Modeling and dynamic assessment of urban economyâ€“resourceâ

Journal Identification = ECOIND Article Identification = 785 Date: May 6, 2011 Time: 7:25 pm

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Ecological Indicators 11 (2011) 1333–1344

Contents lists available at ScienceDirect

Ecological Indicators

journa l homepage: www.e lsev ier .com/ locate /eco l ind

odeling and dynamic assessment of urban economy–resource–environmentystem with a coupled system dynamics – geographic information system model

ongjie Guana,∗, Weijun Gaob, Weici Suc,d, Haifeng Lia, Kazunori Hokaoa

Faculty of Science and Engineering, Saga University, 1, Honjo-machi, Saga 840-8502, JapanFaculty of Environmental Engineering, The University of Kitakyushu, 1-1, Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, JapanFaculty of Geographic Science, Chongqing Normal University, 12, Tianchenlu Road, Shapingba District, Chongqing 400047, ChinaInstitute of Mountain Resources, Guizhou Academy of Science, Guiyang 550001, China

r t i c l e i n f o

rticle history:eceived 30 June 2010eceived in revised form 1 February 2011ccepted 10 February 2011

eywords:conomy–resource–environmentystem dynamicscenario simulationustainable development

a b s t r a c t

At present, environmental issues associated with rapid economic development are becoming critical con-cerns that arouse government’s and people’s particular attention. A large amount of influencing factorsand especially their complicated interactions have always thrown confused insights into assessing thedynamic evolvement and sustainable development of urban economy–resource–environment (ERE) sys-tem and programming the developing strategies. A combination of system dynamics (SD) and geographicinformation system (GIS) is expected to explicitly understand the synergic interaction and feedbackamong a variety of influencing factors in time and space, since SD model can extend the spatial analysisfunctions of GIS to realize both dynamic simulation and trend prediction of an ERE system development.According to connotation and framework of sustainable development, this study proposes a dynamiccombination method of SD–GIS to model and evaluate the urban development in Chongqing city ofChina suffering from depletion of resource and degradation of environment. To compare different pol-icy inclinations with regard to potential ERE effects, typical scenarios (current, resource, technology and
environment scenarios) are designed by adjusting the parameters in the model and changing the specifi-cation of some variables. Integrated assessment results indicate that the current ERE system of Chongqingis not sustainable; environment scenario is more effective to sustainable development of urban ERE sys-tem in a long run. Under the considerations of development features and regional differences, as well asregular discipline on urbanization, a coordinated combination of environmental, resource and technology
o real
scenarios is anticipated t
. Introduction

In recent years, environmental issues associated with rapidconomic development are becoming critical concerns sufferedy the national and local governments. Aiming at promotingconomic development, as well as public awareness of envi-onmental issues, local authorities have been undertaking thenhanced pressure for effective response to these concerns. Ineneral, a reliable path for dealing with this dilemma is to frameffective environmental management regulations and policies forregion-specific system. This decision-making process requiresell understanding of the significant contributors to regional

nvironmental problems and of the way that the environmental
anagement system will react to particular policies (Guo et al.,
001). During this process, it’s essential to understand the interac-ions among a number of related social, economic, environmental,

∗ Corresponding author. Tel.: +81 090 2963 8121.E-mail address: guandongjie [email protected] (D. Guan).

470-160X/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.ecolind.2011.02.007

ize sustainable development of urban ERE system.© 2011 Elsevier Ltd. All rights reserved.

managerial, regulatory and lifestyle factors. These interactions arecomplicated, not only because they simultaneously involve vari-ous system components but also because they dynamically changeover time.

System dynamics (SD) is considered to be an appropriateapproach for predicting dynamic results of the interactions andanalyzing implications of different policies given such complexes(Alexandra et al., 1996; Guo et al., 2001; Evrendilek and Wali, 2001;Sun et al., 2002; Bald et al., 2006; Berling-Wolff and Wu, 2004;Arquitt and Johnstone, 2008). The method can effectively incorpo-rate individual system components within a general framework,and then comprehensively analyze their interactions. It is verymeaningful to provide us with the knowledge of the environmentalconcerns, as well as the relevant policy responses to sustainabilityof urban development. Recently, there are increasing literatureson applications of SD models in urban development and inte-
grated sustainable management systems (Costanza and Gottlieb,1998; John, 1998; Mohammed and Arunee, 2001; Barredo et al.,2003; Tian and Roderic, 2005; He et al., 2005, 2006; Li et al., 2006;Sufian and Bala, 2007). For example, Alexandre et al. (2006) devel-
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1334 D. Guan et al. / Ecological Indicators 11 (2011) 1333–1344

