Modern Economy, 2011, 2, 427-437
doi:10.4236/me.2011.23048 Published Online July 2011 (http://www.SciRP.org/journal/me)
Sustainable Development and Socio-Economic Duality
Using Fuzzy System-A Case Study of Iran
Jalil Khodaparast Shirazi1*, Mehrzad Ebrahimi1, Mazda Moatari2
1Department of Economics & Management, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2Sciences and Research Branch, Islamic Azad University, Fars, Iran
Received December 20, 2010; revised March 16, 2011; accepted M a rch 28, 2011
Abstract
Homogeneous development and decreasing of inequalities is a logical link between local and national pro-
gram in macro level. Recognizing inequalities in the process of sustainable development play the major role
for programmers. In this study, regarding the complicated qualification and qualitative of socio-economic
indices, Iran provinces were ranked by fuzzy system during 2001-2006. In addition, duality in the concept of
gap and differences among provinces were determined using the selected indices. Results showed that al-
though country’s provinces became more homogeneous from the viewpoint of some of sustainable develop-
ment indices, the distance among several provinces is still high and there is no clear relation among prov-
inces during this study. This study shows that one development program cannot be effective for all areas of a
country with socio-economic duality.
Keywords: Sustainable Development, Duality, Economic Index, Health Index, Social Index, Fuzzy System
1. Introduction
Reaching development is a relative affair, but attempt to
reach this development is definite. How to reach the
economic development is a key question for economists
and programmers. After World War II, in all countries,
developmental strategies and various economic growth
models were propounded. To meet the development,
resources were destroyed and environmental pollutions
were increased. It is in a way that the most important
characteristics of development are its potentiality and
frequency.
To keep the potentiality of development sources; sus-
tainability should b e guaranteed. This is only possible by
coordinating socio-economic and environment. Holding
United Nation’s development conference in 1972 in
Sweden which was about environment and keeping its
equilibrium was a subject for supporters of environment
to try to make economic growth undesirable and confront
developed and developing countries. However, it was a
rescuer for developing countries but in a symposium, in
1991, the concept of sustainable development investi-
gated, and it was emphasized that sustainable develop-
ment does not necessarily protect the environment, but it
is a new concept of economic growth ; a growth which is
accompanied by justice and availability of facilities to all
people and all the generations without the destruction of
limited natural sources of the world.
A special definition of sustainable development was
presented in the Johannesburg Summit in 2002. The de-
finition is not in its tradition al concept, but known as any
process which results in changing human’s thoughts
preparing them to promote social responsibility [1].
However, how it is possible to increase social responsi-
bility of humans and make a continuous relation among
these three sides of triangle: society, economic, and en-
vironment. In fact, durable relation between economy of
society and environment is a big challenge.
The problem is more serious in developing countries
such as Iran regarding un-uniform social and economical
characteristics among areas. This study attempts to help
effective programming by highlighting the dualities am on g
provinces by using some of social-economic in- dices. In
the next step, suggestions for efficient planning have
been provided.
2. Sustainable Development Indices
Most of social and economic indices directly or indi-
rectly influence each other such as the relationship be-

J. K. SHIRAZI ET AL. 428
tween inflation and felon y which has been clearly shown
in combinational indices such as HDI. In this research,
the merged indices, called socio-economical indices,
were used in the study of sustainable development indi-
ces.
There are different and variable indices to measure the
development. At first, income per capita or GDP per ca-
pita were used to show the development, but th e re- sults
were not satisfactory. In fact, measuring the devel- op-
ment has led to more investigation to find compound
indices. On the other hand, compound indices had a
compound correlation with income per captia and GDP
per capita which resulted in preparing Human Develop-
ment Index (HDI) (1990) by the United Nations Devel-
opment Program (UNDP) [2].
Since sustainable development is the commonality of
society, different indices have been proposed for each
parts of sustainable development: economy and envi-
ronment in present and future. The important fact is that
in sustainable develop ment topic, there is a possibility of
recognition between development and sustainable de-
velopment and also recognition of sustainable develop-
ment from unsustainable development. UNDP stated 23
social-economic indices for sustainable deve lopment [3].
