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EurasianAcademyofSciencesSocialSciencesJournal
2016Volume:8S:135‐148
PublishedOnlineMarch2016(http://socialsciences.eurasianacademy.org)
http://doi.org/10.17740/eas.soc.2016.V8‐07
COMPARISON OF CLEAN GROWTH OF COUNTRIES IN
TERMS OF SUSTAINABLE DEVELOPMENT
Suna Özyüksel*, Ünal H. Özden**, Özlem Deniz Başar ***
* İstanbul Ticaret Üniversitesi, **İstanbul Ticaret Üniversitesi, ***İstanbul Ticaret
Üniversitesi
E-mail: sozyuksel@ticaret.edu.tr, uozden@ticaret.edu.tr
Copyright © 2016 Suna Özyüksel, Ünal H. Özden, Özlem Deniz Başar. This is an open access
article distributed under the Eurasian Academy of Sciences License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The irreversible type of consumption of the natural resources after Industrial Revolution and
the rapid growth after World War 2 have had negative effects on environment; and the counter-
relation between development and environment was clearly revealed. Today, one of the most
primary problems that prevent the success of sustainable development is the climate change.
Excessive climate events directly affect the socio-economic factors; and they have a negative
impact on the productivity of labor force, GDP and sustainable development. The aim of this
study is to measure and analyze the relative total activities in terms of greenhouse gas emission
during the countries' economic activities; which is one of the most important factors for the
realization of climate events. This study begins by explaining the concepts used in one of the
methods used in measuring the efficiency of countries, data enveloping analysis (DEA); then
detailed information is provided about the CCR models used in DEA. Finally, the efficiency of
top 50 economies in the world in terms of greenhouse gas emissions are ranked by using input
oriented CCR models.
Keywords: climate change; data enveloping analysis; economic system; greenhouse gas
emission; sustainable development.
JEL-Clasification:
ÜLKELERİN SÜRDÜRÜLEBİLİR KALKINMA AÇISINDAN TEMİZ
BÜYÜMELERİNİN KARŞILAŞTIRILMASI
ÖZET
Sanayi Devrimi ile birlikte doğal kaynakların geri dönüşü mümkün olmayan bir şekilde
tüketilmeye başlanması ve 2. Dünya Savaşı sonrasındaki hızlı büyümenin çevre üzerinde
yarattığı olumsuz etkiler, kalkınma ile çevre arasındaki o güne kadar görülen ters yönlü ilişkiyi
açıkca göstermiştir. Günümüzde sürdürülebilir kalkınmanın başarılmasının karşısındaki en
136
Comparison of Clean Growth of Countries in Terms of Sustainable Development
öncelikli sorun, iklim değişikliğidir. Aşırı iklim olayları, sosyo-ekonomik faktörleri doğrudan
etkilemekte, işgücünün verimliliğini, üretimi, GSYH’yı ve sürdürülebilir kalkınmayı olumsuz
etkilemektedir. Bu çalışmanın amacı, ülkelerin ekonomik faaliyette bulunurken iklim
olaylarının gerçekleşmesinde en önemli faktörlerden olan sera salınımı açısından görece toplam
etkinliklerinin ölçülerek analiz edilmesidir. Çalışmada ilk olarak, sistemlerin etkinliklerinin
ölçülmesinde kullanılan yöntemlerden biri olan Veri Zarflama Analizi’nde (VZA) kullanılan
kavramlar açıklanmış ikinci olarak VZA’da kullanılan CCR modelleri ile ilgili ayrıntılı bilgi
verilmiştir. Son olarak da en büyük ekonomiye sahip 50 ülkenin ekonomik faaliyette
bulunurken sera gazı salınımı açısından ne kadar etkin oldukları girdi yönelimli CCR modelleri
kullanılarak hesaplanıp incelenip sıralanmıştır.
Anahtar Kelimeler: İklim Değişikliği, Veri Zarflama Analizi, Ekonomik Sistem, Sera Gazı
Salınımı, Sürdürülebilir Kalkınma
1. Introduction
Since 1970s, as the developing technology and development has accelerated; the question raised was
what to do in order to pass the natural resources to the next generations; and the concept of sustainable
development started to be argued for the first time. First definition of sustainable development was made
in Brundtland Report published by UN World Environment and Development Commission (Brundtland
Commission) in 1987 as "to meet the current requirements without compromising from the ability of
next generations to meet their own requirements in the future".
One of the primary problems that prevent successful sustainable development is the climate change. As
it is known, as a result of greenhouse effect created by man-made gases' release into the atmosphere;
there is an increase in world atmosphere and in oceans, which is named as global warming. The
greenhouse effect mentioned in here stems from an increase of the accumulation of carbon dioxide,
methane and diasod monoxide gases in the atmosphere. Before the Industrial Revolution, the carbon
dioxide accumulation in atmosphere was approximately 280 parts per million (ppm); and in 2012, this
has reached to 390,5 ppm. It is assumed that this amount will be around 500 ppm in 2050 (Erim, 2005:
2). Excessive situations about the climate prevent the achievement process of sustainable development.
Excessive climate situations directly affect the socio-economic factors. For instance flood will pollute
the water resources and destroy the agricultural products; and it causes poor nutrition and illnesses. As
a result; climate changes have negative effects on the productivity of labor force, on GDP and on
sustainable development.