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ped a SD model to analyze complex interrelationships among thegents affecting the Sepetibza Bay environment. Güneralpa andeto (2008) constructed a simulation model of dynamic interactionf social, economic, and environmental processes to identify somenvironmental benefits and challenges of urbanization. Dyson andhang (2005) forecasted municipal solid-waste generation in a fast-rowing urban region by SD modeling. However, as a “top-down”odel, SD model’s ability to represent a spatially changing process

s constrained, because it cannot deal with the spatial distributionnd situation of variables in a system.

Meanwhile, geographic information system (GIS) technology isn extensively used computer-based tool to deal with spatial issuesKarimi and Houston, 1996; Pei and Zhao, 2000; Matejicek et al.,003; Lubos et al., 2006; Li et al., 2007; Guan et al., 2008). Especially,IS technology integrates common database operations, such asuery and statistical analysis, with the benefits of unique visualiza-ion and geographic analysis offered by maps. These abilities makeIS technology valuable in explaining events, predicting outcomes,nd planning strategies, etc. However, as a “bottom-up” model, GISoes not support a dynamic forecast. Thus, a combination of SD andIS is expected to fulfill a synergic feedback in time and space. This

s because that using GIS as pre-processing tool of SD model canase the data preparation, enhance the spatial data display capa-ilities, and reveal the hidden spatial relationship; SD model canxtend the spatial analysis functions of GIS to realize both dynamicimulation and trend prediction of system behavior.

This paper presents a loose-coupled SD-GIS assessmentethod to understand the changing variables of urban

conomy–resource–environment (ERE) system in both spacend time. Firstly, a SD model is established to explore the relation-hip between urban economic growth and environment impact.econdly, using this model, different scenarios are designed toimulate the economic growth, resource consumption, and envi-onmental developing tendencies from 2008 to 2050. Thirdly, thehange of spatial distribution of factors in different scenarios is
ccomplished with GIS technology. Finally, the integrated dynamicnd spatial assessments of sustainability are performed to proposehe harmonious scenario for sustainable development of EREystem.
ngqing in China.

2. Study area

This study takes the city of Chongqing in China shown in Fig. 1as a case study. Chongqing was approved as one of China’s fourprovincial-level municipalities by the State Council on April 18,1997, in order to accelerate Chongqing’s economic development.Thus, we perform this study with year 1998 as a starting point. Ithas a largest registered population of 31,442,300 in 2005 amongfour municipalities. Its area spans over 82,300 km2. Chongqinglocates in Three Gorges’ tail area, which makes Chongqing andThree Gorges depend on each other. Since social economic driv-ing of Chongqing interacts with vulnerable ecological environmentin Three Gorges, economy–resource–environment (ERE) system inChongqing is quite unique and vulnerable (Guan et al., 2009). Aseries of severe environmental problems are restricting sustainabledevelopment of economy-society and eco-security in Chongqingmetropolis. Therefore, it is meaningful to simulate developmentstate of ERE system in Chongqing and to program its developmentscenario in a scientific way. The research results can contributeto guide the construction of Chongqing, to promote more coordi-nated development of ERE system, and to safeguard the ecosystemsecurity of Three Gorges.

3. Methods

This study applies SD model software VENSIM to simulate theimpacts of economic growth, resource depletion, and environmen-tal deteriorations on Chongqing’s urban development by designingfour scenarios. Spatial technique of GIS is employed to analyzethe dynamic change of spatial distribution of indicators, which arecontained in SD model. The establishment of SD model and thedetermination of indicators were previously described in detail(John, 1998; Trista et al., 2004; Alexandre et al., 2006; Helldén,2008; Guan et al., 2010). We also describe them in the followingspecific sections. For integrated evaluation, because some evalua-
tion models which are developed by traditional methods, usuallyintroduce some subjective factors, we use a hierarchical multi-levelmethod to establish an evaluation model for regional sustainabledevelopment level.


