Population/ GRP module Luohu District Futian District Water demand / pollutant generation module Wastewater
Water supply module
Wastewater reclamation and reuse
489
Socio-economic system
Untreated wastewater
Luofang WWTP Shawan & Sewer …… WWTP & Sewer Binhe WWTP & Sewer WWTP&Sewer module
Water infrastructure
Treated wastewater Abstraction
Shenzhen River catchment
Water transferred from other catchments
Fig. 3. Conceptual integrated water system model in the Shenzhen River catchment.
For further details of the SyDWEM, please refer to Qin et al. (2011). In SyDWEM, the aforementioned individual water environment models are integrated using the System Dynamics platform. The integrated model can be applied to calculate the GRP, population, water consumption and water quality at the catchment scale. Furthermore, the integrated model can consider the effects of socio-economic factors (e.g., industrial structure, labor productivity, water use efficiency) or engineering factors (e.g., volumetric wastewater treatment rate, WWTP pollutant removal rate, wastewater reuse ratio) on the whole water system. Considering the spatial variation of socio-economic development, the population/GRP module and water demand/pollutant generation module were developed for Futian District, Luohu District and Buji Town, respectively. Fig. 3 shows the relationship between each administrative district, sub-catchment, and WWTP within the Shenzhen River catchment. COD is one of the main pollutants in Shenzhen River and is taken as the representative water quality indicator in this study. The decision variables in the model (e.g., industry structure, reclaimed wastewater ratio, volumetric wastewater treatment rate and pollutant removal rate) can be regarded as input parameters and determined by historical statistics or future development policy/planning. In the case study, GRP per labor force and water consumption per unit GRP for different industries are described by exponential growth model. For further details of model calibration and validation, please refer to Qin et al. (2011).
of Shenzhen (2007e2020),” “Industrial Distribution Studies and Planning of Shenzhen,” “Shenzhen Development Strategy in 2030” and “Water conservation planning of Shenzhen (2005e2020).” Instead of attempting to simulate each policy, the measures are summarized into four groups: M1, M2, M3 and M4. The groupings are based on the intended goals of policy measures rather than their mechanism of implementation. 2.3.1.1. Increasing the GRP proportion of tertiary industries (M1). The industry structures in the study area can be categorized into primary, secondary and tertiary industries. The tertiary industry (this is a Chinese term, also known as the service industry) is characterized by the production of services instead of end products. In the study area, the tertiary industry currently (2009) has a slightly lower labor productivity than the secondary industry (see Table 1). However, it has much higher water use efficiency (around 1.6 times) than the secondary industry. Moreover, the pollutant load from the tertiary industry is assumed to be only related to residential activities. Compared with manufacturing industries, the tertiary industry usually has lower levels of water pollutant emissions. Therefore, it is possible to reduce water consumption and pollutant load by increasing the proportion of the tertiary industry. According to Shenzhen’s socio-economic planning, some tertiary industries such as trade, financial, cultural, and exhibition industries will be encouraged to grow and expand. The GRP proportion of tertiary industry in the catchment has the potential to increase by 0e10% by 2020, while the corresponding GRP proportion of the secondary industry is predicted to decrease.