In this study, due to the limitation of preparing statis-
tics for Iranian provinces, 10 indices were selected. The-
se indices were categorized into 4 groups: economic, so-
cial, sanitation, and political. To increase the accuracy of
calculations, 3 indices close to sustainable indices, in-
cluding the portion of hospital’s bed to 1000 persons, the
portion of student to professor, and the inflation rate
were added to 7 the previous indices.
3. Structure of Fuzzy Model
Basically, although fuzzy systems describe unclear and
non-exact phenomenon, fuzzy theory is an exact theory
[4]. In fact, the real world is too complicated to present
an exact definition of it. Therefore, we should introduce
an approximate analytical and acceptable definition. In
scientific systems, main information comes from two
sources. One of the sources is specialists who define
their knowledge about the system with natural language.
The second source is the measurements and mathematic-
cal models, which are derived from the principles of
physics. Therefore, the main subject is to combine these
two sources of information to design systems. To do so,
the key question is how we can formulate knowledge of
human beings in a frame similar to mathematical models
[4].
In other words, converting human knowledge to ma-
thematical formulae is the main issue. Basically, fuzzy
system can address such conversion. Fuzzy rules have
been employed to model the deductive phenomena. This
will help us to establish structure when unclear and not
exact information is available. Then, this structure can be
used as a base for predicating, recognizing simul- tane-
ous effects, and further operations.
Therefore, one simple method is to add one fuzzyifier
to the input which converts variables with real amount to
a fuzzy set, and one defuzzifier which convert a fuzzy set
to a variable with real amount in output. After fuzzifica-
tion on input variables, fuzzy rules will be formed for
results. In this step, the logical relation between inputs
and outputs are shown which are described in the frame
of “if…then” rules. This operation is shown as follows
(Figure 1).
4. Pattern Results
Ranking the provinces is possible by determining fuzzy
system as a pattern and specification of sustainable de-
velopment indices. It should be mentioned that if 10 se-
lected indices apply once as input, several problems will
be emerged. First, the number of fuzzy rules will be in-
creased (for example if each of the inputs would have 3
membership functions, the number of fuzzy rules will be
310). Second, it will decrease the accuracy of calculation.
Third, the complexity of operation will be increased and
fourth, calculations will be increased.
To eliminate these problems, the 10 selected indices
divided to in 4 groups. This classification eliminated the
problems and on the other hand, economic, social, and
sanitation considered individually. In fact, separate
comparison of economic, social, health and political in-
dices provided the chance of new information and
stronger analysis.
The first group: economic index. Income per capita,
the ratio of exports to GDP, unemployment rate, and
inflation rate were categorized as economic indices. Data
were obtained from Centra l Ban k of Ir an (200 1-2 006) [5].
In fuzzy rules, it should be consid ered that an increase in
income per capita and export’s ratio to GDP of provinces
have positive influence on economy, but increasing un-
employment rate and inflation rate have negative influ-
ences. Figure 2 shows the four above mentioned indices
Figure 1. The main structure of fuzzifier and defuzzifier
systems.

J. K. SHIRAZI ET AL.429
Figure 2. Classification of indices to four groups (economic
s input of fuzzy system and economic index as output
up
w
ealth index. Two indices were
pl
Input (Economic indices) Output
,
social, health, and political indices) and running the fuzzy
model separately. Four Indices groups assumed as inputs of
fuzzy system and economic, social, health, political indexes
as outputs.
a
for final ranking o f provinces (Table 1 and Table 2).
The second group: social index. Indices in this gro
ere included as: the ratio of student to professor, the
ratio of student to teache r, and the edu cation al budget per
capita. Data were obtained from Central Bank of Iran
(2001-2006) [5]. In fu zzy rules, the decrease of student’s
proportion to professor, student’s proportion to teacher,
and increase of educational budget per capita have posi-
tive influences. Indices were used as input and social
index an ou tput for f inal ra nking of pr ovinces. ( Figure 2,
Table 3 and Ta ble 4).
The Third group: h
aced in this group: ratio of hospital beds per 1000 per-
sons and health budget per capita (Figure 2). Data were
obtained from Statistics Canter Iran (2001-2006) [6]. it
should be considered that the increase of hospital beds
per 1000 persons and health budget per capita have posi-
tive influences. Two indices were used as input and
Table 1. Fifty four logical relations of if-then which pre-
sents experts opinions by using four selected economic in-
dices. H (High), M (Medium), L (Low). The output of this
Table is used to rank the provinces based on economic in-
dex.