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The procedure of present work follows four steps. First step is toetermine research objective and restriction conditions in obtain-

ng the data. Second step is to establish a stock-flow diagram ofrban ERE system and achieve the evaluation indicators. Then, fourypical scenarios (current, resource, technology, and environmentcenarios) are designed by adjusting the parameters in the modelnd changing the specification of some variables, with an aim atomparing the effects of policy inclinations on ERE system. Thirdtep is to analyze dynamic change and spatial distribution of indi-ators by using SD and GIS technologies. Final step is to predictynamic and spatial changes of the integrated sustainable devel-pment level by using the future values of indicators.

.1. Establishment of SD model

SD model studies the sustainability of ERE system according toybernetics, system theory, and information theory. It can elucidatehe interaction and relationship among many influencing factorso perform a dynamic simulation test. The purpose of this test is tonvestigate the changing behaviors and tendencies of ERE system inlternative scenarios for supporting the corresponding policies andanagements. A stock-flow diagram of ERE subsystem is shown in

ig. 2. All equations in the SD model are illustrated in Appendix A.he core of the SD model is a Cobb-Douglas production functionJohn, 1998). Six variables, GDP, technology in production, labornput, capital input, renewable- and non-renewable resources, arencluded in the production function. They correspond to the sixub-systems in the whole ERE system. Besides the six subsys-ems, environment is another indispensable sub-system in parallel.otally, the seven sub-systems determine the probable scenariosf ERE system through interaction and feedback among them. Theodel is written in VENSIM software with a time step of 1 year, and

ime span is 52 years from 1998 to 2050.

.2. Spatial analysis based on GIS

Chongqing includes 40 counties and districts, 1453 villages andowns; hence every indicator has one present value in one villager town. Based on these present values of indicators, the futurealues of indicators can be calculated according to their dynamichanging tendencies in the above SD model. Subsequently, we usehe future value of every indicator at every village or town at theame year to construct a new GIS database of the future indicatoralue. Finally, the future spatial distribution of every indicator isbtained by using GIS software.

.3. Sustainability assessment of urban environment

.3.1. Assessment indicatorsAt present, the more frequently used assessment methods are

o evaluate index systems by selecting some reasonable and typicalndicators for determining the state of urban environment sustain-bility (Dong et al., 2003; Steven and David, 2003; Guan et al., 2009;an et al., 2009). However, it is less research to carry out dynamicssessment for future state of urban ERE with future indicator val-es. Here, the essential indicators of urban sustainability based onhe above SD model are used to carry out the further dynamicssessment of ERE sustainability in the same development scenar-os (current, resource, environment, and technology scenarios). Theasic hierarchy of composing indicators into the urban sustainableevelopment is shown in Table 1.

.3.2. Assessment weightAnalytic hierarchy process (AHP) is a structural technique for

ealing with complex decisions. Referring to mathematics and psy-hology, AHP was developed by Thomas L. Saaty in the 1970s and

ors 11 (2011) 1333–1344 1335

has been extensively studied since then (Júlíus, 2003; Xiong et al.,2007; Hafeez et al., 2002; Li et al., 2007). The AHP provides acomprehensive and rational framework for structuring a decisionproblem, for representing and quantifying its elements, for relat-ing those elements to overall goals, and for evaluating alternativesolutions. This paper uses AHP to determine the indicators weightsof three subsystems – economy, resource, and environment. Thedetailed analysis process is written in Appendix B. According to theAHP calculations, weight of assessment factor of urban ERE systemsustainability is summarized in Table 1.

3.3.3. Assessment modelThis study uses a hierarchical multi-level method to establish

an evaluation model for regional sustainable development level.Firstly, because assessment indicators in the urban ERE systemare expressed in different units, it is necessary to carry out thenormalization for each indicator by Eqs. (1) and (2). After theirnormalizations, we can incorporate variables which have differ-ent measurement units (i.e. physical, economic, etc.) to fulfill theclear compatibility of different indicators. Secondly, the calculationof the sustainability of urban ERE system is a step-by-step pro-cedure of grouping various basic indicators into the sustainabilitysubsystems (Damjan and Peter, 2005). According to this progres-sive relationship, sustainability of urban ERE subsystems can beevaluated with Eqs. (3) and (4). Finally, the sustainability of urbanERE system is calculated by using Eq. (5).

