an evaluation of turkey's energy dependency
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An Evaluation of Turkey's EnergyDependencyA. Sözen a , İ. Alp b & Ü. İskender c
a Faculty of Technology, Energy Systems Engineering Department ,Gazi University , Ankara , Turkeyb Faculty of Sciences, Statistics Department , Gazi University ,Ankara , Turkeyc Turkish Airlines , İstanbul , TurkeyPublished online: 24 Oct 2013.
To cite this article: A. Sözen , İ. Alp & Ü. İskender (2014) An Evaluation of Turkey's EnergyDependency, Energy Sources, Part B: Economics, Planning, and Policy, 9:4, 398-412, DOI:10.1080/15567249.2010.509080
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Energy Sources, Part B, 9:398–412, 2014Copyright © Taylor & Francis Group, LLCISSN: 1556-7249 print/1556-7257 onlineDOI: 10.1080/15567249.2010.509080
An Evaluation of Turkey’s Energy Dependency
A. Sözen,1 I. Alp,2 and and Ü. Iskender3
1Faculty of Technology, Energy Systems Engineering Department, Gazi University,
Ankara, Turkey2Faculty of Sciences, Statistics Department, Gazi University, Ankara, Turkey
3Turkish Airlines, Istanbul, Turkey
Energy dependency (ED) is the energy import level upon which an economy relies in order to meet its
energy needs. ED is calculated by dividing net imports by the sum of gross inland energy consumption
plus bunkers. This study aims to obtain performance scores to evaluate of Turkey’s ED based on basic
energy indicators (Model 1), sectoral energy consumption (Model 2), and primary energy production
as sources (Model 3) by using data envelopment analysis. The performance evaluation of 25 European
Union member states, Norway, Switzerland, Croatia, and Turkey, a candidate country, was performed
in order to draw up a good energy policy in the future for Turkey. Since the data on Malta and Cyprus
were incomplete, these two nations were excluded from the evaluation. The data set covers the years
between 1998 and 2006 and energy indicators used in the analysis were taken from Eurostat for all
countries. The Results of the analysis show that Turkey’s most important goal for the future is to
produce proper energy policies in order to decrease ED.
Keywords: data envelopment analysis, energy dependency, energy efficiency
1. INTRODUCTION
Energy is one of the most important factors affecting the economy, politics, and development ofcountries. Highly industrialized countries with high life standards are those that maintain a properenergy balance. Turkey has improved its economic situation and the need for energy has increasedaccordingly, which implies more energy consumption and more energy imports. The increase inthe consumption rate of energy is 8% per year. Turkey should revise its energy production planso as to meet the increased energy demand.
For proper energy planning, balance between energy input and output should be predictedsince the time period between setting up energy production systems and starting the productionis considerably high for the countries like Turkey that can never ignore the whole of economicstability.
Many studies in the literature are on Turkey’s energy consumption or prospects regardingTurkey’s sector-specific energy consumption in the upcoming years (Ediger and Tatlıdil, 2002;Hamzaçebi, 2007; Kılıç and Kaya, 2007, Say and Yücel, 2006; Sözen et al., 2007; Öztürk et al.,2005; Lise and Montfort, 2007; Altinay and Karagöl, 2004). Of all those studies, there have been
Address correspondence to Adnan Sözen, Gazi University, Faculty of Technology, Energy Systems EngineeringDepartment, 06500 Teknikokullar, Ankara, Turkey. E-mail: [email protected]
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AN EVALUATION OF TURKEY’S ENERGY DEPENDENCY 399
a few that analyzed the relationship between economic indicators and energy consumption (Sözenand Arcaklıoglu, 2007a; Lise and Montfort, 2007; Altinay and Karagöl, 2004). Currently, theratio of energy dependency (ED) is considered as a more important parameter for Turkey than theenergy consumption forecasts are. Thanks to this study, it is possible to determine the decisionmaking units (DMUs D countries in this study) which establish the most optimal relation betweenthe inputs and outputs of energy indicators (i.e., the efficient countries), and to find out in whichenergy indicators there were redundancies or shortfalls regarding the inefficient countries. In thisway, it will be possible to shape the energy policies in an accurate manner in accordance with theresults obtained. There is a multi-dimensional need for the evaluation of Turkey’s ED:
� Thanks to her geopolitical position, Turkey is an important candidate to be the “energycorridor” for the transmission of the rich oil and natural gas resources of the Middle East,Caspian Area, and Asian countries to the Mediterranean countries and to the demand centersof the West (Öztürk et al., 2005).
� Turkey is an important candidate to be a European Union (EU) member in the future.� The Turkish economy has had a boom and bust cycle recently.� This study is very important as it enables Turkey to revise its rapidly increasing energy
dependency situation in relation to the EU countries and it also helps Turkey to shape itsenergy dependency policies.
It is the method of data envelopment analysis (DEA) that will enable one to attain these objectives.Thus, the DEA method was used in the study. DEA is a relatively new approach for evaluatingthe performance of a set of organizations (or firms, nations, players, branches) called DMUswhich transform multiple inputs to outputs. DEA has gained great popularity in energy andemissions modeling during the last decade (Zhou et al., 2008; Honma and Hu, 2009; Honma andHu, 2008; Ramakrishnan, 2006; Lozano and Gutierrez, 2008). These are among the studies in theliterature conducted in relation to Turkey by using the DEA method: electricity distribution utilities(Bagdadioglu et al., 1996), assessment of performance on greenhouse gas emissions (Onut andSoner, 2006), and evaluation of sectoral energy performance (Sözen and Alp, 2009). The purposeof this study was to determine the changes in ED efficiencies of Turkey and the selected nations byyears and to shed light on how to determine the ED policies. This study reveals the most accuratemodel out of the models that can be conducted for the evaluation of ED performance in Turkey:
� Model 1, which evaluates the ED based on main energy indicators;� Model 2, which evaluates the ED based on sectoral energy consumption;� Model 3, which evaluates the ED based on primary energy production as sources.
