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Methodology for modeling of citysustainable development based onfuzzy logic: a practical caseF. Jaderia, Z.Z. Ibrahima, N. Jaafarzadehbc, R. Abdullaha, M.N.Shamsudina, A.R. Yavarid & S.M.B. Nabaviea Faculty of Environmental Studies, Universiti Putra Malaysia,Darul Ehsan, Malaysiab School of Health, Ahvaz Jundi Shapur University of MedicalSciences, Ahvaz, Iranc Environmental Technology Research Center, Ahvaz Jundi ShapurUniversity of Medical Sciences, Ahvaz, Irand Faculty of Environment, University of Tehran, Tehran, Irane Faculty of Marin Biology, Khorramsahr University of MarineScience and Technology, Khorramsahr, IranPublished online: 14 Apr 2014.

To cite this article: F. Jaderi, Z.Z. Ibrahim, N. Jaafarzadeh, R. Abdullah, M.N. Shamsudin, A.R.Yavari & S.M.B. Nabavi (2014) Methodology for modeling of city sustainable development basedon fuzzy logic: a practical case, Journal of Integrative Environmental Sciences, 11:1, 71-91, DOI:10.1080/1943815X.2014.889719

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Methodology for modeling of city sustainable development basedon fuzzy logic: a practical case

F. Jaderia*, Z.Z. Ibrahima, N. Jaafarzadehb,c, R. Abdullaha, M.N. Shamsudina,

A.R. Yavarid and S.M.B. Nabavie

aFaculty of Environmental Studies, Universiti Putra Malaysia, Darul Ehsan, Malaysia; bSchool ofHealth, Ahvaz Jundi Shapur University of Medical Sciences, Ahvaz, Iran; cEnvironmentalTechnology Research Center, Ahvaz Jundi Shapur University of Medical Sciences, Ahvaz, Iran;dFaculty of Environment, University of Tehran, Tehran, Iran; eFaculty of Marin Biology,Khorramsahr University of Marine Science and Technology, Khorramsahr, Iran

(Received 11 September 2013; accepted 28 January 2014)

Information on sustainability can be used for future development planning. This studypresents an approach for assessing urban sustainability. Delphi and fuzzy logicmethods and the Kruskal–Wallis test were the discovery and verification techniquesused. The city system comprised social, economic, and environmental subsystems. Theseven orientors of existence, effectiveness, freedom of action, security, adaptability,coexistence, and psychological need were measured using different indicators. Thefinal sustainability output was obtained by aggregation of the multiple orientors andsubsystems sustainability values into a unified measure. A fuzzy sustainability indexwas developed to compare the importance of the sustainability orientors andsubsystems. The model was applied to Mahshahr, an industrialized coastal city in Iran.The model output for the subsystems showed significant differences between theeconomic and environmental subsystems and the social subsystem. The finalsustainability output showed that the effectiveness orientor gave the highestsustainability value. The model is dynamic and can be modified for different purposesby changing the indicators. With this model, policy-makers can evaluate existing citysustainability and predict future sustainability by varying the indicators. This can bedone on local, regional, and global scales for security and adaptation strategies,mitigation plans, and sustainable development management.

Keywords: sustainability; orientor; fuzzy sustainability index; sustainable development

1. Introduction

Our lifestyles are becoming more unsustainable. The concept of sustainability stresses the

interconnections between social, economic, and ecological systems (O’Dwyer and Owen

2005). The World Commission on Environment and Development addresses the need to

measure many facets of sustainable development (SD) and has developed sustainable

development indicators (SDIs). Numerous research initiatives have examined the SDIs

and established frameworks to organize the recommended indicators (UNCSD 1996;

Segnestam 2002; OECD 2003, 2008; Hezri 2004; Shi et al. 2004; DEFRA 2006; Muga

and Mihelcic 2008; Palme and Tillman 2008). The indices may potentially be robust tools

for sustainability policy, but only when appropriately used (Parris and Kates 2003; Audrey

q 2014 Taylor & Francis

*Corresponding author. Email: fereshtehjaderi@yahoo.com

Journal of Integrative and Environmental Sciences, 2014

Vol. 11, No. 1, 71–91, http://dx.doi.org/10.1080/1943815X.2014.889719

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and Mayer 2008). SD requires the construction of interdisciplinary models to evaluate and

quantify the effects of on-going economic policies on the delicate interplay between living

populations, natural resources, and economic development.

To assess SD, Munda (2006) introduced the multi-criteria decision analysis. Erol et al.

