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PROSPECTS OF RENEWABLE AND NON-RENEWABLE

ENERGY SOURCES IN SOUTH ASIAN ECONOMIES: AN

ANALYSIS

BY

SADIA ALI

Registration No: 2013-GCUF-05034

Thesis submitted in partial fulfillment of

the requirement for the degree of

DOCTOR OF PHILOSOPHY

IN

ECONOMICS

DEPARTMENT OF ECONOMICS

GOVERNMENT COLLEGE UNIVERSITY FAISALABAD

2017

ii

CERTIFICATE OF APPROVAL

This is to certify that the research work presented in this thesis, entitled, “Prospects of

Renewable and Non-Renewable Energy Sources in South Asian Economies: An Analysis”

was conducted by Mrs. Sadia Ali (Regd. No 2013-GCUF-05034) under the supervision of Dr.

Sofia Anwar.

iii

CERTIFICATE BY SUPERVISORY COMMITTEE

We certify that the contents and form of a thesis submitted by Miss Sadia Ali,

registration No. 2013-GCUF-05034 has been found satisfactory and in accordance with

the prescribed format. We recommend it to be processed for the evaluation by the

External Examiner for the award of the degree.

Supervisor

Co-Supervisor

Member of Supervisory Committee

Chairperson

Dean / Academic Coordinator

iv

AUTHORS DECLARATION

I Sadia Ali, Reg. No 2013-GCUF-05034, here by state that my Ph.D. thesis title

“Prospects of Renewable and Non-Renewable Energy Sources in South Asian

Economies: An Analysis” is my own work and has not been submitted previously by

me for taking any degree from Government College University, Faisalabad or anywhere

else in the country/ world. At any time if my statement is found to be incorrect even

after my graduate the university has the right to withdraw my Ph.D. degree.

v

PLAGIARISM UNDERTAKING

I solemnly declare that research work presented in the thesis titled “Prospects of

Renewable and Non-Renewable Energy Sources in South Asian Economies: An Analysis”

is solely my research work with no significant contribution from any other person. Small

contribution/help wherever taken has bees been duly acknowledged and that complete thesis

has been written by me.

I understand the zero-tolerance policy of the HEC and Government College University,

Faisalabad towards plagiarism. Therefore, as an author of the above titled thesis declare that no

portion of my thesis has been plagiarized and any material used as reference is properly

referred/cited.

I undertake that if l am found guilty of any formal plagiarism in the above titled thesis

even after award of Ph.D. degree, the University reserve the rights to withdraw/revoke my Ph.D.

degree and that HEC and the University has the right to publish my name on the

HBC/University Website on which names of students are placed who submitted plagiarized

thesis.

vi

DEDICATION

Dedicated to

“My Parents”

For Their Love, Encouragement and Prayers

“Muhammad Amir Javed”

For His Support and Believe in Richness of Learning

“Muhammad Husnain Amir, Muhammad Ali Amir and Zainab Amir”

For Their Love, Prayers and Made Me Keen for Learning

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ACKNOWLEDGEMENTS

First of all, I would praise the Almighty Allah, the Gracious and the Most Merciful, for

giving me the quality and inspiration to complete this examination.

I might want to thank my Supervisor, Dr. Sofia Anwar not just for her few astute

remarks concerning the bearing and the substance of this proposition, yet in addition

for her human state of mind and for being understanding. She prepared me how to make

inquiries and express my thoughts. She indicated me diverse approaches to approach

an exploration issue and the need to steady keeping in mind the end goal to achieve any

objective. She showed me how to buckle down. I sincerely value the time she has

committed to regulate my exploration.

I might want to formally recognize and thank various individuals, particularly my co-

supervisor, Dr. Samia Nasreen who empowered, supported and helped me through the

testing a very long time of my Ph. D examinations. Thanks to Dr. Muhammad Rizwan

Yasin for agreeing to serve on my committee. I am also thankful to Dr Muhammad

Sohail Amjad Makhdom for his encouragement and support during my PhD.

Last, yet not slightest, I thank my family: my parents, for giving me life in the first

place, for instructing, educating, support and consolation to seek my interests. My

husband, my siblings for tuning in to my protests and dissatisfactions, and for having

faith in me.

SADIA ALI

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TABLE OF CONTENTS

CERTIFICATE OF APPROVAL…………………………………………ii

CERTIFICATE BY SUPERVISORY COMMITTEE………………………..….......iii

AUTHORS DECLARATION…………………………………………....…………..iv

PLAGIARISM UNDERTAKING…………………….…………………………........v

DEDICATION………………………………………………………………………..vi

ACKNOWLEDGEMENTS…………………………………………………....….…vii

ACRONYMS AND ABREVIATIONS……………….…………………………....xxii

ABSTRACT………………………………………………………………………..xxiii

1. INTRODUCTION……………………….………………………………….………1

1.1 Research Motivation…………………………………………………………..…...8

1.2 Research objectives………………………………………………………….……..9

1.3 Research Contribution………………………………………………………..…..10

1.4 Research Structure………….…………………………………………………….10

2. REVIEW OF LITERATURE………………………..…………………………….12

2.1 Empirical Literature.........................................................................................…...12

2.1.1 Renewable and nonrenewable energy and economic growth..…………………11

2.1.2 Renewable and nonrenewable energy consumption and environment

quality…………………………………………………………………………...……30

2.1.3 Demand for Renewable and nonrenewable energy…….…………...…………..38

2.1.4 Renewable and Non-Renewable Energy Sources, Energy Intensity, Economic

Growth………………………………………………………………………………..47

2.2 Theoretical Literature………………………………………………….…………51

2.2.1 Prospects and Potential of Renewable and Non-Renewable Energy

Sources………………………………………….……………………..…….…….....51

3. METHODOLOGY....…………..……………………………………………...71

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3.1 Data……………………………………………………………………….………71

3.2 Model Specification……………………………………………………………....71

3.2.1 Renewable and nonrenewable energy with growth……..…..…………………..72

3.2.1.1 Model 1. Relationship between Renewable and Non-renewable Energy,

Institutions and Economic Growth …………………………………………..………72

3.2.1.2 Model 2: Relationship between Renewable and Non-renewable Energy,

Urbanization and Economic Growth…………..…………………………..………….73


Financial Development and Economic Growth ………………...….………………....74

3.2.1.4 Data Sources…...…………...….…………………………………..…………75

3.2.2 Renewable and nonrenewable energy with Environment Quality…….….……..75


Population Density and Environment ………………………..……………...………..75


Urbanization, Energy Intensity and Environment …….…………………..….……....76

3.2.2.3 Data Sources……………………………………………………………….…78

3.2.3 Demand Elasticity of Renewable and nonrenewable energy…………...………78

3.2.3.1 Model 6: Relationship between Renewable Energy Demand, Economic Growth,

Industrialization, Technological Changes and Energy Price ………………..……….78

3.2.3.2 Model 7: Relationship between Non-Renewable Energy Demand, Economic

Growth, Industrialization, Technological Changes and Energy Price …………….…79

3.2.3.3 Data Sources…………………………………………………………….……81

3.2.4 Renewable and nonrenewable energy with energy Efficiency……………….…81


Economic Growth, Urbanization and Energy Intensity ………………...…….………82


Economic Growth, Trade Openness and Energy Intensity ……………...……………82


Economic Growth, Industrialization, Technological Progress and Energy Intensity....82

3.2.4.4 Data Sources………………………………………………………………….83

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3.3 Econometrics Methodology……………...…………………………………….…84

3.3.1 Time-Series Methodology………………..………………………………….....84

3.3.1.1 Unit Root Tests…………….…………………………………………………84

3.3.1.2 Johansen Co-integration Test ……………………………………………..….85

3.3.2 Panel Data Methodology…………………………………………...…………...88

3.3.2.1 Panel Unit Root Tests………………………………………………………....88

3.3.2.1.1 LLC Unit Root Test…….………………………..………………...………..88

3.3.2.1.2 IPS Unit Root Test………………………..………………………………...90

3.3.2.2 Panel Co-integration Test…………………………………………..…………91

3.3.2.2.1 Estimation of Panel Co-integration Regression……….……………...…….93

3.3.3 Panel Granger Causality Test …………………………………………………..94

4. RESULTS AND DISCUSSIONS……………………….…………………………96

4.1 An Overview of South Asian Economies…….…….………….…………...…….96

4.1.1 Economic Structure………………..…………………………………………...96

4.1.1.1 Economic Growth………………..……………………………………...……96

4.1.1.2 Inflation………...………………..……………………………………...……97

4.1.1.3 Industrial Share …………..……..………………………….…………...……98

4.1.2 Population Desity ….……………...………………………….…………...……99

4.1.3 Environment………………..……..……………………….…………….....…100

4.2 Energy Situation In South Asia ………………..………….………………….…101

4.3 Importance and Potential of Renewable Energy Sources ……………............…105

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4.4 Empirical results and discussions……………………………………………..…108

4.4.1 Impact of renewable and nonrenewable energy on economic growth……....…108


Institutions and Economic Growth ………..……………………………..…….….108

4.4.1.1.1. Time Series Results…………………………………….……...................108

4.4.1.1.1.1. Unit Root Test Results………………………..…..……………….…...108

4.4.1.1.1.2. Johansen Co-integration Test Results…………………………..……....110

4.4.1.1.2 Panel Results…………………………………………………………....…111

4.4.1.1.2.1 Panel Unit Root Results………………………………………..….…….112

4.4.1.1.2.2 Panel Co-integration Results………………………...…………………..113

4.4.1.1.3 FMOLS Estimates……………...…………………………...…..…………113

4.4.1.1.3.1 FMOLS Estimates Country Wise…………………………….…………113

4.4.1.1.3.2 FMOLS Panel Estimates……………………….……………………..…115

4.4.1.1.4 Panel Causality Results…………...…………………………………...…..116


Urbanization and Economic Growth ……………………………………………..…117

4.4.1.2.1 Time Series Results……………………………………………………..…117

4.4.1.2.1.1 Unit Root Test Results……………………………...……………..…….117

4.4.1.2.1.2 Johansen Co-integration Test Results………………………....………...118

4.4.1.2.2 Panel Results………………………...…………………..……………...…120

4.4.1.2.2.1 Panel Unit Root Results ……………………………..…..………….…..120

4.4.1.2.2.2 Panel Co-integration Results…………………………..…..…………….121

4.4.1.2.3 FMOLS Estimates……………………………...………………………….122

4.4.1.2.3.1 FMOLS Estimates Country Wise……………………………….………122

4.4.1.2.3.2 FMOLS Panel Estimates………………………………………...………123

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4.4.1.2.4 Panel Causality Results……………………………………………..……..124


Financial Development and Economic Growth ………….…….………………..….125

4.4.1.3.1 Time Series Results………..…….………………….…………………..…125

4.4.1.3.1.1 Unit Root Test Results……………………………….…...…………..…125

4.4.1.3.1.2 Johansen Co-integration Test Results…………....………………...……126

4.4.1.3.2 Panel Results……………………………………………...……….……...128

4.4.1.3.2.1 Panel Unit Root Results………….………………………………...……128

4.4.1.3.2.2 Panel Co-integration Results………………………………………….....129

4.4.1.3.3 FMOLS Estimates……………………………………………………...….130

4.4.1.3.3.1 FMOLS Estimates Country Wise………..………………………………130

4.4.1.3.3.2 FMOLS Panel Estimates………………………………………………...131

4.4.1.3.4 Panel Causality Results……………………………………………………132

4.4.2. Impact of Renewable and Non-Renewable Energy Sources on Environmental

Quality………………………………………………………………………...…….133


Population Density and Environment ………………………………………………133

4.4.2.1.1 Time Series Results………………………………..………………………133

4.4.2.1.1.1 Unit Root Test Results…………………….…..……………………..….133

4.4.2.1.1.2 Johansen Co-integration Test Results……..…...…………...…………...135

4.4.2.1.2 Panel Results…………………………………….……………………...…137

4.4.2.1.2.1 Panel Unit Root Results…………………………………………………137

4.4.2.1.2.2 Panel Co-integration Results………...………………………………..…138

4.4.2.1.3 FMOLS Estimates…………………..………………………………..……139

4.4.2.1.3.1 FMOLS Estimates Country Wise……………………………………..…139

4.4.2.1.3.2 FMOLS Panel Estimates……...……..………………………………..…140

4.4.2.1.3.4 Panel Causality Results………………………………………….………142

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Urbanization, Energy Intensity and Environment…..………………………...…….143

4.4.2.2.1 Time Series Results………………………………..………………………143

4.4.2.2.1.1 Unit Root Test Results…………………….…..……………………..….143

4.4.2.2.1.2 Johansen Co-integration Test Results……..…...…………...…………...145

4.4.2.2.2 Panel Results…………………………………….……………………...…147


4.4.2.2.2.2 Panel Co-integration Results………...………………………………..…148

4.4.2.2.3 FMOLS Estimates…………………..………………………………..……149


4.4.2.2.3.2 FMOLS Panel Estimates……...……..…………………………………..151

4.4.2.2.3.4 Panel Causality Results………………………………………….………152

4.4.3 Demand for Renewable and Non-Renewable Energy Sources…………...……153

4.4.3.1 Model 6: Demand for Renewable Energy Sources……………………….….153

4.4.3.1.1 Time Series Results…………………………………………………….….153

4.4.3.1.1.1 Unit Root Test Results……………………………………………...…...153

4.4.3.1.1.2 Johansen Co-integration Test Results………………………………...…158

4.4.3.1.2 Panel Results……...…………………………………….…………………156

4.4.3.1.2.1 Panel Unit Root Results…………..…………………………..…...…….156

4.4.3.1.2.2 Panel Co-integration Results……………………………………..……...158

4.4.3.1.3 FMOLS Estimates……………………………………………………...….158

4.4.3.1.3.1 FMOLS Estimates Country Wise………………………………..………158

4.4.3.1.3.2 FMOLS Panel Estimates………………………………………………...160

4.4.3.1.4 Panel Causality Results……………………………………………………161

4.4.3.1.5 Test of Forecasting Renewable Energy Demand…………………………162

4.4.3.1.5.1 Variance Decomposition of RE Demand…...………………...…….…...162

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4.4.3.1.5.2 Impulse Response Function of RE Demand…………………………......162

4.4.3.2. Model 7: Demand for Non-Renewable Energy Sources…………...………..162

4.4.3.2.1 Time Series Results…….………………….………………………………162

4.4.3.2.1.1 Unit Root Test Results……………………………………………….….162

4.4.3.2.1.2 Johansen Co-integration Test Results…………………………………...164

4.4.3.2.2 Panel Results..……………………………………………………………..166


4.4.3.2.2.2 Panel Co-integration Results…………………………………………….167

4.4.3.2.3 FMOLS Estimates……………………………………………….………...167

4.4.3.2.3.1 FMOLS Estimates Country Wise………………………………….…….167

4.4.3.2.3.2 FMOLS Panel Estimates…………………………………………….…..169

4.4.3.2.4 Panel Causality Results………………………………………………....…170

4.4.3.2.5 Test of Forecasting Non-Renewable Energy Demand……………………171

4.4.3.2.5.1 Variance Decomposition of NRE Demand……………………………...171

4.4.3.2.5.2 Impulse Response Function of NRE Demand…………………………...171

4.4.4. Impact of Renewable and Non-Renewable Energy Sources on Energy

Intensity……………………………………………………………………..171


Economic Growth, Urbanization and Energy Intensity ………………..……171

4.4.4.1.1 Time Series Results……….………………………………………….……171

4.4.4.1.1.1 Unit Root Test Results….…………………………………………...…..171

4.4.4.1.1.2 Johansen Co-integration Test Results………………………………...…173

4.4.4.1.2 Panel Results………………………………………………………………175

4.4.4.1.2.1 Panel Unit Root Results………………………………………………....175


4.4.4.1.3 FMOLS Estimates…………………………………………..……..………177

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4.4.4.1.3.1 FMOLS Estimates Country Wise……………………………....………..177

4.4.4.1.3.2 FMOLS Panel Estimates…………………………………….……..……178

4.4.4.1.4 Panel Causality Results……………………………………….………...…179


Economic Growth, Trade Openness and Energy Intensity ………………………….180

4.4.4.2.1 Time Series Results………………………………………….…………….180

4.4.4.2.1.1 Unit Root Test Results…………………………………………………..180

4.4.4.2.1.2 Johansen Co-integration Test Results……………….…………………..182

4.4.4.2.2 Panel Results………………………………………………………………183

4.4.4.2.2.1 Panel Unit Root Results……………………………………………...….183


4.4.4.2.3 FMOLS Estimates……………………………………….…...……………185


4.4.4.2.3.2 FMOLS Panel Estimates……………………………………….………..187

4.4.4.2.4 Panel Causality Results…………………...………………………...……..188

4.4.4.3. Model 10: Relationship between Renewable and Non-renewable Energy,

Economic Growth, Industrialization, Technological Progress and Energy Intensity

………………………………………………………………………………….…...189

4.4.4.3.1. Time Series Results……………………………………………………….189

4.4.4.3.1.1. Unit Root Test Results……………………………………….…………189

4.4.4.3.1.2. Johansen Co-integration Test Results…………………………………..191

4.4.4.3.2 Panel Results …………………………………………………………..….192

4.4.4.3.2.1 Panel Unit Root Results………………………………………………....192

4.4.4.3.2.2 Panel Co-integration Results………………………………...…………..194

4.4.4.3.3 FMOLS Estimates…………………………………………………………195

4.4.4.3.3.1 FMOLS Estimates Country Wise……………….………………...…..…195

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4.4.4.3.3.2 FMOLS Panel Estimates…………………………………………..…….196

4.4.4.3.4 Panel Causality Results……………………………………………….…...197

5. CONCLUSIONS AND RECOMENDATIONS…………………………….....…199

5.1 Summary and Conclusion…….……………………………………………...….199

5.2 Policy recommendation...………………………………………………….........202

REFRENCES……………………………………………………………………….204

Appendices ………………………………………………………………………....238

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LIST OF TABLES

Table 2.1. Summary of Empirical studies……………………………………………65

Table 4.1 Energy Dependence of Selected South Asian Countries………………….102

Table 4.2 Production and Use of Energy ……………………………………………102

Table 4.3 Non-renewable Energy Sources (oil)……………………………..………103

Table 4.4 Non-renewable Energy Sources (Gas)……………..………………….....103

Table 4.5 Non-renewable Energy Sources (Coal)…………………………..………104

Table 4.6 Renewable Energy Sources (Hydro)………………………………...……104

Table 4.7 Renewable Energy Sources……………………………………………….104

Table 4.8 Importance of Renewable and Non-renewable energy sources……..……105

Table 4.9 Renewable Energy potential………………………………………………107

Table 4.10 DF GLS Unit root Test……………..…….……………………...…..….109

Table 4.11 Johansen Co-integration Test results.....………………………...……….110

Table 4.12 Panel Unit root Test…..……………………………………….....………112

Table 4.13 Panel co-integration Test …………………………..………...….………113

Table 4.14 FMOLS Country Specific Results…………...………………,………….114

Table 4.15 FMOLS Panel Estimates………………………...………...…….………116

Table 4.16 DH panel causality Test …………………………………..……..………116

Table 4.17 DF GLS Unit root Test …………………………….…..……………......117

Table 4.18 Johansen Co-integration Test results……………..…..…………………119

Table 4.19 Panel Unit root Test…..…………………………..……..………………120

Table 4.20 Panel Co-integration Test ……………………………...………………..121

Table 4.21 FMOLS Country Specific Results…………………….…………………122

Table 4.22 FMOLS Panel Estimates……………………………...…………………124

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Table 4.23 DH Panel Causality Test ………………………….……………………..124

Table 4.24 DF GLS Unit root Test…………………...……………………………..125

Table 4.25 Johansen Co-integration Test results.…………………………...……….127

Table 4.26 Panel Unit root Test……………………..……………………..……..…128

Table 4.27 Panel co-integration Test …………………….…………….………...…129

Table 4.28 FMOLS Country Specific Results………………………………….…...130

Table 4.29 FMOLS Panel Estimates…………………………………….……….….132

Table 4.30 DH panel causality Test ……………………………….……..……….…133

Table 4.31 DF GLS Unit root Test …………………………………………..….......134

Table 4.32 Johansen Co-integration Test results…………………….………..….…135

Table 4.33 Panel Unit root Test…………………………………………..…………137

Table 4.34 Panel Co-integration Test …………………..…………………………..138

Table 4.35 FMOLS Country Specific Results………………………………………139

Table 4.36 FMOLS Panel Estimates……………………………………..…….……141

Table 4.37: FMOLS Country Specific Results ……………………………………...142

Table 4.38 DF GLS Unit root Test…………………...………………………….….143

Table 4.39 Johansen Co-integration Test results.…………………………...………145

Table 4.40 Panel Unit root Test……………………..……………………..………..147

Table 4.41 Panel co-integration Test ………………...………………….………..…148

Table 4.42 FMOLS Country Specific Results……………………………………....149

Table 4.43 FMOLS Panel Estimates……………………………………..…….……151

Table 4.44 DH panel causality Test …………………………..…………..…….…...152

Table 4.45 DF GLS Unit root Test…………………...………………….……….….153


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Table 4.48 Panel co-integration Test …………………..……………….………..…158


Table 4.50 FMOLS Panel Estimates…………………………………….……..……160

Table 4.51 DH panel causality Test ……………………………………..……..…...161

Table 4.52 DF GLS Unit root Test…………………...………………….……….….163


Table 4.54 Panel Unit root Test……………………..……………………..…….…..166

Table 4.55 Panel co-integration Test ………………..………………….………...…167

Table 4.56 FMOLS Country Specific Results…………………………………….....168

Table 4.57 FMOLS Panel Estimates…………………………………….…….….…169

Table 4.58 DH panel causality Test ……………………………………..…….…....170

Table 4.59 DF GLS Unit root Test…………………...………………….….………172

Table 4.60 Johansen Co-integration Test results.…………………………...……….173


Table 4.62 Panel co-integration Test ……………………….………….….…….….176


Table 4.64 FMOLS Panel Estimates…………………………………….…….……179

Table 4.65 DH panel causality Test ……………………………………..…….….....179

Table 4.66 DF GLS Unit root Test…………………...………………….………….180



Table 4.69 Panel co-integration Test …………………………….…….…..…….…185

Table 4.70 FMOLS Country Specific Results……………………………….……....186

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Table 4.71 FMOLS Panel Estimates…………………………………….…….……187

Table 4.72 DH panel causality Test ……………………………………..……...…...188

Table 4.73 DF GLS Unit root Test…………………...………………….…….…….189

Table 4.74 Johansen Co-integration Test results.…………………………...….……191


Table 4.76 Panel co-integration Test ………………………….……….……...….…194

Table 4.77 FMOLS Country Specific Results………………………………….…....195

Table 4.78 FMOLS Panel Estimates…………………………………….……..……197

Table 4.79 DH panel causality Test ……………………………………..……..…...198

Table A1 Variable Description ………………………………………….……..……238

Table B1 Summary Statistics of the variables Panel data 1980-2014………………240

Table B2 Summary Statistics of the variables Time Series data 1980-2014….…….241

Table C1 Variance Decomposition of RE Demand…………………………………244

Table C2 Impulse Response Function of RE Demand………………………………245

Table C3 Variance Decomposition of NRE Demand……………….……………….246

Table C4 Impulse Response Function of NRE Demand……………………………..247

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LIST OF FIGURES

Figure 4.1: Trends in Economic Growth.…….………………………………...……..97

Figure 4.2: Trends in Inflation ……………….………………….………...………….98

Figure 4.3: Trends in Industrial share of GDP……………...….…………..………….98

Figure 4.4: Trends in Total Population ………………….…….…………..………….99

Figure 4.5: Trends Population Density………………….…….…………..………….99

Figure 4.6: Trends in per capita Co2 ………………………….…………...….……100

Figure 4.7: Trends in Non-Renewable energy Sources…………………….…….…101

Figure 4.8: Trends in Renewable energy Sources…………………………..………102

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ACRONYMS AND ABBREVIATIONS

DF Dickey-Fuller

ECM Error Correction Mechanism

EC Energy Consumption

FBR Federal Board of Revenue

EI Energy Intensity

EFI Energy Efficiency

EKC Environment Kuznets Curve

FY Financial Year

GDP Gross Domestic Product

GLS Generalized Least Square

IEA Information Energy Authority

IND Industrialization

OLS Ordinary Least Squares

INS Institutions

FMOLS Fully Modified Ordinary Least Square

NRE Nonrenewable Energy

PD Population Density

RE Renewable Energy

TO Trade Openness

URB Urbanization

WDI World Bank Indicator

xxiii

ABSTRACT

The core target of this exploration is to inspect the effect of renewable and

nonrenewable energy sources on economic growth and environmental degradation in

selected South Asian Economies. The thesis explores renewable and non-renewable

energy sources in four ways. Firstly, it empirically investigates the relationship between

renewable, non-renewable energy, and economic growth. Secondly, it empirically

investigates the impact of renewable and non-renewable energy sources on

environmental quality. Thirdly, it examines the determinants of demand for renewable

and non-renewable energy sources including industrial sector. Fourthly, the study

explores the impact of aforementioned energy sources and economic growth on energy

intensity. The thesis comprises on four South Asian countries: Pakistan, India,

Bangladesh and Sri Lanka for empirical investigation, based on the availability of data.

The study employs both time series and panel data analysis for the period of 1980 to

2014. To analyze the long run and causal relationship between variables, we have

applied Johansson co-integration, Larsson panel co-integration and Granger causality

Dumitrescu and Hurlin (DH) approaches. Empirical results confirm the presence of

long run co-integration between economic growth, renewable energy sources, non-

renewable energy sources, institution quality, population density, financial

development, inflation, and trade openness in various models. The impact of renewable

and non-renewable energy on economic growth is found positive which increases per

capita GDP. The analysis of fully modified ordinary least squares (FMOLS) panel

estimates shows that the values of all coefficients are statistically significant and their

signs are in line with the economic growth theory.

Secondly, the results reveal that economic growth, population density, and non-

renewable energy have a positive impact on per capita CO2 emission. However, the

negative sign of renewable energy sources tends to decrease per capita CO2 emissions,

indicating that environmental quality deteriorates with non-renewable energy sources

and improves in case of renewable energy sources. The findings additionally encourage

the Environmental Kuznets Curve (EKC) hypothesis which accepts an altered U-shaped

path amongst affluence and per capita CO2 emissions. Moreover, the study found the

evidence of bi-directional panel causality between CO2 and renewable energy sources

as well as population destiny. Investigations give the evidence of unidirectional

xxiv

causality running from CO2 to non-renewable energy sources. Results also give proof

evidence of feedback relationship between environment and both energy sources.

In the other model of the causal relationship between environmental quality and energy

intensity with the presence of urbanization with both energy sources and population,

outcomes affirm the existence of co-integration between these factors. FMOLS

discovered the positive effect of affluence, urbanization, energy intensity and non-

renewable energy sources on CO2 emissions. Notwithstanding, the negative indication

of renewable energy sources demonstrate that it will prompt diminishing per capita

CO2.

Thirdly, the empirical analysis of renewable and non-renewable energy demand shows

that the price and income are the major determinants of the demand for energy. The

impact of income on energy demand is positive in both energy sources whereas, in case

of price, it has a negative impact. The results of technological progress show a negative

impact on energy demand, which suggests that technological progress is energy saving.

Results of FMOLS indicate that increase in economic growth, industrialization and

population total increases energy demand (renewable and non-renewable). These

results show that higher per capita real income should result in greater economic

activity which in turns accelerate the use of energy. The degree of industrialization, as

a measure of economic structure, is also expected to enhance the demand for renewable

energy. However, the negative sign of P and T indicates that increase in energy price

and technical progress will lead to decrease energy demand. The negative sign of

technical progress shows that the technology is energy saving.

Lastly, the impact of economic growth, population density and renewable energy on

energy intensity is found negative, which suggests that there is a reduction in energy

intensity with the increase in aforesaid factors. On the other hand, reduction in energy

intensity leads to the improvements in energy efficiency. Results of panel FMOLS

indicate that 1 percent increase in economic growth, renewable energy sources, and

population density decrease the energy intensity by about 0.600 percent, 0.027 percent

and 0.960 percent respectively which leads to increase in energy efficiency. Moreover,

the study found the evidence of bi-directional panel causality between energy intensity

and economic growth, urbanization as well as between energy intensity and population

density. Results provide evidence of feedback relationship between energy efficiency

xxv

and output. There is also unidirectional causality running from energy intensity to

renewable and non-renewable energy sources.

Keywords: Economic Growth, Environmental quality, Renewable energy, Non-

renewable Energy, Institutional quality, Industrialization, Energy Demand, Energy

Price, Population Density, South Asia

1

CHAPTER 1

INTRODUCTION

Energy is a fundamental component of human needs. Although it is not viewed as

compulsory requirement but is essential for the fulfillment of daily human needs (Yuko,

2004). Due to the rapid progress and industrialization improvements, the level and

power of energy utilization is essential for a nation's economic development. The

fundamental sources of energy are separated into two vital categories that is traditional

and maintainable energy sources. Traditional sources can be classified as energy from

non-renewable assets. These sources have various difficulties that include

contamination and an unnatural weather change; this has rolled out nation’s

improvement strategies to support reception of greener innovations in renewable power

sources. Renewable or maintainable energy sources are characterized as energy that can

be accomplished from assets which are actually renewed on a human extent, for

example daylight, biogas, wind, hydropower, tides, waves and geothermal warmth.