The purpose of using three different models was to witness the effects of basic energy indicators(Model 1), sectoral energy consumption (Model 2) and primary energy production as sources(Model 3) on decreasing ED. In that way, by measuring income and outcome amounts definingefficiency and determining the redundancies and shortfalls in Turkey’s incomes, it will be possibleto direct the policies related to both basic energy incomes and sectoral share ratios aiming todecrease ED.
If a decision-making unit (in this study, countries) consumes energy more than necessary anddoes not have the capacity to produce it, it will have to import the energy. Thus, its energydependency will rise, which is an undesired situation for DMUs. Therefore, it should configureits energy production and consumption in a way to decrease ED. To achieve this, excesses inenergy consumption and shortages in energy production need to be determined. This study aims todetermine DMUs’ excesses in energy consumption and shortages in energy production to decreaseED. In addition, by considering energy consumption at the sectoral level, it was revealed which
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400 A. SÖZEN ET AL.
sectors consume energy more than they are supposed to. It was ensured to assess the shortages inenergy production according to energy resources by adding a third model to the study.
2. ENERGY INDICATORS OF TURKEY
Turkey has many kinds of energy resources, most of which are limited. The main sources arehydroelectric, thermal, and lignite. Besides, Turkey has significant hydroelectric power potential,with more than 113 plants and a total installed hydroelectric generating capacity of 12.6 GW(EIA, 2008). Lignite has the biggest share in total primary energy production. More than half ofthe net energy consumption is met by imports, and the share of imports continues to increaseeach year. Currently, half of Turkey’s energy usage is based on oil and natural gas. However, theshare of natural gas has increased in recent years. Turkey’s share in the total Organization forEconomic Cooperation and Development (OECD) production was 2% in 1995 and it is expectedto reach 7% in 2020 (Dinçer and Dost, 1996). Nuclear power plants have not been installed yet.In 2005, the shares of oil and natural gas in electricity generation across the country were 3.6%and 43.8%, respectively (Sözen and Arcaklıoglu, 2007b). Similarly, the shares of hydroelectricand lignite were 24.6% and 18.6%, respectively.
The main energy indicators (per capita) are shown in Figure 1. As seen in Figure 1, therehad been a substantial increase in the final energy consumption per capita from 0.77 tones ofoil equivalent (toe) in 1998 to 0.95 toe in 2006 (European Commission, 2006). The net primaryenergy import per capita has increased from 0.67 in 1998 to 0.96 in 2006, while total production ofprimary energy per capita has decreased from 0.45 toe in 1998 to 0.36 toe in 2006. Naturally, thenet primary energy import has increased to serve as a back-up reserve for a possible insufficiencyin total production. Due to this fact, the net per capita import of natural gas increased from 0.13to 0.34 toe throughout the same period. The variation in sectoral energy consumption per capitain Turkey is given in Figure 2. Energy consumption can be simply summarized as the sum ofenergy used in industry, transportation, household appliances, services, and agriculture. As seen inFigure 2, the industry sector is the biggest consumer of electricity in Turkey. Household consump-tion owns the second biggest share of energy consumption. Forecasts are quite important for theeffective implementation of electricity policies. Accurate forecasts of electricity consumption are
FIGURE 1 Main energy indicators per capita of Turkey. (color figure available online)
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AN EVALUATION OF TURKEY’S ENERGY DEPENDENCY 401
FIGURE 2 Sectoral energy consumption per capita in Turkey. (color figure available online)
important since government investments are generally managed by decision makers (Hamzaçebi,2007). Evaluation of ED based on sectoral energy distribution is important from the point ofgeographical location and the capacity of the power plants and the investment costs.
ED is the energy import level upon which an economy relies in order to meet its energyneeds. ED is calculated by dividing net imports by the sum of gross inland energy consumptionplus bunkers. Turkey’s energy dependency in 2006 was 13% higher than that in 1998 (Figure 3).Even though energy dependency decreased in certain years, there had been an overall tendency
FIGURE 3 The energy dependency of Turkey.
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402 A. SÖZEN ET AL.
to increase. This was an indication of the fact that the energy balance of Turkey will be totallyforeign-dependant. Evaluation of ED is important as it helps Turkey to meet its energy demandin the future through correct investments and policies.
3. MATERIALS AND METHODS
3.1. Materials
The study materials have been constituted by means of a thorough study of Eurostat. The inputand output variables selected for the ED performance evaluation are presented in Table 1. Thesecriteria have been considered in the selection of input and output variables:
1. Determination of shortages and excesses in the main energy indicators in order to decreaseED. For example, it is necessary to determine whether Turkey, who imports 97% of thenatural gas it consumes, imports natural gas more than necessary or if there exist shortagesto convert the imported natural gas into energy.
2. Sectoral energy consumption were selected in order to determine the energy consumptionexcesses at the sectoral level to be able to see the effects of energy consumption on the EDperformance.
3. Primary energy consumption variables were selected in order to be able to see the excessesand shortages of the production from different energy resources.