(2011) presented a conceptual decision model utilizing the analytical hierarchy process.

Labuschagne et al. (2005) reported on development of an urban development

sustainability assessment model. Phillis et al. (2011) introduced the SAFE model using

fuzzy logic and Robert and Schmidt-Bleek (2002) defined five interdependent and

hierarchical levels for a systems approach to strategic SD.

To conceptualize sophisticated environmental–economic interactions with robust

capability, SD has been employed in environmental systems (Deaton and Winebrake

1997; Ford 1999b; Chang et al. 2008; Muneepeerakul and Qubbaj 2012) modeling SD

(Forrester 1971; Meadows et al. 1992; Van den Bergh and Nijkamp 1994; Saeed and

Radzicki 1998; Jin et al. 2009), ecological modeling (Costanza and Gottlieb 1998;

Costanza and Voinov 2001; Arquitt and Johnstone 2008; Huang et al. 2008), energy

planning (Ford 1999a, 1999b; Jin et al. 2009), sustainable supply chain management

(Seuring and Muller 2008a), sustainability assessment (Krajnc and GlaviA 2005; Shmelev

2011; Van Zeijl-Rozema and Ferraguto 2011), and urban environmental sustainability (Yu

and Wen 2010; Ianos et al. 2012). Uncertainty and ambiguity still exist in expert responses

(Hwang and Lin, 1987; Chang et al. 2000; Shen et al. 2010).

The results of SD are uncertain in the sustainability output of qualitative and

mathematical sustainability assessment. It can integrate subsystem sustainability using

fuzzy logic to achieve a sustainable model for a city system. The main objectives of this

research are to identify sustainability indicators and model for the city system and to

evaluate and unify the sustainability indicators and subsystems outputs. This research is

one of the first SD model studies in an industrial city in the southwest coastal zones of

Iran. This study used SD modeling to evaluate and quantify the action and interaction

between living populations, natural resources, and economic development through a city

system in this area. The considerations were the type and availability of data, finding

indicators by surveying developed models, aggregating methods and applying them to the

city system.

To develop a city system SD model, the SDIs of the environmental system were

identified, a SD model of the environmental system based on subsystems and orientors

were obtained and the sustainability value of subsystems, orientors, and the

environmental system were unified. The main approach of the study was to unify

measurement of the system and subsystem sustainability as one number. The fuzzy

sustainability index (FSI) was used to define the orientors and subsystems indices. This

study is one of the early integrated city sustainability models based on orientors and

subsystems using Delphi and fuzzy logic techniques. The novelty of this study is to

quantify and unify measurement of the city sustainability and to achieve a three

dimensional sustainability model. Also the novelty of this work is the unified

measurement of the sustainability by FSI and fuzzy outputs based on orientors and three

subsystems.

The significance of this study is that it can assist government policy-making to

establish an SD management system. Policy-makers can employ these scales and

indicators for environmental planning and management and the SD management in city

systems. The city sustainability indicators, model, sustainability output, and indices were

applicable for the local development, municipality, planning and management plan, and

sustainability management plan.

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2. Methodology

As mentioned above the goal of this study was to model the development of city system

sustainability. The sustainability model was measured and modeled using fuzzy logic

because it is very effective in handling vague and complex concepts. The framework

defined the research used to develop specific outputs. In this research, the hierarchy

structure was developed for a city system. The hierarchy structure categorized the

sustainability system, subsystems, orientors, and indicators. The hierarchy structure to

identify and evaluate SDIs is presented in Figure 1.

Sustainability subsystems contain seven basic orientors: existence, effectiveness,

freedom of action, security, adaptability, coexistence, and psychological needs. Fuzzy logic

can be used to measure and model the subsystems and orientor sustainability. City

sustainability is assessed by identifying and evaluating the SDIs. The city of Mahshahr on

the southwest coast of Iran was selected to identify and evaluate the indicators. The

Mahshahr coastal area is the largest petrochemical economical free zone (Petzone) in Iran. It

lies near Shadeganwetlands, an important coastal fishery center in southwest Iran. This area

is facing serious and challenging hazards because of the lack of an integrated SD system and

model. For example, as Zurizade (2010) reported, human interference and rising pollution

have lowered UNESCO ranking for the wetlands. Shadegan wetland once ranked 5th, but

now ranks 22nd. The city system sustainabilitymodel encompasses the unique and sensitive

area that includes Shadeganwetlands, the city ofMahshahr, and the petrochemical industry.