Renewable energy sources can be substituted regular or traditional sources in four

recognizable zones: power era, high temp water/space warming, engine fills, and

countryside (off-network) energy administrations. Petroleum product from fossil fuel

which incorporates coal, oil and flammable gas drove world economic development,

however this energizes arrival of carbon dioxide CO2 into the earth air, is viewed as

the fundamental driver of a dangerous atmospheric deviation and environmental change

(Stern, 2006). Renewable sources such as solar, wind, geothermal, tidal, and biomass

have an unlimited supply and these are being recycled or replaced. While energy

coming from fossil fuels, coal, oil and natural gas are non-renewable sources of energy

which is exhaustible up to a limit . We cannot enjoy the benefits of these non-renewable

energy sources forever, because they become unavailable once exhausted or in other

words these are one time use resources. Even the over-consumption of such sources can

leads to its threshold level, from where they cannot be regenerated for centuries.

Energy can be used for illumination, lighting, warming, and cookery on the one hand

and also as a medium of transportation on the other hand. Energy is required for

manufacturing consumer goods as well as capital equipment both in household and

production sectors. Globally, the demand for energy is increasing day by day. The

reasons are multiple, for instance, shift to industrialization, the introduction of new

2

technology, modernization and most importantly the desire for having a more

comfortable standard of living.

Energy is measured as an essential source for the socio-economic development of a

country whether it is renewable energy or non-renewable energy. Thus, each and every

sector of the economy has shared involvement of energy like in agriculture, industry,

transportation, social and public sector, household and business sector or trade, the use

of energy has become inevitable. Most of the developing and underdeveloped nations

are facing the issue of shortage of energy relative to its needs. (Chaudhry et al., 2015).

Traditional sources such as oil, natural gas, coal and electricity from oil are the most

important sources of energy and this energy supply approximately 90 percent of total

energy requirements at world level (Riaz, 1984). The sharp increase in oil prices and a

shortage of energy creates barriers to bring the rapid growth of the economy (Chaudhry

et al., 2015).

Economic growth of a country is denoted by the gain in Gross Domestic Product (GDP)

or the value of country’s output. On the other hand, economic development of a country

is related to the rise in the living standards of people of the country. Which enhances

consumption, investment and income level in a country. Viable development fulfills the

needs of the present with making future generations capable enough to meet their own

necessities.

The energy is used more extensively in those countries where lifestyle is more

mechanical. So it is important to examine the relationship between energy consumption

and economic growth to formulate energy policies in a country (Chaudhry et al., 2015).

There are number of factors that lead to increasing in demand for renewable energy

sources such as dependency on foreign energy sources, volatility of oil prices,

environmental problems and government policies to promote renewable energy sources

(Apergis & Payne, 2010)

Apergis and Payne (2011) derived four hypothesis relating to energy consumption and

economic growth; First, Growth hypothesis that sights the unidirectional causality

between energy consumption and economic growth. Energy consumption has a vital

role in determining economic growth both directly and indirectly by adding labor and

capital in the production process. If an increase in energy consumption affects economic

growth positively, then there will be a negative impact of energy preservation on

economic growth which is used to reduce consumption of energy. Second,

3

Conservation hypothesis which suggests that if there is a unidirectional causal

connection between economic growth and energy consumption, energy conservation

policies do not affect economic growth negatively. These policies may include a

reduction in greenhouse gas emissions, improvement in energy efficiency, and demand

management measures. Third, Feedback hypothesis that postulates bidirectional

causation between energy consumption and economic growth. It implies that energy

conservation policies may impact economic growth and in turn, economic growth may

also energy conservation policies positively. Lastly, Neutrality hypothesis, it states that

there is no causal link between energy consumption and economic growth. In other

words, energy consumption has no significant impact on real output of a country. In

this hypothesis, energy preservation policies will not affect economic growth inversely.

(Apergis et al, 2012).

Energy utilization is the greatest contributing element to global temperature change,

and additional people mean more energy spending. In fact, 75% of worldwide energy

expenditure happens in urban areas. That utilization is likely to raise as we knew the

movement from 54% of the world’s inhabitants living in city areas in 2014 to 66% by

2050 (Konz, 2015).There are four main essential components of energy use (1) energy

use in residential building, for example, for lighting, boiling, cooking, heating, cooling

and domestic appliances (2) energy use in office building and other non-industrial and

non-residential buildings, such as energy use for lighting, cooling, heating, auxiliary

appliances and office work (3) energy use in building used for entertainment and

leisure, for example shopping malls, theatres and gym centers (4) energy use for urban

traveler transport including both private vehicles and public transportation system

(CCICED Policy Research Report 2009). Rapid urbanization, industrialization, and

development of speculation have a large effect on energy utilization not only in

developed countries but also in developing countries (Shahbaz et al., 2015).

The consumption of energy from renewable sources is expected to rise at the rate of

2.6% per year from 2007 to 2035 whereas; electricity generation share will rise to 23%

in 2035. Hydroelectricity and wind energy shares were 54% and 26% respectively.

These sources have the largest share as compared to other sources of energy (Kaygusuz

et al., 2007; Kaygusuz, 2007; Sovacool, 2009).In 2012 world total energy supply

through RE was 13.2% that increased 22%. IEA has forced to increase that share by

26% in 2020 (IEA, 2015). India is the 5th largest power generator of world energy and

4

its energy demands are rising day by day (Patwardhan, 2010) while China is the world’s

largest power producer (Usage and Population Statistics, 2015) and the 2nd largest

energy consumer (IEA, 2013).

The government of Pakistan made the Alternative Energy Development Board (AEDB)

to promote the renewable energy in Pakistan in May 2013. Its main purpose was to

develop national policies and plans for mobilization of renewable’s with the goal to

attain a 10% share of RE in the total energy mix share in the country by 2015 (Business

Recorder, 2015). Pakistan with huge population is facing very high risks of economy

plus energy crises (Chien & jin, 2008) and country has to tackle this problem of

electricity shortfall at priority (Ahmed et al., 2016). Pakistan has very low oil reserves

so it spends USD 7 billion on the import of fossil fuels for electricity generation

(Khattak et al, 2006; Ashraf et al, 2009). On the backdrop of Inadequate fuel resources

coupled with dwindling economic conditions owing to low capital investment, outdated

power generation system, and liabilities in the forms circular debts supply of energy is

affected badly (GOP, 2014-15).

Potential of renewable or non-renewable energy is energy which can be provided by

specific source annually. Potential depends on technical, economic and geographical

limitations. (Boyle 2004).Energy is an essential part of society and plays a vital role to

improve social and economic living standard of society. With the passage of time man

used various types of resources to generate energy which is starting from wood to

nuclear energy. (Mirza at el., 2008)

Renewable energy resources include wind, solar, hydro, tidal waves, biomass, and

biodiesel, geothermal and non-renewable energy resources include oil, gas, and coal.

Renewable energy resources contribute to 14% of world energy consumption.

(Smaragdaki et al., 2016). In world level the requirement of electricity is increasing

sharply due to population growth and technological development. The demand of

energy overall the world reached 12,730.4 million tons oil equal (mtoe) in 2013, nearly

double that was 6629.8 million Tons oil equivalent in 1980. Among energy resources

Oil has highest percentage for electricity generation 32.9% .It is predictable that by

2100 the global energy demand could boom to 5 times of current energy demand.

Currently fossil fuels full fill three forth of global energy demand. Due to usage of fossil

fuels CO2 emitted in environment which caused greenhouse gases. (Halder et al., 2015)

5

Due to the problem of fossil fuels reduction, problems on energy security and

surroundings issues lead the societies to utilize various sources of energy. Renewable

energy resources are used for electricity production as there are energy scarcities

problems are facing by many countries around the world. As ASEAN countries produce

electricity from fossil fuels. In 2009, about 94.5% of electricity in Malaysia was

produced by usage of fossil fuels. (PTM annual report 2009)

The growth rate of real per capita income of these selected four South Asian countries

is not comparable. According to the statistics revealed by the World Bank in 2014, India

ranked at 7th in the world with having 5.9% growth rate, Sri Lanka ranked at 8th in the

world with 5.7% growth rate and Bangladesh ranked at 12th with 5.2% growth rate. For

the case of Pakistan it ranked at lowest with 2.0% growth rate. South Asia is also one

of the most populous region on the universe.

In South Asia problem of energy, crises are common. Pakistan has to face worst

electricity crises in 2007 when electricity production goes down to 6000 MW.

According to the report of International Energy Agency 38% population is without the

facility for electricity (Nawaz et al). Annual energy demand growth was 8% during

2005 to 2010 if this trend continues than total demand would reach 474 GW in 2050.It

was estimated that Pakistan electricity demand would reach 50,000MW in 2050. (GOP,

2009). In Bangladesh, only 30 % of rural households have the facility to electricity and

in Nepal, load shading goes to almost 20 hours during the dry season (Halder at el.,

2015).

South Asian countries highly depend on imported oil. The region has a vast variety of

resources such as natural gas, oil, coal, wind, solar and hydropower. India is at first

rank in oil resources in South Asia and has the potential of 5,576 million tons of oil

equivalent (Mtoe).Pakistan oil potential is 3,600 million tons and potential in

Bangladesh is 0.96 million tons. Afghanistan tops the region in gas resources with 120

billion cubic meters. Pakistan has the potential of 7,985 billion cubic meters. The

potential of Coal reserves in India are 245,690 million tons, in Pakistan are 185,000

million tons and Bangladesh has 2,715 million tons of coal potential. The region has an

enormous potential for hydro power and only 9% has been utilized. (Regional repot,

2015)

6

There is a huge gap in the region of power demand and supply. Electricity generates

through different resources in India and Pakistan. Bhutan and Nepal depend on

hydropower. Bangladesh is severely reliant on gas and Sri Lanka on oil. Afghanistan

imports from central and west Asia to full fill its energy demand. Nepal has the

80,000MW potential of hydro but installed only 1.83 MW. India, Pakistan, Bhutan have

gross hydropower potential 148,700 MW, 100,00 MW, 30,000MW and installed

capacity is 39,060 MW,6555MW ,1,488MW respectively. Energy assistance could

solve various regional problems but there was a huge potential for each region exist that

need to explore. (Wijayatunga& Fernando, 2013).

Energy is fundamental to the quality of our lives. Nowadays, we are totally dependent

on energy for living and working. It is a key ingredient in all sectors of modern

economies either it is household, industrial, agricultural, transport or any other sector,

it is an important to input for economic development (Turkekul & Unakıtan, 2011).

Energy demand is fulfilled through different sources including both renewable

(sun, wind, wave, biomass and geothermal energies etc) and non-renewable energy

sources (coal, natural gas, oil etc). Energy demand is derived demand (Peach, 2011),

which is required to meet the demand for lighting, cooking, electricity generation

among many other uses. With the increase in population and technological

advancement, energy consumption also increases, while energy supply is limited which

leads to a price increase (Turkekul & Unakıtan, 2011).

Elasticities are calculated to analyze energy demand and supply, which indicates that

how much responsive the energy demanded and supplied is to the relevant variables.

The major energy demand drivers include price of energy, income and substitute price.

Price elasticity of energy measures the degree of responsiveness of energy

demand/supply to change in energy this will be own price elasticity. Price elasticity is

equal to percentage change in quantity divided by percentage change in price. While

cross-price elasticity shows the sensitivity of energy demand to change in the price of

another energy source. Income elasticity measures the degree of responsiveness to real

income. Income elasticity is equal to percentage change in quantity divided by

percentage change in income (Stiglitz et al., 2013).

According to economic theory own price elasticities are typically negative, indicating

the reciprocal relationship between demand and price, while income and substitute

https://en.wikipedia.org/wiki/Wind

https://en.wikipedia.org/wiki/Surface_wave

https://en.wikipedia.org/wiki/Biomass

https://en.wikipedia.org/wiki/Geothermal_gradient

7

elasticities are expected to be positive. If price elasticity of demand is equal to zero then

it is perfectly inelastic, value between zero and one indicates that demand is inelastic

(this occurs when percentage in demand is less than percentage in price) if value is

greater than one so demand more elastic or perfect elastic (demand is affected to a

greater degree by changes In price) (Fan & Hyndman). Changes in income impact

energy demand more than the other components of demand. (Peach, 2011).

Urbanization is also an important demand driver of energy consumption, as

urbanization increases energy consumption increases (Mensah et al., 2015). In addition

to these factors, climate change also affects the energy consumption and production.

When temperature changes requirement for heating and cooling also changes like we

need air conditions in summer and heater for winter. (Dagher, 2011; Fan & Hyndman,

2011; Jamil & Ahmad, 2011; Peach, 2011; Okajima & Okajima, 2013) analyzed the

impact of climate on energy consumption.

Most of the studies estimate the elasticity of specific source of energy and these are

synonym energy elasticity. For example elasticity of electricity demand or oil demand

can be regarded as the elasticity of energy demand. In other words, specific energy

source is a subset of total energy (Peach, 2011).

Estimation of elasticity has great economic importance. Elasticity allows us to predict

behavioral responses to state energy policy. These elasticities are helpful in the

formulation of tax policy, if energy demand is price inelastic, then with the imposition

of tax on those energy sources will lead the consumers to adjust their expenditures on

other goods (Hamilton, 2011).

Elasticity estimates are helpful to control energy consumption and emission of CO2

(Burke & Liao, 2015). Non-renewable energy sources cause CO2 emission and lead to

global warming. The issue of climate change can be addressed with the use of

renewable energy, as the greenhouse gas emission reduces its use (Sadorsky, 2009).

Along with GHG emission and global warming , the issues of energy security is

emerging , Renewable energy plays an important role in reducing an emerging

country’s dependence on imported energy products (like oil and gas)., one way to deal

with these problems is to substitute renewable energy for non-renewable energy sources

8

(Sadorsky, 2009). Estimation of demand and supply elasticity of energy sources

separately will describe substitute ability of these sources energy (peach, 2011).

Between 2005 and 2030, renewable energy demand in China and India is expected to

grow at an annual average rate of 9.9% and 11.7%, respectively (IEA, 2007, p. 119).The

emerging economies have more opportunity to increase the usage of renewable energy

(Sadorsky, 2009). Emerging economies have more than 40% of existing renewable

electricity capacity, more than 70% of existing solar hot water capacity, and 45% of

biofuels production, for renewable power generation China and India both rank in the

top five countries (REN21, 2008).

1.1 Research Motivation

An extraordinary number of exact investigations have managed distinctive parts of

energy and development issues utilizing both hypothetical and experimental proof. The

audit of writing states that a relationship exists between energy use and economic

development. Be that as it may, with regards to whether energy use is an aftereffect of,

or an essential for, economic progress, there are no certain conclusions in the writing.

Stern and Cleveland (2004) see energy as a fundamental factor of creation

notwithstanding capital, work, and materials and in this manner proposed that energy

is vital for development. As opposed to the above view, Toman and Jemelkova (2003)

contended that economic advancement affects energy use. Empirically, Masih and

Masih (1996) find that energy use is essential for economic development in India. Then

again, Ghosh (2002), who additionally analyzed the connection between energy use and

economic development in India, find that energy use is a consequence of economic

development. In this manner, additionally look into on the connection between energy

use and economic development might be expected to deliver this issue because of the

blended hypothetical perspectives and observational discoveries in the literature.

As specified before, the connection between renewable and non-renewable energy

sources and economic development is related to the financial matters of energy demand.

Energy demand gauges have been utilized by various analysts and strategy chiefs to

research request conduct and furthermore to forecast, request administration and outline

of fitting energy approaches (Halicioglu, 2007). The greater part of these examinations

regularly investigate the long run and here and now effect of GDP and energy price on

9

total utilization of at least one fills, in singular segments. Since non-renewable energy

source has engaged the world economic development for a long time. The consumption

of non-sustainable power sources and the issue of an Earth-wide temperature boost, be

that as it may, have as of late pulled in wide consideration toward creating substitute

energy sources (Simsek and, Simsek, 2013). With the advances in innovation and task

for environmental manageability, renewable energy sources are winding up

progressively noteworthy options. In the meantime, renewable energy sources in the

vast majority of developing and rising economies are to a great extent undeveloped

while at the same time these nations are taking part in a worldwide progress to spotless

and low-carbon energy frameworks.

Thus, the emerging issue for empirical examination in energy led economic

development literature is that whether a progress from nonrenewable energy to, the

renewable energy source can manage economic progress in developing nations (Maji,

2015; Bhattacharya, et al. 2016). In reality, examination of the relative impacts of,

renewable energy and non-renewable energy sources on economic progress gives

profitable experiences to outline and execute reasonable energy and environmental

strategies (Apergis and Payne, 2012; Omri, 2014). The target of the present examination

is to analyze the effect of both, renewable and non-renewable energy sources use on

monetary development in India, Pakistan, Bangladesh and Sri Lanka.

1.2 Research Objectives

The main objective of the study will be to examine the prospects of renewable and non-

renewable energy sources in selected South Asian countries namely: India, Pakistan,

Bangladesh and Sri Lanka over the period of 1980 to 2014. In order to fulfill this central

objective, the specific objectives are as follows:

1. To examine empirically the impact of renewable and nonrenewable energy

sources on economic growth in South Asian economies.


sources on environmental quality in South Asian economies.

10

3. To examine the determinants of demand for renewable and nonrenewable

energy sources in South Asian countries.

4. To estimate the elasticity of demand for renewable and nonrenewable energy

market in South Asian countries.


sources on energy intensity in South Asian economies.

6. To explore the potential and prospects for renewable and nonrenewable energy

sources at South Asian economies.

7. To suggest policy implications for the concerned stakeholders to help in

decision making.

1.3 Research Contribution

There is a limited literature on South Asian energy sources including both renewable

and non-renewable energy sources. Previously studies mentioned in literature reviews

explored the relationship between energy consumption and economic development and

environment. This research work fill this gap by considering the renewable and non-

renewable energy sources with their demand elasticity in the south Asian region.

Another contribution of this study will be to undertake both regional level analysis

using panel data techniques and country-specific analysis using time-series data

techniques. Last commitment of this work will be the employment of the most recent

panel data analysis techniques for empirical analysis. Further, it will explore the

causality linkages between indicators by applying the recently evolved causality

procedure of Dumitrescu and Hurlin (2012).

1.4 Research Structure

Keeping in view the above mentioned research objectives of the thesis, the structure of

the research work will be consists of five chapters: first chapter introduces the research

background and gives an overview of the central objectives that can be achieved later

by this research. Second chapter is about the review of the literature related to the

prospects of renewable and non-renewable energy sources. Third chapter provides a

11

theoretical framework for the investigations. This chapter also discuss the methodology

and data sources for the study. The results of the data analysis are presented in fourth

chapter. Conclusions and policy recommendations drawn from the analysis are

discussed in fifth chapter. All reference of the cited literature and appendix are given in

the end.

12

CHAPTER 2

REVIEW OF LITERATURE

Both with regards to developed and developing countries, there has been far reaching

hypothetical and observational exploration to date that endeavors to concentrate on

renewable and nonrenewable energy sources and growth as well as on environmental

degradation. This section presents a brief review of the previous literature in two folds:

empiciiacal as well as theoretical.

2.1 Empirical Literature

According to the research objectives the empirical literature further categorized into

different parts. It explores firstly, the relationship between economic growth and both

energy sources with various model specifications. Secondly, the impact of both energy

sources on CO2 emissions and energy intensity. Thirdly, it investigates the factors of

energy demand and lastly it explores the relationship of energy sources, economic

growth and energy intensity.

2.1.1 Renewable and Non-Renewable Energy and Economic Growth

At the macroeconomic level, various examinations can be discovered supporting each

of the previously mentioned theories. Kraft and Kraft's (1978) investigation of the

United State started the literature. Their examination investigates the connection

between energy utilization and GDP. They found unidirectional causality from gross

domestic progress to vitality utilization yet not the other way around. Yu and Jin (1992)

was the first investigation to apply co-joining investigation to the United States. No

proof is found for a co-incorporating relationship inside the information supporting the

finish of Kraft and Kraft (1978). Co-integration investigation has turned into the

prevailing strategy to test for the nearness of the energy output speculations. Soytas and

Sari (2003) analyzed the G-7 and selected developing economies. They affirm the

finding of no causality from energy utilization to income in the United States. Lee

(2006) utilizing per capita GSP and total energy utilization find bidirectional causality

in the United States. Lee (2005) and Keppler (2007) are astounding outlines of different

economies. This little specimen of the writing proposes an accord has not risen up out

of the exact trial of this issue.

13

Stern (1993) found in the United States of America, the causal relationship between

energy (use) and gross domestic product. He used a weighted index of energy quality

by shifting lower quality energy like coal to higher quality energy like electricity.

Author employed multivariable vector autoregressive and causality tests. In the analysis

he found total use of energy did not Granger cause GDP. Again Stern (2000)

investigated the causal relationship between energy use and gross domestic product in

United States of America from 1948 to 1994. He contradicted the previous studies by

including the factors GDP, labor, capital and quality weighted energy and established

the insignificant role of labor, capital and technical change in output determination.

Cheng and Lai (1997) investigated the causal relationship between energy consumption

and economic growth in on side and the also computed causal relationship between

energy consumption and employment in the other side. Authors employed annual data

on energy consumption, gross domestic product and consumer price index for the case

of Taiwan from 1955 to 1993. They utilized Hsiao’s Granger causality and found a

positive impact of energy on growth and employment and concluded that for the

progress of economy energy is the essential ingredient. Furthermore, they advocated

that the higher level of output will influence the use of energy positively which in turn

raise the employment level.

Adjaye (2000) computed the association between energy use and per capita income for

the case of India, Thailand, Indonesia, and Philippines. He included the variables such

as energy for marketable, per capita income and prices of energy (a proxy of CPI). He

used different time spams for a couple of countries such as from 1973 to 1995 for the

case of India and Indonesia and from 1971 to 1995 for the case of Thailand and

Philippines. By utilizing Granger causality through error correction model he found

unidirectional causality between energy and income for India and Indonesia, whereas

bidirectional causality between energy and income for Thailand and Philippines.Ghosh

(2000) found no long run but unidirectional affiliation between income and electricity

utilization from 1950 to 1997 in India.

In the case of Pakistan Aqeel and Butt (2001) investigated the said Cheng and Lai

(1997) similar relationship with the extension that consumption of petroleum would be

possible due to the rapid growth. He also found there was no relation between the

economy in growth and gas consumption. However, in the power sector, he found that

14

electricity consumption led to economic growth without feedback. Finally, positive

relationship between energy and employment was found.

Altinay and Karagol (2004) estimated the link between energy consumption and

economic progress in Turkey from 1950 to 2000. They measured economic growth by

gross domestic product and energy by consumption of total energy and found no

causality by applying Hsiao’s Granger causality approach. Altinay and Karagol (2004)

also estimated the link between electricity consumption and per capita GDP again in

Turkey from 1950 to 2000 with structural break. Unidirectional causality was found

from electricity consumption to real income by Standard Granger causality approach.

Ghali and Sakka (2004) measured china’s energy situation with the connection of

energy use and per capita income including labor and capital for the era of 1961-1997.

They used multivariable co-integration approach and vector error correction model on

the anticipated structure based on neo-classical single factor aggregate production

technology. The said structure treated capital, labor and energy as separate input. The

empirical results showed that output, labor, capital, and energy share two common

stochastic trends.

Shiu and Lam (2004) measured the connection between electricity use and per capita

income in China for the era of 1971-2001. They found a co-integrated relation between

electricity use and income by applying Johanson (1988) maximum likelihood approach

and also found unidirectional causality running from electricity use to income.

Siddiqui (2004) investigated causality between economic growth and energy use in

Pakistan in the era of 1971-2003. Author measured the part of the energy in economic

progress by including the indicators of labor, capital human capital formation and

exports along with various energy sources (gas and electricity). The study found a

significant impact of electricity on growth whereas, no link between gas and output was

found.

Oh and Lee (2004) investigated the relationship between energy consumption and

growth rate of the economy including labor and capital for Korea on time series data

from 1970 to 1999. Johanson and Juselius (1990) co-integration approach and Granger

causality approach through vector error correction model were applied. They found a

positive long-run relationship between variables and unidirectional causality from

growth to energy. Oh and Lee (2004) reinvestigated the causal relationship in Korea for

15

the era of 1981-2004 and found long-run unidirectional causality from growth to energy

but no causality in short run.

Paul and Bhattacharya (2004) investigated the causality between consumption of

commercial energy and real income with the gross fixed capital formation and labor for

India from 1950-1996. Johansen multivariable approach (1991) was applied and the

positive causal relationship was found in short and long run. Authors concluded that

non-commercial energy should be substituted by commercial energy with the use of

technological progress and increased income in long run.

Yoo (2005) estimated the causal link between electricity use and real income in four

members (Indonesia, Singapore, Malaysia, and Thailand) of the Association of South

East Asian Nations for the era of 1971-2002. Using modern time series techniques he

found two-way causality between electricity use and real income in Malaysia and

Singapore whereas, there was one-way causality between both factors in Indonesia and

Thailand. In other two countries like Malaysia and Singapore, the results indicated that

electricity use had a direct effect on economic progress in turns the economic progress

stimulated more electricity use in both nations.

Lee (2005) computed the causal relationship between energy consumption and gross

domestic product in eighteen developing countries for the era of 1975-2001. He applied

FMOLS (Full modifies ordinary least square) and Granger causality techniques and

found long-run positive relationship between said indicators. He also found short-run

causality from energy consumption to gross domestic product and on the basis of results

he concluded that in developing countries energy conservation would be harmful to

economic growth.

Narayan and Singh (2006) examined the causal link between electricity use, real gross

domestic product and labor force for the case of Fiji. They applied ARDL bounds co-

integration and Granger causality approaches in the era of 1971-2002 and found

electricity use, income, and labor force were only co-integrated when income was used

as an endogenous variable. They also found unidirectional causality from electricity use

and labor force to income and concluded that conservation policies of energy could be

effect adversely on economic progress. Ho and Siu (2006) estimated the causal link

between electricity use and real income including consumer price index in Hong Kong

by using co-integration and VECM model for the era of 1966-2002. They found long-

run equilibrium link between electricity use and real income and also found

16

unidirectional causality running from electricity use to real income in short run.

Similarly, Yuana et al. (2006) projected the causal connection between electricity use

and real income (GDP) in China for the era of 1978-2004. The results of co-integration

and Granger causality showed the long run relation between variables under

consideration and one way causality running from electricity use to real income.

Rufael (2006) investigated the per capita electricity consumption and per capita real

GDP in 17 Countries of Africa for a period of 1971 to 2001. Time series data were used

to analyze long-run causal relationship between per capita consumption of electricity

and per capita real GDP by applying Pesaran et al. (2001) co-integration test. The

author also used the reformed description of Granger causality test as a result of Toda

and Yamamoto (1995). The author found evidence of log run relation between the said

two variables in 9 countries out of 17 countries and Granger causality in only 12

countries. Similarly, Rufael (2006) found a causal relationship between different kind

of energy from industries and real GDP in the time spam of 1952- 1999 in Shangai.

Author found unidirectional causality running from total energy and coke, electricity

and coal to economic growth. No causality was found between oil and economic

growth.

Lund (2007) determined the renewable energy resources to make strategies for

sustainable development in Denmark. Three technological changes were considered for

sustainable development. These changes were energy saving on demand side,

improvement in producing energy, and use of other sources of renewable energy rather

fossil fuels. This paper discussed the problems/perspectives of conversion of present

energy systems into a 100% renewable energy system. Which include transport sector

in future strategies. It was focused that whether a 100% renewable energy system was

a possibility for Denmark or not. Energy PLAN model was used to analyze the

country’s utilization of renewable energy sources in long run. Results showed all

technological changes lead to decrease in fuel consumption. The conclusion for

development was possible which was that there must be less use of oil transportation

than other sources. Second, use of small CHP plants in the system. Third, include a

focus on the wind power of the electricity supply. If these technological improvements

were achieved the renewable energy system could be created for sustainable

development.

17

Zamani (2007) examined the causal link between energy in various forms and economic

progress for the case of Iran for the era of 1967-2003. The author also analyzed the link

between agricultural and industrial sectors of Iran. The results confirmed the causality

between agricultural and industrial sectors, long-run bidirectional causality between

GDP and energy (gas) was also found.

Tang (2008) examined the causal link between electricity use and economic progress

in Malaysia for the era of 1972-2003. They applied ARDL and VECM models and

found no co-integration in Malaysia. However, the standard Granger’s test and

MWALD test found two way link between electricity use and economic growth.

Erdal et al., (2008) investigated the inter-link between energy consumption and

economic growth in the case of Turkey from the time period of 1970 to 2006. Granger

causality panel root unit tests and co-integration tests were used in this study. There

was bi-directional relationship between economic growth and energy consumption

which differed from previous studies. This showed that increase in energy consumption

increases economic growth. It was recommended that the dependence on external

sources of energy like fossil fuel or imported oil should be reduced in the case of

Turkey. Policies regarding environmental protection and energy supply security should

be taken into consideration.