The ultimate aim of the study is to demonstrate Turkey’s position among the EU countries in termsof energy dependency by using the main energy indicators. Initially, populations of countries weredivided by 100,000. Then, values of all input and output variables were modified in accordancewith the population rates obtained. In other words, the input and output variables were calculatedas per 100,000 persons. The variables were modified before the analysis to maintain an accurate
TABLE 1
Energy Indicators Used in the Analysis
Variables
Model 1
I/O
Model 2
I/O
Model 3
I/O
Energy dependency (%) (ED) O O OTotal production of primary energy (1,000 toe) (TPPE) INet imports of natural gas (1,000 toe) (NING) INet imports of primary energy (1,000 toe) (NIPE) INet imports of crude oil and petroleum products (1,000 toe) (NICOPP) ITotal Gross Electricity Generation (GWh) (TGEG) IFinal Energy Consumption (1,000 toe) (FEC) IGross inland consumption of primary energy (1,000 toe) (GICPE) IFinal Energy Consumption by Industry (FEC-I) IFinal Energy Consumption by Transport (FEC-T) IFinal Energy Consumption by Households (FEC-H) IFinal Energy Consumption by Agriculture (FEC-A) IFinal Energy Consumption by Service (FEC-S) IPrimary production by renewable energy (PPRE) (1,000 toe) IPrimary production by natural gas (PPNG) (1,000 toe) IPrimary production by crude-oil (PPCO) (1,000 toe) IPrimary production by coal and lignite (PPCL) (1,000 toe) I
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AN EVALUATION OF TURKEY’S ENERGY DEPENDENCY 403
evaluation. Since some countries had exports rather than imports, their input variables wereproperly modified in such a way that negative values would be meaningful. Each variable wasmodified so that max export would be 0.01 for each one.
3.2. Methodology: Data Envelopment Analysis
In the simplest case, the efficiency of a DMU that has one input and one output can be calculatedas:
Efficiency D output/input: (1)
In the case of too many outputs and inputs the calculation may get complicated and the definitionof efficiency changes into:
Efficiency D weighted sum of outputs/weighted sum of inputs: (2)
In this last definition, unbiased and objective determination of weights has been a matter for along time. Charnes et al. (1978) recommended calculating the weights by Eq. (3). The ratio ofinput to output ho is the objective function choosing optimal input weights. The first restrictionguarantees, with the same weights, all DMUs’ efficiency rates not to be bigger than unit size.Other restrictions are 2> 0 any positive small number and makes Model 3 defined on a closedset. If the efficiency degree obtained at the result of the solution is ho D 1, the DMU is absolutelyefficient. A fractional model approach given in Eq. (3) is appropriate in terms of explanationbut has difficulties from the point of calculation. Therefore, its structure can be substituted by asuitable one, namely by a linear programming model. An objective function can be made linearwith a constant denominator while aiming to maximize the numerator.
max ho D
sX
r�1
uryro=
tX
i�1
vi xio
subject to
sX
r�1
ur yrj =
tX
i�1
vi xij � 1
sX
r�1
vi =
tX
i�1
vi xio � "
sX
r�1
ur =
tX
i�1
vi xio � "
j D 1; : : : ; n r D 1; : : : ; s i D 1; : : : ; t:
(3)
Thereby, a traditional linear programming model along with calculation advantages is attained.Model 4 is easy to solve by the software package in hand. As in the previous model, in Model 4
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404 A. SÖZEN ET AL.
o is the index number of the DMU whose efficiency will be calculated.
max wo D
sX
r�1
mr yro
subject to
tX
i�1
vi xio D 1
sX
r�1
mr yrj �
tX
i�1
vi xij � 0
mr � "; vi � "; " > 0
j D 1; : : : ; n r D 1; : : : ; s i D 1; : : : ; t:
(4)
vi and mr shows the weights of input and output, which will maximize the efficiency, score ofDMU, " which is any positive small number makes the weights of input and output positive. Since1978 many DEA models have been developed. DEA models can be classified with respect to twocriteria: type of scale effects and model orientation. The first criterion determines the assumptionsconcerning the scale effects accepted in the model (constant returns to scale [CRS] or variablereturns to scale [VRS]). In DEA one uses a series of linear programming problems to determinea production frontier. The efficiency of each DMU is evaluated with regard to this frontier. Hencethe efficiency of each DMU is evaluated against the performance of other DMUs. Both inputand output oriented models can be used, depending on which variable is the target variable. Forexample, if the objective is to produce as much output as possible using the given input, oneshould use an output-oriented model. If the objective is to produce a given output using the leastamount of inputs, an input-oriented model is more suitable. In this study we used the input-oriented constant returns to scale (CRS or CCR) and the input-oriented variable returns to scale(VRS or BCC) models. In these models, the efficiency score is determined by holding outputsconstant and assessing the extent to which the inputs would have to be improved (decreased) inorder for a DMU to be considered efficient. The input-oriented form of the CCR (CRS) modelcan be described as:
min ho D � � " �
sX
rD1
SC
r � " �
mX
iD1
S�
i
Subject to
�:xio �
nX
j D1
�j xij � S�
i D 0
nX
j D1
�j yrj � SC
r D yro
�j :S�
i ; SC
r � 0
j D 1; : : : ; n; i D 1; : : : ; m; r D 1; : : : ; s;
(5)
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AN EVALUATION OF TURKEY’S ENERGY DEPENDENCY 405
where � is the scalar variable that represents the possible radial reduction to be applied to allinputs so as to obtain the projected input values, S�
i , SC
r are the slacks in the i th and the r thinput and output, respectively. In the CCR model, input reduction is accomplished by the variable�. If � is less than 1.0 and/or the slacks are not 0, then the DMU in question is inefficient.To improve and shift the DMU towards the frontier, a proportional decrease of for all inputsis required, followed potentially by an adjustment of the individual slacks. Extending from theoriginal model, Banker et al. (1984) proposed models (namely BCC) that incorporated variablereturns to scale. The input-oriented form of the BCC (VRS) model can be shown as:
min ho D � � " �
sX
rD1
SC
r � " �
mX
iD1
S�
i
Subject to
�:xio �
nX
j D1
�j xij � S�
i D 0
nX
j D1
�j yrj � SC
r D yro
nX
j D1
�j D 1
�j :S�
i ; SC
r � 0
j D 1; : : : ; n; i D 1; : : : ; m; r D 1; : : : ; s:
(6)
A DMU is BCC efficient if and only if the efficiency score is 1.0 and all slacks are 0 as in theCCR model. It is the presence of uo that distinguishes the BCC model from the CCR model. TheCCR model evaluates both technical and scale efficiency (so named as overall technical efficiency[OTE]), combining both measures in a single efficiency score, on the other hand the BCC modelevaluates pure technical efficiency (PTE) of DMUs. If a DMU is fully (1.0 or 100%) efficientin both the CCR and BCC scores, then it is operating at the most productive scale size (MPSS)(Banker and Thrall, 1992). The average productivity of an input-output mix is maximum at theMPSS for that input-output mix.
