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Turkey's Performance of Sectoral EnergyConsumptionİ. Alp a , A. Sözen b & Ş. Kazancioğlu c

a Faculty of Science, Statistics Department, Gazi University, Ankara,Turkeyb Technology Faculty, Energy Systems Engineering Department,Energy Section, Gazi University, Ankara, Turkeyc Turkish Railway, TCDD, Ankara, TurkeyVersion of record first published: 05 Nov 2012.

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Energy Sources, Part B, 8:94–105, 2013Copyright © Taylor & Francis Group, LLCISSN: 1556-7249 print/1556-7257 onlineDOI: 10.1080/15567240903261761

Turkey’s Performance of Sectoral Energy Consumption

I. Alp,1 A. Sözen,2 and S. Kazancioglu3

1Faculty of Science, Statistics Department, Gazi University, Ankara, Turkey2Technology Faculty, Energy Systems Engineering Department, Energy Section, Gazi

University, Ankara, Turkey3Turkish Railway, TCDD, Ankara, Turkey

In this study, an energy performance evaluation of 25 member states of the European Union, Norway,

Switzerland, Croatia, and Turkey, a candidate country, regarding sectoral energy consumption was

conducted by using data envelopment analysis in order to allow us to draw up a good energy policy

in the future. Since the data on Malta and Cyprus was 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 are taken from the European Statistical System (EUROSTAT) for all countries. Results

of the analysis show that Turkey’s most important goal for the future is to produce proper sectoral

energy policies.

Keywords: data envelopment analysis, energy efficiency, energy indicators

INTRODUCTION

Turkey has a significant geopolitical position in terms of being an energy corridor between Asia,the Middle East and Europe. Turkey has been one of the fast-growing markets in the world for thelast two decades through her younger population, low electricity consumption per capita, rapidurbanization and strong economic growth (WEC-TNC, 2000). On the way to being a EuropeanUnion member, Turkey has made significant progress towards transferring the EU acquit about theenergy sector into the national legislative framework and is currently intensifying its efforts towardscompletion of this process (Patlitzianas et al., 2006). However, “energy” is still not included amongthe titles opened throughout the negotiations within the EU harmonization process. In the case ofan opening regarding this title, Turkey needs to analyze its current condition in order to determinea roadmap. With this purpose in mind, this study made comparisons with EU states and someother developed nations with the aim of determining the sector-specific performance of energyconsumption through the energy indicators selected under the heading “energy.” Not only to gainaccess to EU but also as a fast-developing country that regards itself as a bridge of energy betweenAsia and Europe, Turkey should conduct an analysis of efficiency so as to control its sector-specificbalance of energy. In addition, Turkey signed the Kyoto Protocol on February 17, 2009, which

Address correspondence to Adnan Sözen, Gazi University, Technology Faculty, Energy Systems EngineeringDepartment, Energy Section, Teknikokullar, Ankara, 06500, Turkey. E-mail: asozen@gazi.edu.tr

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is available now. Therefore, Turkey should form a balance in the distribution of sector-specificenergy consumption in a way that will decrease its greenhouse gasses (GHGs) in order to reduceits GHG emissions to the level stipulated by the protocol. Many studies in the literature are onTurkey’s energy consumption or prospects regarding Turkey’s sector-specific energy consumptionin the next years (Ediger and Tatlıdil, 2002; Hamzaçebi, 2007; Kılıç and Kaya, 2007; Say andYücel, 2006; Sözen et al., 2006a, b; Sözen and Arcaklıoglu, 2007a, b; Sözen et al., 2007; Sözenet al., 2009; Öztürk et al., 2005). Of them all, there are a few studies that analyzed the relationshipbetween economic indicators and energy consumption (Sözen and Arcaklıoglu, 2007b; Lise andMontfort, 2007; Altınay and Karagöl, 2004). Thanks to this study, it is possible to determinethe Decision Making Units (DMUs, the countries in this study) which establish the best relationbetween the inputs and outputs of energy indicators, i.e., efficient ones, and to find out in whichenergy indicators there are 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. It is the data envelopment analysys (DEA) technique that is used in this study.DEA is a relatively new approach for evaluating the performance of a set of organizations (orfirms, nations, players, branches) called DMUs which transform multiple inputs to outputs. DEAhas gained great popularity in energy and emissions modeling during the past decade (Zhou et al.,2008; Honma and Hu, 2008, 2009; Ramakrishnan, 2006; Lozano and Gutierrez, 2008). Most ofthe research articles on DEA studied on the linkages among CO2 emissions, energy consumptionand gross domestic product growth. Among the studies in the literature on Turkey using the DEAtechnique are electricity distribution utilities (Bagdadioglu et al., 1996), an energy efficiencyassessment for the Antalya Region hotels in Turkey (Onut and Soner, 2006) and assessment ofperformance on GHG emissions (Sozen and Alp, 2009).