Several international meetings and protocols were arranged in order to emphasize the negative effects
of climate change and to struggle with it. As a result of First Evaluation Report of Intergovernmental
Panel on Climate Change (IPCC) (Houghton et al., 1990); 1992 United Nations Framework Agreement
on Climate Change (UNFACC) was agreed on in Rio De Janeiro and came into force in 1994 in order
to set the ground for a global reaction against the problem of climate change. 194 countries have signed
the agreement. Although the agreement has no legal binding; it has encouraged the UN member
countries to reduce the greenhouse gas emissions, to collaborate on research and technology and to
protect the greenhouse gas reserves. Following this agreement, the infrastructure of Kyoto Protocol was
prepared through annual "Conferences of the Parties- COP" and the protocol was signed in 1997.
Kyoto Protocol (1997) is a legally binding document of UNFACC in terms of reducing or limiting the
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Eurasian Academy of Sciences Social Sciences Journal 2016 Volume:8
137
greenhouse gas emissions. With this feature, Kyoto Protocol is the single framework document in order
to provide struggle on global warming and climate change (Özyüksel and Oksay, 2014:95). Countries
that have signed this Protocol have accepted to reduce the emission of gases that cause greenhouse effect
and to increase their rights by emission trade, as indicated in the annex of Protocol. A total of 169
countries and state-bound organizations have signed the agreement since December 2006. Some of the
countries which have not signed the agreement are; developed countries such as USA and Australia, and
developing countries such as Turkey (Turkey has signed Kyoto Protocol in February 2009 and by the
decision of Parliament on condition that it is included to the countries listed in Protocol, Annex-2 and
without any reduction in carbon emission). Although the required number of signatures is reached for
its validation; the non-signature by USA, that produces the highest amount of greenhouse gas emissions
with 23-24 %, has made the applicability of Kyoto Protocol impossible. Countries with high emission
rates such as USA and China have joined the Protocol in 2014. USA and China have been hesitant about
stopping climate change and they are responsible for 45 % of carbon emissions worldwide. And, they
have undertaken for the first time that they will reduce greenhouse gas emissions, providing a due date.
USA has undertaken that greenhouse gas emissions will be reduced by 26-28 % in 2025 when compared
with 2005 figures; and China has declared that the emissions will be reduced from 2030 onwards.
Countries which signed the protocol have accepted that the historical and current global greenhouse gas
emission was mainly done by developed countries; that the gas emissions per person in developing
countries is still very low and that the global emissions of developing countries will increase according
to social and developmental needs.
Annex-A of the Protocol includes the names of greenhouses gases caused by human activities and
equivalent to carbon dioxide. These gases are Carbon dioxide (CO2), Methane (CH4), Nitrous oxide
(N2O), Hydro fluorocarbons (HFCs), Per fluorocarbons (PFCs) and Sulphure hexafluoride (SF6). In
Annex-1 at Protocol Annex-B, emissions limitations and decrease obligations were indicated. A
common goal of reducing the greenhouse gas emissions by 5 % when compared with 1990 data was set
for all countries in the Protocol. Net changes in the greenhouse gas emissions as a result of utilization
of field and deforestation since 1990 with direct human effect were indicated in the protocol. Along
with this, protocol has made it possible for the countries to purchase carbon credits from other countries
(to make emissions trade) in order to reach their greenhouse gas emissions targets.
The aim of this study is to analyze and determine those countries among the top 50 economies in the
world in terms of the ones that pollute the nature least (by doing the least amount of greenhouse gas
emission), thereby producing in the most efficient way. As there are multiple input and output variables
in the analysis, input oriented CCR models in Data Enveloping Analysis is used in order to calculate the
relative total efficiencies of countries. In addition to this, the target values and potential enhancement
rates that need to be realized in order for the non-efficient countries to be efficient.
2. Methodology
Each system has its unique goals. These goals are generally expressed by performance indicators such
as high productivity, efficiency, profit maximization, minimization of costs, served satisfactions, growth
and respectability (Barutçugil, 2002:13). One of the methods used in measuring the efficiency of
systems is the data enveloping analysis. In data enveloping analysis, how efficient and productive the
system resources (inputs) are used while producing goods and services (outputs) will be determined.
Data envelopment analysis (DEA) was first developed by Charnes, Cooper and Rhodes (1978) with the
aim of measuring the relative efficiency of systems that produce similar goods or services and named
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Comparison of Clean Growth of Countries in Terms of Sustainable Development
as Unit of Decision-Making (UDM). This method is a Linear Programming (LP) based approach that
provides the chance to measure the relative total factor efficiency of UDMs in cases where there are too
many input and output variables with different measuring units and they cannot be downgraded to a
common scale (Aydemir, 2002:45). Efficiency measurement will be done under the assumption that
production function is known (production limit, also known as efficiency limit) and the efficiency of
systems are relatively measured by comparing with production limit (Yolalan, 1993:65). The efficiency
measures used in DEA are "total", "technical" and "scale" efficiency measures. By using the input
compound about the system in the most appropriate way, generating the most possible outputs is named
as "technical efficiency"; whereas the efficiency calculated by multiplying the technical efficiency and
scale efficiency is named as "total efficiency" (Cıngı and Armagan, 2000:3).
DEA compares each UDM only with the best UDMs. UDMs determined as the best ones establish the
efficiency limit; and the efficiency of any UDM is measured according to this limit. Method evaluates
the best UDMs above the efficiency limit as relative efficient and these units are stated as the reference
cluster. Other UDMs which are not above the efficiency limit are the units that are relatively not
efficient. DEA is a method that guides the managers and decision-makers about what to do for the
enhancement of relative inefficient decision making units' efficiencies (Reisman, 2004:3-4).