In these models, the efficiency score is determined by holding outputs constant and assessingthe extent to which the inputs would have to be improved (decreased) in order for a DMU tobe considered efficient. Also note that in DEA efficiency it is only a relative measure, whichmeans that efficient DMUs perform relatively better than the other DMU. After the evaluationof the relative efficiency of the present set of units, DEA shows how inputs and outputs have tobe changed in order to maximize the efficiency of the inefficient target DMUs. DEA suggeststhe benchmark for each inefficient DMU at the level of its individual mix of inputs and outputs.Typically, these guidelines stem from benchmarking against a set of good performers in order fora poor performer to improve its performance (Thanassoulis, 2001).
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406 A. SÖZEN ET AL.
4. RESULTS AND DISCUSSION
CCR and BCC input-oriented models were used to determine technological efficiency in allmodels. OTE (CCR) score and PTE (BCC) scores were calculated for the models (Tables 2–4).Redundancies in input and shortfalls in output were determined by reference countries that areinefficient for 2006 (Table 5). Prepared by Dortmund University, the Efficiency MeasurementSystem was used for efficiency measurement. When there are statistically meaningful differencesbetween CRR and BCC efficiencies, one should use CRS values.
In order to calculate the exact values of redundancies in input and shortfalls in output, oneshould take the previous transformations into consideration. This is also the case for calculatingtarget input-output values in order to make the inefficient countries efficient. According to Model1, Norway and Luxemburg are countries that had achieved local (VRS, BCC) and global (CRS,CCR) technical efficiency in each year between 1998 and 2006 (Table 2). Thus, they can be saidto have operated in the MPSS. In other words, during that time period, average efficiency ofthe input-output structure of Norway and Luxemburg had been at the maximum level. Accordingto Model 2, Norway and Romania are countries that had achieved local and global technicalefficiency in each year between 1998 and 2006 (Table 3). Furthermore, they had operated inthe MPSS during that time period. According to CCR and BCC efficiency scores, Norway isglobally efficient for all years in the models. Norway is the reference for inefficient countriesfor all years according to Models 1–3. Besides, Romania is the only reference for inefficientcountries in Model 2. According to Model 3, Norway is the country that had achieved local andglobal technical efficiency in each year between 1998 and 2006 (Table 4). Turkey could not be anefficient country in terms of decreasing ED since it has failed to convert different energy resourcesinto energy production (Table 4).
According to the CRR efficiency scores of the models, Turkey had not been globally efficientduring that time period; it had been inefficient. Turkey was not able to use the inputs and outputsefficiently when compared to EU states. Turkey’s CCR efficiency scores had been 100% in allyears except 2001 and 2003 according to Model 1 (Table 2). Also, Turkey’s BCC efficiency scoreshad been 100% in all years according to Model 1 and Model 2. It cannot deliver full performanceby using its basic energy indicators in an efficient manner. However, it is technically efficientaccording to BCC efficiency scores. That is, it is locally efficient (Table 5). According to Model1, like Turkey, certain developed countries of the EU such as Germany, the United Kingdom,and France experienced a drop in their global efficiency values (CRS) between 2001 and 2003(Table 2). However, while the VRS efficiency scores of these countries fluctuated, it remainedstable at 1.00 for Turkey (Table 2).
According to Model 1, in 2006 (Table 5), Turkey was technically efficient considering its VRSand CRS efficiency scores and it proved to be a reference to other inefficient 18 and 19 countriesin relation to VRS and CRS efficiency scores, respectively (Table 5). The number 27 in the firstcolumn indicates the ranking number of Turkey among all countries while the scores in bracketsindicate the percentage by which the other countries resemble Turkey (in bold). This suggests thatTurkey used its energy indicators (inputs) efficiently both locally and globally with regard to ED.
In Model 2, on the other hand, CRS efficiency scores for Turkey fell (Table 2). However, VRCefficiency scores remained stable and continued to be 100% (Table 2). Turkey, according to Model2, is not globally efficient but locally is. This means that its sectoral energy consumption had notbeen as efficient as EU countries in keeping a low level of energy dependence but its nationwidesectoral energy distribution had been efficient.
According to Model 2, in 2006, the CRS efficiency score of Turkey was 17.07% whereas thatof Norway was 100% (Table 5). Sectoral analysis showed that Turkey’s performance was 17.07%in terms of energy dependence when compared to Norway. In 2006, Turkey’s CRS efficiency score
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030.
9755
0.40
690.
9971
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250.
9825
0.44
180.
9969
0.97
820.
9782
0.97
570.
9757
0.97
820.
9788
Fra
nce
0.95
430.
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380.
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0.78
350.
9387
0.55
800.
9968
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170.
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430.
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880.
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280.
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0.95
270.
9527
Ital
y0.
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atvi
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9723
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560.
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Lux
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urg
1.00
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Hun
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550.
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9815
Net
herl
ands
1.00
001.
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1.00
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0000
0.82
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0000
0.72
111.
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Aus
tria
0.97
820.
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550.
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320.
9629
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0000
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Pol
and
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970.
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Por
tuga
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960.
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760.
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love
nia
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570.
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630.
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9998
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320.
9632
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Slo
vaki
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inla
nd0.
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wed
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MIN
IMU
M0.
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ST
.DE
VIA
TIO
N0.
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990.
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1936
0.02
510.
0219
0.02
190.
0301
0.02
360.
