Energy and Sustainability

Energy and Sustainability: Theoretical and Applied Perspectives

Ed. Oktay KIZILKAYA & Emrah KOÇAK

We work with leading authors to develop the strongest academic materials with bringing cutting-edge thinking and best learning practice to a global world.

SRA Academic Publishing (Strategic Researches Academy) visit us on the World Wide Web at: www.stracademy.org

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a license permitting restricted copying. All trademarks used herein are the property of their respective owners. The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does use of such trademarks imply any affiliation with or endorsement of this book by such owners. ISBN: 978-605-67769-3-9 Copyright © 2017 by Oktay KIZILKAYA & Emrah KOÇAK Printed in the United States of America

Library of Congress Cataloging-in-Publication Data Energy and Sustainability: Theoretical and Applied Perspectives / edited by Oktay KIZILKAYA & Emrah KOÇAK p. cm. ISBN 978-605-67769-3-9

Editors Oktay KIZILKAYA & Emrah KOÇAK

Energy and Sustainability: Theoretical and Applied Perspectives

i

ii

CONTENTS

CHAPTER I GREEN GROWTH: EFFECTS OF RENEWABLE AND NON-RENEWABLE ENERGY CONSUMPTION IN THE OECD COUNTRIES Kubilay Çağrı YILMAZ & Doğan UYSAL 1. Introduction ................................................................................................................................................................1 2. Literature Review .....................................................................................................................................................5 3. Data and Method.......................................................................................................................................................8 3.1. Stationarity Tests .............................................................................................................................................8 3.2. Panel Cointegration Tests .............................................................................................................................9 3.3. Estimation of Long Term Coefficients .................................................................................................. 10 3.4. Results of Short and Long Term Causality .......................................................................................... 11 4. Conclusion ................................................................................................................................................................ 12 Appendix ........................................................................................................................................................................ 13 References ..................................................................................................................................................................... 14

CHAPTER II INDUSTRIAL OUTPUT AND INDUSTRIAL ENERGY CONSUMPTION IN TURKEY: AN EMPIRICAL ANALYSIS OF AGGREGATE AND DISAGGREGATE DATA Şerife ÖZŞAHİN 1. Introduction ............................................................................................................................................................. 17 2. Literature Review .................................................................................................................................................. 18 3. Energy Consumption in Turkey: An Overview.......................................................................................... 20 4. The Data and the Econometric Model ........................................................................................................... 26 5. Methodology ............................................................................................................................................................ 26 6. Empirical Results ................................................................................................................................................... 28 7. Conclusion ................................................................................................................................................................ 33 References ..................................................................................................................................................................... 34

iii

CHAPTER III INCENTIVE POLICIES FOR RENEWABLE ENERGY SOURCES IN TURKEY Mustafa MIYNAT & Osman GÜLDEN 1. Introduction ............................................................................................................................................................. 39 2. Overview of The Renewable Energy Concept ............................................................................................ 40 3. Renewable Energy in the World...................................................................................................................... 41 3.1. Renewable Energy in Turkey ................................................................................................................... 42 4. Turkey's Renewable Energy Policies ............................................................................................................ 43 5. Incentıves And Subsidies For Renewable Energy In Turkey............................................................... 45 5.1. Renewable Energy Support Policies and Types ............................................................................... 47 5.1.1. Regulatory Incentive Mechanisms ............................................................................................ 47 5.1.2. Financial Incentive Mechanisms ................................................................................................ 48 6. Policy Suggestions for Turkey .......................................................................................................................... 49 7. Conclusion ................................................................................................................................................................ 50 References ..................................................................................................................................................................... 52

CHAPTER IV THE EFFECT OF INDUSTRIALIZATION AND URBANIZATION ON ENERGY INTENSITY IN TURKEY Emrah KOÇAK 1. Introduction ............................................................................................................................................................. 55 2. Theory and Empirical Literature .................................................................................................................... 56 2.1. Theory ................................................................................................................................................................ 56 2.2. Empirical Literature ..................................................................................................................................... 57 3. Model, Data and Methodology.......................................................................................................................... 59 4. Results ........................................................................................................................................................................ 62 5. Conclusion ................................................................................................................................................................ 65 References ..................................................................................................................................................................... 67

iv

CHAPTER V ECONOMICAL DIMENSION OF CURSE OF NATURAL RESORCE (CNR) Yeşim KUBAR 1. Introduction ............................................................................................................................................................. 69 2. Energy Resources ................................................................................................................................................. 70 2.1. Resource and Natural Resources ............................................................................................................ 70 2.2. Classifications of Natural Resources ..................................................................................................... 71 2.3. Hypothesis of Curse of Natural Resource ............................................................................................ 72 2.4. The effects of Curse of Natural Resource ............................................................................................ 74 2.5. The Concepts Accounting for Curse of Natural Resource ............................................................. 76 2.5.1. Volatility and Prices of Natural Resources ........................................................................... 80 2.5.2. Matsuyama Model ............................................................................................................................ 81 2.5.3. Civil War ............................................................................................................................................... 82 2.5.4. Rent-Seeking Behavior................................................................................................................... 82 2.5.5. Role of Institutes ............................................................................................................................... 84 3. Dutch Disease .......................................................................................................................................................... 85 3.1. Economic Effects of Dutch Disease ........................................................................................................ 88 3.2. Country Examples of Dutch Disease................................................................................................... 89 4. Conclusion ................................................................................................................................................................ 92 References ..................................................................................................................................................................... 94

CHAPTER VI THE IMPACT OF OIL PRICES ON INFLATION EXPECTATIONS IN TURKEY Umit BULUT 1. Introduction ............................................................................................................................................................. 99 2. Motivation, contribution, and literature ................................................................................................... 102 3. Data .......................................................................................................................................................................... 103 4. Methodology ......................................................................................................................................................... 104 4.1. Narayan and Popp (2010) unit root test .......................................................................................... 104 4.2. Hatemi-J (2012) asymmetric causality test..................................................................................... 104 5. Estimation results .............................................................................................................................................. 106 6. Conclusion ............................................................................................................................................................. 107 References .................................................................................................................................................................. 109

v

CHAPTER VII OIL PRICES PASS-THROUGH TO AGRICULTURAL COMMODITY PRICES Taner TAŞ 1. Introduction .......................................................................................................................................................... 113 2. Literature Review ............................................................................................................................................... 116 3. Methodology ......................................................................................................................................................... 118 3.1. Data .................................................................................................................................................................. 118 3.2. Method ............................................................................................................................................................ 118 3.3. Empirical Results........................................................................................................................................ 120 4. Results ..................................................................................................................................................................... 123 References .................................................................................................................................................................. 126

CHAPTER VIII POLLUTION HAVEN HYPOTHESIS AND CLIMATE CHANGE: ANALYSIS FOR NEWLY INDUSTRIALIZED COUNTRIES Emrah SOFUOĞLU 1. Introduction .......................................................................................................................................................... 129 2. Literature ............................................................................................................................................................... 131 3. Econometric Analysis ....................................................................................................................................... 133 3.1. Method ............................................................................................................................................................ 133 3.2. Model and Data Set .................................................................................................................................... 134 3.3. Empirical Findings ..................................................................................................................................... 135 4. Conclusion ............................................................................................................................................................. 136 References .................................................................................................................................................................. 139

CHAPTER IX INTERNATIONAL TRADE AND ENERGY INTENSITY: CAUSALITY ANALYSIS FOR MANIFACTURING INDUSTRY OF TURKEY Aykut ŞARKGÜNEŞİ 1. Introduction .......................................................................................................................................................... 143 2. Theoretical Background and Empiric Literature .................................................................................. 146 3. Material and Method ......................................................................................................................................... 148 4. Empirical Results ................................................................................................................................................ 150 5. Conclusion Remarks .......................................................................................................................................... 152 vi

References .................................................................................................................................................................. 154 Appendix A: Sectoral Codes (ISIC Rev. 4) ...................................................................................................... 156

CHAPTER X AN OVERVIEW OF DEVELOPMENT STUDIES AND POLICIES RELATED TO GEOTHERMAL ENERGY IN TURKEY Mustafa KAN, Arzu KAN & Hasan Gökhan DOĞAN 1. Introduction .......................................................................................................................................................... 157 2. Changes in Development Policies in Turkey (From Traditional Development to Sustainable Development) ........................................................................................................................................................... 159 3. Developments in Sustainable Energy Policies of Turkey ................................................................... 163 4. Interaction of Geothermal Source Usage Policies with the other Policies (Energy, Climate, Agriculture, Tourism, and Health) and Supports ....................................................................................... 165 5. Universities in Policies; Vision and Strategies of Ahi Evran University on Geothermal ....... 177 6. Results and Suggestions .................................................................................................................................. 179 References .................................................................................................................................................................. 181

vii

viii

CHAPTER I GREEN GROWTH: EFFECTS OF RENEWABLE AND NON-RENEWABLE ENERGY CONSUMPTION IN THE OECD COUNTRIES Kubilay Çağrı YILMAZ1 & Doğan UYSAL2 1. Introduction There has been a global increase in the demand of energy and therefore, in energy consumption with rapid development of technology, diversification of demands and needs and deepening of the consumption era. Due to the scarcity of the energy sources, their unbalanced distribution in regional sense as of natural return and being inadequate to meet the decreasing energy demands, countries had to develop an energy policy constantly. Energy, taking an important place among production input, has the key role with the role of decreasing the individuals’ and countries’ welfare levels in the production foot. When it is evaluated in this respect, the relationship between the energy and the development is gaining importance in economic literature. Due to the fact that countries’ economic policies will be influenced by the results that are going to be obtained from the discussed studies, the studies that have been done in this field have been reserving its currency and the studies have been continuing. In addition, acceleration of productivity activities after industrial revolution and the geographic acceleration in rapid urbanization and population growth are another factors of energy consumption increase. Especially, after the second half of the 20th century, there has been rapid increase countries’ energy needs and actions have been taken to meet the relevant needs with the motive of producing more energy. At the end of the 20th century, because of the scarcity of fossil energy and the negative effects of the carbon dioxide that is excreted by the fossil energy, orientation to alternative energy sources decreased. In addition to more energy production attempts to meet the energy needs, countries also are drawing a route map that will reduce the greenhouse gas emission in the atmosphere. International Energy Agency has been reported that the current orientation in the need and the usage of the energy is unsustainable in the sense of economic, environmental and social factors, unless determined and permanent precautions are taken, in 2050, energy related carbon dioxide CO2 emmision will be 1

Res. Asist., Manisa Celal Bayar University, [email protected] Dr., Manisa Celal BAYAR University, doğ[email protected]

2 Prof.

1

more than twice, and the growing petrol demand will decrease the security consideration in the petrol demanding countries (Apergis and Payne 2010, p.2255). Developed and developing countries that follow their policies in this context are heading towards renewable energy sources and they are running their courses to restrict the fossil energy consumption. Authorities that runs their works on environment emphasizes in their studies on which they are investigating the global warming and the consequences of it that the main reasons of the global warming and CO2 emission are the increase in the global economy, increase in the production capacity and the inevitable increase in energy consumption. Because of the scientifically agreed increase in the CO2 emission after fossil fuel consumption and moreover, to prevent the possible causes that it may be cause years later, Kyoto protocol was prepared in 1997 and by this means, it was requested to restrict the percentage of the greenhouse emissions of the signer countries. Protocol entered in force in 2005. CO2 emission occurs when the fossil fuel is burned and it provides the biggest contribute to greenhouse emission by its own. CO2, one of the contaminatives that cause the climate change, composes the %60 of the all greenhouse gases. On the occurance of this development, worldwide rapid economic growth that has been showing trend since 1970s has been effective and accordingly, CO2 emission’s effect has been decreased. For this reason, predictions and analysis about CO2 emission claim that energy consumption and economic growth constitute the most important part of the clean energy economy (Pao, Fu, Tseng, 2012,p. 400). In addition to the devastation that the fossil energy consumption creates, as a result of this basic input unstable distribution, countries may have difficulties in the sense of cyclical purchasing. In the last 20 years, developing countries have been heading towards alternative energy sources because of the current deficit level that is the result of the fossil fuel importation. Also Turkey, one of the developing countries, is one of the main countries that are foreign-dependent in the sense of energy. In fact, Turkey eliminates the three of four its current energy consumption by importation. Turkey is one of the top 20 countries in the world in energy consumption and this exhibits the fact that its foreign-dependency in the sense of energy. Turkey that has current deficit at the highest levels, in spite of its richness in the sense of renewable energy sources, can not benefit from sun, wind, bioenergetics and geothermal energy sources. Turkey’s current deficit, in addition to known as energy gap, this current deficit effects a lot of macro indicators. This situation makes it necessary to make new investments about renewable energy sources. ‘’ Turkey, with its geopolitical power thanks to the bridge between its most energy demanding district in the world and its district that has the most intense energy sources, needs to demand multinational business cooperation in the areas such as the transfer and the process of the energy sources that will prodive Turkey the access to traditional energy sources such as petrol and natural gas by paying lower cost.’’ (Demir, 2013: 3). Numerous studies have been needed about global warming, climate change that occurs as a result of worldwide CO2 emission, cyclical and political problems that occurs during 2

energy supply process, economic growth as a result of negative returns of economic factors such as current deficit and relationship between energy consumption and environmental pollution. An important part of these studies occured in the context of petrol shocks and precessor and successor of Kyoto Protocol agreement. Again, studies about environmental pollution has been taken into account within the frame of Kuznets’ (1995) study that reveals the relationship between income distribution and economic growth. Kuznet claims that individual earnings will increase with the economic growth, however, this increase will increase the inequality in the income distribution in the first phase of the development. He emphasizes that later on, the development will grow in maturity and after this point, inequality will disappear in time. This hypothesis is called ‘’Backwards U’’ or ‘’Bell Shaped Curve’’ because of the fact that this relationship will draw a parabol in the shape of backwards U between economic growth and income inequality. Based on Backwards U hypothesis, in 1990s, Grossman and Krueger (1991, 1995) supported the idea that economic growth will cause environmental pollution in first years, however, it time, with the completion of the growth and with taking precautions to decrease environmental pollution, environmental pollution will decrease. In other words, they evaluate the fact that in developing countries, from the beggining, it will be high the negatively affected rate of environmental factors, however, this process will be compensated after the country completes its development . The hypothesis that explains this relationship is called Environmental Kuznets Curve (EKC) in literature. Figure 1. Environmental Kuznets Curve

Resource: Yandell et. al, 2004:3

Economic theories, energy consumption do not directly express that there is a clear relationship between CO2 emission and the economic growth, and there are numerous empirical studies about the existence of short-term and long-term relationship between relevant parameters. Empirical studies that have been done with these parameters constitute one of the most important areas of energy economy in the late twenty years. In these studies, it has been concluded that at the cost of neglect the active technologies and high current deficit, developing countries runs their high developing rates mostly by decreasing the energy consumption amounts (Tiwari, 2011: 95). On the other hand, it is also known that developed countries provide sustainability in their current growth

3

number by reducing fossil energy sources and heading towards renewable energy sources in years. As a result, apart from undeveloped countries, while developing countries are carrying their economic growth by fossil energy consumption, developed countries follow more environmentalist and more beneficial policies in long-term about the usage of alternative energy. In a way, even it is difficult for developing countries to target growth by reserving their capital to renewable energy sources infrastructure, in long-term period, it is doubtless that this investments will create positive effects in the sense of economic growth, environment and current deficit. It has been shown that total energy consumption in the world in years and contribution of varios energy types in this consumption in the Figure 2 retrived from BP 2016 energy report. Figure 2. Total Energy Consumption in the World

Resource: BP Statistical Review of World Energy (2016)

In the world, as it is seen in the Figure—2, in the last 25 years, agricultural sector has lost its importance and industry and service sector has come into the forefront, energy consumption has increased by rapid urbanization and population growth. Because of the fact that relevant change in the general structure of the economy requires more energy usage, demand to fossil fuels especially petrol, natural gas and coal also increased. Lately, it has been brought into question that the effects of decreasing energy consumption on the national output level. However, there are different visions about the direction of the causative relation in the world. In addition, while the relationship between energy consumption and the growth, causative relation that the energy has been included has been neglected in numerous studies. The aim of this study, is to analyse the relationship between primary energy consumption, renewable energy consumption, CO2 emission and economic growth for Chosen 12 OECD countries, with the help of the annual data between 1980-2013 by using different cointegration techniques and Granger causalty tests3. In the second part Mentioned countries are the countries that have the most renewable energy consumption since 1960, Turkey, Spain, Italy, America, Canada, Norway, Japan, France, Switzerland, Sweden, Mexico and Finland. 3

4

of the study, results of the discussed studies are being evaluated by including the empirical studies that have been tested the studies that have similar qualifications in the World and in Turkey and the existence of Environmental Kuznets Curve. In the chapter three, there are techniques that are going to be used in the study, empirical application results related to the inspected period and later on, there is evaluation about the results obtained from the study and the study is being bringing to an end with the policy suggestions.

2. Literature Review The relationship between the energy and the gworth is a subject for many studies and it was studied by Kraft and Kraft (1978) for the first time, for the USA economy. They studied depending on 1947-1974 data’s and they concluded that there is causality from the growth to the energy consumption. The relationship between the energy and the growth gathers under two roofs in literature. The first one is the causality relationship between the economic growth and various energy-based parameters (energy consumption, renewable energy consumption, CO2 emission etc.). The second one is the studies that the Environmental Kuznets Curve is being tested. Below, there are abstracts of the mentioned two types energy topical empirical testings. In the hypothesis developed by Kuznets (1955), it is suggested that in times during the countries’ development gains speed, with the rapid industrialization, firstly, the incomes and the savings of the capital owners’ will decrease and this will cause a sort of inequality. It also claims that later on, the growth’s benefits will reflect to the other individuals as high prices and savings in time and there is a backwards U shaped relationship between the income and the growth. This curve is called Kuznets Curve. In 1990s, it has been claimed that a relationship that is similar with Kuznets’ hypothesis is between the income and the environmental pollution and this has been argued for long years. Grossman and Krueger (1991), suggested that there is a relationship that is similar with Kuznets Curve between the environmental pollution and the income for the first time. Environmental Kuznets Curve hypothesis caused an argument about the fact that economic growth’s negative effects on the environment has rapidly increased in its first years, however, when the growth gains sustainabilty, pollution will be in tendency to reduce. In their studies, Grossman and Krueger (1995) pointed out both Environmental Kuznets Curve is valid and with the countries’ growth, increase in the pollution levels will be weaker when it is compared with the similar causality in the previous terms. They pointed out that it will be possible with the transfer of an experience that is gained from the developed countries in the previous years, high technology anad green energy consumption. Apergis and Oztürk (2015) tested the Environmental Kuznets Curve’s validity for 14 Asian countries for the period that covers 1990-2011 years by using GMM panel method The reason of the choice of the countries that have high renewable energy consumption is the existence of the renewable energy potential

5

and they concluded results that support the hypothesis fort his countries. Shahbaz et. al (2015), in his study, tested the validity of Environmental Kuznets Curve’s for Portugal between the years 1971-2008 by using ARDL bound testing. Test results support the hyptohesis. Oztürk and Al-mulali (2015) tested the validity of Environmental Kuznets Curve that covers the years 1996-2012 for Kampuchea and they concluded that this hypothesis is not valid for this country. Shahbaz et. al (2014) tested the validity of Environmental Kuznets Curve between the years 1971-2010 for Tunisian with ARDL bound testing approach. EKC is unvalid for Tunisian. In Lau et. al (2014) study the validity of EKC hyptohesis has been tested between the years 1970-2008 for Malaysia. According to results of ARDL bound testing approach, EKC hyptohesis is valid in Malaysia. Chandran and Tang (2013) tested the validity of EKC hyptohesis for Association of Southeast Asian Nations by using Johansen cointegration test for the period that covers the years 19712008. It has been concluded that EKC hypothesis in invalid. Wang et. al (2011) tested the validity of EKC hyptohesis between the years 1995-2007 for 28 districts of China. According to Pedroni cointegration test results, EKC hypothesis is not valid. Jalil and Mahmud (2009) tested the validity of EKC for the years 1975-2005 for China by using ARDL bound testing and it has been concluded that EKC hypothesis is valid for China. Ozcan, B. (2013) tested the EKC hyptohesis for middle east countries. He found in the analysis that he made with the help of the data from the years 1990-2008 that by using FMOLS method, EKC is valid in 5 of 12 countries. Bilgili, Koçak and Bulut (2016) in their studies that was published in 2016 tested the validity of Environmental Kuznets Curve with the data of the years 1977-2010 for 17 OECD countries and they found that for both FMOLS and DOLS, EKC is valid in 12 countries. Hondroyiannis, Lolos and Papapetroa (2002) studied the relationship between the energy consumption and the growth by using vector error-correction modal for Greece by using the data between 1960-1996. In empirical study, it has been observed that energy consumption and growth parameters are cointegrated in long term and energy consumption is affective in determining the economic growth. Paul and Bhattacharya (2004), analyzed the causal cohesion between the energy consumption and economic growth for India. By applying Engle-Granger cointegration and standard Granger causal tests, with the help of the data from 1950-1996, they showed that parameters are in interaction with each others. Lise and Montfort (2007), tested the relationship between the energy consumption that the data from 1970-2003 is being used, results of cointegration and vector errorcorrection modal showed that parameters act in cohesion in long term and causality occurs from GSYIH to energy consumption. Kar and Kınık (2008), anaylsed the relationship between the total electric consumption and economic growth for the years 1975-2005 by using Johansen cointegration 6

approach and vector error-correction modal and as a result, they found that parameters are cointegrated and there are long-term relationship between these parameter pairs. Halicioglu (2009), pointed out that in the increase of the pollution level, income, energy consumption and foreing trade are affective respectively by using the data from 19602005 in his study that analysis the relationship between the pollution and income for Turkey. Mucuk and Uysal (2009), analysed the relationship between the energy consumption and economic growth in Turkey by using unit root, cointegration and Granger causality analysis with the data from 1960-2006 periods. They showed that in long term, energy consumption and the growth are acting in cohesion and by using Granger causality test, they showed that the relationship between the parameters occur from the energy consumption to the economic growth and energy consumption affects the growth in a positive wat. Güvenek and Alptekin (2010) analysed the causality relationship between the energy consumption and the economic growth by using Panel Data method for 25 OECD countries. In the study that covers 1980-2005 period, GSYIH and energy consumption data is taking into consideration in cointegration analysis. According to the results of the analysis, economic growth is affecting the energy consumption and there is cointegration between the parameters. Tsani (2010), analysed the Granger causality and VAR according to the data from 19602006 and made evaluations to the industry sector and energy consumption. In the study, they observed that causality occurs from the energy consumption to the real GSYIH. Yanar and Kerimoğlu (2011), analysed the relationship between the current deficit, economic growth and energy consumption for 1975-2009 in the study. As a result of Johansen cointegration, action and reaction and varians decomposition, it has been seen that an increase that occurs in energy consumption affects GSYIH in a high rate. Hossain (2011), searched the relationship between CO2 emission, energy consumption, economic growth, trade gap and urbanization rate of 9 less developed countries (Brazil, China, India, Malaysia, Mexico, Philippines, South Africa, Thailand and Turkey) by using panel data and causality tests. It has been concluded that parameters are cointegrated şn Fisher panel test. In Granger causality tests, there were no long-term causal relationship, however, in long-term, there were Granger causal from economic growth and trade gap to CO2 emission, from economic growth to energy consumption, from trade gap and urbanization to economic growth and from trade gap to urbanization. In long-term, energy consumption related CO2 emission flexibility is bigger than short-term flexibility and it has been stated that this will worsen the environmental pollution by increasing CO2 emission of energy consumption in the examined countries. Altıntaş, (2013), studied the relationship between primary energy consumptioni CO2 emission and the growth with the help of causality analysis. In the study, he suggested that income per capita and energy consumption are the results of carbon dioxide 7

emission for short-term in Granger causality tests. In long-term, it has been seen that there is a one way causality relationship from the income per capita and energy consumption to the carbon dioxide emission. Tas and Uysal (2016) were tried to examine the relationship between urbanization and carbon dioxide emissions for Turkey. In order to determine this relationship, the effect of the population living in urban areas of Turkey and other factors of urbanization on the emission of carbon dioxide was investigated using the annual time series data covering 1968-2011 period. According to the results of the analysis, urbanization has a positive and significant effect on carbon dioxide emissions in the long run. There is a one-way relationship between urbanization and carbon dioxide emissions. Kızılkaya, Sofuoğlu and Çoban investigated (2016), the relationship between carbon dioxide emissions in Turkey, energy consumption in transportation sector, economic growth and openness in Turkey. Using Johansen (1990) Maximum Likelihood-Trace tests, the cointegration relation was obtained in the data of 1967-2010. A long-run relationship between carbon dioxide emissions, transport sector energy consumption, economic growth and openness has been identified in the study, and a positive impact of economic growth and openness on carbon dioxide emissions has been emphasized. When these results are evaluated collectively, it can be seen that CO2 emissions in Turkey are seriously affected by economic activities

3. Data and Method In this study, the logarithms of carbon dioxide emissions (CO2), gross domestic product (GDP), non-renewable energy consumption (fec) and renewable energy consumption (renew) data of the 12 developed and developing countries including Turkey obtained from websites of World Bank and OECD covering the period between the years 1980 and 2013 has been used. The model used in the study is as follows: gdpit = f(co2it, fecit, renewit)

(1)

Equation (1) can also be written as: gdpit = co2itβ1i, fecitβ2i, renewitβ3i

(2)

Finally, if we take the natural logarithm of equation (2), we obtain the following equation: gdpit = αi + δit + β1ico2it + β2ifecit + β3irenewit+ɛit

(3)

i = 1, …, N refers to country t =1, …, T to time, (ɛ) to stochastic error term, αi ve δit, parameters to stationary effect or deterministic trend specific to the country in equation (3).

3.1. Stationarity Tests Stationarity levels of variables require to be examined before proceeding to investigate long-term relationships between variables that are subject to analysis. Levin, Lin and Chu (2002) LLC, Im, Pesaran and Shin (2003) IPS, Breitung (2000), Fisher-ADF and 8

Fisher-PP unit root tests have been used for this. Table 1 contains the unit root results of the ground and first difference levels for the variables. These results show that the variables generally include unit root at the ground level but they are stationary i.e. they do not contain the unit root when the first differences are considered. Table 1: Results of Panel Unit Root Test co2 fec gdp renew 1.034 -0.922 -1.5244 -2.8038 Level (0.849) (0.178) (0.0637)* (0.0025) LLC First -15.225 -9.736 -12.6931 -18.1292 derivative (0.0000)*** (0.0000)*** (0.0000)*** (0.0000)*** 2.363 3.052 2.8027 1.2338 Level (0.990) (0.998) (0.9975) (0.8914) Breitung First -7.6161 -3.1777 -6.6025 -10.4222 derivative (0.0000)*** (0.0007)*** (0.0000)*** (0.0000)*** 0.608 0.322 0.4705 -2.1277 Level (0.728) (0.626) (0.6810) (0.0167)** IPS First -15.2799 -12.3899 -10.4323 -18.8426 derivative (0.0000)*** (0.0000)*** (0.0000)*** (0.0000)*** 35.119 50.499 19.4776 46.3331 Level (0.066)* (0.0012)*** (0.7261) (0.0040)*** ADF First 212.394 168.941 135.647 293.549 derivative (0.0000)*** (0.0000)*** (0.0000)*** (0.0000)*** 33.904 52.119 21.4654 50.8006 Level (0.086)* (0.0008)*** (0.6111) (0.0011)*** PP First 871.631 571.457 224.596 1073.51 derivative (0.0000)*** (0.0000)*** (0.0000)*** (0.0000)*** Note: Probabilities of the statistics are presented in parantheses. ***, ** and * indicate that the test statistics is significant at %1, %5 and %10 levels respectively. The Schwarz information criterion (SIC) has been used to determine the optimal lag length. Method

3.2. Panel Cointegration Tests Application of panel unit root tests were followed by cointegration tests of Pedroni (1999, 2004) and Kao (1999) for I (1) series integrated at the first level to determine the existence of a long-term relationship between variables. The Pedroni (2004) cointegration test consists of seven statistics distributed over the two cointegration test sections. The first section covers four panel statistics and includes v-statistics, rhostatistics, PP-statistics and ADF-statistics. These statistics are classified as indimensional and take into account common autoregressive coefficients between regions (provinces). The second part covers three groups of statistics, including the rho statistic, the PP statistic and the ADF statistic. These tests are categorized on dimensions and are based on individual autoregressive coefficients for each region (province). The null hypothesis suggests that there is no cointegration, and the alternative hypothesis indicates cointegration between variables. Kao (1999) developed a fault-based test statistic based on the null hypothesis that there is no cointegration. Unlike Pedroni tests, Kao tests provide homogeneity because it does not allow the slope coefficients in equation (3) to vary between singular members of the panel.

9

Table 2: Results of Panel Cointegration Tests Pedroni Panel Co-Integration test Result Weighted t-statistics P-value P-value t-statistics Panel v- statistics 0.398 0.345 -0.733 0.768 Panel rho- statistics -0.641 0.260 -1.836 0.033 Panel PP- statistics -1.995 0.023** -3.858 0.000*** Panel ADF- statistics -1.572 0.057* -3.999 0.000*** Group rho- statistics -0.300 0.381 Group PP- statistics -2.928 0.001*** Group ADF- statistics -1.757 0.039** Kao Panel Co-Integration test Result t-statistics P-value ADF -2.347 0.009*** Note: Trend assumption: Individual intercept. Automatic lag length selection based on Schwarz information criterion (SIC). Newey-West automatic bandwidth selection and Bartlett kernel. ***, ** and * indicate that the test statistics is significant at %1, %5 and %10 levels respectively.

When the results of the Pedroni panel cointegration tests in Table 2 are examined, it is seen that most of the test statistics reject the null hypothesis that there is no cointegration. Similarly, the Kao panel cointegration test results also indicate the existence of a cointegration relationship. In this case, a long-term relationship among income, CO2 emissions, renewable and non-renewable energy consumption has been achieved.

3.3. Estimation of Long Term Coefficients In the last stage, an estimation of long-term coefficients between CO2 emission, renewable and non-renewable energy from the independent variables in equation (3) and dependent variable income has been conducted. In the context of the panel estimation, as the estimator of ordinary least squares (OLS) deviate, the fully modified least squares (FMOLS) and dynamic least squares (DOLS) panel approximations proposed by Pedroni (2001, 2004) have been used in addition to least squares (OLS) in the process of estimating the coefficients of equation (3). Table 3: Estimation of Panel OLS-FMOLS-DOLS Long Term Coefficient Method/Variables Co2 fec renew 0.506 -0.846 0.060 OLS (0.000)*** (0.0000)*** (0.068)* 1.002 -1.693 1.052 FMOLS (0.000)*** (0.000)*** (0.000)*** 0.992 -1.152 1.257 DOLS (0.000)*** (0.048)** (0.000)*** Note: Cointegration equation deterministics: intercept. All variables are measured in natural logarithms. . ***, ** and * indicate that the test statistics is significant at %1, %5 and %10 levels respectively.

When OLS, FMOLS and DOLS long term coefficients estimated for the equation (1) in Table 3 are examined, it is seen that the three methods used produce similar results both in terms of the signs of the coefficients and in statistical significance. According to

10

these results, it increases by 0.506%, -0.846% and 0.060% respectively, when the Co2, fec and renew variables are increased by 1%.

3.4. Results of Short and Long Term Causality Since there is a cointegration relation between the variables used in the study, an error correction model (VECM) can be established to determine the direction of this relationship. The error correction model used in the study can be written as: (4)

(5)

(6)

(6) (7) In the above equations, Δ is the first difference operator; Q is the delay length automatically determined by the Schwarz information criterion; μ is the random error term; ECT is the modification term derived from the long-term relation obtained by equation (3). The above equations allow examining both short- and long-term causality relations. The short-term causal relations were determined by Wald test and the longterm causality relations were determined by the statistical significance of the error correction coefficient and the direction of the sign. Table 4: Results of VEC Granger Short Term Causality Test Short Term Long Term Δgdp Δco2 Δfec Δrenew ECT 0.407 0.183 0.712 -0.012*** (0.523) (0.668) (0.398) (0.001) Δco2 0.000 2.411 0.695 0.003 (0.985) (0.120) (0.404) (0.392) Δfec 0.082 0.850 0.572 0.005 (0.774) (0.356) (0.449) (0.041) Δrenew 0.907 7.496*** 25.087*** 0.003 (0.340) (0.006) (0.000) (0.164) Note: Lag lengths selected is 1 based on the Schwarz information criterion. P-value listed in parantheses. . ***, ** and * indicate that the test statistics is significant at %1, %5 and %10 levels respectively.

Dependent Variable Δgdp

According to the results in Table 4, a one-way causal relationship was found in the short term, from only Co2 emission and non-renewable energy consumption towards renewable energy consumption. In the long term, there is a one-way causal relationship

11

from CO2 emission, non-renewable energy consumption and renewable energy consumption towards product.

4. Conclusion As a result of this study, the relationship between primary energy consumption, renewable energy consumption, CO2 emission and economic growth for Chosen 12 OECD countries is analysed with the help of the annual data between 1980-2013 by using different cointegration techniques and Granger causalty tests. All parameters were tried as an independent variable to observe all relationship combinatorially. Thus, we found fit model which clarify dependances of variables. Our findings suggest that use of non-renewable energy causes negative impact on growth for selected countries. On the contrary, economic growth is affected positively thanks to renewable energy consumption. Moreover, while fossil energy consumption have increased CO2 emission, renewable energy has been inversely related with it. When OLS, FMOLS and DOLS long term coefficients are examined according to the equation 1 in Table 3, the coefficients for the three methods used show similar results in terms of signs and statistical significance. According to these results, Co2 increased 0.506%, -0.846% and 0.060%, respectively, when the FEC and regeneration variables increased by 1%. According to the results in Table 4, there is a one-way causality relation between renewable energy consumption and Co2 emission and fossil energy consumption in the short term. In the long run there is a one-way causal relationship from CO2 emissions, non-renewable energy consumption and renewable energy consumption to growth. According to the information obtained as a result of the analysis, there is a negative relationship between total fossil energy consumption and growth. One of the main reasons for the existence of this relationship should be the fact that the 10 selected countries are developed countries and that these countries have reduced their fossil energy consumption by years and have turned to renewable energy sources. The graph of the energy consumption of the countries is given in Figure 3. In this result light, it is revealed that the growth can be realized by reducing the fossil energy consumption. The cost of moving to renewable energy sources at the top of the main draws of developing countries and the negative impact on growth is also unfounded. As a result, it is possible to protect the environment and grow by using renewable energy resources.

12

Appendix Figure 3. Fossil Energy Consumption (Country by country) FEC Canada

Finland

4.40

4.3

4.38

4.2

France

Italy

4.6

4.56 4.52

4.4

4.36

4.1 4.48

4.34 4.0

4.2

4.32

4.44 3.9

4.30

4.0

4.26 1980

4.40

3.8

4.28

1985

1990

1995

2000

2005

2010

3.7 1980

1985

1990

Japan

1995

2000

2005

2010

3.8 1980

1985

1990

Mexico

4.56

2000

2005

2010

4.36 1980

1985

1990

Norway

4.51

1995

2000

2005

2010

2000

2005

2010

2000

2005

2010

Spain

4.15

4.56 4.52

4.50

4.52

1995

4.10 4.48

4.49 4.48

4.05

4.44

4.00

4.40

4.48 4.44 4.47 4.40 4.36 1980

4.36 3.95

4.46

1985

1990

1995

2000

2005

2010

4.45 1980

1985

1990

Sweden

1995

2000

2005

2010

3.90 1980

4.32

1985

1990

Switzerland

4.2

1995

2000

2005

2010

1985

1990

Turkey

4.3

1995

US

4.55

4.52

4.50

4.0

4.28 1980

4.50

4.2 4.45

3.8

4.48 4.1

4.40

3.6

4.46 4.35 4.0

3.4 3.2 1980

4.44

4.30

1985

1990

1995

2000

2005

2010

3.9 1980

1985

1990

1995

2000

2005

2010

4.25 1980

13

1985

1990

1995

2000

2005

2010

4.42 1980

1985

1990

1995

References Al-Mulali, U., & Ozturk, I. (2015). The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in the MENA (Middle East and North African) region. Energy, 84, 382-389. ALTINTAŞ, 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). Apergis, N., & Payne, J. E. (2010). Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy policy, 38(1), 656-660. Apergis, N., & Ozturk, I. (2015). Testing environmental Kuznets curve hypothesis in Asian countries. Ecological Indicators, 52, 16-22. Apergis, N., & Payne, J. E. (2010). The emissions, energy consumption, and growth nexus: evidence from the commonwealth of independent states. Energy Policy, 38(1), 650-655. Bilgili, F., Koçak, E., & Bulut, Ü. (2016). The dynamic impact of renewable energy consumption on CO 2 emissions: a revisited Environmental Kuznets Curve approach. Renewable and Sustainable Energy Reviews, 54, 838-845. Uysal, D., Tandoğan, A., & Taş, T. (2016). Kentleşme, Enerji Tüketimi ve Çevresel Bozulmalar (Kirlilik-CO2) Arasındaki İlişkinin Yapısal Kırılmalar Altında İncelenmesi: Türkiye Örneği. Uşak University Journal of Social Sciences, 9(28). DEMİR, M. (2013). Enerji ithalati cari açik ilişkisi, Var analizi ile Türkiye üzerine bir inceleme. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD), 5(9). Levin A, Lin CF, Chu CS. “Unit Root Tests in Panel Data: Asymptotic And Finite Sample Properties”. J Econ 2002;108:1e24. Lise, W., and Montfort, K. V. 2007. Energy consumption and GDP in Turkey: Is there a cointegration relationship? Energ. Econ. 29:1166–1178. Im KS, Pesaran MH, Shin Y. “Testing For Unit Roots in Heterogeneous Panels”. J Econ 2003;115:53e74. Breitung, J. (2000). The Local Power of Some Unit Root Tests for Panel Data, in: B.Baltagi (Ed.) NonStationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics, 15, 161-178, JAI Press, Amsterdam. Govindaraju, V. C., & Tang, C. F. (2013). The dynamic links between CO 2 emissions, economic growth and coal consumption in China and India. Applied Energy, 104, 310-318 Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American free trade agreement (No. w3914). National Bureau of Economic Research. 14

Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The quarterly journal of economics, 110(2), 353-377. Güvenek, B., & Alptekin, V. (2010). Enerji tüketimi ve büyüme ilişkisi: OECD Ülkelerine İlişkin bir panel veri analizi. Enerji, Piyasa ve Düzenleme, 1(2), 172-193. Halicioglu, F. (2009). An econometric study of CO 2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy, 37(3), 1156-1164. Hadri, Kaddour. 2000. “Testing for Stationarity in Heterogeneous Panel Data,” Econometrics Journal 3: 148-161. Hondroyiannis, G., Lolos S, & Papapetroa, E. (2002). Energy consumption and economic growth: assessing the evidence from Greece. Energy Economics, 24, 319-336. Hossain, M. S. (2011). Panel estimation for CO 2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries. Energy Policy, 39(11), 6991-6999. Jalil, A., & Mahmud, S. F. (2009). Environment Kuznets curve for CO 2 emissions: a cointegration analysis for China. Energy Policy, 37(12), 5167-5172. Kao, Chihwa. 1999. “Spurious Regression and Residual - Based Tests for Cointegration in Panel Data,” Journal of Econometrics 90: 1-44. Kar, M. and Kınık, E. (2008), “Turkiye’de Elektrik Tuketimi Çeşitleri ve Ekonomik Buyume Ekonometrik Bir Analizi”, Afyon Kocatepe Uni., 10(2), 333-353. Kızılkaya, O., Sofuoğlu, E., and Çoban, O. (2016). “Ekonomik Büyüme, Enerji Tüketimi Ve Çevre Kirliliği Analizi: Türkiye Örneği”, Kırıkkale Üniversitesi Sosyal Bilimler Dergisi, 6(2). Kraft, J., & Kraft, A. (1978). On the relationship between energy and GNP. The Journal of Energy and Development, 401-403. Kuznets, S. (1955). Economic growth and income inequality. The American economic review, 1-28. Lau, L. S., Choong, C. K., & Eng, Y. K. (2014). Investigation of the environmental Kuznets curve for carbon emissions in Malaysia: Do foreign direct investment and trade matter?. Energy Policy, 68, 490-497. Mucuk, M. and Uysal, D. (2009), “Türkiye Ekonomisinde Enerji Tüketimi ve Ekonomik Büyüme”, Maliye Journal, 157, 105-115. Ozcan, B. (2013). The nexus between carbon emissions, energy consumption and economic growth in Middle East countries: A panel data analysis. Energy Policy, 62, 1138-1147. Pao, H. T., Fu, H. C., & Tseng, C. L. (2012). Forecasting of CO 2 emissions, energy consumption and economic growth in China using an improved grey model. Energy, 40(1), 400-409. 15

Paul, S., & Bhattacharya, R. N. (2004). Causality between energy consumption and economic growth in India: a note on conflicting results. Energy economics, 26(6), 977-983. Pedroni P. “Critical Values For Cointegration Tests in Heterogeneous Panels With Multiple Regressors”. Oxford Bulletin of Economics and Statistics 1999;61 syf:653-670. Pedroni P. “Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The PPP Hypothesis”. Econ Theory 2004;20 syf:597-625. Pedroni P. “Purchasing Power Parity Tests in Cointegrated Panels”. Rev Econ Statistics 2001;83:727e31. Shahbaz, M., Khraief, N., Uddin, G. S., & Ozturk, I. (2014). Environmental Kuznets curve in an open economy: A bounds testing and causality analysis for Tunisia. Renewable and Sustainable Energy Reviews, 34, 325-336. Tsani, S. Z. (2010). Energy consumption and economic growth: a causality analysis for Greece. Energy Economics, 32(3), 582-590. Tiwari, A. K. (2011). Energy consumption, CO2 emissions and economic growth: Evidence from India. Journal of International Business and Economy, 12(1), 85122. Wang, S. S., Zhou, D. Q., Zhou, P., & Wang, Q. W. (2011). CO 2 emissions, energy consumption and economic growth in China: a panel data analysis. Energy Policy, 39(9), 4870-4875. Yanar, R., & Kerimoğlu, G. (2011). TÜRKİYE’DE ENERJİ TÜKETİMİ, EKONOMİK BÜYÜME VE CARİ AÇIK İLİŞKİSİ. Ekonomi Bilimleri Dergisi, 3(2).

16

CHAPTER II INDUSTRIAL OUTPUT AND INDUSTRIAL ENERGY CONSUMPTION IN TURKEY: AN EMPIRICAL ANALYSIS OF AGGREGATE AND DISAGGREGATE DATA Şerife ÖZŞAHİN1 1. Introduction Energy is one of the sources that accelerates a country’s economic and social development. It is widely used both in houses to meet daily needs and in the sectors of industry, transportation, and trade (TP, 2016: 4). Mainly production in industrial sector requires use of energy sources effectively in addition to labor and capital factors. The impact of energy use on production has led many researchers to examine the relationship between energy consumption and output level (Qazi et al., 2012: 1). Increasing income level and population brought about by urbanization and industrialization are among the major factors leading to an increase in world primary energy consumption (ETKB, 2016: 3). Many different stages of industrial production necessitate the consumption of various types of energy. According to the IEA 2016 data, industry sector accounts for 31.6% of worldwide primary total energy consumption. Industrial energy consumption increased by 89% during the 1950-2016 period. The need for energy sources to meet the energy demands is continuously growing as well. Almost 75% of oil and natural gas reserves are in the Middle East, Europe, Russia, and Central Asia. Thus, Turkey has a very significant geopolitical role in transmitting the energy sources in Central Asia to the world markets (Erdal et al., 2008: 3838). Moreover, Turkey, which has limited energy sources and which is an importing country particularly fossil energy sources, demands a considerable amount of energy to be used in production in many sectors or for final consumption (Yılmaz and Atak, 2010: 224). Consequently, it can be said that there is a close relationship between energy consumption and production level. The relationship between economic growth and energy consumption in Turkey has been investigated in many studies such as Soytas and Sarı (2003), Altınay and Karagöl (2004, 1

Assist. Prof. Dr., Necmettin Erbakan University, Department of Economics, [email protected]

17

2005), Sarı and Soytaş (2004), Şengül ve Tuncer (2006), Lise and Montfort (2007), Jobert and Karanfil (2007), Erbaykal (2008), Erdal et al. (2008), Kar and Kınık (2008), Mucuk and Uysal (2009), Acaravcı (2010), Ağır and Kar (2010), Kaplan et al. (2011), Yalta (2011), Acaravcı and Öztürk (2012), Çetin and Seker (2012), Saatçi and Dumrul (2013), Tuğcu (2013), Akpolat and Altıntaş (2013), Bayar (2014), Erdoğan and Gürbüz (2014), Pata and Terzi (2016), and Kızılkaya et al. (2016). The majority of these studies on Turkish economy examined the long-term relationship and the causality interaction between total energy consumption and economic growth. However, the effect of the industrial sector where energy is used the most and the disaggregated energy consumption on industrial production in Turkey has not yet been explored. To fill the gap in the literature, this study examines the long-term relationship between the total and disaggregated energy consumption in Turkey, and industrial production through the bounds test and autoregressive distributed lag (ARDL) method. The remainder of this paper is organized as follows. The studies which analyzed energy consumption and industrial production through disaggregated data and their findings are explored in Section 2. In Section 3, a brief evaluation of energy consumption in Turkey is made. The data set and the econometric model are explained in Section 4, followed by information on the econometric method. Section 6 interprets the empirical findings, and the study ends with the conclusion section.

2. Literature Review Having adequate and safe energy sources is among the factors that increase a country’s economic welfare and the level of development. In countries without adequate sources, import becomes mandatory to meet the energy need. Fields of activity which require high amounts of energy consumption are industry, residents, and transportation. A limited number of studies has so far examined the relationship between the industrial value added and energy consumption. These studies and their findings are explored below. Ewing et al. (2007) examined the effect of traditional energy sources of coal, fossil fuels and natural gas, and the renewable energy sources of waste, hydroelectric, solar, wood, and wind energy on industrial production in the US using the monthly data for the period between January 2001 and June 2005. In another study, Sarı et al. (2008) explored the relationship between eight different disaggregated energy consumption variables and industrial production in the US through the ARDL method. The findings of the analysis conducted with the monthly data for the January 2001 and June 2005 period revealed that industrial production is the primary determinant factor behind fossil fuel, conventional hydroelectric power, solar, waste and wind energy consumption. Jobert and Karanfil (2007) examined the relationship between industrial production and energy consumption for the 1960-2003 period in Turkey using the Johansen cointegration and Granger causality analysis. The tests showed that there is no relationship between industrial energy consumption and the industrial value added. 18

Zamani (2007) tested the relationship between industrial value added and industrial total energy, electricity, natural gas and oil consumption in Iran using the VECM method. The results of the analysis carried out with the data on the 1967-2003 period indicated that there is uni-directional causality from industrial value added to industrial total energy, oil products, and electricity consumption. Furthermore, there is bi-directional causality between industrial gas consumption and industrial value added. Soytaş and Sarı (2007) investigated the relationship between Turkish manufacturing sector electricity consumption and value added through a model which includes manufacturing industry fixed capital investment and manufacturing industry employment. The yearly data for the 1968-2002 period were analyzed with the Johansen cointegration test. The results revealed that electricity consumption in Turkey is among the important determinants of manufacturing sector production. Granger causality test results indicate that there is uni-directional causality running from electricity consumption to manufacturing value added in the long-run. Ziramba (2009) examined the causality relationship between South African coal, electricity and oil consumption, and industrial production with the Toda and Yamamoto method. The findings gathered from the data for the 1980-2005 period indicate that there is bi-directional causality between industrial production and oil consumption. However, no causality relationship was observed between coal and electricity consumption, and industrial production. Kouakou (2011) examined the relationship between per capita GDP and industrial value added, and electricity consumption for Cote d’Ivoire using the data for the 1971-2008 period. The results of the analysis indicate that there is causality between industrial value added and per capita electricity consumption in short and long-run. Zheng and Luo (2013), who conducted a study on China, tested the relationship between industrial sector oil consumption and industrial output using the data for the 1978-2009 period. The findings of VECM and Granger causality methods indicate that although industrial production is the Granger cause of industrial oil consumption in the long-run, there is no such relationship in the short-run. Qazi et al. (2012) examined the relationship between disaggregated energy consumption and industrial output in Pakistan using the data for the 1972-2010 period. The findings of Johansen cointegration test point to the positive effects of disaggregated energy consumption on industrial output. Another study by Abid and Mraihi (2015) investigated the relationship between aggregated and disaggregated energy consumption and industrial production for the Tunisian economy. In the analysis conducted with the yearly data for the 1980-2007 period, the Johansen cointegration test and Granger causality method were used. The findings indicate a uni-directional causality running from total energy consumption to industrial output in the short-run. Thus, it can be said that industrial sector growth rate in Tunisia depends highly on total energy consumption. The analysis conducted using the disaggregated energy data indicated that electricity consumption in the short-run and oil consumption both in the short and long-run are the Granger cause of industrial production. In another study on Tunisia, Abid and Sebri (2012) did cointegration analysis for the 1980-2007 period and 19

found a uni-directional causality from industrial value added to industrial energy consumption. This finding points to a close relationship between energy consumption and industrial production performance.