Income Unemployment Inflation Economic
per capita
The ratio
of exports
to GDPrate rate Index (EI)
L L L LL
L L L M L
L L L H L
L L M L L
L L M M L
L L M H L
L L H L L
L L H M L
L L H H L
L H L L M
L H L M M
L H L H M
L H M L M
L H M M M
L H M H L
L H H L L
L H H M L
L H H H L
M L L L M
M L L M M
M L L H M
M L M L M
M L M M M
M L M H M
M L H L L
M L H M L
M L H H L
M H L L H
M H L M M
M H L H M
M H M L H
M H M M M
M H M H L
M H H L M
M H H M L
M H H H L
H L L L H
H L L M H
H L L H H
H L M L M
H L M M M
H L M H M
H L H L L
H L H M L
H L H H L
H H L L H
H H L M H
H H L H H
H H M L H
H H M M H
H H M H M
H H H L M
H H H M L
H H H H L

J. K. SHIRAZI ET AL. 430
Table 2. Ranking of provinces from the viewpoint oonomic index. Output of this Table is one of the inputs for final rank-
Year
f ec
ing of provinces.
Province 2001 2002 2003 2004 2005 2006
Azarbayjan Shar ghi 0.1 0.11 0.15 0.31 0.4 0.45
Azarbayjan Gharbi 0.1 0.1 0.1 0.11 0.16 0.31
Ardebil 0.1 0.1 0.1 0.14 0.19 0.37
Isfahan 0.47 0.46 0.43 0.46 0.46 0.48
Ilam 0.1 0.1 0.12 0.44 0.49 0.1
Bushehr
ahal Bakhtiari
luchestan
in
stan
hah
yrahmad
n
an
an
0.12 0.26 0.5 0.53 0.5 0.52
Tehran 0.29 0.42 0.46 0.47 0.49 0.5
Chaharm0.1 0.1 0.1 0.11 0.17 0.35
Khorasan 0.1 0.12 0.17 0.18 0.33 0.43
Khuzestan0.46 0.18 0.42 0.47 0.52 0.45
Zanjan 0.46 0.42 0.32 0.18 0.37 0.45
Semnan 0.11 0.16 0.29 0.45 0.46 0.48
Sistan Ba0.1 0.1 0.1 0.1 0.1 0.1
Fars 0.1 0.11 0.15 0.27 0.39 0.45
Qazv0.12 0.15 0.23 0.39 0.45 0.48
Qom 0.1 0.1 0.13 0.23 0.36 0.43
Korde0.1 0.1 0.12 0.31 0.42 0.33
Kerman 0.46 0.45 0.48 0.2 0.41 0.2
Kermans0.1 0.1 0.36 0.18 0.44 0.12
Kohgiluye Bo0.5 0.5 0.51 0.5 0.46 0.4
Golestan 0.1 0.1 0.11 0.17 0.24 0.37
Gilan 0.16 0.1 0.14 0.19 0.35 0.43
Loresta0.1 0.1 0.1 0.1 0.15 0.1
Mazandar0.1 0.14 0.23 0.38 0.44 0.46
Markazi 0.35 0.35 0.41 0.46 0.47 0.49
Hormozg0.47 0.14 0.2 0.44 0.5 0.5
Hamedan 0.1 0.1 0.1 0.16 0.23 0.4
Yazd 0.1 0.15 0.22 0.36 0.45 0.47
Table 3. Twenty seven logical relation of if-then which presents experts opinions by using 3 selected social indices. H (High),
Input (Social indices) Output
M (Medium), L (Low). The output of this table (Social Index, SI) is used for final ranking of provinces socially.
The ratio of student to pro of student to teacher Educational budget per capita Social Index (SI) fessor The ratio
L L L M
L L M H
L L H H
L M L M
L M M M
L M H H
L H L L
L H M L
L H H M
M L L L
M L M M
M L H H
M M L M
M M M M
M M H M
M H L L
M H M L
M H H M
H L L L
H L M M
H L H M
H M L L
H M M M
H M H M
H H L L
H H M L
H H H L

J. K. SHIRAZI ET AL.431
Table 4. Ranking of provinces from the viewpoint of social index. Output of this table (social index) is of the inputs for
Year
one
final ranking of provinces.