Lee and Chang (2008) computed the causal relationship between energy consumption,

capital stock, labor input and real gross domestic product in sixteen developing

countries for the era of 1975-2001. He applied FMOLS (Full modifies ordinary least

square) technique to estimate co-integration vectors for heterogeneous panels. They

also applied vector error correction model for dynamic analysis of heterogeneous

panels. They found a positive long run positive relationship between said indicators. In

short run, there existed no causality between variables.

Sadorsky (2009) presented an empirical model of renewable energy consumption for

Group of 7 economies to analyze economic and societal issues regarding energy

security and global warming. In this paper, renewable energy was assumed to be a

substitute for oil. Annual data for G7 countries was collected on renewable energy

consumption, real GDP, population, CO2 emissions and oil prices. Panel co-integration

unit root tests were used to compute long run elasticities and Error correction model

(ECM) approach was used for short term elasticity. In long run real GDP per capita and

CO2 emissions were found to be a driver for renewable energy consumption. While in

18

short run renewable energy consumption were carried by its movement back to long

term equilibrium due to short run shocks. Study suggested that there would be a greater

reliance on renewable energy consumption due to energy security and global warming

concerns.

Apergis and Payne (2009) employed a panel data set for six Central American countries

to show the causal relationship between energy consumption and economic growth by

taking into account the other variables Labor and Capital. Error correction model,

Heterogeneous panel co-integration tests, panel root tests and heterogeneity tests were

used by taking annual data from 1980-2004. Heterogeneous panel co-integration test

revealed that there was the positive impact of real GDP, capital formation and labor

force on energy consumption. While Error correction model extended that there exist

short term as well as long term Granger causality from energy consumption to economic

growth. This study focused and suggested that government must divert its attention to

implement effective energy supply and demand policies in long term.

Ma et al., (2010) surveyed china’s unfavourable energy situations, it's high energy

intensity and need of development programs for environmental protection. Paper

reviewed Renewable energy laws and development policies and gaps in reviewing the

development of the country. Continuous improvement in the mobilization of renewable

energy economy was relayed on Government support programs. More research was

required to investigate and address many issues in China's economy. Renewable energy

researcher and economists needed to pay more attention to Grain-based biofuel energy

production and renewable energy substitution possibilities with fossil energy for

renewable energy economic development.

Lund et al., (2010) discussed 37 tools review individually in detail. The energy tools

were diverse because of their structure, operation, and application. Previous studies did

not provide a vast analysis of any other tools. Each of energy tool reviewed individually

in this paper. It was objected that which energy tool could make 100% renewable

energy system. The survey on these tools was conducted by tools developer’s project

at the University of Limerick. There was no computer single energy tool that solved all

issues related to the inclusion of renewable energy. Relative to the electricity sector,

CHP facility was attainable by the energy PRO tool. Different types of energy tools

were available for different conditions of analysis.

19

Apergis. N and Payne J E (2010) extended the investigations on the causal relationship

between renewable energy consumption and economic growth to the region of Eurasia.

Two panel data sets were used to conduct the empirical analysis. To define the

understanding of the results the data sets included with and without Russia. Panel data

from 1978 to 2007 of 13 countries within Eurasia was taken from World Bank

development indicator. Heterogeneous panel co-integration, panel error correction

model, and FMOLS were applied to estimate the results. Apergis and Danuletiu (2014)

also found same investigations in the case of 8o countries. In the US for the period of

1949-1960 Bowden and Payne (2010) found the positive and significant relationship

among GDP, renewable and non-renewable energy consumption in individual sectors.

Apergis and Payne (2010a) investigated the linkage between sustainable power source

utilization and monetary development over the period 1985-2005 inside the

multivariate structure. Past examinations utilized renewables to address the present

vitality utilization issues yet additionally demonstrated reasonable improvement. The

investigation contained research on twenty OECD nations to think about the degree to

which monetary development was affected by utilization of sustainable power sources.

Heterogeneous board co-coordination finding guaranteed a long-run connection

between genuine GDP, sustainable power source utilization, real gross fixed capital

formation, and the labor force. A panel vector error correction showed the existence of

both short-run and long-run bidirectional connection between utilization of

inexhaustible and financial development. Paper recommended government

arrangements required to diminish nation's reliance on outside sources.

Apergis and Payne (2010b) extended the research on the causal relationship between

renewable energy consumption and economic growth with the inclusion of measures

for capital and labor in the case of Eurasia. This relationship was categorized into four

hypothesis under multivariate panel data framework. Annual data from 1992 to 2007

were obtained from the World Bank Development Indicators for 13 Eurasian countries.

The test revealed real GDP, consumption of renewable energy, capital formation, and

the labor force has a significant relation in long run. There was a need for insertion of

development policy and utilization of renewable energy by giving tax credits or

subsidies for production and consumption of renewables.

Fang (2011) found a positive impact of renewable energy consumption on real GDP,

per capita GDP, per capita rural income and per capita urban income in China over the

20

period of 1978 – 2008. Multivariate OLS was applied to assess the renewable energy

consumption and its share in economic welfare under the framework of Cobb–Douglas

production function. Further, the effect of renewable energy consumption on economic

welfare found insignificant, and an expanding offer of REC contrarily influenced the

growth of economic welfare to a specific degree. In short, the author found that

renewable energy has expanded their share of power era that is electricity production

of China in the course of the most recent years because of security of supply and

environmental alarms.

Shah et al., (2011) dealt with the issue of unawareness of public regarding the potential

of renewable energy technologies for sustainable development. The survey was

launched at PCSIR laboratories, Hyderabad in 2009. 300 questionnaires were

distributed for the judgment of awareness level of visited stakeholders there. 69 % of

the stakeholders did not have knowledge regarding solar thermal energy devices.

Political favouritism and less trained S&T professionals were the main hurdles in

sustainable development. National education policies must be redesigned and

community participation should be raised.

Peach (2011) analyzed the causal relationship between energy consumption and

economic growth at the state level. Paper took three states of USA California,

Wyoming, and Arizona. The study used Time Series data from 1970-2007, Granger

causality test and co-integration techniques. The analysis was made in two ways: first,

only specifying demand of energy second by considering both demand and supply of

energy. Energy consumption, energy prices, manufacturing employment and price

index of primary energies as a determinant of demand and supply of energy were the

variables used in it. Gross state product (GSP) was taken as a proxy for economic

growth. Results were different for all states by only specifying demand in Arizona the

consumption did not impact the growth while in California and Wyoming causality

existed. So it was concluded that consumption of energy at the aggregate level, energy

prices lead to economic growth. Consumption of different energy sources affected

economic growth in different ways.

Apergis. N and Payne J E (2011a) investigated a positive and significant impact of

renewable energy consumption on economic growth for panel data from 1900 to 2007

in six Central American countries. There existed the bidirectional causality between

economic growth and renewable energy consumption. Apergis. N and Payne J E

21

(2011b) also found a positive and significant impact of renewable and non-renewable

energy consumption on economic growth with the increment of explanatory variables

as labor and capital for panel data from 1990 to 2007 in developed and developing

countries. There existed the bidirectional causality between economic growth and

renewable energy consumption. For the case of developing market countries, Apergis.

N and Payne J E (2011c) included electricity consumption from renewable and non-

renewable sources instead of energy consumption and also positive and significant

impact of renewable electricity consumption on economic growth over the period of

1990 to 2007. There existed the bidirectional causality between economic growth and

renewable electricity consumption. Apergis et al., (2012a, 2012b) also conducted same

studies and found similar results in eighty countries and in Central America,

respectively.

Ahmad (2012) investigated the partial impacts of trade-openness and institutional

quality on monetary development crosswise over nations utilizing panel informational

collection using the accessible panel arrangement for institutional quality. Utilizing

GMM technique which consumed interior instruments, they assessed a specification for

the standard equation of development condition. The assessments considered the

endogeneity of the independent variables and found both trade openness and

institutional quality had a significant and strong share of growth. Author additionally

discovered partial impacts of trade-openness on per-capita GDP was higher for the

developing nations, while neither trade-openness nor institutional quality was

discovered significant for developed nations.

Yildrim et al., (2012) determined the issue that economic growth should be carried

through the consumption of renewable energy. Model consisted of total output,

employment, investment and different types of renewables consumption. Timespan of

1949-2010 by using panel root unit tests and optimal lag order were used. Bootstrap

corrected causality test and toda-yamamoto test which was based on asymptotic

distribution were used in the analysis. Study found inter-relationship between biomass

wastes derived energy consumption and real GD. Results revealed that countries that

used other energy sources did not took any advantage from energy through waste. And

suggested that country should take energy producing from waste as an alternative

option.

22

Garces E and Daim T U (2012) empirically investigated the positive impact of R&D

investment in renewable energy technology in short and long run. The Study also

explored that in long run technological innovation positively affected the US economy.

The conclusion was made that energy from renewable technologies will play an

important role in the future economy. The dynamic relationship among variables was

estimated by co-integration analysis. A model was constructed on the base of GPT

theoretical assumptions. Time series data of 33 years from 1976 to 2006 was used to

study the two effects on multifactor productivity. The effects were the R&D investment

and the R&D investment in renewable energy technologies of the US economy.

Shahbaz et al. (2012) investigated the relationship between per capita gross domestic

product and renewable and non-renewable energy consumption using the framework of

Cobb-Douglas production function. They used annual data for the period of 1972-2011

in Pakistan. ARDL bounds test and Gregory and Hansen (1990) structural break co-

integration approaches for long run were applied. Findings confirmed the existence of

co-integration between renewable, non-renewable energy consumption, and economic

growth and showed that both renewable and nonrenewable energy consumption

improved economic growth. Causality investigation by the VECM Granger

authenticated the existence of feedback hypotheses between economic growth and both

renewable and non-renewable energy consumption.

Tugcu et al. (2012) investigated causal relationships between renewable and non-

renewable energy consumption and economic growth in G7 economies over the period

of 1980 – 2009 in long run. Authors made a comparative analysis between both sources

of energy by assessing various production function as traditional and augmented

production functions. For co-integration analysis, Autoregressive Distributed Lag

approach was utilized. Likewise, causality among energy utilization and economic

development was researched. The computed results showed both energy consumption

(RE and NRE) matters for GDP growth and the measured relationship is more

effectively explained by augmented production function than traditional. Then again,

albeit bidirectional causality is found for all nations if there should arise an occurrence

of traditional production function, combined outcomes are found for every nation when

the augmented production function was applied.

23

Hossain (2012) inspected the connection between energy utilization and exports by

including remittances from foreign and economic development as extra determinants

contributing factors in the region of SAARC. The author examined the countries

particularly Pakistan, India, and Bangladesh from the region. The authors exposed the

no causality between exports and demand for electric power control.

In OECD countries, Dedeoglu and Kaya (2013) inquired the association between

exports, imports, and energy use. Authors also included financial improvement as

additional determinant of trade responsiveness and energy use. They processed the

panel co-integration methodology developed by Pedroni (2004) and Canning and

Pedroni (2008). Their examination exhibited the co-integration between the aforesaid

factors. They found positive impact of economic improvement, exports and imports on

energy use. The bidirectional causality was found between exchanges (imports) and

energy usage.

The institutional quality effect on growth has been explored by Emmanuel and Ebi

(2013) who inspected the relationship between "institutional quality, oil assets and

economic development" in Nigeria, Brazil, and Canada in the period of 2000 to 2010

utilizing a difference-in-differences approach. The investigation found that there were

contrasts in the growth rate of Nigeria and Canada from one perspective, and Nigeria

and Brazil and that such contrasts in the scrutinized rate of economic growth between

were because of contrasts in the level of violation among the nations. Besides, it was

watched that there exist bidirectional causality between the distinctions in the level of

defilement and the distinctions in government viability.

Al-Mulali et al. (2013) calculated the two way long run association among GDP growth

and renewable energy consumption by categorizing different economies on the basis of

income. Ordinary least square was applied after modification and found that there exist

two-way positive relationship between GDP growth and renewable energy

consumption for 79 percent economies in long run. Contrarily these variables do not

show any relation to 19 percent countries in the long run. Two percent economies

showed unidirectional long run relation from GDP growth to renewable energy

consumption. So, the results were not same for all countries but it was found that the

two way long run relation was more significant between GDP growth and renewable

energy consumption.

24

Ocal and Aslan (2013) found a negative impact of renewable energy consumption on

GDP growth in Turkey by using data from 1990-2010. Results from ARDL method

shows that the impact of renewable energy consumption was negative on economic

growth. The unidirectional causality was also found from economic growth to

consumption of renewable energy with the help of Toda-Yamamoto tests. Energy

consumption from renewables was observed to be costly to develop nations and ascend

in GDP is a noteworthy determinant of expanded renewable energy utilization.

Okoh and Ebi (2013) researched the effect of the nexus between the investment in

groundwork and institutional quality-capture utilizing corruption and the

implementation of agreements, and economic growth in Nigeria. The examination

found that corruption had a negative furthermore, huge significant impact, while

interest in framework had a positive and noteworthy development effect. Then again,

the institutional quality-infrastructural venture nexus had an insignificant development

effect. In a comparable tone, Ologunla et al. (2014) inspected the nexus organizations

assets curse nexus in Nigeria in the period of 1986 to 2012, utilizing the Granger

causality test. The investigation utilized economic freedom (ECF) of the world as an

intermediary for institutional quality. The aftereffects of the examination demonstrated

the presence of a negative relationship between's the nearness of solid establishments

and asset curse in Nigeria.

Fu H C and Pao H T (2013) investigated the impact of various four types of energy on

economic growth in Brazil over the period of 1980 – 2010. Co-integration and VECM

approaches were applied which revealed long run relation between all variables. He

found the positive and significant impact of the ratio of renewable energy (non-

hydroelectric) consumption to total renewable energy consumption on economic

growth. The impact of the ratio of non-renewable energy consumption to the total

primary energy consumption on economic growth was insignificant. The results from

the vector error correction models reveal a unidirectional causality from renewable

energy (non-hydroelectric) consumption to economic growth, a bidirectional causality

between total renewable energy consumption and economic growth, and a

unidirectional causality from economic growth to renewable energy consumption or

total energy consumption in long run deprived of the hypothesis of feedback.

25

Iyoboyi and Latifah (2014) analyzed the connection between "institutional limit and

macroeconomic execution" in Nigeria in the period of 1961 - 2011 utilizing the VECM

method. The aftereffect of the impulse response work demonstrated that a one standard

deviation improvement on institutional limit has a negative effect on the execution of

the Nigerian economy in the short, medium and long run. Then again, the outcomes of

the variance decomposition uncovered that noteworthy measure of the adjustments in

the macroeconomic execution in the nation was not inferable from changes in the limit

of institutions.

Aïssaa et al. (2014) clarified the relationship among growth, energy consumption from

renewable source and exchange by utilizing annual data of 11 nations from Africa

nations over the period of 1980 - 2008. They applied panel ECM approach and found

significant and positive impact of renewable energy and trade on growth. There also

existed uni-directional causality from growth to trade in short run. There was no

causality from growth to trade and energy consumption in long run.

Al-mulali et al. (2014) investigated the impact of electricity consumption from both

renewable and non-renewable sources on GDP in eighteen countries of Latin American.

The author included control variables trade, labor and capital formation (gross fixed)

and applied Pedroni co-integration, panel Dynamic Ordinary Least Squares (DOLS)

and Vector Error Correction approaches on panel data over the period of 1980 – 2010.

He found the existence of long-run co-integrating relationship among all variables and

significant impact of those variables on GDP in all countries. There also existed

feedback causality among all the concerned variables. The study specified that in

stimulating GDP, renewable electricity consumption from renewable sources was more

significant than from non-renewables.

Shahbaz et al., (2015) extended the association between consumption of renewable

energy and growth in Pakistan. Cobb-Douglas production function was used over the

period 1972Q1-2011Q4 to check association between renewable’s consumption and

economic growth with labor and capital. The ARDL approach to co-integration checked

the long run relationship between these variables. Renewable energy consumption

raised economic growth, which was proved through various tests and similar analysis

could be used for non-renewable energy consumption as well.

26

Apergis and Eleftheriou (2015) used panel data from 1995 to 2011 in selected countries

of Europe, Asia, and Latin America. To find the association of different institutional

and political factors with renewable energy consumption they used error correction

model and causality approach in the long run and short run. They found a statistically

significant and strong effect of an institutional and political factor on the consumption

of renewable energy by keeping economic environment constant. They pointed out

some institutional and political hurdles in the way of renewable energy strategies like

the traditional behaviour of households and old legislation. They suggested making

clean energy policies on the basis of study outcomes.

Mulali and Ozturk (2015) examined the trials that initiated the environmental

deterioration in MINA state. The ecological footprint was used as a good sign of ED

over the period 1996 – 2012 probing fourteen MENA countries. The Padroni test

exposed that ecological footprint, trade openness; URB, EC, political stability and

industrial development are Cointegrated. The FMOLS test revealed the long run

relationship of ED with URB, trade openness, EC and industrial expansion while the

political stability has negative relationship with environmental deterioration decrease

in the long run. The Granger causality test concluded that all the variables used in the

study have short run and long run causal association with the ecological footprint. The

policy recommendation suggested that MENA countries should reduce their ED.

Shahbaz et al. (2015) anticipated the effect of URB, affluence and trade openness on

EC by employing the STIRPAT model in Malaysia on the working period of 1970Q-

2011Q. ARDL model was utilized to check the short run and the long run relationships

between variables. Further dummy variable had been also incorporated into detention

the structural break raised in the sequences. The unidirectional causality is found among

URB to EC by causality analysis and the bidirectional relationship is also found in

energy and capital. Trade openness was also caused EC and bidirectional causality

exists between EC and exchange openness.

Guan and Zhou (2015) examined the decomposed effecting factors of URB on EC of

China from the time period 1980 to 2012 by using LMDI strategy, the change of EC in

the two segment is decomposed into several effects related to URB.A fast growth rate

increase in urban population is throwing a significant effect on both residual and

production sector in China, affecting the EC. The empirical results showed that the

27

change of energy in production sector is the dominating element of EC in China.

Among the affecting elements, the economic growth urged by URB is the primary

component driving the expansion of EC, while technical change is the key element in

energy saving. In the residential sector, the EC distinction amongst URB and rural

resident is also confirmed.

Bhattacharya et al., (2016) found the effect of renewable energy as compared to non-

renewable energy consumption on the economic growth in most countries.

Heterogeneous panel techniques were used to check long run relationship of real GDP

for 38 countries renewable energy consumption over the period of 1991-2012. Paper

defined production function with traditional inputs, renewable, and non-renewable

energy sources into the production process. Analysis revealed these countries were

largely dependent on international trade so low carbon energy mix will have no

significant effect on economic growth. The investigation concluded investment,

development of human capital political barriers were strategies needed to mobilize

renewables across countries.

Sharif and Raza (2016) studied the dynamic relationship between URB, EC, and

income in Pakistan by taking the time series data from 1972 to 2013. The study used

three methodologies for Co integration and affirmed positive interaction between urban

people and CO2 outflows. The strength of the Co integration vector is further checked

by utilizing FMOLS and DOSL test and the outcome approves the long run coefficient.

The consequences of VDM display the uni-directional causality between carbon

dioxide discharge and URB runs from URB to carbon dioxide outflow. It was

subsequently noticed that policy in which the government needs to assign more portion

of environment protection and EC saving framework and making a chain of increasing

elements of environmental protection and energy saving.

Sitharam and Dhindaw (2016) Studied that URB had happened quickly in India mainly

because of social, monetary and political drivers and had offered enhanced personal

satisfaction, access to convenient and economic opportunities for many. However, this

had been accompanied by challenges that incorporate energy, absence of urban

transportation, poor conveyance of basic services, resulting in adverse environmental

effect, urban sprawl and congestion. India’s URB was placed incredible demand on the

country’s resources. Providing energy to all while keeping up a low carbon emission

was a worldwide need. Although economic development was tied down by both URB

28

and industrialization, URB it was a noteworthy determinant of energy use, including

energy use identified with transportation. There was a lack of urban planning and

management of urban cities, though to be the overcome of India’s urban environment

is to meet the increasing desires of an extending urban population and provide an

atmosphere consistent with rapid, comprehensive and sustainable growth. India’s

energy demand in 2030 was likely to be doubled that of current demand and achieving

a greener future economically with low energy cost can be tended to by measures, for

example, particular measures such as renewable, investment in technology and

empowerment of local government to meet the low carbon energy needs in India.

Khobai and Roux (2017) investigated the relationship between energy consumption,

carbon dioxide emission, economic growth, trade openness and urbanization. They

employed annual data for the period for 1971 to 2013 on South Africa. For the long run

relationship between energy consumption, carbon dioxide emission, economic growth,

trade openness and urbanization, Johansen test of co-integration was applied. The

results showed the existence of long run relationship between variables. Outcomes of

Vector Error Correction Model (VECM) Granger causality indicated that there was

bidirectional causality flowing between energy consumption and economic growth in

the long run. The VECM results further found a unidirectional causality flowing from

carbon dioxide emissions, economic growth, trade openness and urbanization to energy

consumption and from energy consumption, carbon dioxide emissions, trade openness

and urbanization to economic growth. These results posited a fresh perspective for

creating energy policies that will boost economic growth.

Kahouli (2018) investigated the causal relationship between energy consumption,

financial development and economic growth in short and long run. For the

investigation, they applied multistep techniques such as ARDL, VECM and co-

integration in SMCs for the period of 1995-2015. They found in the long run co-

integration exists between energy consumption, financial development and economic

growth. And for the short run Granger causality was applied which gives mixed results.

Pramati et al. (2018) investigated the relationship between renewable, non-renewable

energy sources and economic growth in 17-G20 countries. They used the panel data for

the period of 1980-2012 and applied panel co-integration and FMOLS techniques. They

found positive and significant impact of renewable and non-renewable energies on

29

economic growth and also concluded that renewable energy consumption contribute

more in economic growth than non-renewable energy.

Natonas et al. (2018) investigated the relationship between renewable, non-renewable

energy sources, gross domestic product, labor force and gross fixed capital in 25

European countries. They used the panel data for the period of 2007-2016 and applied

autoregressive distributed lag model with cluster technique. They found positive impact

of renewable and non-renewable energies on gross domestic product.

Bilan et al. (2019) investigated the relationship between renewable energy sources and

gross domestic product including other macroeconomic variables such as political

stability and carbon emissions. They used the panel data for the period of 1995-2015

on the member of EU countries and applied OLS, FMOLS and Pedroni panel co-

integration techniques. They found positive impact of renewable energies on gross

domestic product.

Chandio et al. (2019) investigated the co-integration and causal relationship between

energy consumption and economic growth in Pakistan. They used the time series data

from 1983-2017 and applied ARDL approach to finds the link between variables. They

examined the link between energy use from coal, gas and electricity for the industrial

sector with the renewable energy consumption and financial development of Pakistan.

They found the long-run co-integration link between renewable energy and economic

development. They also found the bi-directional causality between industrial sector and

economic growth.

Jabeur (2019) investigated the relationship between renewable energy consumption and

economic growth with labor force in France. They applied ordinary least square,

dynamic ordinary least square, fully modified ordinary least squares methods for the

time series data for the period of 1987-2017. The results showed the renewable energy

consumption, total labor force and gross fixed capital contributes in gross domestic

product.

Ahmed and Shimda (2019) investigated the effect of renewable energy on sustainable

economic development using panel data of 30 emerging and developing countries. They

applied dynamic and fully modified ordinary least square methods. They found that in

selected Asian, South Asian and African countries there was a significant impact of

30

renewable energy on economic growth. In addition, they also found that economic

growth and nonrenewable energy leads to increase carbon emissions.

2.1.2 Renewable, Non-Renewable Energy Sources and Environmental

Quality

Soytas et al., (2007) analyzed that energy consumption Granger cause per capita

emissions in the case of United States but output does not. He concluded that in the

long run only output cannot be solve the environmental problems. Chiu and Chang

(2009) found negative impact of renewable energy on CO2 emissions and positive

impact of economic growth and non-renewable energy on CO2 emissions.

Zarzoso (2008) worked on a data from 1975 to 2003 pertaining to different income

groups of countries, classified into various groups. The special feature of his work is

that resident is treated as an indicator in the model, rather than utilizing whole

population as a variable with the ambition of having connection with CO2 production.

Further prevalence of heterogeneity in the sample was also considered along with a

check for the unvarying estimated elasticities over time. The outcomes showed that,

though the effect of population expansion of emission greater than one and somewhat

atypical for all countries. The variables pertaining to population parameters, especially,

URB showed a very different effect on emission for LIC; HIC and upper MIC (middle

income countries). The countries in first group showed the responsiveness of emission

was greater than 1 for URB while in second group, the elasticity is 0.72 which is in

agreement with the higher the environmental effect was observed in less developed

countries. Though, in upper MIC the elasticity, is negative, albeit the heterogeneous

effect of URB on CO2 might be considered in further research work and policies to

tackle environmental issues.

Varun et al., (2009) compared conventional fuel based systems for extent of choice of

renewable energy sources to country’s sustainability. The aim of this paper was to

review different energy and CO2 life cycle analyses for electricity generation systems

with renewable sources. LCA methodology was applied to check the impact of

electricity generation on the environment. And allowed producers to make better

decisions for environmental protection. Results showed the Life cycle emissions had

very high in conventional sources as compared to renewable energy sources. Tendency

towards the fact that there should be the use of renewable energy technologies.

31

Lund and Mathiesen (2009) presented the method of analysis of 100% renewable

energy systems in Denmark in years 2030 and 2050. Danish Association of Engineers

project in which 1600 participants were included designed IDA Energy Plan model.

Inclusion of high amount of renewable sources into electricity supply was its main

target. Hour by hour computer simulations with technical and economic analysis of no.

of experts and their feedback on each individual proposal was the methodology used in

this paper. Results showed if 100% renewable energy system proposed for year 2050

was implemented then primary energy supply will fall and CO2 emission will be zero.

100% renewable energy supply was dependent on country’s biomass sources.

Apergis et al., (2010) examined the connection between emission and renewable energy

use in 19 developed and developing countries. The results of Panel Granger causality

tests showed a negative and significant relationship between nuclear energy and per

capita, CO2 emissions in contrast results showed a positive and significant relationship

between energy from renewables and per capita CO2 emissions and revealed that

renewable energy consumption does not contribute to a reduction in CO2 emissions.

That might be because of the absence of sufficient stockpiling innovation to beat

discontinuous supply issues subsequently electricity makers need to depend on

emissions creating energy sources to take care of the highlighted load of electricity

demand.

Menyah et al., (2010) investigated the relationship between economic growth, energy

consumption and CO2 emissions including the variables labor and capital in the

analysis. The purpose was to check the effect of labor and capital combined with energy

on economic growth and environmental degradation. In previous studies absence of

these variable created inconsistent and false results. Autoregressive distributive lag

(ARDL) to co-integration and Granger causality tests were used in the case of South

Africa. Empirical results suggested short run and long run positive relationship between

pollutant emissions and economic growth. While direct relationship was held between

CO2 emissions and economic growth, and then from energy consumption to economic

growth and from energy consumption to CO2 emissions. The investment or policies

should be made to increase utilization of clean energy sources to reduce pollutant

emissions as well as to increase economic growth in the country.

Li et al., (2011) re-investigated the connection between energy use and economic

progress for thirty Chinese provinces for era of 1985-2007. Panel unit root test and

32

panel co-integration tests were used to check the relationship in short term and long

term in East and West China. Empirical investigation showed that time series data may

yield unreliable and inconsistent results with short term span of data sets. There was a

long run positive relationship between energy consumption and economic growth. And

a one percent rise in real income increased carbon dioxide emissions between 0.41%

and 0.43% in China. Furthermore, paper analyses concluded that in East China was

based on extensive energy consumption, low efficiency of energy use with high CO2

emissions. Study suggested that energy-saving and CO2 emissions control policies in

production field should be made in future.

Wang et al., (2011) conducted a research in twenty eight provinces of China for the

period of 1995-2007. Author observed the causal relationships between CO2 emissions,

energy consumption and real economic growth. Results showed that there was long run

co-integrated relationship between CO2 emissions, energy consumption and economic

growth. They concluded that CO2 emissions were bi-directionally linked to energy

consumption. Wang et al., (2016) also conducted research in China for the period of

1990-2012 and found co-integration relationships between CO2 emissions, energy

consumption and real economic growth. Results showed that there is long run co-

integrated relationship between CO2 emissions, energy consumption and economic

growth. They also concluded that CO2 emissions were bi-directionally linked to energy

consumption

Amer and Daim (2011) explored four types of renewable energy options for electricity

generation in Pakistan for socio-economic, political, technical, and environmental

purposes. Paper explained the potential of each primary energy sources in the country.

Analytic hierarchy process (AHP) for the first time and Multi-criteria decision analysis

(MCDA) were used by decision makers to resolve decision problem. A survey was

conducted in universities and industry to obtain subjective judgments from experts.

Pairwise comparisons were used to rank the energy alternatives for decision making.

Results revealed biomass and wind energy were preferable options for the country to

reduce CO2 emissions, and dependence on fossil fuel. It was suggested that this

proposed model can be used for long term energy policy, formation and development

of renewable energy sources in future.