Mahshahr is the center of the petrochemical industry and a crossroads for trade in the

region. As are other industrialized cities, Mahshahr is becoming a megacity with all of an

industrial city’s problems. The study area includes areas of different types of land use.

Petrochemical plants, oil export facilities, local fisheries, a sugar cane refinery, and large

areas of agricultural land all exist there (Zamani-Ahmadmahmoodi et al. 2010). Several

studies have pinpointed the sources of contamination in the study area. This includes use

of fertilizers, herbicides, and pesticides for agriculture, hazardous substance spills from

refineries and Bandar Imam Petrochemical factories (Zolfaghari et al. 2007), threats to

Figure 1. Hierarchy to identify and evaluate SDIs.

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water usage, availability and quality (Sima and Tajrishy 2006), heavy discharge from

industry, heavy withdrawal of water for irrigation, and saline discharge from the local

sugar cane refinery (Zamani-Ahmadmahmoodi et al. 2010). The area was also bombarded

with chemical weapons during the Iran/Iraq war in the 1980s (Literathy 1993;

Kanyamibwa 1998; Zamani-Ahmadmahmoodi et al. 2009, 2010) and the wetlands have

been damaged by acid rainfall during the 1991 Gulf War (Scott 1995). The Mahshahr area

is an oil field and has oil reservoirs and petrochemical companies (Alhashemi et al. 2011).

Mahshahr is a city system that surrounds the oil and petrochemical industries. Most of

the action and interaction occurs between the petrochemical industry and the city system.

Identifying SDIs using the Delphi method for Mahshahr is useful to achieve a workable

SD fuzzy model.

2.1 Identification of SDIs

The system was categorized into social, economic, and environmental subsystems, and the

sustainability of the system was achieved based on these subsystems. Sustainability

indicators were then identified for the subsystems. The Balaton method (Bossel 1977,

1987,1998, 2000)was applied to find the subsystems indicators based on the seven orientors.

The Delphi method was used at this stage to identify the indicators for the

environmental city system. Two rounds of surveying were implemented based on the

Delphi method. In the first round, a questionnaire was prepared to identify the indicators.

There were two main criteria for the selection for this group. The first round focused on the

sensitivity and importance of the selected study area. This importance stems from the

natural resources, Shadegan wetlands, the Persian Gulf, and oil and petrochemical

resources. The second identified SD to be implemented first in the study area.

The survey group in this research effort comprised lecturers and experts, especially

local environmentalists. In 2002, the Balaton Group developed a method for identifying

subsystem indicators based on the orientors. The main criteria for the indicators were

importance, usability, accuracy, and localization of the indicators. A literature review

identified various indicators for the subsystems. The respondents were asked to propose

indicators to find SDIs for Mahshahr.

The second questionnaire (Q2) prioritized the status of the proposed indicators.

Different sources were used to prepare Q2, including indicators from the government and

agencies, national and international reports and studies (Malaysia 1996; Bossel and Group

1999; Bossel 2000; Hezri and Dovers 2006; Poor Asghar Sanghachin 2006; Esquer peralta

2007; Esty et al. 2008). Q2 prioritized the proposed indicators from most important to less

important for each orientor in each system.

Respondents were asked to prioritize the selected indicators for Mahshahr based on a

0–100 category scale. The scale of the rating ranged from very weak (low; 0–20) to very

good (high; 80–100). The results of the sustainability orientors and systems were

measured and modeled using the fuzzy logic method.

2.2 Development of environmental system sustainability model

The fuzzy logic can work with uncertainties and inaccuracy and can solve problems where

there are no sharp borders and exact values (Zadeh 1965). In light of lack of precise

mathematical model that can describe system behavior, the fuzzy logic toolbox is an

excellent tool for solving the problems. It allows using if–then logic rules to illustrate

the behavior of the system (Nedeljkovic 2004). Fuzzy operations perform precisely as the

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corresponding crisp sets where the range of membership grades is restricted to the

individual fuzzy set {0, 1} (Klir and Yuan 1995). Information flows through three major

transformations before existing the system as output information, also known as

fuzzification, fuzzy inference, and defuzzification. The fuzzification modules transform

the crisp, normalized value of the indicator into a linguistic variable to make it compatible

with the rule base (Zadeh 1965).

