Application of System Dynamics and DPSIR framework for sustainability assessment of urban residential areas
Xu Zhao Marta Bottero PhD student Dr. DITAG- land Environment and DITAG- land Environment and Geo-Engineering Department, Geo-Engineering Department, Politecnico di Torino Politecnico di Torino Italy Italy [email protected] [email protected]
Summary Constructing and improving urban residential areas is an eternal critical subject in the process of the whole territorial development which is connected with a series of challenges and problems such as population pressure, environmental pollution, public safety, etc. In this paper, DPSIR (Driving Forces-Pressure-State-Impact-Response) framework has been employed for better systematizing the sustainability indicators of urban residential areas. Due to the urban activities cause impacts not only on local level but also a broader scale, a simulation model, using system dynamics (SD) methodology, is structured to quantitatively investigate the developmental tendency of the indicators. The integration of system dynamics (SD) and DPSIR framework can better explain the interaction and the variation with time of the sustainable indicators of urban residential areas. Hence it’s able to support the Decision Maker to view the sustainable level of urban residential areas more comprehensively. At last the paper will consider an application to a real case concerning the development of a residential area in China.
Key words: DPSIR framework, System Dynamics, urban residential areas, sustainable development
1. Introduction
Urban residential areas show increasing signs of sustainable problems. Major concerns for cities, especially in the emerging countries where residential areas are expending very quickly, are the quality of air, traffic jam, water conditions, limited land resources and related social-economy problems. Furthermore, with the rapid economic growth, people are demanding more and more in the quality of their residence and living level. The concept of sustainable development has put in evidence the impossibility of separating environment and development; for this reason, sustainability assessment should be based on multidisciplinary approaches that reflect the complex network of interactions between human and environmental systems. Many researchers have studied the issue of sustainability assessment from different perspectives with various evaluation methodologies. For example, Nessa and Montserrat (2007) summarized the conceptualization of sustainable development and housing from the international statements and contended that housing is an underdeveloped indicator and called for more attention to be paid to the importance of aspects of housing for sustainable development and the measurement of progress towards it via social indicators. Bidone and Lacerda (2003) developed a Driving Forces- Pressure-State-Impact-Response (DPSIR) framework in a practical context to integrate natural and social-economic indicators. They also discussed the strengths and limitations of cost-benefit analysis (CBA) included in DPSIR which is used to evaluate losses and benefits resulting from policies. Shen et al., (2009) applied a system dynamics model for the sustainable land use and urban development in HongKong. They utilized the model to test the outcomes of development policy scenarios and make forecasts. In this paper, we try to integrate the System Dynamics (SD) methodology and DPSIR framework to evaluate the sustainable development of urban residential areas. System dynamics model can be used to explore the complex and inter-dependent relationships between the various indicators within social-economic environment and DPSIR framework can been employed for better systematizing the sustainability indicators. The study is aimed at identifying and calculating the quantitative criteria of sustainable level of urban residential areas. And the results analysis and discussion are expected to give some suggestions to support the Decision Maker to view the sustainable level of urban residential areas more comprehensively.