C+j,it

=I+j,it

− I+min,j,i

I+max,j,i− I+min,j,i

(1)

C−j,it

= 1 −I+j,it

− I+min,j,i

I+max,j,i− I+min,j,i

(2)

Sj,t =m∑

j,it

Wj,iC+j,it

+m∑

j,it

Wj,iC−j,it

(3)

m∑

j,i

Wj,i = 1, Wj,i ≥ 0 (4)

At =m∑

j,t

WjSj,t (5)

where C+j,it

is the normalized indicator i (with positive impact) for j

subsystem at time (year) t; I+j,it

is indicator i (with positive impact)

for j subsystem at time (year) t; I+min,j,iis the minimum indicator i

(with negative impact) for j subsystem at the total time (year) t; C−j,it

is the normalized indicator i (with negative impact) for j subsystemat time (year) t; I−

j,itis indicator i (with positive impact) for j sub-

system at time (year) t; m is the total amount of indicators. I+max,j,i

is the maximum indicator i (with negative impact) for j subsystemat the total time (year) t. Sj,t is the sustainability for j subsystem attime (year) t. Wji is the ith indicator weight for j subsystem. At isthe sustainability of the ERE system at time (year) t; Wj denotes thejth subsystem weight.

4. Results and discussion

4.1. Dynamic analysis results based on SD model

4.1.1. Validation of SD modelIt is important to empirically calibrate the SD model for test-

ing reasonability and feasibility of simulation results. This can befulfilled by a matching test of historical behavior in the current



nomy

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Fig. 2. Stock-flow diagram of eco

cenario. We chose several variables with available values for val-dation. They include total population, GDP, labor input, capitalnput, stock volume of renewable resource, discharged volumes ofOD, SO2 and solid waste, and investment of environmental pro-

–resource–environment system.

tection. To validate the SD model, we firstly compared the real datain 1998, as well as the simulated value in 2007, with the real data in2007, respectively. The model testing results are shown in Fig. 3. Itis evident that the simulated values in 2007 of variables are closer to


D. Guan et al. / Ecological Indicators 11 (2011) 1333–1344 1337

Table 1Weight of assessment indicator for urban ERE system.

System Subsystem Weight Indicators Weight

Assessment indicators of urban ERE system (A) Economy system (B1) 0.3334 Per capita GDP (C1) 0.43856Technology level (C2) 0.12866Capital input (C3) 0.1144Labor input (C4) 0.31838

Resource subsystem (B2) 0.3333 Nonrenewable resource stock (C5) 0.75Renewable resource stock (C6) 0.25

Environment subsystem (B3) 0.3333 Waste water pollution index (C7) 0.65217Air pollution index (C8) 0.21739Solid waste pollution index (C9) 0.13044

ical da

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Fig. 3. Verification result of histor

he real values in 2007 than the real data in 1998. This suggests theeasonability of model. Secondly, the simulated values of variablesn 2007 have the low relative errors of ±5% compared to the real val-es in 2007. This indicates the validity of model. So, the developedD model is reliable to elucidate the causal feedback relationship,nd predict the dynamic change of Chongqing’s ERE system.

.1.2. Setting of different scenariosFor different developing purposes in the ERE system, some sim-

lation scenarios can be assumed through adjusting variables andarameters. Subsequently, these assumed strategies can be testedhether and how they can improve system’s performance, with

he established SD model. The study simulated different scenarioshange with 1998 year as the starting values; because the abovealidation test indicates that the simulation of SD model with realata in 1998 is reliable. We also expect that the changing tendencyf indicator in the developing scenarios would be more obviousithin a longer simulation period of 1998–2050.

According to Cobb–Douglas production function, these
djustable variables and parameters are highlighted in Fig. 2.ccordingly, the detailed settings of variables and parameters in
ypical scenarios are compared in Table 2. Firstly, for reference,urrent scenario is to sustain base run results of all subsystems,

ta with the established SD model.

keeping all parameters and variables at their current values.Secondly, in resource scenario, we reduced elastic coefficients ofrenewable resources and nonrenewable resources for decreas-ing resource consumption. Meanwhile, for balancing economygrowth, we should increase the input of labor force and capital,as highlighted with yellow background in Fig. 2. Thirdly, in envi-ronment scenario, we increased the investment of environmentprotection to improve environmental system, as highlighted withpink background in Fig. 2. Finally, in technological scenario, we canensure sustainable development of economy system and enhancescience technology level for increasing output, as highlighted withgreen background in Fig. 2.