3. Energy Consumption in Turkey: An Overview Turkey is the candidate for being the energy corridor between the Middle East and Asian countries, and Europe. However, while energy demand is increasing, energy deficit is met through import as energy production is low in Turkey (WEC-TNC, 2002: 3). Total primary energy supplies refer to total amount of supply consumed or transformation. In line with the energy need that emerged together with the economic growth from the 1970s to present in Turkey, energy supply also started to increase. As seen in Table 1, following the year 2000, energy demand has increased in parallel with the growth in the Turkish economy. Total final energy consumption, which was 57.8 million tonnes of oilequivalent (Mtoe) in 2000, decreased because of 2008 global financial crisis, but in the following years, this increase was compensated and it reached 87.3 million tonnes of oilequivalent (Mtoe) in 2012 with 18% increase, and 85.75 Mtoe in 2014. The total primary energy supply, which was 129.7 Mtoe in 2015, indicates that energy demand has increased by 54% in the last 10 years (IEA, 2016a: 22). Year 1971 1980 1990 2000 2010 2011 2012 2013 2014

Table 1: Key Energy Statistics of Turkey (ktoe) Total Primary Energy Total Final Production Imports Exports Supply Consumption 13809 6215 -105 19544 16173 17137 14671 -288 31449 26317 25815 30035 -962 52717 40072 25857 52227 -1330 75957 57846 32398 82223 -7113 106658 77750 32227 89636 -8369 113506 81667 30718 98709 -8560 118223 87331 31497 96419 -8153 116937 85220 31348 101567 -7845 121541 85751 Source: IEA, Headline Global Energy Data, 2016 Edition.

When Turkey’s present situation is examined concerning energy sources, it can be said that domestic energy sources cannot meet the total energy demand. Turkey, which is the net importer of fossil energy sources, met the 26.6% and 25.1% of energy demand with domestic sources in 2013 and 2014, respectively (ETKB, 2016a: 14). As seen in Graph 1, Turkey’s energy dependence has increased since 1990, which reached 75% in 2014 (TP, 2016: 25).

20

Figure 1: Energy Dependency of Turkey (1990-2014) (%) 80 70 60 50 40 30 20 10 0

67,2 51,6

72,4 72,7 73,8 71,8 70,6 69,4 71,9 73,4 73,5

75

57,7

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: TP, 2016: 26.

When the primary energy consumption across sectors is examined, it is seen that 30% of the total energy is used in cycle sector, or in electricity production, while 24%, 23%, and 19% is used in household and service sector, in industry, and in transportation, respectively (TP, 2016: 25). Figure 2 shows the process of total final energy consumption from 1970 to present. While energy consumption in each sector increased in time, this growth accelerated even further after the 1990s. Energy has been consumed in four main fields of activity in Turkey. In 1970, energy consumption in residents, commerce, industry, and transportation was the 20%, 12%, 44%, and 24% of the total domestic energy consumption, respectively. In 2000, energy consumption in residents followed the same trend. However, the share of industrial sector in total energy consumption decreased to 35%, and the share of transportation and commerce sector in the total energy consumption increased to 17% and 27%, respectively. In 2016, total final energy consumption occurred at 31.7% in the industrial sector, at 21% in residents, at 28.7% in transport, and at 18% in commercial and other service sectors. Figure 2: Total Final Energy Consumption by Sectors in Turkey (1971-2014) 120000 100000 Transport

80000

Industry

60000

Commercial 40000

Residental

20000 0 1970 1980 1990 2000 2005 2010 2011 2012 2013 2014 2015 2016 Source: IEA (2017), Energy Consumption by Sectors. http://www.eia.gov/totalenergy/data/monthly/dataunits.cfm

Parallel to high growth performance of developing countries, energy and electricity consumption demands increased as well. As per capita income grew, individuals who reached higher life standards had increased demands of lighting and home appliances, which led to an increase in electricity consumption. First five countries in electricity 21

production all over the world in 2014 are China, the USA, India, Russia, and Japan. Turkey is the 19th on the list with about 252 billion kWh electricity production (ETKB, 2016a: 5-6). The successful restructuring and privatization in electricity distribution sector between the years 2008 and 2013 led to a considerable amount of private investment in this sector. Turkey’s electricity generation has improved greatly particularly in the last ten years, and it broke the record in 2015 with 259.7 terawatt-hours (TWh) (IEA, 2016a: 131). Turkey’s total electricity generation in 2015 was met by natural gas, coal, hydrolic sources, and wind with 37.7%, 28.2%, 25.7%, and 45%, respectively (ETKB, 2016a: 15). Figure 3: The Source of Electricity Production in Turkey (2015) Geothermal 1.6% Coal 28.2%

Wind 4.5%

Natural Gas 37.7%

Hydro 25.7% Fuel Oil 2.3%

Source: ETKB, 2016a: 15.

As a result of technological development, the decrease in costs, and a considerable amount of incentives given by governments in many countries, renewable energy has started to compete with other energy sources. Renewable energy has recently been used in more fields considering the harms that other energy sources give to the environment (ETKB, 2016a: 6-7). Turkey’s natural gas consumption has significantly increased in recent years, which may be attributed to the use of natural gas in heating homes and for electricity generation in other fields with the effect of expansion in domestic gas distribution network (IEA, 2016a: 25). As only 24.8% of the need for natural gas and oil is met with domestic sources in Turkey, dependence on import particularly for these energy sources is high (IEA, 2016: 22). In 2014, 92% of oil, 99% of natural gas, and 94% of coal consumed in Turkey was met through import (ETKB, 2016a: 14). Based on this data, it can be said that 25% of primary energy demand was met with domestic production in 2014. As this rate, which is the lowest in the last ten years, shows, Turkey’s energy dependence is at the level of 75%. The rate of foreign energy dependence increased in parallel to the increasing natural gas consumption particularly after the 1990s, and it reached 70% in the 2000s (TP, 2016: 25).

22

Figure 4: Total Energy Consumption by Source in Turkey (1980-2014) 100% Electricity

80% 60%

Renewable

40%

Natural gas

20% 0% 1980

1990

2000

2005

2010

2014

Source: IEA, Headline Global Energy Data, 2016 Edition.

The distribution of energy sources across energy consumption starting from 2000 is seen in Figure 4. The mostly consumed energy types were oil (45.3%), coal (18.7%) and electricity (14.3%) in 2000. However, this list has changed over the years. While the consumption of oil did not change in the total final energy use with 35.6% in 2014, this rate was followed by natural gas with 22.6% and electricity with 20.6%. These figures reveal that Turkey has recently witnessed a considerable increase in natural gas consumption. Figure 5: Total Energy Consumption by Source in Turkey in 2000 and 2014

2000 ElectricitOther 1% y 14.3%

2014 Other Coal 2.4% 12.3% Electricity 20.6%

Coal 18.7%

Renewa ble 12.7% Natural gas 8.5%

Renewable 6.5%

Oil 35.6%

Natural gas 22.6%

Oil 45.2%

Source: IEA, Headline Global Energy Data, 2016 Edition.

Oil, which is in the first place in the world regarding primary energy consumption, is the main energy source for transportation, while natural gas and coal are primarily used in electricity generation. In 2015, 32.6% of world’s energy demand was met with oil, while 23.7% of it was met with the natural gas (TP, 2016: 4). Countries with the richest oil reserves are Venezuela with 17.5%, Saudi Arabia with 15.7%, and Canada with 10.2%. Iran follows this countries with 9.3% and Iraq with 8.8%. However, despite the high level of oil reserves, Venezuela is not among the top ten countries in oil production (ETKB, 2016c: 11). Saudi Arabia, Russia, and the US carry out the production of crude oil at 12.9%, 12.6%, and 12.1%, respectively. The contribution of these three countries to 23

world oil production was 38% in 2014. These countries are followed by China and Canada, whose share in oil production amounts to 5%. The share of Iraq in crude oil production is 4%. The production of these top six countries constitutes 50% of the world crude oil production (ETKB, 2016b). According to the IEA 2014 data, about 73% of Turkey’s 30.5 mtoe final oil consumption is met through import. Turkey mostly imports oil from Iraq (28.7%), Russia (17.7%), and Iran (14.1%). 61% of total oil import is from these countries (EPDK, 2016b: 6). Figure 6: Oil Consumption by Sectors in Turkey (2015) Commerce and Public Services 8.8%

Industry 13.6%

Residential 0.8%

Transport 61.8%

Source: Republic of Turkey (2016), Ministry of Energy and Natural Resources, Balances of Energy Statistics 2015.

In Turkey, transportation and industry sectors are the major fields of activity where oil is used greatly. In 2015, the transportation sector, industry sector, commerce and public service sector, and residents made up 61.8%, 13.6%, 8.8%, and 0.8% of the total oil consumption, respectively. Most of the world’s natural gas reserves are found in Russia, former Soviet Republics, and the Middle East. Only Russia, Iran and Qatar have 54% of world’s natural gas reserves (ETKB, 2016a: 4). According to the 2014 data, the countries with the biggest share in world natural gas production are the USA (20.7%), Russia (18.3%), and Iran (4.8%). These countries produce 43.8% of world’s natural gas (ETKB, 2016b). Turkey’s natural gas reserve is not sufficient to meet the final consumption. In 2015, the highest amount of natural gas production was conducted in the cities of Tekirdağ (66.17%), Kırklareli (14.41%) and Düzce (11.56%) (EPDK, 2016a: 3). Since Turkey’s natural gas reserves and production volume are not adequate to meet the domestic demand, natural gas import has become a necessity (EPDK, 2016a: 7). According to the IEA 2014 data, Turkey provided 99% of primary domestic natural gas supply through import. As seen in Figure 7, natural gas consumption was met through import due to insufficient domestic production, and this trend has been growing over the years.

24

Figure 7: Natural Gas Production and Import in Turkey (2000-2014) 45000 40000 35000

Import Production

30000 25000 20000 15000 10000 5000 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: IEA, Headline Global Energy Data, 2016 Edition.

Turkey imports a substantial amount of natural gas from Russia. In 2015, Turkey imported 55.3% of natural gas from Russia, 16.1% from Iran, 12.7% from Azerbaijan, and 8.1% from Algeria. Table 2: Natural Gas Import in Turkey (Million Sm3) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Russia Iran Azerbaijan Algeria Nigeria Others 17.524 4.248 0 3.786 1.013 0 19.316 5.594 0 4.132 1.100 79 22.762 6.054 1.258 4.205 1.396 167 23.159 4.113 4.580 4.148 1.017 333 19.473 5.252 4.960 4.487 903 781 17.576 7.765 4.521 3.906 1.189 3.079 25.406 8.190 3.806 4.156 1.248 1.069 26.491 8.215 3.354 4.076 1.322 2.464 26.212 8.730 4.245 3.917 1.274 892 26.975 8.932 6.074 4.179 1.414 1.689 26.783 7.826 6.169 3.916 1.240 2.493 Source: EPDK (2016a). Doğal Gaz Piyasası 2015 Yılı Sektör Raporu, s. 8.

Total 26.571 30.221 35.842 37.350 35.856 38.036 43.874 45.922 45.269 49.262 48.427

Coal production is different from other energy sources as it is conducted in a wider area. However, according to the 2014 data, 72% of world coal reserves are found in the USA, Russia, China, Australia, and India. China, which is among the top three countries with the largest coal reserves, produced 47% of the coal in the world in 2014 (ETKB, 2016a: 4). China is followed by the USA with 11.4% of coal production, India with 8.3% of production and Australia with 6.1% of production. China, which produced almost half of the world’s coal in 2014, also consumes nearly half of the world’s coal by itself (TKİ, 2016: 6, 11). Domestic coal production in Turkey accounted for 12% of energy consumption in 2004, while this rate increased to 13.2% in 2014. Coal import was at very low levels before the year 1980. However, it increased to 10 million tones in 1990 and over 20 million tones in the 2000s. In 2014, coal import showed an 11% increase compared to the previous year, and it rose 79% between the years 2004 and 2014. The main reason behind this increase in import is the increase in demand for steam coal for the generation of electricity (TKİ, 2016).

25

4. The Data and the Econometric Model This study aims to examine the effect of energy consumption in the industrial sector in Turkey on industrial output through time series analysis methods. The study is different from the other studies in the literature in that the empirical analysis is conducted with industrial sector total energy consumption as well as disaggregated energy consumption data. The existence of a long-term relationship between industrial energy consumption and industrial production is analyzed through six different components, namely total energy consumption, coal, oil, natural gas, renewable energy, and electricity consumption. The empirical model to be estimated is given in Equation 1: (1) In Equation 1, represents industrial sector total production, while , , and represent industrial sector total energy consumption, error term, and time dimension, respectively. In addition to the industrial total energy consumption ( ), the consumption amounts of coal ( ), oil ( ), natural gas ( ), renewable energy ( ) and electricity energy ( ), which are different components of energy consumption, were also taken into account, and in total six models were estimated. The time period for the study was determined as the years between 1971 and 2014 considering the accessibility of data. Industrial value added (constant 2010 US$) was derived from the World Bank World Development Indicators 2016 dataset to measure industry sector total production, while International Energy Agency (IEA) Energy Data 2016 was used for energy consumption. Based on the literature, the natural logarithm of all the series was calculated, and the empirical analysis was conducted with Eviews 6 softwarei.

5. Methodology Cointegration analyses are used to examine the existence of a long-term relationship between the variables which theoretically act together in the long-run. The most commonly used cointegration tests in the literature, including two-step Engle and Granger (1987), Stock and Watson (1988), Phillips and Ouliaris (1990), and Johansen (1991, 1995) tests, require the series to be stationary at the same level, or in other words, to be integrated at the same level. However, these methods are invalid for series which have stable at different levels. The bounds test developed by Pesaran et al. (2001) is different from the other methods in that it makes it possible to check the existence of cointegration between integrated series at different levels (Pesaran et al., 2001: 289290). In this study, the long-term equation in Equation 1 was estimated using the time series cointegration methods with the annual data on Turkish economy for the 1971-2014 period. For this purpose, before investigating whether there is a long-term relationship between the variables given in Equation 1, it is necessary to determine the stationary levels of the series. Augmented Dickey-Fuller (Dickey and Fuller, 1979), Phillips-Perron (Phillips and Perron, 1988), and Kwiatkowski et al. (KPSS, 1992) unit root tests were used to determine the stationary levels of the series. Following the unit root analyses, 26

the bounds test by Pesaran et al. (2001) was used to examine whether the variables in the long-term equation have a relationship in the long-run. Then, long-term coefficients were obtained through Pesaran and Shin (1999) ARDL (Autoregressive Distributed Lag) method. According to these methods, the dependent variable must be stationary at the first difference, while no such condition exists for the independent variables. The bounds test first estimates unrestricted conditional error correction model to determine whether there is a relationship between the variables in the long-term equation. The unrestricted conditional error correction model to be estimated in this study is given in Equation 2.

(2)

In this Equation, is the optimal lag length that needs to be determined before estimating the unrestricted conditional error correction model. While determining the lag length, the values recommended by Schwarz or Akaike information criteria are used. is a vector representing external variables like dummies. Also, represents the constant term, while ∆ expresses the first difference operator and 1 and 2 represent the long-term coefficients of the variables. In the null hypothesis, the bounds test examines that there is no long-term relationship between the variables, while in the alternative hypothesis, the existence of a long-term relationship is investigated. The null and alternative hypotheses to be tested in this study are as follows:

The Bounds testing requires the estimation of the long-term equation using the ordinary least squares method in the first stage. The models to be estimated using the optimal lag length must be controlled for the autocorrelation problem (Pesaran and Shin, 1999: 373, 386). F statistics derived as a result of the estimation and the critical values given by Pesaran et al. (2001) are compared, and consequently, either the null hypothesis or the alternative one is accepted. Pesaran et al. (2001) present two different critical values, the upper bound and the lower bound. If the F statistics is higher than the Pesaran et al. (2001) upper bound critical value, then the null hypothesis which states that there is no long-term relationship between the variables is rejected. When the calculated F value is lower than the lower bound critical value, the null hypothesis is accepted and it is concluded that there is no long-term relationship between the variables. When the calculated F-statistics is between the upper and lower bound critical values, the bounds test does not point to a precise result (Pesaran et al., 2001: 290). If the bounds test indicates a long-term relationship between variables, the regressions which show the long and short term relationships among these variables are obtained 27

through the autoregressive distributed lag (ARDL) method. To reach long-term equations, first, the long-term conditional ARDL model, in which industrial sector production is the dependent variable, must be created based on the optimal lag lengths recommended by Akaike (AIC) or Schwarz (SBC) information criteria. The long-term conditional ARDL model is given in Equation 3. This model represents the optimal lag lengths for p1 industrial production and q1 industrial energy consumption. The shortterm error correction model is given in Equation 4. (3)

(4) In Equation 4, is the short-term coefficient of industrial production, and is the short-term coefficient of industrial energy consumption. Also, coefficient ( is negative and statistically significant, which means that the error correction model works well and converge to equilibrium (Açıkgöz et al., 2012: 182-183). Engle and Granger (1987) state that when two series, which have stationary in their first difference, are cointegrated in the long-term, there may be at least one causality relationship between these series (Granger, 1988: 199). In this study, Granger causality tests for the variables of industrial production and industrial energy consumption are conducted using Equation 5 and 6. (5) (6) Equation 5 examines causality from industrial energy consumption to industrial production, while Equation 6 investigates causality from industrial production to industrial energy consumption. F- statistics for the lag of explanatory variables indicates short-term causality effects, while t statistics for the (coefficient of lagged error correction term) shows the importance of long-term causality relationship (Güngör et al., 2014: 42).

6. Empirical Results This study analyzes whether industrial energy consumption and industrial production act together in the long-run within the framework of ARDL-bounds test. It is necessary to determine the stationary levels of series before applying the cointegration tests. In this study, Augmented Dickey Fuller (ADF), PP (Phillips-Perron), and KPSS (Kwiatkowski–Phillips–Schmidt–Shin) unit root tests were used to conduct the stationary analysis of the series. ADF and PP unit root methods propose that in the null 28

hypothesis the series have unit root, while in the alternative hypothesis the series have stationary. KPSS test, on the other hand, proposes that in the null hypothesis the series have stationary. The unit root test results for each series are displayed in Table 3. Table 3: Unit Root Test Results ADF PP KPSS ConstantConstantConstantConstant Constant Constant Trend Trend Trend Level -1.170 -2.78 -1.149 -2.785 0.844*** 0.120*** First Diff. -5.66*** -5.62*** -5.626*** -5.591*** 0.09 0.040 Level -2.191 -3.820** -3.219** -3.764** 0.840 0.191 First Diff. -8.361*** -8.551*** -9.477*** -20.07*** 0.406* 0.303 Level -2.618* -2.289 -3.071** -2.289 0.770 0.207 First Diff. -8.444*** -8.894*** -8.761*** -12.40*** 0.496 0.780 Level -0.310 -0.733 -0.738 -0.733 0.219 0.188 First Diff. -5.018*** -4.520*** -5.026*** -5.724*** 0.551 0.051 Level -1.577 -1.570 -1.571 -1.570 0.652 0.178 First Diff. -6.367*** -6.417*** -6.366*** -6.455*** 0.153 0.065 Level -1.031 -1.609 -1.031 -1.730 0.714 0.126 First Diff. -6.280*** -6.223*** -6.280*** -6.223*** 0.109 0.092 Level -2.693 -2.582 -4.748*** -2.805 0.842 0.209 First Diff. -5.543*** -5.984*** -5.510*** -6.977*** 0.532 0.134 Note: ***, ** and * denote rejection of null hypothesis at significance level 1, 5 and 10%, respectively.

Variable

Although the industrial production has unit root at the level according to ADF, PP and KPSS tests, it becomes stationary when it is subjected to unit root test again after taking the first difference. The unrestricted conditional error correction model given in Equation 2 must be estimated to determine whether there is a relationship between the variables in the long-term equation with the bounds test. lag length recommended by Akaike (AIC) and Schwarz (SBC) criteria was taken into account in choosing the suitable lag lengths to be used in the estimation of this model. Table 5 shows that AIC and SBC criteria yield similar results in choosing optimal lag lengths. Furthermore, the recommended lag lengths and their residuals obtained from the models to be estimated should not have serial correlation problem. The results in Table 4 reveal that there is no autocorrelation problem in the optimal lag lengths and the estimated models.

29

Table 4: Statistics for Selecting the Optimal Lag Order Constant Constant and Trend χ2(1) χ2(4) P AIC χ2(1) χ2(4) 2.40(0.12) 5.58(0.23) 2 -3.33 2.47(0.11) 8.97(0.06) 1.07(0.29) 3.77(0.43) 2 -3.19 1.64(0.19) 6.89(0.14) 0.62(0.43) 3.05(0.54) 1 -2.90 3.45(0.06) 5.44(0.24) 0.50(0.47) 2.39(0.66) 1 -3.12 2.06(0.15) 4.33(0.36) 0.81(0.36) 2.44(0.65) 1 -2.99 3.89(0.04) 5.22(0.26) 5.38(0.02) 8.78(0.06) 1 -3.80 1.61(0.20) 1.97(0.74) Constant Constant and Trend p SBC χ2(1) χ2(4) P SBC χ2(1) χ2(4) 1 -3.05 2.40(0.12) 5.58(0.23) 2 -3.03 2.47(0.11) 8.97(0.06) 1 -2.91 1.07(0.29) 3.77(0.43) 1 -2.94 3.43(0.06) 6.48(0.16) 1 -2.71 0.62(0.43) 3.05(0.54) 1 -2.78 3.45(0.06) 5.44(0.24) 1 -2.78 0.50(0.47) 2.39(0.66) 1 -2.92 2.06(0.15) 4.33(0.36) 1 -2.72 0.81(0.36) 2.44(0.65) 1 -2.78 3.89(0.04) 5.22(0.26) 1 -3.62 1.47(0.22) 2.51(0.64) 1 -3.59 1.61(0.20) 1.97(0.74) Note: χ2(1) and χ2(4) are residual autocorrelation test statistics for 1 and 4 lag order. p-values of χ2 statistics are given in parenthesis. p 1 1 1 1 1 3

AIC -3.22 -3.07 -2.87 -2.94 -2.89 -3.81

Bounds test results for the six models to be estimated within the study are given in Table 5. To reach the conclusion that there is a long-term relationship between the variables as a result of the bounds test, the obtained test statistics (F-iii, F-iv, F-v, t-iii, tv) must be higher than the Pesaran et al. (2001) upper critical value. According to F-iv and t-v statistics, in all models, and according to the t-iii statistics, in all the models except for the one in which electricity consumption is the independent variable, the null hypothesis stating that there is no cointegration is rejected. As it was identified with the bounds test that the variables in Equation 1 act together in the long-run, the coefficient of each variable in the long-term was estimated with the ARDL method in the next step. Table 5: The Bounds Test F- and t- Statistics for Cointegration Relationship with the constant and deterministic trend with constant P F-iv F-v t-v p F-iii t-iii 2 5.782* 7.043 -5.798* 1 4.088 -4.474* 1 5.774* 6.960 -5.711* 1 3.311 -3.788* 1 6.543* 6.644 -5.678* 1 3.463 -3.937* 1 7.953* 9.893* -5.563* 1 5.069 -5.017* 1 5.530* 6.355 -5.485* 1 3.694 -3.422* 1 5.841* 6.312 -5.542* 1 6.011* -3.085 Note: * denotes the statistical significance at the 5%. Lower and Upper-bound critical values are taken from Pesaran et al. (2001). [F-iv (4.68 5.15), F-v (6.56 7.30), t-v (-3.41 -3.69), F-iii (4.94 5.73), t-iii (-2.86 -3.22)].

The long-term coefficient estimation results obtained with the ARDL model, in which lag lengths determined by the SBC criteria are used, are given in Table 6. It was found through the constant term model that industrial total energy consumption and electricity consumption affect industrial production positively at 99% significance level, while industrial sector coal and natural gas consumption affect industrial sector production positively at 95% significance level. According to the long-term coefficient estimations with the constant model, industrial sector oil consumption affects industrial production negatively, but this result is not statistically significant. According to the results of the constant and trend model, all types of energy consumption have a positive effect on industrial production in the long-run; however, among these results, only electricity consumption is statistically significant. 30

This result indicates that total energy consumption and coal, natural gas, and electricity consumption are important determinants of industrial value added in Turkey. This result coincides with the findings of Soytaş and Sarı (2007), who found that there is a significant relationship between electricity consumption and industrial value added in Turkey (Soytaş ve Sarı, 2007: 1152-1153). Thus, in order to increase the value added, it is of great importance for the firms in the industrial sector to adopt new technologies in which they will use coal, electricity and natural gas particularly more efficiently, or to employ new practices that will increase energy production capacity. Table 6: The Estimation Results for the Long-Run Model with constant Coefficient t statistics Std.Error Selected Model

0.922*** 7.526 0.122 (1,1)

0.759** -0.249 0.043** -0.0080 1.960 -0.239 2.217 -0.133 0.391 1.039 0.019 0.606 (1,1) (1,0) (1,0) (1,0) with the constant and deterministic trend

Coefficient 0.124 0.057 0.020 0.006 0.002 t statistics 0.576 0.626 0.335 1.499 0.416 Std.Error 0.56 0.092 0.061 0.004 0.005 Selected Model (2,1) (2,1) (1,0) (1,0) (1,0) Note: ***, ** and * indicate statistical significance at 1, 5 and 10%, respectively.

0.762*** 29.51 0.025 (3,1)

0.372* 1.646 0.229 (1,1)

The results of the error correction model, which gives the short-term coefficients for the six models are reported in Table 7. The results revealed that the error correction term has a negative and statistically significant coefficient in all equations. Moreover, according to the constant and constant-trend models, industrial total energy consumption, coal consumption, and electricity consumption affect industrial sector production positively at 99% significance level. Although the error correction model estimations show that in industrial sector natural gas and renewable energy consumption affect industrial production negatively in the short-term, these findings are not statistically significant. Also, according to the estimations of the constant-trend model, in the first equation, where industrial sector total energy consumption is the independent variable, industrial production of the previous period affects the current period production positively at 99% significance level. In the second model, in which coal consumption is the independent variable, the production of the industrial sector in the previous period has a positive effect on the current production at 95% significance level.

31

Table 7: Error Correction Model and Estimations of Short-Run Coefficients with constant

0.120

0.299*** -0.098* 0.125*** -0.030* 0.015 -0.015* -0.001 -0.053** -0.003 -0.007* 0.138 0.888*** -0.324*** with the constant and deterministic trend

0.365*** 0.336**

0.290*** -0.299*** 0.126*** -0.323*** 0.015 -0.288*** -0.002 -0.383*** -0.0004 -0.301*** 0.803*** -0.236*** Note: ***, **, * indicate statistically significance at 1%, 5% and 10% levels, respectively.

Based on the results of the error correction model and the short-term coefficients of the disaggregated energy consumption variables, it can be said that the coefficient of industrial electricity consumption is higher than the other energy types, which indicates that the most commonly used energy type in industrial sector production is electricity. After determining the short and long-term coefficients of the variables, Granger causality analysis was conducted using Equations 5 and 6 to reveal the causality relationship between the variables. The results of this analysis are shown in Table 8. Table 8: Granger Causality Test Results for Short-run and Long-run with constant

with constant and deterministic trend

Y / X -0.17 -0.194 -0.44 -2.69** 1.35 -1.476 0.51 -0.102 -0.20 -0.12 -0.02 -2.89*** 0.43 --0.05 0.17 --0.61 -0.008 -0.85 -0.13 -3.18*** 0.42 --2.37** 0.11 --0.480 -0.01 -1.65* -0.67 -4.07*** --0.805 0.20 -0.40 0.34 -0.01 -0.74 -0.44 -3.38*** -1.68* 0.69 --0.58 0.10 -0.02 -0.41 -0.01 -1.87* 0.006 -1.70* 0.007 -0.20 Note: ***, **, * indicate that the test statistic is statistically significant at 1%, 5% and 10% levels, respectively.

The results indicate that there is no short-term causality relationship between industrial production and total energy consumption; coal, oil, natural gas, and renewable energy; and electricity consumption. However, the results of the constant and trend model revealed that in the long-run, there is uni-directional causality running both from industrial total energy consumption and from coal, oil, natural gas, renewable energy and electricity consumption to industrial sector total output.

32

7. Conclusion Energy is an important input in the production process needed to facilitate economic development. Particularly industrial sector necessitates the consumption of different energy types in many stages of the production. It is widely believed that increasing energy consumption will lead to an increase in production level. This study examines the effect of industrial energy consumption on industrial output in Turkey using the data for the 1971-2014 period. Different from the existing literature, this study conducted empirical analysis with industrial total energy consumption as well as disaggregated energy consumption data. The existence of a long-term relationship between industrial energy consumption and industrial production was tested with total energy consumption and with five different dissaggregated energy consumption variables, such as coal, oil, natural gas, renewable energy, and electricity consumption. The findings obtained using the bounds test and the ARDL method revealed that total energy consumption has a positive and statistically significant effect on industrial value added. The findings obtained from disaggregated energy types, on the other hand, indicate that industrial sector coal, electricity and natural gas consumption affect industrial value added positively and significantly. According to the long-term coefficients of these three energy types, electricity energy has the biggest impact on industrial value added. The Granger test conducted to examine the causality relationship revealed that there is no causality between industrial energy consumption and industrial value added in the short-term. However, when the long-term findings are examined, it is seen that there is uni-directional causality relationship from both industrial total energy consumption and coal, oil, natural gas, renewable energy, and electricity consumption to industrial value added.

33

References Abid, M., & Sebri, M. (2012). Energy consumption–economic growth nexus: does the level of aggregation matter? International Journal of Energy Economics and Policy, 2(2), 55-62. Abid, M. & Mraihi, R. (2015). Energy Consumption and Industrial Production: Evidence from Tunisia at Both Aggregated and Disaggregated Levels. Journal of the Knowledge Economy, 6(4), 1123-1137. Acaravcı, A. & Öztürk, I. (2012). Electricity consumption and economic growth nexus: a multivariate analysis for Turkey. Amfiteatru Economic, 14(31), 246-257. Acaravcı, A. (2010). Structural breaks, electricity consumption and economic growth: evidence from Turkey. Journal for Economic Forecasting, 2, 140-154. Açıkgöz, Ş., Balcılar, M. & Saracoğlu, B. (2012). Openness and financial development: time series evidence for Turkey. International Journal of Banking, Accounting and Finance. 4(2), 172-201. Ağır, H. & Kar, M. (2010). Türkiye’de elektrik tüketimi ve ekonomik gelişmişlik düzey ilişkisi: yatay kesit analizi, Sosyoekonomi Dergisi, Özel Sayı, 150 -175. Akpolat, A. & Altıntaş, N. (2013). Enerji tüketimi ile reel GSYİH arasındaki eşbütünleşme ve nedensellik ilişkisi: 1961-2010 dönemi. Bilgi Ekonomisi ve Yönetimi Dergisi, 8(2), pp.15-127. Altınay, G., & Karagöl, E. (2005). Electricity consumption and economic growth: evidence from Turkey. Energy Economics, 27, 849-856. Altinay, G. & Karagol, E. (2004). Structural break, unit root, and the causality between energy consumption and GDP in Turkey. Energy Economics, 26(6), 985-994. Bayar, Y. (2014). Türkiye’de birincil enerji kullanımı ve ekonomik büyüme. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 28(2), 253-269. Çetin, M., & Şeker, F. (2012). Enerji tüketiminin ekonomik büyüme üzerindeki etkisi: Türkiye örneği. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(1), 85-106 Dickey, D. & Fuller, W.A. (1979). Distribution of the estimates for autoregressive time series with unit root. Journal of the American Statistical Association, 74(366), 427-431. Erdal, G., Erdal, H. & Esengün, K. (2008). The causality between energy consumption and economic growth in Turkey. Energy Policy, 36, 3838-3842. Engle, R. F., & Granger, C. W. J. (1987). Cointegration and error correction representation: estimation and testing. Econometrica, 55, 251-276. Ewing, B. T., Sarı, R. & Soytaş, U. (2007). Disaggregate energy consumption and industrial output in the United States. Energy Policy, 35, 1274-1281. 34

EPDK (2016a). Doğal Gaz Piyasası 2015 Yılı Sektör Raporu. Strateji Geliştirme Dairesi Başkanlığı, Ankara. EPDK (2016b). Petrol Piyasası 2015 Yılı Sektör Raporu. Strateji Geliştirme Dairesi Başkanlığı, Ankara. Erbaykal, E. (2008). Disaggregate energy consumption and economic growth: evidence from Turkey. International Research Journal of Finance and Economics, 20, 1-8. Erdal, G., Erdal, H. & Esengün, K. (2008). The causality between energy consumption and economic growth in Turkey. Energy Policy, 36, 3838-3842. Erdoğan, S. & Gürbüz, S. (2014). Türkiye'de enerji tüketimi ve ekonomik büyüme ilişkisi: yapısal kırılmalı zaman serisi analizi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 32, 79-87. ETKB (2016a). Elektrik Üretim Sektör Raporu 2015, EÜAŞ Araştırma Planlama ve Koordinasyon Dairesi Başkanlığı. ETKB (2016b). Mavi Kitap 2016: Enerji ve Tabii Kaynaklar Bakanlığı ile Bağlı, İlgili ve İlişkili Kuruluşlarının Amaç ve Faaliyetleri. Ankara. ETKB (2016c). Dünya ve Ülkemiz Tabii Kaynaklar Görünümü. Strateji Geliştirme Başkanlığı, Sayı 14. Ankara. Güngör, H., Çiftçioğlu, S. & Balcılar, M. (2014). Does financial development increase private savings? the case of Turkey. International Journal of Business and Social Science. 5(2), 36-47. Granger, C.W.J. (1988). Some recent development in a concept of causality. Journal of Econometrics, 39(1-2), 199-211. IEA-International Energy Agency (2017). Energy consumption http://www.eia.gov/totalenergy/data/monthly/dataunits.cfm

by

sectors.

IEA-International Energy Agency (2016a). Energy policies of IEA countries: Turkey 2016 Review. OECD/IEA, Paris. IEA-International Energy Agency (2016b). Headline Energy Data 2016. OECD/IEA, Paris. IEA-International Energy Agency (2015). Key World Energy Statistics 2015. OECD/IEA, Paris. Jobert, T., & Karanfil F. (2007). Sectoral energy consumption by source and economic growth in Turkey. Energy Policy, 35, 5447-5456. Johansen, S. & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169-209.

35

Johansen, S. (1988). Statistical analysis of cointegrating vectors. Journal of Economic Dynamic and Control, 12(2–3), 231-254. Johansen, S. (1991). Estimation and hypothesis testing of cointegrating vectors in Gaussian vector autoregressive models. Econometrica, 59, 1551-1580. Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press: Oxford. Kaplan, M., Öztürk, İ. & Kalyoncu, H. (2011). Energy consumption and economic growth in Turkey: cointegration and causality analysis. Romanian Journal of Economic Forecasting, 2, 31-41. Kar, M. & Kınık, E. (2008). Türkiye’de elektrik tüketimi çeşitleri ve ekonomik büyüme arasındaki ilişkinin ekonometrik bir analizi. Afyon Kocatepe Üniversitesi, İ.İ.B.F. Dergisi, X(II), 333-353. Karagöl, E., Erbaykal, E. & Ertuğrul, H.M. (2007). Türkiye’de ekonomik büyüme ile elektrik tüketimi ilişkisi: sınır testi yaklaşımı. Doğuş Üniversitesi Dergisi, 1, 7280. Kızılkaya, O., Sofuoğlu, E., & Çoban, O. (2016). Ekonomik Büyüme, Enerji Tüketimi ve Çevre Kirliliği Analizi: Türkiye Örneği, Kırıkkale Üniversitesi Sosyal Bilimler Dergisi, 6(2). Kouakou, A. K. (2011). Economic growth and electricity consumption in Cote d’Ivoire: evidence from time series analysis. Energy Policy, 39, 3638-3644. Kwiatkowski, D., Phillips, P.C.B., Schmidt, P. & Shin, Y. (1992). Testing the null of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root?. Journal of Econometrics, 54(1), 159-178. Lise, W., & Montfort, K.V. (2007). Energy consumption and GDP in Turkey: is there a cointegration relationship?. Energy Economics, 29, 1166-1178. Mucuk, M. & Uysal, D. (2009). Türkiye ekonomisinde enerji tüketimi ve ekonomik büyüme. Maliye Dergisi, 157, 105-115. Pata, U.K. and Terzi, H. (2016). The relationship between aggregated–disaggregated energy consumption and economic growth in Turkey. Business and Economics Research Journal, 7(4), 1-15. Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis. S. Strom (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371-413), Cambridge: Cambridge University Press. Pesaran, M. H., Shin, Y., & Smith, R. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289-326. Phillips PCB. & Ouliaris, S. (1990). Asymptotic properties of residual based tests for cointegration. Econometrica, 58, 165-193. 36

Phillips, P.C.B. & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335-346. Qazi, A. Q., Ahmed, K. & Mudassar, M. (2012). Disaggregate energy consumption and industrial output in Pakistan: an empirical analysis. Economics Discussion Paper, No: 2012-29. Saatçi, M. & Dumrul, Y. (2013). The relationship between energy consumption and economic growth: evidence from a structural break analysis for Turkey. International Journal of Energy Economics and Policy, 3(1), 20-29. Sarı, R.,& Soytaş, U. (2004). Disaggregate energy consumption, employment and income in Turkey. Energy Economics, 26, 335-344. Sarı, R., Ewing, B. T., & Soytas, U. (2008). The relationship between disaggregate energy consumption and industrial production in the United States: an ARDL approach. Energy Economics, 30, 2302-2313. Soytas, U. & Sarı, R. (2003). Energy consumption and GDP: causality relationship in G- 7 countries and emerging markets. Energy Economics, 25, 33-37. Soytas, U., & Sarı, R. (2007). The relationship between energy and production: evidence from Turkish manufacturing industry. Energy Economics, 29, 1151-1165. Stock J. & Watson, MW. (1988). Testing for common trends. Journal of the American Statistical Association, 83, 1097-1107. Şengül, S. & Tuncer, İ. (2006). Türkiye’de enerji tüketimi ve ekonomik büyüme: 19602000. İktisat İşletme ve Finans, 21(242), 69-80. TKİ -Türkiye Kömür İşletmeleri Kurumu (2016). 2015 Kömür Sektör Raporu. Enerji ve Tabi Kaynakları Bakanlığı, Ankara. TP-Türkiye Petrolleri (2016). Ham petrol ve doğal gaz sektör raporu. Türkiye Petrolleri Strateji Geliştirme Daire Başkanlığı. Tuğcu, C. T. (2013). Disaggregate Energy Consumption and Total Factor Productivity: A Cointegration and Causality Analysis for the Turkish Economy. International Journal of Energy Economics and Policy, 3(3), 307-314. World Energy Council-Turkish National Committee (WEC-TNC) (2002). 2002 Türkiye Enerji Raporu. Ankara. Yalta, T. (2011). Analyzing energy consumption and GDP nexus using maximum entropy bootstrap: the case of Turkey, Energy Economics, 33, 453-460. Yılmaz, M. & Atak, M. (2010). Decomposition analysis of sectoral energy consumption in Turkey. Energy Sources Part B, 5, 224-231.

37

Zamani, M. (2007). Energy consumption and economic activities in Iran. Energy Economics, 29, 1135–1140. Zheng, Y., & Luo, D. (2013). Industrial structure and oil consumption growth path of China: empirical evidence. Energy, 57, 336-343. Ziramba, E. (2009). Disaggregate energy consumption and industrial production in South Africa. Energy Policy, 37, 2214-2220.

The calculations in the bounds test and the ARDL method were made using the program codes developed by Prof. Mehmet Balcılar. i

38

CHAPTER III INCENTIVE POLICIES FOR RENEWABLE ENERGY SOURCES IN TURKEY Mustafa MIYNAT1 & Osman GÜLDEN2 1. Introduction Energy economy, as one of the key elements of the country in terms of social and environmental development should be taken into consideration in the growth and development strategy is a very important factor. Despite the increase in energy demand in the world, the limited availability of energy sources has led countries to focus more on energy politics. Today, economic growth in many countries to support sustainable development and national energy policies are applied and states to achieve these objectives by determining long-term goals in this area have shown that they do their best. The high rate of energy demand of the country depending on population growth increased, increased, this increase across made on energy security with worries about living and climate change issues in intensive discussions considerable interest in the renewable energy sources in our country as well as worldwide. (Yurdadog and Tosunoglu, 2017). The sustainability of economic activities depends on the availability of energy claims. Much of the energy demand is provided by fossil sources. Fossil resources, in addition to its rapid depletion, cause great damage to the environment and create significant economic and social external costs. Especially in the countries which are insufficient in terms of fossil resources, the economic costs arising from import dependency are getting worse. Renewable resources are the energy resources that are peaceful, indigenous, clean and uninhabitable, which can be an alternative to fossil resources in solving these problems. The use of renewable energy sources in energy production requires high investment costs at the outset, due to the inadequate development of the technologies in this area. In addition, to compete with fossil resources, these resources are used for many years in power. At this point, the right and on-site incentive policies that can be implemented as an instrument of fiscal policy can overcome the disadvantages of bidding on renewable resources (Sen, 2017). 1 2

Prof. Dr., Manisa Celal Bayar University, Department of Public Finance, [email protected] Res. Assist., Manisa Celal Bayar University, Department of Public Finance, [email protected]

39

Given the history of humanity, the public has been busy for many years the fact that nonrenewable fossil fuels such as coal, oil, and natural gas will be consumed in the near future. The presence of new reserves in the Middle East and Central Asia will prolong this period to some extent, but it can not provide a fundamental solution to the energy problem that is waiting for the world. Especially after the 1973-1979 oil crises, nations are turning to domestic and renewable energy sources located in the country. Many countries are aware of the drawbacks of external dependence in a sine qua non of energy as a result of the crises that live in them. However, the use of renewable energies in the global carbon trading system, which was established after the Kyoto Protocol and in which almost all countries participate or are influenced, even under different conditions, is rewarded. In this context, investors who build plants can sell and generate income and contribute to the financing of the project by providing them with the emission rights they obtain according to the quality of the projected greenhouse gas emissions (Uluatam, 2010). The importance of renewable energy sources in the World is increasing besides the costs of the consumable energy resources, with the air, water and noise pollution. Various national and intrnational implementation has been realized to rise usage these resources. Besides enviromental pollution, the importance of renewable resources such as wind, solar, geothermal is increasing for Turkey which has a foreign dependent economy such as oil and natural gas. Turkey endowed with many geographical and geological features and high capacity to use of these resources. This study focuses on incentive policies to increase capacity of renewable resources in recent years. In additon examined the impact on Turkey's energy usage and assessment the legal infrastructure of these policies.

2. Overview of The Renewable Energy Concept The rapidly increasing world population and increasing energy demand in parallel with industrialization are not met with increasingly limited traditional energy sources. On the other hand, fossil fuels that meet most of your energy needs are one of the major causes of environmental pollution today. As a result of industrial activities, about 20 billion tons of carbon dioxide, 100 million tons of sulfur compounds, 2 million tons of lead and other toxic chemical compounds are released into the atmosphere every year. Fossil fuel based energy use; dependence on foreign fuel, among major drawbacks such as high import costs and environmental problems, due to the rapid depletion of fossil fuel reserves in the world increases the importance of renewable energy sources. Renewable energy sources, as well as being the cause of sustainable continuity is of paramount importance also be found in every country of the world. In addition, the environmental impact is very small compared to non-renewable energy sources. Renewable energy sources are considered to be the most important energy source in the 21st century if the existing technical and economic problems are resolved. (Çukurova Development Agency, 2012). In recent years, changes in renewable energy markets, investments and policy framework have changed so rapidly that the need to revise targets for renewable energy 40

in a way that adapts to such changes. On the other hand, when the renewable energy sector is considered as a whole, 100,000 people in this sector in many countries the number of people employed, the employment directly created by the renewable energy sector is seen as a global score 3.5 million. (REN21, 2011). Renewable energy sources are mainly classified as "solar", "wind", "geothermal", "hydraulic", "biomass", "wave" and "hydrogen". Sun is the main source of a large portion of these energy types and can be said to be the direct or indirect effect on them. Even coal, oil and natural gas, which are known as fossil fuels, are actually shapes of solar energy. For these reasons it is possible to define the sun as the most important energy source of the world. List of renewable energy types and their sources are listed below. (Karagol & Kavaz 2017): Table 1. Renewable Energy Varieties and Resources Types of Renewable Energy Source of energy Solar Energy Sun Wind Power Wind Geothermal Energy Underground Waters Hydraulic Energy River and Streams Biomass Energy Biological Waste Wave Energy Ocean and Seas Hydrogen Energy Water and Hydroxides

3. Renewable Energy in the World Energy is one of the most basic and immovable requirements of the economic and social development of an country. In this regard, "energy security" cases, economic security and national security is one of the vital elements. Energy is an indispensable input for almost all processes necessary for us to sustain our social lives; Industrial, transportation, residential and commercial sub-sectors. While the energy consumed in the world today is derived from a large number of energy sources, Fossil resources such as oil, natural gas and coal make up 87% of these resources. Petroleum has the largest share of the world's primary energy consumption, especially as the main source of energy for the transportation sector. Natural gas and coal, which follow petroleum, are used for electricity generation to a large extent. By the first year of the year 2015, oil accounted for 32.6% of world energy demand and 23.7% of natural gas. Until now, according to various projections made by various international institutions (International Energy Agency, US Energy Administration, BP, ExxonMobil, etc.), it is predicted that oil and natural gas will also protect their share in primary energy consumption in the long term (TP, 2016).

41

Figure 1: World Primary Energy Consumption Ratio Year 2015 Nuclear 4%

Hydro 7%

Renewable 3% Oil 33%

Natural Gas 24%

Oil

Coal

Natural Gas

Coal 29% Nükleer Hydro

Renewable

Source: BP Energy Outlook to 2035

When Graph 1 is examined, it is seen that fossil fuel consumption is dominant in the world as of 2015. Renewable energy consumption is only about 3%. Although energy policies are focused on renewable energy sources, the world has a lot of ways to go about this. The long-term expectations of renewable energy continue to be positive, with stable growth in all sectors. Research and analysis suggests that between now and 2040: (KPMG, 2016) 

Total energy demand expected to increase by more than 30%.



Renewable energy will grow and account for 56% of total electricity capacity.



Renewable energy capacities of developing countries expected to be three times higher than developed countries.



The penetration of renewable energy into the market will double to reach 46% of electricity generation.



Wind energy costs expected to decrease by 32% and solar energy costs by 48%.



Solar energy expected to create more than a third of global capacity growth.

3.1. Renewable Energy in Turkey As a result of the collapse of the energy sources used in an unbalanced way in the world of fossil fuels is increasing dependence on the outside due to be supplied from other countries. The countries where these fossil fuel reserves are to be consumed after a certain time have led to the use of renewable energy sources. Sources such as wind energy, solar energy, water power, biomass energy, seawater power, geothermal energy and so on are emerging as renewable energy sources. Regarding the energy profile in 42

Turkey, the place of renewable energy sources is very important. Increasing the use of solar and wind energy in particular will contribute significantly to Turkey's energy budget. The importance of strategies, plans and policies for achieving accurate, complete and effective utilization of renewable energy sources is increasing and reaching great proportions (Cakır, 2010). Environmental problems experienced during the production and use of energy are one of the main reasons for the abandonment of old technologies. In addition to local destruction of coal, oil and natural gas plants in the region where they are established, there are also threats globally threatening the whole world. When fossil fuels are burned, carbon dioxide, sulfur dioxide, nitrous oxide, dust and dust emitted from the atmosphere and polluting the environment, carbon dioxide and other greenhouse gases cause global climate change and threaten life in all countries of the world (Gençoğlu, 2012). When the energy profile in Turkey is reviewed, the place and the importance of renewable energy sources are clearly seen. However, the use of renewable energy sources is at very low levels (1% and below) and these energy types are not adequately addressed. In particular, the use of solar and wind energy will provide significant contributions to Turkey's energy budget. The importance of strategies, plans and policies for benefiting from renewable energy sources in a correct and healthy renewable way is increasing and reaching important dimensions (Mutlu, 2013). Turkey is a country rich in potential and diversity of renewable energy sources. Our country has 8% of world potential in geothermal energy which is not available in many countries. In addition, because of its geographical position, solar energy is getting bigger. Turkey is one of the few countries in the world in terms of hydropower potential. The wind energy potential is estimated at about 160 TWh. The cost of these energy sources is rather low, they are renewable and are not a significant threat to the environment and human health, unlike ineffective and conventional fuels (Gençoğlu, 2012).