2. Sustainable development indicators and urban residential areas
Since the Earth Summit in 1991, many countries and international organizations have been working on indicators about sustainable development. And sustainable development is increasingly linked with the concepts of quality of life, well-being and livability (Michalos 1997; Moore and Scott 2005; Low Choy, 2004, 2005). However, housing is one of the more neglected aspects of sustainability and the availability of housing indicators in international sustainable development indicator sets is extremely limited, despite relatively advanced sustainable development policies which refer to the importance of housing. (Nessa, W., & Montserrat, P.E. 2008) • The Organization for Economic Cooperation and Development (OECD) has been working on environmental indicators since 1989. The work of OECD mainly focuses on indicators that have to be used in national, international and global decision making; furthermore the approach may also be used to develop indicators at a sub-national or ecosystem level (OECD, 2003); • The United Nations Commission on Sustainable Development (UNCSD) produces some indicator sets in the field of sustainability assessment. UNCSD provides a very useful and timely forum for the discussion of national-level indicators with the involvement of governments, international organizations and various stakeholders. UNCSD indicators play an important role in “helping countries make informed decisions concerning sustainable development”, and they are “applied and used in many countries as the basis for the development of national indicators of sustainable development” (United Nations, 2007); • The European System of Social Indicators (EUSI) is part of a cross-national European project which began in the late 1990s and which aims at monitoring and assessing welfare development and social change in Europe (Berger-Schmitt & Noll, 2000). The EUSI framework links sustainability to other welfare concepts, such as social cohesion, social exclusion, social capital and quality of life; • The Chinese Academy of Sciences, through the Chinese Urban Development Centre (CUDC), has proposed an indicator system for urban sustainability assessment (CUDC, 2002). This system concentrates on the strategic context, strategy objective, strategy mission and the strategy design of sustainable urban development in China. As far as residential urban areas are concerned, the above mentioned sustainability indicator systems focus on different issues, such as environmental quality, well-being of the population, economic aspects, etc. We could select a representation of the indicators that are available for the assessment of the sustainable development of residential urban areas, according to the main dimensions that have been identified: housing, economy, environment and society.
3. Methodology Environmental indicators and evaluation methods should be incorporated in sustainability assessment. Qualitative analysis of environmental indicators was based on an index system established in this study, while evaluation methods was discussed based on DPSIR framework and System dynamics (Fig. 1). The two aspects were combined to obtain a comprehensive evaluation system.
3.1 System dynamics (SD) model
This paper applies system dynamics theories to formulate, simulate and validate the sustainable development of urban residential areas. System dynamics is a methodology and computer simulation modeling technique for framing, understanding and discussing complex issues and problems [1]. And it is widely used to gain understanding of a system with complex, dynamic and nonlinearly interacting variables [2].
System dynamics has four elements defined within the system: (a) stock, (b) flow, (c) converter, and (d) connector (HPS, 1997; Mohapatra, 1994), as shown in Fig. 2. A stock collects all those in-flows and also serves as the source from where out-flows come. A flow serves as a vehicle to deliver information to or drain information from the stock. The connector is the information transmitter connecting elements [2].
This methodology has been applied into various fields, including but not confined to, global system of mobile telecommunication (Tung & Claudia, 1996), the interaction among various water cycle components (Shahbaz et al., 2007), urban transportation system and its application (Wang et al., 2008), forecasting demand and evaluating policy scenarios (Erma et al., 2010), building design and operation (Benjamin & Lawrence, 2009), the sustainable land use planning and development (Shen et al., 2009), sustainable performance of construction projects (Shen et al., 2004).
3.2 DPSIR framework
The DPSIR framework is able to illustrate the complexities of the system interactions in sustainable development and urban residential areas. The framework can be summarized as follows: • The Driving Forces are processes and anthropogenic activities that able to cause pressures during the development of urban residential areas; in other words, they are the reasons that cause the changes in the development process. • The Pressures are the direct stresses, deriving from the anthropogenic activities and affecting the environment, economic and society. • The State reflects the actual conditions of urban residential areas; • The Impact is the measure of the environmental effects due to the development of urban residential areas; • The Response refers to specific actions oriented to reduce the pressure and promote development in terms of economic or administrative measures.