2.3. Potential measures to improve the water environment In order to improve the water environment in Shenzhen River, local policy makers have proposed many measures over the years. These policies can be categorized into two main groups: socioeconomic measures and engineering measures. 2.3.1. Socio-economic measures Many socio-economic measures have been proposed to improve the water environment according to development planning policies. The relevant planning policies include “Urban Master Planning
Table 1 Labor productivity, water use efficiency and pollutant load of different industries. Type of industry
Labor productivity (103 Yuan per capita)
Water use efficiency 103 Yuan/m3
COD load per GRP t/109 Yuan
Secondary industry - Labor-intensive - Technology-intensive - Capital-intensive Tertiary industry
59 24 59 218 56
0.38 0.15 0.62 0.17 0.59
755 1612 121 24 /
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2.3.1.2. Increasing GRP proportion of certain secondary industries (M2). The secondary industry (also known as the manufacturing industry) generally manufactures finished goods or goods that are suitable for use by other businesses, for export, or for sale to domestic consumers. Within the study area, there are three types of secondary industries: labor-intensive, technology-intensive and capital-intensive. As seen in Table 1, capital-intensive industry has much higher labor productivity and lower COD load per GRP than labor-intensive industry. Therefore, it is possible to decrease the population growth and reduce pollutant loads by increasing the proportion of capital-intensive industry. According to Shenzhen City socio-economic planning guidelines, some capital-intensive (e.g., biotechnology and pharmaceutical industry, new material and new energy industry and fine chemistry industry) will be encouraged and enhanced, and the GRP proportion of capitalintensive industry in the catchment has the potential to increase 0e5% by 2020, while the labor-intensive industry would have a corresponding decrease. 2.3.1.3. Improving labor productivity (M3). Labor productivity can be measured by GRP per capita or GRP per employee (Stephen and Douglas, 2005). The labor productivities of the secondary and tertiary industry in Shenzhen are currently much lower than those of developed countries/areas. For example, in 2009, the labor productivity of the secondary industry in the Shenzhen River catchment was only 40% and 63% of those in Japan and Hong Kong, respectively, whereas the labor productivity of the tertiary industry was only 28% of that in Hong Kong (Census and statistics department of Hong Kong, 2010; Japan Productivity Center, 2010). Some policies for labor productivity improvement have been proposed by socio-economic planners, including increasing the education level of the laborers, increasing the industry entry barrier and promoting trade, financial, cultural and exhibition industries, which have high labor productivity (Development and Reform Commission of Shenzhen, 2009). In this way, the productive efficiency has the potential to improve 0e10% by 2020. 2.3.1.4. Improving water use efficiency (M4). The water use efficiency of economic activities can be measured by GRP per unit water consumption. In 2009, the water use efficiency in the study area was 0.417 $ 103 Yuan/m3, which is only one third of that in Hong Kong or Singapore. There are various potential means to improve water use efficiency, including the following measures: a. Upgrading water recycling technology: Industrial water recycling rate refers to the ratio of recycled water to total water consumption of an industry. The water recycling rate is as high as 90% in some developed countries (e.g., Japan). However, the recycling rate is only 39% in Shenzhen due to the obsolete technologies in use. According to the “Catalogue Guideline for Major Industry Clean Production Technology” issued by the National Development and Reform Commission (NDRC), obsolete technologies should be replaced by advanced technologies in the next 10 years. By upgrading industrial technology, the recycling rate is expected to increase to 50% by 2020. b. Decreasing pipeline leakage: Water loss due to network leakage is less than 5% in developed countries. However, it is estimated that nearly 10% of water leaked from the water supply pipeline in the study area in 2004 (Shenzhen Municipal Water Affairs Bureau, 2007). Thus, there is a plan to decrease the network leakage rate to 5% by 2020 by renovating urban water supply pipelines. c. Promoting water-saving appliances: Local statistical data indicate that only 50% of public utilities were equipped with