There are several models used in DEA that assume each system will choose the weight of inputs and
outputs in a way that will multiply its own efficiency grade most. The way to determine which way to
be used in general depends on the scope of research and the assumptions to be used. If it is assumed that
UDMs have the fixed asset according to the scale and is the total efficiencies of the units are needed to
be determined; CCR or unidirectional models can be used. If the variable input assumption is valid for
UDMs according to scale and if only the technical efficiencies of the units is to be calculated; BCC or
additive models would be sufficient to utilize.
Also, CCR and BCC models used in DEA can be installed in two different ways as input-oriented and
output-oriented. If the control on inputs is low (or absent) an output-oriented model; and if the control
on outputs is low (or absent) an input-oriented model should be established. In input oriented models,
least amount of input should be used to generate the existing output and in output oriented models, it is
aimed to generate the most output with the existing input (Dinç and Haynes, 1999:470). The study will
measure the total efficiency of countries with CCR models. Whether the CCR models are input or output
oriented have no impact on efficiency values. In other words, both input and output oriented total
efficiency values provide the same result. Therefore, as the countries' controls on inputs are higher while
producing; input oriented CCR models are used.
If there is m amount of inputs and s amount of outputs in DEA that belong to n number of decision
making unit; and the i input amount for j decision making unit Xij ≥ 0 ; and for r output amount produced
by j decision making unit Y rj ≥ 0; then the input oriented fractional DEA model is shown as
(1)
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Eurasian Academy of Sciences Social Sciences Journal 2016 Volume:8
139
(2)
(3)
form. In the model:
Max : Maximization
ur : weight given to output r by k decision unit,
vi : weight given to input i by k decision unit,
Yrk : r output generated by k decision unit,
Xik : i input used by k decision unit,
Yrj : r output generated by j UDM,
Xij : i input used by j UDM,
ε : A positive and very small value
as it is. If there are n number of UDMs in DEA; then the models to be generated will be n number and
in order to measure the relative efficiency of each UDM; n number of best models need to be solved.
The goal function of models is the maximization of the ratio of total weighted outputs (virtual outputs)
for k decision making unit to total weighted inputs (virtual inputs) (Cooper et al., 2004:8-21). Charnes
et al. have made the transformation of the fractional model defined above in order to solve it with linear
programming (LM) solution methods in 1962 and they have mentioned the model as
(4)
thereby developing the LP model. This model is also named as input-oriented CCR model and it
measures the total efficiency with the assumption of fixed income to the scale and provides the best
solution with the fractional model.
Dual model in data enveloping analysis requires less mathematical transactions than the primal model
to reach the best solutions; and it also provides important administrative information; therefore it is used
widely. The dual version of input-oriented primal CCR model in Model 4 is given in Model 5.
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Comparison of Clean Growth of Countries in Terms of Sustainable Development
(5)
If we assume that the dual model indicates (θk*) as the best model; in order to say that a decision making
unit in data enveloping analysis is relative total efficient;
i. θk*=1
ii. Si-=0 ve Sr+=0 (6)
conditions must be met together. Si- indicates the idle variables related with over-used inputs in order to
transform the inequalities in LP models to equalities; and Sr+ indicates the idle variables related with
deficient produced outputs. If the value of a goal function related with a UDM is less than 1 (θk*<1)
and/or the idle variables have different values than zero (Si≠0 and/or Sr≠0); then the result will be to say
that UDM is not relative total efficient. In the literature, fulfilling the first condition only is defined as
"weak" relative efficiency; and if the second condition where the idle variable values are equal with zero
along with the first condition is met, it is defined as "strong" relative efficiency (Cooper et al., 2007:43-
128).
To determine the possible input excessiveness and output lack of UDMs of which the relative total
efficiency is investigated and whether the propositions provided in Equation 6 have been realized
together or not; require the two-phase solution of Model 7 (Cooper et al., 2004:8-21).
(7)
At the first phase of the solution of this model, Model 5 will be solved and the best value of goal function
θk* will be reached. In the second phase; θk* value reached with the solution of Model 5 at first phase
will be put to its place in Model 7 and the values of dual variable will be found as λ*, Si-* and Sr+*.
When we assume that there is a relative inefficient decision making unit and the reference cluster of this
UDM is indicated with Rk ; the efficient UDMs at Rk will constitute an example for inefficient UDMs;
and the theoretical (reference) UDM represents a single point that inefficient UDM has to imitate. More
clearly, in order to become as efficient as the UDMs in reference cluster; the UDMs that are not relative
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efficient should aim for the input and output levels of theoretical UDM. In order to reach the targeted
input and output levels, the potential enhancements (PE) about the relative inefficient UDM's input and
outputs can be calculated as percentage by the following formula.
PE (%) =
Target - Obser ved
Observed .100
(8)
In order to make the relative inefficient UDM efficient; the value of the variable which had negative PE
percentage should be reduced as much as PE, and the positive value should be increased as much as PI.
If it is zero, then there is no need to make any kind of enhancement (Babacan et al., 2007:103).