4. The integration of System Dynamics and DPSIR framework in sustainability assessment
The procedure which aims at integrating System Dynamics and DPSIR indicators can be can be divided into 6 steps, as follows: Step1. Selection of indicators
The availability and reliability of data, the usability of the available data within DPSIR framework, and the sensitivity of indicators to reflect the underlying social and economical processes were used as the criteria to establish the indicators system proposed in this work. The indicators system contains different kinds of indicators including social, economic, environmental and housing indicators. From indicators identified in the four indicator-systems, we selected a synthetical and concise indicators system which is suitable for dealing with sustainability assessment of urban residential areas. Table 1 represents the 24 indicators selected for the application of the sustainability assessment of urban residential areas, according to the DPSIR framework. In this way, the overall system is described as different layers: categories of the DPSIR framework, thematic areas and indicators. The structure here illustrated is represented in Fig 3. Table 1 Assessment indicators for sustainable development of urban residential areas Thematic Area Indicator DPSIR Category Housing • The floor space completed of residential housing; S • Living space per capita; S • Dwellings in deficient state of repair; S • The total demand of the residential houses; D • The total supply of the residential houses; R Economy • GDP; D • Disposable income per capita; D • Land price per floor area P • Average urban housing price; P • Average rent price; P • Exploitation and investment of real estate; R Environment • Crime in residential area; P • Environmental pollution abatement and control expenditure; R • Urban air emissions (SOx,NOx,VOC); I • Ambient water conditions in urban areas; I • Generation of waste; I • Green coverage ratio; I • Share of renewable energy sources in total energy use. R Society • Urban population; D • Urban infrastructure; D • Dependency rate; P • Road traffic volumes. P • Proportion of population living below national poverty level; S • Stock of road vehicles. S Step2. Determination weight of indicators
The weight of each indicator was determined using Multicriteria Analysis (MCA). The Analytic Hierarchy Process (AHP) method (Saaty, 1980) has been developed. On the basis of the knowledge about sustainability assessment of urban residential areas, it is possible, using the AHP, to discuss the weight of each indicator by consulting the opinions from experts. Judgment matrixes have been established. For example, Table 2 represents the judgment matrix for the comparison of the importance of the different DPSIR categories for the assessment of the sustainability of urban areas. The process has been repeated for the whole assessment system and a relative stable weight result has been reached. Table 3 represents the indicator system structure; the values in the square brackets reflect the weight of the element with reference to the overall system.
Fig. 3 The structure of DPSIR framework
Table 2 Pairwise comparison matrix for DPSIR category assessment. D P S I R Weight D 1 2 3 3 1/2 0.25 P 1/2 1 2 2 1/3 0.15 S 1/3 1/2 1 1 1/5 0.08 I 1/3 1/2 1 1 1/5 0.08 R 2 3 5 5 1 0.44
Table 3 The sustainable indicator system in the DPSIR framework. System DPSIR categories Thematic areas Indicators Housing [0.25] 1: The total demand of the residential houses [1] 2: GDP [0.5] Driving forces Economy [0.5] 3: Urban disposable income per capita [0.5] [0.25] 4: Urban population [0.5] Society [0.25] 5: Urban infrastructures [0.5] Environment [0.33] 6: Crime in residential areas [1] 7: Dependency ratio [0.5] Society [0.33] 8: Road traffic volumes [0.5] Pressure [0.15] 9: Land price per floor area [0.25] Economy [0.34] 10: Average urban housing price [0.5] Sustainable 11: Average rent price [0.25] development 12: The floor space completed of residential housing [0.24] of urban Housing [0.75] 13: Living space per capita [0.53] residential areas[1] State [0.08] 14: Dwellings in deficient state of repair [0.23] 15: Stock of road vehicles [0.25] Society [0.25] 16: Proportion of population living below national poverty level [0.75] 17: Urban air emissions [0.28] 18: Generation of waste [0.28] Impact [0.08] Environment [1] 19: Ambient water conditions in urban areas [0.28] 20: Green coverage ratio [0.16] Environment [0.34] 21: Share of renewable energy sources in total energy use [1] 22: Environmental pollution abatement and control expenditure [0.4] Responses [0.44] Economy [0.33] 23: Exploitation and investment of real estate [0.6] Housing [0.33] 24: The total supply of the residential houses [1] Step3. Calculation of the indicator value
The assessment model should include two basic function parts: evaluation and prediction. The two basic function parts should be related, respectively, to historical data (previous indicators) and estimate values (future indicators). As far as the environmental indicators are concerned, it is possible to obtain the information required by referring to historical data derived from statistical yearbooks, social-economic development reports, environmental observations, recordings, etc. As far as the housing-economy-society indicators are concerned, it is possible to estimate the values in System Dynamics model. In this paper, the System Dynamics model has four level variables, Urban population, Urban GDP, the total demand of the residential houses and the total supply of the residential houses; Six rate variables, the population growth, the economy growth, the demand growth, the supply growth, the actual demand and the supply completed. We can establish the System Dynamics model by the software “vensim” (Fig 4).