Tiwari et al., (2011) analyzed the multidimensional relationship between CO2

emissions, renewable energy consumption, and economic growth. Results did not find

33

any proof of co-integration among the three variables in India. The innovations analysis

of study revealed that positive and negative shocks on the consumption of renewable

energy source in different ways. In the first case, it increases GDP and decreases CO2

emissions and in the later case, the GDP has a significant and positive impact on the

CO2 emissions.

Alam et al., (2011) used dynamic modelling approach to investigate the causal

relationship among income, energy consumption and CO2 emissions in India. He found

bi-directional causality between energy consumption and CO2 emissions in long run.

No causality found in real GDP and energy consumption and also in real GDP and CO2

emissions. For the case of Bangladesh Alam et al., (2012) found dynamic causality

existed among GDP growth, energy consumption, electricity consumption and CO2

emissions. ARDL and Johansen co-integration approach were applied for long run

relation, VECM was applied for short run analysis. He found uni-directional causality

running from energy consumption to GDP growth and bi-directional causality running

from electricity consumption to GDP growth.

Arouri et al., (2012) extended the relationship between energy consumption, economic

growth, and CO2 emissions in 12 Middle East and North African countries. Paper

aimed to test for EKC hypothesis in the MENA region for CO2 emissions investigated

EKC existence in each country, and check the nature of causality between these

variables. Panel root unit tests and panel co-integration techniques were used from the

period of 1981-2005. At regional level, results showed in MENA region energy

consumption had a positive impact on reduction in CO2 emissions in long run. EKC

hypothesis was valid at regional level but it was not valid at country level. It was

suggested in the paper that if GDP in MENA region continued to grow then there would

be a significant reduction in CO2 emissions in future as well.

Sulaiman et al. (2013), Baek and Pride (2014) found renewable energy reduced CO2

emissions and economic growth and non-renewable energy increased CO2 emissions.

Bölük and Mert (2014) examined the future aspects of renewable energy sources in

Turkey. The results indicated that in the long run, renewable energy sources and Co2

emissions were negatively and significantly related. This effect was observed positive

and statistically significant in short run revealing that with one year lag, renewable

electricity production tends to contribute towards the enhancement of environmental

34

quality. The results also suggested a U-shaped (EKC) relationship between income and

per capita GHGs.

Shafiei and Salim (2014) found the factors affecting carbon dioxide CO2 emissions

with the help of STIRPAT model by using panel data of OECD countries from 1980-

2011. The results of study indicated that CO2 emission increased as consumption of

non-renewable energy increased and it decreased as renewable energy consumption

decreased. Results showed that GDP per capita, population size, urbanization and

industrialization had a positive and significant impact on carbon dioxide CO2

emissions. They also construct an environmental Kuznets curve among urbanization

and carbon dioxide CO2 emissions which shows the decrease in environmental impact

with higher urbanization level. They recommended that policies should formulate

keeping attention on urban planning and clean energy development. The policies in the

favour of renewable energy can clean climate and the use of non-renewable energy.

Government should make policies to attract the investors to invest in renewable energy

for continuous growth.

Jebli et al., (2015) found statistically significant positive relationship between

emissions, gross domestic product (GDP), renewable energy consumption and

international trade in 24 sub-Saharan Africa countries from 1980 to 2012. Granger

causality results showed bidirectional causality from economic growth to CO2

emissions. The results also revealed a positive relationship between CO2 emissions and

real exports and unidirectional causality from real imports to CO2 emissions. The long-

run estimates suggested that the inverted U-shaped EKC hypothesis was not supported

in case of those countries. Jebli et al. (2016) also investigated same findings in the case

of twenty five OECD countries in the period of 1980–2010. In that analysis inverted

U-shaped (EKC) assumption was verified.

Azam and Khan (2015) studied the effect of URB alongside some other logical

variables on natural deterioration measured by CO2 emanation for four nations from

the SAARC countries in particular Bangladesh, India, Pakistan and Sri Lanka. The time

series data was utilized from 1982 to 2013. After utilizing different statistical test, the

technique of Least squares estimate showed that the effect of URB on the atmosphere

is fixed. In case of India and Bangladesh relationship between URB development

furthermore, environment found was essentially negative, while the effect of URB on

35

environment was altogether positive if there should be an occurrence of Sri Lanka and

insignificantly positive for Pakistan in the period under the study. The discovery of the

study recommend that the arrangement producer need to figure-fitting strategy for long

run urban arranging which can absolutely mitigate generally CO2 outflow.

Farhani and Ozturk (2015) empirically analyzed the fundamental relationship between

Real GDP, CO2 emission, trade openness, EC, and urban population of Tunisia

covering the working period of 1971 to 2012. CO2 emissions were used as dependent

variable, per capita, of real GDP, financial progress, trade openness, EC, and URB had

also been used as independent variables. The results from using ARDL to Co-

integration procedure found a positive relationship among all these variables (financial

development, real GDP, and CO2 emission). It means that result did not sustenance the

validity of Environmental Kuznets Curve (EKC). Additionally, explored the causal

correlation between variables by applying Granger causality model and it was

concluded that financial development plays an important part in the Tunisian economy.

Heidari et al. (2015) showed the relationship between CO2 emission, EC and financial

development with the plan to check the stability of the EKC in five Association of South

East Asian Nation (ASEAN) countries (Malaysia, Philippines, Thailand, Singapore,

and Indonesia,) by employing the PSTR model. The PSTR model was more appropriate

and suitable for describing the heterogeneity and time instability. The outcomes

demonstrated that economic growth raised ecological degradation while the reversed

trend in the second regime and EC direct to increase CO2 emission either the first or

the second. The outcomes demonstrated that EC expanded CO2 emissions in both

regimes. The general results bolster the stability of the EKC.

Nejat et al. (2015) observed the current trend and condition of EC, CO2 emission and

energy policies in the residential sector in top ten CO2 emitting countries, including

(China, Japan, India, US, South Korea, Germany, Russia, Iran, Canada, and UK). They

considered the EC, URB and economic growth are the major driving factor of CO2

emissions and found that the household sector spreads 27% and 17% of worldwide EC

and CO2 emissions. The global household EC increased by 14% from 2000 to 2011.

Greenhouse gas emission and residential EC have a significant role in reducing EC.

There was a reducing trend of CO2 emissions in the UK, Russia, Germany, and South

Korea, Canada, but the increasing trend of CO2 emissions in India, Iran, and China.

36

Ara Begum et al. (2015) studied the dynamic effects of population expansion, the

growth of GDP and EC on CO2 discharges for Malaysia over the period of 1970-1980.

The DOLS (dynamic ordinary least squares) and SLMU (sasabuchi-Lind-Mehlum U)

test was used to test the stability of the variables. The hypothesis of EKC was not valid

in Malaysia during the study period. The results demonstrated both GDP per capita and

per capita of EC was a long run positive effect on CO2 emission, but population growth

had been no significant effect on CO2 emission in Malaysia. The significant

transportation of low carbon technologies such as energy efficiency and renewable

energy could contribute to decrease the carbon emission and maintain the long run

economic development.

Li and Lin (2015) concentrated on the impact of mechanization and URB on EC and

CO2 discharges. The STIRPAT model was utilized to perceive the ratchet effect and

heterogeneity of a balanced panel dataset of 73 nations over the era of 1971 to 2010

divided into four groups. The outcomes demonstrated URB lessened EC and expanded

CO2 outflow in low income country. Industrialization diminished EC and expanded

CO2 outflow in middle and high income group country. In middle, high income country

URB and industrialization were an insignificant effect on EC and CO2 emission.

Finally, findings reveal that improvement techniques of URB and industrialization

conserve energy and diminished outflow.

Cetin and Ecevit (2015) explored the affiliation between URB, EC and carbon dioxide

CO2 emanations in Sub-Saharan nations over the period 1985-2010. Kao and Pedroni

Cointegration techniques and the Granger causality based on the VECM test was

utilized to conduct this empirical analysis. It is additionally found that there exists bi-

directional Granger causality between a few variables like in the short run as well as in

the long run, between EC and CO2 outflows. The outcomes suggest that the EC and

URB are the fundamental component of natural contamination in these countries. The

policy implication should be taken to reduce the ED.

Bilgili et al. (2016), Al-Mulali and Ozturk (2016), Dogan and Seker (2016b, c), and

Bento and Moutinho (2016) also found negative impact of renewable energy on CO2

emissions which mitigate the pollution and positive impact of economic growth and

non-renewable energy on CO2 emissions which leads to pollutant environment.

37

Ahmed et al., (2016) reviewed comparatively the renewable energy sectors of and

sharing opportunities between China, India and Pakistan (CIP). The paper discussed

future energy demands and plans of energy mix of CIP. CIP consisted 40% world

population and 29% contribution to energy consumption. Pakistan was far less energy-

consuming country with 85.75 MTOE but through energy sharing opportunities it could

do better. By overcoming overall world energy demands and per capita energy

consumption resulted economic growth of the country. CIP had high nuclear capability

and energy sharing potential for socio-economic development and world prosperity.

Non-renewable fossil fuel energy which was costly because of CIP’s dependency,

which lead to higher CO2 emissions to environment. While cleaner resources were

hydro, solar, biogas, wind and geothermal energy. Paper also reviewed that exploitation

of CIP’s dependency on non-renewable energy and energy sharing plans could

contribute to worldwide prosperity.

Chang (2016) investigated the intensity of extraction of carbon emissions and use of

energy sources on China’s economy. Multivariate co-integration Granger causality tests

and Vector error correction model (VECM) were used in it. Results showed bi-

directional causality from GDP to CO2 emissions and electricity consumption to GDP.

And revealed that this increased GDP growth or energy consumption would stimulate

CO2 emissions. The critique on this study was that it only focused on fossil fuel energy

in the analysis. It was estimated that CO2 emissions could be reduced by effective

government policy or plan. While any energy conservation policy could negatively

affect economic prosperity.

Kumar (2016) extended that Energy security and carbon emissions reduction as higher

priorities for energy supply for consecutive growth of economy and development all

over the world at reasonable costs. Renewable’s potential in Indonesia and Thailand

between 2010 and 2050 were determined by using three major dimensions. The LEAP

energy model was applied to estimate the energy demand, consumption and carbon

emissions reduction possibilities and future options for electricity supply. Investigation

revealed huge potential of renewable energy to ensure supply security and reduction in

CO2 emissions. Analysis concluded increasing the share of renewables in the energy

mix brought high socio-economic benefits to Southeast Asia.

Mitic et al. (2017) analyzed the co-integration relationship between GDP (real) and

CO2 emissions in Transitional countries using the time series data from 1997 to 2014.

38

They applied Dynamic OLS and FMOLS and found existence of co-integration long

run relationship between real gross domestic product and CO2 emissions. Also, they

found positive impact of real GDP on CO2 emissions. They suggested that in

transitional economies environmental taxes should be applied to reduce CO2 emissions

with the attainment of economic growth.

Kahouli (2018) investigated the causal relationship between energy from electricity

consumption, economic growth, R&D stock and CO2 emissions in MCs for the period

of 1990-2016. They used the methodology of simultaneous equations by SUR, three-

stage least square (3SLS) and generalized method of moments (GMM) techniques.

They found strong and positive feedback effects between energy from electricity

consumption, carbon emissions, R&D stock and gross domestic product.

Ramli et al. (2019) investigated the impact of energy consumption, gross domestic

product, industrial index and employment on 𝐶𝑂2 emissions in Malaysia. They used

time series data for the period of 1986-2018 and applied OLS and found positive and

significant impact of energy on CO2 emissions and least impact of gross domestic

product on CO2 emissions. They suggested that renewable energies such as solar

energy should be adopted in Malaysia to control the environmental pollutions.

Cahia et al. (2019) examined the impact of renewable energy consumption, foreign

direct investment and economic growth on carbon emissions. They used panel data of

12 Middle East and North African countries for the period of 1980-2012. They used

vector panel autoregressive model with multi-domain analysis framework and Granger

causality techniques. They contributed that economic growth lead to environmental

degradation while renewable energy, foreign direct investment and international trade

contribute to decrease carbon emissions CO2.

2.1.3 Economic Growth and Demand Elasticity for Renewable and Non-

Renewable Energy

The connection between demand for energy and per capita real GDP (economic growth)

has grown into a divisive matter, Cheng and Lai (1997), Adjaye (2000), Shiu and Lam

(2004), Rufael (2004), Altinay and Karagol (2005), Mahadevan and Adjaye ( 2006) and

Aqeel and Butt (2001) investigated the said connection. They found positive

relationship between demand for energy and economic growth. In almost all developing

nations, the demand for energy is growing due to the growing population and economic

39

development. Demand for energy is usually accompanying with rise in per capita

energy utilization and income Hislop (1992). A nation’s economy has strongly linked

with the energy due to the huge influence of energy prices, demand and supply on the

all over aspects of economic development. According to Riaz (1984) process of

industrialization, speedy economic growth and gradually rising income increased the

demand for energy at global level.

The elasticity of energy demand is also important for the development of growing

economies. Bentzen and Engsted (1993) estimated demand elasticity of energy in

Denmark. For long run elasticities, co-integration was applied and error correction

model was applied for short run elasticities. Danish annual data for the period of 1948

– 1990 on energy consumption, real prices and real GDP was used for computed

elasticity estimates. The estimates were used for forecasting energy consumption. The

estimated income elasticities were 0.666 in short run and 1.213 in long run. While the

own price elasticities were -0.135 in short run and -0.465 in the long run. These

estimates indicated that the energy demand is less income elastic and more price elastic

in short run. The results indicated that the structural change in energy demand was not

caused by increased prices of energy.

Liu (2004) calculated the price and income elasticities of energy demand. Study

covered OECD’s residential and industrial area. They used panel data techniques on the

data from 1978-1999. The study applied one-step GMM model and energy demand as

specified by the partial adjustment model (ADL). The results indicated that price

elasticities of energy demand were higher in absolute value. Price elasticities were

higher in residential area than industrial sector. Short run price elasticity ranged from -

0.17 to 0.1.6 and long run price elasticity were -0.52 to 0.59. While income elasticities

were higher in industrial sector and lower in residential sector. Estimated income

elasticity was around unity.

Ziramba (2010) estimated the price and income elasticities of crude oil import demand

in South Africa. Short run and long run elasticities were estimated by applying the

Johansen co-integration multivariate analysis on annual time series data for the time

period 1980-2006. Error correction model was applied to analyze the short run demand

behaviour. According to results, crude oil import demand was inelastic in short run,

while the long run price and income elasticities were -0.147 and 0.429 respectively.

40

Crotte at el., (2010) estimated short run and long run price, income, vehicle stock and

cross elasticities of demand for gasoline in Mexico. They used time series data from

1980 to 2006, applied co-integration technique and GMM model on penal data of 30

states over period 1993-2004. This study estimated elasticities at both national and local

level and the elasticities were different at the national and local level and changed over

time. The estimated long run elasticities at the local level in Mexico City were -0.2 to -

0.26, and at the national level, it ranged from -0.6 to-0.8. The results showed that the

fuel efficiency elasticities were smaller and vehicle stock had more impact on gasoline

demand than its consumption.

Chaudhry (2010) Estimated price and income elasticities of electricity demand in

Pakistan. This study was based on two level analysis one for whole economy and other

on firm level. It used penal data of countries including China, Indonesia, India, Korea

and Pakistan over the time period1998-2008 to analyze income elasticities. The

estimated income elasticity in Pakistan was 0.69, and the results indicated that the

elasticities were high in low and middle income countries as compared to high income

countries. For the firm level analysis, this paper used Cobb-Douglas production

function and used annual data of electricity consumption, output of firm and electricity

prices. The estimated price elasticity for firm was -0.6.

Chaudhry (2010) estimated income elasticity of electricity demand at household level

in Pakistan. It considered income of consumer and ownership of appliances as demand

drives of electricity. For this purpose it used household survey data of appliances

ownership of 2003/2004, this study was based upon MICS and used GMM and

maximum likelihood estimator. Households were divided into four tiers elasticity was

estimated for each household tier separately. The income elasticity was measured as

virtual expenditures which had positive elasticity of magnitude 0.21. Elasticity of 4 tiers

was 0.16, for 2nd tier 0.19. This paper used elasticity for tariff structure, they suggested

that high burden of tariff should be upon high income groups.

Yanagisawa (2011) estimated the price elasticity of energy demand in Japan for

different sectors including commercial, residential, passenger, freight sector. It made

two types of analysis of elasticity one included the structural change and others

completely excluded its impact on price elasticity. Price elasticity was estimated by

calculating the ratio of actual energy consumption in each sector to the reference it will

41

exclude the impact of structural change, it also used the base year and compare the

results to estimate structural change. The estimate short run price elasticities were -0.01

to -0.06 and in long run, it ranged from -0.1 to -0.2 the results indicated that when

structural changed were excluded price elasticity became higher.

Moore (2011) Estimated oil demand elasticity in Barbados and used monthly data of

time period 1998-2009. He considered price, income, electricity consumption and

lagged oil demand as demand drivers and applies single equation co-integration model

Pesran (2001), unrestricted error correction model and ARDL approach to find short

run and long run elasticities. The results showed that all the variables had significant

impact in the short run. In the long run, oil demand was inelastic with respect to price

and income elasticity was insignificant in the long run. Estimated long run elasticities

of price, electricity consumption and income were -0.552, 1.43 and 0.91 respectively.

The price inelasticity indicated the oil tax as good source of revenue and suggested the

Barbados to produce electricity with source other than oil.

Peach (2011) estimated demand and supply of energy with their price elasticities in

energy market at the state level. The study covered the period of 1970-2007. To analyze

the variables affecting demand and supply of energy author used time series data.

Energy consumption, energy prices, Gross State product, manufacturing employment,

climate changes hand input prices are the factor that are analyzed in this study by using

3SLS technique. The study indicated that price elasticity of supply was more elastic

than demand so more burden was on consumers. Demand was more elastic in short run

than in long run. Price elasticity of demand ranged from -0.23 to -0.06. Price elasticity

of supply ranged from 2.43 to 7.64 these are not accurate because elasticity supply can’t

be captured in energy market. Estimated that changes in income and prices of energy

had impact on energy consumption. Climate changes had impact on both demand and

supply of energy. But its impact is different for each state and source of energy. It was

concluded that price change of renewable resources will be more prominent than non-

renewable resources.

Lin (2011) estimated supply and demand in the world oil market. Annual data was used

from 1965-2000 to choose instruments, monthly data from 1981-2000 and applied

many econometric techniques (OLS, 2SLS, SUR, 3SLS) and instrumental variable

were also included. The study assumed the oil market static and perfectly competitive

42

and discussed timeless issue of demand and supply of oil. Some of the variables used

in this study are GDP, population, price (OPEC and non-OPEC oil price), oil reserves

and electricity production from oil gas. From all the above techniques 3SLS gave better

results all the variables had expected signs by this technique. The results showed that

the instruments chosen from annual data could not identify the demand and supply

while the instruments from annual data strongly identified demand and supply. Oil

demand was inelastic for the prices from OPEC and other companies. The assumption

of static and perfectly competitive market was proved unrealistic.

Fan and Hyndman (2011) attempted to estimate price elasticity of electricity demand

in South Australia. Annual and each half hour per day price elasticities were estimated

for different demand quartiles. This study used time series data from 1st July 1997 to 30

June 2008 and applied Log-Linear econometric model while separate model was

estimated for each half hour periods. It used annual population, Gross State Product

(GSP), electricity price and climate as explanatory variables. The results indicated that

the price elasticity was high in the peak times (high in morning, low at midnight), high

in winter as compared to summer. The price elasticity changed both annually and in

hourly periods. The estimated annual price elasticity of electricity ranged from -0.21 to

-0.48 and half-hour period elasticities ranged from -0.60 to -0.505 for different demand

quantiles.

Turkekul and Unakıtan (2011) Attempted to estimate price and income elasticities of

energy demand in Turkish agricultural sector .this study estimated long run and short

run elasticities for two types of energy diesel and electricity which were assumed

substitute for each other for irrigation. Diesel and electricity consumption were used as

dependent variable and real agricultural GDP, real diesel and electricity price as

explanatory variables. All the variables were measured per capita. This study used

annual time series data from 1970 to 2008 and applied co-integration technique and

error correction model to estimate price and income elasticities. ECM was based on

Maximum Likelihood Estimator. Elasticities of substitute price were also estimated.

The results indicated that short run income and price elasticities were insignificant for

both energies, in the long run, electricity demand was price inelastic in other words

electricity demand was irresponsive to price change and diesel demand was income

elastic in the long run. The estimated income elasticity of diesel and electricity demand

43

was 1.47 and 0.19 while price elasticities were -0.38 and -0.72 respectively in the long

run.

Jamil and Ahmad (2011) estimated electricity demand elasticity in Pakistan at

aggregate and sector level. The determinants of electricity demand included in this

paper were economic activity which was represented by real GDP, electricity price,

price of substitute (diesel), stock of energy and temperature. They used annual data of

time period 1961-2008 and applied Johansen’s approach to co-integration model and

used vector error correction model to estimate elasticities. The sector analysis included

residential, commercial, manufacturing and agricultural sector. The estimated income

elasticity was greater than one and economic activity had highest impact on electricity

demand. Price elasticity was also elastic for all the sectors in long run but lowest in

agricultural sector due to excessive subsidy. Short run elasticities were almost

insignificant in all sectors. According to results substitute price elasticity was inelastic.

The estimated aggregate income and price elasticities were 0.32 and -0.07 respectively.

Maria de Fatima .S.R (2011) estimated price and income elasticities of demand for

energy in Mozambique. This study applied econometric techniques on a household

survey from (IAF) of 2002-2003. Five sources of energy including firewood, charcoal,

candles, kerosene, and electricity were analyzed. The results indicated that low grade

energy sources (firewood, charcoal) were less responsive to price and income change

than high grade sources (candles, kerosene, electricity). The estimated price and income

elasticities were charcoal -0.28, 0.32, firewood -0.41, 0.45, candles -0.88, 0.93,

kerosene -0.79, 0.84 and electricity -0.60, 0.69 respectively.

Dagher (2012) attempted to estimate residential natural gas demand elasticity at utility

level in Colorado. This study applied Autoregressive Distributed Lag Model (ARDL)

and took monthly data from January 1994 to September 2006. The explanatory

variables included in this paper were the price of natural gas, the price of electricity,

income, weather and seasonality to explain natural gas consumption. The dynamic

elasticities were estimated and results indicated that the natural gas demand was much

less responsive even in long run for both income and price increase. Its own price

elasticity ranged from -0.091 to -0.237 while cross price elasticity ranged from 0.153

to 0.398. The analysis indicated that market adjustment took place in just 18 months.

44

Sita at el., (2012) estimated price and income elasticities of gasoline demand for

Lebanon in short run. According to this study, gasoline demand was affected by

structural breaks. For this purpose, it used monthly data from January 2000 to

December 2010 and analyzed the elasticity with no structural break, single and multiple

structural breaks. And the results indicated that demand elasticities increased with

structural breaks, estimated elasticities were high in multiple structural breaks than no

structural change. Short run elasticities were found inelastic in short run, price and

income elasticity with no structural change were-0.623 and 0.309 while elasticity with

single structural change was -0.915 and 0.424 respectively.

Neto (2012) Estimated time varying elasticity of gasoline demand in Switzerland. It

estimated price and income elasticity in different times. This study used quarterly time

series data from 1973:Q1 to 2010:Q4 and applied time varying co-integration model.

He took consumption of gasoline as dependent variable and for independent variable

real GDP as income variable and real prices of gasoline were used. The results indicated

that income elasticity remain the same overall sample periods which was 0.69 on

average. For price elasticity there were two periods in which demand response to price

change was different. In the first period from 1973 to 1994 the price elasticity was -

0.37 while in the mid of 90’s. The price change had insignificant impact or not

responsive to price change but in last sample, gasoline demand started to be price

elastic. Furthermore, this study analyzed the feasibility of tax on gasoline to reduce the

CO2 emission and concluded that tax will be effective only if the demand is not price

inelastic.

Okajima and Okajima (2013) estimated the elasticity of residential electricity demand

in Japan. This paper analyzed price elasticities for the regions with different weather

and for different income groups in short run and long run. Penal data was used for the

time period 1990-2007 and applied the generalized method of movement (GMM)

estimator. There was the very small difference between estimated short run and long

run elasticities. The results indicated that the price elasticity of Japan’s residential

energy consumption was both income and weather dependent. The short run price

elasticities in high, middle and low income groups were - 0.479,-0.425 and -0.383

respectively and elasticities in cold, medium and hot regions were - 0.493, -0.403 and

-0.305 respectively. By these elasticity estimates, the authors gave policy suggestion

about taxation policy to reduce energy consumption.

45

Lim.K.M at el., (2014) estimated price and income elasticities of electricity demand in

Korean service sector both in short-run and long-run . They used annual data of time

period 1970-2011 and applied co-integration technique and error correction model to

find demand function. The results indicated that the electricity demand was inelastic in

short-run but elastic in long-run in Korean service sector. The estimated short-run and

long-run price elasticities were -0.421 and -0.1002 while income elasticities were 0.855

and 1.092 respectively. They suggested pricing policy as a more effective tool to

stabilize electricity demand in Korean service sector in long-run.

Bernstein and Madlener (2015) estimated electricity demand elasticity at subsectoral

level for German industries. They estimated short run and long run elasticities and took

economic activity and electricity price as explanatory variables. The subsectors

included pulp, paper and printing, wood products, textile and leather, non-metallic

minerals metal and machinery, food and tobacco and transport equipment. This study

used annual time series data for time period 1970-2007and applied standard

cointegration analysis, Granger causality test and error correction model. Estimated

short run price elasticities were -0.57 for non-metallic industry -0.31 for transport

equipment and it was significant for all other sectors. And the elasticity of economic

activity was also significant in all sectors elasticity estimates were 0.17 in sector food

and tobacco, 1.02 in pulp and paper, 0.51 in non-metallic minerals and 0.48 in transport

equipment. The long run elasticities were higher than short run elasticities.

Mensah et al., (2015) attempted to analyze energy demand elasticity in Ghana at

disaggregate level. Energy sources included in this paper were gasoline, residual fuel

oil, electricity, diesel, solid biomass, kerosene, and LPG. They consider income, price,

urbanization and economic structure as demand drivers. The study used time series data

for the time period 1979-2013 and applied the autoregressive distributed lag model and

partial adjustment model to find the elasticities in short run and long run. The results

indicated that demand for electricity, gasoline, and LPG was responsive to price and

income change, increase in income led to increasing demand for these energy sources.

The income elasticity was positive and price elasticity was negative for kerosene,

residual fuel oil, and solid biomass because these were treated as inferior energy in

Ghana, as income increases they switched to another energy source. The results showed

that urbanization increased demand for all energy sources in the long run while in the

short run income and price were major drivers of energy demand.

46

Burke and Liao (2015) estimated the elasticity of coal demand at province- level for 30

provinces in China. They used penal data for the time period 1998-2012 of provinces

and applied the simple log-log model to estimate energy demand. They analyzed the

responsiveness of coal consumption to price change over time and estimated point

elasticity and two-year elasticity .the results indicated that the price elasticity of coal

demand increased over time in China, people became more responsive to price change

as they had more substitutes of coal. Estimated point elasticity ranged from -0.3 to -0.7

for 2012 while in the previous years it was almost equal to zero or inelastic. This paper

initiated to suggest the reduction in CO2 emission by reducing coal consumption in

China.

Sun and Ouyang (2016) Estimated price and expenditure elasticities of residential

energy demand during urbanization in China. The study analyzed different income level

and different regions which was based on household survey from China’s Residential

Energy Consumption survey of January 2013. Almost ideal demand system was used

to analyze the impact of energy pricing policy on energy consumption. The study

estimated elasticities for three type of energies; electricity, natural gas and

transportation fuel, the results indicated that people were more sensitive to income

change than price change. Price elasticities were higher in low income household than

high income households. Due to pricing policy for energy its demand was inelastic in

residential area.

Zhang and Broadstock (2016) extended the existed studies based on constant structure,

they adopted time varying techniques to measure the relationship between energy and

GDP in China. They mentioned that China being second-largest economy worldwide is

experiencing rapid industrialization and tremendous urbanization that has given rise to

massive energy demand.

Lu et al. (2017) investigated the effect of energy policies in China on GDP they viewed

that in order to satisfy this growing energy demand, massive energy supply is required,

thus generating demand for huge investments in energy sector. This investment may

bring improvement in energy industry technologies and quality of fuels, making the

existing industrial sector of economy work more efficiently, hence increasing the

industrial output.

47

Mrabet et al., (2019) investigated the relationship between nonrenewable energy

demand, urbanization, carbon emissions, gross domestic product, industrial share and

with oil price. They applied advanced heterogeneous panel techniques (such as

augmented mean group) for the period of 1980-2014 on developed and emerging

countries. They found the largest impact of GDP and oil prices on non-renewable

energy demand.

2.1.4 Renewable and Non-Renewable Energy Sources, Energy Intensity,

and Economic Growth

The ratio of energy consumed to gross domestic product is imagined the energy

intensity and the ratio of percentage change in energy consumed to percentage change

in gross domestic product is measured the elasticity of energy intensity.