3. Data
The aim of the countries in the world is to utilize the existing resources in order to provide the economic
welfare and to increase the life quality of their citizens. As most of the economic activities necessitate
utilization of energy, this increases the greenhouse gas emission. Therefore, in order to provide the
homogeneity of UDMs, only top 50 economy countries were included to the study. For the determined
countries; 1990, 2005 and 2010 were considered as set by Kyoto Protocol , implemented and when the
reports were crated. But, as the 1990 data for Belgium, Russia and Ukraine were missing; it was thought
that the efficiency results that rely on relativity would be arguable, these countries were excluded from
the scope of analysis in 1990.
It was not possible to use all input and output variables used in the literature. Because, as efficiency is
measured by DEA; using too many input and output variables would make it harder to separate relative
efficient and inefficient UDMs. The number of UDMs that could be analyzed in the study is 47 for 1990;
and 50 for 2005 and 2010. Total of input and output variable numbers is 6. In this analysis, 5 input and
1 output variables were used from which accurate data could be obtained and those that do not have
high correlation within themselves. The data and the descriptions about these variables were received
from the official website of World Bank. The descriptions about the variables used in research can be
found below.
Input variables
a) Energy Use per Person (EUPP): The definition of energy as used in here means the primary energy
used before transforming into other fuels. This value was reached by adding import and stock changes
to local production, and by excluding the exported fuel and the fuel provided to planes and ships that
conduct international transportation. The variable used in this study was generated by proportioning this
value with the population values of countries. Lozano and Gutierrez (2008) have determined the
economic growth and energy utilization as the factors with the most impact on carbon dioxide emission.
Altıntaş (2013), in the study on Turkey, has says that energy generation and investments are effective
on economic growth; but energy generation causes over pollution. Therefore, the EUPP variable was
used as an input variable in this study.
b) Non-Forest Area (%) (NFA): The naturally existing or planted trees which have the height of 5 mt.
or more consist the "forest". Fruit trees, agricultural forestry systems, parks and gardens are not within
this scope. As the variable used in this study is generated; the forest field rate that was rationed
according to the fields of the countries were subtracted from 100 % and the non-forest area percentage
for the related countries was obtained. According to the report published by Food and Agriculture
Organization (FAO) (2010); it has been stated that carbon emissions stem from deforestation is an
important issue that should be handled within the climate change policies. Therefore, deforestation or
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Comparison of Clean Growth of Countries in Terms of Sustainable Development
increasing the non-forest field will cause changes in carbon emissions.
c) Agricultural Area (%) (AA): The variable of agricultural field that is used in this study states the
percentage of cultivable and cultivated field, when the surface of the related countries is considered.
Agricultural lands are used as input variable as it is a factor that increases the greenhouse gas emission.
d) Methane Emission Per Person (MEPP): Methane emission is calculated by values obtained from
human activities such as agriculture and from industrial methane production. The variable used in this
study was obtained by rationing the methane emission value to the population of related countries.
e) Carbon dioxide Emission Per Person (CEPP): Carbon dioxide emission stems from the burning of
fossil fuel and from cement production. These are the carbon dioxide values that appear as a result of
burning of solid, fluid and gas fuel. The variable used in this study was obtained by dividing the carbon
dioxide values acquired for countries to the populations of related countries. Jebli and Youssef (2015)
have determined in their study that carbon dioxide emission has a positive and statistically significant
impact on GDP.
Output variable
f) Gross Domestic Product Per Capita (GDPPC): This value is obtained by dividing the GDP value to
the middle of the year population of the related country. The data used in this study were calculated as
Dollar in order to have a common meaning.
Within the framework of determined variables, CCR models were used in order to calculate the relative
total efficiencies of countries on clean growth. To do this, DEA Solver 3.0 package program, which is
an add-on to MS Excel was used.
4. Results
Kyoto Protocol has set a common goal that presumes the countries to reduce their greenhouse gas
emissions 5 % less than 1990 level. In order to achieve this, member countries should make these
arrangements and also prevent the GDP and sustainable development to be negatively affected from
this. In short, the most important goal of the countries is to increase their production volume and to keep
their greenhouse gas emissions at a minimum. The aim of this study is to measure and analyze the
relative total activities in terms of greenhouse gas emission during the countries' economic activities; in
other words, their amount of efficiency was determined by data enveloping analysis.
As a result of calculations done by CCR models, countries that are relatively total efficient are Brazil,
Canada, Finland, France, Italy, Japan, Norway, Sweden, Switzerland, United States of America and
England. Relative efficient countries are the same for 2005 and 2010. These are; Finland, Japan,
Norway, Singapore, Sweden and Switzerland. 6 countries (USA, Brazil, France, England, Italy, Canada)
were relative efficient in 1990; but they have lost their relative efficiency in 2005 and 2010, becoming
inefficient countries. On the other hand, one country (Singapore) was not relative effective in 1990; and
it became relative efficient in 2005 and 2010. Countries which are relative total inefficient are those
which have efficiency scores below Table 1.
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Table 1: Five Countries with Lowest Efficiency on Year Basis
1990 2005 2010
CountryValueCountryValueCountryValue
Romania0.081Ukraine0.038Ukraine0.046
Poland0.116Iran0.071Iran0.093
Hungary0.139China0.081China0.106
Venezuela0.143India0.098India0.106
South Africa0.161Indonesia0.101South Africa0.116
As it is seen on Table 1, the countries with the lowest efficiency value among those relative total
inefficient in 1990 are Romania, Poland, Hungary, Venezuela and South Africa, in consecutive order.