Fig. 4 Stock-flow diagram of housing-economy-society indicators in “vensim”
Step4. Standardization of the indicator value
In order to take into consideration the positive and negative values of the indicators, it is necessary to calculate the standard value of each indicator as in equation (1):
(1) Where xi is the standard value of indicator i in the temporal period considered for the analysis, x is the actual value of indicator i in each part of the considered period, x is the average value of indicator i in the period and s is the standard deviation of the indicator i in the period.
Step5. Calculation results
On the basis of steps 2 and 3, it is possible to obtain the value of the subsystems layer related to the DPSIR categories, as in equation (2): nm Ywxkjii= ∑∑σ () (2) ji==11 where Yk is the value of the subsystem layer related to category k of the DPISR framework, σj is the weight of thematic area j corresponding to Yk, n is the number of the thematic areas under Yk, wi is the weight of indicator i, m is the number of indicators under thematic area j and xi is the standard value of indicator i. The final value of the sustainability level of the system is derived from the weighted sum of the five subsystems k, as in equation (3): 5 Z = ∑ μ kkY (3) k =1
Where Z is the final value of sustainability of the system, μk is the weight of category k of the
DPSIR framework and Yk is the value of category k. It is possible to observe that Z is a composite index that results from the value of the Driving Forces, Pressure, State, Impact and Response categories; furthermore, the value of Z is included in the (0, 1) domain.
Step6. Results analysis
After the calculations made in step5, it is possible to obtain a set of values of the composite index years of the period Z and of the Driving forces, Pressures, State, Impact or Response subsystems for the different considered in the analysis, as illustrated in Table 4 where n represents the single year in the considered period. Table 4 Assessment results of the sustainability level in the considered period (year 1 – year n+2). 1 2 … n
Driving forces YD1 YD2 … YDn Pressure YP1 YP2 … YPn State YS1 YS2 … YSn Impact YI1 YI2 … YIn Response YR1 YR2 … YRn
Z Z1 Z2 … Zn It is possible to observe that when Z is very low, it will be difficult to continue a sustainable urban development, because some indicators indicate a bad performance (for example GDP or urban infrastructures). However, the previous consideration does not mean that the higher the value of Z, the higher the sustainability level of the system. According to the environmental impact assessment classification (Chen and Chen, 1992), a five-grade classification is utilized to classify the sustainability of residential urban areas (Table 5). Table 5 A five-grade classification of the sustainability of residential urban areas.
In other words: • Z is very large in grade I. This shows that in the social, economic and environmental aspects the residential urban areas are developing excessively. For example, the excessive emphasis on the environmental quality of residential areas can lead the actual demands of the inhabitants being ignored; this is particularly true in emerging countries, where the need to raise income and welfare is higher that the need to have green spaces. • Z has a reasonable value in grade II and III. This means that the residential urban areas are developing at a steady rate (grade III) or quickly (grade II). This is the optimum condition for sustainable development. • Z has a smaller value in grade IV than in grades II and III. In this case, the sustainable development of residential urban areas is able to bear the pressure. The situation can be ameliorated by a sequence of special measures adopted to strengthen environmental, social and economic development. • Z has the smallest value in grade V. At this point, the sustainable development of residential urban areas is bearing great pressure. It can be observed that the social-economic sustainability and environmental sustainability confront a major obstacle.