4. Turkey's Renewable Energy Policies In the fifth five-year development plan enacted in 1984 on renewable energy sources, it was stated that initiatives needed to take advantage of new and renewable resources shortly were necessary. The sixth five-year development plan is to utilize the renewable energy sources such as geothermal and solar energy, especially hydraulic; It is stated that the use of renewable energy resources should be expanded in the seventh five-year development plan. In the eighth five-year development plan, renewable energy sources are mentioned in detail, In the world and in Europe, the use of these resources, the incentives given, the effects on the environment are mentioned in detail. In addition, the requirements for using these sources are summarized in the conclusion (DPT, 2001). Government programs have also stated the necessity of using renewable resources for reasons such as being indigenous, not harming the environment, and not having security of supply of fossil fuels. However, no significant investments were made in the four plan

43

periods and no incentives were given. For these reasons, little has been done on renewable energy other than a few small-scale studies (Gorez & Alkan, 2005). In the Tenth Five-Year Development Plan, carried out between 2014 and 2018, the share of domestic resources including domestic and foreign oil and natural gas inferences, which is 28% in primary energy generation in 2011, will be increased to 35% by 2018, Increase of lignite-based electric energy production to almost 60 billion kWh in 2018, which was almost 39 billion kWh in 2012, Evaluation of domestic coal for electricity generation, evaluation of water resources not yet rated as competent for electricity generation purposes, Reduction of energy density, increasing energy efficiency is targeted. The main reason for this is that in 2011 and 2012, 45% and 62% of the foreign trade deficit was attributable to net energy imports. Importing oil, natural gas and coal increased to meet the rapidly growing demand, resulting in external dependency, current account deficits and increased pressure on supply security. Turkey is the world's 17th largest economy. Along with the growing economy and growing population, the energy demand in Turkey is growing rapidly, which places energy supply security at the top of the government agenda, both for electricity and other primary energy sources. The Turkish economy is dependent on imported energy sources and by 2012, 90 per cent of primary energy consumption is based on imported fossil fuels. It is foreseen that the economic development process of Turkey will continue in the coming years and it is expected that the energy demand will continue to increase. The government has set very ambitious targets for electricity generation based on these sources for 2023, moving from the high potential of renewable energy sources such as hydraulics, wind, solar, geothermal. With the further use of these resources, the share of renewable energy sources in electricity generation will increase to at least 30% in 2023 (ETKB, 2014). In parallel to its prosperity and development goals, Turkey is developing R & D studies and technologies for the use of high renewable energy potential to reduce its dependence on fossil fuels causing climate change. In this direction, are being made various residues from combustion and process gases, such as molten carbonated fuel batteries, fuel preparation and hydrogen production, solar energy heating systems, photovoltaic batteries, combustion and gasification systems, electric and hybrid vehicle technologies, energy storage and management systems, technological applications (Turan & Güner, 2017). Turkey's renewable energy sources are rich and varied in potential, and are the second largest source of energy after coal in the country. The main renewable energy sources in Turkey include hydropower, biomass, wind, biogas, geothermal and solar energy. As of 2008, the share of renewable energy in total electricity generation is 16.75%, while the share of natural gas is 48.19%. For the period 2006-2020, annual growth in total electricity generation is estimated to be 8%. The additional production capacity needed until 2020 requires a great investment (Önal & Yarbay, 2010).

44

5. Incentıves And Subsidies For Renewable Energy In Turkey Given the history of humanity, the public has been busy for many years the fact that nonrenewable fossil fuels such as coal, oil, and natural gas will be consumed in the near future. The presence of new reserves in the Middle East and Central Asia will prolong this period to some extent, but it can not provide a fundamental solution to the energy problem that is waiting for the world. Especially after the 1973-1979 oil crises, nations are turning to domestic and renewable energy sources located in the country. Many countries are aware of the drawbacks of external dependence in a sine qua non of energy as a result of the crises that live in them. However, the use of renewable energies in the global carbon trading system, which was established after the Kyoto Protocol and in which almost all countries participate or are influenced, even under different conditions, is rewarded. In this context, investors who build plants can sell and generate income and contribute to the financing of the project by providing them with the emission rights they obtain according to the quality of the projected greenhouse gas emissions (Uluatam, 2010). Countries have included a variety of fiscal, monetary, credit and foreign trade policies they have implemented within their economic systems in order to ensure their social and economic development and to enhance their competitiveness in the international zone. In these policies incentives are used at various concentrations in all countries as a means of fiscal policy (Giray, Koban & Gercek, 1998). In this context, the fiscal policy aims to accelerate economic growth and development, to achieve full employment (Pınar, 2010) consistently, through public expenditure and public revenues, which are instruments of fiscal policy. There are also some applications in the nature of regulation policies. Incentives are defined as financial and / or intangible support, assistance or encouragement given by the state in various ways to ensure that a particular industry and or region develop more and faster than others (Selen, 2011). While the benefits of renewable energy sources are known, investments made with these resources still can not compete with traditional resources, as investments carry high risks. Because investors see investments more risky when the performance of a technology is unconfirmed or limited. State subsidies reduce the costs and risks of investments by providing investors with these risks. Until now, in areas where renewable energy support mechanisms have been used extensively around the world, installed capacities have increased significantly compared to other technologies and costs have significantly declined. Incentives can be applied through public expenditure and taxation, such as tax exemptions and exemptions, low interest loans or grant aids, energy reductions, land acquisition, state capital participation and financing facilities. There are basically two aims of incentive instruments. The first is to increase the funds that this sector will allocate by mitigating the costs of the private sector; The second is to direct the public to areas that are thought to be beneficial for the country's economy in order to increase more economic activities than others. However, it should be emphasized that; The 45

incentive instruments will not increase or increase the private sector investment volume and the extent to which entrepreneurs will direct the savings they have gained through these (Sen, 2017). Table 2: Classification of Renewable Energy Support and Incentive Mechanisms Regulatory Policies Financial Incentives Public Investments Capital subsidies, grants, Public investments, loans and Renewable Energy Targets discounts grants Investment and other tax Fixed Price Guarantees credits Reductions in sales, energy, Quota Obligations CO2 consumption, value added tax Energy generation payments Tender (Bidding) System or tax credits Renewable Energy Certificates Net Metering System Biofuels Liability Policies Heat Obligation Policies Source: (KPMG, 2013)

Various mechanisms have been developed in many countries to promote the use of renewable energy resources. These can be grouped under three main headings: Incentives that set price and quantity obligations, cost-cutting investment policies and incentives to promote public investment and the development of the RE market. The incentives that bring price-setting and quantity obligations consist mainly of feed-in tariff and renewable energy portfolio standards (RPS). According to this, while the purchasing guaranteed tariffs vary from country to country, it is mainly based on the receipt of the electricity generated by the government by the government through electricity distribution companies at a price determined by the state. It is envisaged that a defined amount of electricity produced in RPSs in a particular region or country will be produced from sources of RPS. Subsidies and reductions constitute a pillar of costcutting investment policies (Uluatam, 2010). Another method is tax deduction. These include investment tax credits, accelerated depreciation, production tax credits, property tax credits, income tax incentives, VAT exemptions, environmental tax exemptions, import tax reductions, grants, equipment credits and similar applications. These incentives are applied for small-scale individual installations as well as for large-scale investments. Therefore, such policies are not only directed at investors who represent supply in the energy market, but also at consumers who represent demand. Finally, public investments and incentives for the development of the RE market are made up of infrastructure policies covering public-interest funds, construction and design, site determination and permits, equipment standards, contractor certification and network connectivity. Among these incentives, however, are the legislation on the ground that bureaucratic obstacles can be reduced to a minimum (Uluatam, 2010).

46

In terms of the effective use of public resources, the following points need to be taken into account in the application of incentives for renewable energy: (Yurdadog and Tosunoglu, 2017). 

Reducing management cost with simple and transparent applications,



Increasing the diversity of incentive mechanisms with more than one application,



Establishment of investor trust,



Providing low production costs,



The effect of reducing consumer prices,



Development and improvement of the market,



A gradual and easy transition from the existing system,



Contributing to feel the benefit of locally all renewable resources,



Establishing public opinion on renewable resources,



Elimination of external factors

5.1. Renewable Energy Support Policies and Types Due to differences in resource potentials and costs of renewable technologies, a single support tool is not sufficient for the development of renewable energy sources. For this reason, countries are benefiting from the combination of these different incentive mechanisms, according to the type of energy to be used, according to the market structure. It is possible to examine the incentives given in this context in two categories: regulatory and financial incentives (Eser & Polat, 2015). Turkey, where a significant portion of imports are made up of energy and its derivatives, is located in the category of energy importing countries. That energy external dependence can make our country vulnerable to both exchange rate risks and shocks experienced at energy prices. This situation; It may be a problem in terms of our country which is in the path of development that some uncertainties can be experienced in the planning and programming process. In addition, per capita GDP per capita in the year of 2014 in our country has been realized as 10.400 US dollars and this result can be expressed as a country in the middle income bracket. The reduction of external dependency on energy demand is seen as a fundamental goal for our country to be able to emerge from this money, which is accepted between USD 10,000-20,000. In terms of supporting this goal, it is also the wind energy; has shown an increasing development in recent years due to the high level of development and environmental positive externalities (Ozen, Sasmaz & Bahtiyar, 2015).

5.1.1. Regulatory Incentive Mechanisms The most interest in promoting regulatory mechanism is fixed price guarantee application. The fixed price guarantee is a long term purchase agreement used to accelerate renewable energy investments. With this incentive, governments guarantee 47

annual energy purchases from producers using their renewable energy sources above the market price. The amount of energy to be taken depends on the source and economically feasible applicability of the source. Because with a simple method applied by many countries this incentive mechanism to reward producers who contributed to the development of innovative and green technologies (bonus) is given. Since initial installation costs are high in the use of REC, the application of fixed price guarantees is generally given in the periods when production facilities enter the first activity. This contributes to lowering the cost of the technologies used and increasing the amount of energy generated per unit. In this way it is also alleviated the financial burden on the government due to the decrease in the fixed tariff prices along with falling costs (Eser & Polat, 2015). The premium guarantee is basically a method similar to the fixed tariff guarantee and also gives a guarantee of the purchase of the production of the renewable manufacturer but the manufacturer includes the payment by adding some premium over the market price instead of a fixed price. The amount to be added to the market price can often be obtained directly from the consumer, depending on the nature of the applied country and the resource situation, or sometimes it can be funded by the public budget. In this method, the premium can be fixed or it can be changed depending on the market price, it is possible to adapt the method to different needs by bringing different approaches to prime (OKA, 2014). The quota system being implemented in many countries; quota for a certain percentage of consumption, sales or production portfolios of consumers, suppliers or producers to be generated from renewable sources. The execution of the quota system requires production actually carried out the official documentation specified in the relevant legislation based on renewable resources. These certificates, given in different countries under the names "green certificate", "green label" or "renewable certificate", are a means of proof for the parties with quotas. The quota approach is often implemented in conjunction with the issuing or selling of the mentioned certificates at the organizing or over-the-counter markets (OKA, 2014).

5.1.2. Financial Incentive Mechanisms At the beginning of financial incentives for energy production from renewable sources are exemptions and reductions through various taxes. Financial incentives can be applied at each stage of production, investment and consumption and can be used as complementary to regulatory incentive mechanisms. Governments are attempting to encourage renewable energy generation with incentive mechanisms such as energy taxes, various environmental tax exemptions, VAT exemptions, accelerated depreciation, property tax exemptions, in particular carbon tax, in order to enable producers producing energy from renewable sources to compete with conventional sources and to eliminate unfair competition (Abolhosseini and Heshmati, 2014). Financial incentives, such as tax exemptions and tax reductions, are often used as complementary support instruments. RE producers who are faced with unfair 48

competition on external costs within the traditional energy sector are exempted from some compensatory taxes (eg CO2 tax). The effectiveness of such financial incentives depends on the tax rates applied. In Scandinavian countries that apply high energy taxes, these tax exemptions may be sufficient to promote the use of renewable energy; countries that implement low energy tax rates need to take extra precautions. Capital subsidies for RE often fall behind the real effect in practice. But it is quite easy to promote capital subsidies in government decisions within the diversity of existing tax exemptions for a long time. Therefore, capital subsidies are widely used in practice. (Liptow & Remler, 2012).

6. Policy Suggestions for Turkey There are no sector specific arrangements in tax incentives. Taking advantage of general incentives is far from decisive for an investor who is willing to invest his resources. Even if a special incentive such as corporate tax reduction is given, these incentives do not affect the investment decisions as they are related to future income taxation. Perchance, as applied in some countries, the ability to qualify for a tax credit through expenditures on renewable energy investments and to use it not only for corporate taxes but also for all taxes and public debts can affect investment models more positively (KPMG, 2016). The financing of investments is an arrangement in which investors must be assessed according to the type (small or large scale, domestic or foreign). For example, support now being provided through development agencies may be the right method for local and small-scale producers. Yet in order to significantly increase the share of renewable energy in Turkey's energy production portfolio, larger scale investments must also be financially viable. As we have seen so far, accessing the private financing resources of these projects is a difficult and expensive alternative. Companies that are not strong in their own resources are also having difficulty in passing these investments. For this reason, many projects are either in the planning phase or their implementation is delayed. Facilitating arrangements should be studied to finance these investments. A further problem with infrastructure is technical and financial issues related to the connection of such facilities to the main distribution line as a result of such distant and scattered installations of renewable energy investments mostly from settlements. In this regard, the administration may also place another obstacle in front of investments by providing the necessary coordination between electricity generation, distribution and transmission companies and by providing an arrangement that provides fair sharing between the public and the private sector the costs of improving the technical infrastructure. In order to increase the share of renewable energy investments in Turkey's energy production portfolio faster and more efficiently, larger scale investments by the public administration (with wider financial opportunities of the public) can be considered as an alternative model by transferring the private sector through the wholeness or partial privatization. In this case the resource to be transferred to the incentive of small scale and relatively less efficient private sector investments can be used more efficiently (KPMG, 2016). 49

7. Conclusion The energy inputs of today's industrial societies are largely based on fossil fuels. It is fully understood that due to the energy and environmental problems arising from fossil fuels, the problems of fossil fuels expected to be reduced. It is vital to analyze the status of technological developments in the world in the field of renewable energy and to determine its applicability in terms of technical and economic aspects in our country. The study of technology investments with potential viability and the identification of required legal regulations should be adopted as an appropriate approach. In recent years, factors such as industrialization, population growth, urbanization and rising living standards have increased energy consumption not only in Turkey but also in the world, which has led to the rapid depletion of fossil energy resources and thus environmental pollution. As a consequence of all of this, we have given great speed to the work on the development of the renewable energy sector in the world in order to reduce environmental pollution. There are various energy potentials of all the countries of the world. Turkey also has a variety of energy sources. The evaluation of these resources is important to activate and develop energy resources. However, with the development of energy potential, Turkey meets its energy needs from external sources as its resources are inadequate. In other words, it is in the category of a country that is dependent on the outsider for energy. This dependence brings with it many problems like economic performance decline, foreign trade deficit, current account deficit, unemployment. Static energy in Turkey, which is a country with a high degree of external dependence on energy, is absolutely necessary to be converted into kinetic energy. Among the static energies that it possesses, especially solar and wind energy, comes to the forefront in terms of green economy. The permanence of these resources and their low cost make them attractive for investing. In terms of our country, the existence of a very high potential in terms of these resources is accepted as scientific. The wind and solar energy is one of the rare sources that the earth will never end. It can significantly reduce the external dependency of our country on energy with the right policies and it is important enough to enable the foreign exchange to stay in the country. Incentives are, as is known, defined as intangible support, assistance or encouragement by the state in various ways to ensure that a particular sector and / or region develop more and faster than others. In accordance with this definition, incentives can be expected to be used to increase renewable energy production. However, Turkey, which is dependent on foreign energy and which separates the largest part of the current account deficit every year for energy imports, can considerably reduce this problem by making use of renewable energy sources. On the other hand, progress in this area will create new employment areas, as well as providing energy supply security and avoiding external costs caused by environmental problems. The use of renewable energy can be encouraged by setting a renewable energy-intensive road monitoring requirement or by providing financial assistance in energy companies' 50

targets and investments. Buying guarantee should open up the front of investors by following high policies in terms of time, quantity and pricing. The renewable energy policy, which increases the economic competitiveness, can reduce the ecological damage caused by the irreversible global warming. Production and consumption of renewable energy can be promoted through various economic and financial instruments. In this context, Turkey's incentives for renewable energies are thought to benefit from the experience of leading countries in this area.

51

References Yurdadog V., Tosunoglu, S. (2017). Renewable Energy Support Polıcıes In Turkey, Eurasian Academy of Sciences Eurasian Business & Economics Journal, 9, 1-21. Sen, S. (2017). "Incentives as a Fiscal Policy Tool in Renewable Energy Production: Experiences of Selected Selected European Countries", Journal of Life Economics, 11, 59-76. Uluatam, E. (2010). "Renewable Energy Incentives", Economic Forum Review Turkish Union of Chambers and Commodity Exchanges - Monthly Publication Organization, October, 34-41. Topcu, C., Yünsel, T. (2012). Renewable Energy Report (Pub No:03). Cukurova Development Agency: Author. Karagol, E., Kavaz, İ. (2017). In The World And Turkey Renewable Energy, Seta Analiz, 197, 1-31. Turkey Petroleum. (2016). Crude Oil And Natural Gas Sector Report, Ankara. BP. (2015). Energy Outlook 2035, London, United Kingdom. KPMG. (2016). Taxes And İncentives For Renewable Energy, KPMG International, https://assets.kpmg.com/content/dam/kpmg/pdf/2016/05/tr-yenilenebilerenerjiye-yonelik-vergi-ve tesvikler.pdf Accessed from (14.02.2017). Cakır, M. T. (2010). Wind Energy Potential of Turkey and its Place in EU Countries, Journal of Polytechnic, 14(4), 287-293. Mutlu, Y. (2012). The energy potential of Turkey and its importance of renewable energy sources in terms of electricity production, Ankara University Journal of Environmental Sciences, 4(2), 33-54. DPT, (2001). Electric Energy Commission Report. DPT Publication, Ankara. Gencoglu, M.T. (2002). The İmportance of Renewable Energy Sources For Turkey, Firat University, Journal of Science and Engineering Sciences, 14(4), 57-64. Gorez, T., Alkan, A. (2005). Turkey's Renewable Energy Sources And Hydropower Potential, TMMOB EMO, III. Renewable Energy Resources Symposium, Mersin. ETKB, (2014). 2014 Annual Report, Republic of Turkey Ministry of Energy and Natural Resources, Ankara. Güner, E. D., Turan, E. S. (2017). The Impact of Renewable Energy Sources on Global Climate Change, Artvin Çoruh University Natural Hazards Application and Research Center Journal of Natural Hazards and Environment, 3(1), 48-55. Onal, E., Yarbay, Z. (2010). The Potential and Future of Renewable Energy Sources in Turkey. ICU Journal of Science and Technology, 9(18), 77-96. Giray, F., Koban, E. & Gerçek, A. (1998). Tax Incentives and Comparative Evaluation for Investments and Exports in the European Union and Turkey, Bursa: Minevra. 52

Pınar, A. (2010). Fiscal Policy, 3rd Edition, Natural Publications, Ankara. Selen, U. (2011). Incentive Applications as A Fiscal Policy Tool, Bursa: Ekin Yayınevi. Eser, L. Y., Polat, S. (2015). The Incentıves for The Use of Renewable Energy Resources in Electrıcıty Generatıon: Turkey And The Scandınavıan Countrıes Practices. Gumushane University Electronic Journal of Social Sciences, 12, 201-225. Ozen, A., Sasmaz, M. U., Bahtiyar, E. (2015). A Renewable Energy Source in Terms of Green Economy in Turkey: Wind Energy, KMU Journal of Social and Economic Research, 17(28), 85-93. Abolhosseini S., Heshmati, A., Altmann, J. (2014). A Review of Renewable Energy Supply and Energy Efficiency Technologies, IZA Dıscussıon Paper Series (Pub. No.8145), Germany:Bonn. Liptow, H., Remler, S. (2012). Legal Frameworks For Renewable Energy - Policy Analysis For 15 Developing And Emerging Countries, Germany: BMZ Federal Ministry for Economic Cooperation and Development.

53

54

CHAPTER IV THE EFFECT OF INDUSTRIALIZATION AND URBANIZATION ON ENERGY INTENSITY IN TURKEY Emrah KOÇAK1 1. Introduction Turkey is a country with young population, industrializing, urbanizing and experiencing a rapid economic growth process. While the share of population living in cities in Turkey was 24.8% in 1950, this rate increased to 70.5% in 2012. During the same period, Turkey's national income per capita reached from $ 2.000 to $ 10.000. As a result of increasing income, industrialization and urbanization process, Turkey's energy demand has also increased considerably. Also, until 2050, It is expected that the energy demand of the Turk will increase five times (Yüksel, 2010). On the other hand, Turkey supplies a large part of the energy demand from fossil sources such as coal, oil and natural gas. Today, the share of fossil resources in Turkey's total energy consumption is approximately 91% (Kotcioglu, 2011). Moreover, Turkey is considered as a country with energy dependency by importing more than 74% of its energy needs (Balat 2010). Because of this dependency, Turkey faces a serious energy security problem in the 21st century (Kaygusuz, 2010). The growth in energy demand in Turkey also brings with it environmental problems. Over the last three decades, CO2 emissions have increased significantly. According to estimates, Turkey's greenhouse gas emissions have increased by about 65% since 1990 (Kotcioglu, 2011). Today, Turkey's total CO2 emission is around 400 million tonnes (mt), and Turkey is one of the countries with an important role in global carbon emissions. It should also be noted that Turkey ratified the Framework Convention on Climate Change in 2004. For this reason, it has to reduce carbon emissions. To this end, the Turkish government develops various strategies. The success of these strategies depends on the decline in energy intensity. Energy intensity is the ratio of energy consumption to GDP and is defined as the amount of energy required to produce a unit product. Energy intensity is also a key indicator of energy efficiency and provides a general idea of the country's industrial structure, technology level and energy use performance (Liu ve Xi, 2013). Thus, countries' energy independence and low 1

Dr., Ahi Evran University, [email protected]

55

greenhouse gas emissions targets are basically realized with the reduction of the energy intensity. In this context, the purpose of the study is to investigate the effect of the industrialization and urbanization process on energy intensity in Turkey. It is expected that these findings will lead the authorities to design industrialization, urbanization and energy politics. In addition, as far as we know, there is no similar research in the literature regarding the case of Turkey. Thus, this study aims to fill the gap in the literature. In the context of these goals, the rest of the paper is organized as follows: In the second section, the theoretical background of the relations between industrialization, urbanization and energy density are evaluated and empirical findings in the literature are discussed. In the third section, the method is presented. In the fourth section, research findings are given. In the fifth section, which is the conclusion section, findings are evaluated and various policy proposals are presented.

2. Theory and Empirical Literature 2.1. Theory Industrialization and urbanization are a process of modernization and development. Economists view this process as a major achievement on the path of wealth and prosperity. Because, industrialization and urbanization are emphasized as a developmental transformation that occurs in all areas of social and economic structure. In particular, the process of transition from agricultural economic structure to industry and service based economic structure emerges with industrialization and enrichment. As a result of this transformation, the labor industry in the agricultural sector moves to the industrial sector and migrates towards the cities. The increase in urban population encourages more economic activities in cities. On the other hand, as a result of this process enormous increases in energy demand. In today's world, it is stated that about 70% of global energy consumption is city-based. Moreover, as industrialization and urbanization continue, it is expected that the need for energy will increase further (Madlener ve Sunak, 2011). Numerous studies in the literature show that urbanization increases energy consumption (Parikh and Shukla, 1995; Liu, 2009; Zhang and Lin, 2012; Wang etal., 2014). Well, how does industrialization and urbanization as a modernization and development indicator affect energy efficiency/intensity? Sadorsky (2013) emphasizes that the effect of industrialization on energy intensity differs according to the phases of preindustrialization, industrialization and post-industrialization. In the pre-industrial phase, agriculture is a dominant sector and the traditional manufacturing sector produces basic consumer goods with low energy intensity. In the industrialization phase, the production infrastructure is built to meet mass production and mass consumption. In this phase, with the accumulation of the capital, various industrial sectors emerge and the energy intensity gradually increases. Finally, in the postindustrial phase, the share of the manufacturing industry with high energy intensity in GDP decreases and the share of information, communication and service sectors with 56

low energy intensity increases. In addition, the technological advances that arise in this phase also increase energy efficiency. As a result, the energy intensity decreases in the post-industrial phase. In other words, the effect of industrialization on the energy intensity differs according to the level of production / industry structure and technological development of economies. Bilgili et al. (2017) explains the impact of urbanization on the energy intensity as follows: (a) Urbanization increases urban mobility, individual motorized vehicle use and transportation needs. Therefore, this movement in the transport sector increases the energy intensity. On the other hand, as urban regions developed, some urban practices that increase energy efficiency, such as urban logistics concepts and efficient logistics systems, cause a mitigating effect of energy intensity. (b) With urbanization, demand for multi-storey buildings, road networks, sewage, cleaning and drainage systems, communication networks, office buildings and electricity networks increases even more. In short, urbanization needs more infrastructure expenditures and infrastructure expenditures require more energy. Consequently, the increase in infrastructure expenditures drive the pressure on the energy intensity. However, with the encouragement of economic developments, renewable energy production, green building applications and asphalting solutions increase energy efficiency in cities and reduce energy intensity. (c) Urbanization also affects consumer needs and the lifestyles of households. For example, demand for more energy-consuming products such as refrigerators, microwave ovens, appliances and private cars increases along with urbanization. The increased demand for these products leads to more electricity usage and once for all, the energy intensity is further increased. Besides, increasing prosperity stimulates the demand for a cleaner environment in communities and develops the idea of sustainable development. This perspective in society forces producers to use environmentally friendly technologies and make cleaner production.

2.2. Empirical Literature How are the results of empirical studies? Numerous studies in the literature examine the relationship between industrialization, urbanization and energy consumption. However, very few studies in the literature focus on energy intensity instead of energy consumption (Bilgili etal, 2017). In one of these studies, Sadorsky (2013) investigates the effects of income, industrialization and urbanization on the energy intensity in 76 developing countries of the 1980-2010 period. Sadorsky (2013) uses heterogeneous panel data analysis methods such as mean group (MG), augmented mean group (AMG) and common correlated effects (CCE) estimators for empirical analysis. The following findings are obtained for the long-run: (i) An increase of 1% in income reduces the energy intensity by -0.45% to -0.35%. In other words, income growth as an indicator of economic development reduces energy intensity. (ii) A 1% increase in industrialization increases energy intensity by 0.07 to 0.12. The effect of industrialization on energy 57

intensity is positive. (iii) The effect of urbanization on the energy intensity is mixed. According to the MG and AMG estimators results, urbanization affects the energy intensity positively. But, According to the CCE estimation result, urbanization affects the energy intensity negatively. Liu and Xie (2013) investigate the relationship between urbanization and energy intensity in the People's Republic of China in the period 1978-2010 with nonlinear cointegration method and causality analysis. The study results confirm a long-run equilibrium relationship between urbanization and energy intensity. According to this, there is a bi-directional causality relationship between urbanization and energy intensity. In addition, according to another important finding of the study, the energy intensity in the People's Republic of China increased faster than the urbanization. Belloumi and Alshehry (2016) investigates the effect of economic growth (GDP per capita), industrialization and urbanization on energy intensity in Saudi Arabia in the period 1971-2012. In the research, autoregressive distributed lag (ARDL) bounds test, the fully modified ordinary least squares (FMOLS) regression and the dynamic OLS regression (DOLS) estimation methods are used. According to the ARDL bounds test results, there is no significant relationship between economic growth and energy intensity. On the other hand, industrialization and urbanization have a positive effect on energy intensity. FMOLS and DOLS tests for robustness control of results do not differ greatly and support the results of ARDL. Bilgili et al. (2017) investigate the impact of urbanization, economic growth (GDP per capita), ruralization, exports, renewable energy and non-renewable energy consumption on energy intensity in ten Asian countries in the period 1990-2014. In the research, recently developed Panel Data Models with Cross-Sectional Dependence and Heterogeneity method is used. The results obtained in the research are as follows: (i) Urbanization has a negative effect on energy intensity. (ii) Ruralisation has a positive effect on energy intensity. (iii) Exports have a positive effect on energy intensity. (iv) Renewable energy consumption has a reducing effect on energy intensity, while nonrenewable energy consumption has an increasing effect on energy intensity. The results of the study, in Asian countries, urbanization is an increasing effect on energy efficiency. Elliot et al. (2017) examines the direct and indirect effects of economic growth (GDP per capita), industrialization and urbanization on energy intensity, coal intensity and electricity intensity in thirty provinces of the People's Republic of China in 1995-2012. In the study, the total energy intensity indicator is used to describe the direct effect. The intensity of energy in construction, transportation, wholesale and residential sectors is used to reveal the indirect effect. For the analysis, AMG and AMG estimation methods are considered. The findings of the research are as follows: (i) Economic growth in longrun (or increase in GDP per capita) reduces total energy intensity, coal density and electricity intensity. (ii) Industrialization and urbanization increase the total energy intensity, coal intensity and electricity intensity. (iii) Similar findings are obtained for 58

the energy intensity in construction, transportation, wholesale and residential sectors. (iv) However, industrialization and urbanization are more influential on the energy intensity in the construction sector.

3. Model, Data and Methodology This study aims to estimate the effect of industrialization and urbanization on energy intensity in Turkey. For this purpose, Sadorsky (2013), Belloumi and Alshehry (2016) and Elliot et al. (2017) models are followed. lneit= β0 + β1 lnyt + β2 lnindt + β3i lnurbt + εt

(1)

Where lnei, lny, lnind and lnurb represent energy intensity, per capita income (constant 2010 prices in USD), industrialization (industry, value added as a % of GDP) and urbanization, respectively. Furthermore, the t and ε indices indicate the time and error terms. β0, β1, β2 and β3 are the elasticity coefficients of the independent variables. Energy intensity is the ratio of primary energy supply divided by GDP measured at 2011 purchasing power parity. Urbanization is measured by the percentage of population living in urban areas. All variables are used in natural logarithmic form. The study covers the period from 1980 to 2014 and the data is based on annual observations. All of the data is obtained from the World Bank. The descriptive statistics and correlation matrix for the variables are shown in table 1. One notes that all descriptive statistics of lny are greater than those of lnei, lnind and lnurb. One may notice, as well, that lnei is positively correlated with lny, lnind and lnurb. It is aimed to show some of the initial or preliminary information with the descriptive statistics and correlation matrix. Table 1: Descriptive statistics and correlation matrix for variables Descriptive statistics Mean Median Maximum Minimum Std. Dev. Observations

2.172 2.203 2.285 1.925 0.087 35

lnei lny lnind lnurb

1 0.774 0.178 0.892

lnei

lny 8.909 8.910 9.327 8.473 0.257 35 Correlation matrix 1 -0.201 0.855

lnind

lnurb

3.383 3.352 3.565 3.170 0.106 35

4.115 4.145 4.289 3.779 0.139 35

1 0.023

1

Figure 1 shows the histogram and trend graphs of the series. Thus, some preliminary information about the changes, distributions and dynamics of the series is observed. However, beyond the observations in table 1 and figure 1, econometric methods such as unit root, cointegration and causality tests will be used to obtain more reliable and effective results.

59

Figure 1: Dynamics of the lnei, lny, lnind and lnurb series lnei

lny

2.3

9.4

9.2

2.2

9.0 2.1 8.8 2.0

8.6

1.9

8.4 1980

1985

1990

1995

2000

2005

2010

1980

1985

1990

lnind

1995

2000

2005

2010

2000

2005

2010

lnurb

3.6

4.3 4.2

3.5

4.1 3.4 4.0 3.3 3.9 3.2

3.8

3.1

3.7 1980

1985

1990

1995

2000

2005

2010

1980

1985

1990

1995

Estimation of long-run relationship parameters between macroeconomic variables is an important area of interest in the literature. For researchers, the Gauss-Markov statistical properties must fulfill the following two conditions. First, each of the regression variables must be stationary at the level values. In other words, all of the series must be integrated of order zero [I(0)]. Second, if each variable is stationary in the first difference [I(1)], in order for the regression to be statistically significant, it must have at least one cointegration relationship between the series. For this reason, in the first step of the econometric analysis, the stationarity of the series is investigated by unit root tests. Traditional unit root tests (ADF and PP, respectively) developed by Dickey and Fuller (1979; 1981) and Phillips and Perron (1988) are widely used in econometric literature. In this study, ADF and PP unit root tests will be used for stationarity testing by following the relevant literature. On the other hand, unit root analyses often show that the series are not stationary in level values, but are stationary at first differences. In this case, there must be a cointegration relation between the series before the estimation of the regression parameters. Engle and Granger (1987), Johansen (1988) and Johansen and Juselius (1990) tests are widely used for cointegration analysis in the literature. Following the literature, Johansen (1988) and Johansen and Juselius (1990) cointegration tests (JJ tests) will be used in this study. However, the JJ cointegration tests assume that long-run cointegration parameters do not change over time. That is, in the test of the 60

cointegration relationship, the structural breaks occurring over time are not considered. To fill a this gap, Gregory and Hansen (1996) developed a cointegration test that takes into account a structural break. Recently, Hatemi-J (2008) develops a cointegration test that determines two structural breaks. In this study, Hatemi-J (2008) cointegration test will be used for more robust findings after JJ tests. For the analysis, the following equation is first considered: yt = a + β ′ xt + u ve t= 1, 2, …. , n.

(2)

The equation (2) is expanded as follows considering the effect of two structural breaks. yt = α0 + α1 D1t + α2 D2t + β'0 xt + β'1 D1t xt + β'2 D2t xt + ut

(3)

In the equation (3), D1t and D2t are dummy variables that include structural breaks, and the dummy variables are defined as follows: D1t =

0 if t 1 if t

n n

1

D2t =

0 if t 1 if t

n n

2

(4)

1

2

In equations (4), the parameters 1 (0,1) and 2 (0,1) show the realization time and the integer part of the regime change. The symbol “t” indicates the breaking date. Hatemi-J (2008) estimates three models using the Augmented Dickey-Fuller (ADF) test proposed by Engle and Granger (1987) and the Zα and Zt test statistics developed by Phillips (1987) to test the null hypothesis with no cointegration. The first of these, the model 2 (C) level shift model. Secondly, the model 3 (C/T) has shifts in level and trend. Third, model 3 (C/S) has shifts in level and cointegrating slope coefficients. Test statistics used for analysis have non-standard distributions and Hatemi-J (2008) produces new critical values with Monte-Carlo simulations. As a result of cointegration analysis, if there is a cointegration relationship between the variables, the next step is to estimate the long-run parameters. For this purpose, this study will use the dynamic least squares (DOLS) estimation method developed by Stock and Watson (1993). The DOLS estimator is a dynamic estimator that includes the potentials and lags of the explanatory variables. In addition, the DOLS estimator solves the problems of autocorrelation and endogeneity of OLS estimators. In order to check the validity of the DOLS results, this study also employs fully modified least squares (FMOLS) developed by Philips and Hansen (1990) and canonical cointegration regression (CCR) estimation methods developed by Park (1992). Finally, the causality test will be used to determine direction of the relationship between variables. Because the relationship between the economic variables can sometimes be unidirectional or sometimes bidirectional. For example, while industrialization and urbanization affect energy intensity, energy intensity can also affect industrialization and urbanization. Therefore, It is important to determine the direction of the 61

relationship between the variables. In this study, Hacker and Hatemi- J (2012) bootstrap causality test will be used for causality analysis. The null hypothesis for the test is defined as there is no a causality relationship from first variable to second variable. The Wald statistic is calculated to test the null hypothesis. The Wald statistic is calculated to test the null hypothesis. If the Wald statistic is greater than the bootstrap critical values obtained by the Monte Carlo simulation, the null hypothesis is rejected.

4. Results In the first stage of empirical analysis, ADF and PP unit root tests are applied to investigate the stationary of the series. Table 2 shows the unit root test results. According to the unit root test results, the series used in the analysis are not stationary. When the first differences of the series are taken and the unit root is applied again, it is observed that all of the series are stationary. In other words, all variables are [I(1)]. Variable lnei lny lnind lnurb ∆lnei ∆lny Δlnind Δlnurb 1% 5% 10%

Table 2: ADF and PP unit root tests ADF test statistic PP test statistic Intercept Intercept and trend Intercept Intercept and trend -1.185 -2.096 -1.172 -1.856 -0.588 -3.112 -0.479 -3.091 -2.308 -2.964 -2.365 -3.166 -1.073 -3.780b -2.552 -2.632 a a a -9.910 -4.517 -9.833 -9.978a a a a -6.475 -6.373 -7.857 -7.704a a a a -5.067 -5.327 -5.493 -5.725a -7.946a -5.092a -4.525a -4.216a Critical values -3.639 -4.252 -3.639 -4.252 -2.951 -3.548 -2.951 -3.548 -2.614 -3.207 -2.614 -3.207 Notes: ∆ is the first difference operator. a and b denote significance at the 1% and 5% levels respectively.

After the unit root test, the JJ cointegration test is performed to test for the existence of a long-run relationship between variables. For the JJ cointegration test, first the VAR model is estimated and the lag length is determined according to the information criteria. Then, it is determined by Breusch-Godfrey LM test and White test that the model established in this lag length does not include autocorrelation and heteroscedasticity problems. Finally, the cointegration relation is examined by JJ trace and maximum eigenvalue tests. In this study, the lag length is determined as 1 with the Schwarz information criterion and there is no autocorrelation and heteroscedasticity in this model. Table 3 shows the JJ cointegration test results. JJ trace and maximum eigenvalue test results indicate that the variables have at least one long-run relationship. Figure 2 shows the inverse roots of AR characteristic polynomial and cointegration graph for the estimation model. The distribution of the roots in the circle and their symmetrical projections imply that the model is not a problem in terms of stationarity. Also, the AR roots graph confirms that the cointegration relationship has a normal distribution and works with an appropriate mathematical form. Furthermore, the 62

fluctuation of the cointegration graph around zero reveals that the linear combination of variables are stationary. Table 3: JJ cointegration tests Trace statistic Maximum eigenvalue statistic Null Alternative Test Critical Null Alternative Test Critical hypothesis hypothesis statistic value hypothesis hypothesis statistic value (H0) (H1) (5%) (H0) (H1) (5%) r=0 r>0 57.263* 47.856 r=0 r=1 31.132* 27.584 r 1 r>1 26.131 29.797 r=1 r=2 16.954 21.131 r 2 r>2 9.176 15.494 r=2 r=3 8.788 14.264 r 3 r>3 0.387 3.841 r=3 r=4 0.387 3.841 Notes: r represents the number of cointegrated vectors. * denotes significance at the 5%. Figure 2: Inverse roots of AR characteristic polynomial and cointegration graph 1.5

.5 .4

1.0

.3

0.5 .2

0.0

.1 .0

-0.5

-.1

-1.0 -.2 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 14

-1.5 -1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Cointegrating relation 1

This study also applies Hatemi-J (2008) cointegration test with structural breaks for more robust cointegration findings. The results for Hatemi-J (2008) cointegration test are depicted in Table 4. Hatemi-J (2008) test results reveal that there is a cointegration relationship between variables. According to these results, there is a long-run balance between energy intensity, economic growth, industrialization and urbanization in Turkey between 1980 and 2014. Besides, the break dates which are determined by the Hatemi-J (2008) test results show the economic crises of 1993-1994 and 2000–2001 in Turkey. During this crises, Turkey experienced significant economic problems such as economic recession, unemployment, inflation and banking problems. All these economic problems in the crisis period caused important structural disorders in the Turkish economy.

63

Table 4: Hatemi-J (2008) cointegration tests (C/S)* (C/T)* (C)* ADF test Model 4 Model 3 Model 2 AR lag 1 1 1 t-stat. -7.676b -4.838 -7.504b First break point (ADF) 0.400 0.657 0.657 First break date 1993 2002 2002 Second break point (ADF) 0.514 0.714 0.714 Second break date 1997 2004 2004 Phillips test Zt* -11.053a -8.419a -7.699b * First break point (Zt ) 0.428 0.657 0.400 First break date 1994 2002 1993 Second break point (Zt*) 0.600 0.714 0.657 Second break date 2000 2004 2002 Zα* -50.513 -46.331 -44.062 First break point (Zα*) 0.457 0.657 0.400 Kırılma Tarihi 1995 2002 1993 * Second break point (Zα ) 0.600 0.714 0.714 Second break date 2000 2004 2004 Notes: * Critical values are obtained from Table 1 in Hatemi-J (2008). a and b denote significance at the 1% and 5% levels respectively.

After the long-run relationship is determined, long-run parameters are estimated to reveal the effects of economic growth, industrialization and urbanization on energy intensity. First of all, the breaking dates obtained from the Zt* statistic in Hatemi-J (2008) model 2 are included in the regression as dummy variables. Then, the long-term regression coefficients are estimated by the DOLS method. FMOLS and CCR estimation methods are also applied in order to check the robustness of DOLS results. Table 5 shows the results of long term parameter estimation. DOLS estimation results are as follows: (a) The effect of economic growth on energy intensity is negative and statistically significant at 1% level. In other words, the increase in GDP per capita or economic growth encourage energy efficiency in the long-run. (b) The effect on industrialization energy intensity is positive and statistically significant at 5% level. Consequently, the increase in the share of the industrial sector in GDP pressures the energy intensity in the long-run. (c) The effect of urbanization on energy intensity is positive and significant at 1% level. Accordingly, the urbanization process in Turkey causes an increasing influence on the energy intensity. (d) The coefficient of Dummy 1 (1993) is significant and the coefficient of dummy 2 (2002) is insignificant. This result shows that the structural changes (crisis) of 1993 in Turkey is an increasing effect on energy intensity. Because of the crisis in 1993, economic problems have affected energy efficiency in the negative direction. (e) FMOLS and CCR estimation results for robustness control are not different and support greatly the results of the DOLS.

64

Table 5: Long-run estimations (Dependent variable: lnei) DOLS FMOLS

CCR

Variables Coefficient t statistic Coefficient t statistic Coefficient t statistic lny -0.292a -2.921 -0.352a -3.732 -0.334a -3.310 lnind 0.017b 2.164 0.048c 1.891 0.056 1.519 lnurb 0.910a 4.815 0.993a 5.465 0.927a 4.750 Dummy1 (1993) 0.060a 2.967 0.058a 3.009 0.061a 2.951 Dummy2 (2002) -0.004 -0.168 0.019 0.745 0.023 0.774 constant 1.058 2.127 1.014 2.097 1.099 2.203 Adj. R2 0.868 0.895 0.890 Durbin- Watson 1.829 1.647 1.588 Notes: a, b and c denote significance at the %1, 5% and 10% levels respectively.

Finally, the Hacker and Hatemi-J (2012) bootstrap causality test is applied to determine the relationship between variables. Table 6 shows the results of the causality test. According to the test results, there is a one-way relationship from economic growth, industrialization and urbanization to energy intensity. In other words, changes in economic growth, industrialization and urbanization affect energy intensity. However, changes in energy intensity do not affect economic growth, industrialization and urbanization. Essentially, findings of causality support the cointegration relation and long term coefficient findings. Table 6: Bootstrap causality test MWALD Bootstrap Critical Values* Decision statistic 1% 5% 10% a lny does not cause lnei 3.672 7.820 4.107 2.856 Reject lnei does not cause lny 0.822 8.222 4.520 3069 Fail to reject lnind does not cause lnei 2.975a 7.613 4.139 2.269 Reject lnei does not cause lnind 0.817 7.799 4.300 3.008 Fail to reject lnurb does not cause lnei 3.537a 7.615 4.211 2.909 Reject lnei does not cause lnurb 2.668 7.674 4.249 2.983 Fail to reject Notes: * Critical values are obtained through 10.000 bootstrap replications. a denotes significance at the 10%. Null hypotheses

5. Conclusion The process of industrialization and urbanization is an important development indicators. This development process dramatically increases energy demand. However, how industrialization and the urbanization process affect energy efficiency/intensity is an important debate. Within the scope of the relevant discussion, this study investigates the effect of industrialization and urbanization on the energy intensity of 1980-2014 period in Turkey. In this research, unit root tests, Hatemi-J (2008) cointegration test, Stock and Watson (1993) DOLS estimator and Hacker and Hatemi-J (2012) bootstrap causality methods are followed. At the end of the empirical analysis, the following findings are obtained: (1) There is a long-run equilibrium relationship between industrialization, urbanization and energy intensity. (2) Industrialization and urbanization have a positive effect on the energy intensity. (3) There is a one-way causal relationship from industrialization and urbanization to energy intensity. These results (1-3) support Sadorsky (2013), Belloumi and Alshehry (2016) and Elliot etal. (2017)

65

research results. Accordingly, industrialization and urbanization process in Turkey increases energy intensity and affects energy efficiency negatively. Today, Turkey is considered as a country that has an important role in CO2 emissions from energy and faces the risk of energy security. It is very important to reduce the energy intensity to solve these problems. Because, the reduction of energy intensity is an important indicator of energy efficiency. For this reason, the following suggestions are recommended for Turkey to reduce the energy intensity: (i) Higher energy efficiency technologies can be used in industrial enterprises. (ii) Industrial organizations can increase spending on energy-related research and development specifically. (iii) Renewable resources can be used as fuel in the transport sector (Bilgili etal. 2017). In order to increse renewable energy production, Turkey should also focus on licencing activities (Kızılkaya et al. 2016). (iv) A variety of applications can be developed taking into account energy efficiency in buildings. For example, green building code applications, such as Leadership in Energy & Environmental Design (LEEDS) certifications, have significant potential to reduce energy intensity in buildings (Sadorsky, 2013). Undoubtedly, these proposals provide significant contributions to Turkey's energy policy objectives in the long-run.

66

References Belloumi, M., & Alshehry, A. S. (2016). The impact of Urbanization on energy intensity in Saudi Arabia. Sustainability, 8(4), 375. Bilgili, F., Koçak, E., Bulut, Ü., & Kuloğlu, A. (2017). The impact of urbanization on energy intensity: panel data evidence considering cross-sectional dependence and heterogeneity. Energy, 133, 242-256. Balat, M. (2010). Security of energy supply in Turkey: Challenges and solutions. Energy Conversion and Management, 51(10), 1998-2011. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 10571072. Elliott, R. J., Sun, P., & Zhu, T. (2017). The direct and indirect effect of urbanization on energy intensity: A province-level study for China. Energy, 123, 677-692. Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251276. Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of econometrics, 70(1), 99-126. Hacker, S., & Hatemi-J, A. (2012). A bootstrap test for causality with endogenous lag length choice: theory and application in finance. Journal of Economic Studies, 39(2), 144-160. Hatemi-j, A. (2008). Tests for cointegration with two unknown regime shifts with an application to financial market integration. Empirical Economics, 35(3), 497505. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of economic dynamics and control, 12(2-3), 231-254. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210. Kaygusuz, K. (2010). Sustainable energy, environmental and agricultural policies in Turkey. Energy Conversion and Management, 51(5), 1075-1084. Kızılkaya, O., Sofuoğlu, E., & Çoban, O. (2016). Ekonomik Büyüme, Enerji Tüketimi ve Çevre Kirliliği Analizi: Türkiye Örneği, Kırıkkale Üniversitesi Sosyal Bilimler Dergisi, 6(2). 67

Kotcioğlu, İ. (2011). Clean and sustainable energy policies in Turkey. Renewable and Sustainable Energy Reviews, 15(9), 5111-5119. Liu, Y., & Xie, Y. (2013). Asymmetric adjustment of the dynamic relationship between energy intensity and urbanization in China. Energy Economics, 36, 43-54. Madlener, R., & Sunak, Y. (2011). Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management?. Sustainable Cities and Society, 1(1), 45-53. Parikh, J., & Shukla, V. (1995). Urbanization, energy use and greenhouse effects in economic development: Results from a cross-national study of developing countries. Global Environmental Change, 5(2), 87-103. Park, J. Y. (1992). Canonical cointegrating regressions. Econometrica: Journal of the Econometric Society, 119-143. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 335-346. Phillips, P. C., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I (1) processes. The Review of Economic Studies, 57(1), 99-125. Sadorsky, P. (2013). Do urbanization and industrialization affect energy intensity in developing countries?. Energy Economics, 37, 52-59. Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: Journal of the Econometric Society, 783-820. Wang, S., Fang, C., Guan, X., Pang, B., & Ma, H. (2014). Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces. Applied Energy, 136, 738-749. World Bank. (2016). World Development Indicators. http://data.worldbank.org/ (date of access: 10.12.2016). Yüksel, I. (2010). Energy production and sustainable energy policies in Turkey. Renewable Energy, 35(7), 1469-1476. Zhang, C., & Lin, Y. (2012). Panel estimation for urbanization, energy consumption and CO 2 emissions: a regional analysis in China. Energy Policy, 49, 488-498.