The purpose of this study is to determine the changes in the sector-specific performance ofthe energy consumption of Turkey and the selected nations by years and to shed light on how todetermine the sector-specific energy policies.

THE ENERGY CONSUMPTION OF TURKEY

The main energy indicators per capita are shown in Figure 1. As seen in Figure 1, there is asubstantial increase in the final energy consumption per capita, namely from 0.77 (toe) in 1998to 0.95 (toe) in 2006 (European Comission, 2010). The net import of primary energy per capitaincreased from 0.67 in 1998 to 0.96 in 2006, while total production of primary energy per capitadecreased from 0.45 (toe) in 1998 to 0.36 (toe) in 2006. Naturally, net import of primary energyincreases in the case of insufficient total production. Due to this fact, the net import of naturalgas per capita increased from 0.13 to 0.34 (toe) through the same period. The sectoral energyconsumption per capita has increased since 2001 in Turkey as seen Figure 2. The industry sectorhad the highest energy consumption. The economic growth rate of all the sectors in Turkey hasbeen raised by the free market economics introduced in the 1980s. The growth of the industrysector resulted in a considerable increase in energy consumption. The energy consumption inhouseholds tends to increase in parallel to rapid urbanization. The other sectors had a slightlyincreasing trend.

MATERIALS AND METHOD

Study materials have been constituted by means of thorough study of European Statistical System(EUROSTAT). In this study, the input variables and output variables selected for the sectoral energy

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FIGURE 1 Main energy indicators per capita of Turkey (Source: © European Union 2010). (color figure availableonline)

performance evaluation are presented in Table 1. The input and output variables without grossdomestic product (GDP) were converted for accurate evaluation before the analysis. Initially, thepopulation of countries was divided by 100,000. Then, all input and output variables proportionedin accordance with the population rates obtained. In other words, the input and output variableswere calculated as per 100,000 persons. Since some countries had exports rather than imports,their input variables were properly converted in a way in which negative values observed in theirinput variables would be meaningful. Each variable was translated in a way in which the maxexport would be 1 for each one. Statistical data on input and output variables are presented inTable 2.

Data Envelopment Analysis

DEA is a relatively new approach for evaluating the performance of a set of organizations (or firms,nations, players, branches) called DMUs which transform multiple inputs to outputs. Charneset al. (1978, p. 432) in their original study described DEA as a “mathematical programmingmodel applied to observational data that provides a new way of obtaining empirical estimates ofrelations—such as the production functions and/or efficient production possibility surfaces—thatare cornerstones of modern economics.”

In the beginning, DEA was used specifically for measuring technological efficiency in the publicand not-for-profit sectors, where prices may not be available or reliable and the assumption ofcost minimizing or profit maximizing behavior may not be appropriate. Then it was successfully

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FIGURE 2 Sectoral energy consumption in Turkey. (color figure available online)

applied in many different sectors to assess the comparative/relative efficiency of homogeneousoperating units such as banks, hospitals, markets and universities, which are either in the publicor private sector (Said et al., 2004; Cook and Seiford, 2009).

DEA is a non-parametric technique, used for performance measurement and benchmarking. Ituses linear programming to determine the relative efficiencies of a set of homogeneous (compa-

TABLE 1

Energy Indicators Used in the Analysis

Variable

Input (I)/

Output (O)

Total production primary energy (toe) (TPPE) INet imports of natural gas (toe) (NING) INet imports of primary energy (toe) (NIPE) INet imports of crude oil and petroleum products (toe) (NICPP) ITotal gross electricity generation (MWh) (TGEG) IGross inland consumption of primary energy (toe) (GICPE) OFinal energy consumption by industry (FEC-I) OFinal energy consumption by transport (FEC-T) OFinal energy consumption by households (FEC-H) OFinal energy consumption by agriculture (FEC-A) OFinal energy consumption by service (FEC-S) OGross domestic product (GDP) O

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TABLE 2

Summary Statistics of Untransformed Data for 2006

Input/Output Mean Std. Deviation Min. Max.