Energy intensity has been considered a global indicator of the link between income and

energy consumption because the reflection of the amount of energy required to produce

income per unit has been purified by energy intensity. Damstadler et al. (1977) made a

comparative analysis by applying this concept of energy intensity on households,

industrial groups, and transportation sector in 7 countries including US, Canada,

western countries and Japan. They analyzed how those industrial countries utilized

energy. Authors found that there existed complex methodological issues in comparison

at international level. They also found differences in the ratio of energy consumption

to income (GDP). The task of energy intensity covered the reasons for differences, but

the task was proved problematically.

Samouilidis and Mitropoulus (1984) inspected the interconnected path of economic

growth and energy demand in an industrializing economy. Price and elasticity of energy

were computed through Translog function. Strong positive relationship of value share

of industries in connection with energy demand and energy intensity was observed.

They concluded that Substitution among energy, capital and labor take put, however to

a restricted degree, as shown by the substitution elasticity of total measure of

energy/non-energy. All findings seemed to propose that energy policymaking, in an

industrializing nation like Greece, will be of low adequacy until certain basic changes

in the economy are figured it out.

Tabti and Mandi (1985) evaluated energy intensity ratio over the 35 preceding years at

the global level. They included not only the energy and oil but also included other

48

sources such as electricity, gas, and coal in the evaluation of energy intensity ratio. The

evaluation revealed the indication of relation with energy in terms of other parameters

like technological change and industrialization, more efficient use of energy with its

conservation in leading years and energy price.

Yu and Choi (1985) calculated average and the ratios of marginal efficiency to gross

domestic product for selected economies1from 1963 to 1976. They computed that the

ratios of aggregate energy consumption to the gross domestic product were relatively

stable in developed and diversified nations. They found that market economies (United

States) and centrally planned economies (Poland) were fitted for that approach and

energy intensity found as an exogenous variable in those economies.

Benardini and Riccardo (1993) explored the negative relationship between energy

intensity and income. They found three stages of reduction in energy intensity as an

increment in income. In stage I energy intensity declined due to changed structure of

energy demanded in the process of industrialization, in that stage energy intensity lower

in service sector than manufactures sector, in stage II technical improvement reduced

the energy intensity, and in third stage, the less energy intensive materials used ad

substitution energy which caused reduction in energy intensity.

Galli (1998) concentrated the likelihood of an altered U molded example for energy

intensity in a specimen of 10 Asian economies over the period 1973–1990. Panel

models were assessed utilizing fixed effects furthermore, random coefficients

estimators. There found some proof of a reversed U moulded connection between

energy intensity and income for the fixed effects determination but no measurably

significant confirmation of this relationship in the random coefficients determination.

Miketa (2001) introduced an examination of energy intensity advancements in

manufacturing sectors in developing and industrialized nations. The reviews secured

the advancement of energy intensity after some time and its association with the

departmental economic improvement. Three factors were investigated as for their effect

on energy intensity, departmental fixed capital formation and prices of industrial

energy. Panel examination was computed for ten assembling ventures utilizing pooled

information of 39 nations in the vicinity of 1971 and 1996. The review found that capital

1 Poland, South Korea, United States, United Kingdom, and the Philippines

49

development has the impact of expanding energy intensity and this impact is more

powerful where the departmental output is bigger.

Alam et al. (2007) assessed the result of population growth, energy intensity

improvement, economic development and urban extension of natural decay in Pakistan

in the working time of 1971-2005. The gross domestic product was utilized as an

intermediary variable of ward and CO2, EC, add up to the populace and URB had been

utilized as informative factors. The results indicated that 0.24% augmentation in the

rising rate of per capita CO2 because of 1% extension in the energy intensity

development rate and an expansion 1% in GDP advancement brought on just about

0.84% expansion in the development rate of CO2. The CO2 outflow, energy utilization

created economic development significantly and positively. The Co incorporating

vector comes about demonstrated that an extension of populace development and URB

influences adversely to the economic improvement, while positively to ED over the

long period.

Li and Yao (2009) observed URB and its effect on the energy efficiency of building

and building EC in China. The fast URB, development created emerging issues relating

to building effectiveness and building EC. Several reasons were responsible for this

lack of consensus. First, mismatch between design and construction affect actual energy

savings. Second, a lack of cost estimation of energy-efficient building. Third, property

developers had underestimated the demand for energy-efficient building. The policy

implication argued that Training course, Education, research and development

programs are needed to be enhanced.

Ma et al., (2010) surveyed china’s unfavorable energy situations, it's high energy

intensity and need of development programs for environmental protection. Paper

reviewed Renewable energy laws and development policies and gaps in reviewing the

development of the country. Continuous improvement in the mobilization of renewable

energy economy was relayed on Government support programs. More research was

required to investigate and address many issues in China's economy. Renewable energy

researcher and economists needed to pay more attention to Grain-based biofuel energy

production and renewable energy substitution possibilities with fossil energy for

renewable energy economic development.

50

Alam et al., (2012) investigated dynamic linkage of economic growth, energy

consumption, pollutant emissions, and electricity consumption in Bangladesh.

Johansen co-integration test, Auto-regressive distributive lag bound test, Granger

causality in ECM, and Stationary tests were used. Unidirectional Granger causality was

running between these variables of analysis both in short run and long run, while bi-

directional causality existed between economic growth and electricity consumption in

short run only. In short run rise in energy usage lead to increase in carbon emissions

while in long run CO2 emissions lead to increase energy consumption. Policymakers

should use remarkable energy sources and plans to reduce environmental degradations.

Rafiq et al. (2016) concluded that population density and economic growth expand

energy intensity in twenty-two progressively developed rising economies from 1980 to

2010. They utilized three second-age heterogeneous linear panel models just as of late

created nonlinear panel assessment strategies considering cross-sectional reliance.

They found renewable energy sources seems to be relaxing in energy intensity and

emissions while non-renewable energy increases both energy intensity and CO2

emissions.

Belloumi and Alsherhry (2016) examined the causal relationship between energy

intensity, GDP per capita, urbanization and industrialization in Saudi Arabia in long

run. The review covered the time of 1971 to 2012, ARDL test and Granger causality

were computed. Urbanization was taken as explanatory variable and outcomes

demonstrated the co-integration relationship in variables. Authors additionally found

that urbanization has a positive effect on energy intensity in long run and also in short

run. Over the long run test of Causality showed that urbanization caused economic

development that brought about energy intensity.

Sheng et al., (2017) investigated the impact of urbanization on energy efficiency and

energy consumption using data of 78 countries for the period of 1995-2012.

Generalized method of moments applied for estimation and found the positive impact

of urbanization on energy consumption and negative impact on energy efficiency. In

addition, they found that the extent to which energy inefficiency correlates with

urbanization is greater in countries with higher gross domestic product per capita.

Li et al., (2018) investigated the urbanization and provincial energy efficiency in China.

They used panel data of 30 provinces of China for the period of 2003-2014. Stochastic

51

Frontier Model was applied and found negative impact of urbanization on energy

efficiency.

2.2 Theoretical Literature

2.2.1 Prospects and Potential of Renewable and Non-Renewable Energy

Sources

Renewable energy flows are very large in comparison with human kind’s use of energy.

Therefore, in principle, all our energy needs, both now and into the future, can be met

by energy from energy sources. Technologies exist that convert renewable energy flows

of modern energy carries or directly into desired energy services.

Chaudhry (2007) investigated the potential of wind power at Gharo, Sindh (Pakistan).

The study used three-year data from April 2002 to March 2005. For investigation of

wind, power data was collected at 10 meters and 50 meters height. Power law was used

to compute wind speed at different heights. Annual average speed was computed 6.8m/s

and average power destiny was 360 𝑤 𝑚2⁄ at 50 meters wind speed. Result showed

that at giving height 600 kW hypothetical wind turbine could produce total annual

electricity 1401 MWh which made the location economically feasible for commercial

wind farms.

Henrik Lund (2007) determined the renewable energy resources for development in

Denmark which involved three technological changes. These changes were: efficiency

improvement in production, energy saving on demand side and substitution of non-

renewable energy sources by different renewable energy sources. This paper discussed

the problems as well as perspectives of converting high share of intermittent existed

energy systems into a 100% renewable energy system. The main analysis was about the

possibility of a 100% renewable energy system in Denmark. Energy PLAN model was

used for a number of analysis of large-scale integration of renewable energy. Results

showed all technological changes lead to decrease in fuel consumption. The conclusion

for development is possible which is oil transportation must be replaced by other

sources e.g. electricity. Second, include small CHP (combined heat and power

production) plants in the regulation. Third is the inclusion of wind tribunes in the

voltage and frequency regulation of the electricity supply. If these technological

improvements are achieved the renewable energy system can be created for viable

development.

52

Chaudhry et al., (2009) diverted focus on the prospects and challenges regarding

renewables in Pakistan. Paper explained the primary energy sources and future energy

options to overcome energy shortages. Primary energy supplies were not sufficient to

meet even the present needs of the country. Pakistan aimed at to achieving 10 % of

electricity supply from renewables by 2010. Small activities were done on the

implementation of renewables e.g. heat, wind, thermal, hydropower and biomass to

current energy systems. Some of the organization promoted or worked for renewable

energy products but remained unable to expand it in long run production levels.

Connolly et al (2010) has discussed 37 tools review individually in detail. The energy

tools were diverse in terms of their structure, operation and application. Previous

studies did not provide an extensive analysis of other tools. This paper contained an

individual description about each of energy tool reviewed. The objective of this study

was to identify energy tools that could simulate a 100% renewable energy system. The

survey on these tools was conducted by tools developer’s project at the University of

Limerick. Results showed there were no computer energy tool that addressed all issues

related to integration of renewable energy. In relation to the electricity sector, the

energy PRO tool could analyze the feasibility of a new power plant or CHP facility.

The survey concluded that wide range of different energy tools available in terms of

their regions, technologies and objectives.

Keyhani et al., (2010) estimated wind energy potential as a power generation source in

the capital of Iran, Tehran. Time series data of eleven years (1995-2000) were used to

find out the wind energy potential in Iran. Two methods meteorological and Weibull

were used to assess wind characteristics. It was indicated that the numerical values of

scale(c) and shape (k) parameters for Tehran were varied. The yearly value of k ranged

from 1.91 to 2.26 with a mean value of 2.02 and value of c were in the range of 4.38 to

5.1 with a mean value of 4.81. Values for monthly data were found for parameter K in

the range 1.72 to 2.68 and for parameter C 4.09 to 5.67. Result showed that the highest

wind potential were in April and lowest were in August and mean wind speed values

were higher in day time. And the most probable wind direction was 180 degrees as west

winds. It was concluded that site studied were not suitable for large scale electricity

application due to cost factor, however in long run with development of wind tribune

technology the utilization of wind energy could be possible.

53

Kumar et al., (2010) discussed particular coverage interventions for overcoming the

boundaries and improving deployment of renewables for the future. The goal of this

study was to review the current status, obtainability and future capacity of renewable

resources in India. It was analyzed that 33% renewable energy were used for primary

consumption in India. Author said that the currently coal, oil and petroleum were used

for energy consumption but for sustainable future energy security and to control carbon

emissions the use of renewable resources would be better choice. As biomass power

capacity was more than 5000 million units of electricity. Wind potential analyzed

45,000 MW but installed capacity of wind was only 5310MW. Geothermal energy has

not used currently but government has plan to use these resources in future. Study

showed that India had planned to use renewable sources for electricity supply up to

60,000 MW by 2032. Study proposed government should be used of suitable measures

to increase energy security in future for social and economic benefits.

Shafie at el., (2012) investigate the electricity production through biomass utilization

in Malaysia. The aim of this study was to analyze potential to produce energy from

biomass residue in Malaysia. They investigated that Malaysia had a great potential in

renewable energy the most potential is using empty fruit bunch, fibre and shell of palm

oil. Malaysia was producing 94.5% of its energy from fossil fuels in 2009 which caused

a lot of climate damage that the average temperature of Peninsular Malaysia increased

from 0.5 to 1.5 degrees and 0.5 to 1 degrees in East Malaysia. And the consumption of

energy can triple till 2030 which can cause more harm to climate as well as the

economy. The study showed that the biomass residue is the best option for Malaysia to

produce renewable energy because country produce more than enough waste from palm

oil, rubber, cocoa, wood and timber which can be used to harvest clean energy. Study

showed that Malaysia is 2nd biggest producer of palm oil in world there are 532 mills

in Malaysia working on palm oil sector producing 90.07 MT palm oil waste which can

be used to produce 406.18 Pet joule energy in 2011 Malaysia showed an increasing

pattern in the production of palm oil production reached 98.45 MT which forecast that

in 2020 palm oil will have the potential to generate 70TW/h energy plus Malaysia

produces 700,000 of sugarcane which has the potential to produce 0.421 million BOE

energy per year assuming that a ton of bagasse(sugarcane waste) is equal to 1.6 barrel

of fuel oil pineapple, coconut and rice’s husk is also available in considerable amount

in the country. Study showed that Malaysia can use direct Combustion, Gasification

54

and Pyrolysis to harvest energy but gasification is the best way in terms of efficiency

but there are some challenges to produce energy from biomass in Malaysia like no local

expertise, lack of awareness, lack of new technology, Lack of interest from commercial

investors, insufficient training and education.

Liu at el., (2012) discussed the perspective and potential of renewable energy in China

and also compared with Denmark’s. The study showed that having a 10% GDP growth

rate with associated energy demand China is one of the biggest fossil fuel consumers

in the world. China used coal to produce most of its energy at a very high rate because

the consumption of energy very high which causes a very big climate damage. China

was already using renewables for energy at a huge level as annual average power

generation from hydropower plants was 2474 TW h in 2011 which ranks first in the

world, world’s largest producer and consumer of solar water heaters and second in

ranking for the use of wind power with capacity 26.01 million kW but still it was not

more than 20% of total energy produced in the country china has enough potential to

increase the use of renewable resources to meet the total demand of energy in 2030. As

China had 3886 rivers with estimated gross theoretic hydropower reserves of 694 GW,

throughout China’s vast landmass and long coastline there is a rich resource of wind

energy with great development potential, more than two-thirds of the country’s area can

receive a radiation of more than 5000 MJ/m2 yr. and more than 2200 h of sunshine. But

because of constantly increasing demand china need to invest huge money to generate

most of the power from renewable resources and to decrease the use of coal. Total

amount of potential renewable energy sources per capita is less than twice as high in

Denmark as in China and one GDP unit in China required more energy consumption

than in Denmark. China had great potential but renewable energy is presently under-

exploited in China but China has potential and investment to adopt a 100% renewable

energy system.

Farooq and Kumar (2013) compared the current installed capacity and future potential

of renewable energy resources for electricity generation in Pakistan. Timespan from

2010 to 2050 were used. Aim of study was to estimate the current and future

geographical and technical potential of solar, biomass, wind, small hydro resources for

electricity generation. Study estimated the modern and future capability of renewable

energy sources for electricity generation via most promising technologies. Presently

electricity production in Pakistan relies on fossil fuels, but due to increasing price of

55

fossil fuels electricity production was lower than demand. .It was analyzed that in 2010,

the country had 168GW installed potential of renewable energy which was 8 times more

than overall electricity demand of 21 GW. Government had planned to increase the 5%

installed capacity of renewable resources till 2030. Techinal potential for small

hydropower, wind, biomass and solar for 2010 was estimated 2.7 GW, 12.8GW, 3.6GW

and 148.7 GW respectively and technical potential of these resources was estimated

2.7, 12.8, 9.8 and 168.7 by 2050. Study explored that current Nation action plans

exploited small hydro and wind resources but huge potential of solar and biomass

resources also exist. It was concluded that use of solar and biomass resources would

help country to overcome electricity security as well as environmental pollution issue

caused by fossil fuels.

Latif and Ramzan., (2014) studied the position of energy sector in Pakistan. In this

paper potential of electricity production from different sector has been studied. In

Pakistan, power produce from fossil fuels and did not pay attention to renewable

resources which cause power shortage. Load shedding has adverse effect on industrial

sectors. Pakistan fulfils only 0.79% energy requirement from coal. In Pakistan, total

coal reserves have shown 185,175 million tons. Pakistan has potential to produce

electricity about 10000 MW from coal. It was analyzed annual potential of biogas

production from waste was 8.8 to 17.2 billion cubic meters. Hydropower potential from

hydropower was estimated about 50000MW, and 6,595 MW has established, only 13%

water has stored annually. Wind energy potential in Pakistan was estimated about

50000MW. Study was estimated that Pakistan has potential of 300,000MW for

electricity production from wind and solar. It was foreseen that electricity disaster can

be conquer by using the efficient usage of these resources as Pakistan has capacity to

produce electricity more than demand.

Mahmood at el (2014) attempted to compare the IPI, TPI and LNG gas projects and

energy potential of Pakistan. The goal of this paper was to assess the energy potential

in context of major issues, energy crises, future prediction and impact of energy import

options. Electricity deficit was recorded in 2010 about 4522 MW and it was expected

that it would reach up to 7000MW in May 2011. 63.7 % of electricity generation in

Pakistan primarily depends on thermal power plants. Pakistan’s damn storage capacity

has only 8%. Paper showed that current circular debt was 313 billion rupees of power

sector. Pakistan producing 32%of electricity from natural gas and has 27.5 trillion cubic

56

feet of gas reserves. Wind potential in coastal region of Pakistan about 55MW.Potential

of biodiesel was around 400,000 MW and producing 7.6% voltage from nuclear power.

Study showed that currently power shortfall from 4 to 7 GW and expected to 14GW by

2014. However, Pakistan has huge potential geothermal but not explored yet. Coal input

was 6% of energy mix and only o.1% of power generation. It compared the LNG

(liquefied natural gas), IPI (Iran-Pakistan-India) and TAPI (Turkmenistan-Afghanistan-

Pakistan-India) projects. Power generation from IPI project was estimated 4,000MW

and total cost 7.5 US dollar and power generation from TAPI 2500 MW and total cost

was estimated 7 to 8 billion dollar. It was stated that LNG was better option than TAPI,

IPI gas pipelines.

Thomas B Johansson at el., (2014) demonstrates the potential of renewable energy

resources, barriers and policy option for energy innovation. In a broad sense, renewable

energy recourse refers to the hydropower, biomass energy, solar energy, wind energy,

geothermal energy and ocean focus on modern and sustainable energy.

Talwar and Lata (2014) evaluated the impact of status and potential of renewable

energy resources on economic growth with reference to India. This paper evaluated

status and the potential of various renewable sources in India and recognized obstacles

and techniques for utilize sources. Secondary information was used to analyze the

impact of renewable energy on economic growth. There had a huge potential of energy

resources in country as biomass had potential to produce electricity about 22,536 MW,

geothermal 1000MW. Energy consumption of India has been increased due to

population increased, urbanization and economic development. It was estimated that

domestic electricity demand of India turned into 918 billion units in 2012 and with

annual increase at 9.8% the demand would be attain 1,640 billion units by using 2020.

As India had large potential of different renewable resources so use of renewable energy

would help to tackle issues like energy scarcity, variations in fuel prices and help India

to sustain economic growth.

Karatayev and Clarke (2014) presented the current energy scenario in Kazakhstan

including energy produced from fossil fuels and renewable resources and investigated

the policy options for energy sector. The aim of the study was to analyze the available

energy resources and future potential of renewable resources of Kazakhstan. It

discussed the barriers to adoption of renewable energy. Study investigated that the 80%

57

of Kazakhstan’s overall electricity was produced by the way of thermal power plants

which become responsible for carbon dioxide emissions of 275 MT co2. In Kazakhstan

total installed potential was 19.8 GW while available potential was 15 GW .It was

analyzed that due to rapid growth in economy energy demand would increase as

consumption of energy rose in 1999 to 62.03 Mtoe in 2013. Study showed that country

has good potential to produce renewable energy as 50% of Kazakhstan’s territory has

average wind speeds suitable for energy generation (4–6 m/s) with the strongest

potential in the Caspian Sea and northern regions, areas with high insolation that could

be suitable for solar power particularly in the south of the country receiving between

2200 and 3000 hours of sunlight per year which equals 1300-1800 kW/m² annually, an

average grain yield of 17.5-20 Mt which equates to roughly 12-14 Mt of biomass wastes

and at least 400,000 households are known to keep cattle, horses and sheep. Study

showed that the production of renewable energy could contribute to significant

reductions in greenhouse gas emissions, air pollution, and minimizes the impact on

environment as well as it could increase the coal, gas and oil exports of the country.

But still, some barriers to address as lack of awareness, insufficient government support

and financial barriers as low electricity price in country.

Habibullahat el., (2015) has evaluated the potential of biodiesel as a renewable energy

source in Bangladesh for sustainable development and future energy needs. It was

compared that by using biodiesel CO and HC emission were low, but a slight increase

in NOx was observed. In this paper current energy scenario of Bangladesh, available

potential biodiesel feedstocks, characteristics of diesel engines, comparison of cost

analysis and future direction were discussed. It was analyzed that cost of biodiesel

production was expensive as compared to conventional diesel fuel. The study showed

that projected cost for pure biodiesel in range of us$1.6/L to 23.96/L compared with

diesel fossil cost was US$0.71 to 0.91/L. Production cost would be lower if biodiesel

produced at high volume. The study concluded that production of biodiesel was

possible and could assist in future energy needs.

Halder at el., (2015) extended the research on potential of renewable energy and energy

scarcity in Bangladesh. Paper represented the prevailing energy scenario of country and

available potential of renewable energy sources. .Electricity generation was highly

dependent on fossil fuels especially natural gas and oil. It was analyzed that the amount

of indigenous nonrenewable energy sources was very limited and was not sufficient to

58

full fill electricity demand in future. Socio-economic and industrial development has

been affected due to insufficient amount of power. Bangladesh had great amount of

renewable recourses among which biogas, biomass and solar energy considered most

effective. Study showed that in Bangladesh only 2.5% electricity was produced by

renewable resources. Installed electricity capacity was increased by 10.709MW but

amount was still sufficient to meet electricity demand. Government of Bangladesh has

planned to enhance 4000MW nuclear electricity in power technology till 2030 but it

might not be enough to satisfy power demand about 34,000mw. Study showed that

Bangladesh has potential of 50,174 of solar grid energy and 1897 MW potential of

hydro energy. However geothermal resources were still needed to be exposed. And no

initiative has been taken to expose energy of tidal and ocean waves. Study proposed

that private, government and international contributor agencies must work together to

promote indigenous renewable energy resources for economic and social progress.

Kurbatovaat el., (2015) reported the state of the day for non- renewable and renewable

energy in Ukraine and state policy of its development in long run perspective. This

paper discussed the renewable and nonrenewable resource potential in Ukraine. The

purpose of the research was to study the available potential of renewable energy sources

in Ukraine development of which can provide significant economic, ecological and

social benefit. It was presented that Ukraine depended on imported energy resources

and on future with increasing levels of energy consumption and depletion of existing

reserves, this dependence will only grow and negative effect on energy security of the

state. The study showed that the Ukraine government emphasized on production of

organic fuel increasing rather than renewable energy sources. The study showed that

there should be the use of renewable energy resources in the future.

Nawaz and Masood (2015) studied the electricity energy crises facing by Pakistan and

its solutions. Energy infrastructure of Pakistan reflected underdeveloped and

disappointingly managed. The aim of this was to discuss the potential of renewable and

non-renewable energy potential in country and electricity supply and utilization of

resources. Electricity power generation reduce due to dependency on fossil fuels.

Pakistan has facing electricity crises through many decades of its independence and

worst shortfall was in 2011 of 4000MV. Paper showed potential of different renewable

resources for electricity generation. Pakistan has potential of over 300,000 MW of wind

and in Punjab canal system potential for power generation was estimated at 350MW.

59

As sugar industry has potential more than 1000MW of power generation from biomass.

Paper showed the tendency towards the fact that government should make use of

renewable energy resources that would reduce energy shortfall issues and would also

increase employment opportunities.

Abbas (2015) presented the wind characteristics and wind strength capability for south

coast of Thatta, Sindh province, Pakistan. Hellman exponent law was used to estimate

the wind speed at various heights. Variation to month to month wind speed at different

heights as 10m,20m,30m,40m,50m were analyzed. Coefficient of performance 𝐶𝑝 as

0.40 and 0.50 were used to calculate the power destiny for these heights. Result showed

that during spring and moon soon season exhibit the high wind speed. Highest wind

sped was observed in July was5.01 𝑚𝑠−1 at 10m height and 6.28 𝑚𝑠−1 at 50m. April

to September wind potential was beneficial with variation of wind speed from 2.81

𝑚𝑠−1 5.01 𝑚𝑠−1at 10m height and from.52 𝑚𝑠−1 to 6.28 𝑚𝑠−1at 50 m height. So the

study showed wind situation become very encouraging in Thatha for efficient usage.

Niblick and Landis, (2016) represented the renewable energy potential of United States

marginal and contaminated sites. The study centered on both Bioenergy and non-

agriculture sources. The United States has 121 million hector of marginal land that

might be used to supply renewable electricity. A study used the GLS model to assess

range of site-specific production potentials on brown fields, closed landfills, and

abandoned mine. Study focused on the symbiotic relationship between the lands,

regional characteristics, and the renewable energy potential available. Five energy

sources have been considered: soybeans, sunflowers, and algae for biodiesel, and solar

and wind energy for electricity. Using soybeans, sunflower and algae, the United States

could produce 39.9×103 TJ- 59.1×103 TJ of renewable fuel in line with 12 months

from biodiesel. The use of solar and wind resources United States could produce 114-

53 TW h per year of electricity. The study showed that five renewable energy sources

that examined could meet up to 39% of the full U.S. demand for diesel and electricity.

It was concluded that most reliable use of land, feedstock and power manufacturing

technology might permit contaminated lands to play a more and more vital function in

fulfilling America energy demand in a sustainable way.

Ali at el., (2016) presented the potential of rice husk mixed with poultry waste as an

alternative source to generate energy in Pakistan. The principal objective of this study

was to investigate the capability of biomass (rice husk and rooster waste) as a source of

60

renewable energy and to estimate the economic benefit and cost of the usage of rice

husk for biogas. The rice husk and poultry waste had widespread potential for

production of power and energy and additionally by way of the usage of this generation,

timber utilized in power plants also can be saved. The paper compares the gain of biogas

plant operated by using rice husk and fowl waste in comparison to timber. Benefit-cost

evaluation confirmed that installation of biogas plant by using of risk husk with poultry

waste was possible in Pakistan. The study proposed that the government of Pakistan

need to allocate funds and technical help to commercialize this technology. Public and

private ownership tasks need to be endorsed. And the media ought to additionally

highlight the benefits of this era.

Branka at el., (2016) attempted to analyze the potential solid biomass energy in

Autonomous Province of Vojvodina for sustainable energy production. The goal of this

study was to offer an outline of solid biomass potential for energy production in

Autonomous province in Vojvodina and compare it with fossil fuel potential. It was

emphasized that local, renewable and sustainable strength resources have to be centred

for sustainable energy generation. Autonomous province Vojvodina is an energy

deficient region, but dominantly agriculture location. The primary reasons for

underdeveloped biomass utilization were: First and predominant, low calorific value

according to the unit quantity of storage, the periodic nature of its boom, high priced

shipping, manipulation and storage, damaging characteristics of the ash, the tendency

for Biochemical degradation and relatively high-priced combustion Gadgets. It became

estimated by using literature and statistical data solid biomass potential in Vojvodina

was o.8 Mote per year. Data showed that solid biomass energy was enough to substitute

absolutely solid and liquid fossil fuel intake in Vojvodina. It was concluded that

biomass can be regarded as a significant power source because of its quantity.

Mijakovski at el., (2016) attempted to estimate the potential usage of renewable

resources within the southeastern Region within the republic of Macedonia. Paper

refers to the potential and usage of renewable energy resources inside the southeastern

planning region within the Republic of Macedonia. The primary goal of the paper was

to investigate opportunities of the usage of the renewable energy sources which

naturally belong to the areas of the ten municipalities placed on this a part of the

Republic of Macedonia. Analysis showed that co2 emissions could be reduced in an

atmosphere by producing electricity through renewable energy resources as compared

61

to electricity production in thermal plant through fossil fuel. It was estimated that

potential of producing electricity from renewable resources in southeastern region was

around 1084 GWH that can reduce CO2 emission around 630 thousand tons compared

to the case when same amount of energy turned into made from fossil fuels. Study

showed the tendency toward the fact that energy production from energy resources

would increase energy supply, reduce emission of CO2 caused by fossil fuel usage and

would increase economic and social development in region.

Kumar at el., (2016) has extended the research on assessing potential of renewable

energy resources for reduction of environmental emission in India. Electricity was

produced by fossil-fueled in power plants which caused CO2 emission. The main

objective of paper was to investigate the sustainable electricity supply in India by the

usage of renewable energy resources. LEAP energy model was used to develop

different scenarios. Study examined the consequences of renewable energy use in

electricity delivery system and estimates the CO2 emission through developing diverse

scenarios under the least-cost approach. BAU (business as usual), RET (renewable

energy technology), ARET (accelerated renewable energy technology) scenarios was

analyzed. Timespan for these scenarios were from 2010 to 2050 with base year 2010.