68

CHAPTER V ECONOMICAL DIMENSION OF CURSE OF NATURAL RESORCE (CNR) Yeşim KUBAR1 1. Introduction The share of oil in world energy consumption actualized as 21% in 1940s; 30%, in 1950s; 45%, in 1980s; and 40%, in 1990-2000s; and after 2010s, it rose to over 50s%. Due to the increase of this share over decades, the maim aims of the countries have become to have oil resources, control the production of energy resources such as oil and natural gas, keep oil pipelines under control, and become successful in international policies for being able to fulfill these aims. In the historical process, energy resources formed the background of many sociopolitical issues and became famous with making a current issue warheads. The effects energy resources will introduce for economies are classified in two ways as positive and negative. The studies containing periodical and country-specific differences show that the effect can realize both negatively and positively. In the studies of early period, the conclusion shows that economic growth becomes slower in the countries having abundant amount of natural resources in time. After 1970s, some studies, carried out on developing countries, point out a paradoxical state in the relationship between natural resource equipment and economic growth. This paradox expresses that the natural resource- rich countries have relatively lower growth rates compared to those having less resources or not having any resource. This case, called curse of resource, is a phenomenon earlier seen and having seen in economic history. In 17th century, Netherland not rich in terms of natural resources moved ahead of Spain, known abundance and wealth in terms of gold and silver. Again, in 19th and 20th century, Japan, a natural resource-poor country, exhibited a better performance than Russia, which is rich in terms of natural resources. The natural resource –poor countries,which pass to rise in the recent years, are put in order as South Korea, Taiwan, and Hong Kong. Just as silver negatively affects South America and gold, California, today, a dozen of valuable mines from oil to diamond are

1

Assist. Prof., Fırat University, Departman of Economics, [email protected]

69

continuing to negatively affect tens of poor countries from Iraq to Sudan and from Sierra Leon to Congo While some of the studies carried out in the scope of the reasons for curse of natural resource and its mediator variable mechanisms examine the hypothesis of Dutch Disease, some deal with importance of institutionalism. Wealth of natural resource, resulting in a control struggle and internal fidgetiness, is considered to lead to bad institutionalism and, thus, low growth, and it is expressed that the great rents obtained from natural resources urge the governments and private organizations to the use of rent, corruption, and illegality. The positive effect of natural resource wealth in increasing economic growth can decrease due to the negative effects arising from simultaneously disturbing the institutional structure and, sometimes, turn into even a negative effect. The hypothesis curse of resources actualizes in the political, economic, and military dimensions. Many states having rich natural resources can exhibit a poor performance compared to the states not having these resources from economic and political point of view. Therefore, economic curse of resources is expressed with the fact that the countries having rich natural resources generally fall behind the resource –poor countries in industrialization and employment areas, while political curse of resources emerge with the fact that as a result of rent economy and high military expenditures the high incomes obtained from the resources make it possible cause the oppressive and anti-democratic regimes to survive in these countries

2. Energy Resources Energy resources changed by diversified throughout history. Energy resource, which was in the form of muscle force, turned into hydraulic and wind energy in Middle Age. Industrial revolution increased energy needs and needed more than provided from the water and wind and this energy converted to coal, which is fuel for the machines operated by steam (Gimple,1996:1). Energy resources are classified with distinctions more than one. Fuels are major energy resources we use at the present days. These fuels are the resources, which cannot be used for one time more after using for one time, and which cannot renew themselves. These resources are energy capital in quality Their existing amounts only increase with finding the new reserves (Tümertekin,1994:406). Energy resources used as driving force take place in the literature as renewable energy resources. Renewable energy resources consist of wind, sun, water, forestry products, and etc. Natural resources both provide energy for production and constitute raw material of goods to be produced.

2.1. Resource and Natural Resources In order to be able to clearly introduce the meaning of the term natural resource, first of all, it is necessary to define the word resource. The concept resource is used in many languages. As a term of hydrography, in the positions, where aquifer intersects 70

with topographic surface, it is defined as a place, where water outcrops by itself. The term resource in physics is defined as object emitting heat, light, and energy. The term is used in the meanings such as the origin, cause, essence, support, remedy , and solution of the objective or subjective entity (Doğanay and Altaş,2013:1). The term resource is economically defined as a whole of objective (every kind of non-living things or living things except human being) and subjective (humanistic) entities supporting the production of goods and services and forming a support for this production. Human being, a variety of mines, water, forests, agricultural lands, meadows and grasslands, domestic and wild animals, all of objective and subjective entities such as sun, industry, trade and transportation constitutes the resources of wealth. That is, everything directly or indirectly supporting human life and economic activities is a resource. As known, the basis of economies (agriculture, industry, trade, tourism, etc.) are the resources of raw material. Therefore, economic sciences and economic geography are based on natural resources. Economic resources, defined by the term natural resource, show a large diversity. even if they are not as comprehensive as the meaning of the term resource. Natural resources are expressed as resources of wealth, which formed by itself in the environment (nature), which are not a product of human mind and technique, and in which there is no formative and destructive role of human in the formation stage (Doğanay and Altaş,2013:2). Natural resources consist of all resources occurred through natural ways and used in meeting human needs (UN,1970:6). Mines (metallic or non-metallic), waters (running waters, lakes, seas), natural vegetation, natural animal populations, and lands(agricultural lands, grasslands, and lands in forestry land)are expressed as natural resources. In addition, the sun, which is a source of the natural and unnatural formation on all earth, takes place among the most leading natural resources. Beside this, winds and rainfall are the other formations taking place in the class of natural resources (UN,1970:6).

2.2. Classifications of Natural Resources Some part of natural resources are living entities such as forest, animal, and vegetation, while the other part of them are non-living entities such as air, water, and land. Since every two sorts of natural resource have an economic dimension, the principle to use them without running out was developed. In the direction of this principle, annual productivity that will be obtained from a living entity should not exceed the rate of annual natural increase of that natural resource (Kışlalıoğlu and Berkes, 1990:206). The private and public decisions regarding the use and management of natural resources have environmental effects from social, economic, and technical point of view. These decisions alternate depending on that the resources are exhaustible or renewable. The subjects such as discussion of hierarchical decisions for natural resource management, distinction between exhaustible and renewable resources, market balance, static activity, producer redundancy, consumer redundancy, and net 71

social benefit take place among economy of natural resources. Natural resource management concerns timing and management of the use of natural resources, resource owners, business managers, and responsible people for the relevant group and public policy (Prato,1998:111). There is a connection between economic growth and environmental pollution that occurs depending on consumption of natural resources. There are two views regarding that economic growth causes environmental pollution. According to the first view, sincethe environmental entities such as air, water, and land are regarded as nonexhaustible, these resources are considered “free goods”. In the direction of the principle of economic paradigm in the form of profit in production and utility in consumption, free goods are evaluated as resources enabling profit to increase, reducing production cost. Undesirable losses in environmental values are expressed as negative externalities in economic area. If there are externalities in market economy, market shows that the condition of Pareto Optimum and affectivity of resource distribution are not satisfied ( Ertürk,2009:188-189). It is known that countries have natural resources in the different sorts and amounts In general, this case is closely related to the geographies and positions of countries. Among natural resources, the first important element is land in terms of process. Therefore, in order to acquire or not to lose the land, the wars and conflictions were experienced in almost every period of history. This case was followed by the valuable mines. Together with industrial revolution, the coal and iron stood out as the main raw material of production. Since these mines are also the main inputs of arm production, they have gained a strategic value. In the proceeding periods,in the demand to coal as well as the several different mines, a rapid increase actualized. This demand is increasingly continuing at the present days as well.

2.3. Hypothesis of Curse of Natural Resource The thought that natural resources may impede economic growth dates to Bodin (1576), French philosopher of 16th Century. Bodin claimed that easy wealth obtained from natural resources led to slackness and laziness. This thought changed in time together with the change and development of economic theories and has again become the subject of examination together with Dutch Disease that became popular in 1970s. The concept of curse of resources is that the asset of foreign currency accumulating in a natural resource-rich country cannot be effectively valued. Asset of foreign currency accumulating in a country leads national currency to be overvalued and, thus, the goods imported from abroad become cheaper and the demand to these goods increases. This case causes the country to give serious current deficits and serious constrictions in the sectors substituted to import. Among corruptive effects the rich natural resources create, other than economic ones, detrimental effects that develop, depending on bad management, also occur. Rich natural resources whet all sector’s appetite especially foreign investors. A large injustice can be experienced in sharing these resources. The possibility that a large part of incomes from natural 72

resource export is transferred to the sub-contractors of the country strengthened by the governments or foreign investors and some part of bureaucrats supporting them can reduce the share received by the people of country. Rich natural resources can form a basis for bad management and outbreak of corruption and bureaucracy can mostly cooperate with strong foreign investors in favor of their own interests. As a result of this, some part of wealth coming from natural resources are transferred to the certain sectors as rent and people cannot receive the share they deserve from this wealth (Ross, 2003:17-41). On the dates, when empirical analyses cannot be strongly supported by the statistical and econometric models, observations and comparative studies examined the use of resources of the countries having abundant amount of natural resources, and identified that socioeconomic indicators of the countries having abundant amount of resource more remained in the background; that the signs for passing a strong democracy were seen less; and that they were more inclined to domestic disturbances. In the next process, economists and political scientists examined this case with the different approaches and instruments and called it curse of resources. Together with the studies Sachs and Warner (1995) realized and identified that negative relationship between natural resource wealth and growth rate, while the other factors are constant, the concept became public and a new study area formed. Following this, a few researchers studied this relationship, using the other explanatory variables such as human capital and institutionalism. In contrast to the first studies and what was thought, it was concluded that the relationship between natural resource abundance and growth was affected by many other factors and there was not any case just as every resource-rich country has slow growth. Gylfason (2001), Bravo Ortega and De Gregorio (2005), Stijns (2006), in their studies, in the countries having resource abundance, concluded that low educational expenditures and mechanisms of low rates of participation in school formed a negative pressure on the growth. A second finding derived from the studies is that negative effect of natural resource wealth on growth can be counterbalanced with high educational level. Another finding is that per capita income obtained from natural resources shows a positive relationship with human capital accumulation. Baland and Francois (2000) associated with this relationship with pursuing rent and income level. Manzano and Rigobon (2000) suggested, in their studies, the primary problem with growth in the counties with high resource equipment was the problem with debt load. The studies of the recent period carried out for curse of natural resource, together with that the literature of institutional economics gains importance, reveal that how a relationship resource abundance and dependency are with economic activities through institutional mechanisms and at what degree they are effectively used. Through the factors such as political stability, illegality, management forms, and degrees of political rights, at what measure development mechanism works well has an important place in explaining the presence of curse of resources. The studies show that growth rates the countries with equal starting conditions in terms of amount of natural 73

resource equipment show in time “t” are very different from each other. Scandinavian, Latin American, and African countries constitute the examples of this case. In the studies carried out, it was concluded that the disappointing effect of natural resource abundance on economic growth resulted from many economic, political, and social conditions. One of the results of depending on natural resources is the disturbances in the structure of government. The second is that it negatively affects democracy, while the third is that it causes conflictions in the countries (Palley, 2003:54-61). In view of this, it is put forward that curse of resource produces the three important problems. The first of these is fluctuation in incomes of government. In this case, in the event of that the budgets of the countries are weakly managed, a disturbance will occur in inflation and the governmental expenditures and, as a consequence of this, economic fluctuations will be seen. The second is that the excessive export of natural resource causes real exchange rate to be valued and competitiveness of manufacturing sector to be impeded. The third is that keeping at hand the control of resources from political point of view causes in-country political confliction. About curse of natural resource, the fact that it causes fluctuations in exchange rate; the value of exchange rate to be determined by some organizations and political managers; the prices of oil and other natural resources to be more volatile than the prices of the products produced in agricultural and manufacturing sector; and resources to have negative effect on the growth are frequently mentioned. In the most of the countries in terms of resources, since economic life is unstable, economic situation is also negatively affected from the existing political instabilities (Auty, 2001:839-846).

2.4. The effects of Curse of Natural Resource In related to curse of resource, it is possible to mention about two kinds of effect. Of these, the first effect is called economic effect and the second effect, sociopolitical effect. Economic effect is divided into two within itself. Market effect is grouped as exchange rate, capital, labor force, and goods demand, while governmental effect is grouped as goods demand and social plans and it is wanted to be identified which issues they affect in economy. Governmental effect is grouped as policy formation, illegality, and governmental function, while political effect is grouped as human capital, saving, and authorization. These effects in order are expressed as transmission mechanism. According to this, abundance of natural resources can lead to the following effects. 

Income fluctuation



Exclusion effects



Dutch Disease



Increase of role of government



Sociocultural and political effects

74



Effects on human capital



Fall of term of trade in long period

Income fluctuation expresses that government is a serious worry resource of the fluctuation in income for financial managers being active in the scope of the need for policy and balanced budget Transmission mechanism of human capital expresses the contribution of the educational levels of workers to the production, and productivity covers information in large rate. It is accepted that human capital is a more important production factor than the natural and physical capital; however, in the developing countries, human capital produces less than two-thirds of income and since a country exporting natural resource does not need for education, it allocates less resource to education. This case is concluded with a negative relationship between resource abundance and human capital. In the resource -poor countries, process positively runs and, with the changes occurring in economy, the tolerance of country against shocks increases in large rate. In the literature, economic run of the phenomenon curse of resource is concluded with formation of anti-democratic government, unfair assets, protected light industrial market, delayed urbanization, delayed demographic change, labor force surplus lasting long, low savings, slowly developing heavy industry, slow skill accumulation, slowly growing social capital, growth –constricted primary sector , and less tolerance against shocks and crises. The economic and social effects of resource abundance are related to each other. In case of resource wealth, the tolerance of society against the economic and social shocks is not high (Auty,2003).In the literature of curse of resource, common thought is that the resource –rich countries made stagnant economic growth. In addition, that the administrations of these countries are antidemocratic makes it difficult worsen the existing state. Another effect of course of resource is on human development index (HDI). The relationships between whether or not natural resources and human development are curse were examined. The results obtained from the studies are in the direction that the variations in HDI can be positively related to natural resource abundance and natural resource abundance, to HDI. In resource-poor countries, economic run is concluded with the developments such as realization of redistributing goods through developmental government, competition first experienced in the area of light industry, fair income distribution, and early urbanization; growing of social capital, with demographic variations to lead labor force change; development of heavy industry together with high saving; realization of liberalization and democratic developments; and their being more tolerable against shocks and crises, compared to the countries having resource abundance (Auty,2003).

75

2.5. The Concepts Accounting for Curse of Natural Resource Gylfason (2001) states that that natural resources are abundant excludes the other factors such as the physical, human, and institutional resources and foreign capital. There are some reasons for this case. At the end of export of the resources such as oil and natural gas a country has in abundant amount, input of abundant amount of foreign currency is provided to country and, as a result of national currency of the country, with increase of the import of the country, and competitive power of domestic production gets harm. That incomes obtained are mostly canalized to the sectors, which natural capitals belongs to, causes the other sectors to be neglected. In such a case, countries, leading the activities such as technological development and education, which will increase the productivity power in the other sectors, to be neglected, cause the country to become import –dependent, unemployment and decrease of production in the other sectors and, as a result, increase of production (Krugman, 1987:49). With the view that abundant amount of resources will have a negative effect on economic growth, the theses of Singer (1950) and Prebish (1950) attracting attention to the disturbed term of trade in terms of emergence process of this relationship verify the thesis by Bhagwati (1958) of impoverishing growth. In terms of the cases of the climate, natural resource, and geographical structure the countries have, geography plays considerably role. In this scope, climactic conditions, affecting the level of agricultural productivity and human capital, become associated with economic growth. Climatic conditions suitable to agriculture, increasing agricultural production and agricultural yield, make positively contribution to economic growth (William and McMillan, 2000:20-21). The factors such as mild climatic conditions, behavior of energetic human, healthy individuals, long life, productive power, and durability affect quality of human i.e. quality and quantity of human capital and, from this direction, becomes associated with economic growth (http://wcfia.harvard.edu). Under unsuitable climatic conditions, the level of agricultural yield and human capital and, thus the rate of economic growth becomes low (www.brookings.edu). The effect of geography on economic growth can also actualize in respect with natural resources. Theoretically, the increase in the amount of natural resources consisting of the areas suitable to agriculture, forests, the mines such as gold and diamond, and energy resources such as coal and oil is expected to positively affect economic growth. However, in natural resource–rich countries, just as the effect of natural resources on economic growth can be positive, it can be negative as well. In African, Latin American, and Gulf countries having natural resources oil, natural gas, and suitable agricultural areas, economic growth remains at very low levels. The main reasons for why natural resources negatively affect economic growth are expressed as the instabilities in export of primary products (Singer-Prebisch Thesis); risks the fluctuations in the prices of goodsbased on natural resources emerge; that the manufacturing sector excludes export of natural resource due to lack of specialization in export of natural resource; and that valuable mines stir up domestic 76

conflictions, and leads to civil wars and weak institutional structure. Dutch Disease, defined in the form of that the incomes obtained from a newly discovered natural resource leads to disturbance in terms of trade and increase in public expenditures, overvaluing national currency, also takes place among the main factors leading to curse of natural resource (Kaplan, 2013:35). Among the traditional approaches toward introducing the relationship between natural resource wealth and economic development, the views of Bhagwati, Nurkse, Myrdal and Prebish-Singer take place. According to the theory developed by Bhagwati, as a result of disturbance in external terms of trade, economic growth can lower the welfare of country to lower level than the previous one. Bhagwati theoretically introduced this view of him in in his papers and called his theory “impoverishing growth”. In 1958, hagwati developed theoretical bases of impoverishing growth and visibly revealed the negative effect of increase in foreign trade on foreign trade rates, introduced by Edgeworth. The theory of impoverishing growth by Bhagwati says that growth will lead to worsen in terms of trade under certain conditions but this worsening may cause the different results such as increase, not varying at all, or actually decrease in real income. According to this, if real income increase, provided by growth, is bigger than the loss arising from external terms of trade, any problem does not occur but if the loss arising from terms of trade goes up this, this means that the real income increase, provided by the growth, is exceedingly eliminated. In this case, country enters the process of impoverishing growth. Although it is not a sufficient condition, the cases, in which export goods are not flexible, and import substitution industry are liquidated, can lead to this result. It is said that developing countries preferring export- oriented growth, economically growing through this way, can cause terms of trade to become worse than the position they previously have. This case is known as immiserizing growth among economists (Krugman and Obstfeld, 1997:12). According to, Nurksein large majority of developing countries in our age, consumer preferences remain under the effect of formations in industrialized countries. Despite large differences in per capita incomes, while that the developing countries imitate consumption model in the rich countries, on the one hand, makes slower capital accumulation, on the other hand, it increases import. In order to meet this increase of import, when the emerging countries fasten to increase the export of traditional agricultural products and raw material (natural resource –intensive products), whose demand flexibilities are low, external terms of trade turn to the expense of developing countries thus, the country can undergo real income loss. On this reason, Nurkse cannot match with the requirement of growth that developing countries attempt to grow, liberalizing foreign trade and increasing export in these conditions (Bal, 2011:93). Myrdal gives weight the differences in technology level and challenges to specialization according to comparative advantage and explains the reason for this as 77

that developing countries exhibit regional differences. According to this, in accordance with the structure taken over from the past, regional income that is more suitable produces industrial goods, whose flexibility is higher. The regions that are poorer content itself with producing the products, whose income flexibility is lower (natural resource- intensive products),like agricultural products and raw materials. Between regions, the differences appearing with the production of the products, whose technology level and income flexibility are different, are ended with shoving dual structure of economy. This dual structure becomes more intensive, when the country are opened to foreign countries; while the rich region becomes richer, poor region becomes poorer. In addition, as a result of liberalization in economy and foreign trade, it is concluded with the escape of skilled labor force and capital. The negative effects of natural resource abundancy were examined by Prebisch and Singer in 1950s. Prebisch, although worldwide increase occurs in incomes, introduced the thought that less developed countries become poorer, when compared to the developed countries. He put forward that this arises from the increase of export and import unbalance between the developed and developing countries. According to Prebisch, unbalance results from the export of agricultural products and natural resource from the developing economies to the developed economies and import of industrial products of the developing economies from the developed economies. As a solution of this balance in trade, he put forward that the third world countries had to be industrialized (Prebisch, 1950). Prebisch and Singer, in the studies they individually carried out in 1950, put forward that the relative price of primary goods (raw material and agricultural products) was in the tendency to decline in time compared to manufactured goods and this view was called Prebisch-Singer Hypothesis in the next years. Prebisch-Singer Hypothesis was more generally defined as hypothesis that net swap terms of trade will show a long term tendency in favor of manufactured goods, in turn, at the expense of primary goods. Prebisch and Singer (1950), as a reason for this state, showed the theory of static comparative advantage by Ricardo. According to the authors, primary goods –exporter countries, in contrast to the countries that have comparative advantage in the production of manufactured goods and that are exporters of these countries, are deprived of the profits industrialization engenders. Since the prices cannot keep in step with productivity, it was emphasized that industrialization provided extra profits arising from the improvements in the technical process and it was put forward that industrialization was an obligation for the countries having comparative advantage in the production of the primary goods. With the assumption that the primary goods are being produced by developing countries and manufactures goods by developed countries, the discussions around hypothesis have become covering the losses and profits of developing countries arising from foreign trade. In this context, according to Prebisch-Singer Hypothesis, it can be put forward that there is a long tern disturbance tendency in terms of trade of developing countries. The direct effects of disturbance tendency put forward that it is present in the terms of trade of the 78

countries concerned, on foreign trade balance can be summarized as follows (http://dergi.kmu.edu.tr ); 

If income effect (Harberger- Laursen-Metzler Effect) the variations in external term of trade causes is dominant over substitution effect these variations cause, foreign trade balance of the country concerned enters a long term worsening tendency.



If substitution effect the variations in external term of trade causes is over income effect this variation causes (if the condition of Marshall-Lerner is satisfied ), foreign trade balance of the country concerned enters a long term improving tendency.



If substitution effect the variations in external term of trade is fully counter balanced with income effect these variations cause, any effect will not occur in foreign trade balance of the country concerned.

Singer identified the problem with long term disturbance for less developed countries in foreign trade conditions. He put forward that the prices of less developed countries for export of main product fell in time compared to the prices they have to pay for the processed products and other products they import from the developed countries (Singer,1950). Prebisch and Singer claim that main commodities are face to face with a long tern fall in foreign trade conditions compared to manufactures. As a result of this, a long term fall in global prices can impede natural resource-based growth. Furthermore, global demand to the main products will grow more slowly compared to the manufactured products. The reason for this, in connection with global income, underlies the fact that demand to main products is inelastic. That is, each increase of 1% in world income is concluded with an increase less than 1% in demand to main products (Singer,1950). The modern views that being rich in terms of natural resources may result in negative effects on economic development are also diverse and are expressed in summary as follows (Sachs and Werner, 1995; Lane and Tornell, 1995; Lal and Myint, 1996; Ross, 1999; Auty, 2000; and Gyflason 2000, Balcılar, 2002): Rent seeking: In industries related to natural resources, first or last, the effective groups seeking rent will emergeand these will enable the policies negatively affecting economic growth to be applied via political pressure, even if they are detrimental for the society. Rent seekers, leading resources to be allocated to ineffective areas, cause economic growth to decrease. As a matter of fact, seeking for acquiring rents made effect aggrandizing the forms of governmental administration, which can be expressed as partisan or freebooter. Rent –seeking activities can also cause even civil wars in some examples of African countries. This encourages elitist administration forms and causes violations to increase in the meaning of democracy and human rights. One step further of this is climb in the phenomenon of illegality and corruption.

79

Secondly, abundant natural resources result in the applications of wrong policy. The natural resource –rich countries feel themselves in more secure and do not show the necessary sensitivity in the implementation of policies. In these countries, when the rent seeking –induced market failures reduce economic growth, policy makers refer to large investment projects they believe in that they will improve economic growth, and foreign debts. The efforts to reduce high inflation and control the rising foreign debts, which can occur in view of this, reduce economic growth much more. Especially in downward fluctuations seen in the prices of natural resources, this kind of investments and import pressure cannot be attenuated and this makes the problem permanent. In the long period, the processes of saving scarce and dependence on foreign finance make economic growth pausing and downgrading. A long term dependence constitute toward prestige-aimed investments as well as natural resource export. This causes effects delaying competitive industrialization processes. Thirdly, primary production based on natural resource abundance reduces the demand to education and negatively affects human capital accumulation in these countries. Such a structure, which can also be expressed as exclusion of social capital, causing the effectiveness of human capital to decrease in the middle and long period, leads to negative effects on economic growth (Zakaria, 2007:76). In the natural resource –rich countries, rapidly reduction of tax rates and subventions reduces the competiveness of individuals and firms and result in laziness. The result is a decreasing growth rate. In these countries, the employment intensifying in the primary sector cause the workers employed in this sector to be less educated. These workers are mostly deprived of information accumulation high technology requires. This situation also impedes the development of high technology sectors, engine of growth (Zakaria, 2007:144). Fourth is the phenomenon of Dutch Disease. This phenomenon is related to that the rent acquiring- based industries in the countries in natural resource abundance can survive with high exchange rates and pay for higher wages. Fifth is the examples of freeloading showing the tendency to become widespread in economic agents. The main reason for this case, expressed as an indirect effect, in natural resource-rich countries, is that the tendency of government to levy is weak and that public services are provided in free of charge and in the conditions similar to this. While the phenomenon of freeloading blunts the sense of competition in economic area, it can encourage laziness; forms a basis the attempts to becoming widespread of social aid programs; and causes negative effects in the meaning of productivity and competitive power of industry

2.5.1. Volatility and Prices of Natural Resources Exports of natural resources can harm to economies through the various ways. First of all, exports of natural resource create volatility in government revenues and, if they are not successfully managed, can cause the cycles of sudden rise and fall in inflation and government expenditures. High volatility in commodity prices can cause 80

irregular effects on the income flows and economic growth of an economy, whose exports are largely based on the primary natural resources. Thus, export incomes remain dependent on market price Volatility causes uncertainty and reluctance about investors’ allocating their money to the productive activities and, this case is not a desirable case for the growth (Ploeg, 2009). The prices of oil and natural gas have a higher volatility compared to the prices of the resources such as mines and agricultural products. In short and middle term, dealing with a natural resource like oil as having relatively high volatility are due to the fact that oil has a quite low flexibility in term of price regarding supply and demand. Therefore, even the smallest variations in demand requires detailed adjustments in price to balance supply and demand. That natural resources have more fluctuate prices compared to the prices of the other goods and services leads the primary commodities to be concluded with higher uncertainty for producers. Uncertainty spreads to the other sectors of the economies in natural resource dependency and, the higher uncertainty, the lower capitalization factor becomes and this state is concluded with lower economic growth. The reason for this is that just as uncertainty raises risk, it raises also the option value of waiting. An economy should choose to diversify the goods and services in economy instead of specializing for attenuating the risks volatility contains. The volatility of the prices of natural resource becomes a good reasons for explaining why natural resource abundance can impede economic performance of a country

2.5.2. Matsuyama Model Matsuyama model deals with two major sectors as manufacturing sector qualified with learning by doing and agricultural sector not qualified as learning by doing (Frenkel,2010:13). In addition, there are two assumptions of this model. According to the first assumption, the preferences are not homothetic and demand to agricultural goods has an income flexibility that is less than unit income flexibility. The second assumption is that manufacturing productivity increases in time due to learning by doing. All forces driving economy from manufacturing to agriculture, considering that manufacturing grows as learning-induced, lower economic growth rates (Sach,1995:5). In Matsuyama model, two scenarios take place as the cases of closed economy and open economy. Scenario of closed economy: In this scenario, there is a positive relationship between agricultural productivity and industrialization. The increase of agricultural productivity means so many foods suitable for growing population and, this also means that it is necessary to use less labor for producing large amount of food, and that there is more labor, which can be used for manufacturing sector. In addition to this, high income produced by agricultural sector encourages domestic demand for manufactures and increases the supply of domestic savings to support industrialization.

81

As a result of this, in a closed economy, an increase in agricultural sector, accelerating economic growth, shifts labor to manufacturing sector (Matsuyama,1992:317). Scenario of Open Economy: In this scenario, prices are determined by the situation of global market. An economy having high yield and output in agricultural sector, excluding the previous one, is concluded with the transition of labor from manufacturing sector to agricultural sector. Otherwise, an economy having low agricultural yield allow more suitable labor for manufacturing sector, which is concluded with faster and more economic growth, and in which productivity is qualified with learning by doing Thıs, in contrast to scenario of closed economy, in the scenario of open economy, there is a negative relationship between agricultural yield and industrialization, in which the lower agricultural yield, the higher economic growth will be (Matsuyama,1992).

2.5.3. Civil War Since the mid- 1990s, the studies om the causes of civil wars were intensified. The leading finding, which is surprising and themost important, is that natural resources have a large importance in triggering, sustaining, and financing these conflictions. Natural resources leading to conflictions are predominantly oil and valuable mines such as coltan, diamond, and gold. However, the other valuable mimes and substances such as timber can lead domestic conflictions. Beside this, if we count “drugs” among natural resources, these are also seen to play role in many conflictions. Among the valuable mines playing major role in the conflictions related to natural resources, the goods such as oil, natural gas, drug, and timber take place (Ross,2003:17). Natural resources increase the risk of outbreak of civil war and, when a civil war begins, and makes difficult to solve this. Resource wealth raises the danger of civil war; however, while almost all of resource-rich countries expose to conflictions, some can avoid this situation. Directing resource wealth to the areas such as education, health, and struggle against poverty and developing better policies form an effect decreasing the thoughts in the direction of that natural resources lead to civil wars (Ross, 2003:18). Natural resource-dependent countries, especially oil-dependent countries, provide social peace by applying populist policies or suppressing (as long as terrorism and international intervention are not present ) the rivals at home. Powerful energy importers supports political stability in these countries by military aids or ignoring anti-democratic applications and human right violations in that country (Le Billion, 2001:10-11).

2.5.4. Rent-Seeking Behavior In sharing rich natural resource, great injustices can be experienced, since that the returns of natural resource are actualizing at extremely high levels whets appetite of many sectors. This, transferring some part of natural resource incomes to rentseeking groups, also strengthens the possibility that the share the people of that country will have is quite low. Rich natural resource can form a basis for bad 82

management and outbreak of corruption and bureaucracy is mostly prone to cooperating with strong foreign investors. As a result of this, some part of wealth from natural resource is transferred to certain sectors as rent, while the people cannot receive the share they deserve from this wealth (Ross,2003:17-22). Rent – seekers, leading the resources to be allocated to unproductive areas, cause economic growth to decrease. Rent-seeking behavior, as experienced in some natural resourcerich countries, can lead to even civil wars. This, also encouraging elitist administration form, impedes the development of democracy and causes the increases in human right violations. As expressed by Stiglitz, curse of resources creates rich countries, in which the poor people live (https://www.theguardian.com). Governments having natural resource abundance show the tendency to lose the view of the need for useful and adequate growth. Instead of creating wealth with the precautions of encouraging manufacturing sector, they follow policies toward consuming the resources as immediate as possible. In the periods, when rent-seeking results in market failure and reduce economic growth, policies, instead of offering solutions, thoroughly finding out the problem, go toward solution salvaging the day. In this case, they attempt to large investment projects and refer to foreign debts. In the long term, the processes such as saving scarcity and foreign debt-dependence impede economic growth. The reason for this is that in natural resource –rich countries, the tendency to levy is weak and that public services are provided in free of charge and in the conditions similar to this. While freeloading weakens and eliminates the sense of competition, it encourages laziness and leads social aid programs to become widespread. Torvik expresses that the countries claimed that they escape from curse of resource are those having higher saving rates (Torvik, 2009:249). The most important mechanism of the phenomenon curse of resource, which affects economies in the governance area is the increasing illegalities. Considerably strong evidence is obtained toward the hypothesis that as income a country obtains from natural resources, it exposes to more illegalities. Two causes related to this are mentioned about. First is that governments only register a certain amount of natural resource income that is rapidly increasing. Natural resource wealth provides much more income than governments can effectively manage. The second results from volatility in natural resource incomes. The fast rises and falls in the prices, negatively affecting budgetary processes, attenuate the institutes in the country( Ross,2003:1742). A number of study was carried out regarding the hypothesis of curse of resource, which puts forward that natural resource abundance is related to the level of democracy. The results of the study are in the direction that natural resource rent strengthens autocratic administration rather than supporting democracy. Autocracy prevailing in Middle East countries, oil producer, is accepted as an indicator of this relationship. It is seen that this state is also valid for many resource-dependent countries in the other regions other than Middle East ( Billion, 2001:10).

83

2.5.5. Role of Institutes One of the explanations of misfortune of natural resource is the indicators of poor institutionalism and governance. There are two main approaches related to the possible effects of natural resource equipment on economic growth. In the first approach, in which natural resource wealth is a blessing, natural resources provide the necessary investment capital and technological development for the relevant country. According to this approach, the natural resource-rich countries are expected to exhibit a faster growth trend compared to those having relatively scarce resource. As a result, natural resources a country has increase the export incomes of that country and, thus, growth performance through different channels. In the second approach, a production and export structure based on rich resource equipment impedes human and physical capital accumulation and development of economic and politic institutes in non-resource sectors (Guriev et al., 2009: 6). This approach attracting attention to regression especially in manufacturing industry is based on the negative relationship between natural resource wealth and economic growth. Some part of literature regarding the phenomenon of curse of natural resource touches on the role of institutes as a critical factor. This case is expressed with the results of the study. Elite and Weidman (1999), in contrast to food and agricultural raw material among natural resources, reached the findings that fuel and non-fuel minerals encourage illegalities. In the studies of Wantchekon (1999) and Ross (2001), it was suggested that there was a positive relationship between dependence on oil production and authoritarian regimes. Wantchekon (1999) expressed that increase of 1% in natural resource dependence, measured with the share of primary product export in GDP, increased the possibility of authoritarian administration about by 8%ü and that resource-rich countries experienced unsuccessful experiences in transmission to democracy. Ross (2001) concluded that oil and mineral wealth caused less democratic regimes. The mechanism here develops through two separate channels. First is “the effect of rent-seeking government”expressing that oil incomes are used to survive authoritarian regimes via low taxing and regime –protective expenditures. The second is “the effect of pressure”expressingthat authoritarian regimes in oil aim at remaining in power, using oil incomes for defense and security expenditures. In this scope, natural resource wealth is not only cause lower growth but also impedes the democracy and individual freedoms. Mehlum et al. tested the hypothesis of curse of resources and emphasized in their study that in exposition of the countries to curse of resource, Dutch Disease and quality of institution, in a larger rate, gained importance. The institutes providing support to the producers and industrialists, apart from avoiding from the negative effects of the phenomenon of curse of resources, can transform this wealth to a comparative advantage (Mehlum et al, 2006:16). Gylfason and Zoega, in their study, concluded that natural resource equipment negatively affected the institutional 84

environment and democracy and expressed that there was a remarkable negative correlation between natural resource abundance and institutional quality (Gylfason and G. Zoega, 2001).

3. Dutch Disease Dutch Disease, in an economy reaching resource of suddenly getting rich, as a result of that production factors are withdrawn from the other production areas and they go toward the new resource, was defined as a total of production decrease. Dutch Disease defines overvaluing of domestic currency in the country due to natural resource incomes, and the problems with high current deficitand unemployment arising in country economy at the end of the disturbing trade conditions as result of this. Since this case was experienced after oil was discovered in Netherland in North Sea in the early 1970s, it was called Dutch Disease with the name of this country. This name showed development, depending on that the significant change was experienced in the period following that Netherland have high incomes of natural gas and that significant gas reserves were discovered. With the increase of natural resource export, the profits, provided from the other export products, decreased and, the products obtained from the production mostly underwent to harm (http://www.geocities.com). Although the term of Dutch Disease was first used on the date of November 26, 1977, it has been claimed that it is a subject existing in the older periods in economy literature (Corden,1984:359). In the period of 1547-1554, as a result of that Spain discovered the silver mines, increase of real exchange rate that began ended in 1570s. In the study, carried out on this subject, the rate of the price index of the sectors that are the subject of trade to the price index of the sectors that are not the subject of trade was accepted as real exchange rate. Since this definition shows compatibility to the definition of real exchange rate accepted on the core basis of Dutch Disease, it is considered that Dutch Disease exhibits a features that were existing in the previous periods John Cairnes seems to have been the first to use this analytical approach when, in 1857, he studied the effects of the 1851 Australian gold discoveries on other sectors of the economy (Gelb vd., 1988:21). The extensive theoretical literature that followed has been surveyed by Corden (1984) and Neary and van Wijnbergen (1986); the treatment here is therefore concise and selective. In the countries having rich natural resources, unbalances occurring in the structure of economic activity have been the focus of the increasing interest, following oil crisis in 1972. In December 1982, with an article, published by W.Max Corden and J.Peter Near, titled “Booming Sector and De-industrialization in a Small Economy”, the theoretical framework of Dutch Disease was considered in scientific meaning. Therefore, the article by W.M. Corden and J.P. Neary seems to be accepted the main model of Dutch Disease The theoretical approaches to the concept Dutch Disease enabled a large literature to form and led economic processes natural resource -rich countries experience to be examined from more different point of view. 85

Dutch Disease has become one of the subtitles of Hypothesis of Curse of Natural Resource in time. The fact that silver flowing to Spain in colonization period of America was in fact concluded with Dutch Disease was understood from the study, carried out in the framework of economic history and, thus, theoretical framework of Dutch Disease formed. Dutch Disease basically starts with domestic currency overvalued with input of high amount of money and although this case initially seems to be a positive case with the increase of foreign currency input and rise of the value of domestic currency, it leads to rather bad results. The flow of foreign currency in higher amount than natural resource export into the country revalues the domestic currency and, as a consequence of this, trade conditions become difficult (Sarno and Taylor, 2003:107). The discovery of large gas resources was concluded with bounded expansion, strengthened domestic currency, rapid fall of the value of foreign currency, and largely weakened competitive capacity in non –energy sectors of economy. The feature of this disease generally expresses such a situation that a deep and strategically dangerous states underlies this fine course, even though the economic developmental indicators of the country forms a relaxing landscape The effects of curse of natural resource and Dutch Disease show difference. In Dutch Disease, while there were the growth and export diversity in economy, this was not present in the hypothesis of curse of natural resource. In the hypothesis of curse of natural resource, while economic constriction is experienced, there is export diversity; a constriction is seen in manufacturing industry (Ulucak, 2016:87). It is possible to put in order the general characteristics of Dutch Disease (Gurbanlı,2010:62): 

Inclusion (“cause”) of oil dollar, obtained from increase of oil production and export, in foreign currency market increases the value of national currency and this case is evaluated as an “effect”.



Increase of the value of domestic currency reduces competitive capacity of traditional export products and a fall at export level is experienced and, this case is also evaluated as an “effect”.



That transitional trade products lose competitive ability, in whether internal or external markets, is expressed as “cause” and, that mobile production factors flows from those areas to the other areas (non- trade areas and oil sector), as “effect”



That the capacity of crude oil production, leading the competitive capacity of non-oil trade sector to decrease changes the area structure of economy and industry and economy passes to one-directional development profile. This case is evaluated as “effect”.



The flow of production factors is “cause” and the marginal production of labor and capital and decrease of level of economic activity indicators is “effect”. 86



The structural changes in economy are considered as the reason for the loss of balance between supply and demand in country economy; the dependence of economy on import increases; and weakening of circulation capacity in currency of the country is “effect”.



It increases the dependence of revenue of government budget on the capacity of oil export and variation of oil price level in world market.

The cases similar to Dutch disease actualized in different forms inmany countries. Great Britain, Norway, Indonesia, Nigeria, Mexico, Venezuela (raw oil), Thailand (rice), Zambia, and Algeria (copper) take place among these countries. Dutch syndrome emerged with natural gas export and occurred with oil export in the countries such as Norway, United Kingdom, Mexico, Nigeria, and Venezuela and production of mines in Zambia and Algeria. The effect of Dutch Disease is theoretically described based on Salter-Swan-CordenDornbusch. In this context, firstly, it is assumed that there are groups of commercial and non –commercial commodities and both groups of goods cannot be substituted within themselves. Commercial commodities are the ones used in export and import and are tradable in international markets. Therefore, the prices of trade goods are determined in international markets. The prices of non–commercial commodities are determined in national markets (Acosta, et al, 2009:104; Adenauer and Vagassky, 1998:177). When classified from another aspect, three sectors stand out. These are natural resource based sectors (mining, natural gas, oil, etc.), trade subject sectors (agricultural and manufacturing industry, having the outputs of goods and service, which can be the subject of international trade) and non -trade subject sectors ( services including health, education, retail building, and etc.) While the prices of two out pf these three sectors – natural resource-based and trade subject- are determined in international markets, non-trade subject sectors are priced in national markets (Bacak, 2014:2). While the sector generally experiencing gain explosion with Dutch Disease is natural based sector, the sector whose production is constricted is industrial or agricultural sector. Dutch Disease is concluded with the increase of foreign currency incomes flowing to country and revaluation of real exchange rate. This also requires the sartorially redistribution of economic resources. Capita and labor, leaving the sectors such as agriculture and industry, shift to the sectors, where the discovery of natural resource is experienced. The price of non–trade subject sector also increases. In trade subject agricultural and industrial sectors, which face competitive international prices for the goods they produce final result, it shows itself in the form of that the increasing costs and decreasing compatibility appear. The gain explosion in natural resource incomes, making effect such as exclusion effect on the other important sectors of economy, constricts the production of those sectors and cause them to lose competitive power (Gurbanov, 2012).

87

3.1. Economic Effects of Dutch Disease If a natural resource is discovered in a country or if abroad demand of natural resource increases much more, the import of natural resource concerned increases in high amount. Because export increases, the amount of foreign currency flowing to country shows increases fold by fold. However, if all foreign currency flowing to the country is spent for import, monetary supply and demand to local products is not directly affected from the increase of foreign currency (Rudd, 1996:3-4) However, as a result of the flow of much more foreign currency to the country, the competitive power of export goods of the firms taking place other sectors natural resource (natural gas, etc.) in both fixed and flexible exchange rate system weakens and traditional export sector of the country downsizes and, this state is called expense effect . In addition, since oil prices increase, the costs of agricultural and manufacturing sector also increase (www.imf.org). It is possible to put in order the economic effects of raw materials in a country (Pettinger, www.economicshelp.org): Increase of difference of exchange rate: When raw material is discovered or its export increases, the flow of foreign currency to the relevant country will increase and national currency will revalue. The reason for this is that raw material is demanded with national currency. Decrease od competitive power: The increase in the value of national currency decrease competitive power of the other sectors that are subject of international trade. Therefore, the demand to manufacturing sector decreases. In addition, in economy, a decrease is seen from manufacturing sector to the primary sectors. Increase of luxurious goods and service import: At the end of more production and export of raw material, income levels will increase. As a consequence of this, demand to luxurious goods and services will increase. Due to luxurious import, the incomes of domestic firms will decrease. Increase of real wages:. With the rise of welfare level, the demand to those working in service sector will increase. This will also lead real wages to increase in economy. In addition, the increase of real wages is a problem for manufacturing sector. Because those working in manufacturing sector will demand increase in their wages. With the rise of wages in manufacturing sector, costs will also increase. Indirect Industrialization: As a result of increase in real wages and revaluation of domestic currency, with decrease of competition in manufacturing sector, production level will also decrease. Manufacturing sector will fall behind manufacturing sectors in the other countries. In the next years, manufacturing sector will hardly reach high production level in the past years. Income inequality: In general, when raw material is discovered in countries, a large part of population utilizes the income increase. In the countries, in which raw material is discovered, income level rises but the income obtained from raw 88

material is not equally distributed in the society. Therefore, discovery of raw materials, a few of billionaire emerge.

together

with

the

Tax Income: Raw material production at high level enables government to obtain tax income at the important level. Thus, government can have budgetary surplus and, in addition to this, government can make public service such as more infrastructure and educational expense. When oil, natural, gas, and the other resources are discovered in a country, GDP of country increases with export. Thus, tax income of the country will increase, current account will develop, and opportunity of employment will occur. When raw material runs out, the countries face to some realities(Pettinger, 2014): 

Due to decrease in output and investment, reaching the position of exporter economy takes long years.



With raw material export, countries can have current surplus. But when oil export decreases, countries faces to large current deficit.



Together with fall in GDP, top level demand of service will fall and, as a result of this, among those working in service sector, unemployment will increase.

3.2. Country Examples of Dutch Disease In a number of countries, cases similar to Dutch Disease will emerge in various forms in a number of countries (www.geocities.com). The countries such as Great Britain, Norway, Indonesia, Nigeria, Mexico, Venezuela, Zambia, and Algeria, with the use of natural resource specified natural resource, faced to some problems. Great Britain, Norway and Indonesia: In 1970s, Great Britain, which was oil importer, after starting the use of oil beds in North Sea in large rate, turned into a giant oil exporter and experienced Dutch Disease. In Norway, together with the discovery of oil, generous welfare state considerably expanded and a fall occurred in traditional Norway industry. In 1986, together with the fall of oil prices, the constricting competitive power of industry left Norway face to face with a crisis. Norwegian enterprises lost their competitive powers. Unemployment rate considerably rose and that Norwegian economy gets rid of the effects of crisis took time. In the years following the first discovery, the lessons Norway took from this crisis helped it better prepare for the next periods. Therefore, Norwegian Government, effectively using the applications of Pension Fund and Budgetary Rule, could manage the resources it has as well as possible. (www.norgesbank.no/no/om/publisert). In Indonesia, in the period of First Oil Shock, due to inexperience in managing oil shock, several problems occurred. Symptoms of Dutch Disease were mostly seen to emerge in the form of expense effect and assessments of real exchange rate. Increase of oil export, raising export income in the country economy in the rate of 60-70%, resulted in extra incomes of billions of Rupi. The increase of oil prices after 1973 largely increased oil incomes and foreign currency resources of Central 89

Bank. Revaluation of national currency unit versus the other foreign currency caused devaluation. Devolution was conducted in terms of supporting non-oil sectors not the problems with balance of payments (Gelb et al,1988:207-209). As a result, in large scale industrial areas exporting non-oil products, it was concluded with closing of thousands of workplace. It was claimed that negative effects of Dutch Disease emerged as a result of increase of oil prices rather than oil production. The results of Dutch Disease were not strongly supported by empirical findings but it was observed that these effects mostly emerged in the meaning of agricultural sector (Charles and Atifah,1994:269-270). Nigeria, as a result of oil shock experienced in the period of 1972-1974, in the production of agricultural sector, which forms 60% of its export, some decreases occurred, consequently, the share of agricultural products in export decreased to 25%. The share of agriculture in GDP over non-oil sector decreased to 30s% in the period 1960-1980. In addition, government expenses largely increased in the years of 1970- 1980 and, although incomes increased, the budget and foreign balance of payments had deficit and the level of internal and external debts rose in the period 1975-1983. In the sample of Nigeria, it was observed that an excessive constriction in agricultural sector instead of industrial sector. It was seen that de-agriculturization actualized instead of re-industrialization Since Nigeria wasted the resources it obtained from oil incomes, it was obliged to go toward borrowing. When oil incomes fell, since any resource cannot be found for continuing expenditures, borrowing had been a single solution. Expansionary public policies made a contribution to the rise of inflation and led assessment of domestic exchange rate to continue. That public removes supply deficiency via import caused domestic sector to regress. Thus, due to constricting agricultural sector, import of agricultural products, import of agricultural products increased much more (Nyatepe-Coo,1994:328-331). Application of fixed exchange rate in Nigeria strengthened assessment of real exchange rate that is one of the main symptoms of Dutch Disease. Venezuela: After oil shock, GDP of Venezuela increased in higher rate compared to the other Latin American countries. After oil shock, a rapidly increase of poverty level made feel itself and this sharp difference showed itself at the levels of child death and education, in increase tempo of job wage, and in the other indicators (Hüseyinov et al, 2005:8-9). Chili, New Zeeland, Mexico, and Australia: Reforms performed in Chili and New Zeeland in the period 1973-1983, were concluded with Dutch Disease, realization of reforms such as liberalization of trade, decrease of custom tariffs, removal of quota imposed on trade and price control, real regulation of interest rates were concluded with strengthening of capital flow to country. Also, as a result of indexing of wage, the rise of exchange rate attenuated the development of agriculture. As a result of strengthening of capital flow to country, the tendency of decrease of agricultural power was repeated once more. In New Zeeland, fixing program, which has been applied since 1984, caused high interest rates and flow of foreign investments to the 90

country. That government makes agriculture liberal impeded subventions allocated to agriculture; and the conservation level of industry remained high, and ended with the rise of real exchange rate Russia: When Dutch Disease is evaluated in terms of its symptoms, a net result cannot be reached. While the effect of resource distribution remained insignificant due to enclave structure of oil sector and low labor mobility. It is seen that expense effect is tried to be moderated with stability fund formed by means of foreign financial investments. In real exchange rate, even though it is observed that assessment is experienced, that a large part of expense effect is controlled by public sector prevents this effect from growing much more. A a matter of fact, in the recent years, in non-trade subject sectors, that a faster growth is relatively experienced enabled structural change, foreseen in the scope of the theory of Dutch Disease, to become more fluffy. In the period of Soviet Union, between the years 1980-1986, extreme fall of oil prices led to unavoidable fall in welfare level, reducing import of trade subject goods and export of main substance (Fetisov, 2007:57 – 67). It is understood that the relationship between oil prices and Russian GDP also survive at the present days Saudi Arabia In oil sector, this country experiencing an important gain explosion between 1974 -1979 went toward rapidly using the gain it obtained from oil incomes. Public sector enlarged very rapidly. That is, all of national labor force has become employed by public sector. Real exchange rate continuously gained value. While agricultural sector was hold at hands of a small minority, private sector generally went toward buying real estate and foreign investments. Thus, between the years 1974 -1981, the symptoms of Dutch Disease appeared and this disease mostly reduced the compatibility level of non-oil products (Auty,2001b:85). Saudi Arabia, in the meaning of diversifying economic activities, could found petroleum refineries together with insufficient agricultural incentives. Since petroleum refineries are capital- intensive, they did not make much more contribution to employment. Hence, Saudi Arabian economy, with both assessment of exchange rate and deindustrialization and demand toward non-trade subject goods, shows symptoms of Dutch Disease very sharply. Iran: It is an economy, more than 80% of export items consist of crude oil. In Iran economy, in financing budgetary deficit, that the resources taken from Oil Stability Fund gradually increase attracts attention. Iran has begun to accumulate oil incomes in Oil Stability Fund beginning from 2000 Especially in the late 1990s and the early 2000s, real exchange rate became sensitive to the shocks of oil price. This is also accepted as one of symptoms of Dutch Disease (Farzanegan and Markwardt, 2009:134-145). About management of oil incomes, although forming stability fund seems to be a positive step, that the connection between fiscal policy and oil incomes increasingly continues facilitated expense effect begin to run.