Total production of primary energy (1,000toe)

3,9216.14 57,724.11 79 223,650

Net imports of natural gas (1,000 toe) 7,608.07 24,279.97 �73,675 66,434Net imports of primary energy (1,000 toe) 30,999.31 69,692.55 �197,567 215,548Net imports of crude oil and petroleum

products (1,000 toe)18,219.34 41,604.56 �123,096 121,515

Gross inland consumption of primaryenergy (1,000 toe)

68,221.66 87,164.29 4,625 349,026

Total gross electricity generation (GWh) 128,465.00 164,248.22 4,333 636,600Final energy consumption (1,000 toe) 44,481.66 55,221.68 2,775 223,062Final energy consumption by industry

(1,000 toe)12,437.17 13,882.97 615 55,648

Final energy consumption by transport(1,000 toe)

13,732.34 18,078.28 797 63,311

Final energy consumption by households(1,000 toe)

11,578.24 15,940.48 610 69,124

Final energy consumption by agriculture(1,000 toe)

1,150.31 1,346.48 27 4,302

Final energy consumption by service(1,000 toe)

5,061.62 6,410.53 105 25,720

GDP 99.45 48.82 36.50 267

rable) units. Within DEA models weights of inputs and outputs are calculated to determine theefficiency of the units in such a way that each DMU should adopt weights that show it in the mostfavorable light relative to the other DMUs. A non-parametric technique DEA has another majoradvantage on all other similar efficiency measurement techniques because of its methodology thatis directed to frontiers rather than central tendencies. Parametric approaches fit a regression linethrough the center of data while the DEA covers the data, from the top, with a linear or piecewiselinear surface. DEA focuses on every individual observation rather than averages and estimationof parameters associated with parametric approaches. DEA compares the input and output levelsof all DMUs in the data set and defines the efficient frontier by identifying the relatively bestpractice DMUs. Specifically, those DMUs which are most efficient with respect to translatinginputs into outputs will form part of the frontier (and be rated as 1.0 or 100% efficient), whilethose DMUs lying inside the frontier will be deemed to be relatively inefficient (and will be ratedat greater than 1.0 or 100% in an output-oriented model).

DEA models can be classified by two criteria: Type of scale effects and model orientation.The first criterion determines the assumptions concerning the scale effects accepted in the model(constant returns to scale [CRS], or variable returns to scale [VRS]). The CRS surface is repre-sented by a straight line that starts at the origin and passes through the first DMU that it meetsas it approaches the observed population. The models with CRS envelopment surface assumethat an increase in inputs will result in a proportional increase in outputs. The Charnes, Cooper,Rhodes (CCR) model of DEA is based on the CRS assumption. The VRS surface envelops thepopulation by connecting the outermost DMUs. The VRS model allows an increase in input valuesto result in a non-proportional increase of output levels. The Banker, Charnes, Cooper model ofDEA is based on the VRS assumption (Charnes et al., 1978; Ray, 2004; Banker et al., 1984).The model orientation approach indicates whether the objective is the minimization of input(s),such as the cost of production, then called the input-oriented model, or the maximization of a

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SECTORAL ENERGY CONSUMPTION 99

particular output, such as profit, then called the output-oriented model. In this research we usedthe output-oriented CCR (CRS surface) model of DEA. Therefore, only the CCR output-orientedmodel will be discussed detail.

Using this model, the efficiency score is determined by holding inputs constant and assessing towhat extent outputs would have to be improved (increased) in order for a DMU to be consideredefficient. Also note that in DEA, efficiency is only a relative measure, which means that efficientDMUs perform relatively better than the other DMUs. The CCR model evaluates both technicaland scale efficiency (so named as overall technical efficiency, global efficiency), combining bothmeasures in a single efficiency score. In the CCR model, we assume that there are n DMU tobe evaluated. Each DMU consumes varying amounts of m different inputs to produce s differentoutputs. The model can be described as follows:

max ho D � C ":

sX

rD1

SC

r C ":

mX

iD1

S�

i

Subject to

�:yro �

nX

j D1

�j yrj C SC

r D 0

nX

j D1

�j xij C S�

i D xio

�j :S�

i ; SC

r � 0

j D 1; : : : ; n; i D 1; : : : ; m; r D 1; : : : ; s

Where the subscript o represents the DMU being assessed and ho efficiency score of DMUo.� is the sacalar variable that represents the possible radial enlargement constant to be applied toall outputs so as to obtain the projected output values, xij , yrj denotes the input i and output r of

DMUj, respectively. " is an arbitrary small “non-Arcimedian” number. S�

i , SCr are the slacks in

the ith and the rth input and output. Following the CCR output-oriented model, a CCR efficientDMU, j, can be defined to satisfy the following two conditions:

1. ��D 1:0 or 100%, and

2. all S��

i D 0 and SC�

r D 0, .i D 1; : : : ; m/, .r D 1; : : : ; s/.