It was estimated that India renewable energy resources have potential to fulfil future

energy consumption demand. Results showed that the using accelerated renewable

energy technologies CO2 emission would reduce 74% by 2050.study showed the

tendency toward the fact that by using renewable energy in electricity generation would

give more energy independence in future with reducing carbon di-oxide emissions.

Kumar (2016) developed different scenarios to analyze renewable energy potential for

power safety and carbon mitigation in Southeast Asian countries Indonesia and

Thailand. These two countries highly depended on nonrenewable energy such as coal,

oil and natural gas which cause environmental pollution. It was estimated that in 2013

electricity generation from coal was 51%, from natural gas 24%, from oil 13%, from

hydro only 8% and geothermal 4% and Indonesia has target to produce electricity from

geothermal, biomass, hydro, solar, wind and from biofuel by 5% till 2025. Study

showed that in Thailand mostly power generation was derived from fossil fuel.

Electricity generation in Thailand was estimated from oil was 43%, from natural gas

41% from coal 14%, from hydro and other renewables only 1% in 2013. Leap energy

model was used to develop different renewable energy policy scenarios from base year

62

2010 to 2050 and also each scenario cost was estimated. In this paper 3 different

scenarios, reference scenario, renewable energy and renewable potential were

developed to analyze the renewable energy potential in Thailand and Indonesia. Result

showed that in the renewable potential situation, the renewables share in the energy mix

was 40% in Indonesia and 39% in Thailand by 2050. It was also estimated that 81%

and 88% of CO2 emissions would be decreased in Indonesia and Thailand respectively

and cost of production would increase in both countries extensively by enforcing

renewables at large scale. Study concluded that by increasing the share of renewable

resources in energy production would bring socio-economic benefits to Southeast Asian

countries.

Ghafoor at el., (2016) discussed the current status of energy production as well as the

potential to produce renewable energy in Pakistan. The aim of this paper was to analyze

the current status of energy and renewable energy potential in Pakistan for sustainable

future energy. Study showed that the 87% of total energy produced in Pakistan is from

fossil fuels such as natural gas, oil, coal and LPG and due to unstable prices of these

fuels but according to economic situation of the country this amount of energy is not

enough to meet the needs. Paper presented the country has potential to produce

renewable energy; it indicates that Pakistan has solar, Wind, Biogas, Hydropower and

Biomass power potential. On an average solar global insulation 5–7 kWh𝑚−2𝑑−1exists

in most to be had is almost 5.5 kWh m2 d1 having annual mean sunshine period

between 8–10 h d1 and 300 days (1500–3000 h) per 12 month. Total potential of wind

energy was reported 346,000 MW that can be used to produce thousands MW of clean

energy. Potential of almost 45000 MW of hydropower was available in northern areas

plus canals can also be used in Punjab province to provide power. Paper showed that

there were 72 million of animals, 785 million poultry birds, 81 million tons per year of

crop residues across the country that could make 14.86 million 𝑚3biogas per day which

could produce 1012 MW. It was analyzed that bio waste also available in the form of

Sugarcane trash, Cotton sticks, Maize stalks, Paddy straw and Municipal solid waste.

And About 150 million liter biodiesel could be produced using jatropha, kallar and

switch grass. Study showed that Pakistan had great potential to produce renewable

energy because of its geographical location and as an agricultural country but less than

1 % of total energy was being produced by renewables and it necessary to increase this

percentage to fulfil the energy needs of the country.

63

Baran at el., (2016) investigated the future dry combustion in Turkey and gave some

projections on Energy from Waste utilization potentials as secondary local generators.

The paper highlighted that energy from waste was better than landfilling in term of

environmental offset and electricity generation potential but high temperature

combustion rooms should be used not instead of open field incineration because open

field incineration contains the risk of emissions of toxic contaminants and heavy metals

to nature. Study presented a case study investigating energy from waste potential of

Turkey and gave an estimation of energy from waste potential in Turkey for near future.

Study showed that one kilogram of MSW can generate 214.24 kJ of heat energy that is

nearly equal to 0.01316 kWh of electric energy by assuming that 30% of this energy

spent for maintenance, self-consumption of energy from waste incineration plants and

power requirements of waste management system a kilogram of MSW can generate

0:00921 kWh electricity for supply. Turkey is developing country with 74 million

populations out of which 75% population lives in cities that produce 25.4 million tons

SMW yearly 95-96% of which can be turned to heat which indicates that turkey has the

potential of 230 Gig watt hours electricity generation from SMW. Study showed that

Energy potential of energy from waste incineration plant was more stable and

controllable when compared to other renewable resources as energy from waste

incineration plants can postpone their energy production to meet possible excessive

power demand in future. Use of SMW for energy generation promises the fulfilment of

future needs of energy in turkey because when population will rise EFM potential will

also rise due to the increase in MSW mass collection as yearly average MSW mass

production per capita was calculated 416.1 kg according to 2010 population data.

Mushtaq1 et al., (2012) discussed the coal potential for power generation in

Baluchistan. The aim of this study was to explore the potential of coal reserve of

Baluchistan for power generation. Study showed that socio economic improvement in

Baluchistan has always decreased due to electricity crises. Electricity generated from

coal and oil resources while region has huge potential of coal. According to Ministry

of water and power government of Pakistan, total coal reserves in Pakistan was 185,175

Million tons. It was analyzed that share of coal in electricity generation of Pakistan was

only 1% and unexploited coal reserves could produce more than 100MW of electricity

for coming 30 years through coal converted to clean gas. Share of gas, oil and other

energy resources in power generation was estimated more than 99% which cause

elimination of oil reserves. Baluchistan coal reserves were anticipated 217 million tons

64

which incorporate low ash and high sulfur that considered suitable for energy

technology. Final conclusion was that coal should use in Baluchistan for electricity

generation to reduce electricity crises.

Shukla et al. (2017) overviewed the renewable energy resources in the south Asian

countries. They provided the brief and comprehensive description about energy

scenarios, renewable energy potentials and challenges in South Asian countries. They

concluded that national and regional policies can play an important key role in

supporting renewable energy development and implementation, helping South Asian

countries to identify priorities and pathways for renewable energy market.

Baky et al. (2017) presented the thorough review and current status and future

potentials of the renewable energy sector in Bangladesh. They explored that coal,

refined oil and petroleum and natural gas were the main sources of energy in

Bangladesh. They suggested that more research efforts should be given to the

development of the renewable technologies which will be great helpful for improving

renewable energy growth as well as economic growth.

65

Table 2.1. Summary of Empirical studies

Study Technique Country Conclusions Renewable, Non-Renewable Energy and Economic Growth

Kraft and Kraft's (1978) Sims Causality United States of

America

Economic growth determine

energy consumption

Yu and Chio (1985) Co-integration United States of

America

Economic growth determine

energy consumption

Yu and Jin (1992) Granger Causality Test South Korea,

Philippine

Energy determine economic

growth

Cheng and Lai (1997) Hsiao’s Granger

causality

Taiwan positive impact of energy on

growth and employment

Adjaye (2000) Granger causality India, Thailand,

Indonesia, and

Philippines


growth

Soytas and Sari (2003) Co-integration G-7 and selected

economies


growth

Altinay and Karagol

(2004)

Hsiao’s Granger

causality approach

Turkey No relation between economic

growth and energy

Altinay and Karagol

(2004)

Causality with

structural break

Turkey (again) electricity consumption determine

real income

Ghali and Sakka (2004) multivariable co-

integration and VECM

china positive impact of energy on

growth (labor, capital used)

Oh and Lee (2004) VECM South Korea Bi-causality, energy and growth

Paul and Bhattacharya

(2004)

Johansen multivariable

approach

India Energy and technological progress

determine economic growth

Lee (2005) FMOLS 18 developing

countries

Energy conservation would be

harmful to economic growth.

Yuana et al. (2006) Co-integration china Energy(electricity) determine

economic growth

Lee and Chang (2008) FMOLS 16 developing

coutries


growth

Sadorsky (2009) Panel FMOLS 18 emerging

countries

GDP contributes to Renewable

energy

Apergis. N and Payne J

E (2011)

Panel co-integration

test

6 central Asian

coutries

positive and significant impact of

renewable electricity consumption

on economic growth

Fang (2011) OLS China positive and impact of renewable

electricity consumption on

economic growth

Shahbaz et al. (2012) ARDL bounds test Pakistan Bi-causality, energy and growth

Bildirici (2014) Panel OLS,FMOLS Transition

Economies

positive impact of biomass

energies on gdp

Salahuddin et al., (2015) FMOLS, DOSL and

DFE

GCC Countries positive impact of

energy(electricity) on growth

Kasman and Duman

(2015)

FMOLS EU Countries Short run causality from growth to

energy

Sharif and Raza (2016) FMOLS and DOSL Pakistan positive impact of energy,

urbanization on growth

Pramati et al. (2018) Panel co-integration

and FMOLS

17-G20 countries positive impact of both re/ nre

energy on growth, re energy

contribute more

66

Natonas et al. (2018) Cluster ARDL 25 European

countries

Positive impact of re and nre

energies on economic growth.

Kahouli (2017) Co-integration,

ARDL,VECM

SMCs Longrun link exists between GDP,

FD, EC, unidirectional causality

Bilan et al. (2019) OLS, FMOLS, Pedroni EU countries positive impact of renewable

energies on gdp

Chandio et al. (2019) co-integration and

causal relationship

Pakistan Re energy contributes to economic

growth, uni-directional causality

industrial sector and economic

growth.

Jabeur (2019) OLS France renewable energy consumption,

total labor force and gross fixed

capital contributes in gdp

Ahmed and Shimda

(2019)

FMOLS 30 emerging and

developing

countries

significant impact of renewable

energy on economic growth

Renewable, Non-Renewable Energy, Economic Growth and Environmental Quality

Sadorsky (2009) Panel pedroni, FMOLS

and causality test

18 emerging

countries

In short run neutrality hypothesis

and in long run conversation

hypothesis between energy and

carbon emissions

Chiu and Chang (2009) FMOLS OECD member

countries

Negative impact of re energy on

Co2 and positive impact of

economic growth and nre energy

on CO2.

Menyah et al., (2010) (ARDL) co-integration

and Granger causality

South Africa GDP contributes to CO2

conversation hypothesis between

energy and carbon emissions

Apergis (2010) Dynamic Causality 19 developing

countries

Feedback hypothesis between

energy consumption and CO2

Li et al., (2011) panel co-integration thirty Chinese

provinces

long run positive relationship

between energy consumption and

economic growth

Tiwari et al., (2011) panel co-integration

(two Cases)

India No co-integration

1st case: GDP reduces to CO2

2nd case: GDP contributes to CO2

Omri (2013) OLS MENA countries GDP contributes to CO2, FD and

energy(capital) reduces CO2

Zeb et al., (2014) Panel granger causality

OLS,FMOLS

India, Pakistan,

Bangladesh,

Nepal, Sri Lanka

Neutrality hypothesis between re

electricity and CO2, re electricity

contributes to CO2 (growth

Hypoth)

Payne et al., (2014) panel co-integration,

VECM

25 OECD

countries

Feedback hypothesis between re

energy and CO2

Chang (2016) co-integration Granger

causality, VECM

China bi-directional causality from GDP

to CO2and negative impact of

GDP and energy on CO2

67

Mitic et al. (2017) co-integration

OLS and FMOLS

Transitional

countries

integration long run relationship

between real gdp and CO2

positive impact of real GDP on

CO2

Kahouli (2018) SUR, 3SLS, GMM MCs Strong feedback link between

egrowth, electricity, R&D, CO2

Ramli et al. (2019) OLS Malaysia Positive impact of energy on CO2

and least impact of gross domestic

product on CO2 emissions.

Cahia et al. (2019) vector panel

autoregressive Granger

causality

12 middle East

and North

African countries

GDP contributes to Environments

degradation, Re energy, FDI and

trade reduces CO2 emissions

Economic Growth and Demand Elasticities for Renewable, Non-Renewable Energy

Bentzen and Engsted

(1993)

co-integration, VECM Denmark energy demand is less income

elastic and more price elastic

Cheng and Lai (1997) FMOLS Positive relationship between

growth and energy demand

Ziramba (2010) Johansen co-integration South Africa Energy demand for crude oil price

was inelastic in short run, while

in long run it was negative for

price and positive for growth

Crotte at el., (2010) GMM Mexico Fuel efficiency elasticities were

smaller and vehicle stock had

more impact on gasoline demand

than its consumption.

Chaudhry (2010) FMOLS China,

Indonesia, India,

Korea and

Pakistan

Positive income and negative price

elasticities and elasticities were

high in low and middle income

countries as compared to high

income countries

Kahsai et al., (2012) Pedroni co-integration,

Granger Causality

40 SSA

countries

Positive relationship between

growth and energy demand

Bernstein and Madlener

(2015)

co-integration, VECM

Granger Causality

German

industries

Negative for gasoline price and

positive for GDP

Mensah et al., (2015) ARDL Ghana Urbanization increased demand for

all energy sources in the long run

while in the short run income and

price were major drivers of energy

demand.

Sun and Ouyang (2016) OLS China People were more sensitive to

income change than price change.

Price elasticities were higher in

low income household than high

income households

Zhang and Broadstock

(2016)

time varying

techniques

China rapid industrialization and

tremendous urbanization has given

rise to massive energy demand

68

To sum up, the previously mentioned literature, utilized different models and methods

to investigate the reason and outcomes of renewable and non-renewable energy sources.

Most of traditional studies like Kraft and Kraft (1978), Akarca and Long (1979, 1980),

Yu and Choi (1985a, 1985b), Stern (1993), Cheng and Lai (1997), Stern (2000), Yang

(2000), Adjaye (2000), Aqeel and Butt (2001), Ghosh (2002), Paul and Bhattacharya

(2004), Siddiqui (2004), Narayan and Singh (2006), Tang (2008), Erdal et al., (2008),

Narayan et al.,(2008), used only the nexus between energy consumption total to

generate link between energy and economic growth and investigated the relation on

production side. These studies mostly used traditional labor and capital stock as inputs

in energy. Moreover, these studies used Granger Causality to find the causal

relationship between energy consumption and economic growth. Later on, studies cover

the demand side of energy demand and the techniques like vector error correction

models to investigate the relationship between energy demand and economic growth.

These are studies of Oh and Lee (2004), Lee (2005). Most of the studies used CPI index

for the measurement of energy price as proxy such as Masih and Masih (1998), Asafu-

Adjaye (2000), Fatai et al. (2004) as well as Mahadevan and Asafu-Adjaye (2007). In

the era of technology, the traditional production function replaced with modern

Mrabet et al., (2019) augmented mean group developed and

emerging

countries

largest impact of GDP and oil

prices on non-renewable energy

demand

Renewable and Non-Renewable Energy Sources, Energy Intensity, and Economic Growth

Benardini and Riccardo

(1993)

negative relationship between

energy intensity and income

Galli (1998) fixed effects model 10 Asian

economies

a reversed U molded connection

between energy intensity and

income

Rafiq et al. (2016) 2nd generation test in twenty-two

progressively

developed rising

economies

Re energy sources seems to be

relaxing in energy intensity and

emissions whereas non-renewable

energy increases the both factors.

Belloumi and Alsherhry

(2016)

ARDL test and Granger

causality

Saudi Arabia urbanization has a positive effect

on energy intensity

Sheng et al., (2017) GMM 78 countries positive impact of urbanization on

energy consumption and negative

impact on energy efficiency

Li et al., (2018) Stochastic Frontier

Model

China Negative impact of urbanization

on energy efficiency.

69

production function which used the technology as an input. In that context, the energy

used as technical input and the traditional model replacement with modified version of

STRIPAT model. Shi (2003), Fan et al. (2006), York (2007), Poumanyvong (2010),

Die and Liu (2011), Zhong and Lin (2012), Poumanyvong et al. (2012), Liddle (2014),

Shafaie and Salim (2014) and Al-Mulali, Ozturk, (2015), used the said model in various

investigations. Sadrosky (2014) also used the STRIPAT model in emerging markets.

The energy intensity and URB are used as a proxy of T (Technology). Li and Lin (2015)

used two model in STRIPAT into four groups according to their yearly income level.

The first model included the share of industry and URB in EC model as a T

(Technology). While the share of industry, URB and energy intensity are included in

the CO2 model as a T (Technology). Shahbaz et al (2015), Rafiq et al (2016) and Rauf

et al. (2018) analyzed the four model in the STRIPAT and EKC (Environmental

Kuznets curve) models.

Later studies used renewable energy and non-renewable energy instead of traditional

energy such as Apergis and Payne (2009), Amirat and Bourni (2010), Asghar and Rahat

(2011) explored the causal link between energy use and economic growth in single

country using time series data. Masih and Masih (1996), Asghar (2008), Nondo and

Kashi (2009), Noor and Saddiqi (2010), used panel data. Dogan (2015) explored the

electricity consumption from renewable and non-renewable energy sources and

economic growth. Sadorsky (2009) Apergis and Payne (2010a) extended the link

between renewable energy and income. Lee (2005), Rufael (2006), Lee and Chang

(2008), Apergis and Payne (2010b, 2011, 2011b, 2011c and 2012), Tugcu et al. (2012)

and Bhattacharya et al., (2016) Khobai and Roux (2017), Natonas et al. (2018), Jabeur

(2019) and Bilan et al. (2019) explored the causal link between renewable and non-

renewable energy use and economic growth in cross countries using panel series data.


quality on monetary development crosswise over nations utilizing panel data collection

Emmanuel and Ebi (2013) inspected the relationship between institutional quality, oil

assets and economic development. Latter on Nayaran and Smyth (2009), Lean and

Smyth (2010), Halicioglu (2011), Shahbaz et al., (2012) included Dedeoğlu and Kaya

(2013) inquired about the association between exports, imports and energy use and

economic growth. Recently, the new studies Mitic et al. (2017), Khobai and Roux

(2017) and Bilan et al. (2019) also used the renewable energy and non-renewable

70

energy sources separately using FMOLS techniques. Bilgili et al. (2016), Al-Mulali and

Ozturk (2016), Dogan and Seker (2016b, c), and Bento and Moutinho (2016) also found

negative impact of renewable energy on CO2 emissions which mitigate the pollution

and positive impact of economic growth and non-renewable energy on CO2 emissions

which leads to pollutant environment.

In nutshell, the empirical studies recommended a number of leading indicators such as

economic growth, inflation, institution, financial development, population total,

population density, industrialization, and trade openness are found to be important in

the prospects of renewable and non-renewable energy sources. Observational based

empirical investigations likewise give rule with respect with the impact of energy on

economic development and environmental quality. Along these lines literature give a

strong base to the choice of factors to be utilized in observational investigation. At long

last, the literature proposed that there is no solid examination that talks about both the

causes and prospects of renewable and nonrenewable energy sources on account of

South Asia. The ebb and flow research study is an unassuming exertion to fill this hole.

71

CHAPTER 3

METHODOLOGY

This chapter describes and explain the research methodology in order to achieve the

targeted research objectives. The objective of this research is to explore the prospects

of renewable and non-renewable energy sources on the one hand and to analyze the

consequences of renewable and non-renewable energy sources on South Asian

Economies on the other hand. This research intends to use econometric models to

achieve research objectives. Econometrics use economic theory and statistical

influences to analyze and test economic phenomena. Economists use econometric

models to analyze the association between variables, usually with the hope of

determining causality.

3.1 Data

Four countries from the region of South Asia has been selected for empirical

investigation on the basis of data availability. These countries are India, Pakistan,

Bangladesh and Sri Lanka. The time span to be covered in the thesis is 1980-2014. See

table A1 in appendices for variable description.

3.2 Model Specification

Empirical Analysis is conducted to achieve the research objective proposed in this

study. Nine different models are utilized for empirical analysis. The first three models

explain the impact of renewable and non-renewable energy sources on economic

growth and in these models, economic growth is used as dependent variable. The next

model describes the role of renewable and non-renewable energy sources in the process

of economic development, in improving environmental quality taking per capita co2

emission as independent variable. Model 6 and 7 describe the determinants and

elasticities of renewable and non-renewable energy demand taking renewable and non-

renewable energy sources as dependent variables, respectively. The last three models

explore the relationship of energy intensity with economic growth and renewable and

non-renewable energy sources. In these models energy intensity used as dependent

variable. Summary of the all variables is given in appendices (see table B1 and B2).

72

3.2.1 Renewable and Non-Renewable Energy Sources with Economic

Growth

Kraft and Kraft (1978), Akarca and Long (1979, 1980), Yu and Choi (1985a, 1985b),

Stern (1993), Cheng and Lai (1997), Stern (2000), Yang (2000), Adjaye (2000), Aqeel

and Butt (2001), Ghosh (2002), Paul and Bhattacharya (2004), Siddiqui (2004), Oh and

Lee (2004), Lee (2005), Narayan and Singh (2006), Tang (2008), Erdal et al., (2008),

Narayan et al.,(2008), Apergis and Payne (2009), Amirat and Bourni (2010), Asghar

and Rahat (2011) explored the causal link between energy use and economic growth in

single country using time series data. Masih and Masih (1996), Asghar (2008), Nondo

and Kashi (2009), Noor and Saddiqi (2010), used panel data. Dogan (2015) explored

the electricity consumption from renewable and non-renewable energy sources and

economic growth. Sadorsky (2009) Apergis and Payne (2010a) extended the link

between renewable energy and income. Lee (2005), Rufael (2006), Lee and Chang

(2008), Apergis and Payne (2010b, 2011, 2011b, 2011c and 2012), Tugcu et al. (2012)

and Bhattacharya et al.,(2016) Khobai and Roux (2017), Natonas et al. (2018), Jabeur

(2019) and Bilan et al. (2019) explored the causal link between renewable and non-

renewable energy use and economic growth in cross countries using panel series data.


quality on monetary development crosswise over nations utilizing panel data collection

Emmanuel and Ebi (2013) inspected the relationship between institutional quality, oil

assets and economic development. Latter on Nayaran and Smyth (2009), Lean and

Smyth (2010), Halicioglu (2011), Shahbaz et al., (2012) included Dedeoğlu and Kaya

(2013) inquired about the association between exports, imports and energy use and

economic growth.

3.2.1.1 Model 1: Relationship between Renewable and Non-renewable

Energy, Institutions and Economic Growth

The relationship between economic growth (Y), renewable energy sources (RE),

nonrenewable energy sources (NRE), institutions (INS), population density (PD) and

trade openness (To) is modeled as follows:

73

, , , ,it it it it it itY f RE NRE INS PD To 1

1 2 3 4 5it i it i it i it i it i it itY RE NRE INS PD To 2

Where

Y = GDP per capita (constant 2005 US $) is a proxy for Economic Growth

RE = Electricity production from renewable sources (kWh) is proxy for Renewable

Energy sources

NRE = Electricity production from oil sources (kWh) is proxy for Non-Renewable

Energy sources

INS = Institutions (polity 2 score obtain from polity IV is used to measure institutions)

PD = Population Density (People per square Km of land area)

To = Trade openness is measured by ratio of exports + imports to GDP

ε = Error Term


Energy, Urbanization and Economic Growth

The relationship between economic growth (Y), renewable energy sources (RE), non-

renewable energy sources (NRE), institutions (INS), inflation (CPI) and urbanization

(URB) is modeled as follows:

, , , ,it it it it it itY f RE NRE INS CPI URB 3

1 2 3 4 5it i it i it i it i it i it itY RE NRE INS P URB 4



Energy sources


Energy sources

74

INS = Institutions (polity 2 score obtain from polity IV is used to measure institutions)

CPI = Consumer Price Index is used to measure inflation

URB = Urban population (% of total)

ε = Error Term

3.2.1.3. Model 3: Relationship between Renewable and Non-renewable

Energy, Financial Development and Economic Growth

Latter on Narayan and Smyth (2009), Lean and Smyth (2010), Halicioglu (2011),

Shahbaz et al., (2012) included the financial development with trade openness and

energy use from both renewable and non-renewable.

The relationship between economic growth (Y), renewable energy sources (RE),

nonrenewable energy sources (NRE), financial development (FD), population density

(PD) and trade openness (To) is modeled as follows:

, , , ,it it it it it itY f RE NRE FD PD To 5

1 2 3 4 5it i it i it i it i it i it itY RE NRE FD PD To 6

Where



Energy sources


Energy sources

FD = Financial Development (Broad money to total reserves ratio is used to measure

FD)


To = Trade openness is measured by ratio of exports + imports to GDP

75

ε = Error Term

According to the empirical literature presented in the previous chapter, the coefficients

of renewable and non-renewable energy sources are expected to be positive because an

increase in energy sources may lead to increase economic growth. The coefficient of

institutions may be positive or may be negative. The coefficient of urbanization may

also be positive because urbanization process may lead to enhance economic activities.

Industrialization also have positive impact on economic growth. The coefficient of

trade openness may be positive or may be negative.

3.2.1.4. Data Sources

The data on real GDP per capita, renewable energy sources, nonrenewable energy

sources, merchandize exports, merchandise imports, inflation, and financial

development, urbanization and population density are obtained from World

Development Indicators (2014) of the World Bank. The data on polity 2 score is obtain

from polity IV project (Marshall and Jaggers, 2015). The variables real GDP per capita,

renewable energy sources, nonrenewable energy sources urbanization, financial

development, inflation, and population density are in logarithmic form.

3.2.2 Renewable and Non-Renewable Energy with Environment Quality


Energy, Population Density and Environment

The relationship between environmental quality (CO2), economic growth (Y),

renewable energy sources (RE), nonrenewable energy sources (NRE) and population

density (PD) is modeled as follows:

2 , , ,it it it it itCO f Y RE NRE PD 7

1 2 3 4 52it i i it i it i it i it itCO Y RE NRE PD 8

The relationship between environmental quality (CO2), economic growth (Y), square

of economic growth (Y2), renewable energy sources (RE), nonrenewable energy

sources (NRE) and population density (PD) is modeled as follows:

76

22 , , , ,it it it it it itCO f Y Y RE NRE PD 9

2

1 2 3 4 5 62it i i it i it i it i it i it itCO Y Y RE NRE PD 10

Where

CO2 = CO2 emissions (metric tons per capita)


Y2 = Square of GDP per capita (constant 2005 US $) is a proxy for Economic Growth.


Energy sources


Energy sources


ε = Error Term


Energy, Urbanization, Energy Intensity and Environment

The relationship between environmental quality (CO2), economic growth (Y),

renewable energy sources (RE), nonrenewable energy sources (NRE), urban population

(%) of total, economic efficiency and population density (PD) is modeled as follows:

2 , , , , ,it it it it it it itCO f Y RE NRE PT URB EI 11

1 2 3 4 6 7 82it i i it i it i it i it i it i it itCO Y RE NRE PT URB EI 12

Environmental Kuznets hypothesis is presented as:

22 , , , , , ,it it it it it it it itCO f Y Y RE NRE PT URB EI 13

Its general form can be written as:

77

2

1 2 3 4 5 6 7 82it i i it i it i it it i it i it i it itCO Y Y RE NRE PT URB EI 14

Where

CO2 = CO2 emissions (metric tons per capita)

Y = GDP per capita (constant 2005 US $) is a proxy for Economic Growth.

Y2 = Square of GDP per capita (constant 2005 US $) is a proxy for Economic Growth.


Energy sources


Energy sources


EI = EI = Energy Intensity measured by ratio of total energy consumption to GDP

PT = Population Total

ε = Error Term

According to the empirical literature presented in the previous chapter, the coefficient

of renewable energy sources are expected to be negative because an increase in energy

sources may lead to decrease in per capita CO2 emissions so, renewable energy sources

are environment friendly. The coefficient of non-renewable energy sources are

expected to be positive because an increase in energy sources may lead to increase in

per capita CO2 emissions. The coefficient of urbanization may also be positive because

urbanization process may lead to enhance per capita CO2 emissions. Industrialization

also have positive impact on per capita CO2 emissions. The coefficient of trade

openness may be positive or may be negative. Population total or population density

may also increase pollution so the coefficients of population total and population

density are expected to be positive. The coefficient of energy intensity also expected to

be positive.

78

The mentioned above model is the modified version of STRIPAT model. The

specification of the STRIPAT model is:

I =aPibAi

cTidui 15

In the above equation, a is the constant term, while b, c and d are the responsiveness of

environmental impact on P, A, and T correspondingly. The variables µ denotes the error

term and the subscript i represent the country analysis. Shi (2003), Fan et al. (2006),

York (2007), Poumanyvong (2010), Die and Liu (2011), Zhong and Lin(2012),

Poumanyvong et al. (2012), Liddle (2014), Shafaie and Salim (2014) and Al-Mulali,

Ozturk, (2015), Mitic et al. (2017), Khobai and Roux (2017) and Bilan et al. (2019)

used the said model in various investigations. Sadrosky (2014) also used the STRIPAT

model in emerging markets. The energy intensity and URB are used as a proxy of T

(Technology). Li and Lin (2015) used two model in STRIPAT into four groups

according to their yearly income level. The first model included the share of industry

and URB in EC model as a T (Technology). While the share of industry, URB and

energy intensity are included in the CO2 model as a T (Technology). Shahbaz et al

(2015), Rafiq et al (2016) and Rauf et al. (2018) analyzed the four model in the

STRIPAT and EKC (Environmental Kuznets curve) models.