Province 2001 2002 2003 2004 2005 2006
Azarbayjan Shargh 0.5 0. 5 0.48 0.47 0.52 0.56
Azarbayja Gharbi 0.45 0.5 0.5 0.27 0.5 0.53
Ardebil 0.33 0.44 0.48 0.47 0.5 0.52
Isfahan 0.5 0.49 0.47 0.43 0.54 0.6
Ilam 0.42 0.48 0.44 0.24 0.84 0.87
Bushehr
ahal Bakhtiari
luchestan
in
stan
hah
yrahmad
n
an
an
0.15 0.19 0.39 0.27 0.52 0.54
Tehran 0.19 0.4 0.49 0.5 0.5 0.51
Chaharm0.49 0.17 0.48 0.42 0.65 0.87
Khorasan 0.5 0.49 0.46 0.36 0.43 0.64
Khuzestan0.12 0.12 0.14 0.19 0.5 0.58
Zanjan 0.33 0.27 0.4 0.48 0.51 0.75
Semnan 0.48 0.47 0.4 0.46 0.57 0.7
Sistan Ba0.12 0.12 0.12 0.12 0.16 0.33
Fars 0.48 0.48 0.5 0.49 0.53 0.58
Qazv0.44 0.45 0.5 0.5 0.51 0.54
Qom 0.47 0.5 0.5 0.46 0.54 0.56
Korde0.3 0.22 0.47 0.42 0.52 0.64
Kerman 0.5 0.49 0.47 0.45 0.53 0.69
Kermans0.44 0.5 0.5 0.48 0.54 0.85
Kohgiluye Bo0.2 0.12 0.12 0.45 0.6 0.64
Golestan 0.49 0.46 0.44 0.47 0.51 0.55
Gilan 0.29 0.22 0.19 0.33 0.57 0.81
Loresta0.27 0.44 0.5 0.45 0.5 0.51
Mazandar0.24 0.19 0.15 0.17 0.52 0.86
Markazi 0.49 0.5 0.49 0.5 0.52 0.56
Hormozg0.12 0.12 0.12 0.12 0.42 0.5
Hamedan 0.5 0.5 0.5 0.49 0.53 0.7
Yazd 0.26 0.2 0.28 0.19 0.54 0.56
ealth index as output. (Figure 2, Table 5 and Table 6).
th
After ranking 10 selected indices to economic, social,
government’s interference which 2 fuzzy set of high and
selected health indices by ug experts opinions and in-
hThe Fourth group: political index. In this group, only
e ratio of budget to GDP of provinces which has been
considered as an interference index of government was
considered (Figure 2). Data was obtained from The
Management and Planning Organization of Iran (MPO)
[7]. As a result, there was just two fuzzy sets (high and
low) for the political index (Table 7). In making fuzzy
rules, the increase of government’s interference has
negative influence on social and economic indices. Since
there was only one index here, there was no need to
make separate fuzzy system for political index (Table 7).
5. Fuzzy System Phases
health and political, at first 3 fuzzy systems were carried
out for 3 groups (economic, social, and health) by Matlab
software. Then, for each inputs, three fuzzy sets, includ-
ing high, medium, and low were allocated (Table 1, Ta-
ble 3, Table 5 and Table 7). There was 2 exceptions,
including: the ratio of export to GDP and the index of
low were considered (Table 1 and Table 7). For eco-
nomic group, 54 rule bases were generated, while, 27, 9,
Table 5. Nine logical relations of if-then which presents 2
sin
formation. H (High), M (Medium), L (Low). The output of
this Table (Health Index, HI) is used to rank the provinces
from the view point of health index.
Input (Health indices) Output
The ratio of hospital beds
per 100Health budget per pita Health Index 0 person ca(HI)
L L L
L M H
L H H
M L L
M M H
M H H
H L L
H M H
H H H

J. K. SHIRAZI ET AL. 432
Table 6. Ranking of provinces frpoint of health inde Output of this Table is one of tnputs for finanking
Year
om the viewx. he il ra
of provinces.