The fuzzification as the first component of fuzzy system decomposes input variables

(social, economic, and environmental sustainability) with crisp numbers, and maps the

crisp numbers into fuzzy sets. The real numbers must be translated to fuzzy sets through

various available techniques such as triangular (TFN) trapezoidal (ZFN), exponential,

Gaussian function, and so on (Michael 2004; Jafari et al. 2008). For example in this study,

the linguistic values of basic subsystems regarding environmental system sustainability

are very low (VL), low (L), medium (M), high (H), and very high (VH). The linguistic

value, VL, is represented by a fuzzy set that uses the membership function mVL. The

membership function associated with each normalized subsystem value, a number, mVL, in

[0, 1] which represents the grade of membership of subsystems in VL of its equivalency;

the truth value of the proposition subsystems is VL. TFN or ZFN fuzzy members are used

to represent linguistic variables. Thus, let xc be the indicator value for the system whose

sustainability we want to assess. The linguistic value, yc, for ZFN (Equation (1)) and TFN

(Equation (2)) is calculated (Jang et al. 1998; Phillis and Andriantiatsaholiniaina 2001).

ZFN ¼ ycðxcÞ ¼

0; x # a

x2 a

b2 a; a # x # b

1; b # x # c

d 2 x

d 2 c; c # x # d

0; d # x

8>>>>>>>>>><>>>>>>>>>>:

9>>>>>>>>>>=>>>>>>>>>>;

: ð1Þ

In Equation (1) the data for each variable are normalized on a scale between 0 (lowest

level) and 1 (highest level) to allow aggregation and facilitate fuzzy computations. This

computation is done as follows: To each basic variable, x, we assign a target, a minimum,

a, and a maximum value d. The target can be a single value or, in general, any interval on

the real line of the form [b, c ] to represent a range of desirable values for the indicator

(variable) (Jang et al. 1998; Phillis and Andriantiatsaholiniaina 2001).

TFN ¼ ycðxcÞ ¼

0; x # a

x2 a

b2 a; a # x # b

c2 x

c2 b; b # x # c

0; c # x

8>>>>>>><>>>>>>>:

9>>>>>>>=>>>>>>>;

: ð2Þ

In Equation (2) the fuzzy computation is accomplished as follows: to each basic

variable, x, has assigned a target, a minimum, a, and a maximum value c. The target can be

a single value or, in general, any interval on the real line of the form [a, c ] that represents a

range of desirable values for the indicator (variable).

Fuzzy logic was applied to plan this model and find differences between sustainability

of the subsystems and orientors. The model is holistic in that it uses a balanced

representation of the social, economic, and environmental factors. Local and global

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regions generally cannot provide sufficient data for the indicators. This model, however,

can be implemented using expert knowledge.

The structure of the model is a combination of the seven basic orientors used to achieve

the subsystem sustainability. The goal is to identify and evaluate the indicators of the

orientors. If there is more than one indicator for the orientors, it can overlap the related

indicators for every two inputs and fuzzy output subsystem sustainability. Linguistic

variables existed on five levels for the social subsystem and the economic and

environmental subsystems. The rankings were the same as for Q2. Table 1 shows the

linguistic value, notation, and numerical range of the subsystems and inputs. The linguistic

variables for a city system (output) and the five levels of input were categorized.

To apply this approach to the SD model, the social, economic, and environmental

subsystems were modeled as a fuzzy set. The membership function approach was adopted

using trapezoid and TFN fuzzy members. Figures 2 and 3 are the membership functions

for the social, economic, and environmental systems that are modeled as fuzzy sets. The

social, economic, and environmental subsystems were represented by fuzzy sets with

Figure 2. Input membership functions.

Table 1. Linguistic variables for sustainability inputs and sustainability output.

Linguistic value Notation Numerical range (normalized)

Very high VH [0 0 10 20]High H [15 30 45]Medium M [35 50 65]Low L [55 70 85]Very low VL [75 90 100 100]

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ranges taken from Table 1. Figure 3 shows the membership functions for the resulting

sustainable values from 0 to 100.

2.2.1 Elicit and contrast the fuzzy rules for sub-system sustainability

To develop fuzzy rules, the experts were asked to describe how a problem can be solved using

the fuzzy linguistic variables as defined. The knowledge was collected from the experts and

other sources (books, computer databases, flowdiagrams). There are three input variables and

one output variable for this system. The mapping for the three subsystems and city system

sustainabilitywas developed using fuzzy if–then rules.The total is 125when the if–then rules

are used in the fuzzy inference system (FIS) to provide mapping between the inputs and the

single output. The fuzzy rules of the system were the following:

1. If (social sustainability is VL) and (economic sustainability is VL) and

(environmental sustainability is VL) then (system sustainability is VL).