4.1.3. Simulation resultsDeveloping tendencies of economy, resource and environment

subsystems are simulated following the different scenarios, respec-tively. Firstly, Fig. 4 presents the GDP changes in different scenariosfrom 2008 to 2050. It shows that the economy levels of Chongqingin the four scenarios decrease in the following sequence: technol-
ogy scenario, current scenario, environment scenario, and resourcescenario, indicating that technology advancement is more favor-able for the sustainable development of economy system than theother strategies. Also, resource scenario leads to a marked decrease



Table 2Parameter setting of different scenario simulation.

Parameter Scenarios

Current scenario Resource scenario Environment scenario Technology scenario

Elastic coefficient of renewable resources 0.1 0.05 0.1 0.1Elastic coefficient of nonrenewable resources 0.3 0.2 0.3 0.3Elastic coefficient of labor input 0.2 0.25 0.2 0.2Elastic coefficient of capital input 0.4 0.5 0.4 0.4Investment rate of environment 0.02 0.02 0.04 0.02Investment rate of technology 0.002 0.002 0.002 0.004

0

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Resource scenario Technology scenario

Current scenario Environment scenario

Fig. 4. Simulated results of GDP under different scenarios.

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Fig. 5. Simulated results of renewable resource stock under different scenarios.

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ig. 6. Simulated results of nonrenewable resource stock under different scenarios.

n GDP as compared to current scenario, which denotes a stressed
ependence of economic development on natural resources. Onean see that the increase of environment investment will spend amall fraction of GDP.
Fig. 7. Simulated results of discharged volume of SO2 under different scenarios.

Secondly, Figs. 5 and 6 present the consumption changes ofrenewable- and nonrenewable-resources in different scenariosfrom 1998 to 2050. The results indicate that the consumptionspeeds of both renewable- and nonrenewable-resources are thelowest in the resource scenario. The low consumption rates ofnonrenewable- and renewable-resources remain the relativelylarge stocks, so resource stock has a long-term potential to sustainthe ERE system. It should be explained that environment sce-nario has only adjusted the environmental invests and the resourceparameters are the same as those in the current scenario (SeeTable 2), so the resource consumption rates in the two scenarios arethe same as displayed in Figs. 5 and 6. Further combining Fig. 4 withFigs. 5 and 6, it is evident that rapid economic growth after 2014 areconsistent with the accelerated consumption rates of renewable-and nonrenewable-resources. These developing tendencies are inaccordance with the fact that economic development is stronglydependent on and at the expense of the consumption of resources.Consequently, resource scenario can delay the exhaustion speed ofresources to a remarkable extent as compared to other scenarios.

Thirdly, discharged volumes of waste gas SO2, waste water(COD-chemical oxygen demand, an indicator of organic contentin water), and solid waste are recognized as the indicators ofenvironment system’s evolvement, as presented in Figs. 7–9. Thesimulation results show that discharged volumes of SO2, COD andsolid waste have decreasing tendencies in environment scenario,but increasing tendencies in the other three scenarios. Dischargedvolume of SO2, COD and solid waste in other three scenariosdecreases in the following sequence: technology scenario, currentscenario, and resource scenario. These results show that the pollu-tion emission is mostly attributed to the consumption of resourcesand heavy industrial activities. In addition, the accelerated chang-ing tendencies in discharged volumes of SO2, COD and solid wastein four scenarios can be seen after 2014. They also correspond tothe development tendency of GDP as seen in Fig. 4. In summary,
the evaluated model can reveal the dynamic relationships amongthe developments of economy, resource and environment.


D. Guan et al. / Ecological Indicat

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20502046204220382034203020262022201820142010200620021998

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f C

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(To

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Fig. 8. Simulated results of discharged volume of COD under different scenarios.

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the discharged volumes of pollutions as shown in Figs. 7–9. The

ig. 9. Simulated results of discharged volume of solid waste under different sce-arios.