0224
0.02
19
407
Dow
nloa
ded
by [
Uni
vers
ity o
f B
oras
] at
05:
29 0
5 O
ctob
er 2
014
TA
BLE
3
Results
of
CR
Sand
VR
SE
ffic
iencie
sfo
rM
odel
2
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
Co
un
trie
sC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
S
Bel
gium
0.04
570.
3310
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470.
3457
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160.
3687
0.06
810.
3067
0.06
810.
3372
0.11
140.
3190
0.06
380.
3384
0.05
560.
3353
0.05
860.
3772
Bul
gari
a1.
0000
1.00
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zech
Rep
ubli
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2748
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6169
0.61
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enm
ark
0.31
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7782
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9001
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8084
Ger
man
y0.
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2451
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2248
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2253
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3184
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2270
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2166
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93E
ston
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8028
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2766
0.69
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3117
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2254
0.76
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elan
d0.
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0416
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0343
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0278
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reec
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0.66
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1936
0.64
780.
1497
0.70
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1230
0.59
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1230
0.67
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4140
0.73
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1311
0.78
460.
1691
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1150
0.83
85S
pain
0.11
860.
9208
0.10
330.
8528
0.09
480.
8561
0.07
190.
7749
0.07
190.
7593
0.33
840.
7373
0.07
720.
7264
0.06
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7653
0.05
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8211
Fra
nce
0.18
850.
5944
0.21
020.
5402
0.19
830.
5133
0.18
490.
4766
0.18
490.
5223
0.43
030.
5293
0.17
900.
5130
0.20
230.
5533
0.17
700.
6097
Ital
y0.
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0.62
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0724
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Lux
embo
urg
0.00
290.
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560.
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3759
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2884
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4428
Hun
gary
0.23
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7768
0.26
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0.56
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0.22
170.
8705
0.17
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9236
Net
herl
ands
0.16
400.
4279
0.17
270.
4260
0.13
790.
4727
0.11
480.
3921
0.11
480.
3978
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5003
0.13
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4273
0.13
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4851
Aus
tria
0.07
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4026
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3892
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and
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8831
0.41
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9553
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0.36
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8500
0.39
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9161
0.28
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8704
Por
tuga
l0.
0895
0.96
950.
0654
0.92
150.
0616
0.92
400.
0622
0.85
920.
0622
0.82
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2360
0.86
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0592
0.85
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0485
0.88
570.
1024
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oman
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love
nia
1.00
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0000
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960.
5954
0.37
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670.
5472
0.26
580.
5685
0.24
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9549
Slo
vaki
a0.
1394
0.73
720.
1825
0.68
880.
2169
0.68
570.
2183
0.65
280.
2183
0.59
110.
3148
0.65
210.
1958
0.74
430.
2111
0.70
550.
2438
0.78
35F
inla
nd0.
0954
0.26
770.
0993
0.26
930.
0886
0.29
520.
0794
0.26
130.
0794
0.26
970.
2047
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0835
0.28
280.
0827
0.29
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0653
0.30
09S
wed
en0.
1191
0.34
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1658
0.37
380.
1499
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900.
1286
0.32
460.
1286
0.34
630.
3276
0.36
440.
1380
0.36
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1484
0.37
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1329
0.39
14U
nite
dK
ingd
om0.
8766
1.00
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9392
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0.83
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roat
ia0.
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wit
zerl
and
0.20
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7258
0.27
190.
6228
0.39
390.
8130
0.37
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0.37
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7688
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ER
AG
E0.
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343
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694
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707
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20.
745
ST
.DE
VIA
TIO
N0.
334
0.23
40.
306
0.23
40.
303
0.24
50.
276
0.24
990.
2764
0.23
70.
3203
0.24
70.
2757
0.24
10.
2629
0.23
50.
270.
2331
MIN
IMU
M0.
003
0.26
80.
017
0.26
90.
011
0.29
50.
003
0.26
130.
0056
0.27
0.00
650.
278
0.00
710.
283
0.00
690.
298
0.00
40.
3009
408
Dow
nloa
ded
by [
Uni
vers
ity o
f B
oras
] at
05:
29 0
5 O
ctob
er 2
014
TA
BLE
4
Results
of
CR
Sand
VR
SE
ffic
iencie
sfo
rM
odel
3
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
Co
un
trie
sC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
SC
RS
VR
S
Bel
gium
0.02
600.
6630
0.02
900.
4930
0.09
300.
4911
0.02
150.
4725
0.02
110.
4642
0.07
720.
4520
0.02
010.
4490
0.02
550.
4762
0.02
010.
4596
Bul
gari
a0.
0804
0.72
190.
0752
0.55
780.
2457
0.55
460.
0673
0.53
670.
0593
0.53
390.
2233
0.51
980.
0618
0.52
170.
0779
0.54
180.
0631
0.52
88C
zech
Rep
ubli
c1.
0000
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0000
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0000
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1.00
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0.97
781.
0000
1.00
00D
enm
ark
0.13
780.
7113
0.13
740.
5273
0.54
570.
5762
0.12
740.
5035
0.12
390.
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0.47
750.
4775
0.13
670.
4743
0.17
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5030
0.12
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4853
Ger
man
y0.
0679
0.74
800.
0686
0.60
230.
1985
0.58
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0528
0.56
290.
0482
0.55
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1746
0.53
860.
0500
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350.
0613
0.56
500.
0477
0.54
40E
ston
ia0.
2604
0.92
780.
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730.
6381
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850.
2505
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470.
3462
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441.
0000
1.00
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001.
0000
1.00
000.
9010
1.00
00Ir
elan
d0.
0254
0.69
120.
0208
0.53
780.
0648
0.52
480.
0104
0.50
000.
0101
0.48
410.
0400
0.48
320.
0132
0.47
710.
0130
0.49
910.
0089
0.48
12G
reec
e0.
0529
0.73
910.
0567
0.59
940.
1533
0.59
020.
0435
0.57
090.
0375
0.57
380.
1456
0.54
050.
0364
0.55
080.
0524
0.57
470.