2. If (social sustainability is VL) and (economic sustainability is VL) and

(environmental sustainability is L) then (system sustainability is VL).

3. If (social sustainability is L) and (economic sustainability is M) and (environmental

sustainability is M) then (system sustainability is M).

4. If (social sustainability is L) and (economic sustainability is VH) and environmental

sustainability is H) then (system sustainability is H).

5. If (social sustainability isM) and (economic sustainability is VL) and (environmental

sustainability is VL) then (system sustainability is L).

6. If (social sustainability is M) and (economic sustainability is H) and (environmental

sustainability is H) then (system sustainability is H).

7. If (social sustainability is H) and (economic sustainability is H) and environmental

sustainability is L) then (system sustainability is H).

Figure 3. Output membership functions.

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8. If (social sustainability is H) and (economic sustainability is VH) and (environmental

sustainability is VL) then (system sustainability is M).

9. If (social sustainability is VH) and (economic sustainability is VL) and

(environmental sustainability is VL) then (system sustainability is L).

10. If (social sustainability is VH) and (economic sustainability is VH) and

(environmental sustainability is H) then (system sustainability is VH).

The social, economic, and environmental subsystems are the three system inputs, and

system sustainability is the single output. The fuzzy logic toolbox can generate a 3D output

surface by varying any two inputs and keeping any other input constant. Figure 4 shows

the system sustainability fuzzy set. This figure shows the hierarchy for the Mamdani FIS,

where FIS is used for the environmental system sustainability model.

2.2.2 Aggregation of rule outputs

Aggregation is the process of unification of the outputs of all rules. Themembership functions

of all rule sustainability was previously scaled and combined into a single fuzzy set. Thus,

the input of the aggregation process becomes the list of clipped or scaled sustainability

membership functions, and the output is one fuzzy set for each output variable.

2.2.3 Defuzzification

Often environmental system sustainability involves multiple social, economic, or

environmental sustainability categories, such as different zones, orientors, or times. In this

study, variables were combined to provide an overall sustainability value for the city

system. The final output sustainability combined the various orientors into a unified

sustainability measure. The fuzzy sustainability output was then calculated as

Sustainability output ¼PN

i¼1KiSiPNi¼1ki

; ð3Þ

Social Sustainability (5)

Economic Sustainability (5)

Environmental Sustainability (5)

System

Sustainability(Mamdani)

125 Rules

System Sustainability (5)

Figure 4. Fuzzy set of system sustainability.

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where N is the number of variables; Ki is the weight factor based on the selected input;

sustainability is the calculated fuzzy sustainability value for each variable.

This process iterates for the seven orientors separately. The output is the sustainability

of the environmental system for each orientor. The sustainability output of the orientors is

obtained by

Orientor sustainability output ¼PN¼orientor variable

i¼subsystem KiSiPN¼orientor variablei¼subsystem Ki

; ð4Þ

where N is the number of variables for each orientor; Ki is weight factor based on the

selected subsystem as input; sustainability is the calculated fuzzy sustainability value for

each variable.

In this study, the equation for sustainability based on social, economic, and

environmental subsystems is

Subsystem sustainability ¼PN

i¼subsystemKiSiPNi¼subsystemKi

; ð5Þ

where N is the number of variables; Ki is the weight factor based on social, economic, or

environmental inputs; sustainability is the calculated fuzzy sustainability value for each

variable.

The SI was calculated using the above equations and adding N to the sustainability

equation. The FSI value is an average aggregation operator for the environmental system

that can be calculated as

FSI ¼PN

i¼1KiSi=NPNi¼1Ki

; ð6Þ

The SI was calculated for the fuzzy sustainability method outputs and the FSI was

calculated for the sustainability of various orientors and subsystems for Mahshahr.

2.3 Statistical analysis

The results of sustainability analysis determined by the method based on fuzzy logic were

then compared. The Kruskal–Wallis test was used to compare the differences between

more than two variable means. The Mann–Whitney statistical test was used to compare

the differences between two variable means. To assess the identified indicators and find

the differences between the indicator groups, the Kruskal–Wallis test was used. In the

Kruskal–Wallis test, there are more than three consistence indicators in the groups.

3. Results and discussion

3.1 Identified indicator

The first round of surveys had 50 respondents. Utilizing the methodology and the form of

the questionnaire, the respondents proposed indicators for the subsystems with seven

orientors to identify the SD indictors for Mahshahr. The various indicators in this round

were obtained and their indicators were evaluated.