water-saving devices; and 10e30% of total water consumption was wasted due to irrational use of water devices in 2004 (Shenzhen Municipal Water Affairs Bureau, 2007). Watersaving devices may reduce 20e30% of the water use, and water consumption may be reduced to 10% by the year 2020 by promoting water-saving devices and practices. d. Water tariff adjustment: The industrial water price in Shenzhen is 2.25 Yuan/m3 (current price), which is only half of that in Hong Kong. Based on an econometric analysis of the relationship between residential water demand, water price and income (Liu and Lv, 2006), it is estimated that a water price increase of 10% would result in a water demand decrease of approximately 4.1%. Therefore, it is possible to promote water conservation by increasing the water price or implementing a cascade water tariff. If the aforementioned measures are considered, water use efficiency has the potential to improve by up to 20% by the year 2020. 2.3.2. Engineering measures Many engineering measures have been proposed to improve the water environment according to water supply system planning (2006e2020), and Shenzhen municipal wastewater system planning (2002e2020) (Shenzhen Urban Planning and Land Resources Bureau, 2003). The measures can be classified into four groups (M5eM8): 2.3.2.1. Increasing the water transfer quota (M5). Currently, the water supply in Shenzhen is mostly transferred from the upstream parts of Dongjiang River, and the transfer quota is based on an agreement with the Bureau of Dongjiang River basin management. The current water-transfer capability servicing for the Shenzhen River catchment is 1.39 million m3/day. Considering the environmental/ecological flow requirements of Dongjiang River, the maximum quota of water transfer for the Shenzhen River catchment is 1.53 million m3/day. Therefore, the amount of water transfer has the potential to increase by up to 10%. 2.3.2.2. Improving the volumetric wastewater treatment rate (M6). There are three WWTPs in the study area: Binhe, Luofang and Caopu. The wastewater treatment rate was 78% in 2009. According to Shenzhen municipal wastewater system planning, three new WWTPs will be built gradually in the next 10 years (Fig. 1). Although the future wastewater treatment capacity will reach 1.5 million m3/d in 2020, the volumetric wastewater treatment rate is not expected to exceed 95%. The existing separated drainage system built in the mature district of the Shenzhen River catchment has low efficiency due to misconnections between the sewer and storm water pipes, causing some wastewater discharge into the river through storm water pipes. Such misconnections are not expected to be fully rectified in the short term. Therefore, the wastewater treatment rate in the study area has the potential to increase to 85%e95%. 2.3.2.3. Improving the pollutant removal rate (M7). Although existing WWTPs are equipped with secondary treatment, the expected COD pollutant removal rate is only around 80%. According to Shenzhen municipal wastewater system planning guidelines, the existing and new WWTPs will be equipped with tertiary treatment. Therefore, it is possible to improve the COD removal efficiency to 90% by 2020. 2.3.2.4. Wastewater reuse (M8). Currently, the wastewater reuse ratio (percentage of reused water compared to the total treated wastewater) in the Shenzhen River catchment is very low (only 5%
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in 2009). This is because, in the study area, water demand for agriculture accounts for a very small percentage of total water demand, and very few industries in Shenzhen require large amounts of low-quality water. Therefore, municipal water, landscaping and new domestic housing become the potential users of reclaimed wastewater. Considering the potential constraints on wastewater reuse, such as water quality, price of the treated wastewater and user acceptance of reclaimed water, it is expected that the reclaimed wastewater reuse ratio will increase by at most 15% (Shenzhen Urban Planning and Land Resources Bureau, 2003). In summary, eight potential measures (M1 e M8) have been proposed according to local socio-economic planning or water infrastructure planning for water environmental management in the Shenzhen River catchment (Table 2).
2.4. Evaluation criteria In this study, the GRP growth rate, rate of population increase, water shortage index (WSI, defined as the ratio of water demand to potential water supply) and the maximum COD along the river are selected as status indicators of the water environment in an urbanizing catchment. More specifically, the objectives of the proposed measures are to: 1) balance water supply and demand (WSI <1) in the future; 2) meet the requirement of Class V of Environmental Quality Standard for Surface Water in China (COD<40 mg/L); 3) slow down the population growth; and 4) keep GRP growth as stable as possible.
3. Results and discussion 3.1. Results of the baseline simulation The SyDWEM model is used to simulate the population increase and economic growth for the next 10 years (2011e2020) and their impact on water demand and water quality in the Shenzhen River. A base case scenario was proposed based on the assumption that current growth trends will be maintained (0% increase for each possible measure in Table 2). The simulation results indicate that the GRP will increase by 98% (from 141 billion Yuan in 2011 to 279 billion Yuan in 2020), while the population will increase by 10% (from 2.77 million in 2011 to 3.06 million in 2020). In the year 2011, the water shortage index is expected to be 0.98 and the water resource is expected to balance. However, water supply cannot keep up with water demand starting in 2012, and the WSI will increase to 1.33 in 2020 (Fig. 4). The results also indicate that the water quality in the Shenzhen River will worsen in the planning horizon due to pollution load increases. The maximum COD along the river is expected to occur at the confluence of the Buji and Shenzhen Rivers, because most of the untreated wastewater in the catchment discharges into Shenzhen
Fig. 4. GRP, Pop, WSI and COD from 2011 to 2020 (base case).