The data on per capita CO2 emissions, real GDP per capita, renewable energy sources,

nonrenewable energy sources, merchandize exports, merchandize imports and

population density are obtained from World Development Indicators (latest version) of

the World Bank. The data on polity 2 score is obtain from polity IV project (Marshall

and Jaggers, 2015). The variables per capita CO2 emissions, real GDP per capita,

renewable energy sources, nonrenewable energy sources and population density are in

logarithmic form.

3.2.3. Demand Elasticity of Renewable and Non-Renewable Energy

Sources

3.2.3.1. Model 6: Relationship between Renewable Energy Demand,

Economic Growth, Industrialization, Technological Changes and Energy

Price

79

The relationship between renewable energy sources (RE), economic growth (Y),

industrialization (IND), population (PT), energy price (P) and technological changes

(T) is modeled as follows:

, , , ,it it it it it itRE f Y IND PT P T 16

1 2 3 4 5 6it i i it i it i it i it i it itRE Y IND PT P T 17

Where,


Energy sources


EP= energy Price (Price of energy consumption) since energy prices were not available,

this variable was proxed by the consumer price index Adjaye (2000) Mrabet et al.,

(2019).

IND = Industry, value added (% of GDP)

TP = Total population

T = Technical progress or efficiency improvement which is proxed by a deterministic

time trend

ε = Error Term

3.2.3.2. Model 7: Relationship between Non-Renewable Energy Demand,

Economic Growth, Industrialization, Technological Changes and Energy

Price

The relationship between non-renewable energy sources (NRE), economic growth (Y),

industrialization (IND), population (PT), energy price (P) and technological changes

(T) is modeled as follows:

, , , ,it it it it it itNRE f Y IND PT P T 18

1 2 3 4 5 6it i i it i it i it i it i it itNRE Y IND PT P T 19

80

Where,

Y= real GDP per capita (constant 2005 US $ used as proxy of economic growth)

NRE = Non-renewable energy sources proxied by electricity production from oil

sources (kWh)

P= energy Price (Price of energy consumption) since energy prices were not available,

this variable was proxied by the consumer price index (Akinlo, 2008; Galindo, 2005;

Hondroyiannis et al., 2002; Adjaye, 2000; Mrabet et al., (2019).


TP = Total population

T = Technical progress or efficiency improvement which is proxies by a deterministic

time trend

ε = Error Term

According to the studies of Rapanos and Polemis (2006), Hunt and Ninomiya (2005)

and Fouquet et al. (1997), the coefficient of economic growth is expected to be positive

because an increase in economic growth may lead to increase in demand for both

renewable and non-renewable energy sources. The coefficient of industrialization

(value share of industry) is also expected to be positive because an increase in value

share of industry in terms of GDP designates the industrialization process and may lead

to increase in demand for both renewable and non-renewable energy sources

Samoulidis and Mitropoulos (1984). The coefficient of population is expected to be

positive because an increase in population may lead to increase in demand for both

renewable and non-renewable energy sources. The coefficient of energy price is

expected to be negative, an increase in energy price leads to reduction in energy demand

according to Pindyck (1979a) higher energy prices have contributed to lower economic

growth and may lead to change in lifestyles of many nations which leads to reduce

energy demand. Another important determinant of demand for energy is technical

efficiency. In this thesis, the improvement in energy efficiency has been captured by

the standard approach of a deterministic time trend. According to the studies of

Beenstock and Willcock (1981), Kouris (1983), Hunt et al. (2003) and Dimitropolus et

81

al., (2004) there has been a debate in the literature of energy consumption. These studies

concluded that technical progress has always been very difficult to quantify therefore,

time trend has been used to measure improvement in energy efficiency. According to

Hogan and Jorgenson (1991) and Hunt et al. (2013), the coefficient of technical

progress may be positive or may be negative. Technical efficiency improvement leads

to reduction in energy demand which contributes to energy saving as Welsch and

Ochsen (2005), Lin (2003), Popp (2001) and Berndt et al. (1993) found in studies earlier

they conducted.


The data on real GDP per capita, renewable energy sources, nonrenewable energy

sources, energy price and industrialization is obtained from World Development

Indicators (latest version) of the World Bank. The variables real GDP per capita,

renewable energy sources, nonrenewable energy sources, energy price,

industrialization and population density are in logarithmic form.

3.2.4. Renewable and Non-Renewable Energy and Energy Intensity

(efficiency)


Energy, Economic Growth, Urbanization and Energy Intensity

The relationship between energy intensity (EI), renewable energy sources (RE), non-

renewable energy sources (NRE), economic growth (Y), population density (PD) and

urbanization (URB) is modeled as follows:

, , , ,it it it it it itEI f Y RE NRE PD URB 20

1 2 3 4 5 6it i i it i it i it i it i it itEI Y RE NRE PD URB 21

Where,

EI = Energy Intensity measured by ratio of total energy consumption to GDP.


82


Energy sources


Energy sources



ε = Error Term


Energy, Economic Growth, Trade Openness and Energy Intensity


renewable energy sources (NRE), economic growth (Y), population density (PD), and

trade openness (To) is modeled as follows:

, , , ,it it it it it itEI f Y RE NRE PD To 22

1 2 3 4 5 6it i i it i it i it i it i it itEI Y RE NRE PD To 23

Where,




Energy sources


Energy sources


To = Trade openness (export (US$) plus imports (US$) divided by GDP is used to

measure trade openness)

83

ε = Error Term


Energy, Economic Growth, Industrialization, Technological Progress and

Energy Intensity


renewable energy sources (NRE), economic growth (Y), population density (PD),

industrialization (IND) and technological changes (T) is modeled as follows:

, , , , ,it it it it it it itEI f Y RE NRE PD IND T 24

1 2 3 4 5 6 7it i i it i it i it i it i it i it itEI Y RE NRE PD IND T 25

Where,




Energy sources


Energy sources



T = Technical progress or efficiency improvement which is peroxide by a deterministic

time trend

ε = Error Term


The data on total energy consumption, gross domestic product, real GDP per capita,

renewable energy sources, nonrenewable energy sources, merchandize exports,

merchandise imports, urbanization and population density are obtained from World

84

Development Indicators (latest version) of the World Bank. The variables energy

intensity, real GDP per capita, renewable energy sources, nonrenewable energy sources

urbanization and population density are in logarithmic form.

3.3. Econometrics Methodology

3.3.1. Time-Series Methodology

Time-series methods on unit roots, co-integration and causality are commonly applied

in individual country analysis. The brief detail of proposed time-series methodology is

provided below.

3.3.1.1. Unit Root Tests

In time series analysis, it is necessary that data should be stationary. The most

commonly used test of unit root in time series data is the augmented Dickey-Fuller

(ADF) test in which the null hypothesis is non-stationary. ADF test is sensitive to lag

length selection and the presence of negative MA term. This test is also characterized

by problem of poor power and size. In contrast to this test, DF-GLS unit root test

proposed by Elliott, Rothenberg and Stock (1996) have higher power in the sense

that this test is more likely to reject the null hypothesis of a unit root and accept

the alternative hypothesis of stationary. This test is also called de-trending test because

it is used to determine the order of integration of the variables Zt. Further, this test

requires much shorter sample sizes than the conventional ADF tests to attain the same

statistical power. The general equation of DF-GLS test is given below:

* * *

1 1 1 1 1....d d d d

t t t p t p tz z z z 26

Where d

tz the de-trended series and null hypothesis of this test is that tz has a random

walk trend:

0 1ˆ ˆd

t tz z t 27

The DF-GLS test proposed two hypothesis. Firstly tz is stationary with a linear time

trend and secondly, it is stationary without linear time trend with a mean greater than

zero. The test is performed by utilizing the generalized least square technique in an

85

alternative hypothesis. This estimation is investigated by generating the following

variables:

2, 1 ,....., 1t Tz z L z L z

28

2, 1 ,....., 1t TY z L Y L Y

29

And 1, 1tY tT

30

Where T represents the number of observations for tz and is fixed. OLS estimation

is followed by the under given equation:

0 1 t tz Y Y 31

OLS estimators 0 and 1 are utilized for the removal of trend from tz above. OLS is

employed on the transformed variable by fitting the following regression:

0 1

1

md d d

t t i t i t

i

z z z

32

Finally, ADF regression is employed on new transformed variables and test the null

hypothesis: 0 : 0H

3.3.1.2. Johansen Co-integration Test

The concept of co-integration was first introduced by Granger (1981) and elaborated

further by Engle and Granger (1987), Engle and Granger (1987) give the formal

definition of co-integration among two as follows: “ time series Yt and Xt are said to

be co-integration of order d, b where d ≥ b ≥ 0, written as Yt, Xt ̃ CI(d, b) if (a) both

series are integrated of order d, (b) there exist a linear combination of these variables,

say

β1Yt + β 2 Xt which is integrated of order d-b. The vector {β1 β2} is called the co-

integration vector.”

This analysis is used to capture the notion that non-stationary variables may possess

long run equilibrium relationship so that they tend to move together in the long run.

86

The concept of the co-integration is characterized by a situation where the variables in

a given relation should not diverge from each other in the long run. (Banerjee et. al.,

1993)

The presence of a unit root (non-stationary time series) in time series may lead to

spurious development of theory. The individual time series may be I(1), that is they

have stochastic trend, their linear combination may be stationary and the series are said

to be co-integrated. (Gujarati, 2006)

The stationary linear combination is called the co-integration equation. We first identify

the co-integrating relationship i.e., determination of the rank of the matrix Π. When the

rank is zero, it implies that Π. contains only zero elements, on the other hand, an “n”

rank shows that all endogenous variables in the model are I(0). Johansen suggested two

test to determine the rank ‘r’ or the number of co-integration equation.

1. The Trace Test

2. The Maximum Eigen value Test

4.3.4.1. The Trace Test

The Trace test is based upon a sequence of hypothesis tests. The test statistics will be:

LR trace (γ Ho ) = - T ∑ ln (1- λi) 34

Where (γ Ho ) is the number of co-integration equations assuming the null hypothesis is

true and λˆi is the estimated Eigenvalue.

The test sequence is as follows:

r = 0 versus r ≥ 1

Ho : r = 0 (There are no co-integrating equations)

H1 : r ≥ 1 (There are r ≥ 1 co-integration equation)

r = 1 versus r ≥ 2

Ho : r = 1 (There are r = 1 co-integrating equations)

H1 : r ≥ 2 (There are r ≥ 2 co-integration equation)

87

And the sequence continues, until we have hypotheses test:

r = n-1 versus r = n

Ho : r = n-1 (There are (n-1) co-integrating equations)

H1 : r = n (There are maximum co-integration equation)

If LR trace (γ Ho ) > critical values reject Ho in favour of H1 otherwise accept Ho.

If the test indicates the presence of co-integrating combination in the model, error

correction can be applied to it that gives us information about long run equilibrium

relationship and short run dynamics.

4.2.4.2. The Maximum Eigenvalue Test

The test statistics will be:

LR max (γ Ho ) = - n ln (1- λ r Ho +1) 35

This test is also based on the sequence of the hypothesis test thus; the testing sequence

is as follows:

r = 0 versus r = 1

Ho : r = 0 (There are no co-integrating equations)

H1 : r = 1 (There are r = 1 co-integration equation)

The second hypothesis test is

r = 1 versus r = 2

Ho : r = 1 (There are r = 1 co-integrating equations)

H1 : r = 2 (There are r = 2 co-integration equation)

And the sequence continues, until we have the (n-1) th hypothesis test:

r = n-1 versus r = n

Ho : r = n-1 (There are (n-1) co-integrating equations)

H1 : r = n (There are maximum co-integration equation)

88

The thesis have been applied the standard maximum likelihood technique of Johansen

and Juselius (1990) and Johanson (1995) to determine the long run relationship

between the variables or to check whether the variables are co-integrated or not.

3.3.2 Panel Data Methodology

Panel data have two important merits with respect to time series data. Firstly, such data

is large in sample size, more efficient and more reliable and have less co linearity among

variables. Secondly, panel data enables researchers to include country specific affects

and time specific effects to absorb the effects of unobservable variables that are

correlated with explanatory variables included in the panel (Reppas and Chirstopoulos,

2005).

In short, panel data analysis provides large sample size and hence more powerful

significance tests as compared to time-series analysis. This is true not only for unit root

tests but also for co-integration tests. In this section, detail of panel co-integration tests

and panel data causality analysis are provided.

3.3.2.1. Panel Unit Root Tests

The study apply Levine et al. 2002 (LLC), Im et al. 2003 (IPS) with demean panel unit

root tests to check the stationary properties of the variables. These tests apply to a

balanced panel but the LLC can be considered a pooled panel unit root test, IPS

represents as a heterogeneous panel test. Some recent papers (Lyhagen, 2008 and

Wagner, 2008) find that in the presence of cross-sectional dependence (particularly in

the case of trade openness and economic growth due to unobservable common shocks),

the IPS, LLC and other similar tests incorrectly reject the null hypothesis of non-

stationary. Therefore, we also implement the unit root tests on time demeaned series to

deal with cross-sectional dependence. According to Levin et al. (2002), implementing

unit root tests on time demeaned series allows to mitigate the impact of cross-sectional

dependence on panel data.

3.3.2.2 LLC Unit Root Test

Levin et al. (2002) developed a number of pooled panel unit root tests with various

specifications depending upon the treatment of the individual specific intercepts and

time trends. This test imposes homogeneity on the autoregressive coefficient that

89

indicates the presence or absence of unit root problem while the intercept and the trend

can vary across individual series. LLC unit root test follows ADF regression for the

investigation of unit root hypothesis is given below step by step:

1. Implement a separate ADF regression for each country:

ti

p

j

jtijiitiiti

i

yyy ,

1

,,1,

36

The lag order pi is allowable to across individual countries. The appropriate lag length

is chosen by allowing the maximum lag order and then uses the t-statistics for ij b to

determine if a smaller lag order is preferred.

2. Run two separate regressions and save the residuals 1,

~~

, tiit

itti

p

j

jtijtiiti

i

yy~

,

1

,,,

37

1,

~

1,

1

,,1,

titi

p

j

jtijtiiti

i

yy 38

LLC procedure suggests standardized the errors 1,

~~

, tiit by the regressing the standard

error the ADF equation provided above:

^

1,

~

1

~

^

~

~

,

i

ti

it

i

it

it

39

3. Regression can be run to compute the panel test statistics following equation-v:

titiit ,1,

~~

40

The null hypothesis is as follows: 0......,..: 1 nH and alternate hypothesis

is 0......: nAH .

90

3.3.2.2 IPS Unit Root Test

Im, Pesaran and Shin (IPS), (2003) introduced a panel unit root test in the context of a

heterogeneous panel. This test basically applies the ADF test to individual series thus

allowing each series to have its own short-run dynamics. But the overall t-test statistic

is based on the arithmetic mean of all individual countries’ ADF statistic. Suppose a

series ( tiTR , tiEC ) can be represented by the ADF (without trend).

ti

p

j

jtijitiijti

i

xxx ,

1

,,1,,

41

After the ADF regression has different augmentation lags for each country in finite

samples, the term )( TtE and )var( Tt are replaced by the corresponding group averages

of the tabulated values of ),( iT PtE and T ivar (t ,P ) respectively. The IPS test allows for

the heterogeneity in the value i under the alternative hypothesis. This is more efficient

and powerful test than usual single time series test. The estimable equation of IPS unit

root test is modeled as following:

)(1

, i

N

i

tiNT PtN

It

42

wheretit ,is the ADF t-statistics for the unit root tests of each country and iP is the lag

order in the ADF regression and test statistic can be calculated as following:

T T

tT

N(T)[t E(t )]

var(t )

43

As NTt is explained above and values for )]0,([ iiT PtE can be obtained from the results

of Monte Carlo simulation carried out by IPS. They have calculated and tabulated them

for various time periods and lags. When the ADF has different augmentation lags )( iP

91

the two terms )( TtE and )var( Tt in the equation above are replaced by corresponding

group averages of the tabulated values of ),( iT PtE and ),var( iT Pt respectively2.

3.3.3 The Panel Co-integration Tests

Advance panel co-integration tests can be expected to have high power than the

traditional tests. The tests applied for long-run examination are developed by Larsson

et al., (2001).

The panel Larsson et al., (2001) likelihood ratio test statistics is derived from the

average of the individual likelihood ratio test statistics of Johanson (1995). The

multivariate co-integration trace test of Johanson (1988, 1995) is engaged to investigate

each individual cross-section system autonomously, in such a way, allowing

heterogeneity in each cross-sectional unit root for said panel. The process of data

generation for each of the groups is characterized by following heterogeneous VAR (

ip ) model:

tijti

p

j

jiti YYi

,,

1

,,

44

Where TtNi ,.......1;,......,1

For each one, the value of 0,1, ,...... iji YY is considered fixed and

ti, are independent and

identically distributed (normally distributed): ),0(~ iKN , where i is the cross-

2Karlsson and Lothgren, (2000) demonstrate the power of panel unit root tests by Monte Carlo

simulation. The null of all these tests is that each series contains a unit root and thus is difference

stationary. However, the alternative hypothesis is not clearly specified. In LLC the alternative is that all

individual series in the panel are stationary. In IPS the alternative is that at least one of the individual

series in the panel is stationary. They conclude that the “presence or absence of power against the

alternative where a subset of the series is stationary has a serious implications for empirical work. If

the tests have high power, a rejection of the unit root null can be driven by few stationary series and

the whole panel may inaccurately be modelled as stationary. If, on other hand, the tests have low power

it may incorrectly concluded that the panel contains a common unit root even if a majority of the series

is stationary’’ (p. 254). The simulation results reveal that the power of the tests (LLC, IPS) increases

monotonically with: (1) an increased number (N) of the series in the panel; (2) an increased time series

dimension (T) in each individual series; (3) increased proportion of stationary series in the panel. Their

Monte Carlo simulations for N=13 and T=80 reveal the power of the test is 0.7 for LLC tests and

approaching unity for the IPS tests.

92

correlation matrix in the error terms: ),( '

,, titii E . The equation-10 can be

modified in vector error correction model (VECM) model as given below:

jijti

p

j

jitiiti YYYi

,,

1

1

,1,,

45

Where 1......1, piiiand

ijijiji ,1,,,is of order )( kk . If i is

of reduced rank: rank ii r )( , which can be decomposed into 'abi , where i and

i are of order )( irk and of full rank column rank that represents the error correction

form. The null hypotheses of panel LLL (2001) rank test are:

0 i iH rank ( ) r r for all Ni ,.....,1 against

a iH rank ( ) k for all Ni ,.....,1

The procedure is in sequences like individual trace test process for cointegration rank

determination. First, we test for 0 i iH rank( ) r r, r 0 , if null hypothesis of no

co-integration is accepted, this shows that there is no co-integration relationship

i i(rank ( ) r 0) in all cross-sectional groups for said panel. If null hypothesis is not

accepted then null hypothesis 1r is tested. The sequence of procedure is not

disconnected and continued until null hypothesis is accepted, 1 kr , is rejected.

Accepting the hypothesis of co-integration 0r along with null hypothesis of rank

i( ) r 0 (0 r k) implies that there is at least one cross-sectional unit in panel

which has rank 0)( ri . The likelihood ratio trace test statistic for group i is as

following;

)1ln()(/)((ln2)(/)({1

'

p

rl

liiTiT TkHrHQkHrHLR 46

Where '

l is the thl largest eigen value in the thi cross-section unit. The LR-bar statistic

is calculated as the average of individual trace statistics:

)](/)([1

)](/)([1

_

kHrHLRN

kHrHRLn

i

iTiT

47

93

Finally, modified version of above equation is defined as:

_

_

NT k

LRk

N(LR [H(r) / H(k)]) E(Z )[H(r) / H(k)]

VAR(Z )

48

Where )( kZE and )( kZVar are mean and variance of the asymptotic trace statistics,

which can be obtained from simulation. The LLL (2001) prove the central limit theorem

for the standard LR-bar statistic that under the null hypothesis, _

LR

N(0,1) as N and

T in such a way that 1

NT 0,

under the assumption that there is no cross-

correlation in the error terms, that is give below:

i,tE( ) 0 and i

i,t j,tE( , )0

for jiji , 49

LLL (2001) note that T is needed for each of the individual test statistic to

converge to its asymptotic distribution, while N is needed for the central limit

theorem.

3.3.3.1 Estimation of Panel Co-integration Regression

On the off chance that all the variables are co-integrated, the following stride is to assess

the long-run co-integration parameters. Within the incidence of co-integration, an

Ordinary Least Square estimator is recognized as biased and conflicting results. Due to

behind this cause, limited estimators have been offered. For instance, Kao and Chiang

(2000) contend that their parametric panel Dynamic OLS (DOLS) estimator (that pools

the information along the inside measurement of the board) is promising in small

samples and performs well when all is said in done in co-integrated panels. Be that as

it may, the panel DOLS because of Kao and Chiang (2000) does not consider the

significance of cross-sectional heterogeneity in the alternative hypothesis. To take into

consideration cross-sectional heterogeneity in the alternative hypothesis, endogeneity

and serial correlation problems to get steady and asymptotically unbiased estimates of

the co-integrating vectors, Pedroni (2000; 2001) proposed the group mean Fully

Modified OLS (FMOLS) estimator for co-integrated panels. These methodologies used

by the authors in their papers such as Sadorsky, P. (2009), Apergis & Payne (2010a),

Fang, Y. (2011), Al-Mulali et al., (2013) Sadorsky.P (2014), Cho et al., (2016), Jebli

94

et al., (2016), Mitic et al. (2017), Jabeur (2019) Ahmed and Shimda (2019) Ramli et

al., (2019, Natonas et al., (2018) and Bilan et al. ,(2019).

Taking after Pedroni (2001), FMOLS method produces predictable estimates in small

samples and does not suffer from large size distortions in the presence of endogeneity

and heterogeneous progression. The board FMOLS estimator for the coefficient β is

characterized as:

1

1 2 *

1 1 1

ˆ ˆ( ) ( )

N T T

it it it i

i t t

N y y y y z T 50

Where * 0 021 21

21 21 22 22

22 22

ˆ ˆˆ ˆˆ ˆˆ( ) , ( )

ˆ ˆi i

it it it i i i i i

i i

L Lz z z y

L L and ˆ

iL is a

lower triangular decomposition of ˆi . The associated t-statistics gives:

* *

1/2

ˆ ˆ ,1

N

ii

t N t

Where *

1/2

* 1 2

ˆ 0 11,1

ˆ ˆ ( )

T

i i itit

t y y 51

3.4.3.2 Panel Granger Causality Test

The panel Co-integration tests imply the presence of the long runs affiliation among

variables, but it does not notify regarding the direction of the affiliation amongst the

variables. If the relationships of variables are existed, the Granger causality test can be

utilized to research the causal connection amongst the variables. We used the more

advanced form of causality test Granger (1969) for panel data, which is developed by

Hurlin and Venet (2001). Mathematical equation which is utilized is explained below:

( ) ( )

, , ,

1 0

p pk k

it i t k i t k i t

k k

Y y z w

52

Where assume the coefficient of autoregressive( )k and the slopes of equation ( )k

i

remain constant. The null and alternative hypothesis is defined below:

H0: 0i 11,....,i N

H1: 0i 1 11, 2,....,i N N N

95

Where N1 is unknown but satisfies the condition 0< N1/N<1. The formation of this test

is similar to unit root test in a heterogeneous panel presented by IPS (2003). We process

the following Wald Statistics with a specific goal to test these Np linear restrictions:

2 1

1

( ) / ( )

RSS / (1 )hnc

RSS RSS NpF

NT N p p

53

Where RSS2 is the restricted residual sum of square while RSS1 is the unrestricted

residual sum of squares of above equation. HNC hypothesis is accepted if the calculated

value of F-statistics is not significant.

The panel Granger Causality test is also measured in the light of W & Z test

measurements of the Homogenous Non Causality (HNC) theory. In the test, the null

hypothesis of Homogenous Non Causality (HNC) hypothesis implies there is no

relationship for all the cross-units of the panel. Under the alternative, there is a

relationship among variables in the long run. This test is applied on a balance data. The

W measurements compare to the cross sectional normal of the N standard individual

Wald statistics of Granger non-causality tests. The Z measurements compare to the

standardized statistic (for fixed T sample). Both measurements tend to a normal

distribution when both T and N dimension tends to infinity (forW ) or only when N

tends to infinity (for Z ).

96

CHAPTER 4

RESULTS AND DISCUSSIONS

This chapter presented an overview of South Asian economies as well as the application

of econometric techniques in order to achieve the research objectives. 4.1 section of the

chapter provides economic structure of South Asian economies by highlighting

pertinent trends and indicators such economic growth, energy sources, inflation,

population, as well as population density, industrial sector and trade among others. 4.2

section provides the energy situation in South Asia. Section 4.3 discussed the

importance of energy sector including both renewable and non-renewable energy

sources and also presented the potential of renewable energy sources. In the last, section

4.4 provides empirical results.

4.1 An Overview of South Asian Economies

South Asia is a homeland to nearly one quarter of world population. The region always

fascinated to thinkers, researchers, and philosophers due to its cultural diversity, rich

history, social and economic development. South Asia comprises India, Pakistan,

Bangladesh, Nepal, Bhutan, Afghanistan, Sri Lanka and Maldives and its total

population is nearly 1.70 billion, nearly a third of them very poor, having less than US

$ 1 a day to consume. Only Maldives belongs to the category of upper middle income

countries as defined by World Bank. India, Pakistan, Bhutan and Sri Lanka belong to

the category of lower middle income countries whereas, Bangladesh and Nepal are in

the list of low income countries (World Bank, World Country Classification; 2013-14).

4.1.1 Economic Structure

4.1.1.1 Economic Growth

South Asian economies have made stunning work to improve the living standard of its

people. South Asian region has been next only to East Asia in its economic growth

performance since the beginning of 1980’s. Its average annual growth rate of per capita

gross domestic product was 5.6% in 1980,s. The region have a tremendous potential for

economic growth. India has led South Asian growth acceleration since the start of

1980’s due to its superior growth performance. During 1990’s, the region growth rate

was substantially higher than the respective countries. In the last decade, 2001-2010

97

these countries showed improvement in growth rate. The growth rate in India jumped

by two percent points to 7.5 percent, Pakistan too shook off the stagnation of 1990’s

and attained 6.5 percent rate of growth. Bangladesh growth rate further strengthen and

reached up to 6 percent. The same decade, Sri Lanka faced the declined growth rate.

Figure 4.1 explain the trends in growth rate of India, Pakistan, Bangladesh and Sri

Lanka during the period of 1980-2014.

Figure 4.1 Trends in Economic Growth (GDP per capita)

4.1.1.2 Inflation

Growing trend in food and fuel prices before the onset of global and financial crises led

to the high inflation rates in most countries of Asia. The onset crises shifted the pressure

if inflation in all sub region of Asia. The inflationary pressure increased the

vulnerabilities of less and middle income communities. This upward pressure in prices

of primary commodities in most South Asian economies has also worsened due to the

low per capita output in agriculture sector. Due to the high growth in agriculture sector

only the India in this region is less affected by inflationary pressure. Other three

economies Pakistan, Bangladesh and Sri Lanka are highly affected by inflation because

these economies mostly depend on international markets for fuel. So these economies

facing much more inflationary challenges than India. Figure 4.2 describes trends of

inflation rate of selected counters of South Asia during 1980-2014. This table shows

the increasing trend in all the countries. In 2013 the inflation rate in India was 132, in

Pakistan, it was also 132. In Bangladesh and Sri Lanka it was 126 and 123 respectively.

0

1000

2000

3000

4000

5000

6000

GD

P P

ER C

AP

TA

YEARS

Trends In Economic Growth (%)1980 -2014

IND PAK BGD LKA

98

Figure 4.2 Trends in Inflation (Annual Percentage Change)

4.1.1.3 Industrial Share

In the 2013 year industrial share as percentage of gross domestic product of India,

Pakistan, Bangladesh and Sri Lanka was 30.73, 21.08, 27.63 and 32.46 respectively.

This shows that Sri Lanka is sharing more from the industry to the GDP. The figure 4.3

shows the trends in industrial share of percent GDP in all selected countries.

Figure 4.3 Trends in Industrial Share of GDP

0

20

40

60

80

100

120

140

160

1 9 7 5 1 9 8 0 1 9 8 5 1 9 9 0 1 9 9 5 2 0 0 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0

INFL

ATI

ON

(A

NN

UA

LR %

CH

AN

G)

YEAR

TRENDS IN INFLATION 1980 -2014

IND PAK BGD LKA

0

5

10

15

20

25

30

35

40

IND

UST

RY

VA

LUE

(GD

P)

YEARS

TREND IN INDUSTRIAL SHARE OF GDP1980 -2014

IND PAK BGD LKA

99

4.1.2 Population Density

On the earth, South Asia is the most populous region. According to the UN (2015)

report, Bangladesh is ranking 12th in population density in the world followed by India

at 28th, Sri Lanka at 40th and Pakistan at 53rd. Total population in India 1252139596,

in Pakistan 182142594, in Bangladesh 156594962 and in Sri Lanka 20483000 was

recorded in 2013. In 2014 total population in India 1267670638, in Pakistan

185147997, in Bangladesh 158379909 and in Sri Lanka 20320500 was recorded. Table

4.4 and 4.5 describe the trends in population total and population density in India,

Pakistan, Bangladesh and Sri Lanka, respectively. According to WDI (2013) population

density in India Pakistan, Bangladesh and Sri Lanka are 421, 236, 1203 and 327 people

per square meter of land area, respectively.