91

Just as Dutch Disease can occur as a result of the increase of price of a single product in the world or finding a new resource, it is seen that it can appear as a result of large amount of capital flow ((Wahba,1998:362).) It was mentioned about the presence of Dutch Disease for Egypt, Jordan, Lebanon, and Syria. In the sample of Syria, it was claimed that oil incomes caused Dutch Disease, while in Lebanon, Jordan, and Egypt, workers’ incomes. International compatibilities of these countries was observed to decrease( Gretta and M.Ayoub, 2008:220-226).

4. Conclusion In literature on curse of resources, the view, begun to be voiced after 1990s and hitting the top in the early 2000, in which the first results reached showed that resource abundance had an effect braking the growth but together with the next studies, that this was begun to be falsified; that natural resource wealth was not a healthy data on its own in affecting the economic growth of the countries, was revealed in the studies. Incomes of natural resource can give the different reactions, depending on the performance that the factors such as the quality of institutes and human capital show om economic growth. In the countries, where political stability lasts long, the most faced problem is that managerial vulnerabilities arise. The leading one among these is illegality. Illegality causes the effects disturbing the distribution of income and social justice. While natural resource equipment was an important production factor in the growth and development processes, the excessive use of this in unconscious and unplanned way and the use of all other possibilities in the areas related to single factor brimg the countries face to face with an important problem like not developing. While the use of resources unconsciously and in unplanned way eliminates the possibility of sustainable development, wasting all other possibilities with the understanding of natural resource based growth and development helps hypothesis of curse of natural resource realize. Upon that Netherland rapidly extracts natural gas reserves and sells in the world market and, due to natural gas incomes, as a result of that it obtains considerable amount of foreign currency income, Dutch Disease appeared in the form of that its national currency gain value and that natural gas incomes go toward consumption not investment. This disease, in respects with its structure, is concluded with that the natural gas resources the country has negatively affect the production and industry. Dutch Disease begins with the resource discovery, monetary flow, and export explosion in a certain city and becomes fact in the form affecting all macro variables and affecting balance of payments, firstly positively and then negatively. Because it affects macroeconomic variables, it leads to important results. DD become effective in application of monetary policy, on the balance of balance of payments, and public expenditures. As a result of DD, real profit rises; balance of payments is negatively affected as the final and dominant effect; public expenditures increase; expenditures 92

turn into consumption expenditures; and income flows the results of fiscal policy, forming a one-way effect. Natural resource incomes are concluded with rent incomes and lead political power to apply conservative policies, which result in bureaucracy and corruption. In DD model, a sector that stands out and overgrows or a sector that downsizes alternately appears. The downsizing sector is generally manufacturing sector or agricultural sector, when the samples of country examples are examined. Some countries caught to DD, taking lessons from their faults, can be rid of disease and turned the effect of negative growth, expected to appear, into positive.

93

References Acosta, Lartey, Mandelman (2009), Remittances and the Dutch Disease, Journal of International Economics, 79, 102–116, http://www.sciencedirect.com/science /article/pii/S0022199609000889(07.03.2017). Adenauer and Vagassky (1998), Aid and the Real Exchange Rate: Dutch Disease Effects in African Countries”, Intereconomics: Review of European Economic Policy, 33, 177-185. Auty, R. M. (2000), How Natural Resource Affect Economic Development, Development Policy Review, Vol.18, pp.347-364. Auty, R. M., (2001). The Political Economy of Resource-Driven Growth, European Economic Review, 45, 839-846. Auty R. M., (2001b), The political state and the management of mineral rents in capitalsurplus economies: Botswana and Saudi Arabia. Resources Policy, 27. Bacak, Ç. (2014), Hollanda Hastalığının (Dutch Disease) Alternatif Yorumu, Sanayisizleşme ve Çıkış Yolları”, http://anahtar.sanayi.gov.tr/ tr/news/hollanda-hastaliginin-dutch-disease-alternatif-yorumusanayisizlesme-ve-cikis-yollari/692, (17.02.2017). Bal H. (2011), İktisadi Gelişme Ve Doğal Kaynaklar: Geçiş Ekonomileri Çerçevesinde Bir inceleme, Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi, 20 (1), p. 87-104 Balcılar M. (2002), Geçiş Ekonomilerinin Büyüme Etkinliklerinin Bir Değerlendirmesi, Manas Üniversitesi Küreselleşme ve Geçiş Ekonomileri, Uluslararası Sempozyum Bildirileri içinde, 2-4 Mayıs Bishkek. Billion, L. (2001), Fueling War: Natural Resources and Armed Conflicts, Oxford University Press. Bloom D. and Sachs D. J. (1998), “Geography, Demography, and Economic Growth in Africa”, The Brookings Institution Brookings Papers on Economic Activity, https://www.brookings.edu/.../1998/.../1998b_bpea_bloom, (15.01.2017). Corden W. M. (1984), Booming sector and Dutch Disease economics: survey and consolidation, Oxford Economic Papers, 36 (3), http://www.bresserpereira. org.br/terceiros/cursos/2013/07.09.corden.pdf, (07.02.2017). Charles H. And Atifah T. (1994), Oil production and macroeconomic adjustment in the Indonesian economy, Energy Economics,16 (4). Doğanay, H. Ve Altaş N. T. (2013), Doğal http://www.pegem.net/dosyalar/dokuman, (18.03.2017).

Kaynaklar,

Ertürk H. (2009), Çevre Bilimleri, Bursa: Ekin Kitabevi, 3. Baskı. Ebrahimzadeh C. (2003), Dutch disease: Too much wealth managed unwisely, http://www.imf.org/external/pubs/ft/fandd/2003/03/ebra.htm, (25.01.2017). 94

Farzanegan M. R. and G.Markwardt (2009), The effects of oil price shocks on the Iranian economy, Energy Economics, 31 (1). Frankel A. J. (2010), The Natural Resource Curse: A Survey, HKS Faculty Research Working Paper Series, RWP10-005, John F. Kennedy School of Government, Harvard University. Gelb, Alan And Associates (1988), Oil windfalls: blessing or curse?. Washington D.C, Oxford University Press. Gimple J. (1996), Ortaçağda Endüstri Devrimi, Ankara: Tübitak Yay. I.Basım. Gretta S. and Ayoub M. (2008), The Dutch Disease syndrome in Egypt, Jordan, Lebanon and Syria: A comparative study. Competetion Forum, 6 (1). Gjedrem, S. (2001), Norges Bank Årstale - Økonomiske Perspektiver 2001, Norges Banksrepresentantskapsmøte,15.02.2001,http://www.norgesbank.no/no/om/ publisert/foredrag-og-taler, (27.03.2017). Gurbanlı, Ş. (2010), Azerbaycan Ekonomisinde Petrolün Önemi ve Hollanda Hastalığı Açısından Değerlendirilmesi, Anadolu Üniversitesi Sosyal Bilimler Enstitüsü, İktisat Anabilim dalı, Yüksek Lisans Tezi, Eskişehir. Gurbanov, S. (2012), Hollanda Hastalığı, Akis Kitap Yayıncılık, İstanbul. Guriev, S., Plekhanov A., Sonin K. (2009), Development Based on Commodity Revenues, EBRD Working Papers, No. 108, http://www.ebrd.com,14.01.2017. Gyflason, T. (2000), Resources, Agriculture, and Economic Growth in Economies in Transition, Kyklos, July. Gylfason, T. (2002), Natural Resources and Economic Growth: What Is the Connection?, Published in a conference volume entitled Fostering Sustainable Growth in Ukraine, eds. Stephan von Cramon-Taubadel and Iryna Akimova, Physica-Verlag (A Springer-Verlag Company), Heidelberg and New York. Gylfason, T. And Zoega, G. (2001), Natural resources and economic growth: The role of investment, Discussion Paper No 2743., Centre for Economic Policy Research, London, UK. Fetisov G. (2007), The `Dutch Disease` in Russia: Macroeconomic and structural aspects. Problems of Economic Transition, 50 (1). Hüseynov A., v.d.(2005), Paranın Değer Kazanması ve Ekonomik Kalkınma, Sada Neşriyatı, Bakı. Hollanda Hastalığı, http://www.geocities.com/ EnchantedForest/Glade/6314/hollanda.doc (05.01.2017) Kaplan F. (2013), Ekonomik Büyümenin Dolaylı Belirleyicileri, Orta Gelirli Ülkeler Üzerine Panel Veri Analizi, Atatürk Üniversitesi, Sosyal Bilimler Enstitüsü, İktisat Anabilim Dalı, Erzurum. Kışlalıoğlu, M. Ve Berkes F. (1990), Çevre ve Ekoloji, İstanbul: Remzi Kitabevi, 3. Basım. 95

Krugman P. and Obstfeld M. (1997), International Economic Relations, Finance, Addison Wesley Publication , Boston. Lal D. ve Myint, H. (1996), The Political Economy of Poverty: Equity and Growth: AComparative Study, Oxford: Clerendon Press. Lane, P. ve Tornell, A. (1995), Power Concentration and Growth, Harvard Institute of Economic Research Discussion Paper no. 1720, May. Le Billon, P. (2001), Fueling War: Natural Resources and Armed Conflicts, Oxford University Press. Leite, C. ve Weidmann, J. (1999), Does Mother Nature Corrupt? Natural Resources, Corruption, and Economic Growth, IMF Working Papers, No. 85. Matsuyama K. (1992), Agricultural Productivity, Comparative Advantage, and Economic Growth, Journal of Economic Theory 58, December,317-334. Mehlum,H., Moene,K., Torvik, R. (2006), Institutions and the Resource Curse, The Economic Journal, 116: 1–20. Nyatepe-Coo A.A. (1994), Dutch Disease, government policy and import demand in Nigeria, Applied Economics, 26(4). Palley, T. I. (2003), Lifting the Natural Resource Curse, Foreign Service Jorunal, No. 80, pp. 54-61. Prato T. (1998), Natural Resource and Environmental Economics, Iowa State University Press, Iowa. Pettinger T. (2014), Dutch Disease, http://www.economicshelp.org/blog/ 11977/oil/dutch-disease/,(18.01.2017). Ploeg, F. (2009), Aggressive Oil Extraction and Precautionary Saving: Coping with Volatility, OxCarre Working Papers 021, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford. Prebisch R. (1950), The Economic Development of Latin America and its Principal Problems, United Nations Publications, Economic Comission for Latin America, United Nations Department of Economic Affairs, New York: United Nations. Rodrik,D. (2002), nstitutions, Integration And Geography: In Search Of The Deep Determinants Of Economic Growth, http://wcfia.harvard.edu/publications/ institutions-integration-and- geography-search-deep-determinants-economicgrowth. (04.01.2017). Ross, M.L. (1999), “The Political Economy of Resource Curse”, World Politics, Vol.51,pp.297-322. Ross, M. L. (2003), The Natural Resource Curse: How Wealth Can Make You Poor, Editors: Bannon, I., Collier, P., Natural Resorces and Violent Conflict: Options and Actions, Worldbank. 96

Rudd D. (1996), An Empirical Analysis of Dutch Disease: Developing and Developed Countries, Illinois Wesleyan University Honor projects. Sachs, J. D., and Warner A. M. (1995), Natural Resource Abundance and Economic Growth, NBER Working Paper 5398, Cambridge, Massachusetts. Sarno, Lucio, Taylor, Mark P., (2003), The Economics of Exchange Rates, Cambridge University Pres. Singer H. W. (1950), The Distribution of Trade Between İnvesting and Borrowing Countries, The American Economic Review, Vol. 40, No. 2, May. Stiglitz, J. (2004), We can now cure Dutch disease: For some nations, natural resoures are a curse - but it needn't be so”, The Guardian, Ağustos 18, 2004, http://www.guardian.co.uk/business/2004/aug/18/comment.oilandpetrol, (21.02.2017). Tümertekin E. (1994), Ekonomik Coğrafya, İstanbul: İstanbul Üniversitesi Edebiyat Fakültesi Yayınları. Türkiye Çevre Sorunları Vakfı (1984), Türkiye’nin Yeni ve Temiz Enerji Kaynakları, TÇS Vakfı Yayını, Ankara. United Nations (1970), Natural Resources of Developing Countries, United Nations Press, New York. Ulucak R. (2016), Doğal Kaynak Laneti ve Avrasya Bölgesi İçin Bir Uygulama, Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Ekim, 9(4), 85-94. Wahba J. (1998), The transmission of Dutch Disease and labour migration, The Journal of International Trade and Economic Development, 7(3). William A. M. and McMillan s. M. (2000), Climate and Scale in Economic Growth, CID Working Paper, 48. Yamak R. Ve Korkmaz A. (2006), Prebicsh ve Sınger Hipotezi ve Küçük Açık Ekonomi Varsayımı, http://dergi.kmu.edu.tr/userfiles/file/haziran2006/10.pdf, (06.03.2017). Zakaria, F. (2007), Özgürlüğün Geleceği: Yurtta ve Dünyada İlliberal Demokrasi, İstanbul: Kırmızı Yayınları.

97

98

CHAPTER VI THE IMPACT OF OIL PRICES ON INFLATION EXPECTATIONS IN TURKEY Umit BULUT1 1. Introduction A monetary policy strategy/regime usually relies on a nominal anchor that is an intermediate target. This anchor prevents a central bank from trying to increase output through unexpected expansionary monetary policy (Mishkin and Posen, 1997). Bernanke et al. (2001) remark that monetary policy can be more effective with the existence of this anchor and that the success of monetary policy depends on the understanding and attitude of public about the anchor. A central bank adopting a monetary policy strategy tries to affect the intermediate target through its policy instruments and expects that this intermediate target is able to affect its ultimate target. As Froyen (1999) denotes, the ultimate target that the central bank aims at controlling is inflation while the intermediate target is controlled by the central bank. Because, a central bank can’t directly control inflation. Inflation targeting is a monetary policy strategy that has been adopted by many central banks nowadays and that lets central banks implement monetary policy under constrained discretion (Bernanke et al., 2001). A central bank that adopts inflation targeting has an inflation target and tries to achieve this target using its policy instruments. The main policy tool of the central bank is short-term/overnight interest rate and the central bank tries to affect long-term interest rates by controlling shortterm interest rates. Because of the fact that monetary policy has a lagged effect on inflation, a central bank adopting inflation targeting strategy must be forward-looking. “Being forward-looking” is a considerable phenomenon for inflation targeting strategy and must be explained. As Svensson (1997) clarifies, current inflation rate is actually determined by past decisions and contracts and a central bank can affect future inflation. As is known, theoretically, there is a high and positive correlation between public’s inflation expectation and future inflation, and thus inflation expectation is a key driving force behind inflation (Mishkin, 2007). Therefore, the central bank should affect public’s inflation expectation. If the central bank is transparent, reputable, and reliable, the central bank can shape public’s inflation expectation. In other words, the expectation 1

Dr., Ahi Evran University, FEAS, Department of Economics, [email protected]

99

management of the central bank can ensure that inflation target will be equal to public’s inflation expectation. This means anchoring of inflation expectations. Therefore, public’s inflation expectation will be an ideal intermediate target in inflation targeting strategy. Then, end-year inflation will be close to inflation target if inflation expectation is close to inflation target. For this reason, the central bank will aim at minimising the difference between inflation expectation and inflation target. If inflation expectation increases and climbs over inflation target, the central bank will increase short-term interest rate to reduce expenditures and inflation expectations (Svensson, 1997; Clarida et al., 1998, 1999, 2000). This is why and how a central bank adopting inflation targeting must be forward-looking. Inflation expectations indicate credibility of monetary policy of a central bank, and credibility is essential for a central bank to achieve inflation target. Mishkin (2007), Bernanke (2007), and Gerlach et al. (2011) point out that a shock to aggregate demand, to energy prices or to foreign exchange will have a little effect on expected inflation if inflation expectations are well anchored. In other words, public will go on considering inflation target of the central bank. Bernanke (2007) also states that increases in oil prices can affect inflation in medium and long runs only if these increases result in higher expected inflation through wage-price spiral and that a one-time increase in oil prices will not lead to a permanent increase in inflation if inflation expectations are anchored well. After monetary targeting and implicit inflation targeting experiences, the Central Bank of the Republic of Turkey (CBRT) adopted inflation targeting in 2006, and short-term interest rate became the main policy instrument of the CBRT in order to achieve inflation target. As is clearly defined in the inflation reports of the CBRT, the CBRT focuses on expected inflation rates. As a central bank adopting inflation targeting, the CBRT conducts the survey of expectations. The survey of expectations is executed to follow the expectations of the decision makers and experts in financial and real sectors about macroeconomic variables including inflation. Therefore, one can track inflation expectations of the decision makers and experts in Turkey through the survey of expectations.

100

Figure 1: Inflation, inflation expectations, and inflation target in Turkey (%)

2006-M1 2006-M9 2007-M5 2008-M1 2008-M9 2009-M5 2010-M1 2010-M9 2011-M5 2012-M1 2012-M9 2013-M5 2014-M1 2014-M9 2015-M5 2016-M1

14 12 10 8 6 4 2 0

12-month ahead expected inflation 12-month ahead inflation target 12-month ahead inflation

Source: CBRT

Using consumer price index (CPI) data, Figure 1 presents actual inflation rates, inflation expectations, and inflation targets in Turkey during the period January 2006-August 2016. Inflation data in the figure are annually calculated. One may make a few observations for Turkey by following Figure 1: (1) Actual inflation rates considerably deviated from inflation targets in most of the periods. Even though inflation is also affected by other factors along with monetary policy and central banks have imperfect control on inflation (Svensson, 1997), it is very hard to argue that the CBRT is successful regarding inflation. (2) Actual inflation rates were usually greater than inflation expectations. Therefore, one might argue that the decision makers and experts could not forecast future inflation in Turkey. In other words, he/she might conclude that the decision makers and experts were greatly optimistic about future inflation in Turkey. (3) Inflation expectations were usually greater than inflation targets. For this reason, one might contend that inflation expectations were not well anchored in Turkey. Why does the CBRT miss inflation target? As a lately produced paper by Bulut (2016a) points out, long monetary transmission lags, the depreciation of the Turkish Lira, and increases in prices of food and non-alcoholic beverages have a role in missing inflation rates in Turkey. By observing Figure 1, one may also argue that one of the reasons of missing inflation target is that inflation expectations in Turkey are usually high compared with inflation targets and usually low compared with actual inflation rates. Therefore, by following Figure 1, one can easily argue that it is very hard to argue that inflation expectations are anchored well in Turkey. Therefore, the views of Mishkin (2007), Bernanke (2007), and Gerlach et al. (2011) mentioned previously do not prevail in Turkey. One can also assert that there does not seem to be a strong correlation between inflation expectations and actual inflation rates in Turkey. Then, what determines and does not determine inflation expectations in Turkey? Do decision makers and experts in Turkey consider all available and necessary information while they are forming their inflation expectations? More clearly, as (i) energy is an essential input and all production activities require the usage of energy, and (ii) sharp increases in oil prices increase production cost and are mentioned as examples of negative supply 101

shocks (Brown and Yucel, 2002; Askari and Krichene, 2010; Cavalcanti and Jalles, 2013), do energy prices affect inflation expectations in Turkey? The answers of these questions help researchers and policy makers to find out the reasons of low inflation expectations and are very considerable in terms of the success of monetary policy in Turkey. This study focuses on the last question. In other words, this study examines whether oil prices affect 12-month ahead inflation expectations obtained from the survey of expectations in Turkey by utilizing monthly data from April 2006 to August 2016. The remainder of the study is organized as follows: Section 2 reveals the motivation of the author, the contribution of the study, and brief literature. Section 3 gives data. Section 4 presents methodology. Findings are depicted in Section 5. Section 6 concludes the study.

2. Motivation, contribution, and literature This section presents the motivation of the author, the contribution of the study, and the empirical literature on the relationship between energy/oil prices and inflation/inflation expectations. Accordingly, the motivation and contribution are fivefold. First, the examination of the effect of oil prices on inflation goes back a long way. Hence many papers have been conducted in order to investigate the effect of oil prices on inflation/oil price pass-through into inflation (Askari and Krichene, 2010; Cavalcanti and Jalles, 2013; Pindyck and Rotemberg, 1983; Burbidge and Harrison, 1984; Mork et al., 1994; Ferderer, 1996; Atkeson and Kehoe, 1999; Lee et al., 2001; Berument and Tasci, 2002; Hooker, 2002; Cunado and De Gracia, 2003, 2005; Barsky and Killian, 2004; Leduc and Sill, 2004; Trehan, 2005; Roeger, 2005; Van den Noord and Andre, 2007; Bermingham, 2008; Cavallo, 2008; Cologni and Manera, 2008; Herrera and Pesavento, 2009; Jacquinot et al., 2009; Castillo et al., 2010; Tang et al., 2010; Alvarez et al., 2011; Wu and Ni, 2011; Mandal et al., 2012; Alba et al., 2013; Valcarcel and Wohar, 2013; Wu et al., 2013; Yoshizaki and Haomori, 2014; Albulescu et al., 2017). Compared with the number of the studies examining the effects of oil prices on inflation, the number of the papers investigating the effects of oil prices on inflation expectations is limited. Nevertheless, some papers have been made on the latter topic especially in recent years (Gerlach et al., 2011; Mehra and Herrington, 2008; Harris et al., 2009; Celasun et al., 2012; Coibion and Gorodnichenko, 2015; Wong, 2015). Second, all these papers, except Mehra and Herrington (2008), yield that inflation expectations are sensitive to oil prices. Therefore, one may argue that this topic is noteworthy. Third, to the best of the author’s knowledge, there are only a few papers examining the determinants of inflation expectations in Turkey (Baskaya et al., 2008, 2010, 2012). Among these papers, Baskaya et al. (2010, 2012) investigate whether oil prices affect inflation expectations in Turkey and yield that increases in oil prices affect both 12-month ahead and 24-month ahead inflation expectations positively. For this reason, one can argue that there is a research gap for Turkey on this topic. Fourth, Turkey is a country that meets a considerable part of energy needs through import. With regard to World Bank (2016) data, the share of 102

energy imports in total energy use was 74.4% in Turkey in 2014. The data for crude oil imports are even more remarkable. Turkey imported 87.7% of its oil supplies in 2014 with regard to Energy Information Administration (EIA, 2016) data. These figures show that Turkey depends on oil imports. Such a high energy dependence is able to create inflationary pressures when energy prices increase in international energy markets. Fifth, contrary to the previously made papers by Baskaya et al. (2008, 2010, 2012) that estimate the coefficients of some independent variables, this study employs a recently developed causality test based on bootstrapping. This causality test was developed by Hatemi-J (2012) and lets researchers examine asymmetric causal relationships between variables. Therefore, this is the first study examining the possible asymmetric relationships between oil prices and inflation expectations in Turkey.

3. Data This study follows time series analysis to examine the relationship between inflation oil expectations and oil prices in Turkey. 12 stand for 12-month ahead expected e and p annual inflation rate and oil prices, respectively. The data are monthly and cover the period April 2006-August 2016. While inflation expectations are extracted from the survey of expectations of the CBRT, data for oil prices (Europe Brent spot price FOB, dollars per barrel) are obtained from EIA. Table 1: Descriptive statistics and correlation matrix e

Mean Median Maximum Minimum Std. deviation Observations 12 e oil

p

2

Descriptive statistics 7.02 6.91 9.13 5.48 0.66 125 Correlation Matrix -0.20

po

83.55 79.44 132.72 30.70 26.34 125 -0.20 -

oil As is seen, all descriptive statistics of poil are greater than those of 12 e . Additionally, p is negatively correlated with 12 e . Figure 2 shows graphical observations of the series.

Figure 2: Dynamics of the series 12-month ahead expected annual inflation rate

oil prices

10

140

9

120 100

8 80 7 60 6

40

5

20 06 07 08 09 10 11 12 13 14 15 16

103

06 07 08 09 10 11 12 13 14 15 16

Descriptive statistics and graphical observations provide one with some initial inspection. However, one should employ some statistical methodologies, such as unit root and causality tests, to obtain efficient and unbiased output.

4. Methodology 4.1. Narayan and Popp (2010) unit root test Narayan and Popp (2010) produce a unit root test with two structural breaks endogenously determined. They propose two models allowing for two structural breaks. The first model, namely M1, allows for two structural breaks in intercept while the second model, namely M2, allows for two structural breaks in intercept as well as trend. The models are exhibited as follows: dM1 t = α + βt +

L

1 DU1,t

dM2 t = α + βt +

L

1 DU1,t +

+

2 DU2,t 2 DU2,t +

where DUi,t = 1 t TB,i , DTi,t = 1 t

(1) 1

DT1,t +

2

DT2,t

(2)

TB,i t - TB,i , i=1,2.

where TB,i , i=1,2 denotes the true break dates. The parameters i and i indicate the magnitude of the intercept and trend breaks, respectively. Narayan and Popp (2010) assert that the inclusion of L lets breaks happen slowly over time. For this reason, the proposed model is an innovative outlier class of models as it is based on the idea that the series responds to shocks to the trend function in a similar way as it responds to shocks to the innovation process, et. The test regressions are the reduced forms of the corresponding structural model. They are showed as follows: yM1 = yt-1 + α1 + β t + t

1D

TB

1,t

yM2 = yt 1 + α + β t + t

1D

TB

1,t

1

DT1,t-1 +

2

DT2,t-1

k j=1 βj ∆yt-j

+ +

2D 2D

TB TB

2,t

+ δ1 DU1,t-1 + δ2 DU2,t-1 +

2,t

k j=1 βj ∆yt-j

+ et (3)

+ δ1 DU1,t 1 + δ2 DU2,t 1 +

+ et

(4)

The break dates are decided by utilizing a sequential procedure (see Narayan and Popp (2010) for the details of this procedure). The null hypothesis of a unit root of = 1 is tested against the alternative hypothesis of < 1, and t-statistics of in Equations 3 and 4 are used. Critical values are generated through Monte Carlo simulations. If calculated test statistics are greater than critical values, the null hypothesis of a unit root is rejected.

4.2. Hatemi-J (2012) asymmetric causality test Hatemi-J (2012) asserts that previous papers on causality assume that the causal impact of a positive shock is the same as the causal impact of a negative shock. Hatemi-J (2012) remarks that positive and negative shocks may have different causal impacts and so develops an asymmetric causality test. Assume that we aim at investigating the causal 104

relationship between two integrated variables y1t and y2t defined like the following random walk processes: y1t = y1t-1 + ε1t = y10 +

t i=1 ε1i

(5)

y2t = y2t-1 + ε2t = y20 +

t i=1 ε2i

(6)

where t = 1,2,…,T, the constants y10 and y20 are the initial values, and the variables ε1i and ε2i signify white noise disturbance terms. The following notation is used to identify -

positive and negative shocks: ε+1i = max ε1i , 0 , ε+2i = max ε2i , 0 , ε1i = min ε1i , 0 , -

-

-

ε2i = min ε2i , 0 , respectively. Then, one can indicate ε1i = ε+1i + ε1i , and ε2i = ε+2i + ε2i . It follows that y1t = y1t-1 + ε1t = y10 + y2t = y2t-1 + ε2t = y20 +

t + i=1 ε1i t + i=1 ε2i

+ +

t i=1 ε1i

(7)

t i=1 ε2i

(8)

Finally, the positive and negative shocks of each variable can be expressed in a cumulative form as y+1t =

t + i=1 ε1i ,

-

y1t =

t i=1 ε1i ,

y+2t =

t + i=1 ε2i ,

-

y2t =

t i=1 ε2i .

Each positive and

negative shock has a permanent impact on the underlying variable. The following step is to test for the causal relationship between these components by using these cumulative sums. In his paper, Hatemi-J (2012) focuses on the case of testing for causal relationship between positive cumulative shocks.2 Assuming that y+t = y+1t , y+2t , the test for causality can be applied by employing the subsequent vector autoregressive model of order p, VAR (p): y+t = v + A1 y+t-1 +…+ Ap y+t-1 + u+t

(9)

where y+t is the 2 x 1 vector of variables, v is the 2 x 1 vector of intercepts, and u+t is a 2 x 1 vector of error terms. The matrix Ar represents a 2 x 2 matrix of parameters for lag order r (r = 1, …, p). After determining the optimal lag order through criterion developed by Hatemi-J (2003, 2008, hereafter HJC), the null hypothesis that kth element of y+t does not Granger cause the ωth element of y+t is tested.3 This null hypothesis is defined as the following: H0: the row ω, column k element in Ar is equal to zero for r = 1, …, p

(10)

The VAR (p) model can be defined as follows: Y = DZ + δ

(11)

-

-

To carry out tests for causality between negative cumulative shocks, the vector y-t = y1t , y2t is utilized. Other combinations are possible. 3 Hatemi-J (2012) remarks that an additional unrestricted lag is included in the VAR model to take into account the effect of unit root as Toda and Yamamoto (1995) suggest. 2

105

The subsequent Wald test statistic can be utilized in order to test the null hypothesis of non-Granger causality defined as H0 : Cβ = 0: Wald = Cβ

C

ZZ

-1

-1

SU C

Cβ

(12)

where β = vec(D) and vec stands for the column-stacking operator, indicates the Kronecker product, and C is a p x n(1 + np) indicator matrix with elements ones for restricted parameters and zeros for the rest of the parameters. SU represents the estimated variance-covariance matrix of the unrestricted VAR model that is estimated as SU =

δU δU T-q

, where q is the number of parameters in each equation of the VAR model. When

the assumption of normality is held, the Wald test statistic in Equation (12) has an asymptotic χ2 distribution with the number of degrees of freedom which is equal to the number of restrictions to be tested (in this case it is equal to p). However, some data may not be distributed normally and there may be autoregressive conditional heteroscedasticity (ARCH) effects for some data. To fix these problems, the bootstrap simulation technique can be employed. If the calculated Wald statistic is greater than bootstrap critical values, the null hypothesis of non-Granger causality is rejected (see Hatemi-J (2012) for details).

5. Estimation results Table 2 reports the results of the Narayan and Popp (2010) unit root test. Accordingly, the null hypothesis of a unit root can be rejected at first differences for both variables. Put differently, the variables are integrated of order 1. Variablea 12 e oil

p Δ 12 e Δpoil Critical valuesb

Table 2: Narayan and Popp (2010) unit root test Test statistic Break dates M1 M2 M1 M2 -4.23d -2.86 Apr. 2008, Dec. 2008 Apr. 2008, Dec. 2008 -1.46 -6.03c Jul. 2008, Dec. 2008 Jul. 2008, May 2012 -7.43c -8.16c -8.58c -8.68c 1% -4.95 -5.57 5% -4.31 -4.93 10% -3.98 -4.59

Notes: a ∆ is the first difference operator. b Critical values are obtained from Table 3 in Narayan and Popp (2010). c Illustrates 1% statistical significance. d Illustrates 10% statistical significance.

The break dates obtained from the Narayan and Popp (2010) unit root test indicate considerable periods for the Turkish economy. Accordingly, the global financial crisis may account for the breaks detected in 2008. Besides, the loosening in the liquidity policy of the CBRT and the increase in the global risk appetite may account for the break detected in May 2012 (Bulut, 2016b).

106

Table 3: Hatemi-J (2012) asymmetric causality testa Null hypothesisb

poil

+

Test statistic

Critical valuesc 1%

5%

10%

Null hypothesisb

12 + e

0.05

10.11

6.40

4.84

12 + e

poil

poil

12 e

6.30e

12.97

7.11

5.23

12 e

poil

poil

12 + e

0.88

10.52

6.49

4.97

12 e

poil

12 e

6.66e

12.46

7.56

5.69

12 + e

poil

poil

+

Test statistic +

+

Critical valuesc 1%

5%

10%

6.95d

10.89

6.81

5.12

0.43

11.05

6.59

4.92

0.42

10.69

6.71

4.99

1.64

10.93

6.48

4.87

Notes: a Maximum lag length is 6, and the HJC is used to determine the optimal lag length. b indicates no causality. c Critical values are obtained through 10000 bootstrap replications. d Illustrates 5% statistical significance. e Illustrates 10% statistical significance.

Table 3 presents the results obtained through the implementation of the Hatemi-J (2012) asymmetric causality test. As seen, 3 out of 8 null hypotheses can be rejected. Accordingly, (i) the null hypothesis that a negative shock in oil prices does not Granger cause a negative shock in 12-month ahead expected annual inflation rate can be rejected at 10% significance level, (ii) the null hypothesis that a positive shock in oil prices does not Granger cause a negative shock in 12-month ahead expected annual inflation rate can be rejected at 10% significance level, and (iii) the null hypothesis that a positive shock in 12-month ahead expected annual inflation rate does not Granger cause a positive shock in oil prices can be rejected at 5% significance level. Based on the findings, the Hatemi-J (2012) asymmetric Granger causality test reveals that both increases and decreases in oil prices decrease 12-month ahead inflation expectations in Turkey. If the findings of this test are considered with Figure 2 that shows the graphical observations of the variables, this study presents valuable information about low inflation expectations in Turkey. Accordingly, rapid increases in oil prices during April 2006-May 2008 and November 2008-April 2011 had a decreasing effect on inflation expectations in Turkey. Therefore, the study finds that one of the reasons of low inflation expectations in Turkey, especially in the regarded periods, is that the decision makers and experts in Turkey are not able to forecast the inflationary effects of the increases in oil prices.

6. Conclusion Beginning from 2006, the CBRT began to adopt inflation targeting strategy. A central bank which adopts inflation targeting uses inflation expectation as the intermediate target and tries to achieve inflation target. When one examines the period 2006-2015 in terms of actual inflation rates, inflation expectations, and inflation targets in Turkey, he/she can observe that actual inflation rates were lower than inflation targets only in 2009 and 2010 and that inflation expectations were usually lower than actual inflation 107

rates and greater than inflation targets. Therefore, one can argue that inflation expectations are not anchored well and that one of the reasons of missing inflation target may be low inflation expectations in Turkey. Then, it seems essential whether decision makers and experts in Turkey regard all available and necessary information while they are forming their inflation expectations should be examined in Turkey. Following the recent empirical literature, this study investigates the effects of oil prices on 12-month ahead inflation expectations of the decision makers and experts in Turkey using monthly data from April 2006 to August 2016. After conducting a unit root test with two structural breaks, the study performs the asymmetric Granger causality test. The findings of the asymmetric Granger causality test indicate that both increases and decreases in oil prices reduce inflation expectations in Turkey. Hence the study yields that the decision makers and experts in Turkey are not able to forecast inflationary effects of increases in oil prices and asserts that they should follow oil prices closely and should be aware of that rises in oil prices may have inflationary effects. Thus, considering the findings of this study, they can help the CBRT to achieve inflation target since greater inflation expectations will lead to higher interest rates in Turkey. In spite of its contribution to the literature, this study has some limitations. First, Turkey imports not only oil but also natural gas and some other energy sources. Hence future research may focus on examining the effects of prices of other energy sources on inflation expectations. Second, the only determinant of inflation expectations is not energy price. Therefore, the effects of other macroeconomic variables, such as inflation target, output gap, exchange rates etc., on inflation expectations may be investigated in future empirical studies.

108

References Alba, J. D., Chia, W. M., & Su, Z. (2013). Oil shocks and monetary policy rules in emerging economies, Applied Economics, 45(35), 4971-4984. Albulescu, C. T., Oros, C. and Tiwari, A. K. (2017). Oil price–inflation pass-through in Romania during the inflation targeting regime. Applied Economics, 49(15), 15271542. Atkeson, A. and Kehoe, P. J. (1999). Models of energy use: Putty-putty versus putty-clay, The American Economic Review, 89, 1028-1043. Alvarez, L. J., Hurtado, S., Sánchez, I. and Thomas, C. (2011). The impact of oil price changes on Spanish and euro area consumer price inflation, Economic Modelling, 28(1), 422-431. Askari, H. and Krichene, N. (2010). An oil demand and supply model incorporating monetary policy, Energy, 35(5), 2013-2021. Barsky, R. B. and Kilian, L. (2004). Oil and the Macroeconomy since the 1970s, The Journal of Economic Perspectives, 18(4), 115-134. Baskaya, S., Kara, H. and Mutluer, D. (2008). Expectations, communication and monetary policy in Turkey, CBRT Working Paper, 01. Baskaya, Y. S., Gulsen, E. and Orak, M. (2010). 2008 hedef revizyonu öncesi ve sonrasında enflasyon beklentileri, CBRT Working Paper, 01. Baskaya, Y. S., Gulsen, E. and Kara, A. H. (2012), Inflation expectations and central bank communication in Turkey, Central Bank Review, 12 (2), 1-10. Bermingham, C. (2008). Quantifying the impact of oil prices on Inflation, Central Bank of Ireland Quarterly Bulletin, 1, 85-94. Bernanke, B. S., Laubach, T., Mishkin, F. S., and Posen, A. S. (2001). Inflation targeting: lessons from the international experience, Second Edition, New Jersey: Princeton University Press. Bernanke, B. S. (2007). Inflation expectations and inflation forecasting, https://www.federalreserve.gov/newsevents/speech/bernanke20070710a.ht m, (date of access: 01.11.2016). Berument, H. and Tasci, H. (2002). Inflationary effect of crude oil prices in Turkey, Physica A: Statistical Mechanics and its Applications, 316(1), 568-580. Brown, S. P. and Yucel, M. K. (2002). Energy prices and aggregate economic activity: an interpretative survey, The Quarterly Review of Economics and Finance, 42(2), 193-208. Bulut, U. (2016a). May monetary transmission lags have a role in missing inflation targets in Turkey? Cointegration tests with structural breaks and structural VAR analysis, International Journal of Economics and Finance, 8(4), 93-103. 109

Bulut, U. (2016b). Do financial conditions have a predictive power on inflation in Turkey?, International Journal of Economics and Financial Issues, 6(2), 621-628. Burbidge, J. and Harrison, A. (1984). Testing for the effects of oil-price rises using vector autoregressions, International Economic Review, 25(2), 459-484. Castillo, P., Montoro, C. and Tuesta, V. (2010). Inflation, oil price volatility and monetary policy, Banco Central de Reserva del Peru Working Papers, 002. Cavalcanti, T. and Jalles, J. T. (2013). Macroeconomic effects of oil price shocks in Brazil and in the United States, Applied Energy, 104, 475-486. Cavallo, M. (2008). Oil prices and inflation, FRBSF Economic Letter, 31. Celasun, O., Mihet, R. and Ratnovski, L. (2012). Commodity prices and inflation expectations in the United States, IMF Working Paper, 89. Central Bank of the Republic of Turkey, http://tcmb.gov.tr/, (date of access: 02.11.2016). Clarida, R., Gali, J. and Gertler, M. (1998). Monetary policy rules in practice: some international evidence, European Economic Review, 42(6), 1033-1067. Clarida, R., Gali, J. and Gertler, M. (1999). The science of monetary policy: a new Keynesian perspective, Journal of Economic Literature, 37(4), 1661-1707. Clarida R., Gali, J. and Gertler, M. (2000). Monetary policy rules and macroeconomic stability: evidence and some theory, Quarterly Journal of Economics, 115(1), 147-180. Coibion, O. and Gorodnichenko, Y. (2015). Is the Phillips curve alive and well after all? Inflation expectations and the missing disinflation, American Economic Journal: Macroeconomics, 7 (1), 197-232. Cologni, A. and Manera, M. (2008). Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries, Energy Economics, 30(3), 856888. Cunado, J. and De Gracia, F. P. (2003). Do oil price shocks matter? Evidence for some European countries, Energy Economics, 25(2), 137-154. Cunado, J. and De Gracia, F. P. (2005). Oil prices, economic activity and inflation: evidence for some Asian countries, The Quarterly Review of Economics and Finance, 45(1), 65-83. Energy Information Administration, http://www.eia.gov/, (date of access: 20.09.2016). Ferderer, J. P. (1996). Oil price volatility and the macroeconomy, Journal of Macroeconomics, 18(1), 1-26. Froyen, R. T. (1999). Macroeconomics: theories and policies, Sixth Edition, New Jersey: Prentice-Hall.

110

Gerlach, P., Hordahl, P. and Moessner, R. (2011). Inflation expectations and the great recession, BIS Quarterly Review, 39-51. Harris, E. S., Kasman, B. C., Shapiro, M. D. and West, K. D. (2009). Oil and the macroeconomy: lessons for monetary policy, Proceedings of the US Monetary Policy Forum Report, February. Hatemi-J, A. (2003). A new method to choose optimal lag order in stable and unstable VAR models, Applied Economics Letters, 10(3), 135-137. Hatemi-J, A. (2008). Forecasting properties of a new method to determine optimal lag order in stable and unstable VAR models, Applied Economics Letters, 15 (4), 239243. Hatemi-J, A. (2012). Asymmetric causality tests with an application, Empirical Economics, 43(1), 447-456. Hatemi-J, A. and Uddin, G. S. (2014). On the causal nexus of remittances and poverty reduction in Bangladesh, Applied Economics, 46(4), 374-382. Herrera, A. M. and Pesavento, E. (2009). Oil price shocks, systematic monetary policy, and the “Great Moderation”. Macroeconomic Dynamics, 13(01), 107-137. Hooker, M. A. (2002). Are oil shocks inflationary? Asymmetric and nonlinear specifications versus changes in regime, Journal of Money, Credit, and Banking, 34(2), 540-561. Jacquinot, P., Kuismanen, M., Mestre, R. and Spitzer, M. (2009). An assessment of the inflationary impact of oil shocks in the euro area, The Energy Journal, 30(1), 4983. Leduc, S. and Sill, K. (2004). A quantitative analysis of oil-price shocks, systematic monetary policy, and economic downturns, Journal of Monetary Economics, 51(4), 781-808. Lee, B. R., Lee, K. and Ratti, R. A. (2001). Monetary policy, oil price shocks, and the Japanese economy, Japan and the World Economy, 13(3), 321-349. Mandal, K., Bhattacharyya, I. and Bhoi, B. B. (2012). Is the oil price pass-through in India any different?, Journal of Policy Modeling, 34(6), 832-848. Mehra, Y. P. and Herrington, C. (2008). On the sources of movements in inflation expectations: a few insights from a VAR model, Economic Quarterly, 94(2), 121146. Mishkin, F. S. and Posen, A. A. (1997). Inflation targeting: lessons from four countries, NBER Working Paper, 6126. Mishkin, F. S. (2007). Inflation dynamics, International Finance, 10(3), 317-334.

111

Mork, K. A., Olsen, Ø. And Mysen, H. T. (1994). Macroeconomic responses to oil price increases and decreases in seven OECD countries. The Energy Journal, 15(4), 1935. Narayan, P. K. and Popp, S. (2010). A new unit root test with two structural breaks in level and slope at unknown time, Journal of Applied Statistics, 37(9), 1425-1438. Pindyck, R. S. and Rotemberg, J. J. (1983). Dynamic factor demands and the effects of energy price shocks. The American Economic Review, 73(5), 1066-1079. Roeger, W. (2005). International oil price changes: impact of oil prices on growth and inflation in the EU/OECD, International Economics and Economic Policy, 2(1), 1532. Svensson, L. E. O. (1997). Inflation forecast targeting: implementing and monitoring inflation targets, European Economic Review, 41 (6), 1111-1146. Tang, W., Wu, L. and Zhang, Z. (2010). Oil price shocks and their short-and long-term effects on the Chinese economy, Energy Economics, 32, S3-S14. Toda, H. Y. and Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes, Journal of Econometrics, 66(2), 225-250. Trehan, B. (2005). Oil price shocks and inflation, FRBSF Economic Letter, 28, 1-3. Van den Noord, P. and Andre, C. (2007). Why has core inflation remained so muted in the face of the oil shock?, OECD Economics Department Working Paper, 551. Valcarcel, V. J. and Wohar, M. E. (2013). Changes in the oil price-inflation passthrough, Journal of Economics and Business, 68, 24-42. World Bank, http://www.worldbank.org/, (date of access: 30.09.2016). Wong, B. (2015). Do inflation expectations propagate the inflationary impact of real oil price shocks? Evidence from the Michigan survey, Journal of Money, Credit and Banking, 47(8), 1673-1689. Wu, M. H. and Ni, Y. S. (2011). The effects of oil prices on inflation, interest rates and money, Energy, 36(7), 4158-4164. Wu, L., Li, J. and Zhang, Z. (2013). Inflationary effect of oil-price shocks in an imperfect market: a partial transmission input–output analysis. Journal of Policy Modeling, 35(2), 354-369. Yoshizaki, Y. and Haomori, S. (2014). The effects of oil price shocks on expenditure category CPI, Applied Economics, 46(14), 1652-1664.

112

CHAPTER VII OIL PRICES PASS-THROUGH TO AGRICULTURAL COMMODITY PRICES Taner TAŞ1 1. Introduction Over the past years due to the excessive volatility in commodity prices, countries are being misled by inflation expectations, and consequently they are experiencing deterioration in the general price level. In this sense, Turkey is one of the countries that have problems in terms of macroeconomic balances. For Turkey, the main determinants of inflation are food prices and changes in energy prices that they are imported products. Due to the fluctuating structure of food prices as well as energy prices, the relationship between the two prices attracts the attention of both governments and international organizations and is often found in economic literature (Abbott, Hurt and Tyner, 2009). This relationship is based on the presence of energy (petroleum, natural gas, electricity) at the beginning of the inputs used when considering the basic agricultural production costs. Despite the numerous studies in the literature on the pass through effect of price changes on exchange rates on consumer and producer prices (Alptekin, Yılmaz and Taş, 2016; Kara and Öğünç, 2012; Şen, 2009), studies that examine the pass through effect from oil price changes to food price changes are relatively few. In general, fluctuations in oil prices are partly changed into cost inflation, also expressed in terms of foreign exchange, and in some cases food inflation (Chen, Kuo and Chen, 2010). As mentioned above, the rapid increases in oil prices in recent years and the subsequent sharp declines are closely related to all sectors, especially those who use energy intensively in production. As can be seen from Figure 1, oil prices, which were around $ 35 in early 2009, reached a peak of $ 125 over a period of almost two years and remained at these levels for several years. However, the decline that began in mid-2014 brought oil prices under the 2009 levels, which made the prices very volatile.

Res. Assist. Dr., Manisa Celal Bayar University, Faculty of Business, Ekonomics and Finance department, [email protected] 1

113

Figure 1: Changes in Oil Prices

As can be seen from Table 1, these fluctuations in oil prices are thought to cause significant effects on the prices of agricultural products as the share of gasoline in production costs can reach up to 20%. Therefore, it is very important to examine the relationship between oil prices and agricultural product prices. Table 1: Share of Fuel in the Costs of Some Basic Agricultural Products Name of the product Cherry Wheat Sunflower Lentil Maize Barley Chickpea Grape Olive Cotton Rice Sugar beet

Share Amount of Diesel Product of Fuel Diesel Used Yield Product Cost Expenditure Cost in the at 1 Decare (Kg/Decare) (YTL/Decare) (YTL/Decare) (YTL/Kg) Costs (Liter) (%) 26.7 59.01 0.33 864 285.12 20.7 6.54 14.45 0.36 220 79.2 18.25 7.5 16.58 0.56 180 100.8 16.44 6.06 13.39 0.73 130 94.9 14.11 11.88 26.25 0.29 700 203 12.93 4.98 11.01 0.34 262 89.08 12.35 6.6 14.59 1.29 97 125.13 11.66 7.02 15.51 0.18 740 133.2 11.65 5.76 12.73 5.5 20 110 11.57 20.76 45.88 1 407 407 11.27 20.4 45.08 0.67 709 475.03 9.49 12.18 26.92 0.09 4521 424.97 6.33 Source: Dellal et al., 2007

The relationship between agricultural product prices and oil prices is explained in three different perspectives. Firstly, the increase in oil prices is directly related to the increase of agricultural product prices as it increases the input costs used in the production of agricultural products. In the latter case, the increase in oil prices causes the increase in the current account deficits of the countries that import oil. This leads to the depreciation of the local currency, which in turn increases both the cost increase and the 114

general level of prices in agricultural product prices (Harri, Nalley and Hudson, 2009). These relationships can be schematized as shown in Figure 2, which is called the trickle down effect in the literature. Figure 2: The Theoretical Relationship Between Agricultural Product Prices, Oil Prices and Exchange Rate

Source: Harri vd. 2009

The third and final case concerns the relationship between oil prices, biofuels and agricultural product prices. Demand for biofuels, which are an alternative to petroleum, is increasing in periods of rising oil prices. Since agricultural products are used in the production of biofuels, the demand increase actually increases the demand for agricultural products and therefore the prices of these agricultural products also increase. In general, information on the agricultural products required for the production of biofuels is given in Table 2. In Turkey, the biggest part of bioethanol production, which is used by mixing with benzine, is realized by using wheat, corn, sugar beet and beet syrup (Akalın and Seyrekbasan, 2015).