In 2005, these countries were Ukraine, Iran, China, India and Indonesia. In 2010, relative most
inefficient five countries are Ukraine, Iran, China, India and South Africa.
Greatest economy in the world, United States of America, was relative total efficient in 1990; in 2005,
it moved back to 13th rank in relative efficiency and 16th rank in 2010. Turkey was 31st in 1990, 27th
in 2005 and 29th in 2010; in terms of relative total efficiency ranking. China was 41st in 1990, and 48th
both in 2005 and 2010. Results about other countries are indicated in detail at Table 2.
When the reference clusters that indicate the countries which should be considered as an example by
relative inefficient countries are examined; Switzerland appears to be the country that should be
considered as a reference in 1990, 2005 and 2010. When it is examined regionally, countries with
developed democracies such as north European ones appear to be those that should be referred to.
Norway, Japan and Sweden are in 2005 reference cluster for USA and in 2010, Switzerland and Sweden
are in reference cluster for USA. In addition, at reference cluster for Turkey; there are Switzerland,
France and Italy in 1990, and Switzerland in 2005 and in 2010. In the reference cluster of China, there
is USA in 1990; and Switzerland in 2005 and 2010.
Table 2: Relative Total Efficiency Values of Countries, Ranking by Efficiency and Reference Clusters
Countries
(DMUs)
19902005 2010
Relative Total
Efficiency
Relative Total
Efficiency Ranking
Reference Cluster
Relative Total
Efficiency
Relative Total
Efficiency Ranking
Reference Cluster
Relative Total
Efficiency
Relative Total
Efficiency Ranking
Reference Cluster
1-USA11 0,607 13 28-37-25 0,5371626-25
2Germany0,9641220-28-1 0,612 12 26-28-37 0,5461426-28-25
3-Angola0,4762526-16 0,192 36 26 0,2783126
4-Argentina0,5542210-28-1 0,212 33 26 0,2673226
5-Australia0,691720-28-1 0,479 23 26-37 0,5491326-25
6-Austria0,7371626-27-28 0,789 926-28-37 0,726826-25
7-United Arab
Emirates0,79314370,452615-370,3892628-37-39
8-Bangladesh0,5951926-16 0,163 39 26 0,1694126
9-Belgium- - 0,537 18 26-28-37- 0,4872226-28-25
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Comparison of Clean Growth of Countries in Terms of Sustainable Development
25
10-Brazil11 0,261 31 26 0,3632726
11-Algeria0,3443226-27-28 0,203 35 26 0,1814026
12-China0,214110,081 48 26 0,1064826
13-Denmark0,811326-27 0,892 826 0,747726
14-Indonesia0,2913710-16 0,101 46 26 0,1514426
15-Finland11 1 1 11
16-France11 0,591 14 26-25 0,5161826-25
17-South Africa0,1614327-28 0,123 42 26 0,1164626
18-India0,3323410-1 0,098 47 26 0,1064726
19-Netherlands0,6211826-16-27-
280,5721626-28-370,5071926-25
20-England11 0,69 10 26 0,5381526
21-Iran0,2244027-28 0,071 49 26 0,0934926-37
22-Ireland0,562126-27 0,934 726 0,692926
23-Spain0,7911526-16-27 0,519 20 26 0,5052026
24-Israel0,442726-37 0,541 17 26-28-37 0,5781126-28-37
25-Sweden11 1 1 11
26-Switzerland11 1 1 11
27-Italy11 0,642 11 26 0,5631226
28-Japan11 1 1 11
29-Canada11 0,52126-37-250,5351728-37-39-
25
30-Qatar0,3872915-37 0,588 15 15-37 0,581037-25
31-Korea Rep.0,3982826-270,4742526-28-37-
250,4232428-25
32-Kuwait0,3273526-370,3412915-37-250,3123028-37-39-
25
33-Hungary0,1394526-16-27 0,258 32 26 0,2283526
34-Malaysia0,2483826-16-27-
280,1743726-370,1993826-25
35-Mexico0,3793020-28-1 0,324 30 26 0,2653326
36-Egypt0,1844226-28-37 0,112 44 26-37 0,1873926-37
37-Norway11 1 1 11
38-Peru0,3163626-16-27 0,35 28 26 0,3492826
39-Poland0,1164620-28 0,21 34 26 0,2123726
40-Portugal0,52426-28 0,474 24 26 0,4562326
41-Romania0,0814726-16-27 0,163 38 26 0,2123626
42-Russia- - 0,107 45 37-25 0,1644326-37-25
43-Singapore0,5912026-37 1 1 1139
44-Saudi Arabia0,2453926-27-28 0,16 40 26-28-37 0,1654226-25
45-Thailand0,3353310-28-1 0,113 43 26 0,1224526
46-Turkey0,3613126-16-27 0,364 27 26 0,3132926
47-Ukraine- - 0,038 50 26 0,0465026
48-Venezuela0,1434426-27-28 0,147 41 26-37 0,2593426-37-25
49-New
Zealand0,5342326-16-280,4812226-370,4222526-25
50-Greece0,4642626-27 0,521 19 26 0,4892126-25-25
Potential enhancement percentages for 1990, 2005 and 2010 of inefficient countries are provided in
Table 3. The total inefficiency of USA when compared with countries in its reference cluster (Sweden
and Switzerland) is rooted in percentage in agricultural area, methane emissions per person and carbon
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dioxide emissions Per Person; and deficiency in Gross Domestic Product per capita. As such, if USA
could decrease agricultural area rate by 39,5 %, methane emissions per person by 12,9 % and carbon
dioxide emission per person by 53,8 %; and if it increases the Gross Domestic Product per capita by
86,3 %; then it will become total efficient as Sweden and Switzerland in its reference cluster.