It was important to determine the priorities and consistency to achieve sustainability

from the indicators. The Q2 prioritized the indicators based on their importance to the

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Table

2.

Topindicators

forsubsystem

sandorientors

forMahshahr.

Mahshahr

Social

subsystem

Economic

subsystem

Environmentsubsystem

No.

Orientors

Topindicators

Value

Topindicators

Value

Topindicators

Value

1Existence

Poverty

reduction

3.96

Employment

4.44

Environmentdem

olition

5.12

2Effectiveness

Unem

ployment

6.70

Personal

income

3.34

Industrial

andurban

sewage

treatm

ent

5.72

3Freedom

ofaction

Literacyrate

5.33

Energyproductivity

4.59

Pollutionreduction

4.30

4Security

Governmentfinancial

security

5.69

Water

pollution

4.39

Supply

drinkingwater

5.24

5Adaptability

Trainingprograms

4.20

Capital

flow

5.83

Wetlandschanging

6.81

6Coexistence

Multilanguagepopulation

4.23

Environmentalaccounting

4.46

Airqualityplan

4.20

7Psychological

needs

Occupational

health&safety

3.75

Qualityofpublichealth

andcity

cleanliness

6.09

Anxiety

aboutenvironment

5.91

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study. The top indicators had the highest importance values. The list of top indicators and

their values for the subsystems and orientors are presented in Table 2.

The total indicators for each subsystem were compared using statistical analysis. The

results showed the indicators of unemployment or low income (social), quality of public

health and sanitation (economic), and changes in the wetlands and estuaries

(environmental) had the highest values of importance at 6.7, 6.09, and 6.81, respectively.

The fuzzy logic toolbox can generate a surface to help analyze system performance.

The resulting output surface enveloped for the Mamdani method for two fuzzy inputs

(economic and social) and the fuzzy output sustainability is shown in Figure 5.

Two inputs were available in this model; for every two of three inputs, the model was

the same as the presented model. The data used were obtained from the survey by applying

the Delphi method. The results for the sustainability using fuzzy logic and the Delphi

method were compared. A total of 31 respondent proposals were evaluated for city system

sustainability, and the social, economic, and environmental subsystem data used as inputs

for the Mahshahr system were identified. The outputs of the seven orientors obtained from

the fuzzy logic method are presented in Table 3.

The output was the aggregated result of three inputs for the social, economic, and

environmental subsystems. The Kruskal–Wallis test indicates that there are significant

differences between the outputs. TheMann–Whitney test was used to find consistency and

differences between the orientors. Table 4 shows the differences between orientor outputs

based on the Mann–Whitney test.

This comparison shows that the effectiveness orientor was significantly different from

all orientors except those for security and adaptability. The freedom of action orientor was

Figure 5. Environmental system sustainability model.

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not significantly different from the security orientor. The security orientor was not

significantly different from the existence, freedom of action, and adaptability orientors.

The adaptability orientor was not significantly different from existence and security.

The co-existence orientor was not significantly different from the psychological needs

orientor, but was from all other orientors. The overall results show that the effectiveness

orientor was significantly different from all other orientors. Both psychological needs and

co-existence are not significantly different from each other, but are significantly different

from all other orientors.

Environmental system sustainability often involves multiple aspects of social,

economic, or environmental sustainability, such as different zones, purposes, orientors, or

times. These variables are combined to provide an overall sustainability value for the

environmental system.

The results in Table 4 and the statistical analysis shown in Table 3 indicate that the

effectiveness orientor was highest for the level of sustainability at 61.66% for the high and

good levels. Of the subsystems of the effectiveness orientor, social sustainability was

63.06% for the good range. The effectiveness orientor was significantly different from the

other orientors. The co-existence and psychological needs orientors were significantly

different from all orientors and were the lowest level of sustainability in the moderate

range, with values of 46.29% and 48.41%, respectively.

Comparing the social, economic, and environmental subsystems in Tables 3 and 4

shows that the economic subsystem sustainability is at the highest level of subsystem

sustainability with a value of 54.87% in the moderate range. Social subsystem

sustainability is the lowest sustainability with a value of 53.85% in the moderate range.

The final sustainability output of Mahshahr is in the moderate range with 54.49% for

sustainability. The FSI values calculated for the various orientors of Mahshahr are shown

in Table 5.

The results of the FSIs show that the effectiveness orientor lies in the highest FSI. The

results for orientor sustainability show that there is no significant difference for FSI for the

environmental and economic subsystems, but the social subsystem orientors show

significant differences for FSI.