From the CCR model, output augmentation is accomplished through the variable �. If � isgreater than 1.0 (or 100%) and/or the slacks are not 0, then the DMU under investigation isinefficient. To improve and shift the DMU towards the frontier, a proportional increase of � forall outputs is required, followed potentially by an adjustment of individual slacks.

RESULTS AND DISCUSSION

CRS solutions are obtained through a consideration of the the notion of “global output-orientedtechnical efficiency” (GOOTE; Tables 3 and 4). Those with greater than 100% efficiency refer to(inefficient) decision-making units. That is, more energy resources are used for the same amount

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TABLE 3

Global Output-oriented Technical Efficiency for EU Countries and Turkey

Countries 1998 1999 2000 2001 2002 2003 2004 2005 2006

Belgium 100.00% 100.00% 102.90% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Bulgaria 258.20% 256.80% 260.65% 281.46% 229.35% 233.75% 226.80% 233.60% 224.63%Czech Republic 134.76% 131.42% 137.53% 130.62% 132.89% 127.86% 124.64% 123.37% 130.39%Denmark 101.19% 100.00% 100.00% 102.74% 100.00% 100.00% 100.00% 100.00% 100.00%Germany 127.71% 128.71% 128.29% 124.52% 119.35% 117.64% 119.84% 120.46% 110.88%Estonia 155.69% 155.99% 153.83% 155.99% 134.75% 139.26% 133.65% 134.21% 127.63%Ireland 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Greece 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Spain 161.05% 148.96% 138.78% 153.10% 143.57% 126.44% 110.18% 123.60% 135.53%France 117.92% 116.44% 132.27% 125.90% 130.33% 125.23% 124.38% 123.01% 123.69%Italy 118.52% 122.87% 121.45% 119.05% 107.41% 106.62% 111.98% 116.70% 108.20%Latvia 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Lithuania 212.41% 213.18% 201.29% 233.33% 202.63% 214.33% 203.44% 167.80% 148.05%Luxembourg 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Hungary 117.86% 115.43% 102.75% 100.00% 106.81% 102.32% 100.00% 100.00% 100.25%Netherlands 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Austria 107.02% 104.35% 110.81% 100.00% 100.00% 100.00% 101.80% 100.47% 100.94%Poland 121.28% 121.67% 122.39% 123.93% 127.00% 129.32% 119.69% 124.07% 125.29%Portugal 100.00% 100.00% 100.00% 127.57% 117.06% 127.09% 100.00% 108.34% 128.01%Romania 181.17% 264.83% 257.60% 336.70% 125.55% 132.49% 125.35% 130.06% 140.27%Slovenia 128.38% 118.63% 164.11% 132.69% 158.48% 174.31% 179.74% 185.31% 180.81%Slovakia 100.00% 100.00% 100.00% 100.00% 101.14% 111.87% 100.00% 109.19% 103.80%Finland 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Sweden 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%United Kingdom 131.28% 124.10% 123.93% 127.88% 119.72% 122.70% 119.61% 122.40% 121.22%Croatia 143.86% 119.13% 110.78% 124.35% 123.75% 127.35% 130.08% 120.92% 122.33%Turkey 100.00% 100.00% 101.35% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Norway 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%Switzerland 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

of sectoral energy consumption. Efficiency scores of 100% refer to efficient decision-makingunits.

Pegging the inputs and maximizing outputs, CCR output-oriented models were used for deter-mining GOOTE. Prepared by Dortmund University (Dortmund, Germany), the Efficiency Mea-surement System was used for efficiency measurement. GOOTE were calculated for selected EUcountries (Tables 3 and 4). Figure 3 shows the distribution of GOOTE for selected countries. InTable 3, the countries that are shaded are efficient. According to CCR efficiency scores (Table 3),Ireland, Greece, Latvia, the Netherlands, Finland, Sweden, Norway, and Switzerland are globallyefficient for all years. These globally efficient countries are a reference for inefficient countriesfor all years. Belgium, and Turkey can be considered globally efficient except for 2000. Romania,Lithuania and Bulgaria have the highest score of GOOTE. According to CCR efficiency scores,Turkey can be considered globally efficient; in other words, it is efficient. But, Turkey is inefficientin 2000 (GOOTE D 101.35%).