   

Table 2: Biofuels by Plant and Agricultural Product Source 1. Generation biofuels 2. Generetaion biofuels 3. Generation biofuels Biodiesel Bioethanol Bioethanol Bioethanol, Bioethanol, etc. Palm oil  Corn  Willows  Algae Rape seed  Sugar cane  Poplars Sunflowers  Sugar  Grass beets Soy beans  Waste products from  Potatoes agricultural  Wheat  Waste products from forestry Source: Biemans et al., 2008

115

This study aims to examine the short and long - term relationship between oil prices, exchange rates and agricultural product prices in Turkey for the period 2009-2016, based on the VECM (Vector Error Correction Mechanism) model. In addition, impulse response functions were established for each model, and the response of agricultural product prices to the shocks that occurred in oil prices and exchange rates was investigated. In the first part of the study, the theoretical relation between oil prices and agricultural product prices was mentioned, followed by literature reviews and similar studies were examined and empirical applications and results were included in the last part.

2. Literature Review When the relevant literature is examined, a number of studies investigating the effects of fluctuations in oil prices on food prices have been carried out. These studies differ from each other both in terms of the agricultural products examined and the methods used in the study of the relationship. As can be seen from Table 3, there is often no consensus on the existence of a long-term relationship between oil prices and agricultural commodity prices, which is often the case when the Johansen cointegration test is used. In most of these studies, only agricultural products are used with oil prices, while exchange rates are also included in some studies (Harri et. al, 2009; Nazlıoğlu and Soytaş 2011, 2012). Moreover, it has been determined that shocks of oil prices generally do not have significant effects on agricultural product prices as a result of the impulse-response functions of the variables (Kaltalıoğlu and Soytaş 2009, Mutuc, Pan and Hudson, 2010; Nazlıoğlu and Soytaş 2011). Campiche, Bryant, Richardson and Outlaw (2007), using Johansen cointegration method, could not get the existence of a long-term relationship between crude oil prices and corn, sugar, soybean, soy oil, palm oil and sorghum for periods 2003-2007. Similarly, Zhang, Lohr, Escalante and Wezstein (2010) use oil prices and maize, soya, wheat, sugar and rice prices for the 1989-2008 period in their studies that they could not reach a long-term relationship, only short-term causal relationships between some agricultural products. Kaltalıoğlu (2010) does not find a long-term relationship between variables in the study of the relationship between corn, wheat, soybean, rice and oil prices between 1998 and 2009, using johansen cointegration test. Therefore, an impulse response analysis was conducted with the established VAR model and it was observed that agricultural product prices do not react to shocks in oil prices. Mutuc et al. (2010) set up a SVAR model for their 1976-2008 period in which they find no cointegration relationship between oil prices and cotton prices, and examined the situation of cotton prices in response to shocks in oil prices. However, they do not reach any significant relation as in other studies. Nazlıoğlu and Soytaş (2011) examine the short and long-term relationships between variables in their studies of oil prices, wheat, corn, cotton, soybean and sunflower prices by including foreign exchange rate between 1994-2010 periods. After the Toda-Yamamoto causality approach, as a result of the impulse-response functions is applied, they reach the conclusion that agricultural prices in Turkey do not react significantly to oil and exchange rate shocks. There are studies in the literature that show that oil prices and agricultural commodity prices are in a longrun relationship and that agricultural commodity prices are affected by the shocks that occur in oil prices. Harri et al. (2009) examine the cointegration relationship between oil 116

prices, exchange rates, corn, cotton and soya prices for the 2000-2008 period and determined that only maize, cotton and soybean prices were in a long-run relationship with oil prices, but between oil prices and wheat prices they can not find such an association. Another study by Nazlıoğlu and Soytaş (2012) examine the relationship between 24 different agricultural product prices, world crude oil prices and exchange rates in the world between 1980 and 2010 using panel data analysis. The results show that oil prices have a strong impact on agricultural commodity prices, and the positive impact of the weak dollar on agricultural commodity prices has also been proven. İşcan et al. (2016) examine the relationship between world energy prices and world food prices for the 2009-2015 period using the ARDL cointegration test and the error correction model and they find a long-term relationship. Abdlaziz et al. (2016) have investigated the relationship between oil prices and food prices using the NARDL method and have concluded that there is a strong relationship between variables, both short-run and long-run. Another study that use the same method, Altıntaş (2016), carried out an analysis covering the period between 2000-2013 using food prices, oil prices, real national income, energy prices. And as a result, the existence of both shortterm and long-term relationships between oil prices and food prices has been identified. Table 3 : Studies on the Cointegration Relationship between Oil Prices and Agricultural Product Prices The name of the work Author Method Result Examining the Evolving Correspondence Between Petroleum Prices and Agricultural Commodity Prices. Food Versus Fuel: What Do Prices Tell Us?

Jody L. Campiche, Henry L. Bryant, James W. Richardson, Joe L. Outlaw. (2007). Zibin Zhang, Luanne Lohr, Cesat Escalante, Michael Wetzstein. (2010).

Price Transmissions Between Food and Oil.

M. Kaltalıoglu. (2010).

Response of Cotton to Oil Price Shocks.

M, Mutuc, S. Pan and D. Hudson. (2010).

Oil Price, Agricultural Commodity Prices and the Dollar: A Panel Cointegration and Causality Analysis.

S. Nazlioglu, U. Soytas. (2012).

Enerji Fiyatlarının Dünya Gıda Fiyatları Üzerine Etkisi: Bir Sınır Testi Yaklaşımı

Neşe Algan, Erhan İşcan, Duygu Serin. (2016).

The Relationship Between Oil, Exchange Rate and Commodity Prices.

A. Harri, L. Nalley and D. Hudson. (2009).

Oil and Food Prices Cointegration Nexus for Indonesia: A Non-linear Autoregressive Distributed Lag Analysis. Petrol Fiyatlarının Gıda Fiyatlarına Asimetrik Etkisi: Türkiye İçin NARDL Modeli Uygulaması.

R., A., Abdlaziz, K., A., Rahim, P., Adamu. (2016).

World Oil Prices and Agricultural Commodity Prices: Evidence From an Emerging Market.

H. Altıntaş. (2016). Nazlioglu and U. Soytas. (2011).

117

Johansen cointegration test

No cointegration

Johansen cointegration test

No cointegration

Johansen cointegration test

No cointegration

Johansen cointegration test Pedroni cointegration test ARDL cointegration test Johansen cointegration test NARDL cointegration test NARDL cointegration test TodaYamamoto causality test

No cointegration There is cointegration There is cointegration There is cointegration There is cointegration There is cointegration Impulse response functions are meaningless.

3. Methodology 3.1. Data In this study, Brent oil prices (oil), USD / TL exchange rate (exc), wheat (whe), corn (mai), soybean (soy), sunflower (sun), cotton (cot), lentil (bar), rice (ric), sugar beet (sug), olive (oli), chickpeas (chi) is used. And the scope of data for the study covers the period between January 2009 and January 2017. The oil price data is obtained from investing.com, the exchange rate series is obtained from the CBRT electronic data distribution system, and all other agricultural product prices is obtained from TUIK. According to the table 1, agricultural products are included in the analysis that the share of diesel fuel in agricultural production cost is 10% or more (Dellal, Özat and Özüdoğru, 2007). Also in figure 3, the price movements of the agricultural products during the analysis period are seen. Figure 3: Price Movements of Agricultural Products 6,000 Wheat

5,000

Maize 4,000

Sunflower Cotton

3,000

Lentil Barley

2,000

Rice 1,000

Sugar beets Olives Ara-16

Şub-16

Tem-16

Eyl-15

Nis-15

Kas-14

Haz-14

Oca-14

Ağu-13

Mar-13

Eki-12

May-12

Ara-11

Tem-11

Eyl-10

Şub-11

Nis-10

Kas-09

Oca-09

Haz-09

0,000

Chick peas

3.2. Method In this study, impulse-response analysis and Granger causality analysis and the vector error correction model (VECM) (Johansen, 1988) are used to examine the short and long-term effect of oil prices and exchange rate on agricultural product prices. The steps of the analysis are shown in figure 4. In order to avoid spurious regression, it is necessary to check whether the series are stable at the level (I(0) or whether they contain unit roots (I(d)) (Engle and Granger, 1987, Granger and Newbold, 1974). For this purpose, the stability levels of the series are first determined by using augmented Dickey Fuller (ADF) (Dickey and Fuller, 1979, Phillips-Perron (Phillips and Perron, 1988) and KPSS (Kwiatkowski, Phillips, Schmidt and Shin, 1992) unit root tests. For example, the equation used in the unit root test for the ADF test statistic is as follows:

118

Against the = 0 null hypothesis, the < 0 alternative hypothesis is tested. In addition, the appropriate number of delays is selected according to the information criteria. Figure 4: Path to Reach Short And Long-Term Relationships Between Variables

Source: Singh and Singh, 2016

After determining the stationary level, I(1), for the series, the stage of determining whether there is cointegration between the variables has been passed. The most general definition of cointegration is the existence of a long-term relationship between the nonstationary series. If the linear combination of two or more non-stationary (but integrated at the same degree) series is stationary, it is assumed that these series are cointegrated (Hendry and Juselius, 2000; Wojcik, 2011). The most commonly used methods for determining the cointegration relationship are Engel and Granger (1987), the two-stage single equation method and Johansen's (1995) maximum probability approach. Johansen (1995) cointegration test is used in this study because it allows the identification of more than one cointegration relationship. Johansen cointegration test uses two test statistic. First, the trace statistics that test the null hypothesis that the degree of matrices, (rank) (Π), is less than or equal to the number of cointegration vectors (r), the second is the maximum eigenvalue statistic that tests the null hypothesis for the existence of cointegration vectors. If the exogenous variables have a cointegration relationship, the vector error correction (VECM) model (Johansen, 1988) can be used, but if there is no cointegration relation, the differentiated series vector autoregressive (VAR) model (Sims, 1980) can be applied. Since there is a cointegration relation between the series in this study, the VECM model is used. The VECM model is a multi-factor system that brings error correction features to the VAR model. The most important feature of the model is that it allows the identification of a long-run equilibrium relationship that can be used to increase the success of long-term estimates of the series in the system. The long-term equilibrium relationship can be determined from the cointegration vector. The error correction model with a degree of cointegration of r ( n), denoted VECM (p), can be written as:

119

In the above equation, r is the number of cointegration vectors, Δ is the difference operator, , α and β n x r matrices, and , n x n matrices. The cointegration vector β is the long term parameter and α is the adjustment coefficient. In the case of cointegration with external variables, the exogenously variable VECM, VECMX (p, m), can be written as:

According to Granger causality concept (Granger, 1988), when yt can be used in the forecasting process for xt, yt becomes the granger cause of xt. The existence of the causal link between the series is examined under the following equality features:

In the above equation, Δ is the delay operator, ECTt-1 is the delayed error correction term derived from the long-term cointegration relationship. μ1t and μ2t are independent random error terms. The dependent variable is estimated in relation to its own and other past values. The optimal delay length p in this process is based on the maximum likelihood procedure of Johansen and Juselius (1990). However, the causality test does not allow to have an idea about the dynamic system features beyond the sample period. After the sample period, the interaction between variables is tested by impulse-response functions (Pesaran and Shin, 1998). Although impulse-response functions tells us about the effect on current and future values of all external variables, these functions can not provide information about the size of this effect.

3.3. Empirical Results In order for the Johansen cointegration test to analyze the long-run equilibrium, the variables must be integrated at the same degree. Regarding this situation, according to the results of unit root test in Table 4, all variables are stationary at the level value while they are stationary at the first differences. Moreover, since all variables are I (1), the cointegration relation between them can be examined.

120

Variable Bar Soy Chi Sug Exc Cot Oli Ric Mai Oil Len Sun Whe

Table 4: ADF, PP and KPSS Unit Root Test Results ADF PP KPSS I(0) I(1) I(0) I(1) I(0) I(1) -0.61 -5.38*** -0.44 -5.30*** 1.13 0.12*** 1.55 -6.89*** 1.80 -6.91*** 1.23 0.48* -0.39 -4.09*** -0.28 -7.32*** 0.98 0.18*** -0.18 -10.25*** 0.01 -10.39*** 1.27 0.07*** 1.55 -6.89*** 1.80 -6.90*** 1.23 0.48*** -1.83 -7.28*** -1.78 -7.23*** 0.14*** 0.12*** 0.05 -8.34*** -0.18 -8.33*** 0.53* 0.39** -0.64 -7.87*** -0.95 -7.95*** 0.80 0.10*** -2.68* -10.01*** -2.66* -10.53*** 1.02 0.04*** -1.44 -8.19*** -1.51 -8.20*** 0.45* 0.52* 0.21 -7.57*** 0.31 -7.62*** 0.80 0.35** -0.75 -8.49*** -0.79 -8.49*** 1.23 0.05*** 0.12 -7.04*** -0.25 -7.09*** 1.24 0.07***

Note: Unit root tests consist of a fixed term and lag lengths selected is based on the Schwarz information criterion. ***, ** and * indicate that the test statistics is significant at %1, %5 and %10 levels respectively.

Appendix 1.1 and 1.2 show the results of Johansen cointegration test of oil prices, exchange rates and agricultural products separately. When the results are investigated, it is understood that only the wheat (whe) and corn (mai) variables are cointegrated with oil prices and the exchange rate. Thus, only these two variables were used as dependent variables in the establishment of VECM models and long-term causal relationships were examined. When the results in Table 5 are analyzed, it is seen that the ECT (error correction) coefficient of the first model is negative and statistically significant, and therefore the long-run relationship between corn prices, oil prices and the exchange rate also includes a long-run causal relationship at the same time. More precisely, oil prices and exchange rates are the cause for corn prices in the long run. When long term coefficients are considered, it is seen that increases in oil prices have reduced maize prices, while increases in exchange rates have increased maize prices. When a similar study is done for wheat prices, it can be seen that there is no long-term causal relationship, and according to long-term coefficients, increases in both oil prices and exchange rates seem to reduce wheat prices. Model Mai-oil-exc Whe-oil-exc

Table 5: Estimates of Long Run and the Adjustment from ECM Regressors Parameter Estimates t-test β1 0.003 0.10 β2 - 0.157 -2.84 ECTt-1 - 0.242 -3.39*** β1 0.369 2.12 β2 0.040 0.15 ECTt-1 0.0001 0.015

In addition to this, according to the short-run VEC Granger causality relation, the results of which are shown in Table 6, only two results are statistically significant. According to the results, in the short term, maize prices are the granger cause of oil prices and wheat prices are the graanger cause of exchange rates.

121

Table 6 : VECM Granger Causality Results VEC GrangerCausality / BlockExogeneityWaldTests Mai-oil-exc Δma Δo Δexc 0.058 0.853 (0.808) (0.355) 2.946 0.188 (0.086)* (0.663) 0.091 0.071 (0.762) (0.788) Whe-oil-exc Δwhe Δo Δexc 0.785 0.163 (0.375) (0.685) 0.421 0.124 (0.516) (0.723) 3.592 0.121 (0.058)* (0.727)

DependentVariable (chi-sq) Δma Δo Δexc

Δwhe Δo Δexc

Note: Lag lengths selected is 1 based on the Schwarz information criterion. P-value listed in parantheses. . ***, ** and * indicate that the test statistics is significant at %1, %5 and %10 levels respectively.

Finally, when the impulse-response functions in Figure 5 are examined, it appears that maize prices have a very limited response to shocks occurring in the price of oil. When wheat prices were initially negative, this response decreased from the fourth month on. For exchange rate shocks, both agricultural products reacted positively at the beginning, but from the fourth month onwards, these reactions seem to disappear. Figure 5: Impulse Response Function from VECM Response to Cholesky One S.D. Innovations

Response to Cholesky One S.D. Innovations

Response of MAI to OIL

Response of WHE to OIL

.006

.002

.005 .001

.004 .000

.003 .002

-.001

.001 -.002

.000 -.001

-.003

5

10

15

20

25

30

35

5

Response of MAI to EXC

10

15

20

25

30

35

30

35

Response of WHE to EXC

.006

.002

.005 .001

.004 .000

.003 .002

-.001

.001 -.002

.000 -.001

-.003

5

10

15

20

25

30

35

122

5

10

15

20

25

4. Results There are three ways in which the price increases that occur in the oil prices shifting to the prices of agricultural products. The first is that increases in oil prices directly increase the prices of agricultural products. Because energy reaches up to 20 percent of agricultural production costs (see table 1). In the second case, the increase in oil prices causes the local currencies to depreciate as the oil-importing countries have deteriorationed their current balances. Thus, prices of agricultural products are also increasing due to increases in the level of general prices. The third and last reason is the growing biofuels, especially in recent years, where production and consumption are increasing. Because agricultural products are used in the production of biofuels. As it is an alternative to petroleum use, demand for biofuels is also increasing, especially when oil prices are very high. Thus, demand and prices for agricultural products used in biofuel production are increasing. In this study, the effects of changes in oil prices and exchange rates on various agricultural commodity prices (wheat, maize, soybean, sunflower, cotton, lentil, barley, rice, sugar beet, olive, chickpea) was investigated. In this sense, firstly the cointegration relations between the variables were discussed, then short and long term causality studies were carried out. For this, 2009-2017 period, Brent oil price, Usd / TL exchange rate and price data of agricultural products obtained from Turkish Statistical Institute were used. Johansen (1988) cointegration test was applied to variables determined to be suitable for stationary levels cointegration analysis and it was found that only wheat and maize move in long term together with oil and exchange rate. Then, a vector error correction (VECM) model was developed to examine the short- and long-term causality of the variables that identified the cointegration relation. When the long-term causality is examined, it is seen that the error correction coefficient in the model that only maize prices are dependent variable is significant. Therefore, it can be said that oil prices and exchange rate are the reasons of maize prices. But such a causal relation is not mentioned for wheat prices. Moreover, when the long term coefficients are considered, it is determined that increases in oil prices have reduced maize prices, while increases in exchange rates have increased maize prices. For wheat, increases in both oil prices and exchange rates have reduced wheat prices. According to the short-term VEC Granger causality relation, it is revealed that the maize prices are the granger cause of the oil prices and the wheat prices are the granger cause of the exchange rate. According to the impact-response functions created at the end of the analysis, maize prices reacted very little to shocks in oil prices and wheat prices responded negatively in the beginning but this response decreased from the fourth month. For exchange rate shocks, it was observed that both agricultural products initially reacted positively, but from the fourth month onwards, these reactions were left behind. As a result, the fact that the prices of agricultural products are not influenced by changes in oil prices and exchange rates poses a positive situation for Turkey, which is an oil importer. From another point of view, it can be said that the openness of the agricultural sector in Turkey is not much, and the prices of agricultural products are determined according to the supply and demand conditions in the country. One of the reasons why 123

the prices of agricultural products are not affected by oil price changes is that biofuels in Turkey do not take up much space in energy diversity. The results of the application demonstrate why only wheat and corn have a long-term relationship with oil prices and exchange rates, as these are two of the four most commonly used crops in biofuel production in Turkey. This situation indicates that there may be some changes in equilibrium relations with the increase in the use of biofuels as an alternative energy source in the next years. Appendix 1.1: Johansen Cointegration Test Results Oil, exc vs Bar (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Chi (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Cot (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Len (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Mai (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Oli (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2

Model 2 Test Statitic

Critical Vaues (0.05)

31.40 -

Model 4 Decision

Test Statitic

Critical Vaues (0.05)

Decision

35.19 -

Not rejected -

41.05 -

42.91 -

Not rejected -

18.06 -

22.29 -

Not rejected -

26.00* 10.44

25.82 19.38

Rejected Not rejected

31.43

35.19

Not rejected -

34.53

42.91

Not rejected -

16.96

22.29

Not rejected -

20.26

25.82

Not rejected -

26.98 -

35.19 -

Not rejected

30.64 -

42.91 -

Not rejected -

14.03 -

22.29 -

Not rejected

15.19 -

25.82 -

Not rejected -

34.03 -

35.19 -

Not rejected -

37.40 -

42.91 -

Not rejected -

20.97 -

22.29 -

Not rejected -

22.28 -

25.82 -

Not rejected -

39.25* 13.65

35.19 20.26

Rejected Not rejected

43.83* 18.51

42.91 25.87

Rejected Not rejected

25.60* 8.35

22.29 15.89

Rejected Not rejected

25.32* 11.10

25.82 19.38

Rejected Not rejected

34.99 -

35.19 -

Not rejected -

39.56 -

42.91 -

Not rejected -

17.75 -

22.29 -

Not rejected -

21.05 -

25.82 -

Not rejected -

Lag interval (in first differences): 1 to 1. Trend assumption; Model 2: No deterministic trend, Model 4: Linear deterministic trend. * denotes rejection of the hypothesis at the 0.10 level based on Mackinnon-Haug-Michelis (1999) p-values.

124

Appendix 1.2: Johansen Cointegration Test Results Oil, exc vs Ric (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Soy (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Sug (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 Sun (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2 whe (lag=1) λtracestatistics H0 : r=0 vs H1 : r≥1 H0 : r 1 vs H1 : r≥2 λmaxstatistics H0 : r=0 vs H1 : r=1 H0 : r 1 vs H1 : r≥2

Test Statitic

Model 2 Critical Vaues (0.05)

Decision

Test Statitic

Model 4 Critical Vaues (0.05)

Decision

24.75 -

35.19 -

Not rejected -

30.06 -

42.91 -

Not rejected -

15.34 -

22.29 -

Not rejected -

15.21 -

25.82 -

Not rejected -

36.41* 18.30

35.19 20.26

Rejected Not rejected

43.76* 22.31

42.91 25.87

Rejected Not rejected

18.10 -

22.29 -

Not rejected -

21.44 -

25.82 -

Not rejected -

38.70* 22.56*

35.19 20.26

Rejected Rejected

38.90 -

42.91 -

Not rejected -

16.14 -

22.29 -

Not rejected -

16.06 -

25.82 -

Not rejected -

28.00 -

35.19 -

Not rejected -

36.63 -

42.91 -

Not rejected -

15.17 -

22.29 -

Not rejected -

22.27 -

25.82 -

Not rejected -

31.00 -

35.19 -

Not rejected -

44.87* 18.22

42.91 25.87

Rejected Not rejected

16.85 -

22.29 -

Not rejected -

26.64* 10.31

25.82 19.38

Rejected Not rejected

Lag interval (in first differences): 1 to 1. Trend assumption; Model 2: No deterministic trend, Model 4: Linear deterministic trend. * denotes rejection of the hypothesis at the 0.10 level based on Mackinnon-Haug-Michelis (1999) p-values.

125

References Abbott, P. C., Hurt, C., & Tyner, W. E. (2009). What's driving food prices? March 2009 Update (No. 48495). Farm Foundation. Abdlaziz, R. A., Rahim, K. A., & Adamu, P. (2016). Oil and Food Prices Co-integration Nexus for Indonesia: A Nonlinear ARDL Analysis. International Journal of Energy Economics and Policy, 6(1). Akalın, B., and Seyrekbasan, A. M. (2015). Dünyadaki Biyoetanol Politikalarının Türkiye Koşulları ile Karşılaştırmalı İncelenmesi ve Türkiye Şartlarına Uygunluk Açısından Biyoetanol Üretiminde Kullanılan Hammaddelerin Değerlendirilmesi. Journal of Agricultural Faculty, 29(1), 157-168. Alptekin, V., Yılmaz, K. Ç., and Taş, T. (2016). Döviz Kurundan Fiyatlara Geçiş Etkisi: Türkiye Örneği. Selcuk University Social Sciences Institute Journal, (35). Altıntaş, H. (2016). Petrol Fiyatlarının Gıda Fiyatlarına Asimetrik Etkisi: Türkiye İçin Nardl Modeli Uygulaması. Yönetim ve Ekonomi Araştırmaları Dergisi, 14(4). Biemans, M., Waarts, Y., Nieto, A., Goba, V., Jones-Walters, L. and Zöckler, C. (2008). Impacts of Biofuel Production on Biodiversity in Europe. Holanda, ECNC, European Centre for Nature Conservation. Campiche, J. L., Bryant, H. L., Richardson, J. W., & Outlaw, J. L. (2007, July). Examining the evolving correspondence between petroleum prices and agricultural commodity prices. In The American Agricultural Economics Association Annual Meeting, Portland, OR. Chen, S. T., Kuo, H. I., & Chen, C. C. (2010). Modeling the relationship between the oil price and global food prices. Applied Energy, 87(8), 2517-2525. Dellal, İ., Özat, H. E. ve Özüdoğru, T. (2005) Tarımda Mazot Kullanımı ve Mazot Destekleri, Çalışma Raporu, No.173, Tarımsal Ekonomi Araştırmaları Enstitüsü. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 10571072. Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251276. Granger, C. W. (1988). Some recent development in a concept of causality. Journal of econometrics, 39(1-2),199-211.

126

Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111-120. Harri, A., Nalley, L., & Hudson, D. (2009). The relationship between oil, exchange rates, and commodity prices. Journal of Agricultural and Applied Economics, 41(02), 501-510. Hendry, D. F., & Juselius, K. (2001). Explaining cointegration analysis: Part II. The Energy Journal, 75-120. İşcan, A. P. D. E. Enerji Fiyatlarının Dünya Gıda Fiyatları Üzerine Etkisi: Bir Sınır Testi Yaklaşımı The Impact of Energy Prices on World Food Prices: A Bounds Testing Approach. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of economic dynamics and control, 12(2-3), 231-254. Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press on Demand. Kaltalıoğlu, M. (2010). Price Transmissions Between Food and Oil. Unpublished masters thesis. The Graduate School Of Socıal Scıences Of Mıddle East Technıcal Unıversıty. Kaltalioglu, M., Soytas, U., (2009). Price transmission between world food, agricultural raw material, and oil prices. GBATA International Conference Proceedings, pp. 596–603. Prague, 2009. Kara, H., ve Öğünç, F. (2012). Döviz kuru ve ithalat fiyatlarının yurt içi fiyatlara etkisi. İktisat İşletme ve Finans, 27(317), 09-28. Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of econometrics, 54(1-3), 159178. Mutuc, M., Pan, S., & Hudson, D. (2011). Response of cotton to oil price shocks. Agricultural Economics Review, 12(2), 40. Nazlioglu, S., & Soytas, U. (2011). World oil prices and agricultural commodity prices: evidence from an emerging market. Energy Economics, 33(3), 488-496. Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 10981104. Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1), 17-29. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 335-346. 127

Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48. Singh, A. and Singh, M. (2016). Inter-linkages and causal relationships between US and BRIC equity markets: An empirical investigation. Arab Economic and Business Journal II, 115-145.

Şen, Bahar, (2009), “Asymmetric Behavior of Exchange Rate Pass Through in Turkey”, Yayınlanmamı ş Yüksek Lisans Tezi, Bilkent Üniversitesi, Ekonomik ve Sosyal Bilimler Enstitüsü, Ekonomi Anabilim Dalı , Ankara. Wójcik, P. (2011). Non-stationarity and its testing, cointegration testing and error correction model. University of Warsaw. Retrieved from 〈http://coin.wne.uw. edu.pl/pwojcik/macro_en.html〉. Zhang, Z., Lohr, L., Escalante, C., & Wetzstein, M. (2010). Food versus fuel: What do prices tell us?. Energy Policy, 38(1), 445-451.

128

CHAPTER VIII POLLUTION HAVEN HYPOTHESIS AND CLIMATE CHANGE: ANALYSIS FOR NEWLY INDUSTRIALIZED COUNTRIES Emrah SOFUOĞLU1 1. Introduction As a notion of sustainable development has started to be debated in all around the world since 1980's. Briefly, this notion means that usage of today’s resources efficiently without detriment to next generations’ resources. In this sense, countries make environmental adjustments, enhance the investment in renewable energy, trying to increase the energy efficiency and decrease the carbon density. Thus, these countries are trying to supply their energy with clean, green and sustainable resources instead of the fossil fuel-based economy in the context of sustainable development. In spite of the fact that developed countries come a long way with this transition, developing countries have been slow due to the economic issues arising from the high costs. Technological developments have a strong effect of reducing energy consumption which decrease the amount of greenhouse gas emission in the atmosphere per output. In this regard, multinational companies directly penetratein the developing countries’ markets by way of foreign capital investments and these companies also lead to economic, technological and governmental changes in the respective developing countries. However, these changes have been mostly accepted positively, from time to time even if they have been seen negatively before. There are some positive developments that could be explained on countries which have foreign direct investments inflow:

1



Technology transfer



Rising of investment



Employment increase



Increasing the management skills



Positive impacts on balance of payments

Research Assistant, Ahi Evran University, FEAS, Department of Economics, [email protected]

129



Increasing the domestic competition

There are also some negative effects of foreign direct investment inflow which have been discussed by economists in the context of the environment. Accordingly, there are some difficulties that developed countries faced such as strong environmental regulations, higher environmental standards, and environmental taxes. So, multinational companies invest in developing countries due to lower taxes, weak legislation regulations, and lower environmental standards. Xing ve Kolstad (2002) highlight that foreign direct investments might have negative effects on the environment, although they have positive effects on the economy. On the other hand, Shahbaz et. al. (2011) argue that developing countries could push environmental issues into the background due to attract to the foreign direct investments. Researchers, who defend this point, generally study on the Pollution Haven Hypothesis. Researchers, who criticize that point, study on Pollution Halo Hypothesis. According to Zarsky (1999), this kind of foreign direct investments provide the technology transfer and develop their management skills to the developing countries. Thus, manufacturing process has become more efficient, the amount of energy has decreased per output and contribute to reduce the greenhouse gas emission. According to World Investment Report 2016, FDI flows increased by 38% to $1.76 trillion, the highest level since the global economic and financial crisis of 2008–2009. The value of announced greenfield investment remained at a high level, at $766 billion. Developing economies witnessed their FDI inflows reach to $765 billion, 9% higher than in 2014. Foreign direct investment has been especially increased since 1980's. In addition to this, there are some guards against the environmental issues after these years. Foreign direct investment has been attracted the attention in the means of that transfer the technological developments to other destinations to supply energy efficiency to solve the climate change. Thanks to the foreign direct investment i) clean and renewable energy sources investment has been increased in all around the world. ii) it could be seen that the amount of energy per output has been decreased and carbon density has been reduced thanks to the increase in the energy efficiency. iii) There are some contributions could be provided to decrease the emission due to new technology developments. Climate change issue has been debated since 80’s all over the world. Earth has faced with climate change because of increasing greenhouse gas emissions in the atmosphere layer. In this context, governments have been trying to find a solution to reduce emissions to protect their future generations. There are some conference that might accepted as a turning point for climate negotiations. In 1992, United Nations Environment and Development Conference was held in Rio and goverments debated on destruction of the ozone layer, biodiversity and climate change. In 1997, Kyoto Protocol developed in 3th Conference on Parties. According to this protocol, Annex-I countries have confirmed taking responsibility to reduce greenhouse gas emissions for the period 130

of 2008-2012 by 5% below 1990 levels and to limit global temperature below 2°C. However, in 2009, goverments could not come into agreement since IPCC suggested that developing countries should also decrease their greenhouse emissions to limit the surface temperature. After this failure, there was no significant improvement until 2015. However, in 2015, 196 countries came together to negotiate and reconciled in 21th Conference of Parties. According to the Paris Agreement, countries committed to reduce global temperature well below 2°C (about 1,5°C). Developed countries are required to provide $100 billion climate finance to developing countries to transform into low carbon climate resilent economies. Paris Agreement might be accepted as scientific based, dynamic and a long process agreement. Most importantly, Paris Agreement aims to develop climate-resistant and low-carbon social transformation.(Karakaya, 2016). The aim of this study is to answer the following questions: How do industry production and FDI affect CO2 emissions which cause the climate change in NIC countries? According to analysis results, which economic policies could be more effective to decrease CO2 emissions? This paper tries to contribute to the literature by analyzing the relationship between industry production, FDI and CO2 emissions over the period 2000-2016 for NIC countries. The data is chosen to test whether Pollution Haven Hypothesis is valid in NIC countries or not. In this sense, the first part will give some information which related to FDI and Pollution Haven Hypothesis. The second part will focus on the empirical literature and in the third part, model and dataset are introduced, the methodology of analysis method will be presented and results of the analysis will be reported. Finally, empirical findings will be discussed and some policy recommendations will be suggested.

2. Literature In the literature, there are many studies focus on the environmental issues. Some hypotheses have been proposed in the field of economic activity and environmental pollution. Environmental Kuznets Curve, Pollution Haven Hypothesis and Pollution Halo Hypothesis are the prominent hypotheses that draw attention by researchers. In this study, literature related to Pollution Haven Hypothesis and the relationship between FDI, industry production and CO2 emissions is summarized. Many economists agree that FDI leads to increase the CO2 emissions in developing countries. Because of having less environmental arrangements, less tax, fewer standards and less bureaucratic procedures, multinational corporations tend to invest in developing countries. Thus, FDI leads to enhance industry production and so that CO2 emissions increase. According to Lucas et al. (1992), Law and Yeats (1992), and Birdsal and Wheeler (1993) polluting product production and export increases in developing countries and decreases in developed countries. Kızılkaya (2017), examined the impact of economicgrowth and FDI on CO2 emissions for Turkey over the period 1970-2014 by utilizing ARDL bounds testing method. Analysis findings indicate that economic growth 131

and FDI have a positive effect on CO2 emissions. However, the study could not find significant relationship between FDI and CO2 emissions for Turkey. Linh and Lin (2015), found that there is a bi-directional causality relationship between CO2 and energy consumption in the short term and there is a uni-directional causality relationship between CO2 emissions and energy consumption, FDI and GDP in the long term in most populated 12 Asia Countries. Kivyiro and Arminen (2014), focus on 6 Africa countries and indicate that CO2, FDI, energy consumption and GDP move together in the long term. Tang and Tan (2015) also imply that there is a bidirectional relationship between CO2 and income, FDI and energy consumption in Vietnam. Omri, Nguyen and Rault (2014), state that there is a bidirectional causality relationship between FDI and CO2 for 54 countries. Mahmood and Chaudary (2012) express that FDI, manufacturing, and population have a positive impact on CO2 emissions. Arouri et al. (2012) found that energy consumption has a positive and significant impact on CO2 emissions in 12 MENA countries. Halıcıoğlu (2009) states that income is the most significant variable in explaining the carbon emissions in Turkey and energy consumption and foreign trade also explain CO2 emissions. According to Pao and Tsai (2011), there is a strong relationship between CO2 emissions and FDI in BRIC (Brazil, Russia, India and China) countries. Jalil and Mahmud (2009) found that energy consumption and income level are significant variables in explaining CO2 emissions. According to Merican et al. (2007), while FDI increases CO2 emissions in Malaysia, Philippines and Thailand, there is no impact in Indonesia and Singapore. However, Yılmazer and Ersoy (2009) analyzed the same model with Merican et al. (2007) and could not find cointegration relationship between variables in ASEAN-5 countries. There are also some studies found different findings in the literature. Mani and Wheeler (1998) confirms pollution haven hypothesis but they suggest that it is temporary. Letchumanan and Kodama (2000) claim that FDI leads to technology transfer to developing countries and it helps to produce an environment-friendly product so that FDI decreases CO2 emissions in developing countries. Şahinöz and Fotourehchi (2014) investigated Pollution Haven Hypothesis and Factor Endowment Theory over the period of 1974-2011 for Turkey by using OLS and it has been seen that Factor Endowment Theory is valid for Turkey. Lieter et al. (2011) precipitated that environmental regulations have a positive impact on industry investment in European countries over the period of 1998-2007.Gökalp and Yıldırım (2004) express that dirty industry products demand is met by import. That is why environmental pollution is transferred to all over the world. Moreover, they state that there is no intense environment in Turkey. According to Chandran and Tang (2013), the long-run elasticity estimation suggests that income and transportation of energy consumption significantly influence CO2 emissions whereas FDI is not significant in ASEAN-5 countries. Sbia (2014) examined United Arab Emirates and found that FDI and openness decrease CO2 emissions. Besides, economic growth and clean energy increase energy consumption.

132

In literature, there are also some studies that reject Pollution Haven Hypothesis and confirm Pollution Halo Hypothesis. Unlike Pollution Haven Hypothesis, Pollution Halo Hypothesis argues that FDI leads to a better environment in developing countries by using high technology, having more productivity and efficiency and management skills. Zeren (2015) analyzed Pollution Haven Hypothesis and Pollution Halo Hypotheses for 1970-2010 by utilizing panel FMOLS and CCR cointegration estimators. Results show that while pollution Halo Hypothesis is valid for the U.S, France and England, Pollution Haven Hypothesis is confirmed for only Canada. Akın (2014) investigated 12 highincome group countries over the period 1970-2012 by employing panel data method. Findings depict that there is a negative relationship between FDI and CO2 emissions. Blanco et al. (2013) examined 18 Latin American countries and they claim that there is an uni-directional causality relationship from FDI to CO2 emissions in dirty-intense industries. Al-mulali and Tang (2013) investigated the validity of PHH for 1980-2009 for GCC countries. Results indicate that FDI does not lead to environmental pollution. Zhang (2011), investigated China for1980-2009 and argues that FDI has an important role in increasing CO2 emissions. Yang et al. (2008) and Sha and Shi (2006) asserted similar results for China as well. Finally, Acharyya (2009) pointed out that FDI is not the only variable to explain Pollution Haven Hypothesis for India.

3. Econometric Analysis In this study, relationship between CO2, FDI and industry production in NIC countries over the period of 2000-2016 was analyzed by using panel data analysis. Empirical analysis consists of three steps. In the first step, stationarity of variables is checked with unit root tests. In the second step, long run relationship between the variables is determined by panel cointegration method. Finally, long run equilibrium relation is estimated by utilizing panel FMOLS and panel DOLS estimators.

3.1. Method To avoid the problem of spurious regression in econometric analyses, series employed in analyses need to be checked whether they have unit root or not. Fisher-ADF test was developed by Maddala and Wu (1999) and IPS test was developed by Im et al. (2003) utilized for unit root tests. In Fisher-ADF test, p values obtained from unit root tests for each i crossing are taken into consideration and have advantage of not to be bound to different lag lengths in individual ADF regressions. Fisher-ADF test is non-parametric and has chi-square distribution with 2n degree of freedom. Fisher-ADF test statistics is calculated as follows: λ = -2

n i=1 loge(pi)

χ22n(d.f.)

(1)

n refers to number of crossings creating panel and pi refers to p values obtained from ADF unit root tests for pi unit in equation (1). Im et al. (2003) specifies LLC panel unit root test and calculates t statistics for every section and average the sections forms panel. IPS test permits units that forms the panel to vary for every p value, test statistics can be obtained as follows: 133

Δyit = µi + yit-1 +

m j=1 αj∆yit-j

+ δit +

t

+ εit

(2)

In unit root analysis, each i calculated for p = 0 and at least one i calculated versus p < 0 alternative hypothesis. Rejection of null hypothesis implies that series do not have unit roots which means series are stationary. If unit root exists, first differences of series need to be taken and proceed to unit root analysis. If the series are stationary at first difference level while they are not stationary at level value in consequence of unit root test, existence of cointegration relation needs to be determined before estimating parameter between variables. Long run cointegration relationship in econometric analysis is generally analyzed by panel cointegration test which developed by Pedroni (1999; 2004). Pedroni, developed 7 different test statistics to test null hypothesis as “there is no cointegration relation”. Pedroni (1999, 2004) takes these statistics from the residuals of the panel cointegration test. Four of these statistics consist of in-group statistics (panel-v, panel- , half parametric panel-t and parametric panel-t), and the rest consists of intergroup statistics (group- statistic, half parametric group-t statistic and parametric group-t). After comparing these seven statistics to critical values, it will be confirmed that cointegration relation is accepted or rejected. At the end of t test, if related statistics are greater than critical values, null hypothesis is rejected and hypothesis with regard to long-term cointegration relation between variables is accepted. If there is cointegration, long run cointegration parameters will be estimated. FMOLS (panel fully modified ordinary least squares) and DOLS (panel dynamic ordinary least squares) tests developed by Pedroni (2000, 2001) are general methods chosen by researchers. FMOLS and DOLS estimators are developed after getting biased results between series that move together in the long run and estimated by least squares methods. The benefit of FMOLS is correction autocorrelation and endogeneity problem with the non-parametric approach. DOLS method is fixing autocorrelation problem by using lagged values and provides better and more dynamic estimator.

3.2. Model and Data Set The aim of this study is to analyze the relationship between CO2 emissions, FDI, industry production and energy consumption in NIC countries. Energy consumption data is considered as proxy data in the model. The model is as below: CO2it = β0i + β1i FDIit + β2i IPit + β2ECεit

(3)

i; represents the number of countries composes the panel and t; represent the period. In the model, CO2 emissions and industry production data were obtained from World Bank database, FDI and primary energy consumption data were obtained from UNCTAD and BP Statistical Review of World Energy 2016, respectively. All variables in the model used in logarithmic form.

134

South Africa - 00 South Africa - 06 South Africa - 12 Mexico - 01 Mexico - 07 Mexico - 13 Brazil - 02 Brazil - 08 Brazil - 14 China - 03 China - 09 China - 15 India - 04 India - 10 India - 16 Indonesia - 05 Indonesia - 11 Malaysia - 00 Malaysia - 06 Malaysia - 12 Philippines - 01 Philippines - 07 Philippines - 13 Thailand - 02 Thailand - 08 Thailand - 14 Turkey - 03 Turkey - 09 Turkey - 15

South Africa - 00 South Africa - 06 South Africa - 12 Mexico - 01 Mexico - 07 Mexico - 13 Brazil - 02 Brazil - 08 Brazil - 14 China - 03 China - 09 China - 15 India - 04 India - 10 India - 16 Indonesia - 05 Indonesia - 11 Malaysia - 00 Malaysia - 06 Malaysia - 12 Philippines - 01 Philippines - 07 Philippines - 13 Thailand - 02 Thailand - 08 Thailand - 14 Turkey - 03 Turkey - 09 Turkey - 15

10

8

6

4

30

29

28

27

25

24 South Africa - 00 South Africa - 06 South Africa - 12 Mexico - 01 Mexico - 07 Mexico - 13 Brazil - 02 Brazil - 08 Brazil - 14 China - 03 China - 09 China - 15 India - 04 India - 10 India - 16 Indonesia - 05 Indonesia - 11 Malaysia - 00 Malaysia - 06 Malaysia - 12 Philippines - 01 Philippines - 07 Philippines - 13 Thailand - 02 Thailand - 08 Thailand - 14 Turkey - 03 Turkey - 09 Turkey - 15

South Africa - 00 South Africa - 06 South Africa - 12 Mexico - 01 Mexico - 07 Mexico - 13 Brazil - 02 Brazil - 08 Brazil - 14 China - 03 China - 09 China - 15 India - 04 India - 10 India - 16 Indonesia - 05 Indonesia - 11 Malaysia - 00 Malaysia - 06 Malaysia - 12 Philippines - 01 Philippines - 07 Philippines - 13 Thailand - 02 Thailand - 08 Thailand - 14 Turkey - 03 Turkey - 09 Turkey - 15

LNCO2

Figure 1: Dynamics of Series LNFDI

15

9 14

13

12

7 11

10 9

5 8

7

6

LNINDUSTRY_PRODUCTION LNENERGY_CONSUMPTION

7.0

6.5

6.0

5.5

5.0

26 4.5

4.0

3.5

3.0

3.3. Empirical Findings

Results of Fisher-ADF test developed by Maddala and Wu (1999) and IPS panel unit root test developed by Im et al. (2003) are depicted in Table 1. According to the test results CO2, FDI, IP and EC are not stationary at level value, however, they are stationary at first difference level. Cointegration relation needs to be analyzed before estimating coefficients of variables.

Table 1: Panel Unit Root Test Results Variables IPS ADF-FISHER CO2 0.81 0.23 FDI 0.28 0.37 IP 0.66 0.24 EC 0.89 0.72 ∆CO2 0.01* 0.02* ∆FDI 0.00* 0.00* ∆IP 0.02* 0.04* ∆EC 0.00* 0.01* * refers to %1 significance level. Prob values were presented in the table.

135

Panel cointegration results are as below in Table 2. According to the test results, four of seven statistics in fixed model and two of seven statistics in fixed and trend model reject the null hypothesis by indicating that there is no cointegration. In other saying test results for two country groups supports long run counterbalanced relation among FDI, corruption and corruption2 variables. Table 2: Panel Cointegration Test Results Test Statistics Fixed Panel v 1,51** Panel rho 0.66 Panel PP -1.41** Panel ADF -2.10* Grup rho 2.77 Grup PP 0.85 Grup ADF -2.30* * refers to %1 significance level, ** refers to %5 significance level. t values of the variables are presented.

The third step after cointegration relationship is found is estimating long run cointegration parameters. Panel FMOLS and panel DOLS test results used for estimating cointegration parameters are report are indicated in Table 3. Table 3: Panel FMOLS, Results FDI IP EC NIC 0.04 0.08 0.62 [7.18]* [2.11]** [10.88]* *, ** and *** refers to %1, %5 and %10 significance levels respectively.

When the Table 3 is examined; Considering panel FMOLS results, t statistics of all variables are statistically significant. According to the long term coefficicient estimation, FDI, industry production and energy consumption have positive effects on CO2 emissions. Therefore, it might be thought that a rise in FDI, industry production and energy consumption leads to higher CO2 emissions in NIC countries. Table 4: Panel DOLS, Results FDI IP EC NIC 0.02 0.18 0.72 [1.25] [1.93]** [7.06]* *, ** and *** refers to %1, %5 and %10 significance levels respectively.

Table 4 shows similar results with panel FMOLS test results when panel DOLS analysis applied to the variables. According to the findings, industry production and energy consumption variables are statistically significant, FDI is not statistically significant. Thus, it might be considered that energy consumption and industry production increase CO2 emissions in NIC countries.

4. Conclusion The aim of this study is to investigate whether foreign direct investment in developing countries increases CO2 emissions or not and to investigate what these results mean in terms of climate change. For this purpose, it was researched whether the Pollution Haven Hypothesis in Newly Industrialized Countries is valid or not. In this context, the 136

period 2000-2016 is examined and panel unit root, panel cointegration, panel FMOLS and panel DOLS methods are utilized. Countries' CO2 emissions, direct foreign investment, industry production and energy consumption data have been used to test the Pollution Haven Hypothesis. Analysis results can be listed as follows: I) According to the panel unit root tests, all of the variables are not stationary at the level value but they stationary at the first difference. This suggests that there will be no spurious regression problem when the model is predicted. II) According to the results of the Pedroni cointegration test, variables have a cointegration relation. Accordingly, in the long run, all variables are in relation to each other. III) The attainment of the cointegration relation makes it possible to pass panel FMOLS and panel DOLS analyzes to predict the direction of the relationship between the variables. In the study, panel FMOLS coefficient estimatation analysis shows that foreign direct investment, industry production and energy consumption increase CO2 emissions. All variables are statistically significant. According to the panel DOLS analysis, the coefficient of the FDI variable is not statistically significant, although direct foreign capital investments, industry production and energy consumption increase CO2 emissions. IV) When the findings from the analyzes are evaluated, the validity of the Pollution Haven Hypothesis is supported in the NIC countries between the period of 2000-2016. Empirical results obtained from the analysis show parallelism with Lucas et al. (1992), Law and Yeats (1992), Birdsal and Wheeler (1993), Kivyiro and Arminen (2014), Mahmood and Chaudary (2012) and Pao and Tsai (2011)’s studies. It is thought that foreign direct investment increase industrial production in NIC countries, which leads to an increase in energy consumption and an increase in CO2 emissions. As newly industrialized countries are considered, both the new and the energy consumptions increase more seriously than in the past, so the emissions released in these countries are increasing. Pollution Haven Hypothesis increases the environmental pollution by foreign direct investment in developing countries, in this content, this study supports this hypothesis. Because the countries in question want to increase the direct foreign direct investment capital inflows in order to increase their economic growth. In this regard, economic growth is encouraged in particular by increasing investments in countries. In order to achieve relevant economic growth, environmental taxes, environmental standards and bureaucracy in developing countries are less when compared to developed countries. This situation leads to a decline in investment costs of multinational companies and accelerates getting into the market. The problem of climate change is not only developing countries’ problem but also problem of developed and less developed countries. In this sense, every instrument that contributes to greenhouse gas reduction leading to climate change needs to be considered. In this study, climate change issue is related to the role of foreign direct investment and in the direction of empirical results, direct foreign investment is one of the factors causing the problem of climate change. It is believed that the main reason for capital inflows in the countries is low costs. Countries are ignoring environmental

137

problems and promoting foreign direct capital flows to maintain their economic growth. This leads to an increase in debates of climate change. There is another hypothesis which argues that foreign direct capital flows reduce environmental problems in developing countries. This view is called Pollution Halo Hypothesis. Accordingly, foreign direct investment in developing countries increases energy efficiency by providing technology transfer and management skills to these countries. Developments in technology reduce the amount of energy per input and contribute to the reduction of CO2 emissions. Climate change which is a global problem threatens next generations unless necessary precautions are taken. In this sense, environmental regulations, standards and environmental concerns need to be increased in developing countries. Economic losses that happen because of environmental standards should be financed by established funds and developed countries. The solution of the climate change problem is possible with reconciliation of developed and developing countries. Climate change negotiations will not be sustainable unless countries do not want to try to find a solution, incur responsibility and pay a price of their welfare.