3.1.1 Prioritization and selection of identified indicators

The indicators are tools for determining sustainability. Time series data for the indicators

can be used to evaluate and forecast the sustainability of the system. In this study, the

respondents were asked to rate the selected indicators in their areas. Table 6 shows the

means for the top indicators for the orientors and the total system.

Table 3. Output sustainability of subsystems and orientors.

OrientorsSocial

sustainabilityEconomicalsustainability

Environmentalsustainability

Systemsustainability

Existence 53.05 53.35 54.46 53.62Effectiveness 63.06 60.88 61.03 61.66Freedom of action 55.89 56.19 56.95 56.34Security 55.30 56.58 54.50 55.46Adaptability 54.00 54.92 54.31 54.41Coexistence 44.49 47.05 47.35 46.29Psychological needs 47.57 49.76 47.90 48.41Final output 53.85 54.87 54.74 54.49

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Table

4.

Comparisonofoutputorientors

usingMann–Whitney.

Asympsig.

Existence

Effectiveness

Freedom

ofaction

Security

Adaptability

Coexistence

Psychological

needs

Existence

–0.00

0.039

0.151

0.889

0.00

0.011

Effectiveness

0.00

–0.003

0.004

0.002

0.00

Freedom

ofaction

0.039

0.003

–0.658

0.031

0.00

0.002

Security

0.151

0.004

0.658

–0.187

0.00

0.00

Adaptability

0.889

0.002

0.031

0.187

–0.00

0.004

Coexistence

0.00

0.00

0.00

0.00

0.00

–0.562

Psychological

needs

0.011

0.00

0.002

0.00

0.004

0.562

–

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The results for the three subsystems indicate that all fall in the moderate range for

sustainability and it is recommended to increase these values to the good or high range to

be a quality of SD. A comparison shows that the social subsystem had the lowest score for

sustainability. The population of Mahshahr is multilingual; the indicators for adaptability

and the occupational health and safety for the psychological needs orientor were 44.49%

and 47.57 %, respectively.

The average for the total system shows that the system is at the medium level of

sustainability with a value of 54.49%, and that the social subsystem average value of

53.83% is lower than the total system average. The average for the environmental

subsystem of the coexistence orientor had the lowest value at 47.35% because of the air

quality management plan. The co-existence orientor for the social subsystem had the

lowest value at 44.49%. The social, economic, and environmental sustainability of

Mahshahr based on the seven orientors are depicted in Figures 6–8.

Figure 6 shows the social sustainability of the seven top indicators for the seven

orientors. The results show that the population is multi-lingual. It has the lowest value for

coexistence in the social subsystem. The highest rank was for unemployment and income

at 63.06% in the good range. The economic sustainability is shown in Figure. Figure 7

indicates that average personal income in the effectiveness orientor was highest with a

60.88% value and the lowest was environmental accountability by firms at 47.05%, at the

moderate range of sustainability. Figure 8 for environmental subsystem sustainability

shows that industrial and urban sewage treatment is an effectiveness orientor. It is in the

highest level at 61.03% in the good range.

The results are based on the proposed scores from the respondents for the top

indicators for each orientor. Figure 9 shows total sustainability for Mahshahr based on the

orientors. Figure 10 shows the total sustainability based on the social, economic, and

environmental subsystems.

The air quality management and anxiety about the environment were ranked lowest at

47.35% and 47.9%, respectively, at the moderate range of sustainability. Figure 9 shows

that the coexistence and psychological need orientors place lowest for sustainability at

46.29% and 48.41%, respectively, for sustainability at the moderate range. The results also

show that the effectiveness orientor is rated highest for sustainability at 61.66% in the

good or high range of sustainability.

Figure 10 compares the subsystems based on social, economic, and environmental

dimensions. Thefigure shows that the social subsystem is in the lowest level of sustainability

at 53.85% and the moderate range of sustainability. The economic subsystem was in the

highest level of sustainability. Statistical analysis shows that there were no significant

differences between environmental and economic subsystem sustainability; however, both

showed significant differences for social subsystem sustainability.

Table 5. FSI of subsystems and orientors.

Orientor Social (FSI) Economic (FSI) Environmental (FSI)

Existence 1.71 1.72 1.76Effectiveness 2.03 1.96 1.97Freedom of action 1.80 1.81 1.84Security 1.78 1.83 1.76Adaptability 1.74 1.77 1.75Coexistence 1.44 1.52 1.53Psychological needs 1.53 1.61 1.55

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Table

6.