0362
0.55
36S
pain
0.03
500.
6845
0.03
000.
5269
0.09
710.
5215
0.02
730.
5039
0.02
010.
4921
0.08
630.
4716
0.02
190.
4666
0.02
320.
4922
0.01
790.
4749
Fra
nce
0.06
270.
6688
0.05
870.
5221
0.19
680.
5146
0.04
970.
4954
0.04
300.
4821
0.17
560.
4577
0.04
530.
4515
0.05
660.
4764
0.04
430.
4605
Ital
y0.
0235
0.67
040.
0202
0.50
230.
0498
0.49
780.
0159
0.47
960.
0117
0.47
010.
0568
0.45
510.
0141
0.45
150.
0183
0.47
860.
0120
0.46
16L
atvi
a0.
0505
0.66
430.
0540
0.55
830.
1655
0.53
860.
0414
0.52
760.
0371
0.51
870.
1399
0.49
180.
0291
0.49
020.
0438
0.50
670.
0318
0.49
87L
ithu
ania
0.06
230.
6635
0.05
330.
5047
0.15
530.
5013
0.05
220.
4826
0.04
980.
4746
0.19
300.
4544
0.04
790.
4505
0.04
870.
4773
0.03
290.
4607
Lux
embo
urg
0.00
220.
9431
0.00
320.
4965
0.00
080.
4962
0.00
210.
4756
0.00
090.
4678
0.00
450.
4520
0.00
170.
4490
0.00
230.
4762
0.00
100.
4596
Hun
gary
0.13
070.
8758
0.06
010.
5341
0.18
710.
5270
0.05
020.
5048
0.04
170.
4978
0.15
090.
4838
0.03
960.
4768
0.04
700.
4987
0.03
670.
4822
Net
herl
ands
0.09
320.
6662
0.08
380.
5941
0.26
110.
5808
0.06
630.
5553
0.05
770.
5273
0.22
800.
5023
0.06
350.
5048
0.07
310.
5286
0.05
720.
5064
Aus
tria
0.03
930.
6769
0.04
150.
5825
0.14
610.
5690
0.03
630.
5565
0.02
870.
5423
0.11
360.
5064
0.02
760.
5022
0.03
290.
5159
0.02
520.
5111
Pol
and
0.43
170.
9109
0.51
020.
8890
0.73
440.
8025
0.29
390.
7921
0.28
300.
8013
0.66
860.
7627
0.28
790.
7917
0.31
490.
7953
0.24
830.
7800
Por
tuga
l0.
0238
0.69
070.
0145
0.51
860.
0602
0.51
770.
0144
0.50
190.
0138
0.48
700.
0526
0.45
940.
0150
0.45
300.
0136
0.47
620.
0155
0.46
16R
oman
ia0.
0981
0.68
670.
1006
0.53
050.
3332
0.53
120.
0818
0.50
960.
0746
0.50
460.
2887
0.49
180.
0726
0.48
920.
0949
0.51
330.
0735
0.50
05S
love
nia
0.07
670.
7237
0.06
780.
5896
0.22
700.
5896
0.06
160.
5666
0.05
640.
5665
0.20
230.
5276
0.05
850.
5309
0.07
250.
5485
0.05
790.
5369
Slo
vaki
a0.
0382
0.68
090.
0419
0.51
800.
1403
0.51
340.
0390
0.49
780.
0325
0.48
770.
1252
0.46
810.
0303
0.46
620.
0424
0.48
940.
0342
0.47
15F
inla
nd0.
0589
0.75
770.
0705
0.74
470.
2177
0.69
130.
0506
0.68
190.
0509
0.69
700.
1942
0.68
690.
0465
0.67
200.
0657
0.68
310.
0580
0.77
95S
wed
en1.
0000
1.00
000.
0805
0.68
570.
2857
0.68
050.
0665
0.66
690.
0563
0.62
060.
2338
0.60
480.
0608
0.60
850.
0772
0.63
440.
0599
0.63
02U
.K
ingd
om0.
1718
0.72
060.
1681
0.58
550.
5261
0.57
230.
1246
0.54
670.
1113
0.52
910.
4173
0.50
760.
0982
0.50
090.
1103
0.51
870.
0774
0.49
56C
roat
ia0.
0650
0.66
870.
0534
0.50
550.
1845
0.50
170.
0476
0.48
260.
0347
0.47
140.
1537
0.45
770.
0390
0.45
430.
0485
0.48
200.
0417
0.46
60T
urke
y0.
0549
0.69
960.
0500
0.52
610.
1441
0.52
000.
0382
0.49
840.
0305
0.48
860.
1063
0.46
850.
0289
0.46
590.
0349
0.49
250.
0270
0.47
99N
orw
ay1.
0000
1.00
001.
0000
1.00
001.
0000
1.00
001.
0000
1.00
001.
0000
1.00
001.
0000
1.00
001.
0000
1.00
001.
0000
1.00
001.
0000
1.00
00S
wit
zerl
and
0.07
670.
7237
0.06
780.
5896
0.22
700.
5896
0.06
160.
5666
0.05
640.
5665
0.20
230.
5276
0.05
850.
5309
0.07
250.
5485
0.05
790.