138

References Acharyya, J. (2009). FDI, Growth and Environment: Evidence from India on CO2 Emission during the Last Two Decades. Journal of Economic Development, 34(1), 43-58. Akın, C. S. (2014). Yabancı Sermaye Yatırımlarının CO2 Emisyonu Üzerine Olan Etkisi: Dinamik Panel Veri Analiz. Akademik Bakış Dergisi, Sayı 44. Al-mulali U., Tang C., F. (2013). Investigating the validity of pollution haven hypothesis in the gulf cooperation council (GCC) countries. Energy Policy, (60), 813-819. Arouri M.E.H., Youssef A.B., M’henni H., Rault C. (2012). Energy consumption, economic growth and CO2 emissions in Middle East and North African countries. Energy Policy, (45), 342-349. Birdsall, N. and D. Wheeler (1993). Trade Policy and Industrial Pollution in Latin America: Where Are the Pollution Havens?”. Journal of Environment & Development 2(1), 137-147. Blanco L., Gonzalez F. ve Ruiz I. (2013). The Impact of FDI on CO2 Emissions in Latin America. Oxford Development Studies,41(1), 104–121. Chandran, V. G. R., and Chor, F.T. (2013). The impacts of transport energy consumption, foreign direct investment and income on CO2 emissions in ASEAN-5 economies. Renewable and Sustainable Energy Reviews, 24, 445-453. Gökalp M.F., Yıldırım A. (2004). Dış Ticaret ve Çevre: Kirlilik Sığınaklar Hipotezi Türkiye Uygulaması. Yönetim ve Ekonomi Dergisi, 11(2), 100-113. Halicioglu, F. (2009). An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy, 37(3), 1156-1164. Im, K. S., Pesaran, M. H. A. and Shin, Y. (2003). Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 115(1), 53-74. Jalil, A., and Syed F.M. (2009). Environment Kuznets curve for CO2 emissions: a cointegration analysis for China. Energy Policy, 37(12), 5167-5172. Karakaya, E. (2016). Paris İklim Anlaşması İçeriği ve Türkiye Üzerine Bir Değerlendirme, Adnan Menderes Üniversitesi, Sosyal Bilimler Enstitüsü Dergisi, 3(1), 1-12. Kızılkaya, O. (2017). The Impact of Economic Growth and Foreign Direct Investment on CO2 Emissions: The Case of Turkey. Turkish Economic Review, 4(1), 106. Kivyiro P., Arminen H. (2014). Carbon dioxide emissions, energy consumption, economic growth, and foreign direct investment: Causality analysis for Sub-Saharan Africa. Energy, (74), 595-606. Leiter A.M., Parolini A., Winner H. (2011). Environmental regulation and investment: Evidence from European industry data. Ecological Economics, 70(4), 759-770. 139

Letchumanan R. and F. Kodama (2000). Reconciling the Conflict Between the “Pollutionhaven” Hypothesis and an Emerging Trajectory of International Technology Transfer. Research Policy, Vol 29/: 59-79. Linh, D.H., and Shih-Mo, L. (2015). Dynamic causal relationships among CO2 emissions, energy consumption, economic growth and FDI in the most populous Asian Countries. Advances in Management and Applied Economics, 5(1), 69. Lucas, R., P. Wheeler and H. Hettıge (1992). Economic Development, Environmental Regulation and International Migration of Toxic Pollution 1960- 1988, In Low (1992). Maddala, G. S. A. and Wu, S. (1999). A Comparative Study Of Unit Root Tests With Panel Data And A New Simple Test. Oxford Bulletin of Economics And Statistics, 61(1), 631-652. Mahmood, H., and Chaudhary, A.R. (2012). FDI, population density and carbon dioxide emissions: A case study of Pakistan. Iranica Journal of Energy & Environment, 3(4), 354-360. Mani M. and D. Wheeler (1998). In Search of Pollution Havens? Dirty Industry in the World Economy 1960 to 1995. The Journal of Environment & Development, 7(3), 215-247. Merican, Y., Yusop, Z., Noor, Z. M. ve Hook, L. S. (2007). Foreign Direct Investment and the Pollution in Five ASEAN Nations. International Journal of Economics and Management, 1(2), 245-261. Omri, A., Duc, K. N., and Christophe, R. (2014). Causal interactions between CO 2 emissions, FDI, and economic growth: Evidence from dynamic simultaneousequation models. Economic Modelling, 42, 382-389. Pao H., Tsai C. (2011). Multivariate Granger causality between CO2emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy, (36), 685-693. Pedroni, P. (1999). Critical Values For Cointegration Tests in Heterogeneous Panels With Multiple Regressors. Oxford Bulletin of Economics and Statistics, 61(1), 653-670. Pedroni, P. (2000). Fully Modified OLS For Heterogeneous Cointegrated Panels. Advances in Econometrics, 15, 93-130. Pedroni, P. (2001). Purchasing Power Parity Tests in Cointegrated Panels. Review of Economics And Statistics, 83(4), 727-731. Pedroni, P. (2004). Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests With an Application to the PPP Hypothesis. Econometric Theory, 20(3), 597-625.

140

Sbia, R., Shahbaz, M. and Helmi, H. (2014). A contribution of foreign direct investment, clean energy, trade openness, carbon emissions and economic growth to energy demand in UAE. Economic Modelling, 36, 191-197. Sha, W., Shi, T. (2006). Environmental Effect of Foreign Direct Investment Analysis on Panel Data of China’s Provinces. World Economy Research, (6), 76- 82. Shahbaz M., Nasreen S., Afza T. (2011). Environmental Consequences of Economic Growth and Foreign Direct Investment: Evidence from Panel Data Analysis. MPRA, No 32547. Şahinöz A., Fotourehchi (2013). Çevresel Kuznets Eğrisi: İndirgenmiş Ve Ayrıştırılmış Modellerle Ampirik Bir Analiz. H.Ü. İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(1), 199-224. Tang C.F., Tan B.W. (2015). The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam. Energy, (79), 447454. UNCTAD, (2016). World Investment Report2016, Investor Nationality: Policy Challenges. United Nations. Wu, S-Y. (2006). Corruption and Cross-Border Investment by Multinational Firms. Journal of Comparative Economics, 34(4), 839-856. Xing, Y. ve Kolstad C.D. (2002). Do Lax Environmental Regulations Attract Foreign Investment?. Environmental and Resource Economics, (21), 1-22. Yang, W. P., Yang Y., Jie X.U. (2008). The impact of foreign trade and FDI on environmental pollution. China-USA Business, 7(12), 1-11. Yılmazer, M., Açıkgöz Ersoy, B. (2009), Kirlilik Sığınağı Hipotezi, Doğrudan Yabancı Yatırımlar Ve Kamu Politikaları, Ege Academik Bakış, 9(4), 1441-1462. Zarsky, L. (1999). Havens, Halos and Spaghetti: Untangling the Evidence about Foreign Direct Investment and the Environment. Emerging Market Economy Forum, OECD. Zeren F. (2015). Doğrudan Yabancı Yatırımların CO2 Emisyonuna Etkisi: Kirlilik Hale Hipotezi mi Kirlilik Cenneti Hipotezi mi?. Journal of Yasar University, 10(37), 6442-6448. Zhang Y.J., (2011). The impact of financial growth on carbon emissions: an empirical analysis in China. Energy Policy, 39, 2197–2203.

141

142

CHAPTER IX INTERNATIONAL TRADE AND ENERGY INTENSITY: CAUSALITY ANALYSIS FOR MANIFACTURING INDUSTRY of TURKEY Aykut ŞARKGÜNEŞİ1 1. Introduction With the industrial revolution, mankind began to use natural resources more intensively than ever before. In the same period, the production and consumption patterns of mankind have also changed greatly. It is noteworthy that by the 21st century, the current level of production and consumption reached the level of imposing limits of natural resources. At present, despite being away from depletion level, destruction caused by extreme use of natural resources of the world strikes as a more important problem, apart from the depletion level of natural resources of the world. Problems arising from overuse of natural resources in many areas such as global warming, pollution of water resources, deterioration of air quality, decrease of biological properties of soil are increasing considerably. As natural resources are important economic inputs and they are inseparable parts of human life, irreversible destruction of them can be threatening the current level of living and development we have achieved. Natural resources are the basic input of energy, which is indispensable in both production and consumption processes, as well as being used as input in different areas of the economy. Everywhere from the factory to the agricultural areas, from the workplaces to the houses and entertainment areas, energy is an indispensable part of everyday life. Thus, the use of energy is the most important factor in the use of natural resources. Moreover, since energy production is carried out largely from fossil fuels, it also has a significant share in the destruction of natural resources. Increasing mechanization and changing lifestyles have caused us to become more energy-efficient in production and consumption. According to World Bank (2014), energy consumption per capita was 1336 kgoe (kilogram equivalent oil) in 1971, and 1929 kgoe in 2014. At the beginning of the same period, 84.4% of the total energy consumption was covered by fossil resources, while at the end of the period this ratio decreased to 80.8%, which Assist. Prof. Dr., Bulent Ecevit University, Faculty of Economic and Administrative Science, International Trade and Business Department, [email protected] 1

143

was rather limited. Although the world population growth and per capita energy consumption are considered, the rate of energy obtained from fossil fuels has decreased, considering the world population growth and energy consumption per capita, it has increased in absolute terms in a very important way. The energy supply crisis that emerged in the 1970s and the environmental awareness that began to feel its impact in the 1990s provided an increase in social and academic interest in energy use. The fact that energy consumption has two different consequences, namely environmental pollution and depletion of natural resources, has made it attractive for groups that have both environmental concerns and supply side concerns. Many academic studies have been conducted in different fields related to the subject. International trade is also one of the areas associated with energy use. Our study is also included in this area and it is thought that our work will contribute to the literature that investigates the relation of energy use to international trade in production processes. According to Suri and Chapman (1998), there is a relationship between industrialization processes of countries and energy intensive product productions. After a certain industrialization phase, imports is preferred to production of energy-intensive products, thus creating an international division of labour in the world in the production of energy-intensive products. The production of energy intensive products is now being carried out in Chinese, whereas they used to be carried out largely by England and Germany by England and Germany, at the beginning of the industrial revolution. The issue of our work is to investigate whether Turkey is involved in a similar international division of labour. In all sub-sectors of the Turkish Manufacturing Industry, it is observed that trade openness has increased significantly between 2000 and 2014 (Graph 1). In the same period, the per capita CO2 emissions that we can count as a demonstration of energy use in Turkey has also increased by about one and a half times (World Bank, 2014). This has motivated us to investigate the causality relation between increasing foreign trade volume and sectoral energy use and to show whether or not our country is included in the above-mentioned kind of international division of labour. See Appendix A for sectoral codes.

144

Graph 1: Trade Openness of Turkish Manufacturing Industry Sub-Sectors (2000-2014) STO C10-C12

C13-C15

.28

C16

.60 .56

.24

C17

.6

C18

.8

.20

.7

.5

.52

.16

.6

.20

.48

.4

.16

.44

.3

.12 .5 .08 .4

.40 .12

.2

.36

.08

.32 00

02

04

06

08

10

12

14

00

02

04

C19

06

08

10

12

14

.2 00

02

04

C20

2.0

.04

.3

.1 06

08

10

12

14

.00 00

02

04

C22

1.6

06

08

10

12

14

02

04

C23

1.0

.40

0.8

.36

0.6

.32

0.4

.28

0.2

.24

06

08

10

12

14

08

10

12

14

08

10

12

14

C24 3.0

1.4 1.5

00

2.5

1.2 2.0 1.0

1.0

1.5 0.8 0.5

1.0

0.6 0.0

0.4 00

02

04

06

08

10

12

14

00

02

04

C25

06

08

10

12

14

00

02

04

C26

1.0

06

08

10

12

14

02

04

C27

2.2

06

08

10

12

14

00

02

04

C28

1.6

2.0

0.8

0.5 00

06

C29

2.0

2.5

1.6

2.0

1.2

1.5

0.8

1.0

1.2 1.8 0.6 1.6 0.8 0.4

1.4

0.2

1.2 00

02

04

06

08

10

12

14

0.4 00

02

04

C30

06

08

10

12

14

10

12

14

0.4 00

02

04

06

08

10

12

14

0.5 00

02

04

06

08

10

12

14

00

02

04

06

C31-C32

4.0

1.6

3.5

1.2

3.0 0.8 2.5 0.4

2.0 1.5

0.0 00

02

04

06

08

10

12

14

00

02

04

06

08

The aim of the study is to evaluate the intensity of energy as a factor in increasing the trade openness in the period mentioned in Turkey and to show the causality relation between the energy intensity and trade openness of manufacturing industry production. Our ultimate goal is to contribute to the assessment of the use of energy in the light of the findings and the priority areas of Turkey's international trade and sector development policies. The hypothesis of working towards this aim was formed as "the change in the sectoral energy intensity of the Turkish manufacturing industry subsectors affects the change in the trade openness ". Hypothesis was tested between 20002014 with data set covering 17 sub-sectors of Turkish Manufacturing Industry and Dumitrescu and Hurlin (2012) Causality Analysis. The prominence of energy policies is rapidly increasing with the changing social and economic structure in the world. Especially the energy needs of the developing countries are increasing rapidly. Developed countries, on the other hand, try to get rid of the energy requirements and negative environmental effects necessary for the production of these products by importing energy intensive products. When evaluating the energy consumption of a country that has been opened to a certain extent, not considering export and import will lead to wrong evaluations. Because international trade is an important element shaping the industrial structure of countries and the associated energy use. This has been investigated in the literature in the context of concepts such as 145

carbon and emission trading, and resource consumption at the production region. This study is expected to contribute to the reduction of pollution resulting from the production of energy intensive products, to the more efficient use of energy resources of Turkey and similar countries, and to the evaluation of this situation in relation to foreign trade. In addition, relatively little research on the relationship between international trade and energy use in the literature can be considered as a unique contribution to the study of the literature on the subject of manufacturing industry sub-sectors and the absence of any work on the subject for Turkey. In this frame, the rest of the paper is arranged as follows: Section 2 introduces theoretical background and empiric literature on international trade and the energy density in production relationship. Section 3 presents material and method. Section 4 yields estimation results, and Section 5 evaluates main findings and provides some policy suggestions.

2. Theoretical Background and Empiric Literature Integrating the country's economy into the international economy, in many ways, causes the economy to change. For many years, the international economic literature has examined the changes that have taken place in such fields as growth, specialization, resource allocation, employment, income distribution, productivity, technology transfers, and income distribution in outsourced economies. Studies examining the relation of internationalization to environmental variables have emerged to explore the effects of environmental protection policies implemented in the early 1970s in OECD countries. The focus of studies at that time was to investigate the effects of the industry on the collection of unregulated regions as a result of environmental regulations. Over the following decade interest in this area has decreased, but with the 1990s its popularity has increased. While some of the work suggests that free trade and globalization will reduce environmental standards, others argue that growth and income will increase due to free trade and globalization, and that environmental standards at the long run can be raised only this way. Some studies isolated from both camps have focused on exploring and modelling the link between trade and the environment, often providing arguments for both sides. (Anriquez, 2002). Total energy consumption for production within an economy arise from scale, technique and composition of production (Grossman and Krueger 1995, Copeland and Taylor 2004). The scale effect relates the size of the economy and total energy consumption. All things equal, as the economy expands, energy use increases. The technique effect identifies the energy intensity of production. As the level of internationalization of firms and industries increases, competition will increase and the use of technologies will increase productivity in many ways, including the use of energy. The third influence is about the changes in the composition of national production related with its trade patterns. All three factors are influenced by international trade volume. Frankel (2008) classified energy use and environmental impacts of international trade into income and non-income channels. Classification according to the income channel is the 146

Environmental Kuznets Hypothesis, which has been studied very differently in the literature. International trade affects energy use and environmental quality positively or negatively with non-income channels. On the negative side, open countries carry relatively lower environmental concerns than less open countries in order not to break away from international competition in general. It is less sensitive to the depletion of natural resources, the magnitude of the energy consumed and the pollution it generates, even when the energy is dependent on the outside. As a result, some countries are able to specialize in the production of energy-intensive and polluted products under the influence of external openness and export these products to countries that are more sensitive to the depletion of energy resources and the pollution they create. On the other hand, as a positive effect, openness to trade could encourage technical innovation, ratchet up environmental standards, or lead to the exercise of consumer power and adoption of corporate codes of conduct. It is thought that the increase in the external openness of Turkish manufacturing industry in the period that includes my work may be due to the above mentioned theoretical relations. On the one hand, our work will contribute to the emergence of empirical evidence for the relationship between openness of the economy and energy consumption which is theoretically different, while looking for the answer to this question. When the empirical literature on this subject is examined, it is observed that the studies generally investigate the relation between growth, foreign trade-energy consumption, growth, foreign trade-CO2 emissions. Other studies have focused on testing the existence of the Environmental Kuznets Curve. These studies have focused on exploring the relationship between per capita income and environmental quality. Studies investigating the relationship between openness and energy intensity are quite limited. Empirical studies that we think are related to our work can be listed as follows; Suri and Chapman (1998) investigated the relationship between industrialization levels of countries and energy intensive product productions. Both industrialising and industrialized countries have increased their use of energy for the production of their export products, but industrialized countries have reached the point at which they can succeed in lowering their energy demands through the import of product. The results of the panel data set for 33 countries show that there is a relationship between foreign trade and the structural change of manufacturing industry production and an international division of labour between the industrializing countries and the industrialized countries in the production of energy intensive products. Kander et al. (2016) in his study covering 1870-1935, it was shown that in the first stages of the industrialization, Britain and Germany met most of the world's demand for energy-intensive products, but after 1900’s the dirty and energy intensive production began to fall rapidly in these countries. It can be said that a similar situation applies to China nowadays. Countries increase energy use for products that they will not consume at certain stages of the industrialization process and create environmental damage. This work also points to the international division of labour in the production of energyintensive products. 147

Lin and Sun (2009) in a study of China's input-output analysis for 2005, investigated the relationship between foreign trade and CO2 emissions and supported the above studies. In fact, about 17% of China's total CO2 emissions came from production for external demand. Liu, Xi and Li (2010) between 1992 and 2005, analysed the energy intensities of export goods produced in China according to the input-output structural decomposition model. In the study, it was concluded that the increase in the embody energy in export products during the period and the increase of China's foreign trade were also the effects of the production of energy intensive goods. Kohler (2013) in a study of South Africa covering the 1960s and 1900s, used the Granger Causality Analysis method and found no significant causality relationships between South Africa's increase in trade openness and pollution-intensive or high-emission activities. Sadorsky (2011) used the Granger Causality Analysis methodology for the period of 1980-2007 for 8 Middle East countries and investigated the effects of increases in foreign trade on energy consumption. In the study, a 1% increase in per capita exports increases per capita energy consumption by 0.11%, a 1% increase in per capita imports increases per capita energy consumption by 0.04%. Ozturk and Acaravcı (2012) in their study for Turkey covering 1960-2007, Granger Causality Analysis was used and its founded that an increase in trade openness increases the CO2 emissions. Hossain (2011) in the study covering 1971-2007 for the newly industrializing countries, used the Granger Causality Analysis methodology and found one-way causality relationship from the trade openness to the CO2 emission of the country. It is also found that the variables trade openness and urbanization have negative significant impacts on carbon emissions. The theoretical relationship between international trade structure and energy use seems to be confirmed by empirical studies at large. However, findings from studies do not point to the same direction. In addition, in order to find more effective political proposals, it may be necessary to analyse the production based approach as well as the consumption based approach of the countries. Likewise, we believe that doing sectoral based analysis rather than treating the economy as a whole will contribute to making more effective political proposals. For these reasons, unlike the listed studies, our work was done on a sectoral basis. This has allowed us to examine the economy or the manufacturing industry not as a whole but on a sectoral basis, so that the sectors with different characteristics can be analysed separately. In the study, only the amount of energy used for production is considered, not total energy use.

3. Material and Method Graph 1 and related theoretical and empirical studies show that the energy intensity of Turkey's manufacturing industry may be regarded as a factor in increasing the trade 148

openness between 2000 and 2014. From this point of view, our hypothesis is formed as "the change in the sectoral energy intensity of the Turkish manufacturing industry subsectors affects the change in sectoral trade openness ". For the variables used in the model, data belonging to 17 sub-sectors (N = 17) of Turkish manufacturing industry obtained from 2000-1994 (T = 15) World Input-Output Database (2016) were used. In our model for testing this hypothesis, we used the Sectoral Trade Openness (STO), which is calculated as the ratio of the sector's foreign trade volume to output, to represent the openness of the sector, and the Sectoral Energy Intensity (SEI), which is calculated as the ratio of sector’s energy consumption to sector’s output . In order to test the dynamic causal relationship between international trade and sectoral energy density, we apply a testing procedure that involves 3 steps. At the first step cross sectional dependency and heterogeneity of parameters are investigated. The second step is to test the stability of the series with the panel unit root tests and at the last step we researched the causality relationship by the Dumitrescu and Hurlin (2012) estimation method which considers the cross sectional dependency and heterogeneity of parameters slopes. To find the most suitable estimation method, firstly, possible cross-section dependency and heterogeneity were investigated. In panel data models cross section dependence can arise due to spatial or spill over effects, or could be due to unobserved common factors. Cross sectional dependence is important in fitting panel-data models. Otherwise the estimation results might be inconsistent, inefficient and estimated standard errors might be biased. Also, homogeneity is important among the regression coefficients. Pooled methods can only applicable if homogeneity is valid. Otherwise serious deviations may be seen in the estimates. To test for cross-sectional dependence, Breusch and Pagan (1980) propose the Lagrange multiplier (LM) test statistic. Pesaran (2004) states that this test is not applicable when N is large. For large panels where T ∞ first and then N ∞, Pesaran (2004) proposes the scaled version of the LM test. CD test may present substantial size distortions when N is large and T is small. To test for slope homogeneity, Pesaran and Yamagata (2008) follow delta ∆ tests. The null hypothesis of slope homogeneity (H0: βi=β for all i) is tested against the alternative hypothesis of slope heterogeneity (H1: βi β for a non-zero fraction of pair-wise slopes for i j). When the error terms are normally distributed, the ∆ tests are valid as (N, T) ∞ without any restrictions on the relative expansion rates of N and T. Our model’s cross-section dependence and homogeneity tests results are presented in Table 1. Heterogeneity and cross sectional dependence are also important to select the appropriate unit root test. Based on our heterogeneity and cross sectional dependence tests results, we used the panel unit root test (CADF), which takes into both heterogeneous slope parameters and cross-section dependence, developed by Pesaran (2007). Unit root test results are presented in Table 2.

149

For the purpose of finding the relationship between international trade and energy intensity for the panel of 17 manufacturing subsectors of Turkey can be expressed as in equation 1. This equation is adapted form of Dimitrescu and Hurlin (2012) linear model for testing Granges causality in heterogeneous panels. STOi,t = αi +

k K k=1 i

STOi,t-k +

k K k=1 βi

SEIi,t-k + εi,t

(1)

Model allows the autoregressive parameters i(k) and the regression coefficients slopes βi(k) to differ across groups but parameters i(k) and βi(k) are constant in time. Monte Carlo experiments show that our standardized panel statistics have very good small sample properties, even in the presence of cross-sectional dependence. Thus the model with two variables constitutes the basic framework for studying Granger causality in our panel data context (Dimitrescu and Hurlin, 2012). Dumitrescu and Hurlin (2012) suggest the use of ZHnc N,T test statistic when the time dimension is larger than the cross-sectional dimension (T N), and the use of the ZHnc N test statistic when the time dimension is smaller than the cross-sectional dimension (T N). Equation 2 shows the calculation of the test statistic that is appropriate for our data set (T = 15, N = 17). ZHnc N =

(2)

4. Empirical Results The null hypothesis of cross-section independence is tested against the alternative hypothesis of cross-section dependence for all statistics. The null hypothesis of slope homogeneity is tested against the alternative hypothesis of slope heterogeneity. As seen in Table 1, both null hypothesis are rejected at %1 significance level. According to this sections are dependent and parameters slope are heterogeneous. These results are determining for the methods used for unit root testing and model estimating. Table 1: Cross-Sectional Dependence and Heterogeneity Tests Test Statistic p-value Cross-sectional dependence tests LM 246.519a 0.000 CDLM 9.766a 0.000 b CD 1.385 0.083 LMadj 13.815a 0.000 Homogeneity tests 21.355a 0.000 ∆ 15.311a 0.000 ∆adj Note: a denotes 1% statistical significance. b denotes 10% statistical significance.

Statistical values of this tests is compared with the Pesaran (2006) CADF critical table values. The null hypothesis indicates that there is a unit root while the alternative hypothesis indicates that there is no unit root. The null of unit root is rejected if CADF 150

critical table values are lower than CADF statistical values. The panel unit root tests results are given in Table 2. Test results support that panel series are stationary in first difference levels. CIPS tests statistics are higher than CADF critical table values in 1% statistical significance. Table 2: Cadf Unit Root Test Cross section/sectors C10-C12 C13-C15 C16 C17 C18 C19 C20 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31-C32 Panel (CIPS)

Test statistic energy intensity -1.273 -2.051 -0.921 -0.435 -0.388 -0.321 -0.039 -1.121 -0.875 0.518 -0.933 -1.125 0.089 0.052 -0.082 -1.023 -0.328 0.603

∆energy intensity -2.464c -3.176b -2.271 -3.351b -1.363 -1.571 -1.276 -3.062b -1.279 -2.791b -2.325c -1.412 -1.593 -1.316 -3.684a -4.118a -1.421 -2.802a

trade

∆trade

-1.872 -1.137 -2.624c -2.403c -2.179 -0.136 -2.186 -1.690 -2.460c -1.927 -2.136 -0.371 1.355 -2.272 -7.113a -2.105 1.225 -1.766b

-1.382 -2.751b -1.180 -1.758 -1.516 -2.161 -13.825a -1.773 -1.263 -2.418c -0.795 -1.121 -0.711 -2.800b -1.217 -2.542c 0.440 -2.282a

Notes: Critical values are obtained from Pesaran (2007) and Δ is the first difference operator. a Illustrates 1% statistical significance. b Illustrates 5% statistical significance. c Illustrates 10% statistical significance.

The relationship between Sectoral Trade Openness and Sectoral Energy Intensity in the study was investigated by Dumitrescu and Hurlin (2012) Causality Test. This test considers possible cross-sectional dependence and heterogeneity between sectors. Estimation results are shown in Table 3.

151

Sectors C10-C12 C13-C15 C16 C17 C18 C19 C20 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31-C32

Table 3: Dumitrescu And Hurlin (2012) Causality Test* Results (2000-2014) STO does not cause SEI SEI does not cause STO Wald stat. P-value Decision Wald stat. P-value Decision c c 2.772 0.092 reject 2.212 0.084 reject 3.264c 0.070 reject 1.876 0.131 fail to reject 1.004 0.316 fail to reject 1.407 0.238 fail to reject 2.873c 0.090 reject 5.079a 0.001 reject c 0.040 0.840 fail to reject 2.077 0.095 reject 8.453a 0.003 reject 6.617a 0.009 reject 4.991b 0.025 reject 4.840b 0.029 reject c 2.818 0.091 reject 0.686 0.560 fail to reject 2.984c 0.085 reject 0.896 0.442 fail to reject 0.846 0.357 fail to reject 1.651 0.175 fail to reject 0.199 0.655 fail to reject 0.836 0.473 fail to reject 0.200 0.654 fail to reject 1.263 0.285 fail to reject 0.382 0.844 fail to reject 1.400 0.240 fail to reject 0.001 0.969 fail to reject 1.205 0.307 fail to reject a 0.044 0.832 fail to reject 3.815 0.009 reject 4.339b 0.032 reject 3.437a 0.010 reject 0.551 0.452 fail to reject 3.431a 0.010 reject

Note:* Critical values are calculated through bootstrap approach a Illustrates 1% statistical significance. b Illustrates 5% statistical significance. c Illustrates 10% statistical significance.

In the period mentioned in Turkey, it is observed that almost all sectors, especially after 2010, have increased their energy efficiency. The existence of the causality relationship from the sectoral trade openness to the sectoral energy intensity is statistically proven in 8 of 17 sectors. Accordingly, the trade openness of the sectors has an effect on the energy intensity (productivity) of the sectors. These sectors are textiles, apparel and leather, paper and paper products, coke and refined petroleum, chemical and chemical products, rubber and plastic, non-metallic mineral products, and other transport vehicles. It is observed that the external openness of all sectors has increased during the period mentioned in Turkey. The existence of the causality relation from the sectoral energy intensity to the sectoral trade openness is statistically proven in 8 of 17 sectors. Accordingly, the energy intensity of the sectors has an effect on the trade openness of the sectors. These sectors are food and beverage tobacco, paper and paper products, printing industry, coke and refined petroleum, chemical and chemical products, motor vehicles and trailers, other transportation vehicles, furniture and other manufacturing sectors.

5. Conclusion Remarks According to findings, openness of Turkish manufacturing industry increases energy efficiency in many sub-sectors. On the other hand, the openness of some sectors is determined by energy intensities. According to this, the openness of some manufacturing industries sub-sectors in Turkey depends on their energy intensities. In

152

other words, Turkey is the subject of the international division of labour in the energy intensive products in some sectors. It is expected that the findings obtained from the study will contribute to the reduction of pollution resulting from the production of energy intensive products, to the more efficient use of the country's energy resources and to the evaluation of this situation in relation to foreign trade production. In some sub-sectors of Turkey's manufacturing industry, there is a foreign trade structure that will increase our energy needs and environmental adverse effects. This study which is conducted for a country like Turkey, with limited energy resources and increasing energy needs, on the other hand, is considered to allow for the evaluation of determining primary fields in international trade and sectoral development policies from the perspective of energy usage.

153

References Anriquez, G. (2002). Trade and the environment: an economic literature survey, University of Maryland Working papers, No: 28598. Breusch, T.S., & Pagan, A.R. (1980). The lagrange multiplier test and its applications to model specification in econometrics, Review of Economic Studies, 47, 239-253. Copeland B. R., & Taylor, M. S. (2004). Trade, growth, and the environment, Journal of Economic Literature, American Economic Association, 42(1), 7-71. Dimitrescu, E. I, & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels, Economic Modelling, 29(4), 1450-1460. Frankel, J. (2008). Environmental effects of international trade, Sweden’s Globalisation Council Expert Report, No: 11. Grossman, G.M., & Krueger, A.B. (1995). Economic growth and the environment, Q. J. Economics, 110, 353–377. Hossain, Md. S. (2011). Panel estimationforCO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries, Energy Policy, 39, 6991-6999. Kander, A., Warde, P., Henriques, S. T., Nielsen, H., & Kulionis, V. (2016). International trade and energy intensity during European industrialization, 1870-1935, Ecological Economics, 139, 33-34. Kohler, M. (2013). CO2 emissions, energy consumption, income and foreign trade: A South African perspective, Energy Policy, 63, 1042-1050. Lin, B., & Sun, C. (2009). Evaluating carbon dioxide emissions in international trade of China, Energy Policy, 38, 613-621. Liu, H., Xi, Y., Guo, J. & Li, X. (2010). Energy embodied in the international trade of China: An energy input–output analysis, Energy Policy, 38, 3957-3964. Ozturk, I., & Acaravcı, A. (2012). The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey, Energy Policy, 36, 262-267. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure, CESifo Working Paper Series No: 1331. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence, Journal of Applied Econometrics, 22, 265-312. Pesaran, M.H. & Yamagata T. (2008). Testing slope homogeneity in large panels, Journal of Economics, 142, 50-93. Sadorsky, P. (2011). Trade and energy consumption in the Middle East, Energy Economics, 33, 739-749.

154

Suri, V., & Chapman, D. (1998). Economic growth, trade and energy: implications for the environmental Kuznets curve, Ecological Economics, 25, 195-208. World Input-Output Database (2016) http://www.wiod.org/release16. World Bank (2014) http://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE

155

Appendix A: Sectoral Codes (ISIC Rev. 4) Codes C10-C12 C13-C15 C16 C17 C18 C19 C20 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31-C32

Sectors Food products, beverages and tobacco products Textiles, wearing apparel and leather products Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials Paper and paper products Printing and recording services Coke and refined petroleum products Chemicals and chemical products Rubber and plastics products Other non-metallic mineral products Basic metals Fabricated metal products, except machinery and equipment Computer, electronic and optical products Electrical equipment Machinery and equipment n.e.c. Motor vehicles, trailers and semi-trailers Other transport equipment Furniture; other manufactured goods

156

CHAPTER X AN OVERVIEW OF DEVELOPMENT STUDIES AND POLICIES RELATED TO GEOTHERMAL ENERGY IN TURKEY* Mustafa KAN1, Arzu KAN2 & Hasan Gökhan DOĞAN3 1. Introduction The most important input of production is energy. As technological developments and increasing populations increase production, demand continues to increase every day in the world. Following the industrial revolution, with increasing energy needs, the countries have constantly updated their energy policies, both in meeting their own consumption and taking their place in the world power wars. Each country is now forming compete position with the extent to which it can supply energy, which is now the most important source of production. For this reason, energy is one of the most important elements in development policies today. Today, the energy policies mainly refers the energy policies obtained from oil and natural gas. Energy is, in fact, an important issue that should not be considered on its own and interacting with all politics. Any decision to be taken in the field of energy directly affects the development of the sectors (agriculture, industry and services) that form the triple hair leg of the economy. How the easy and cheap to get energy which is the main source of the production, the competition on the production will be easy. For that reason, countries are trying to achieve energy diversity in order to minimize both the risk of energy supply and the reduction of energy costs, in other words to ensure energy sustainability. With a rapidly growing economy, Turkey has become one of the fastest growing energy markets in the world. Turkey has been experiencing rapid demand growth in all This work was supported by Ahi Evran University, Scientific Research Projects Coordination Unit. Project Number: ZRT.E2.17.021, Kırsehir-TURKEY 1 Corresponding Author. Asst. Prof. Dr., Ahi Evran University, Agricultural Faculty, Department of Agricultural Economics, Kirsehir, Turkey, [email protected] 2 Asst. Prof. Dr., Ahi Evran University, Agricultural Faculty, Department of Agricultural Economics, Kirsehir, Turkey 3 Asst. Prof. Dr., Ahi Evran University, Agricultural Faculty, Department of Agricultural Economics, Kirsehir, Turkey *

157

segments of the energy sector for decades. Over the last decade, Turkey has been the second country, after China, in terms of natural gas and electricity demand growth. The limits of Turkey’s domestic energy sources in light of its growing energy demand have resulted in dependency on energy imports, primarily of oil and gas. At present, around 25% of the total energy demand is being met by domestic resources, while the rest is being provided from a diversified portfolio of imports (Ministry of Foreign Affairs (MFA), 2017). It is essential for Turkey to substitute for oil and natural gas, which we depend externally in terms of production within the energy supply, to establish long-term sustainable production strategy and to take more place in renewable energy resources that are more friendly with the environment. Turkey, which has a significant potential in terms of renewable energy, ranks seventh in the World and first place in Europe with geothermal potential (Satman, 2007; Akçin, 2015; Akyüz, 2015; MFA, 2017). Priority is also given to the development of hydropower, wind and solar energy, as well as the mentioned energy source. In this context, according to the "National Renewable Energy Action Plan of Turkey" published by the Ministry of Energy and Natural Resources in December 2014, Turkey is aiming to increase total capacity from renewables to 61,000 MW by 2023. 34,000 MW of this total installed capacity will be composed of hydropower; 20,000 MW of wind power, 1,000 MW of geothermal, 5,000 MW of solar and 1,000 MW of biomass energy. Thus, it is targeted that 30% of total electricity production will be covered by renewable energy (Ministry of Energy and Natural Resources (MoENR), 2014). The high cost of obtaining renewable energy types in general, the difficulty of storing the energy obtained intermittently/continuously from many of them, and the limitation of the renewable energy infrastructure, prevent the widespread use of renewable energy in the world. However, due to increasing awareness of global warming and environmental issues and improvements in energy production and transmission technologies, it is expected that demand for renewable energy sources will increase further in the coming years (Bayraç, 2009). On the other hand, Turkey was one of the founding members of the International Renewable Energy Agency (IRENA), an agreement signed at the end of the conference held in Bonn on January 26, 2009, as a prelude to the development of renewable energy resources. It shows that Turkey is proceeding with great determination to be an energy base both the plans with its strategy documents and it’s progress towards building an energy corridor. The most important question to be asked here is how effectively Turkey can use its own energy production to become the energy base. Geothermal resources, which are one of the most important sources that Turkey possesses, are ground heat, hot water, steam, and gases containing chemicals that accumulate in various depths of the earth's crust. Geothermal energy includes all kinds of direct or indirect benefits from geothermal sources. Geothermal energy is a new, renewable, sustainable, inexhaustible, cheap, reliable, environmentally friendly, domestic and green energy (Ministry of Development (MoD) 2015). Geothermal 158

resources are available in 5% of the world's area. While this generation is called the fire ring, Turkey is located on this fire ring. For this reason, Turkey is one of the lucky countries using geothermal energy in the world (Kılıç and Kılıç, 2009). Within the scope of energy diversification, geothermal resources and their use are important in the concept of clean energy and renewable energy. The only site in Turkey where geothermal resources are transformed into energy is in the Aegean region. Turkey has a world-wide potential in terms of geothermal resources and is the first in terms of resource potential among European countries and the third in terms of spa applications. Thermal waters from over 1,500 sources in Turkey located on an important geothermal belt and a young mountain chain called Alpin-Orogenic Belt, have superior qualities than thermal water in Europe in terms of both flow and temperature, and various physical and chemical properties. Having more than 1,500 sources with temperatures ranging between 20-110 0C and 2-500 lt/sec, Turkey is considered among the top seven countries in terms of resource richness and potential. (Ministry of Culture and Tourism (MoCT), 2017a). Geothermal resources are both a source of electricity and heat, and a potential for health tourism, making these resources a multifunctional value. Geothermal resources constitute an important part of the tourism revenues especially in terms of health tourism other than energy. Health tourism is one of the important alternatives and has become a worldwide industry with very rapid growth in recent years. Among the alternative tourism types that Turkey can develop within the scope of tourism product diversification strategy, thermal tourism is in the first place with the features such as being for health purpose, being able to be done all year, being able to integrate with other tourism types, spreading to different regions, length of stay and high average occupancy rate of thermal tourism facilities (Emir, Soyabalı, Baltok, 2008). In this study, evaluation of the change in renewable energy policies within Turkey's energy policies within the scope of sustainability, the potential of geothermal resources for both renewable energy and other uses, and the utilization efficiency of geothermal resources within the scope of existing agriculture, energy, environment and tourism policies have been evaluated.

2. Changes in Development Policies in Turkey (From Traditional Development to Sustainable Development) Despite the multiple definitions of the concept of development, development in general terms is defined as increase of production an per capita national income, development of the people's value judgments in world standards, changes in socio-cultural and economic structure (Korkmaz ve Taşlıyan, 2012). Development is the highest point that every country wants to eventually reach. Development refers to the development of a society with economic, social, environmental and even cultural dimensions. Each country has reached its desired level of development by maximizing social utility by using its internal dynamics. Social benefit is the optimization of all internal and external factors except individuality. In other words, the maximum point of social benefit is the 159

balance point, where production and environment are the most important elements. In other words, it is the balanced use of capital and natural resources within the production factors. At this point, the concept of "sustainable development" emerges. Sustainable development policies that provide the formation of global economic and social development are now one of the priorities of the 21st century world. The concept of sustainability is now in every politics. For the first time in the world, the concept of "sustainable" was used in the Bruntland Report, formally prepared in 1987 by the United Nations World Commission on Environment and Development, which seeks solutions to the subjects such as removal of poverty, equal distribution of benefits from natural sources, population control and development of environmentally friendly technologies in accordance with Sustainable Development Goals. In the Report, the concept of sustainability refers to "the use of existing resources in a way that will lead to future generations" (United Nations (UN), 1987). The concept of sustainability is often used in many areas and is defined as a participatory process in which the social, cultural, scientific, natural and human resources of the society are cautiously used and created a social look on the basis of respect for it. (Gladwin, Krause, Kennelly, 1995). The basic characteristic of the concept used in different fields is that it covers the human future and the preservation of the resources of the area in which it is used. From this point of view, it is seen as the concept that combining economics, social justice, environmental science and management, business management, politics and law. At the same time, it is defined as a dialectical concept that embraces rights, democracy, honesty and other important concepts. (Wilson, 2003). In this direction, discussions are being made on the three dimensions of the concept so that sustainable development can be successful. These; Economic, Social and Environmental Dimension (Haris, 2000; Demirayak, 2002; Gürlük, 2010; Ergün ve Çobanoğlu, 2012; Tıraş, 2012). Economic Dimension: It concerns the use of scarce resources. An economically sustainable system is a system that provides sustainability of internal and external debts, being able to produce according to the principles of continuity of goods and services and avoids sectoral imbalances that damage agricultural and industrial production. Social Dimension: Human-focused. A socially sustainable system is a system that can ensure the adequacy and equal distribution of social services, such as education and health, gender equality, political responsibility and participation. Environmental Dimension: Biological and physical systems are predicted to be balanced. The aim is to ensure that ecosystems are adapting to changing conditions. An environmentally sustainable system should abandon from exploitation of environmental investment functions or renewable resource systems and consume that have been adequately replaced by investments only from renewable sources by keeping the resource base stable. This system also includes the conservation of biodiversity, 160

atmospheric balance and other ecosystem elements that can not be classified as economic resources. The concept of "sustainable development" has been used in Turkey since 2000 with the concepts of "sustainable growth" and "sustainable economy" in basic strategy, policy and plan documents. Concepts such as sustainable agriculture, sustainable energy, sustainable use of natural resources, sustainable rural development, sustainable cities, sustainable transport are also frequently used. The area in which Turkey can best analyze the changes and developments in the development process is the Five-Year Development Plans. Development Plans are the basic policy documents, in which state policies are defined, forming the basic framework for orienting the Turkish economy and set out measures to realize industrialization, economic and social development. When the process of development rhetoric in the Five-Year Development Plans-at the last 10th Five-Year Development Plan has been published (2014-2018)- is examined, since the publication of the First Five-Year Development Plan (1963-1967) in 1963, it appears that the policies created within the framework of economic-environmentsociety interactions have been dealt with in different ways. When the Five-Year Development Plans prepared by the Ministry of Development (formerly the State Planning Organization) are examined, the change and development of sustainable development policies in Turkey over time can be observed. The reflection of environmental protection trends in the global sense has been taken up for the first time in Turkey in the 3rd Five Year Development Plan. After the Stockholm Conference in 1972, environmental issues have been given a separate place as a demonstration of the development of environmental consciousness of Turkey in the Plan (1973-1977) (Egeli, 1996). In 1979 Year Programme, published in the Official Gazette dated 19 December 1978 and numbered 16494, the establishment of an environmental pollution inventory for Turkey was accepted as a principle but the preparation of environmental status reports and the creation of environmental inventories within the framework was added to the agenda after establishment of Environment Inventory Department by the within the Ministry of Environment in 1991 (Ministry of Environment and Urbanisation (MoEU), 1993). The approach, predominantly addressed at the 1992 Rio Conference, aimed at sustainable development first came to the forefront in the Sixth Five-Year Development Plan (1990-1994). During the 6th Five-Year Development period, Undersecretariat of Environment, which could not catch up with industrial development, left its place in 1991 to the Ministry of Environment (Okumuş, 2002). Special Environmental Protection Offices in 6 provinces were structured as headquarters affiliated to the center (Altunbaş, 2004). In the same period, "the prevention of the waste of human and natural resources and the protection of the environment in the conduct of economic and social activities" was adopted as a principle and thus the sustainable development concept was included in the main objectives and policies of the Plan. One of the important features of the plan was to provide an incentive to invest in prevention of environmental pollution as an important link in the context of environment and economy. In the Sixth Five-Year 161

Development Plan, it is seen that the measures for environmental and social development take place in the development targets and policies of the main economic sectors (MoD, 1990). During this period, implementation of the Local Agenda 21 action plan was begun. (Erim, 2000). Turkey was one of the first countries to sign the UN Convention on Biological Diversity (UNCBD) at the Rio Summit in 1992. In addition, Turkey signed the UN Convention on Combating Desertification (UNFCCC) in 1994 with the signing of the Agenda 21 document. The main areas of change in the Seventh Five-Year Development Plan (1996-2000) were the development of human resources, integration with agriculture, industry and the world, increasing the efficiency in the economy, ensuring regional balances and protecting and developing the environment. It was emphasized that the importance of the integration of environmental politics into all economic and social policies was getting increase under the framework of sustainable development approach. In the section "Protection and Improving of Environment" of the Plan"; "In keeping with the sustainable development approach, the basic strategy was to manage the natural resources in a way that will enable continuous economic development while preserving human health and natural equilibrium, and leaving a natural, physical and social environment worthy of coming generations". The strategies to prevent pollution by the measures to be taken, instead of passive approaches that predicted pollution in the development process and tried to refine this pollution was prioritized. The inclusion of environmental and development indicators in decision-making processes is also a major concern in the Plan. In addition, to measure the sustainable development approach was added to the agenda with the measures to start the internalization studies of the protection and development dimensions of the economy in the national income accounts. Policies for sustainable development have been developed in many sectors besides the environment sector (MoD, 1995). The main aim of the Eighth Five-Year Development Plan prepared for the years 20012005 can be summarized as the fact that Turkey gets more share from the world output, acceleration of the integration with the world in the perspective of European Union membership and increasing the quality of life of the society. The main thing here was to realize an uninterrupted growth process during the Plan period. Ensuring sustainable development through the development of a competitive economic structure in the strategy of the plan was identified as a priority. Measures to be taken in relation to the environment in the Plan seem to be related to the increase of competition power of economy for the first time (MoD, 2000). The Ninth Development Plan (2007-2013) was prepared in a quality that prioritizes problems and sets out strategies and targets in this frame, taking macro balances and shapes institutional and structural regulations so as to enable more efficient functioning of markets. The Ninth Development Plan was prepared for a period of seven years, not a period of five years, unlike the other Development Plans. While determining the structure, implementation approach and period of the plan, it was taken into consideration that Turkey's future development strategy and policies were compatible 162

with the legal, institutional and more important financial arrangements of the European Union. Under the development axes, what was to be done in the sectors and areas that would make the most contribution to the priority and development efforts in the Plan were listed as "policy priorities". The principle of "preservation of natural and cultural assets and the environment in a way that considers future generations" took place among the basic principles of the Plan. (MoD, 2006). The Tenth Development Plan (2004-2018) has been designed to cover elements of high, stable and inclusive economic growth as well as the rule of law, information society, international competitiveness, human development, protection of the environment and sustainable use of resources. In the Plan, Turkey's economic and social development process has been dealt with from a holistic and multidimensional point of view and a participatory approach has been adopted within the framework of human-oriented development approach (MoD, 2013a). In summary, in the Development Plans, while at the firstly, environmental policies were based solely on mitigating pollution, after that, preventive policies come the front, and with the Seventh Five-Year Development Plan, the policies were appeared that prioritizing the integration of the environment and the economy in accordance with the concept of sustainable development. Together with the Tenth Development Plan, Turkey's steps towards achieving its 2023 targets has showed that growth is at the forefront, even though it was taken steps towards the development of environmental awareness in development and environmental policies, and integrated into policies. The absence of Turkey's commitment to reduce emissions for greenhouse gas emissions, particularly in relation to climate change, for the period 2013-2020 (MoEU, 2017) is a sign that Turkey will make more complementary moves in the near future.