.2. Spatial analysis results based on GIS

.2.1. Economic development levelPer capita GDP is chosen as an indicator to analyze spatial change

f economy system. Fig. 10 shows the spatial distributions of theensity of per capita GDP in the different developing scenarios in050. In the current, resource, and environment scenarios, higher capita GDP is mainly concentrated in the main urban area inhongqing, and per capita GDP level remains at the lower levelutside the main urban area even in 2050. In comparison, in theechnology scenario, per capita GDP level throughout the stud-ed area exhibits a higher level than that in the other scenarios.hus, the increase of total GDP amount in the technology scenarioccording to dynamic simulation in Fig. 4 will not result in the morenbalanced economic development due to regional differences. Wean see some highlighting red areas in the middle and northeast ofhe studied area that have quite low per capita GDP level in theurrent scenario. These areas acting as novel developing enginesre likely to enhance the economic growth in surrounding areas,eading to harmonious economic development.

.2.2. Resource development levelFig. 11 shows spatial distributions of mineral resource density

ollowing the different scenarios in 2050. In the current scenario,ineral resource density becomes smaller and smaller due to rapid

onsumption speed and rising population. In the resource scenario,istribution density of mineral resource still remains high. In theechnology scenario, distribution density of mineral resource is
igher than that in the current scenario, which can be explained by
ow rates of nonrenewable- and renewable-resource consumptionsn the former one.

ors 11 (2011) 1333–1344 1339

4.2.3. Environment development levelThis study considers water pollution index, air pollution index,

and discharged volume of solid wastes per capita to reflectthe development level of environmental subsystem in 2050(Figs. 12–14).

Seen from Fig. 12, water pollution indices of two districts havethe highest values (pollution index lies in 3.2–5.2, as shown in leg-ends) in the current scenario, which showed that water quality ofthe districts should be improved. One seriously polluted districtappears in the resource scenario; moreover, almost every districtdecreases the water pollution index as compared to current sce-nario, suggesting that the consumption of resources leads to thewater pollution. In contrast, technology scenario causes more seri-ous water pollution. In the environment scenario, water pollutionindex is largely decreased and all seriously polluted areas disap-peared.

Fig. 13 shows spatial distribution of air pollution index underthe different scenarios in 2050. Air pollution index increases inalmost all districts in the technology scenario, which is probablybecause more efficient production process accelerates the emissionof pollution as compared to current scenario. This data also proposethat the environment protection should also be emphasized withthe growing economic development due to technological advance-ment. Similarly to spatial distribution of water pollution indexshown in Fig. 12, almost all districts decrease the air pollution indexin resource scenario. Serious air pollution (index lies in 5.0–7.0, asseen in legends) in the environment scenario disappears.

Fig. 14 shows spatial distribution of discharged volume of solidwaste per capita under different scenarios in 2050. Volume of solidwaste per capita in the technology scenario is the largest amongthe four scenarios. The reason is the same as that for the spatialdistribution of air pollution shown in Fig. 13. Meanwhile, the highdischarged volume areas are distributed outside the main urbanareas, which is because heavy industrial factories are mainly dis-tributed outside the main urban areas. In the case of increasing theinvestment of environment protection, discharged volumes of solidwaste remarkably decrease.

4.3. Sustainability assessment results of urban environment

With the above assessment model, we perform an integratedassessment of sustainable development level with all dynamic indi-cators from current year 2008–2050 under four scenarios in orderto get the best scenario, as shown in Fig. 15.

Fig. 15(a) shows the sustainable development level in currentscenario. The sustainable development grade descends fast duringthe 42-year period, revealing that the system will run into a com-pletely non-sustainable status afterwards. This is because duringthis period, although economical development is kept, resource andenvironment suffer severe damage as discussed above in dynamicanalysis. In a long term, the current scenario is destructive for localeconomical development, social progress and environment protec-tion. Therefore, this scenario must be discarded in the future.

Fig. 15(b) shows that the sustainability of resource scenariofirst keeps at slightly decreasing grade, and then begins increasingto a more sustainable level than that of current scenario, pass-ing through a turning point in 2030. This is because the speedof economy growth becomes remarkably slow in Fig. 4, when thedependence degree of economy growth on resources is decreased.However, the slow consuming speed of resource is favorable forprotecting resource and improving environment due to decreasing

improvement in resource and environment offsets the decrease ofeconomic growth. As a net consequence, the sustainability will beenhanced in a long term.