River via Buji River. The maximum COD will rise from 69.5 mg/L in 2011 to 79.5 mg/L in 2020 (Fig. 5). 3.2. Sensitivity of individual measures The model was applied to evaluate the sensitivity of the water system to each measure while keeping the values of the other measures at 2010 levels. As seen in Fig. 6(a), the GRP and population will slightly increase with the increase of the tertiary industry percentage (M1). Furthermore, because the tertiary industry has a slightly lower labor productivity than the secondary industry in the catchment, labor productivity will slightly decrease under M1 (Table 3), and the population will have a relatively higher increase rate than the GRP. However, water shortages will be alleviated by improvements in water use efficiency, and water quality will improve due to the decrease in COD load per GRP under M1 (Table 3). If the maximum possible increase (10%) of the tertiary industry percentage is implemented, the water shortage index and COD of the river in 2020 will be decreased by 3% and 10%, respectively. As seen in Fig. 6(b), the GRP and population will decrease with the increase in capital-intensive industry (M2). Furthermore, because the capital-intensive industry has higher labor productivity than the labor-intensive industry, the labor productivity will increase under M2 (Table 3), and the population will decrease more than the GRP. In addition, M2 will improve water use efficiency, decrease the COD load per GRP and thus alleviate the water shortage and improve water quality. If the maximum possible increase (5%) of
Table 2 Measures and their possible adjustment range in 2020. Measures/decision variables
The maximum possible % increase relative to base case
M1/GRP proportion of tertiary industry M2/GRP proportion of capital-intensive industry M3/labor productivity M4/water use efficiency M5/water transfer quota M6/volumetric wastewater treatment rate M7/COD removal rate of WWTP M8/Reclaimed wastewater reuse ratio
10% 5% 10% 20% 10% 10% 10% 15%
Fig. 5. Average COD in a tidal period along the river (base case).
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Fig. 6. Sensitivity of the water environment to the individual measures (2020).
Table 3 Labor productivity, water use efficiency and COD load per GRP in 2020. Measure
Base case Individual measure
Integrated measure
M1 M2 M3 M4 M5e8 M9 M10 M11
Labor productivity 103 Yuan
Water use efficiency 103 Yuan/m3
COD load per GRP ton/billion Yuan
119 116 136 131 119 119 136 119 129
0.546 0.585 0.549 0.546 0.650 0.546 0.708 0.546 0.658
267 196 239 265 267 267 144 267 159
the capital-intensive industry percentage is implemented, the water shortage index and COD of the river in 2020 compared to the base case will decrease by 4% and 6%, respectively. As seen in Fig. 6(c), the GRP will slightly decrease but population will significantly decrease with the improvement in labor productivity (M3). Furthermore, M3 will alleviate the water shortage and slightly improve water quality due to the decrease in population. If the maximum possible increase (10%) of labor productivity is implemented, the water shortage index and COD of the river in 2020 compared to base case will decrease by 5% and 2%, respectively. As seen in Fig. 6(d), improvements in water use efficiency (M4) have no impact on the GRP and population growth. Furthermore, although M4 will alleviate water shortages, it will slightly decrease
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Fig. 7. Prediction of socio-economic and water environmental indicators in 2020.