5. Application to a Chinese case study The considered study case refers to the city of Nanjing which is the provincial capital of JiangSu in China. The urban area of the Nanjing megalopolis has been growing rapidly, from 2599 km2 in 2001 to 4723 km2 in 2008. The population has registered a great variation over the years, from 3.71 million inhabitants in 2001 to 5.41 million inhabitants in 2008. Sustainability assessment of the Nanjing urban residential areas has been performed using the 24 indicators which have been observed through a period of 10 years (2001-2010). 5.1 Model Simulation Table 6 gives a short description of the 24 indicators used in the application. Table 6 Description of the quantitative indicators used in the application Indicators Description Unit of measure 1 The total demand of the It indicates the total floor areas which people demand in one year. million m2 residential houses 2 GDP GDP stands for Gross domestic product and it reflects the sum of billion private consumptions, gross investments, government spending and exports, while the imports are subtracted. 3 Urban disposable income Urban disposable income per capita is calculated by adding up all yuan(RMB) per capita sources of income and subtracting current taxes. 4 Natural population This represents the births and deaths in the population of a country million or city. It takes into account migration. 5 Urban infrastructures This represents the investments in urban infrastructures in a year. billion 6 Crime in residential area This is indicated by the number of criminal registered cases per unit n/y of 10000 people per one year. 7 Dependency ratio This represents an age-population ratio who are usually not in the % labor force who registered at an employment agency and those who are usually in the labor force. 8 Road traffic volumes This aims at measuring the urban traffic condition and it is n/10000 p represented by the number of public transportation vehicles per unit of 10000 people. 9 Land price per floor area It is measured by the ratio of basic land price and average plot ratio yuan/ m2 10 Average urban housing This is the ratio of housing prices and the basic price in 2001. yuan/ m2 price 11 Average rent price This is indicated by the growth rate of housing rent. It considers the % rent in 2001 as a basic price. 12 The floor space This is indicated by the floor area completed in one year. million/m2 completed of residential housing 13 Living space per capita This reflects the average level of housing per capita. m2 14 Dwellings in deficient This indicates the households or units relocated due to building n. state of repair demolition. 15 Stock of road vehicles This is represented by the quantity of possessed family cars per n. /100 p 100 urban households. 16 Proportion of population This represents the ratio of the population living below the national % living below national poverty and the full city town population. Low-income families are poverty level urban residents whose average family income is lower than the minimum living standard of NanJing city. 17 Urban air emissions This considers the Air Pollution Index (API). The index has 5 d/y grades, where: grade I (API< 50): the air quality is excellent; Grade II (50
Fig. 5 Line chart of level variables Fig. 6 Line chart of rate variables On the basis of the methodology described in section 4, it is possible to calculate the sustainability level of the subsystems and of the overall system from the values of the indicators of Table 7, for each year of the considered period (Table 8).