5369
409
Dow
nloa
ded
by [
Uni
vers
ity o
f B
oras
] at
05:
29 0
5 O
ctob
er 2
014
TA
BLE
5
Results
of
CR
Sand
VR
SE
ffic
iency
in2006
for
All
Models
Countr
ies
CR
S-M
OD
EL
1-2
006
Benchm
arks
VR
S-M
OD
EL
1-2
00
6
Benchm
arks
CR
S-M
OD
EL
2-2
006
Benchm
arks
VR
S-M
OD
EL
2-2
006
Benchm
arks
CR
S-M
OD
EL
3-2
00
6
Benchm
arks
VR
S-M
OD
EL
3-2
006
Benchm
arks
1B
elgiu
m19
(0.8
7)
27
(0.1
2)
28
(0.0
1)
19
(0.8
7)
27
(0.1
2)
28
(0.0
0)
20
(0.1
0)
28
(0.0
2)
2(0
.40)
19
(0.1
7)
20
(0.4
3)
3(0
.02)
28
(0.0
2)
3(0
.46)
28
(0.5
4)
2B
ulg
aria
19
(0.2
7)
27
(0.6
9)
28
(0.0
1)
26
(0.2
9)
27
(0.6
9)
28
(0.0
2)
20
(0.0
6)
28
(0.0
6)
3(0
.06)
28
(0.0
6)
3(0
.53)
28
(0.4
7)
3C
zech
Rep
ubli
c27
(0.9
5)
28
(0.0
0)
27
(0.9
5)
28
(0.0
5)
20
(0.2
8)
28
(0.0
6)
2(0
.49)
20
(0.4
7)
27
(0.0
3)
28
(0.0
1)
4D
enm
ark
19
(0.0
8)
27
(0.7
9)
28
(0.0
8)
26
(0.0
8)
27
(0.8
2)
28
(0.1
0)
28
(0.1
6)
13
(0.8
8)
28
(0.1
2)
3(0
.11)
28
(0.1
5)
3(0
.30)
6(0
.14)
28
(0.5
7)
5G
erm
any
19
(0.3
1)
27
(0.6
8)
28
(0.0
1)
19
(0.3
1)
27
(0.6
8)
28
(0.0
2)
20
(0.2
6)
28
(0.0
2)
13
(0.3
2)
20
(0.6
8)
3(0
.05)
28
(0.0
4)
3(0
.50)
6(0
.03)
28
(0.4
6)
6E
stonia
27
(0.9
5)
28
(0.0
5)
27
(0.9
5)
28
(0.0
5)
28
(0.0
8)
13
(0.9
1)
27
(0.0
5)
28
(0.0
4)
3(0
.80)
28
(0.0
1)
11
7Ir
elan
d19
(0.0
5)
27
(0.9
7)
20
(0.0
1)
28
(0.0
1)
13
(0.6
0)
20
(0.2
1)
27
(0.1
9)
3(0
.01)
28
(0.0
1)
3(0
.48)
28
(0.5
2)
8G
reec
e16
(0.1
0)
19
(0.8
9)
28
(0.0
0)
16
(0.1
2)
19
(0.6
3)
26
(0.1
7)
27
(0.0
8)
28
(0.0
3)
13
(0.7
6)
27
(0.2
4)
3(0
.04)
28
(0.0
3)
3(0
.55)
28
(0.4
5)
9S
pai
n19
(0.7
9)
27
(0.2
1)
28
(0.0
0)
19
(0.8
4)
27
(0.1
6)
20
(0.0
0)
28
(0.0
2)
27
(1.0
0)
3(0
.02)
28
(0.0
2)
3(0
.47)
6(0
.00)
28
(0.5
2)
10
Fra
nce
19
(0.4
8)
27
(0.4
9)
28
(0.0
3)
19
(0.4
8)
27
(0.4
9)
28
(0.0
3)
20
(0.0
7)
28
(0.0
5)
13
(0.8
3)
20
(0.1
6)
28
(0.0
1)
3(0
.04)
28
(0.0
5)
3(0
.34)
6(0
.12)
28
(0.5
4)
11
Ital
y27
(1.0
2)
28
(0.0
0)
20
(0.0
2)
28
(0.0
1)
2(0
.10)
20
(0.3
5)
27
(0.5
5)
3(0
.01)
28
(0.0
1)
3(0
.46)
28
(0.5
4)
12
Lat
via
28
(0.0
4)
13
(1.0
0)
3(0
.02)
28
(0.0
4)
6(0
.42)
28
(0.5
8)
13
Lit
huan
ia27
(0.9
9)
28
(0.0
1)
27
(0.9
9)
28
(0.0
1)
20
(0.0
2)
28
(0.0
4)
3(0
.03)
28
(0.0
4)
3(0
.42)
6(0
.04)
28
(0.5
4)
14
Luxem
bourg
20
(0.0
0)
28
(0.0
0)
20
(0.6
3)
27
(0.3
7)
3(0
.00)
28
(0.0
0)
3(0
.46)
28
(0.5
4)
15
Hungar
y27
(0.9
9)
28
(0.0
1)
27
(0.9
9)
28
(0.0
1)
28
(0.0
4)
13
(0.9
4)
27
(0.0
6)
28
(0.0
0)
3(0
.04)
28
(0.0
4)
3(0
.48)
28
(0.5
2)
16
Net
her
lands
28
(0.0
7)
2(0
.13)
27
(0.8
3)
28
(0.0
4)
3(0
.05)
28
(0.0
7)
3(0
.41)
28
(0.5
9)
17
Aust
ria
19
(0.5
4)
27
(0.4
5)
28
(0.0
1)
19
(0.5
4)
27
(0.4
5)
28
(0.0
1)
20
(0.1
0)
28
(0.0
2)
2(0
.22)
20
(0.5
1)
27
(0.2
8)
3(0
.02)
28
(0.0
3)
6(0
.40)
28
(0.6
0)
18
Pola
nd
27
(0.9
4)
28
(0.0
3)
27
(0.9
7)
28
(0.0
3)
28
(0.0
9)
13
(0.2
0)
27
(0.7
4)
28
(0.0
6)
3(0
.25)
28
(0.0
7)
3(0
.78)
28
(0.2
2)
19
Port
ugal
20
(0.0
5)
28
(0.0
2)
3(0
.01)
28
(0.0
2)
3(0
.12)
6(0
.34)
28
(0.5
4)
20
Rom
ania
27
(0.9
5)
28
(0.0
1)
12
(0.0
0)
27
(0.9
8)
28
(0.0
2)
3(0
.07)
28
(0.0
8)
3(0
.49)
28
(0.5
1)
21
Slo
ven
ia19
(0.6
1)
27
(0.3
6)
28
(0.0
3)
16
(0.1
1)
26
(0.0
6)
27
(0.8
2)
28
(0.0
2)
20
(0.0
0)
28
(0.0
5)
20
(0.3
9)
27
(0.6
1)
28
(0.0
0)
3(0
.05)
28
(0.0
5)
3(0
.19)
6(0
.35)
28
(0.4
6)
22
Slo
vakia
27
(0.9
9)
28
(0.0
2)
27
(0.9
9)
28
(0.0
1)
20
(0.2
4)
28
(0.0
2)
2(0
.30)
19
(0.0
4)
20
(0.6
7)
3(0
.03)
28
(0.0
4)
3(0
.47)
28
(0.5
3)
23
Fin
land
16
(0.0
4)
19
(0.9
2)
28
(0.0
0)
16
(0.0
4)
19
(0.9
2)
28
(0.0
3)
28
(0.0
5)
2(0
.47)
20
(0.0
0)
27
(0.5
2)
28
(0.0
1)
3(0
.02)
28
(0.0
5)
6(0
.33)
28
(0.6
7)
24
Sw
eden
16
(0.1
7)
19
(0.7
9)
28
(0.0
0)
16
(0.1
7)
19
(0.7
9)
28
(0.0
4)
20
(0.1
5)
28
(0.0
6)
2(0
.78)
20
(0.2
1)
28
(0.0
1)
3(0
.02)
28
(0.0
7)
6(0
.27)
28
(0.7
3)
25
Unit
edK
ingdom
19
(0.0
2)
27
(0.9
2)
28
(0.0
3)
26
(0.0
2)
27
(0.9
3)
28
(0.0
5)
20
(1.1
1)
20
(0.9
9)
28
(0.0
1)