water quality in the river. This is because the average WWTP effluent flow discharged into the Shenzhen River is around three times the river natural base flow (e.g., COD is around 40 mg/L when the COD removal rate of WWTP is 80%), and the water quality of WWTP effluent is much better than the water quality of the river (e.g., COD is 86 mg/L in 2020 under M4). Thus, the WWTP effluent can dilute the pollutants in the river. With an increase in water use efficiency, the WWTP effluent will decrease and the water quality of the river will worsen. If the maximum possible increase (20%) of water use efficiency is implemented, the water shortage index and the COD of the river in 2020 compared to the base case will decrease by 10% and increase by 8%, respectively. In the study, the four proposed engineering measures (M5eM8) were assumed to have no impact on population and GRP growth. In addition, M5 (increase of water transfer quota) has a significant effect on the water resource balance but no effect on the water quality of the river (Fig. 6(e)). If the maximum possible increase (10%) of the water transfer quota is implemented, the water shortage index in 2020 compared to the base case will decrease by 8%. Both M6 (improvement of wastewater treatment rate) and M7 (improvement of pollutant removal rate) can greatly improve water quality but have no contribution to water shortage alleviation (Fig. 6(feg)). If the maximum possible increase in wastewater collection/treatment is implemented, the COD of the river in 2020 under M6 and M7 compared to the base case will decrease by 30% and 23%, respectively. Furthermore, M8 can greatly reduce the water shortage index (Fig. 6(h)). However, the water quality will become a little worse because less WWTP effluent will discharge into the river with increased reclaimed wastewater reuse. If the maximum possible increase (15%) of reclaimed wastewater reuse is implemented, the water shortage index and the COD of the river in 2020 compared to the base case will decrease by 14% and increase by 3%, respectively. To summarize, M1 and M2 have positive effects on both water quantity and water quality in the catchment. M3, M4, M5 and M8 can alleviate the water shortage, but cannot improve water quality
in the river. On the other hand, M6 and M7 can improve the water quality, but have no effect on the water shortage. It should be noted that the status indicators in the study have an almost linear sensitivity to the decision variables with relative variations less than 10% in sensitivity analysis. However, water shortage index shows a slight, but clear nonlinear response to water use efficiency with relative variation ranging from 0% to 20% (Fig. 6(d)). Furthermore, although each individual measure has a certain effect on water resources or the water quality in the Shenzhen River catchment, the goals of water quantity and quality improvement cannot be achieved by the individual measures (M1e8 in Fig. 7); instead, an integrated plan formed by combining the individual measures is needed. 3.3. Impact of integrated measures on the water environment Three integrated measures (M9, M10 and M11) are proposed. M9 is an integrated socio-economic measure that assumes: 1) all four socio-economic measures (M1e4) are followed; 2) the maximum possible increase in each socio-economic measure is implemented; and 3) no water engineering measures are carried out. M10 is an integrated engineering measure that assumes: 1) all four water engineering measures (M5e8) are followed; 2) the maximum possible increase in each water engineering measure is implemented; and 3) no socio-economic measures are carried out. M11 is an integrated socio-economic and engineering measure that assumes that both socio-economic and water engineering measures are implemented (Table 4). Compared with the base case, M9 slightly decreases the GRP but significantly decreases the population (Fig. 7). This is because M9 would increase the labor productivity from 0.119 million Yuan/ capita to 0.136 million Yuan/capita (Table 3). Furthermore, M9 would alleviate the water shortage by improving the water use efficiency and decreasing the population. In addition, M9 would improve the water quality by decreasing the COD load per GRP and decreasing the population. However, the simulation results indicate that the water shortage index and COD can only decrease to 1.2 and
Table 4 Percentage increase of the decision variables in the integrated measures. Measure
Tertiary industry
Capital-intensive industry
Labor productivity
Water use efficiency
Water transfer quota
Wastewater treatment rate
COD removal rate
Reclaimed wastewater reuse
M9 M10 M11
10% 0% 10%
5% 0% 5%
10% 0% 5%
20% 0% 10%
0% 10% 10%
0% 10% 10%
0% 10% 10%
0% 15% 10%