Table 7 Indicator values of the overall system 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 I1 7.12 7.46 7.88 7.8 7.44 9.2 9.57 12.03 14.21 15.2 I2 121.85 134.04 152.4 186.08 227.57 246.69 305.16 361.3 415.5 458.3 I3 14089.3 14042.6 10093.3 12209.6 21891 22912.8 27319.6 31110.3 34372.8 36355.1 I4 3.72 3.82 3.91 4.02 4.16 4.31 4.47 4.65 4.84 5.04 I5 2.92 3.08 3.81 4.65 6.37 6.91 6.71 10.12 11.63 12.83 I6 70 77 71 68 95 90 70 74 69 61 I7 3.59 4.13 4.18 4.03 3.35 3.33 3.3 3.16 2.65 2.25 I8 10.85 9.84 10 9.63 11.4 13.8 14.2 15 17 20 I9 1020.83 1096.08 1161.2 1514.68 1531.2 1525.5 1628.4 1656.75 1728 1749.3 I10 2550.35 2737.18 3132.27 3911.78 3913.88 3938.54 4148.95 4758.44 5256.52 5911.69 I11 100 99.4 103.8 109 109 109.4 111.4 125.3 127.2 133.7 I12 3.05 3.26 3.87 5.17 5.93 6.16 7.62 9.53 12.51 14.41 I13 18.93 19.28 19.9 21.18 22.89 23.45 26.1 28.7 31.16 32.76 I14 13057 20032 21308 13500 15000 15000 16000 25000 24381 27965 I15 0.09 0.3 1.6 2 4.88 6.38 6.63 13 13.3 17.3 I16 0.95 1.6 1.99 2.12 2.4 2 2 2 1.48 0.98 I17 247 215 297 295 304 305 312 322 320 330 I18 1.33 1 1.52 1.66 1.69 1.62 16.2 1.66 1.59 1.5 I19 149.42 242.77 138.62 144.17 154.09 415.29 398.14 409.17 565.3 689.41 I20 40 42.9 43.51 44.46 44.94 45.49 45.92 46.5 46.05 45.67 I21 1.8 1.5 1.43 1.37 1.36 1.31 1.25 1.18 1.15 1.09 I22 176.39 133.12 162.6 233.03 205.93 527.12 585.91 836.8 1100.4 1406.3 I23 10.97 12.06 16.61 26.24 30.04 31.08 41.5 48.78 58.17 63.24 I24 9.72 10.28 11.15 12.98 14.06 16.04 18.16 22.6 28.03 34.67 Table 8 Sustainability level of the subsystems and the overall system Subsystems System Year Driving forces Pressures State Impact Response Sustainable development of urban residential areas 2001 0.08 0.06 0.05 0.05 0.12 0.36 Grade Ⅳ 2002 0.08 0.05 0.03 0.08 0.12 0.36 Grade Ⅳ 2003 0.09 0.06 0.02 0.03 0.13 0.33 Grade Ⅴ 2004 0.09 0.06 0.03 0.03 0.13 0.34 Grade Ⅴ 2005 0.09 0.06 0.03 0.03 0.15 0.36 Grade Ⅳ 2006 0.11 0.07 0.03 0.03 0.19 0.43 Grade Ⅳ 2007 0.13 0.08 0.04 0.03 0.25 0.53 Grade Ⅲ 2008 0.17 0.08 0.04 0.03 0.32 0.64 Grade Ⅲ 2009 0.20 0.11 0.06 0.03 0.36 0.76 Grade Ⅱ 2010 0.22 0.13 0.08 0.04 0.43 0.90 Grade Ⅰ Fig.7 shows the line chart of five subsystems while Fig.8 shows the line chart of the sustainable development situation of residential urban areas in the city of Nanjing for the period 2001-2010.
Fig. 7 Line chart of the 5 subsystems Fig. 8 Line chart of the overall system
5.2 Verification with historical data The developed model is verified using the historical data of 2001, 2002, 2005, 2007 and 2008. The examined variables include 4 indicators: “Urban GDP”, “The floor space completed of residential housing”, “Living space per capita” and “Urban infrastructures”. The method of verification is to make a comparison of the simulation results and historical data, and calculate the relative error and mean square deviation of the variables. eyyyiititit=−ˆ / (4)
n 2 MSEii= ∑ et/ n (5) 1
Here, yit represents the historical data of the indicator “i” in the year of “t”, yˆit represents the simulation result of the indicator “i” in the year of “t”. “n” is the number of the selected indicators in this model verification. ei is the relative error of the indicator “i” and MSEi is the mean square error of the indicator “i”.
Generally, we take the simulation results as an equitable and reasonable answer, when MSEi <5%.
According to function (4), (5) and Table 9, we could get MSEi of the 4 indicators is: 0.04, 0.048, 0.044 and 0.045< 0.05 (Table 10), so this model meets the accuracy requirements theoretically.