Province 2001 2002 2003 2004 2005 2006
Azarbayjan Sharghi 0.26 0.26 0.26 0.26 0.26 0.26
Azarbayjan Gharbi 0.26 0.26 0.26 0.26 0.27 0.27
Ardebil 0.74 0.74 0.74 0.74 0.26 0.26
Isfahan 0.34 0.36 0.37 0.39 0.26 0.29
Ilam 0.74 0.74 0.74 0.74 0.74 0.74
Bushehr
ahal Bakhtiari
luchestan
in
stan
hah
yrahmad
n
an
an
0.74 0.74 0.74 0.74 0.74 0.74
Tehran 0.33 0.33 0.34 0.34 0.26 0.26
Chaharm0.74 0.74 0.74 0.74 0.72 0.74
Khorasan 0.74 0.74 0.74 0.74 0.28 0.72
Khuzestan0.26 0.26 0.26 0.26 0.26 0.28
Zanjan 0.74 0.74 0.74 0.74 0.26 0.26
Semnan 0.74 0.74 0.74 0.74 0.26 0.38
Sistan Ba0.74 0.74 0.74 0.74 0.26 0.73
Fars 0.68 0.7 0.71 0.72 0.26 0.59
Qazv0.7 0.71 0.71 0.72 0.26 0.34
Qom 0.36 0.37 0.39 0.4 0.57 0.74
Korde0.74 0.74 0.74 0.74 0.74 0.39
Kerman 0.72 0.72 0.72 0.73 0.74 0.74
Kermans0.26 0.26 0.26 0.26 0.74 0.74
Kohgiluye Bo0.74 0.74 0.74 0.74 0.74 0.74
Golestan 0.74 0.74 0.74 0.74 0.26 0.73
Gilan 0.66 0.68 0.7 0.71 0.26 0.66
Loresta0.74 0.74 0.74 0.74 0.26 0.66
Mazandar0.26 0.26 0.26 0.26 0.27 0.67
Markazi 0.66 0.68 0.69 0.71 0.72 0.64
Hormozg0.74 0.74 0.74 0.74 0.71 0.69
Hamedan 0.74 0.74 0.74 0.74 0.27 0.63
Yazd 0.74 0.74 0.74 0.74 0.65 0.74
wo logical relations of if-then ofratio of
udget to province’s GDP. H (High) L (Low). Its two out-
Output
Table 7. T
b the ,
puts were used as a political index and as an input for final
ranking of provinces.
Input
The ratio of budget to province’s GDP Index (PI) Political
L L
H H
n 2 rule bases were obtained for and po-
and Table 7).
adsocial health,
litical groups respectively (Table 1, Table 3, Table 5,
and Table 7). Their outputs made economic, social,
health, and political indices (Table 1, Table 3, Table 5,
The obtained results were employed as inputs of the
second phase fuzzy system for the last ranking of prov-
inces which totally produced 36 rule bases (Table 8). In
the next phase, the generated output was a number be
tween zero and one, providing a practical quantity to
compare provinces. The fuzzy system made 3 outputs
for economic group, 2 outputs for social group, 2 for
health, and 2 for political g roup which are summarized in
Table 9. The result of these outputs were 36 cases indi-
cating 36 relation of “if…then” between inputs and out-
puts of system. These rules were obtained by opinions
and experiences of experts (Table 9). By using MAT-
LAB software and entering information from 2001 to

J. K. SHIRAZI ET AL.433
rt’sTable 8. shows 36 logical relation of if- then which uses expe opinions and information and uses socio, economic, health
and political indices. P (positive effect), N (negative effect). The output of this table is used for final ranking of provinces.
Inputs Outputs
P P P N
Rule-Base EI SI HI PI TI
1 V V V L L L L H
2 L L L L V L
V
V
V
V
V V
3 L L H H V V L
4 L L H L V V L
5 L M L H V V L
6 L M L L V L
7 L M H H V L
8 L M H L V L
9 L H L H V L
10 L H L L L
11 L H H H L
12 L H H L L
13 M L L H L
14 M L L L L
15 M L H H M
16 M L H L M
17 M M L H M
18 M M L L M
19 M M H H M
20 M M H L M
21 M H L H M
22 M H L L M
23 M H H H H
24 M H H L H
25 H L L H H
26 H L L L H
27 H L H H H
28 H L H L H
29 H M L H H
30 H M L L V H
31 H M H H V H
32 H M H L V H
33 H H L H V H
34 H H L L V V H
35 H H H H V V V H
36 H H H L V V V H

J. K. SHIRAZI ET AL. 434
Table 9. Clascation of indices and ances of sed phase of fuzystem, H (Hi M (Medium),
Grou Fuzzy Sets Systeutput or ets
ge
sifientrconzy sgh),L (Low).