Table 3: Potential Enhancement (PE) Rates Related with Input and Output Variables of Relative Total
Inefficient Countries
Countries
EUPP
NFA
AA
MEPP
CEPP
GDPPC
YEARS
EUPP
NFA
AA
MEPP
CEPP
GDPPC
Countries
USA
0 0 0 0 0 0 1990 00000 0
Switzerland
0 0 -59 0 -18 64,8 2005 00000 0
0 0 -40 -13 -54 86,3 2010 00000 0
Germany
0 -1,4 0 0 -4,6 3,8 1990 00000 0
Italy
0 0 -28 0 -26 63,4 2005 0 -9,9 -31 -12 -38 55,9
0 0 -25 0 -30 83,3 2010 0 -14 -33 -14 -38 77,6
Angola
-57 -91 -93 0 0 110 1990 00000 0
Japan
0 -79 -87 -89 -22 421 2005 00000 0
0 -73 -83 -86 -35 260 2010 00000 0
Argentina
0 -84 -81 0 0 80,4 1990 00000 0
Canada
0 -61 -62 -87 -34 372 2005 -9 0 0 0 -42 100
0 -55 -59 -83 -36 275 2010 0 0 0 0 -17 87
Australia
0 -0,5 0 0 -22 44,9 1990 -47 0 0 -82 -54 159
Qatar
0 0 -63 -54 -50 109 2005 -58 0 0 -78 -79 70
0 0 -22 -80 -58 82,3 2010 -41 0 0 -79 -59 72,5
Austria
0 0 -24 0 -13 35,7 1990 0 0 -2,2 0 -4,9 151 Korea
Republic
0 0 -46 0 -13 26,7 2005 0 0 -37 0 0 111
0 0 -34 -18 -36 37,7 2010 -1 0 -31 0 -19 136
United
Arab
Emirates
-44 -1,4 0 -47 -67 26,1 1990 0 -31 0 -56 -71 206
Kuwait
-8,8 0 0 -1,5 -46 123 2005 -5,9 0 0 0 -45 194
0 -16 0 0 -42 157 2010 0 0 0 0 -50 220
Bangladesh
-32 -98 -99 0 0 68 1990 0 -20 -39 0 0 617
Hungary
-1,4 -96 -98 -95 0 514 2005 0 -30 -54 -33 -24 287
0 -95 -97 -94 -19 492 2010 0 -32 -51 -53 -25 339
Belgium
- - - - - - 1990 0 -11 0 0 0 304
Malaysia
0 0 -16 0 0 86,3 2005 0 0 -54 -19 -43 474
0 0 -13 0 -5,9 105 2010 0 0 -21 -57 -57 403
Brazil
0 0 0 0 0 0 1990 0 -71 -76 0 0 164
Mexico
0 -38 -61 -92 -1,1 284 2005 0 -54 -68 -71 -38 209
0 -27 -52 -89 -6,3 175 2010 0 -53 -67 -71 -40 278
Algeria
0 -78 0 0 -24 190 1990 0 -94 0 0 -2 444
Egypt
0 -81 -40 -87 -52 392 2005 0 -87 0 -37 -40 797
0 -78 -29 -84 -52 452 2010 0 -87 0 -39 -40 434
China
-38 -95 -95 0 -45 376 1990 00000 0
Norway
0 -66 -74 -75 -51 1000 2005 00000 0
0 -50 -62 -71 -55 842 2010 00000 0
Denmark
0 -16 -26 0 -27 23,4 1990 0 -77 -58 0 0 217
Peru
0 -21 -40 -55 -36 12,2 2005 0 -79 -68 -84 -41 186
0 -18 -36 -53 -38 33,9 2010 0 -71 -56 -81 -51 187
Indonesia -5,2 -58 -48 0 0 243 1990 0 -13 -17 0 -23 761 Poland
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Comparison of Clean Growth of Countries in Terms of Sustainable Development
0 -65 -68 -87 -16 886 2005 0 -31 -49 -75 -52 377
0 -62 -67 -82 -28 561 2010 0 -21 -37 -70 -53 373
Finland
0 0 0 0 0 0 1990 0 -37 -37 0 -10 100
Portugal
0 0 0 0 0 0 2005 0 -20 -34 -63 -35 111
0 0 0 0 0 0 2010 0 -26 -37 -64 -34 119
France
0 0 0 0 0 0 1990 0 -3,1 -17 0 0 1000
Romania
-1,4 0 -30 -36 0 69,3 2005 0 -50 -68 -72 -36 513
-0,6 0 -29 -40 0 93,7 2010 0 -50 -68 -75 -34 372
South
Africa
0 -42 -50 0 -26 521 1990 ----- -
Russia
0 -42 -63 -63 -48 715 2005 0 0 -77 -22 -39 835
0 -38 -60 -59 -54 764 2010 0 0 0 -53 -42 511
India
-2 -93 -93 0 0 201 1990 0 -40 0 -93 -63 69,2
Singapore
0 -88 -91 -82 -39 916 2005 00000 0
0 -84 -89 -78 -47 840 2010 00000 0
Holland
0 -5,7 0 0 0 61,2 1990 0 -20 0 0 -28 308 Saudi
Arabia
0 0 -24 0 -27 74,9 2005 0 0 -48 0 -42 524
0 0 -16 -20 -36 97,1 2010 0 0 -39 -37 -47 508
England
0 0 0 0 0 0 1990 0 -83 -82 0 0 198
Thailand
0 -17 -43 -34 -35 45 2005 0 -52 -57 -79 -38 783
0 -25 -49 -37 -40 86 2010 0 -42 -51 -79 -41 717
Iran
0 -77 -61 0 -13 347 1990 0 -69 -59 0 0 177
Turkey
0 -47 -8,2 -67 -41 1000 2005 0 -71 -75 -75 -43 175
0 -40 0 -61 -45 973 2010 0 -65 -67 -74 -48 219
Ireland
0 -23 -37 0 -22 78,4 1990 ----- -
Ukraine
0 -24 -40 -82 -48 7 2005 0 -28 -54 -62 -32 1000
0 -28 -47 -81 -47 44,4 2010 0 -29 -54 -63 -36 1000
Spain
0 -20 -28 0 0 26,4 1990 0 -11 0 0 -7,4 599
Venezuela
0 -0,8 -39 -25 -36 92,7 2005 0 0 -7,3 -69 -42 582
0 -11 -44 -34 -30 98 2010 0 0 0 -68 -44 287
Israel
0 -49 0 -68 -40 128 1990 -0,1 0 -17 0 0 87,2 New
Zealand
0 -51 0 0 -46 84,8 2005 0 0 -36 -78 -21 108
0 -50 0 0 -39 73,1 2010 0 0 -18 -87 -22 137
Sweden
0 0 0 0 0 0 1990 0 -38 -58 0 -39 116
Greece
0 0 0 0 0 0 2005 0 -23 -3,3 -30 -51 91,9
0 0 0 0 0 0 2010 0 -27 -56 -38 -53 105
Switzerland is in 2010 reference cluster for China. If China could decrease non-forest area rate by 50,20
%, agricultural area rate by 61,50 %, methane emissions per person by 70,80 % and carbon dioxide