For the year 2000 Turkey’s output increased at a rate of 1.35%. Additionally, Turkey completedthe mixed inefficiency in its output in 2000. Therefore, Turkey became active again in 2001.Turkey’s reference number is 1 in 2006, and this referred country is Italy (Table 4). Redundanciesin input and shortfalls in output were determined through reference countries for those countriesthat are not efficient for 2006 (Table 4). In order to calculate the exact values of redundancies

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TABLE 4

GOOTE, Benchmarks, and Slacks of EU Countries in Energy (2006)

Countries Score Benchmarks

1 Belgium 100.00% 42 Bulgaria 224.63% 12 (0.20) 14 (0.43) 16 (0.26)3 Czech Republic 130.39% 1 (0.25) 14 (0.24) 16 (0.22) 28 (0.04) 29 (0.20)4 Denmark 100.00% 25 Germany 110.88% 12 (0.08) 14 (0.38) 29 (0.47)6 Estonia 127.63% 4 (0.12) 12 (0.08) 14 (0.24) 16 (0.18) 29 (0.32)7 Ireland 100.00% 58 Greece 100.00% 39 Spain 135.53% 7 (0.23) 8 (0.39) 14 (0.27) 16 (0.06)

10 France 123.69% 4 (0.01) 14 (0.20) 16 (0.09) 23 (0.11) 28 (0.01) 29 (0.55)11 Italy 108.20% 7 (0.46) 8 (0.11) 12 (0.15) 14 (0.12) 27 (0.14)12 Latvia 100.00% 1313 Lithuania 148.05% 12 (0.70) 14 (0.19) 16 (0.07)14 Luxembourg 100.00% 1515 Hungary 100.25% 1 (0.07) 7 (0.16) 12 (0.65) 16 (0.10)16 Netherlands 100.00% 1317 Austria 100.94% 1 (0.37) 7 (0.22) 14 (0.12) 16 (0.09) 29 (0.15)18 Poland 125.29% 8 (0.04) 12 (0.42) 14 (0.05) 16 (0.44)19 Portugal 128.01% 7 (0.40) 12 (0.29) 14 (0.15)20 Romania 140.27% 12 (0.38) 14 (0.23)21 Slovenia 180.81% 12 (0.01) 14 (0.76) 16 (0.09)22 Slovakia 103.80% 1 (0.50) 12 (0.31) 16 (0.03) 29 (0.11)23 Finland 100.00% 124 Sweden 100.00% 025 United Kingdom 121.22% 12 (0.19) 14 (0.31) 16 (0.03) 29 (0.37)26 Croatia 122.33% 12 (0.84) 14 (0.10) 16 (0.02)27 Turkey 100.00% 128 Norway 100.00% 229 Switzerland 100.00% 7

in input and shortfalls in output, one should take the transformations previously conducted intoconsideration. This is also the case for calculating target input-output values in order to make theinefficient countries efficient. The countries that are efficient in Table 5 remained empty.

A comparison of GOOTE values between the 10 new EU members and Turkey is presentedin Figure 3. As can be concluded from the figure, Latvia, among the new members, is globallyefficient. The nations which are closest to efficiency are Turkey, Hungary and Slovakia. One canobserve fluctuations across years in GOOTE values of other nations. This means that Turkey’sperformance of sector-specific energy performance is satisfying when compared to new members.There are direct correlations between energy and economic indicators. Like Turkey, there weresignificant increases in GOOTE of some countries such as Poland, the Czech Republic andSlovakia, which have benefited from EU funds and had a foreign capital inflow in 2002 (Table 6),and they were able to maintain this situation until 2006. This means that achievement of economicwelfare and a strong economy play an important role in regulation of inputs and outputs of energy.According to scale efficiency values, Turkey’s efficiency is not static. While it was between 101%and 99.9% between 1999 and 2000, it was able to experience a significant increase in efficiencyregarding its sectoral energy consumption performance in 2002 (100%). According to Figure 4,Turkey is globally efficient since 2001; in other words, it is efficient. Turkey was able to use the

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FIGURE 3 Change in Turkey’s and the EU countries’ GOOTE scores by year. (color figure available online)

inputs and outputs selected efficiently when compared to EU states since 2002. Turkey was ableto use the inputs and outputs selected efficiently when compared to EU states.