p Entrance First stage Base entrance to rank the
provinces second sta
Rule m’s oFuzzy s
Economic
1-Income pre capita
of exports to GDP
3-Unemployment rate Econ(EI)
2-The ratio
4-Inflation rate
H M L
H L
H M L
H M L
54
cases
omic Index H M L
Social 5-The ratio of student to professor teacher
dget per capita
H
6-The ratio of student to
7-Educational bu
M L
H M L
H M L
27
cases
Social Index
(SI) H M L
Health 8-The ratio of hospital beds per 1000 persons
9-Health budget per capita H M L H M L 9 cases Health Index (HI) H L
Political budget divided to cases Political Index H L
10-Government’s interference (
GDP) H L 2 (PI)
2006 of econo
om 2001 to 2e final ranking was obtained (Table
etween Provinces
ranainable development in-
dwith fstem f developioun-
try with un-rm areas. Ttained results revealed
es
mical, social, health, and political indices,
006 thfr
10).
6. Considerable Differences and Inequalities
b
In this study, provinces of Iran were compared and
ked in the view of some sust
ices u sy
unifo
zzyas a model ong c
he ob
high distance and difference among provinces of country
according to the above indices (Table 10). Similar to
overall indices, the differences were also observed in
each of social, economic and health groups (Table 2,
Table 4, and Table 6). This finding is in line with pre-
vious observations of Noorbakhsh [8] and Ebrahimi [9].
It should be mentioned that during the investigation
of Iran (2001-2006) by fuzzy system. Table 10. Ranking of different provinc
Total Ranking
Year
Province 2001 2002 2003 2004 2005 2006
Azarbayjan Shar ghi 0.23 0.23 0.0.44 0.5 0.5 24
Azarbi 0.19 0.16 17 0.2 0.41
Ardebil
Chahal Bakhtiari
an
hestan
stan
nshah
rahmad
an
i
n
bayjan Ghar0.18 0.
0.23 0.24 0.24 0.25 0.17 0.49
Isfahan 0.5 0.5 0.5 0.5 0.5 0.5
Ilam 0.24 0.24 0.24 0.49 0.62 0.37
Bushehr 0.13 0.25 0.5 0.49 0.5 0.5
Tehran 0.34 0.5 0.5 0.5 0.5 0.5
harma0.24 0.13 0.24 0.24 0.26 0.6
Khorasan0.24 0.24 0.26 0.26 0.45 0.51
Khuzest0.38 0.09 0.38 0.39 0.5 0.5
Zanjan 0.5 0.49 0.46 0.26 0.48 0.51
Samnan 0.24 0.25 0.42 0.5 0.5 0.52
Sistan Baluc0.12 0.12 0.12 0.12 0.05 0.23
Fars 0.23 0.24 0.25 0.33 0.49 0.5
Qazvin 0.25 0.26 0.29 0.5 0.5 0.5
Qom 0.22 0.22 0.24 0.27 0.48 0.5
Korde0.21 0.15 0.24 0.45 0.5 0.46
Kerman 0.5 0.5 0.5 0.26 0.5 0.31
Kerma0.13 0.13 0.48 0.18 0.5 0.37
Kohgiluye Boy0.49 0.4 9 0.49 0.5 0.5 0.51
Golestan 0.24 0.24 0.24 0.26 0.24 0.49
Gilan 0.22 0.14 0.14 0.25 0.47 0.61
Lorestan 0.18 0.24 0.24 0.24 0.15 0.23
Mazandar0.16 0.1 0.23 0.38 0.5 0.61
Markaz0.48 0.48 0.5 0.5 0.5 0.5
Hormozga0.49 0.13 0.15 0.49 0.5 0.5
Hamedan 0.24 0.24 0.24 0.25 0.23 0.53
Yazd 0.17 0.15 0.24 0.47 0.5 0.5

J. K. SHIRAZI ET AL.435
es are
Table 11. Duality among provinces, by using absolute value of average deviation, in a way that whenever duality increases, (in negative or positive side) provinc at the beginning of the table and whenever duality decre ase s, provinces are at the end of the table.