emission per person by 55,10 %; and if it increases the Gross Domestic Product per capita by 814,5 %,
then it will become total efficient as Switzerland in its reference cluster.
On the other hand, Switzerland is in 2010 reference cluster for Turkey. If Turkey could decrease non-
forest area rate by 64,8 %, agricultural area rate by 67,3 %, methane emissions per person by 74,1 %
and carbon dioxide emission per person by 47,8 %; and if it increases the Gross Domestic Product per
capita by 219 %, then it will become total efficient as Switzerland in its reference cluster.
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Eurasian Academy of Sciences Social Sciences Journal 2016 Volume:8
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4. Conclusions
The most important reason of climate change today is the greenhouse gas emissions released in the
atmosphere that increase by the production increase. Therefore, the countries have to determine policies
that will minimize the emissions, abide by several protocols arranged and to realize "clean" growth.
This study was conducted in order to determine the level of clean growth for countries, and DEA method
was used. The variables of energy usage per person, non-forest area, agricultural area, methane emission
per person and carbon dioxide emission per person were taken as input; and the variable of gross
domestic product per capita was taken as output.
As a result of analysis; it was seen that countries such as Brazil, Canada, Finland, France, Italy, Japan,
Norway, Sweden, Switzerland, United States of America and England were the countries that have used
the input variables most appropriately, efficiently in 1990. In 2005 and 2010; Finland, Japan, Norway,
Singapore, Sweden and Switzerland were determined as relative efficient countries.
Greatest economy in the world, United States of America, was relative total efficient in 1990; in 2005,
it moved back to 13th rank in relative efficiency and 16th rank in 2010. Another great economy in the
world, China, is one of the countries with less relative efficiency in terms of 2005 and 2010
measurements. This indicates that greenhouse gas emissions in China have increased along with its
growth.
In total efficiency ranking; Turkey was at 31st place in 1990, 27th place in 2005 and 29th place in 2010.
Greenhouse gas emissions of Turkey have increased by 133,4 % since 1990.
From the data used by Bosetti and Frankel in their study dated 2014; it is seen that China has the first
place in greenhouse gas emissions according to 2010 data with 10702 mton CO2 . USA follows China
with 6802 mton CO2. Although Turkey does not emit the same amount of greenhouse gas as China or
USA; the per person emission amount (5,9 tons annually) is above the world average.
USA, China and Turkey will be able to achieve efficient and clean growth like the countries in their
reference clusters if they can make the necessary potential enhancements. When we examine other
countries, we see that countries with developed democracies such as the north European ones are being
referenced.
The results of this study indicate that western countries which have signed the Kyoto protocol are
generally and relatively clean growing ones; and the countries such as China and USA that have recently
agreed on Kyoto Protocol are inefficient countries. Considering the fact that western countries that have
caused the climate change problem with greenhouse gas emissions as a result of Industrial revolution,
have completed their development; but the level of succeeding clean growth only is not a sufficient
achievement. Also, when we consider that world foreign direct investment was around 1.47 trillion
dollars in 2014 and more than half of this investment is made to the developing countries; it will be
clearly seen that the developed countries did not fulfill all their growth within their land. Therefore, it
can be said that these countries have greater responsibilities than the developing ones in terms of
decreasing the effects of climate change and adaptation.