3(0
.07)
28
(0.0
8)
3(0
.46)
28
(0.5
4)
26
Cro
atia
16
(0.1
1)
27
(0.8
8)
28
(0.0
5)
13
(0.5
3)
27
(0.4
5)
28
(0.0
2)
3(0
.04)
28
(0.0
5)
3(0
.45)
28
(0.5
5)
27
Turk
ey28
(0.0
3)
3(0
.03)
28
(0.0
3)
3(0
.48)
28
(0.5
2)
28
Norw
ay
29
Sw
itze
rlan
d16
(0.0
2)
19
(0.9
6)
28
(0.0
0)
16
(0.1
8)
19
(0.0
7)
27
(0.7
4)
28
(0.0
1)
20
(0.4
8)
28
(0.0
1)
13
(0.0
9)
20
(0.9
1)
3(0
.05)
28
(0.0
5)
3(0
.19)
6(0
.35)
28
(0.4
6)
Note
:n(N
):n
Dth
ese
quen
cenum
ber
inth
efi
rst
colu
mn
of
the
table
refe
rence
den
able
dco
untr
y;
ND
mult
ipli
edby
the
amount
of
Nin
put
or
the
outp
ut
of
the
refe
rence
countr
y.
410
Dow
nloa
ded
by [
Uni
vers
ity o
f B
oras
] at
05:
29 0
5 O
ctob
er 2
014
AN EVALUATION OF TURKEY’S ENERGY DEPENDENCY 411
was around 17.07% in terms of inputs and outputs of energy when compared to Norway. In order tobe efficient, the inputs (sectoral energy consumption) should be decreased by 82.93%. Furthermore,redundancies in inputs and shortfalls in outputs are to be taken into account. Redundancies inoutputs and shortfalls in inputs are presented in Table 5 in 2006 for all. Another option is thatin order to be efficient Turkey should liken its inputs to Norway’s by 0.03% according to theBenchmark column in Table 5. In addition, Turkey should eliminate redundancies in inputs andmixed inefficiencies in outputs as stated previously.
According to the distribution in Model 2, Turkey is VRS efficient. Turkey proved to be areference for 14 countries regarding VRS efficiency (Table 5). According to CRS efficiency scores,Turkey was not efficient; it adopted Norway as a reference and there was a 99.97% resemblancebetween two countries. Redundancies in inputs that required to be eliminated in order for Turkeyto be efficient are presented in Table 5 for Model 2. According to both the CRS and VRS scoresof Model 3, no significant rise has occurred in Turkey’s efficiency from 1998 to 2006; on thecontrary, its efficiency decreased (Table 5). This finding indicates that Turkey has not been ableto improve its energy production to decrease ED.
5. CONCLUSION
This study is significant since it gives an opportunity to Turkey to reexamine its position inrelation to other EU member countries in terms of its increasing energy dependency as well as anopportunity to align its politics with this objective. Three different models with dissimilar inputshave been developed to exhibit the effects of both basic energy incomes and sectoral energyconsumption on ED. In analysis, the DEA method, a popular method in efficiency research, hasbeen employed. Basic outcomes of this study can be summarized as:
� The results suggest that Turkey used its inputs efficiently when evaluated with a localperspective in terms of energy dependence but could not perform as well as EU countries.As a candidate country for EU membership, Turkey should reduce its dependence in termsof both sectoral and basic inputs in the energy sector by using its own energy resources inorder to be able to get prepared for integration and it should increase its performance in theuse of resources, too.
� According to CRS efficiency analysis results (Model 2), from the point of sectoral energyconsumption, domestic, industrial and agricultural energy consumption took the first threeplaces in disrupting the global ED efficiency of Turkey, respectively. Fort that reason, newpromotions and politics should be formulated to enable a more effective use of the energyparticularly consumed in houses and industry.
� It is necessary for Turkey to efficiently employ especially domestic energy resources inenergy production.
ACKNOWLEDGEMENTS
The authors wish to thank the Department of Scientific Research Project at Gazi University (Projectnumber: BAP: 41/2010-01) for their financial support.
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