J. K. SHIRAZI ET AL. 436
perio2001-2006), these differences decreased and
provinces get closer to the average (Table 10). In other
words, convergence increased among provinces. How-
ever,paring provinces and their rankings revealed
considerable differences and inequalities among prov-
inceserefore, continuous further attempt and investi-
gatio decrease these ineq ualities and move forward to
sustae development in all provinces is highly re-
quiream and Gilan during 2005 and 2006 and Isfah an,
durin001 to 2004, were the highest developed prov-
inces contrast, Sistan Baluchistan during 2001, 2003,
2004d 2005; Khuzestan during 2002 and Lorestan
durin06 were the lowest provinces (Table 10).
7. Duality
Duality is a valuable criterion for evaluating un-uniform
movnt of resources in the field of development [10].
In fay can discover the gap among provinces,
base selected indices. In this work, duality set of
diffe conditions, some of which are desirable and
someon-desirable were considered. If we consider
desirnditions as an average of sustainable devel-
opmices, in order to show the duality among
provsolute value of average deviation, is a reli-
able
We duality increases, (negative or positive
side)ces are at the beginning of the table, and
whenuality decreases, provinces are at the end of
the t1). During the years of investigation
(200), Isfahan and Sistan Baluchestan had the
mosty (Table 11). Because of more movement of
resources into Isfahan, its distance from the average has
beensed in positive side. In contrast, in Sistan
Baluderation movement of resources has
occus result, its distance from average has been
incre negative side. (Tab le 11 ). Figure 3 presents
the dces among 6 provinces which have the high-
est aest ranking among 28 provinces of country
d (
com
. Th
n to
inabl
d. Il
g 2
. In
an
g 20
eme
ct, dualit
d on
rent
are n
able co
ent ind
inces, ab
index.
henever th
provin
ever d
able (Table 1
1-2006
dualit
increa
chestan consi
rred. A
ased in
ifferen
nd low
0
1
2
3
4
5
6
7
2001
0.
0.
0.
0.
0.
0.
0.
2002 20032004 2005 2006
year
Ran k ing
Isfah an
K h uzestan
Il am
Gilan
Lorestan
Sistan & Baluchestan
Figu e differences among 6 provinces which have the
ranking among 28 provinces of country
urinof under investigation.
during the period of under investigation. As it can be
inferred from Table 11, during the period of the study,
the distance among the provinces has been decreased in
relation to mean (overall average ) become more homo-
geneous. However, the absolute distance between prov-
inces has been increased.
8. Conclusions
The results showed that during 6 years of investigation,
some of the provinces were always at the end of the
ranking (Table 11). We suggest that programmers and
politicians should pay more attention to these provinces.
An interesting phenomenon was observed that changes
in ranking of most of the provinces were not constant,
and this is an indicator of lack of coordination and much
variation in the programs of government (Table 10). It is
suggested that programs should be compiled with accu-
rate study and according to the facility of each province.
This study showed that in developing countries such
as Iran, one development program cannot be effective for
all areas of a country with socio-economic duality. In
addition, this work showed that fuzzy system can effi-
ciently be used in clustering of different un-uniform parts
of country.
We suggest that in future works, in addition to fuzzy
models, novel data mining methods such as decision tree
and neural network algorithms can be considered to ap-
ply in development programming. In fact, due to com-
plicated and un-uniform nature of sustainable develop-
ment in developing countries, data mining has the poten-
tial of discovering previously unknown and potentially
interesting patterns in large datasets based on socio-
economic indices [11]. In particular, feature selection
(feature weighting) can dissect different socio- economic
indices from each other clarifying the importance of in-
dices by adding value to them [12]. As a result, more
precise reliable developing program is probable.
9. Acknowledgements
We would like to thank Azad University for supporting
this research. Also, we thank Dr. Esmaeil Ebrahimie
(Shiraz University) for revising the manuscript and his
valuable comments.
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