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Comparison of Clean Growth of Countries in Terms of Sustainable Development
REFERENCES
Altıntaş, H., 2013. Türkiye’de birincil enerji tüketimi, karbondioksit emisyonu ve ekonomik
büyüme ilişkisi: Eşbütünleşme ve nedensellik analizi. Eskişehir Osmangazi Üniversitesi İktisadi
ve İdari Bilimler Dergisi 8(1), 263-294.
Aydemir, Z., 2002. Bölgesel Rekabet Edebilirlik Kapsamında İllerin Kaynak Kullanım Göre
Verimlilikleri: Veri Zarflama Uygulaması, Devlet Planlama Teşkilatı Uzmanlık Tezi, Ankara.
Babacan, A., Kartal, M., Bircan, H., 2007. Cumhuriyet Üniversitesi'nin Etkinliğinin Kamu
Üniversiteleri ile Karşılaştırılması: Bir VZA Tekniği Uygulaması. C.U. İktisadi ve İdari
Bilimler Fakültesi Dergisi 8(2), 97-114.
Barutçugil, İ., 2002. Bilgi Yönetimi, Kariyer Yayıncılık, İstanbul.
Bosetti, V., Frankel, J., 2014. Scorecard Online Data.
http://www.hks.harvard.edu/fs/jfrankel/ScorecardOnlineDataBosettiFrankel2014.xls , Cited 20
July 2015.
Charnes, A., Cooper, W., Rhodes, E., 1978. Measuring the Efficiency of Decision Making
Units. European Journal of Operations Research 2, 429-444.
Cıngı, S., Tarım, A., 2000. Türk Banka Sisteminde Performans Ölçümü DEA-Malmquist. TFP
Endeksi Uygulaması. Türkiye Bankalar Birliği Araştırma Tebliğleri Serisi 1, 3-21.
Cooper, W-W., Seiford, L-M., Tone, K., 2007. Data Envelopment Analysis: A Comprehensive
Text whith Models, Application, References and DEA-Solver Software. 2nd edt. Springer
Science, New York.
Cooper, W-W., Seiford, L-M., Zhu, J. 2004. Data Envelopment Analysis: History, Models and
Interpretations. Handbook on Data Envelopment Analysis. Kluwer Academic Publisher, New
York.
Dinç, M., Haynes, K-E., 1999. Sources of Regional Inefficiency: An Integrated Shift-Share,
Data Envelopment Analysis and Input-Output Approach. The Annals of Regional Science 33,
469-489.
Erim, A., (2005) RIBA İklim Değişikliği Bilgi Notu.
http://www.mo.org.tr/UIKDocs/ribaiklimdegisikligi.pdf Cited 20 July 2015.
Food and Agriculture Organization (2010) Global Forest Resources Assessment 2010. Main
Report, Rome.
Houghton, J-T., Jenkins, G-J., 1990. Climate Change, The IPCC Scientific Assessment.
Ephraums, J.J. (ed.) Cambridge University Press, New York.
Jebli, M-B., Youssef, S-B., 2015. Economic growth, combustible renewables and waste
consumption, and CO2 emissions in North Africa. Environmental Science and Pollution
Research. DOI 10.1007/s11356-015-4792-0
Lozano, S., Gutiérrez, E., 2008. Non-parametric frontier approach to modelling the relationships
among population, GDP, energy consumption and CO2 emissions. Ecological Economics 66,
687–99.
Özyüksel, S., Oksay, G., 2014. İklim Değişikliği, Sürdürülebilir Kalkınma ve Sürdürülebilir
Sigortacılık. TSEV Yayınları, İstanbul.
Reisman, A., 2004. What Can OR/MS Academics Learn From and About Data Envelopment
Analysis? OH, USA : SSRN, OR/MS.
The World bank Database (2015) Agricultural data.
http://data.worldbank.org/indicator/AG.LND.AGRI.ZS. Cited 20 July 2015.
The World bank Database (2015) CO2 emissions data.
http://data.worldbank.org/indicator/EN.ATM.CO2E.KT. Cited 20 July 2015.
The World bank Database (2015) Energy use data.
http://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE. Cited 20 July 2015.
The World bank Database (2015) Forest area data.
http://data.worldbank.org/indicator/AG.LND.FRST.ZS. Cited 20 July 2015.
E
Eurasian Academy of Sciences Social Sciences Journal 2016 Volume:8
149
The World bank Database (2015) GDP per capita data.
http://data.worldbank.org/indicator/NY.GDP.PCAP.CD . Cited 20 July 2015.
The World bank Database (2015) Methane emissions data.
http://data.worldbank.org/indicator/EN.ATM.METH.KT.CE. Cited 20 July 2015.
UN Climate Change Newsroom (1992) United Nations Framework Convention on Climate
Change. https://unfccc.int/resource/docs/convkp/conveng.pdf. Cited 21 Aug 2013.
UN Climate Change Newsroom (1997) Kyoto Protocol to The United Nations Framework
Convention on Climate Change. http://unfccc.int/resource/docs/convkp/kpeng.pdf. Cited 21
Aug 2013.
UN Documents: Gathering a Body of Global Agreements Brundtland Report: http://www.un-
documents.net/our-common-future.pdf. Cited 21 Aug 2013.
Yolalan, R., 1993. İşletmeler Arası Göreli Etkinlik Ölçümü. MPM Yayınları, Ankara.