Table 9 The comparison table of historical data and simulation result The floor space completed of Urban GDP Living space per capita Urban infrastructures year residential housing Historical Simulation Relative Historical Simulation Relative Historical Simulation Relative Historical Simulation Relative data result error data result error data result error data result error 2001 121.85 121.85 0.0% 3.09 3.05 1.3% 19.00 18.91 0.5% 2.98 2.92 2.0% 2002 138.51 134.04 3.2% 3.47 3.26 6.1% 20.10 19.28 4.1% 3.14 3.08 1.9% 2005 224.11 227.57 1.5% 5.65 5.93 4.9% 24.3 22.89 5.8% 6.33 6.37 0.6% 2007 328.37 305.16 7.1% 7.47 7.62 2.0% 26.08 26.10 0.1% 7.32 6.71 8.3% 2008 377.50 361.30 4.3% 8.91 9.53 6.9% 30.84 28.70 6.9% 10.63 10.12 4.8%
Table 10 The mean square error of the 4 indicators The floor space completed Urban GDP Living space per capita Urban infrastructures year of residential housing 2 2 2 2 e1 e1 e2 e2 e3 e3 e4 e4 2001 0.000 0 0.013 0.000169 0.005 0.000025 0.020 0.000400 2002 0.032 0.001024 0.061 0.003721 0.041 0.001681 0.019 0.000361 2005 0.015 0.000225 0.049 0.002401 0.058 0.003364 0.006 0.000036 2007 0.071 0.005041 0.020 0.000400 0.001 0.000001 0.083 0.006889 2008 0.043 0.001849 0.069 0.004761 0.069 0.004761 0.048 0.002304
MSEi 4.0% 4.8% 4.4% 4.5%
6. Discussion
Taking into consideration the five subsystems (Fig.7), it is possible to observe that the State and Impact categories develop smoothly in the considered period, while notable variations appear in the line chart of the Driving Forces, Pressure and Response categories. The Driving Forces and Response in particular rise steadily over the years; this is due to the increase in the values of specific indicators, such as “GDP” (Driving Forces category), “Environmental pollution abatement and control expenditure”, “Exploitation and investment of real estate” and “Exploitation and investment of real estate” (Response category). With reference to the overall system under examination (Fig.8), the model shows that the sustainability level of the Nanjing residential urban area has an ascending trend, with a good performance (Grade II and Grade III) for the latter part of the considered period (from 2007 to 2009). It is possible to notice that the bad performance of the system in the first part of the period (Grade IV in 2001, 2002 and Grade V in 2003) is pushed up towards more sustainable levels by the ascending trend of the Driving Forces and Response categories. Especially, due to this trend of the sustainability level keeping on a rise in these years, it will get into a bad performance (Grade I) again in the period of 2010.
7. Conclusion
In this paper, a system dynamics model that comprises an integrated housing-economy- environment-society system is developed to assist evaluation of the development trend of the urban residential areas in NanJing city, China. The model examines interactions among four subsystems (“Driving forces”, “Pressures”, “State” and “Responses” category) within a time frame of 10 years, and then the result of this model is discussed with the values of environment indicators of “impact” category in DPSIR framework. It can be also concluded that the integration of System Dynamics theory and DPSIR framework is a useful strategy to study the development of urban residential areas in terms of sustainability. A validation is undertaken to verify the model in the end, through comparison with historical data and mean square error. By this study, we can find the development process of our residential areas, and put a further concern on the development of the urban residential areas from various angles. However, there are still a number of opportunities for expanding the study and for validating the results obtained herein. Firstly, only core-indicators were considered in this work. It would be of scientific interest to add other indicators resulting from policies and strategies. Secondly, further research would be required to collect more historical data and optimize the structure of system dynamics model. Finally, in this paper we only considered the historical values of environment indicators of “impact” category in DPSIR framework and didn’t put them in system dynamics model. In system dynamics model, creating equations to describe and quantize the relationship of environment indicators with urban economy, social development and housing needs more work on the monitoring and statistics of environment impacts.