Topindicators

fororientors,subsystem

s,andtotalsubsystem

.

Social

indicators

Social

sustainability

Economical

indicators

Economical

sustainability

Environmentalindicators

Environmental

sustainability

Orientors

Orientor

sustainability

Poverty

reduction

53.05

Employment

53.35

Environmentdem

olition

54.46

Existence

53.62

Unem

ployment

63.06

Personal

income

60.88

Industrial

andurban

sewagetreatm

ent

61.03

Effectiveness

61.66

Literacyrate

55.89

Energyproductivity

56.19

Pollutionreduction

56.95

Freedom

ofaction

56.34

Government

financial

security

55.3

Water

pollution

56.58

Supply

drinkingwater

54.5

Security

55.46

Trainingprograms

54

Capital

flow

54.92

Wetlandschanging

54.31

Adaptability

54.41

Multi-language

population

44.49

Environmentalaccounting

47.05

Airqualityplan

47.35

Coexistence

46.29

Occupational

healthandsafety

47.57

Qualityofpublichealth

andcity

cleanliness

49.76

Anxiety

aboutenvironment

47.9

Psychological

needs

48.41

Social

subsystem

53.85

Economic

subsystem

54.87

Environmentalsubsystem

54.74

System

sustainability

54.49

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The computer model was used for social, economic, and environmental inputs. This

study was based on the indicators determined using the Delphi method. The use of fuzzy

sets and a fuzzy inference engine was well suited for handling the imprecision often

associated with social, economic, and environmental data. In this study, the method and

the model offered an alternative to quantitative sustainability. The 3D sustainability

envelope or surface was generated and used for the computation of the sustainability

values as a replacement for quantitative sustainability.

Using the results of the Delphi method, the sustainability indicators in city systems

were identified and can be successfully employed in other sectors. Sustainability

quantification is applicable when using a hierarchical structure and fuzzy logic systems.

Environmental systems with seven orientors comprised a suitable base for Multi Criteria

Decision Making. Using the inferences and rules of the environmental system, the final

Figure 6. Social sustainability indicators.

Figure 7. Economic sustainability indicators.

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Figure 8. Environmental sustainability indicators.

Figure 9. System sustainability for Mahshahr.

Figure 10. Subsystems sustainability for Mahshahr.

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environmental system sustainability model is based on social, economic, and

environmental sustainability.

The resulting model offers a direct relationship between the subsystems and system

sustainability. The outcome improved on existing qualitative methods and addressed the

gaps in the SDIs. It allowed ranking of sustainability variables based on unified measures.

The final sustainability output and the FSI were adopted for an aggregation of multiple

sustainability orientors or subsystems into a unified measure. When evaluating the

proposed indicators of subsystems and orientors, the subsystems of the same orientors

were achieved for different indicator values. A comparison of the proposed indicators of

subsystems and the orientors shows that the value for the consistent and equal indicators

were different.

4. Conclusion

The final result for the environmental system sustainability shows that Mahshahr

sustainability falls into the medium range at 54.49%. For subsystems, social sustainability

falls into the lowest range at 53.85% and the economic sustainability falls in the highest

rank at 54.87% in the moderate range. The results indicate that fuzzy logic is a constant

and consistent method for modeling SD in environmental or city systems. The model was

applied and tested based on environmental system sustainability and the rank of the system

and subsystem sustainability.

The output of sustainability for the three subsystems presented shows that there is no

significant differences between economic and environmental subsystems in terms of

output. Both of these two subsystems are significantly different from the social subsystem

output. The results of the FSIs show that the effectiveness orientor falls into the highest

FSI. Also, the orientor sustainability results show that, for the environmental and

economic subsystems, the FSI was not significantly different, but the social sub-system

orientors are significantly different for the FSI.

The proposed model is general in nature and provides more output information than

does the quantitative sustainability method. It is applicable to other cities, countries, and

regions for the same purpose. The practical implication of this research is to indicate ways

in which future related researches could be carried out. Policy-makers need a tool based on

scientific information to forecast the effects of future actions on sustainability and

establish better policies for SD. This model allows policy-makers to evaluate existence

sustainability and project future sustainability, by identifying indicators. Using SD

modeling, SD subsystems can be quantified and aggregated. Decision-making information

can be successfully derived and assist government policy-makers to establish stronger SD

for the coastal zones. Policy-makers can also employ these scales and indicators for

security and adaptation strategies, mitigation plans, and SD for overall management.

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