CONCLUSION

It is expected that this study will be helpful in demonstrating the sectoral energy performanceof Turkey among EU countries. As a candidate country for EU accession, Turkey should make

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TABLE 5

Slacks of EU Countries in Energy (2006)

Countries TPPE NING NIPE NICPP TGEG GICPE FEC-I FEC-T FEC-H FEC-A FEC-S GDP

BelgiumBulgaria 21.94 119.93 90.67 0.00 0.00 0.00 9.19 194.49 21.15 1.36 0.00 76.66Czech 0.00 141.30 45.49 0.00 0.00 0.00 0.00 120.57 2.36 2.31 0.00 55.20

RepublicGermany 78.67 68.68 67.56 0.00 0.00 104.34 36.05 175.87 0.00 0.05 0.00 41.07Estonia 93.71 107.05 50.15 0.00 0.00 0.00 35.81 124.17 0.00 0.00 0.00 67.09Spain 0.00 59.37 50.29 0.00 43.94 49.82 0.00 89.28 29.02 0.00 0.00 9.48France 0.00 69.48 45.51 0.00 0.00 0.00 41.09 83.36 0.00 0.00 0.00 19.06Italy 0.00 63.60 66.75 0.00 0.00 26.80 0.00 64.21 12.80 0.00 0.00 11.35Lithuania 10.75 52.47 40.25 0.00 0.00 0.00 23.87 84.41 12.29 2.51 0.00 14.54Hungary 0.00 81.14 84.50 0.00 0.00 0.00 14.54 22.55 4.02 3.94 0.00 14.57Austria 0.00 56.23 63.43 0.00 27.29 99.19 0.00 58.30 0.00 0.00 0.00 15.31Poland 0.00 120.49 54.57 0.00 0.00 43.40 5.91 52.74 0.00 0.00 12.91 31.87Portugal 0.00 193.28 553.25 350.34 0.00 53.36 0.00 65.89 29.04 1.37 0.00 17.36Romania 92.90 568.47 1468.94 902.25 0.00 38.70 0.00 118.56 3.11 2.14 0.00 26.72Slovenia 123.69 84.86 30.12 0.00 0.00 143.07 18.59 292.93 0.00 0.00 3.81 55.73Slovakia 0.00 87.86 119.98 0.00 0.00 41.97 0.00 39.47 28.61 3.72 0.00 28.02U.K. 215.03 74.95 99.86 0.00 0.00 38.51 27.97 106.58 0.00 2.67 0.00 0.00Croatia 16.07 0.00 92.17 73.17 0.00 28.53 4.50 42.77 17.00 0.00 7.75 0.00

TABLE 6

Direct Foreign Capital Investments in New Member States by Years (1997–2002; Million US Dollars)

1997 1998 1999 2000 2001 2002

Czech Republic 1.275 3.591 6.234 4.943 4.820 8.226Hungary 1.741 1.555 1.720 1.123 2.255 598Estonia 130 574 222 324 343 185Slovakia 84 374 701 2.058 1.460 4.007Slovenia 303 221 59 71 371 1.790Latvia 515 303 331 400 151 388Lithuania 328 921 478 375 439 714Poland 3.041 4.966 6.348 8.171 6.928 3.700Bulgaria 507 537 789 1.003 641 430Romania 1.267 2.079 1.025 1.051 1.154 1.080Turkey 805 940 783 982 3.266 585

Note: Data for Turkey from DPT (2009).All other data from IWD (2003).

significant plans dealing with the strategy of basic energy sources. This means that achievementof economic welfare and strong economy play an important role in the regulation of inputs andoutputs of energy.

In the event that Turkey proves to be an efficient country, this will enable it to be a referentfor other inefficient countries regarding maintenance of the balance between inputs and outputsof sector-specific energy. The results will not have a direct effect on the EU accession process.However, the study is highly important in that it will lead the policies developed in order to reviewits energy-specific status and to eliminate its deficiencies within the EU accession process.

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104 I. ALP ET AL.

FIGURE 4 Change in Turkey’s and EU countries’ scale efficiency scores by year. (color figure available online)

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