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Appendix A The core equations used in the software “vensim” to simulate the system. • economic growth= economic growth rate(Time)*urban GDP Units: billion • exploitation and investment of real estate= proportion of real estate investment(Time)*urban GDP Units: billion • GDP per capita= urban GDP/urban population*1e+009 Units: yuan • "housing price-to-income ratio"= living space per capita*urban housing price/urban disposable income per capita Units: Dmnl • land price per floor area=0.75*base land price(Time)/average plot ratio*impact of the proportion of real estate ivestment on land price(proportion of real estate investment(Time)) Units: yuan/m2 • living space per capita=1e-008*urban disposable income per capita*urban disposable income per capita+0.0001*urban disposable income per capita+15.908 Units: m2 • population growth=urban population*population growth rate(Time)/100+immigration population(Time)-emmigration population(Time) Units: people • sales area of dewelling houses=the total demand of residential houses*(0.7+0.1-0.3-STEP (0.05,2001)-STEP(0.05,2002)-STEP(0.05,2003)+ STEP(0.2,2004)+ STEP(0.1,2005) + STEP(0.05,2006))*"impact of housing price-to-income ratio"("housing price-to-income ratio")*"impact of supply-to-demand ratio"("supply-to-demand ratio") Units: thousand m2 • "supply-to-demand ratio"=the total supply of residential houses/the total demand of residential houses Units: Dmnl • the actual demand=sales area of dewelling houses Units: thousand m2 • the demand growth= "impact of housing price-to-income ratio"("housing price-to-income ratio")*impact of loan rate on the demand growth(loan rate)*impact of dependency rate on the demand growth(Dependency rate(Time))*impact of proportion of population living below national poverty level(proportion of population living below national poverty level(Time))*(population growth+dewellings in deficient state of repair(Time)*(0.5+0.15- STEP(0.15,2004))*the urban family size+0.01*urban population*(0.6-0.15-STEP(0.15,2001) +STEP(0.5,2002)-STEP(0.5,2003))+urban population*proportion of population married *(0.85-0.02-STEP(0.03,2001)-STEP(0.2,2003)))*living space per capita/1000 Units: thousand m2 • the floor space completed of residential housing=10*(0.03*exploitation and investment of real estate*exploitation and investment of real estate+10.73*exploitation and investment of real estate+191.7)*impact of urban infrastructure on the floor space completed(urban infrastructure) Units: thousand m2 • the floor space of commercial house sold newly=0.233*exploitation and investment of real estate*exploitation and investment of real estate+177*exploitation and investment of real estate+1902.1 Units: thousand m2 • the supply completed=sales area of dewelling houses Units: thousand m2 • the supply growth=the floor space of commercial house sold newly+the floor space completed of residential housing*(0.15-STEP(0.1,2001) +STEP(0.05,2002) +STEP (0.05,2003)-STEP(0.02,2004) +STEP(0.02,2005) +STEP(0.03,2006)+STEP(0.02,2007) +STEP(0.02,2008)+STEP(0.02,2009)) Units: thousand m2 • the total demand of residential houses= INTEG (the demand growth-the actual demand, 7120) Units: thousand m2 • the total supply of residential houses= INTEG (the supply growth-the supply completed, 9720) Units: thousand m2 • total cost=(construction cost(Time)+land price per floor area)*(1+0.7*2*loan rate/100) Units: yuan/m2 • urban disposable income per capita=GDP per capita*the distribution proportion of GDP Units: yuan • urban GDP= INTEG ( economic growth,121.85) Units: billion • urban housing price= total cost*(1+tax rate)*(1.25+STEP(0.02,2002) +STEP(0.03,2003)+ STEP(0.03,2004) +STEP(0.03,2008)+STEP(0.05,2009)+STEP(0.08,2010))*"impact of supply-to-demand ratio"("supply-to-demand ratio")*impact of urban rent price on urban housing price(the growth rate of urban rent price(Time)) Units: yuan/m2 • urban infrastructure= urban GDP*proportion of urban infrastructure investment(Time) Units: billion • urban population= INTEG (population growth, 3.71882e+006) Units: people