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69 mg/L, respectively, such that the integrated socio-economic measure cannot fully satisfy the requirements of water environment improvement. It should be noted that due to the nonlinearity of the system, the effect of an integrated measure is different from the sum of the individual effects of the each measure when they are implemented independently. For example, M1, M2, M3 and M4 can reduce the water shortage index by 0.04, 0.05, 0.06 and 0.13, respectively, while their combination (M9) can only reduce the water shortage index by 0.14 (Fig. 7). Although the integrated water engineering measure (M10) has no impact on population or GRP growth, it can greatly reduce the water shortage index by increasing water transfer and wastewater reuse (Fig. 7). M9 can also greatly improve water quality by improving the wastewater treatment capacity. However, the simulation results indicate that the water shortage index and COD can only decrease to 1.1 and 51 mg/L, respectively, and thus the integrated water engineering measure cannot fully satisfy the requirements of water environment improvement. Based on the simulation results, socio-economic and water infrastructure planners can consult each other and make trade-offs among various individual/combination measures in order to propose an integrated measure including both socio-economic and water engineering methods. According to the effectiveness of the individual measures, one possible integrated measure (M11) is proposed here (Table 4): 1) improve the percentage of tertiary industry (M1) and percentage of capital-intensive industry (M2) to their possible maximum values (10% and 5%, respectively), since they have positive effects on both water quantity and quality; 2) increase the quota of water transfer (M5) to the possible maximum value (increase quota 10% by 2010), since the quota has been approved by Dongjiang River Catchment Management Bureau; and 3) make trade-offs among other individual measures with the objectives of water resource balance and water quality improvement; the values of model input parameters in these measures can be estimated by trial and error. The simulation results indicate that the GRP and population in Shenzhen River catchment will increase to 275 billion Yuan and 2.72 million, respectively, under M11. Water demand and supply will keep pace in 2020 due to the population decrease, increase in water use efficiency, increase in water transfer and increase in wastewater reuse. Furthermore, water quality will improve gradually and the COD at all sections will meet the Class V standard in 2020 due to the decrease of COD load per GRP and increase of wastewater treatment capacity in the catchment under M11. Therefore, by carrying out the integrated engineering and socioeconomic measures, the objectives of environmental improvement can be fully achieved.
4. Conclusions We use an integrated model (SyDWEM) to describe the dynamic socio-economic system, water infrastructure and receiving water in the rapidly urbanizing Shenzhen River catchment in China. The model is further applied to evaluate the effects of eight proposed socio-economic or water engineering measures on the GRP and population growth, water demand/supply balance and water quality in the catchment for the next 10 years (2011e2020). The results indicate that: 1) All of the measures have little effect on GRP growth; an increase in the percentage of capital-intensive industry (M2) and labor productivity improvement (M3) can significantly decrease population, while other measures have little effect on population growth.
2) An increase in the percentage of tertiary industry (M1) and in the percentage of capital-intensive industry (M2) will have a positive effect on both water quantity and quality in the catchment; and labor productivity improvement (M3), water use efficiency improvement (M4), increase of the water transfer quota (M5) and increase of reclaimed wastewater reuse (M8) can alleviate the water shortage, but cannot improve water quality in the river. However, an increase in the wastewater treatment rate (M6) and the pollutant removal rate (M7) can improve water quality in the river, but has no effect on water shortage. Moreover, individual measures do not meet the goals of water quantity and quality improvement in the catchment. 3) Neither the integrated socio-economic measure (M9) nor the integrated engineering measure (M10) can fully achieve the requirements of water environment improvement. 4) Based on the simulation results of SyDWEM, socio-economic and water infrastructure planners can consult each other and make trade-offs among various individual/combination measures. According to the effectiveness of the individual measures, we propose a possible integrated socio-economic and engineering measure that can achieve water environment sustainability in the Shenzhen River catchment. Therefore, SyDWEM has the capacity to evaluate the effects of both socio-economic and engineering measures on GRP and population growth, the water resource balance and water quality in the river; it also provides a tool for integrating the decision-making of socio-economic and water infrastructure planners.
Acknowledgments The research leading to these results received funding from the European Community’s Seventh Framework Programme under grant agreement n PIIF-GA-2008-220448.
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