Fig. 10. Spatial distribution of per capita GDP under different scenarios in 2050.

ource

dpit

Fig. 11. Spatial distribution of mineral res

Fig. 15(c) shows that the environment scenario also exhibits a
ecreasing trend of sustainability in the initial 20 years, and thenresents increasing tendency from 2022. This indicates that the
ncreasing input of GDP into environment protection will delayhe sustainability of urban ERE in a short term. After some years,

density under different scenarios in 2050.

the positive effect of environment input which improves the envi-
ronmental quality is markedly shown up. Thus, sustainable level isfinally increased by this scenario.
Fig. 15(d) shows that the sustainability of technology scenarioalso exhibits a decreasing trend of sustainability in the initial 20



Fig. 12. Spatial distribution of water pollution index under different scenarios in 2050.

Fig. 13. Spatial distribution of air pollution index under different scenarios in 2050.



Fig. 14. Spatial distribution of solid waste pollution index under different scenarios in 2050.

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Fig. 15. The sustainability of urban ERE system in four scenarios.



tainab

yTstpaFi

twtttoisasdsr

iuIidspw

Fig. 16. Spatial distribution maps of sus

ears, and then presents a slowly increasing tendency from 2030.his indicates that contribution of short-term economic growth toustainable development due to technology advancement is lowerhan the negative effects of resource consumption and numerousollution emissions (see Figs. 7–9) on sustainability. Meanwhile,fter some years, the contribution of rapid economic growth (seeig. 4) becomes higher and offsets the negative effects, thus show-ng a slowly increasing tendency.

According to evaluation results and GIS technology, spatial dis-ribution maps of sustainability under different scenarios in 2050ere also computed, as shown in Fig. 16. It is evident that sus-

ainability values of all districts in the current scenario are lowerhan 0.4, foretelling an unsustainable state of the urban ERE sys-em in 2050. Technology scenario can improve the sustainabilityf all districts, but remaining most districts under the sustainabil-ty value of 0.4. Compared to technology scenario, resource scenariohows more effective to improve the sustainability. And, it is notice-ble that sustainability values of all districts in the environmentcenarios are larger than 0.4, indicating the relatively sustainableevelopment state. As a result, the integrated levels of urban EREystem in four scenarios decrease in the following sequence: envi-onment, resource, technology, and current scenarios.

In summary, resource, environment and technology scenar-os are demonstrated to improve the sustainable development ofrban ERE system in a long term as compared to current scenario.

n contrast, environment scenario is a most effective strategy tomprove the integrated sustainability level, together with spatially
istributed sustainability in every district in 2050. However, theustainability in the three scenarios appears an increasing tendencyassing through a shorter-term development, because all of themill restrict the economic growth (till 2020). Consequently, our
ility under different scenarios in 2050.

results evidently show that environment plays an essential rolein sustainable urban development, and an integrated combinationof the three scenarios with optimizing environment, resource, andeconomic benefits is suggested to guide the harmonious and sus-tainable urban development.

5. Conclusions

According to theory and methodology of SD and GIS, this studydevelops an integrated evaluation model by which four typicaldevelopment scenarios are proposed for both dynamic and spatialanalysis through considering the dynamic evolution of indicators.The optimal countermeasure is suggested to promote the sustain-able development of Chongqing’s ERE system.

Four various scenarios (current, resource, environment, andtechnology scenarios) generate significant differences in thedeveloping tendencies of economic development, resource con-sumption, and environmental issues. Especially, the dynamicchange of spatial distribution of these indicators is clearly shownin different regions with GIS technology. According to a net con-sequence of dynamic and spatial analysis, local government canscientifically put forward the corresponding policies and efficientlydirect the sustainable development. Finally, this paper predicts theintegrated dynamic and spatial assessments of urban sustainabil-ity under the four scenarios. Assessment results indicate that theenvironment scenario is more effective to sustainable develop-ment of urban ERE system. Considering the developing features and
regional differences, as well as regular discipline on urbanization, asynergy programming of environmental, resource and technologyscenarios is believed to realize the continuous sustainable devel-opment of ERE system.


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cknowledgements

This research is supported by Fund of Institute of Lowlandnd Marine Research and Global Environment Research Fund byhe Ministry of the Environment in Japan (No. hc-089), Nationalocial Science Fund in China (No. 06XJY017), Innovative Teamroject of Chongqing Municipal Education Commission in ChinaNo. 201012), as well as Human Geography Key disciplines and GISey Laboratory of Chongqing in China.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at doi:10.1016/j.ecolind.2011.02.007.

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