3. Developments in Sustainable Energy Policies of Turkey Sustainability in energy cannot be ignored, which is the most important input that comes to mind when sustainability is mentioned in production. In order to follow sustainable development policies, a holistic approach to integrating economic, social and environmental dimensions needs to be developed. The issue of energy security at the crossroads of these dimensions is defined as a matter of national security for many developed/developing states, as well as being vital for achieving sustainable development goals. Given the link between energy and economic growth, energy in the country's politics plays a major role. At a point of independence of the countries, it is determined by "potential to meet its own energy". An energyless country politics has been unthinkable because industry cannot be without energy, prosperous and happy societies or the ability to preserve independence cannot be without industry. The energy crises that began in 1973 with the OAPEC countries (Organization of Arab Petroleum Exporting Countries) not exporting oil, continued with the Oil Crisis in 1979, so the 1980s actually showed how important energy policies are in the development of countries. The 1980s have been an important process for re-observing countries' energy policies and consumption. For example, with the "New Energy Policy Strategy" program 163

adopted by the European Council in September 1974 after the first oil crisis in 1973, it adopted a policy of raising consumption to a reasonable level, increasing supply security and protecting the environment in energy production and consumption. Thus, this crisis was caused the Community to set a strategy for the first time in energy policy. Subsequently, in the 1980s, environmental pollution emerged mainly as a consequence of the burning of fossil fuels. In recent years, energy use, greenhouse gas emissions and their potential impacts on global climate change are among the most debated topics. One of the most effective ways to reduce energy use in industry, transport, commerce, housing and agriculture is to increase energy efficiency. In today's industrial world, the use of energy and other resources has reached a significant level. For this reason, on the one side providing the natural resources has begun to decrease, and on the other side, the damages on nature such as environmental pollution have been getting increase. When energy is called today, energy obtained from oil and natural gas comes first. The increasing need for energy with industrialization has led to an increasing adverse effect of energy supplied by oil and natural gas on the environment. In addition to the scarcity of energy resources and reserves, the greenhouse effect resulting from global warming and climate change require the production of energy policies at national and international levels, taking into account the interests of future generations. (Bayraç, 2009). Today's most important goal of developed and developing countries is to ensure sustainable development. Energy is closely related to all of the economic, social and environmental dimensions of sustainable development, and is also an extremely important parameter of internal and external politics. As recent developments in the world have clearly shown, the provision of energy supply security is much more influenced by world politics today than it is in the past. It is clear that the energy policies of today's energy sources and the possibilities offered by the technologies and the onedimensional approaches to energy politics are incompatible with a sustainable energy future. It is not possible to solve the problems related to energy, which have many different dimensions such as economic, social and environmental, by dealing with reducible or one-dimensional approaches by reducing the base excessively. For a reasonable solution, however, an approach that evaluates the complexity caused by different dimensions can help. For this reason, it is imperative to find an optimal solution that addresses all of the problems of different dimensions by considering energy problems in a holistic framework (Saygın, 2004) The most important way of ensuring energy security is ensuring diversity. Diversification is achieved in a number of different ways, including diversification of supply mechanisms (domestic and imported supply, network production and local production balancing), supply countries and energy lines as well as energy sources and technologies. In order to ensure sustainable development, the most appropriate mix of energy and geopolitical factors must be determined without ignoring the specific conditions of the countries and/or regions. (Saygın, 2004). 164

By the time of the recent period, the criterion of the sustainability of the energy system was related to the extent to which consumption was overcome by energy supply. Nowadays, importance is given to the scientific and ethical aspects of sustainable development in terms of energy security and environment security. Global climate changes, particularly those caused by carbon emissions, are at the heart of the sustainability of energy policies. For this reason, the transition to the low-carbon economy has become a focal point of debate about energy politics today (Saygın, 2006). With the impact of environmental problems, significant changes are taking place in terms of preference for energy resources all over the world. Parallel to the whole world, there are also significant changes in the choice of energy sources in Turkey. Turkey has experienced significant developments in the way of becoming an energy base with the moves it has made. Turkey has become a focal point not only with the movements towards oil and natural gas but also with the moves made to find and use renewable energy resources. It is not sufficient to explain only the environmental factors that explain the changes in Turkey's energy source preference, which aims to increase the share of renewable energy resources among the 2023 targets seriously. Especially, we are still a serious importer of oil and natural gas and the large share of energy in the external deficit plays a serious role in giving importance to Turkey's own internal reserves. Turkey has taken an important step towards sustainability in the field of energy, declaring on May 13, 2009 that it was formally a part of the Kyoto Protocol to the UN Climate Change Covenant Convention, as of 26 August 2009. As is known, the energy sector and all its actors have a much more controlled and disciplined structure compared to other sectors in Turkey. In this sense, the Environment Law No. 2872 and the Law No. 5346 on Renewable Energy have also identified the legal framework of energy sector's sustainability. In particular, the incentives and regulations to increase the share of renewable energy sources in total energy production within government plans and increase investments towards the renewable energy sources in the last 10 years in Turkey are important developments which show that Turkey will not be dependent on oil and natural gas in the future. Within the scope of sustainable and reliable energy supply activities for consumers, Turkey give the attractive incentives to inventors such as tariff guarantee, purchase guarantee, connection priorities, license exemption, etc. depending on the capacity and capacity of the power generation plant.

4. Interaction of Geothermal Source Usage Policies with the other Policies (Energy, Climate, Agriculture, Tourism, and Health) and Supports Today, as private sector investments have increased rapidly, geothermal resources are the most important in Turkey with its multifunctionality in renewable energy sources. In terms of geothermal resources, Turkey is a country with rich reserves (Map 1). Especially geothermal resources, which are concentrated in the west of Turkey, have spread widely in Turkey. 165

Map 1. Nanotectonics-volcanic activity and geothermal fields in Turkey (MoENR, 2017)

Being able to be used in agriculture, energy, tourism and health sectors and having multifunctionality feature does not allow the use of geothermal resources to be evaluated with a single policy. For this reason, geothermal policies are closely related to the energy, agriculture, tourism and health sectors. With the introduction of the concept of sustainability within the policies established in all sectors in Turkey, sources such as geothermal resources, wind, and hydraulics have become the rising trend of today. The energy sector is in the first place for the development of the countries and even for their independence. Today, the crises of the Gulf, Middle East or Afghanistan, which can be described as oil wars, show that energies have international independence side as well as industry needs. Within the 10-year period of the Organization for Economic Cooperation and Development (OECD), Turkey has emerged as the country with the fastest increase in energy demand. Likewise, Turkey has become the second largest economy in the world, with the highest rate of increase in electricity and natural gas since 2002 after China. Turkey's stronger position depends on expanding its energy portfolio and increasing energy efficiency with investments. In order to grow within sustainable development goals, it is necessary to use renewable energy resources effectively for the energy sector. In recent years, many regional and national and international policies and campaigns are being implemented to increase the use of renewable energy resources around the world. Particularly, one of the most important issues on the agenda in this new policy, in which climate change is forefront, is about increasing the share of renewable energy sources in total energy resources and energy efficiency. When the 2015 World Energy Outlook is examined, it is expected that the share of renewable energy sources in 2040 will be 12.8% according to the current policy scenario, 15.7% according to the new policy scenario and 25% according to the 450 ppm scenario. It is planned to increase the proportion of renewable energy sources with an increase of about 6%, when the current share of current renewable energy use is thought to be 19% (WEO, 2015). Today around 166

the world, about $ 150 billion in renewable energy sources are supported, 80% of which is given to electricity generation, 18% to the transport sector and about 1% to heating. Estimates suggest that after climbing to a peak of around $ 230 billion per year in the 2030s, this figure is expected to fall in a decreasing trend with the decline in costs and the increase in electricity prices paid by the end user (World Energy Outlook (WEO), 2016). When we analyze the distribution of Turkish electricity energy production according to primary energy sources, the electricity energy we produce from thermal power plants in total electricity production by the end of September, 2016 is 66,2%. In this ratio, the first order is coal-fired power plants with 32.44% rate, followed by natural gas + LNG-based power plants with 32.4% rate. Hydraulic plants are followed by 26.2% share to thermal power plants. As of the end of September 2016, the share of electricity produced in wind power plants increased from 3.4% to 5.6% and the share of geothermal energy increased from 0.9% to 1.72% (Table 1). Especially the rise of geothermal energy by years is striking. Geothermal energy production, which was 436 GWh in 2008, increased to 4214 GWh in 2016 and is expected to rise to 4488 GWh in the estimation of the first quarter of 2017 (Enerji Atlası, 2017). This uptrend is an effect of support and desire for renewable energy resources investments in Turkey. Table 1. Distribution of Turkish Electricity Energy Production According To Primary Energy Sources (GWh) 2014 2015 2016 September Share in Share in Share in Electricity Electricity Electricity Primary Energy Total Total Total Production Production Production Source Production Production Production (GWh) (GWh) (GWh) (%) (%) (%) Coal + Imported 39,647 15.7 44,830 17.12 38,419 18.88 coal + Coal Asphaltite Lignite 36,615 14.5 31,336 11.97 27,594 13.56 Fuel-Oil 1,663 0.66 980 0.37 1,305 0.64 482 0.19 1,244 0.48 Liquid Diesel Fuel Fuel LPG Naphta 20 0.01 Natural Gas+LNG 120,576 47.85 99,219 37.90 65,929 32.40 Renewable+Waste 1,433 0.57 1,758 0.67 1,505 0.74 Thermic 200,417 79.54 179,366 68.52 134,773 66.23 Hydraulic 40,645 16.13 67,146 25.65 53,305 26.20 Wind 8,52 3.38 11,652 4.45 11,318 5.56 Geothermal 2,364 0.94 3,424 1.31 3,506 1.72 Solar 17.4 0.01 194 0.07 589 0.29 TOTAL 251,963 100.00 261,783 100.000 203,491 100.00 Source: MoENR, 2016

While the electricity energy market in Turkey was in the hands of the public at the beginning of the year 2000, it has been currently being directed by the private sector 167

with a rate of 83.1%. It can be said that the private sector has increased its weight especially after 2008 (Table 2). Within this increase, it is important that the share of energy investments obtained from wind and geothermal sources in renewable energy investments. Table 2. Distribution of Turkish Electricity Energy Production According To Producer Establishments and Sources(GWh) 2016 Institutions 2002 2009 2012 2014 2015 September Thermic 50,924 61,115 52,264 47,369 20,355 9,097 EUAS and Hydraulic 26,304 28,338 38,311 23,100 34,964 25,392 affiliated Geothermal 105 0 0 0 0 0 institutions Total 77,333 89,453 90,575 70,469 55,319 34,489 Thermic 44,640 95,808 122,608 153,048 159,012 125,676 Hydraulic 7,380 7,620 19,554 17,545 32,181 27,913 Wind 48 1,495 5,861 8,520 11,652 11,318 Companies Geothermal 436 899 2,364 3,424 3,506 Solar 0 0 0 17 194 589 Total 52,068 105,359 148,922 181,494 206,463 169,002 Thermic 95,564 156,923 174,872 200,417 179,367 134,773 Hydraulic 33,684 35,958 57,865 40,645 67,145 53,305 Wind 48 1,495 5,861 8,520 11,652 11,318 Total Geothermal 105 436 899 2,364 3,424 3,506 Solar 0 0 0 17 194 589 Total 129,401 194,812 239,497 251,963 261,782 203,491 Source: MoENR, 2016

At the end of 2014, it was reported that geothermal power plants had 12640 MWe installed power (Bertani, 2016) and geothermal energy direct utilization power had 70885 MWt (Lund and Boyd, 2016). In the 2009-2014 data period, the first five countries with the absolute increase in MWe were Kenya, the United States, Turkey, New Zealand and Indonesia (Bertani, 2016). The five countries with the highest installed capacity for direct use (including heat pumps) are China, USA, Sweden, Turkey and Germany (Lund and Boyd, 2016). The total installed capacity of 32 Geothermal Power Plants located in Turkey is 851 MWe. It is expected that this power will reach 1700 MWe together with existing and invested geothermal power plants (Table 3). In 2009-2017 period, there was a great increase in the ratio of total consumption to geothermal power generation. The ratio, which was around 0.23% in 2009, rose to 1.80% in 2017. This is an indication that geothermal resources will play an even more active role in the future over the course of energy diversification (Enerji Atlası, 2017).

168

Table 3: Geothermal Power Plants Profile Active Power Plants Number 32 Installed Power 851 MWe Share in Total Installed Power 1.08% Annual Electricity Production ~ 4,600 GWh Rate of Production to Consumption 1.77% Geothermal Power Plants Installed Capacity and Project Capacities Situation Power (MWe) Enable 836 Ongoing Installation 109 Production license received 213 Production pre-license received 292 In the project phase 236 TOTAL 1,700

Rate 49.20% 6.40% 12.50% 17.10% 13.90% 100%

Source: Enerji Atlası, 2017.

The most important geothermal power plants in Turkey are concentrated in the western regions (Marmara and Aegean Regions) in which Aydın, Denizli, Manisa, İzmir, Canakkale, Afyonkarahisar provinces are located (Map 2 and Map 3). The geothermal resources in other regions are mainly directed to use in thermal tourism, heating and other areas due to the fact that the thermal grades are lower. When the maps published by General Directorate of Mineral Research and Exploration are examined, it can be seen that geothermal resources are found in many areas of Turkey. It is important that the necessary investment is directed and the support mechanisms are established in order to use and evaluate this potential more effectively. Map 2. Geothermal power plants and potential areas in Turkey (General Directorate of Mineral Research and Exploration (GDMRE), 2017)

169

Map 3. Geothermal power plants installed in Turkey and some characteristics (GDMRE, 2017)

The public has been busy for many years the fact that fossil fuels such as oil, natural gas and coal will be consumed in the not too distant future when human history is taken into account. The availability of new reserves in the Middle East and Central Asia will only delay this process for a while, but will not bring a lasting solution to the energy problem waiting for the world. Especially after the oil crises of 1973 and 1979, people have turned to sustainable alternative energy sources whose resources are in the nature. Many countries have realised to the inconveniency of external dependence on a vital subject like energy by the effects of these crises. For this reason, support mechanisms for increasing the share of renewable energy resources in total energy production are being carried out. Various mechanisms have been developed in many countries to promote the use of renewable energy resources. These can be grouped under three main headings (Uluatam, 2010; Abolhosseini and Heshmati, 2014): 

incentives that set price and quantity obligations,



Cost-cutting investment policies,



Public investments and incentives for the development of the renewable energy market.

The incentives that bring price-setting and quantity obligations consist mainly of feed-in tariff and renewable energy portfolio standards (RPS). According to this, although the tariffs for purchase guarantee vary from country to country, it is mainly based on the purchase of electricity produced by renewable energy sources by the state through 170

electricity distribution companies at a price determined by the state. It is envisaged that a defined amount of electricity produced in RPSs in a particular region or country will be produced from renewable energy sources. This quota system can be a capacity-based standard that means a certain amount of production until a specified time, or it can be a power generation-based standard that corresponds to a certain percentage of electricity generation. In addition, in some countries, there are also tenders that allow producers to earn a certain amount of electricity from the renewable energy source. In addition, Green Energy Certificates granted to the producers provide concessions to their owners (Uluatam, 2010). Subsidies and reductions constitute a cornerstone of cost-cutting investment policies. Another method is tax deduction. These include investment tax credits, accelerated depreciation, production tax credits, property tax credits, income tax incentives, VAT exemptions, environmental tax exemptions, import tax reductions, grants, equipment credits and similar applications. These incentives are applied to small-scale individual installations as well as to large-scale investments. Therefore, such policies are not only directed at investors who represent supply in the energy market, but also at consumers who represent demand (Uluatam, 2010). Finally, public investments and incentives for the development of the renewable energy market are made up of infrastructure policies, including funding for public benefit, construction and design, site determination and permits, equipment standards, contractor certification and network connectivity. However, renewable energy legislation, where bureaucratic obstacles are reduced to a minimum, can be counted as such incentives (Uluatam, 2010). When the incentives for investments made in Turkey are examined, it can be said that the most important support mechanism started with the Law No. 5346 dated 10 May 2005 on "Renewable Energy Sources". This law is the first legal regulation concerning this issue in Turkey. Renewable Energy Sources Supporting Mechanism-RESSM is an important support for renewable energy. RESSM, which is especially designed to make installation of power plants based on renewable resources attractive, is a very effective support system and this mechanism lies behind the fact that solar, wind, geothermal and biomass investments in Turkey have a very wide range of interest. RESSM is guaranteed of buying the electricity a certain price for 10 years by the state for power plant constituent powered by water, wind, solar, geothermal and biomass. The prices to be applied to the facilities located at RESSM are determined by the Renewable Energy Law and are 7.3 US cents / kWh for production facilities based on hydroelectric and wind energy, 10.5 US cents / kWh for production facilities based on geothermal energy, and 13.3 US cents/kWh for production facilities based on biomass and solar energy. This support shall be applied to the production facilities within the scope of this Law for a period of 10 years, which has entered into or will be in operation from the effective date of the Law, 18/5/2005 to 31/12/2020 (Resmi Gazete, 2013).

171

By the year 2017, a total of 29 operators in total 647operators who apply for RESSM support are producing and selling electricity from geothermal sources. The installed capacity of these enterprises is 752.11 MWe, corresponding to 4.32% of the installed capacity of the total supported enterprises (Enerji Enstitüsü, 2017) Although the private sector investments increased rapidly especially in the electricity generation applications after the legal amendments made in 2007, the percentage of domestic technology usage remained low in the investments made. On the other hand, although geothermal energy is known as environmentally friendly, it is also important to determine the chemical structure of the produced fluid and the environmental effects of the gases produced with the fluid. In this context, there is a great need for R&D studies on geothermal energy technologies in order to develop domestic technologies and to reduce the environmental effects of the produced fluids. TÜBİTAK-ARDEB (The Scientific and Technological Research Council of Turkey-Research Support Programs Directorate) Renewable Energy Resources Call Program "1003-ENE-YENI-2017-1 Geothermal Energy Technologies" has been opened and the accepted projects will be started to be supported with the aim of supporting R & D activities within this scope (TÜBİTAK, 2017). In addition, the European Union's Horizon 2020 Program, which Turkey is involved in, is another support program that can be utilized in the field of geothermal energy. This program is a suitable support program aiming to create a green and competitive economy that is "Sustainable Growth" in the targets of Eropean Union (EU) 2020. GEOTHERMICA, a project participated by TUBITAK within the scope of European Union Horizon 2020 Program, is to establish research and development partnerships among the European countries in the studies carried out by the industry related to geothermal energy and to increase industrial applications related to geothermal energy. Under GEOTHERMICA, an international call for support of R & D projects in the field of geothermal energy has been opened. It has been stated that total 33 million Euro (25 million Euro contribution of 15 countries4 and 8 million Euro contribution of European Comission), will be supported for the research and partnerships in the following issues5; 

Determination and evaluation of geothermal resources, reserves and reservoirs,



Development of geothermal resources and reservoirs,



Supply and Integration to intelligent energy systems,



New approaches to geothermal operations.

In recent years, environmental factors have begun to occupy more and more space in energy politics. Turkey, which is a member of the Organization for Economic Cooperation and Development (OECD), has been placed both in Annex I and Annex II of the United Nations Framework Convention on Climate Change (UNFCCC), together with Belgium, Denmark, France, Germany, Iceland, Ireland, Italy, Netherlands, Portugal, Portugal/Azores, Romania, Slovenia, Spain, Switzerland, Turkey 5 Deadline: 10 Jully 2017, Project Starting Date: 01 May 2018 4

172

developed countries. While supporting the purpose and general principles of the UNFCCC, Turkey, which was not a party to the UNFCCC due to its unfair position, struggled for a long time to change this position. Following the decision in the 7th Conference of the Parties held in Marrakech, Morocco in 2001, Turkey was removed from Annex-II and given special conditions in Annex-I and than Turkey became a party to the UNFCCC on May 24, 2004 and the Kyoto Protocol on August 26, 2009 (MoEU, 2017). As UNFCCC Annex I Party, Turkey has an obligation to develop and implement policies to combat climate change and to inform UNFCCC of the relevant greenhouse gas emissions and emissions. On the other hand, Turkey does not have a commitment to reduce greenhouse gas emissions during the first (2008-2012) and second (2013-2020) periods of the Kyoto Protocol (MoEU, 2017). Turkey's national vision for climate change is to be a country integrating climate change policies with development policies, promoting energy efficiency, increasing the use of clean and renewable energy resources, actively participating in the combating against climate change with special conditions and offering high living standards and welfare to all citizens with low carbon intensity. In this context, one of the projects, in which geothermal projects have also included in Turkey and takes measures against climate change is "Vo untary Carbon Markets (VCM)”. Voluntary Carbon Markets; which are developed independently of the governments' goals and policies to combat climate change, and of which all sectors concerned, from the business world to local governments, NGOs and individuals, can participate with carbon offsetting purposes. Increasing public awareness of climate change and impacts and acknowledgment of the fact that carbon offsetting is a reliable precautionary strategy has allowed these markets to grow rapidly in recent years. Emission credits traded on this market are called Voluntary Emission Reduction Units –VER. Companies that want to balance the greenhouse gases emitted by the atmosphere within their activities calculate emissions (by measuring their carbon footprint) and purchase carbon credits they generate from projects that reduce emissions to reduce and balance these emissions on a social responsibility basis. Although Turkey is not able to benefit from the flexibility mechanisms that are subject to the Kyoto Protocol's emissions trading, the projects for voluntary carbon market, which operate independently from these mechanisms and are based on environmental and social responsibility principles, are being developed and implemented for a long time. Turkey has been hosting the projects in which certificates are being improved in the voluntary carbon market since 2005. Although the voluntary carbon market represent a very small percentage in the World Carbon Market, Turkey, which is already using this market effectively, offers an important opportunity to participate in carbon markets in the future (MoEU, 2017). In the current situation, there are 308 voluntary carbon market projects in Turkey. Greenhouse gas emission reductions of over 20 million tCO2 equivalents per year are 173

expected from these projects. When the sectoral distribution of these projects is examined (Table 4), it can be seen that there are 6 projects on geothermal. Table 4. Sectoral Distribution of Projects Transacted in Voluntary Carbon Market (2014) Annual Emission Project Types Number Reduction(tCO2/year) Hydroelectric power plant 159 8,747,634 Wind Power Plant 106 7,951,391 Energy Production from waste / Biogas 27 3,069,273 Energy efficiency 10 432,081 Geothermal 6 405,309 TOTAL 308 20,605,688 Source: MoEU, 2017

Among the Voluntary Carbon Markets, Turkey is one of the countries that receive significant demand with the sales of 3.2 million tCO2 emissions in international markets in 2015. The value of CO2 emissions sold is $ 1.3 / t and with this value, Turkey is among the cheapest countries selling CO2 emissions in this market. (Hemrick and Goldstein, 2016). So far, the main consumption area of geothermal resources in Turkey have been , heating (housing, greenhouse, thermal facility etc.), electricity generation and health tourism. The biggest share in consumption is the central heating systems with 31.56%. Utilizing geothermal resources is heating, energy production and thermal utilization intensity in terms of capacity. Apart from this, other uses such as chemical production (liquid carbon dioxide), dry ice, leather processing, agricultural drying, heat pump, etc. are also utilized from geothermal sources. However, it has not yet become widespread despite its potential. Another use of geothermal resources is agriculture. In Turkey, the use of geothermal resources in agriculture is usually in the form of greenhouse heating and agricultural drying. Geothermal agriculture drying studies have started in Afyon, Kızılcahamam and Kırşehir in Turkey and research and project studies are continuing in other areas. Agricultural drying is currently carried out at a capacity of 1.5 MWt (Table 5). According to the records of the Ministry of Food, Agriculture and Livestock in the greenhouse activity, a total of 147 enterprises are engaged in geothermal greenhousing activities in the field of 320.2 hectares. 82.62% of the production area is located in Aegean Region. (Ministry of Food Agriculture and Livestock (MoFAL), 2017).

174

Table 5. Evaluation of Geothermal Resources Electricity Generation 635.15 MWe Central Heating (City, Housing) 115,000 Housing Equivelant(1033 MWt) Hot Spring Facilities, Thermal Otels, 46.000 Housing Equivelant (420 MWt) Heating of Circuit Property Plants Greenhouse Heating 424.9 Hectare (770 MWt) Geothermal Heat Pump 42.8 MWt Agricultural Drying 1.5 MWt Total geothermal heat use 2267.3 MWt (252.000 Housing Equivelant) Balneological usage in hot spring, spa, Number (1005 MWt)(18.5 Million 400 thermal facility people/year) Carbon Dioxide Production 240,000 Tone/Year Source: Akkuş ve Alan, 2016 çalışmasından alınan GDMRE, 2017, Mertoğlu ve diğ., 2015,

The use of geothermal resources in agricultural activities in Turkey is significantly supported. 25% -50% discount credit is able to be supplied the greenhouse producers, having controlled conditions at least 1 decare size area, within the scope of the Decree of the Council of Ministers on the Use of Low Interest Investment and Enterprise Loan for Agricultural Production, in accordance with the Regulation on Registration of Greenhouse Production by Ziraat Band and Agricultural Credit Cooperative. Within the framework of the project to support rural development investments, 50% grant support can be provided up to a certain upper limit to greenhouse projects constructed using alternative energy sources (geothermal, solar energy). Ministry of Finance According to 324 and 335 National Real Estate Communiqués published by the General Directorate of National Real Estate, entrepreneurs who will invest in technological and geothermal greenhouses may be allowed to use treasury real estate or easement right within certain criteria. Within the framework of the provisions of the Regulation on the Amendment of the Pasture Regulation No. 27857 dated 25/2/2011, it is possible to make changes in the allocation of purpose with the information and documents required in the allocation of purpose change demands for the geothermal-originated technological greenhouses. In the framework of the Decision on State Aids in Investments, incentives are provided at rates varying according to the regions in greenhouse investments (MoFAL, 2017). Another institution that provides support for geothermal in Turkey is the Agriculture and Rural Development Support Institution (ARDSI). The ARDSI provides support for the use of renewable energy sources in enterprises engaged in agricultural activities, especially in rural areas. These supports are important in terms of diversification of energy in agricultural enterprises and enabling enterprises to produce their own energy. The ARDSI supports energy investments under IPARD II program under 3 headings. 

Renewable Energy Investments for Enterprises to Meet Their Own Consumption



302-7 Renewable Energy Investments



301 Physical Investments to Rural Infrastructure Services

Within the scope of 302-7 renewable energy investments, TKDK supports all kinds of renewable energy activities (excluding hydraulic energy) to be established with the aim of generating electricity, heat, light and gas. These; Biomass, biofuels, concentrated solar 175

energy, geothermal, solar energy, photovoltaic, wind pumps, wind turbines, and combinations of these. In this context, it is obligatory to connect the system to the national network if electrical energy is produced. One of the most important uses of geothermal resources is thermal tourism. Thermal Tourism involves such various treatments as thermomineral baths, thermal water drinking, inhalation, mud baths, etc., combined with climate treatment, physical therapy, rehabilitation, exercise, psychotherapy and diet, together with recreational activities (MoCT, 2017a). Thermals springs in Turkey are ranked 7th in the world and the best in Europe and Turkey is the 3rd in terms of hot spring applications in Europe. In order to enable more effective and productive use of unique and renewable geothermal resources in Turkey, policies are being developed to establish an understanding different from a traditional hot spring use. With regard to development of Thermal Tourism, the goal is to increase not only the number of high quality facilities with cure parks, cure centers and accommodation integration, but also the number of tourists and the tourism income in general. Besides traditional hot spring bathing approach, Thermal Tourism’s new objective is to establish facilities that can provide wellbeing, vitality, recreation, fun, relaxation and sports activities all the year around. Initiatives of the Turkish Ministry of Culture and Tourism regarding health travel and thermal tourism constitute a significant portion of the diversification and extension of tourism activities in Turkey (MoCT, 2017a). Turkey's goal in Thermal Tourism is to be one of the most important of Europe and one of the top five destinations of the world. One of the most important policies implemented within this scope is the establishment of "Culture and Tourism Preservation and Development Region (CTPDG) and Tourism Centers (TC)". Historical, archaeological and socio-cultural tourism values of the country, thermal, winter, hunting and water sports, health tourism and other existing tourism potential are taken into consideration in the determination of CTPDG and TC. Preserving the regions that are intense on historic and cultural values or have high touristic potential, using, and to provide sectoral development and the planned development, the regions that are detected and declared by the decision of Council Of Ministers and their boundaries are suggested by the Ministry of Culture And Tourism. This study aims to create large scale planning, alternative management and business models targeting regional and local development (MoCT, 2017b). The CTPDG is not just a border-setting process, but also involves the use of such powers as planning and allocation within the boundaries in line with pre-determined goals and principles. There are 5 "Thermal Culture and Tourism Conservation and Development Regions" and 78 "Thermal Tourism Centers" in Turkey. Turkey aims to be the world's most important destination as a result of the planning studies with the investment of 50 thousand bed capacity thermal facilities in the short term period (2007-2012), 100 thousand beds in the medium term (2012-2017) and 500 thousand beds in long term (2017-2023). 176

5. Universities in Policies; Vision and Strategies of Ahi Evran University on Geothermal As part of the usage of geothermal resources within the meaning of the law in 2007 the fact that the private sector was started to be supported for renewable energy investment, apart from that, within the context of IPARD II, besides The Ministry of Food, Agriculture and Livestock Agricultural and Rural Development Support Institution supported particular rate of the investments made in renewable energy resources, the most important steps made in 2016 was that the universities in Turkey were supplied to establish regional priority in some matters via mission differentiation. Within the context of “Reg ona Deve opment Or ented M ss on D fferent at on and Spec a zat on of Un vers t es” themed project of which preparatory studies were started in the year of 2015, by supporting the new universities which were founded after the year of 2006 to specialize in particular subjects, they are aimed to be integrated in order to help the regional development. within the context of this project which is coordinated by Higher Education Council(HEC) and carried out coordinately with Ministry of Development, 5 pilot universities were given different missions on the date of 18 October 2016. In this context, Kırşehir Ahi Evran University were given the mission in the area of “Agr cu ture and Geotherma ” and studies were started to make it the pilot university in this area. Within the context of “Reg ona Deve opment Oriented Mission Differentiation and Specialization Self-Assessment Report” which Ahi Evran University prepared for HEC in the date of October 2016, the strategies and plans on the usage of geothermal resources, which the university prepared, can be specified as the following (Ahi Evran University (AEU), 2016); Mission; to become a human – centered, environment – friendly university which researches and educated in the light of science, produces information, technology and services, offers its productions on behalf of society, contributes in regional and national development, makes changes and innovation for permanent perfection, has assimilated national and universal values. Vision; to become a leader university which shapes the future by getting inspired from historical, cultural and art background, is human – centered, enterprising, innovator, productive, has contributions in science, is open to cooperation, is preferred because of its qualified education, is based nationally and universally, is prouded of becoming its member, Ahi Evran University has determined five basic aims in the plan which it has made in line with its mission, vision and values. The first of these aims is directed to contribute in local development by means of social cooperation (Aim 1: To Lead in Local and Regional Development in Cooperation with Shareholders). First two goals of the eight goals whiach are formed in Aim 1 are directly related to the usage of geothermal resources in either energy or agricultural area. (Goal 1: 3 new project and activites will be made for the efficient use of potential energy resources in the region. Goal 2: 10 national and 1 international project will be made, which are aimed to develop regional agriculture and livestock.) 177

One of the most powerful sides of the university is that there have been geothermal water resources and Physical Therapy and Rehabilitation Center(PTR) in its past. The fact that geothermal resources existing in the region are already being used in health care is a big potential for the development of the region in geothermal health tourism are with the contribution of the university. The university which has an important potential in terms of either substructure or education elements is a big candidate for Turkey with respect of being a health center by using the geothermal resource. The university has projects concerning the transformation of the geothermal resource into energy and the use of this energy to heat the serums in agriculture. Within the scope of the priority and the goals determined in 2017 - 2021 Strategy Plan of the University, directed regional development in – years term, renewable energy resources have an important place directly and indirectly in the primary projects which it plans to conduct in cooperation with other sectors. The topics related to this project are (AEU, 2016): 

Renewable Energy Projects



Geothermal Welded Health and Rehabilitation Projects



Sustainable Agriculture and Livestock Projects



Culture, Art and Tourism Projects



Industry Collaboration Projects

Kırşehir province has more than one renewable energy resources depending on its climate and natural environment characteristics. In this context, renewable energy resources such as wind, geothermal, sun and biogas are all together in Kırşehir and it has a potential suitable for production. The use of geothermal resources has an importance in the project topics which Kırşehir Ahi Evran University has determined within its Strategy Plan. Also the university has attached the foundation of a research and application center in order to develop the use of renewable energy resources including the geothermal. Another factor which The University is assertive is the geothermal health complex. Regarding the foundation of a big and integrated Physical Therapy and Rehabilitation Center so as to meet the increasing demand within The University and to make more use of geothermal energy , the studies have been completed; the construction of Physical Therapy and Rehabilitation Application Research Hospital and Physical Therapy and Rehabilitation Academy Complex which is within Bağbaşı side, has daily the capacity of 300 patients 100 of which are in bed and 200 of which are outpatients, has 15.000 square meter indoor space has been started. Associate Degree Programs in The University, Agricultural Faculty, Agricultural Application and Research Center (AARC) and Vocational High School have contributions in the development of the region in terms of education and research. The studies have been started to found a model agricultural running (farm) based on the association of herbal and animal production on the land of 824 decare which was assigned to The 178

University by General Directorate of National Estate for agricultural purpose in 2016. Good agricultural practices, protective zootechnics and veterinary services, breeding animal production, rootstock plant (Kaman walnut) and seed production, drought tolerated plant breeding, feed crop production, application of new watering technologies, biogas production from fertilizer and wastes, vegetables and fruit production in serum utilizing geothermal energy, the use of this energy in coops and broiler production are being planned within The University by the means of model agricultural running which will be founded.

6. Results and Suggestions Turkey is one of the few countries in the world that has made great strides in the way of development in recent years and has tried to solve these legal and structural problems in order to assess the potential of local resources. The geopolitical position grants the potential for natural resources to Turkey. Renewable energy sources need to be exploited in order to effectively prevent environmental problems arising from the direct or indirect use of fossil fuels. For this reason, Turkey has made significant investments in the use of renewable energy resources with significant energy moves in the last 10 years and to be continued. Among the renewable resources, geothermal resources that are prominent with its multifunctionality are increasingly being used in activities such as health tourism, which is also called spa tourism in Turkey, especially geothermal heat pumps in agricultural areas, greenhouses, animal shelters, fish farms, mushroom production, and product drying activities as well as energy fields in Turkey. In our country, investments in renewable energy R&D activities have increased in recent years. Particular emphasis has been given to R&D spending on energy technologies in order to meet the growing demand for energy, to secure energy supply security, and to achieve the goals set out in the Development Plan. Despite the developments that have been recorded, the need to increase both the amount of resources allocated for AR-GE and innovation and the effectiveness to transform it into the desired benefit continues. In particular, the need to strengthen the commercialization process of the technological product manufacturing process, to develop innovative entrepreneurship, to support the development of domestic technologies for public procurement, to improve the productivity in the production process and to support the sustainable production, and to increase the share of high technology sectors in manufacturing industry production and exports maintain its importance. In order to provide the contribution of universities in R & D in Turkey, YÖK has given new missions under the mission differentiation study in 2016 for newly established universities. In this context, the mission of "Ahi Evran University" in the field of "Agriculture and Geothermal" is important for focusing on the specific subjects and directing R & D expenditures. Turkey has been trying to provide an opportunity for the university to play an active role in the research and development of geothermal 179

resources for the last 10 years. Significant future investments will be made with the individuals to be trained in the areas determined within the scope of the development of social and human capital which is the most important phase of development. Increasing the support for geothermal in terms of high investment costs for renewable energy sources is important in terms of finding these resources and exploiting them. Geothermal resources need to be considered not only for energy purposes but also for other functions. It is important that Turkey is an international health tourism destination and to search more possibilities of using renewable energy resources in agriculture including geothermal energy for reaching the 2023 targets. In addition, applications for the provision of energy efficiency, investments for domestic technology and research and development are the factors that ensure the sustainability of these targets.

180

References Abolhosseini, S. & Heshmati, A. (2014). The Main Support Mechanisms to Finance Renewable Energy Development. IZA Discussion Paper No. 8182 May 2014 AEÜ (Ahi Evran Üniversitesi), (2016). Ahi Evran Üniversitesi Bölgesel Kalkınma Odaklı Misyon Farklılaşması ve İhtisaslaşması Öz Değerlendirme Raporu. (Unpublished Report-Basılmamış Rapor) Akçin, M. 2015. Renewable energy policy and biomass energy potential in Turkey, Enerji ve Tabii Kaynaklar Bakanlığı, Retrieved from http://www.irena.org/DocumentDownloads/events/2015/ BioenergyStatisticsPresentations/Administrativeandotherdatasources/Turkey_ EnergyPolicyandBiomassEnergyPotential.pdf Akyüz, E. 2015. Türkiye’nin enerji görünümü ve yenilenebilir enerji kaynaklarının önemi, Akademik Bakış Dergisi, Mayıs-Haziran 2015, 494-504. Akkuş, İ. & Alan, H., (2016). Türkiye’nin Jeotermal Kaynakları, Projeksiyonlar, Sorunlar Ve Öneriler Raporu. TMMOB, Jeoloji Mühendisleri Odası. Retrieved from http://www.jmo.org.tr/resimler/ekler/5ee60fb07fcb1e1_ek.pdf Altunbaş, D. (2004). Uluslararası Sürdürülebilir Kalkınma Ekseninde Türkiye’deki Kurumsal Değişimlere Bir Bakış. Yönetim Bilimleri Dergisi, 1 (1-2), 103-118. Bayraç, H. N., 2009. Küresel Enerji Politikaları ve Türkiye: Petrol ve Doğal Gaz Kaynakları Açısından Bir Karşılaştırma. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 10(1) Bertani, R. (2016). Geothermal power generation in the world 2010–2014 update report. Geothermics 60:31-43. Demirayak, F., (2002). Biyolojik Çeşitlilik-Doğa Koruma Ve Sürdürülebilir Kalkınma, Tübitak Vizyon 2023 Projesi Çevre Ve Sürdürülebilir Kalkınma Paneli İçin Hazırlanmıştır, Aralık Egeli, G. (1996). Avrupa Birliği ve Türkiye’de çevre sorunları. Ankara: TÇV Yayını. Emir, O. Soybalı, H.H. & Baytok, A.. (2008). Afyonkarahisar’da Termal Turizm Potansiyeli ve Geleceğine İlişkin Beklentiler. Termal ve Maden Suları Konferansı (24-25 Nisan, Afyon, ss. 396-407), Ankara. Enerji Atlası, (2017). Jeotermal Elektrik Üretimi İstatistikleri. Retrieved from http://www.enerjiatlasi.com/elektrik-uretimi/jeotermal Enerji Enstitüsü, (2017). Yenilenebilir Enerji Kaynakları Destekleme Mekanizması, 2017 Yılı Nihai YEK Listesi, retrieved from http://enerjienstitusu.com/yenilenebilirenerji-tesvikleri-yekdem-nedir/ Ergün, T. & Çobanoğlu, N., (2012). Sürdürülebilir Kalkınma Ve Çevre Etiği, Ankara: Ankara Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(1):97-123 181

Erim, R. (2000). Çevre İle İlgili Hukuksal Düzenlemeler, Türkiye’de Çevrenin ve Çevre Korumanın Tarihi Sempozyumu. s:177, 179-180. Gladwin, T. N., Krause, T., & Kennelly, J. J. (1995). Beyond eco-efficiency: Towards socially sustainable business. Sustainable Development, 3(1): 35-43. Gürlük, S., (2010) Sürdürülebilir Kalkınma Gelişmekte Olan Ülkelerde Uygulanabilir Mi?, Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi,5(2):85-99 Hamrick, K., & Goldstein, A. (2016). Raising Ambition State of the Voluntary Carbon Markets 2016. http://www.forest-trends.org/documents/files/doc_5242.pdf Haris, J.M., (2000). Basic Principles of Sustainable Development, Global Development and Environment Institute, Working Paper 00-04, June Hasdemir, M, Hasdemir, M, Gül, U. & Ataseven, Z.Y. (2014). Türkiye’de Jeotermal Seracılığın Mevcut Durumu İle Karar Verme Süreçlerinde Etkili Olan Faktörlerin Analizi, Gıda Tarım ve Hayvancılık Bakanlığı, Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü, Tarımsal Ekonomi Ve Politika Geliştirme Enstitüsü TEPGE Yayın No: 227, Ankara. JD

(Jeotermal Derneği), (2017). Türkiye’de Jeotermal, retrieved http://www.jeotermaldernegi.org.tr/sayfalar-Turkiye-de-Jeotermal

from

Korkmaz, H. & Taşlıyan, M. (2012). “Kalkınma Ajanslarının Bölgesel Sorunların Çözümünde ve Kalkınmadaki Önemi ve Katkıları: DOĞAKA, İKA ve ZEKA Örneği”, II. Bölgesel Sorunlar ve Türkiye Sempozyumu, Kahramanmaraş Sütçü İmam Üniversitesi. MoCT (Republic of Turkey, Ministry of Culture and Tourism-Türkiye Cumhuriyeti Kültür ve Turizm Bakanlığı), (2017a). Sağlık ve Termal Turizm, retrieved from http://www.ktbyatirimisletmeler.gov.tr/TR,10392/saglik-ve-termalturizm.html MoCT (Republic of Turkey, Ministry of Culture and Tourism-Kültür ve Turizm Bakanlığı), (2017b). Kültür ve Turizm Koruma ve Gelişim Bölgeleri (KTKGB) ve Turizm Merkezleri, retrieved from http://yigm.kulturturizm.gov.tr/TR,9669/ktkgb-ve-turizm-merkezleri.html MoD, (Republic of Turkey, Ministry of Development-Türkiye Cumhuriyeti Kalkınma Bakanlığı) (1995). Yedinci 5 Yıllık Kalkınma Planı, 1996-2000, Ankara. MoD, (Republic of Turkey, Ministry of Development-Türkiye Cumhuriyeti Kalkınma Bakanlığı) (2000). Sekizinci 5 Yıllık Kalkınma Planı, 2001-2005, Ankara. MoD, (Republic of Turkey, Ministry of Development-Türkiye Cumhuriyeti Kalkınma Bakanlığı) (2006). Dokuzuncu Kalkınma Planı, 2007-2013, Ankara.

182

MoD (Republic of Turkey, Ministry of Development-Türkiye Cumhuriyeti Kalkınma Bakanlığı), (2013a). 10. Kalkınma Planı (2014-2018), Karar No: 1041, Karar Tarihi: 02.07.2013, Ankara. MoD (Republic of Turkey, Ministry of Development-Türkiye Cumhuriyeti Kalkınma Bakanlığı), (2013b). 10. Kalkınma Planı (2014-2018), Madencilik Politikaları Özel İhtisas Komisyonu, Enerji Hammaddeleri Grubu, Jeotermal Çalışma Alt Grubu Raporu. Yayın No: KB: 2926 - ÖİK: 753, retrieved from http://www.kalkinma.gov.tr/Lists/zel%20htisas%20Komisyonu%20Raporlar/ Attachments/260/Madencilik%20Politikalar%C4%B1%20%C3%96%C4%B0K. pdf MoENR (Republic of Turkey, Ministry of Energy and Natural Resources-Türkiye Cumhuriyeti, Enerji ve Tabi Kaynaklar Bakanlığı), (2014). Türkiye Ulusal Yenilenebilir Enerji Eylem Planı (2013-2023), retrieved from https://kusip.gov.tr/kusip/yonetici/tematikAlanEkGoster.htm?id=75 MoENR (Republic of Turkey, Ministry of Energy and Natural Resources-Türkiye Cumhuriyeti, Enerji ve Tabi Kaynaklar Bakanlığı), (2016). Dünya ve Ülkemiz Enerji ve Tabii Kaynaklar Görünümü (01 Ekim 2016 İtibariyle). Strateji Geliştirme Başkanlığı, Sayı: 14, retrieved from http://www.enerji.gov.tr/File/?path=ROOT%2f1%2fDocuments%2fEnerji%20 ve%20Tabii%20Kaynaklar%20G%C3%B6r%C3%BCn%C3%BCm%C3%BC%2f Sayi_14.pdf MoENR (Republic of Turkey, Ministry of Energy and Natural Resources-Türkiye Cumhuriyeti, Enerji ve Tabi Kaynaklar Bakanlığı), (2017). Türkiye’de Jeotermal Enerji. Yenilenebilir Enerji Genel Müdürlüğü, retrieved from http://www.eie.gov.tr/yenilenebilir/turkiyede_jeo.aspx MoEU (Republic of Turkey, Ministry of Environment and Urbanisation), (1993). Çevre ve Çevre Bakanlığı, Ankara: Yeşil Seri. MoEU (Republic of Turkey, Ministry of Environment and Urbanisation-Çevre ve Şehircilik Bakanlığı), (2017). Birleşmiş Milletler İklim Değişikliği Çerçeve Sözleşmesi ve Türkiye'nin Konumu. retrieved from http://www.csb.gov.tr/projeler/iklim/index.php?Sayfa=sayfa&Tur=webmenu& Id=12442 MoFAL (Republic of Turkey, Ministry of Food Agriculture and Livestock-Gıda Tarım ve Hayvancılık Bakanlığı), (2017). Bitkisel Üretim Verileri, retrieved from www.tarim.gov.tr Kılıç Ö. & Kılıç A.M., (2009),"Jeotermal Enerjinin Ülkemiz Açısından Önemi ve Çevresel Etkilerinin İncelenmesi", TMMOB Jeotermal Kongresi, ANKARA, TÜRKIYE, 23-25 Aralık 2009, ss.93-104. Lund, J.W. & Boyd, T.L. (2016). Direct utilization of geothermal energy 2015 worldwide review. Geothermics 60:66-93. 183

Mertoglu, O., Şimşek, Ş., & Başarır, N., (2015). Geothermal Country Update Report of Turkey (2010-2015) Proceedings World Geothermal Congress 2015. Melbourne, Australia, 19-25 April 2015. MFA, (Republic of Turkey, Ministry of Foreign Affairs), (2017). Turkey’s Energy Profile and Strategy. retrieved from http://www.mfa.gov.tr/turkeys-energystrategy.en.mfa GDMRE (General Directorate of Mineral Research and Exploration-Maden Tetkik ve Arama Genel Müdürlüğü), (2017). Enerji Haritaları, retrieved from http://www.mta.gov.tr/v3.0/hizmetler/jeotermal-harita Öztürk, M. & Bayat, M.,(2011). Uluslararası Turizm Hareketlerinde Sağlık Turizminin Rolü ve Kalite Çalışmalarının Önemi Bir Literatür Çalışması. Kahramanmaraş Sütçü İmam Üniversitesi, İİBF Dergisi, 1(2):135-155. Resmi Gazete, (2007). Jeotermal Kaynaklar Ve Doğal Mineralli Sular Kanunu, Resmi Gazete Tarihi: 13.06.2007 Resmi Gazete Sayısı: 26551 Resmi Gazete, (2013). Yenilenebilir Enerji Kaynaklarinin Belgelendirilmesi Ve Desteklenmesine İlişkin Yönetmelik, Resmi Gazete Tarihi: 01.10.2013 Resmi Gazete Sayısı: 28782 Satman, A. (2007). Türkiye’nin Enerji Vizyonu, Jeotermal Enerjiden Elektrik Üretimi Semineri, TESKON2007, VIII. Ulusal Tesisat Mühendisliği Kongresi, 25-28 Ekim 2007, İzmir. Saygın, H., (2004). “Sürdürülebilir Gelişme Gündeminde Nükleer Enerjnin Sorunları”, Elektrik Mühendisleri Odası Dergisi, 42(423): 32–40. Saygın, H., (2006). , “Sürdürülebilir Enerji Politikalarında Nükleer Enerjinin Yeri ve Türkiye”, Türkiye Tekstil Sanayi İşverenleri Sendikası, Konferans, 31 Ağustos 2006, retrieved from http://eppamtr.weebly.com/ uploads/5/6/2/5/5625734/surdurulebilir_enerji_politikalarnda_nukleer_enerji nin_yeri_ve_turkiye. pdf Tıraş, H.H., (2012). Sürdürülebilir Kalkınma ve Çevre: Teorik Bir İnceleme. Kahramanmaraş Sütçü İmam Üniversitesi, İİBF Dergisi, 2(2):57-73. Uluatam, E. (2010). Yenilenebilir Enerji Teşvikleri. Ekonomi Forum, retrieved from https://www.tobb.org.tr/AvrupaBirligiDairesi/Dokumanlar/Raporlar/Yenilene bilirEnerjiTesvikleri.pdf UN, (1987). Our Common Future, From One Earth to One World, Report of the World Commission on Environment and Development, retrieved from http://www.undocuments.net/ocf-ov.htm#1.2 WEO (World Energy Outlook), (2015). World Energy Outlook 2015 Executive Summary, retrieved from http://www.iea.org/publications/freepublications/publication/ WEB_WorldEnergyOutlook2015ExecutiveSummaryEnglishFinal.pdf 184

WEO (World Energy Outlook), (2016). World Energy Outlook 2016 Executive Summary, retrieved from https://www.iea.org/publications/freepublications/ publication/WorldEnergyOutlook2016ExecutiveSummaryEnglish.pdf

185

186