Figure 4.4 Trends in Total Population

Figure 4.5 Trends in Population density

0

500000000

1000000000

1500000000

TOTA

L P

AP

ULA

TIO

N

YEARS

TRENDS IN TOTAL POPULATION 1980 -2014

IND PAK BGD LKA

0

500

1000

1500

2000

2500

PEO

PLE

PER

SQ

MET

ER

YEARS

Trends in Populat ion Densi ty 1980 -2014

IND PAK BGD LKA

100

4.1.3 Environment

Environmental Degradation (ED) is presently an essential topic of the research for

progressing the economic advancement. The requirement of the majority people

depends upon fossil fuels for power creation and the burning fossil fuels emits CO2

around 60 percent air contaminants. The growing urban people have increased the

problem of deforestation, and the problems of sanitation, water effluence and moving

people from rustic areas to cities are increased by URB. It is an essential to recognize

the association between URB, consumption of energy and ecological degradation.

(Irfan and Shaw, 2015).Demographic variables are affected by environment in various

ways. Pollution of water and the air directly effect on morbidity, therefore morality and

through it, fertility substances such as nitrogen oxide, sulfur dioxide, carbon monoxide,

hydrogen and so on are harmful to health. Polluted water causes various diseases and

organic solid wastes rouse the growth of mosquitoes, flies and other insects, which

spread various diseases (Saleemul Huq et al., 1998).

Figure 4.6 Trends in Per Capita CO2 Emissions

The major basis of the greenhouse effect is carbon dioxide CO2 that caused incredible

consideration in recent year. The utilization of fossil fuels is caused by CO2 outflow

i.e. oil, coal and gas the major force of industry and automobiles that are directly

associated with economic expansion and development (Hossain, 2012).

0

0.5

1

1.5

2

2.5

3

3.5

4

PER

CSP

TS C

O2

E

YEARS

Trends In CO 2 Emissions Source(%)1980-2014

IND PAK BGD LKA

101

Large size of population demands more goods and services which in term there will be

the more use of energy consumption. In result, there will be increase in CO2 emissions.

According to World Bank report in 2012, India ranked 3rd in CO2 emission due to the

highly energy consumption, Pakistan at 33rd, Bangladesh 57th and Sri Lanka 90th. In

2013, World Bank reported that CO2 emission in India, Pakistan, Bangladesh and Sri

Lanka were 1.62, 1.0, 0.35 and 0.68 metric ton per capita respectively, (See figure 4.6)

India is major contributor to the CO2 emissions.

4.2 Energy Situation in South Asia

Figure 4.7 and 4.8 reflects the production of the renewable and non-renewable energy

sources in the selected South Asian countries over the period 1980-2014. Non-

renewable energy sources is measures by the electricity production3 from oil and gas

sources and measured in KWH. Renewable energy sources are measured by electricity

production by all renewable sources4 and also measured in KWH.

Figure 4.7 Trends in Non-Renewable Energy Sources

3 Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and

petroleum products. 4 Electricity production from renewable sources includes hydropower, geothermal, solar, tides, wind,

biomass, and biofuels.

0

10000

20000

30000

40000

50000

60000

70000

80000

NO

N R

ENEW

AB

LE E

NER

GY

KW

H

YEARS

Trends In Non Renewable Energy Source(%)1980 -

2014

IND PAK BGD LKA

102

Figure 4.8 Trends in Renewable Energy Sources

Table 4.1 describes comparative situation the energy dependency of selected South

Asian countries. Bangladesh depends on only natural gas 86% of total energy. India,

Pakistan and Sri Lanka depend on gas and petroleum both, Sri Lanka and Pakistan also

depend on hydro. The share of hydro in energy is large in Sri Lanka. Table 4.2 portrays

the production and use of energy in all these selected countries.

Table 4.1 Energy Dependence of Selected South Asian Countries

Country Energy Source Dependence

India Gas, Coal and Petroleum Coal 56%, Gas and Petroleum 35%

Pakistan Gas, Hydro and petroleum Hydro 33%, Gas and Petroleum 66%

Bangladesh Gas from natural sources 86%

Sri Lanka Gas, Hydro and petroleum Hydro 50%, Gas and Petroleum 46%

Source: Mahapatra (2011)

Table 4.2 Production and Use of Energy

Country Energy

Consumed

(Million

Fossil

Fuels5

(% of

Combustible

Renewable

and Waste6

Alternative

and

Nuclear

Energy

Produced8

(Million

Energy

Use

Energy

5 Includes oil, coal, petroleum and natural gas. 6 Includes solid biomass (such as fire wood, liquid biomass, biogas, industrial waste and municipal waste. 8 Refers to form of primary energy including petroleum, natural gas, solid fuels, combustible renewables and

waste, and primary electricity.

0

50000

100000

150000

200000

250000

REN

EWA

BLE

EN

RG

Y K

WH

YEARS

Trends In Renewable Energy Sources 1980 -2014

IND PAK BGD LKA

103

tons of oil

equivalent

)

total

use)

(% of total

use)

Energy7

(% of total

use)

tons of oil

equivalent)

Production

(Mtoe)

India 749.4 72.3 24.7 3 540.9 208.5

Pakistan 84.8 60.9 34.6 4.5 65.1 19.7

Bangladesh 31.3 71.5 28.2 0.2 26.1 5.2

Sri Lanka 10.4 48.7 47.4 3.9 5.3 5.1

Source: World Bank (2014)

Table 4.3 Non-renewable Energy Sources (oil)

Resource

Potential

(mtoe)

Proved

Resources

(mtoe)

Used

so far

(mtoe)

Available

Resource

(mtoe)

Current

Annual

Production

(mtoe)

Resource

Production

Ratio (mtoe)

India 660 1,570 830 740 33.0 44.4

Pakistan 3,600 107 68 39 3.0 13

Bangladesh 0.96 0.96 0.10 0.86 0.0 ------

Sri Lanka 0.0 0.0 0.0 0.0 0.0 0.0


Table 4.4 Non-renewable Energy Sources (Gas)

Country Resource

Potential

(bm2)9

Proved

Resources

(bm2)

Used

so far

(bm2)

Available

Resource

(bm2)

Current Annual

Production

(bm2)

Resource

Production

Ratio (bm2)

India 6310 1,380 460 920 32 29

Pakistan 7,985 1,284 488 795 34 23

Bangladesh 814.5 578.3 144.1 434.2 11.9 36

Sri Lanka 0.0 0.0 0.0 0.0 0.0 0.0


7 Includes geothermal, hydropower, solar power and nuclear (Alternative and nuclear energy is clean energy

does not produced CO2) 9 Billion cubic meters 10 Does not includes new gas finds in deep water wells.

104

Table 4.5 Non-renewable Energy Sources (Coal)

Country Resource

Potential

(mt)

Proved

Resources

(mt)

Used

so far

(mt)

Available

Resource

(mt)

Current Annual

Production (mt)

Resource

Production

Ratio

(Years)

India 245,690 91,631 NA 91,631 410 200

Pakistan 185,000 3,300 200 3,100 3.3 939

Bangladesh 2,715 724 0.0 724 1 724

Sri Lanka 0.0 0.0 0.0 0.0 0.0 0.0

Table 4.6 Renewable Energy Sources (Hydro)

Country Resource

Potential

(mw)

Proved

Resources

(mw)

Used

so far

(%)

Available

Resource

(mw)

Current Annual

Production

(mw)

Resource

Production

Ratio

(Years)

India 301,000 29,500 10.2 0.0 0.0 0.0

Pakistan 40,000 6,500 16 0.0 0.0 0.0

Bangladesh 775 230 30 0.0 0.0 0.0

Sri Lanka 2,000 1,250 62.5 0.0 0.0 0.0


Table 4.7 Renewable Energy Sources

Item India Pakistan Bangladesh Sri Lanka

Biomass Potential 400 25 0.00 12.0

Consumption 340 20 53.5 11.5

Renewable Energy Sources

Wind Potential 45,000 1100-40,000 0.00 24,000

Solar Energy 50,000 v. large 0.00 Large

Micro Hydro 15,000 1000 0.00 300

Biogas Plants 2 million 0.00 0.00 0.00

Ocean Thermal 50,000 0.00 0.00 0.00

105

Sea Wave 20,000 0.00 0.00 0.00

Tidal 9,000 0.00 0.00 0.00


4.3 Importance and Potential of Renewable Energy

Energy access can also stop the occurrence of accidents and diseases. As indicated by

the World Health Association (WHO), around 1.6 million passing happen every year,

for the most part, ladies and youngsters, caused by family cooking fires and the inward

breath of indoor smoke (WHO 2014). Enhanced access to energy also enables families

to boil water and in this way lessens the probability being caused by waterborne

sicknesses. (IEA 2010). Decreasing reliance on non-renewable resources isn't just

environment-friendly but also feasible, sustainable and manageable. Reliance on non-

renewable energy sources makes nations more powerless against oil price stuns and can

prompt macroeconomic instability. Concentrating on renewable energy source won't

just evade such instability but also prompt considerable investment funds. Balance of

payment can also be improved by reduction in cost of imports (for example, oil). Table

4.8 exposes the importance and advantages of renewable energy sources.

Table 4.8 Importance of Renewable and Non-renewable energy sources

Importance of Renewable Energy

Types Solar Energy Hydro

Energy

Wind

Energy

Biomass

Energy

Biogas Energy

Sources photovoltaic

(PV) cells are

placed on the

rooftop of

houses or

commercial

buildings, and

collectors such

as mirrors or

parabolic dishes

that can move

The

hydroelectric

power refers

to

The energy

produced

from water

(Rainfall

flowing into

rivers, etc).

Inexhaustibl

e resource

and

used where

it is

available or

transported

where

needed

Biomass

includes solid

biomass

(organic, non-

fossil material

of biological

origins),

liquid biofuels

(bio-based

liquid fuel

from biomass

biogas produced by

anaerobic digestion of

biomass and

municipal wastes

produced by

residential,

commercial and

public sources

106

and track the sun

throughout the

day

transformation,

mainly used in

transportation

applications),

Uses Cookery,

lighting, and

water heating

Lighting,

agricultural

Processing

Power

generation,

crop

processing,

irrigation,

and water

pumping

Electricity

generation

and heat

Thermal energy;

production of sludge

for fertilizer

Alleviat

ion

Benefit

s

Less

consumption of

fuelwood,

kerosene and

batteries,

improved local

air quality

Reduced

greenhouse

gases,

protection of

land

Decreased

dependence

on

wood/bioga

s, avoidance

of

CO2

emissions

Reduced use of

charcoal and

fuelwood, less

pressure on

natural

resources

Reduced use of

charcoal, fuelwood,

and liquefied

petroleum gas;

reduced use of

pesticides and

fertilizers

Adaptat

ion

Benefit

s

Illumination for

rural education

and access to

information and

communication

technology

Improved

social

resilience

Reduced

vulnerabilit

y to water

scarcity,

more

adaptation

choices

through

irrigated

agriculture

Reduces the

likelihood of

deforestation

and

desertification

Reduces the

likelihood of

deforestation;

adapting to soil

erosion, aridity, and

environmental

degradation

107

Socio

Econo

mic

Develo

pment

Benefit

s

Improved quality

of life as well as

better health and

sanitation

through

streetlights and

boiled water

Improved

agricultural

yield

Income

generation,

improved

quality of

life, reduced

risks of

vector borne

diseases,

improved

water

supply/food

security,

school

attendance

(especially

for girls),

reduced

migration

Creation of

jobs and

livelihood

opportunities,

reduced

drudgery,

reduction of

incidents

related to

indoor air

pollution and

respiratory

infections

Reduced drudgery,

reduction of incidents

related to IAP and

respiratory infections;

better prospects for

agricultural

productivity and

income generation

Source: UNDP (2013)

The countries of South Asia also have huge potential for renewable energy sources.

Table 4.8 summarizes the potential for renewable energy sources such as solar power,

wind power and hydro power. An enormous solar power in India will help the overall

region to overcome the need of energy.

Table 4.9 Renewable Energy potential

Country Hydro Power

Potential MW

Wind Power

Potential MW

Solar Power Potential (Average

in kWh/m2/day)

India 150,000 102.778 5.0

Pakistan 59,000 131,800 5.3

Bangladesh 330 0.0 5.0

Sri Lanka 2000 24,000 5.0

Source: Shukla et al. (2017)

108

4.4 Empirical Results and Discussions

4.4.1 Impact of Renewable and Non-Renewable Energy on

Economic Growth


Energy, Institutions and Economic Growth

4.4.1.1.1 Time Series Results

4.4.1.1.1.1 Unit Root Test Results

There is a basic need to check the stationary properties of the series under consideration

before assessing the log-run relationships among variables. There are numerous

contentions with respect to the use of customary unit root tests. It is obvious from

econometric that ADF tests have low power against the null hypothesis and therefore it

is important to apply other tests like KPSS and DF-GLS to confirm the results of ADF

tests. For present analysis, DF-GLS test is applied both at level and first differences

with constant and with both constant and trend. For the analysis, the null hypothesis of

unit root may be rejected if the calculated values of DF-GLS tests is greater than their

critical values. These critical values are used in absolute terms. In other words the null

hypothesis of unit root is accepted when the calculated values of DF-GLS tests is less

than their critical values (in absolute terms). Similarly, a series is said to be stationary

when the calculated values of DF-GLS tests is greater than their respective critical

values (in absolute terms).

Table 4.10 reports the unit root outcomes of DF GLS test within the sight of trend and

intercept both and only with intercept. The outcomes propose the non-stationery of all

the series at their level shape, however stationary at first difference. According to this

table, the considered variables in selected sample like economic growth (Y), renewable

energy sources (RE), nonrenewable energy sources (NRE), institutions (INS),

population density (PD) and trade openness (To) are integrated at I(1) for individual

country.

109

Table 4.10 DF GLS Unit root Test

Country/ Variables

At Level At first difference

Without

Trend

With

Trend

Without

Trend

With

Trend

India

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

tRE -0.129 -2.465 -6.035* -6.065*

tINS -0.775 -1.919 -5.718* -5.752*

tPD 0.196 -0.521 -1.769*** -7.232*

tTo 0.721 -1.674 -5.086* -5.725*

Pakistan

tY 0.582 -2.287 -3.289* -3.704**

tNRE 0.163 -1.266 -1.782*** -3.083***

tRE -0.314 -2.169 -5.721* -5.926*

tINS -1.392 -1.637 -5.328* -5.348*

tPD -0.013 -1.449 -1.945*** -4.616*

tTo -1.881 -3.123 -1.208*** -5.078*

Bangladesh

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

tRE -4.963 -5.588 -9.152* -9.205*

tINS -1.719 -2.341 -5.597* -5.559*

tPD 0.277 -1.298 -1.847*** -6.669*

tTo -1.081 -2.364 -5.823* -6.181*

Sri Lanka

110

4.4.1.1.1.2 Johansen Co-integration

The unit root test results set the stage for Johansen co-integration approach. The results

are presented in Table 4.11. We find the acceptance of null hypothesis i.e. six co-

integrating vectors in India and Pakistan four co-integrating vectors in Bangladesh and

Sri Lanka. The existence of these co-integrating vectors confirms the presence of co-

integration between the variables. This shows that economic growth (Y), renewable


population density (PD) and trade openness (To) have long run relationship over

selected period of time i.e. 1980–2014.

Table 4.11 Johansen Co-integration Test results

Country liklihood ratio 5% critical value p-value

India

R=0 259.844 95.754 0.000

R≤1 137.920 69.819 0.000

R≤2 88.379 47.856 0.000

R≤3 52.353 29.797 0.000

R≤4 29.210 15.495 0.000

R≤5 7.548 3.841 0.006

Pakistan

tY 0.719 -0.889 -3.406* -4.787*

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

tINS -2.114 -2.483 -5.642* -5.657*

tPD -0.065 -0.844 -4.445* -5.385*

tTo -1.157 -1.104 -5.962* -6.408*

*Denotes significant at 1% level. ** Denotes significant at 5% level. *** Denotes significant at 10% level.

111

R=0 209.662 95.754 0.000

R≤1 122.560 69.819 0.000

R≤2 74.191 47.856 0.000

R≤3 42.875 29.797 0.001

R≤4 17.466 15.495 0.025

R≤5 5.470 3.841 0.019

Bangladesh

R=0 196.150 95.754 0.000

R≤1 103.663 69.819 0.000

R≤2 53.486 47.856 0.014

R≤3 24.471 29.797 0.181

R≤4 11.040 15.495 0.209

R≤5 0.429 3.841 0.513

Sri Lanka

R=0 124.700 95.754 0.000

R≤1 72.806 69.819 0.028

R≤2 45.054 47.856 0.090

R≤3 22.835 29.797 0.254

R≤4 8.702 15.495 0.394

R≤5 0.978 3.841 0.323

4.4.1.1.2 Panel Results

The individual country analysis gives the ambiguity in results that why to overcome the

problem the thesis has been applied panel co-integration approach.

112

4.4.1.1.2.1 Panel Unit Root results

The results based on the LLC, IPS with demean unit root tests with constant and,

constant and trend are reported in Table 4.12. The tests show that all variables are

found to be non-stationary at level. At first difference, all the series are integrated i.e.

I(1).

Table 4.12 Panel Unit root Test

LLC TEST with demean

Variable

Level 1st Difference

Intercept

P-

value

Trend &

Intercept

P-

value Intercept P-value

Trend &

Intercept P-value

itY 0.884 0.812 -1.046 0.148 -1.766* 0.038 -5.849 0.000

itNRE 0.702 0.758 1.932 0.974 -7.199* 0.000 -5.999 0.000

itRE 0.717 0.763 0.975 0.835 -14.064* 0.000 -12.753 0.000

itINS -0.973 0.165 -0.135 0.446 -5.559* 0.000 -4.312 0.000

itPD -2.154 0.937 -1.113 0.133 -2.006 0.022 -1.614 0.053

itTo 1.891 0.971 0.143 0.557 -9.648* 0.000 -7.921 0.000

IPS TEST with demean

Variable


Intercept

P-

value

Trend &

Intercept

P-

value Intercept

P-

value

Trend &

Intercept P-value

itY 0.005 0.988 0.818 0.794 -6.094* 0.000 -5.588 0.000

itNRE -0.885 0.188 -0.786 0.216 -10.687* 0.000 -10.141 0.000

itRE -1.237 0.108 -0.662 0.254 -13.068* 0.000 -12.564 0.000

itINS -0.574 0.283 0.747 0.773 -8.358* 0.000 -7.220 0.000

itPD -0.817 0.207 1.683 0.954 -6.152 0.000 -6.753 0.000

itTo 0.405 0.657 -0.489 0.313 -11.303* 0.000 -10.549 0.000

113

4.4.1.1.2.2 Panel Co-integration Results

This unique order of integration of the variables helps us to apply Johansen panel co-

integration approach to examine the long run relationship between the variables for

selected panel. Table 4.13 presents the results of Larsson et al. (2001) panel co-

integration derived on the basis of likelihood test statistics by Johansen (1995). In the

case of panel, the maximum rank is r =6. We find that the values of maximum likelihood

ratio i.e. 30.124, 19.308, 14.959, 11.883, 9.531 and 4.849 are greater than the critical

value at 1% level of significance. This leads us to reject the null hypothesis of no panel

co-integration between the variables. Hence, the result of Larsson et al., (2001) panel

co-integration indicates the existence of at least five co-integrating vectors in selected

panel of South Asia. Finally, panel co-integrating results confirm a stable long-run

relationship between economic growth (Y), renewable energy sources (RE),

nonrenewable energy sources (NRE), institutions (INS), population density (PD) and

trade openness (To) in four South Asian countries.

Table 4.13 Panel co-integration Test

Hypothesis Likelihood Ratio 1% critical value

R=0 30.124

2.45

R≤1 19.308

R≤2 14.959

R≤3 11.883

R≤4 9.531

R≤5 4.849

4.4.1.1.3 FMOLS Estimates

4.4.1.1.3.1 FMOLS Estimates Country Wise

Table 4.14 displays the results of FMOLS at individual level. In this approach the

coefficients of nonrenewable, renewable energy sources and trade openness are positive

and significant in all selected countries of South Asia except Bangladesh where trade

114

openness is insignificant. All the positive coefficients suggest that increase in

nonrenewable and renewable energy sources leads to increase in economic growth.

Institutional development increases the economic growth in Pakistan. However, in the

case of India, Bangladesh and Sri Lanka, the coefficient of institutions is negative.

Population density have positive and significant impact on economic growth in India.

However, the impact is negative in the case of Pakistan, Bangladesh Sri Lanka, where

the coefficient is reported negative.

Table 4.14 FMOLS Country Specific Results

( tY : Dependent Variable)

Country Variables coefficients p-value

India

tNRE 0.025 0.036

tRE 0.073 0.001

tINS -0.043 0.000

tPD 1.216 0.000

tTo 1.387 0.000

Pakistan

tNRE 0.027 0.000

tRE 0.117 0.000

tINS 0.002 0.000

tPD -5.892 0.000

tTo 0.120 0.000

Bangladesh

tNRE 0.009 0.000

tRE 0.003 0.000

tINS -0.001 0.000

tPD -2.487 0.000

tTo 0.001 0.790

115

Sri Lanka

tNRE 0.003 0.002

tRE 0.063 0.000

tINS -0.014 0.000

tPD -0.310 0.014

tTo 0.067 0.014

4.4.1.1.3.2 FMOLS Panel Estimates

Table 4.15 displays the results of FMOLS panel estimates taking economic growth as

dependent variable. Results shows that all coefficients are statistically significant and

their signs are according to economic theory. Results of FMOLS indicates that 1 percent

increase in renewable energy sources, nonrenewable energy source, population density

and trade openness increases economic growth per capita by about 0.056 percent, 0.159

percent, 0.679 percent and 1.593 percent respectively. Institution shows the negative

impact on economic growth. The estimated results regarding the link between energy

and growth are consistent with the results of Sharma (2010), Kasman and Duman

(2015), Belke et al., (2011), Shahbaz et al., (2013), Raza et al., (2015), Kahouli

(2017,2019). As they mentioned that energy is a major contributor in economic growth.

In the production process energy is an input as it is renewable or non-renewable Ahmed

and Shimada (2019). Our results are contradicts with the study of Lee (2005) as he

concluded that Energy conservation would be harmful to economic growth. .Trade openness

also has positive impact on economic growth. These results are consistent with the

results of Ulasan (2012), Nasreen and Anwar (2014) and Kahouli (2017), as trade

promotes network of transportation which consumes more energy through renewable

or non-renewable sources. In the later study Eris and Ulasan (2013) found no impact of

trade openness on economic growth. Population density also considered major

contributor in the process of production (both agriculture and industrial sector) as well

as in on economic growth here, our results are in line with the results of Simon (1977),

Fredericksen (1981) and Shabani et al.,(2011).

116

Table 4.15 FMOLS Panel Estimates

( itY : Dependent Variable)

Variables Coefficients p-value

itNRE 0.056 0.000

itRE 0.159 0.008

itINS -0.006 0.052

itPD 0.679 0.000

itTo 1.593 0.000

4.4.1.1.4 Panel Causality Results

Table 4.16 represents the direction of causality between variables. The results specify

that there is a bi directional causality between population density and economic growth.

There also exists unidirectional panel causality running from economic growth to

nonrenewable energy sources however, there is no causality between growth and

renewable energy sources, between economic growth and trade openness as well as

between growth and institutions. The results of no causality between renewable energy

and economic growth are contradict with the study of Ahmed and Shimada (2019).

Table 4.16 DH panel causality Test

Direction of Causality ,

HNC

N TW ,

HNC

N TZ P-Value

itNRE → itY 3.449 1.102 0.271

itY → itNRE 4.537 2.032 0.042

itRE → itY 1.697 -0.396 0.693

itY → itRE 3.775 1.379 0.168

itINS → itY 3.079 0.785 0.433

itY → itINS 1.103 -0.905 0.366

117

itTo → itY 3.542 1.181 0.238

itY → itTo 7.248 4.348 1.001

itPD → itY 5.841 3.146 0.002

itY → itPD 7.631 4.675 0.000


Energy, Urbanization and Economic Growth





the series at their level shape, however stationary at first difference or it may be written

as all the considered variables are integrated at I(1) for individual country.


Country/

Variables


Without Trend With Trend Without Trend With Trend

India

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

tRE -0.129 -2.465 -6.035* -6.065*

tINS -0.775 -1.919 -5.718* -5.752*

tURB 0.430 0.053 -1.611*** -2.739***

tP 1.175 -0.742

-2.283*** -2.955***

Pakistan

tY 0.582 -2.287 -3.289* -3.704**

118

4.4.1.2.1.2 Johansen Co-integration Test


are presented in Table 4.18. We find the acceptance of null hypothesis i.e. five co-

integrating vectors in India and Sri Lanka, four co-integrating vectors in Pakistan and

Bangladesh. The existence of two co-integrating vectors confirms the presence of co-

integration between the variables. This shows that economic growth (Y), renewable

tNRE 0.163 -1.266 -1.782*** -3.083***

tRE -0.314 -2.169 -5.721* -5.926*

tINS -1.392 -1.637 -5.328* -5.348*

tURB 4.764 1.083 -1.610*** -2.876***

tP -0.043 -2.399 -2.094** -2.922***

Bangladesh

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

tRE -4.963 -5.588 -9.152* -9.205*

tINS -1.719 -2.341 -5.597* -5.559*

tURB 0.414 -1.271 -1.815* -3.283**

tP 1.172 -2.063 -1.966*** -3.0303*

Sri Lanka

tY 0.719 -0.889 -3.406* -4.787*

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

tINS -2.114 -2.483 -5.642* -5.657*

tURB 0.470 -1.011 -1.848*** -3.839*

tP 0.987 -0.594 -2.529** -4.822*


119


urbanization (URB) and inflation (P) have long run relationship over selected period of

time i.e. 1980–2014.



India

R=0 288.558 95.754 0.000

R≤1 162.206 69.819 0.000

R≤2 101.004 47.856 0.000

R≤3 48.916 29.797 0.000

R≤4 18.925 15.495 0.015

R≤5 0.334 3.841 0.563

Pakistan

R=0 198.806 95.754 0.000

R≤1 112.630 69.819 0.000

R≤2 75.084 47.856 0.000

R≤3 40.473 29.797 0.002

R≤4 15.364 15.495 0.052

R≤5 2.774 3.841 0.096

Bangladesh

R=0 189.014 95.754 0.000

R≤1 119.930 69.819 0.000

R≤2 76.299 47.856 0.000

R≤3 34.965 29.797 0.012

R≤4 13.491 15.495 0.098

120

R≤5 0.045 3.841 0.832

Sri Lanka

R=0 227.051 95.754 0.000

R≤1 124.983 69.819 0.000

R≤2 79.337 47.856 0.000

R≤3 42.987 29.797 0.001

R≤4 18.946 15.495 0.015

R≤5 7.080 3.841 0.008





found to be non-stationary at level. At first difference, all the series are integrated.



Variable


Intercept P-value

Trend &

Intercept P-value Intercept P-value

Trend &

Intercept P-value

itY 0.8840 0.8120 -1.0460 0.1480 -1.7660 0.0380 -5.8490 0.0000

itNRE 0.7020 0.7580 1.9320 0.9740 -7.1990 0.0000 -5.9990 0.0000

itRE 0.7170 0.7630 0.9750 0.8350 -14.064 0.0000 -12.753 0.0000

itINS -0.9730 0.1650 -0.1350 0.4460 -5.5590 0.0000 -4.3120 0.0000

itURB 2.0408 0.9970 -4.4230 0.7460 -25.8320 0.0000 -28.235 0.0000

itP -1.6390 0.9820 -2.8570 0.7630 -7.6670 0.0000 -8.5590 0.0000

121


In table 4.20, the maximum rank is r =5 and the values of maximum likelihood ratio i.e.

31.638, 20.264, 16.410, 10.810 and 6.529 are significant at 1% level of significance.

The results supports rejection of the null hypothesis of no panel co-integration between

the variables. Hence, the result of Larsson et al., (2001) panel co-integration indicates

the existence of at least four co-integrating vectors in selected panel of South Asia.

Finally, panel co-integrating results confirm a stable long-run relationship between

economic growth (Y), renewable energy sources (RE), nonrenewable energy sources

(NRE), institutions (INS), urbanization (URB) and inflation (P) in four South Asian

countries.

Table 4.20 Panel Co-integration Test


R=0 31.638

2.45

R≤1 20.264

R≤2 16.410

itTo 1.8910 0.9710 0.1430 0.5570 -9.6480 0.0000 -7.9210 0.0000


Variable


Intercept P-value

Trend &


Trend &

Intercept P-value

itY 0.0050 0.9880 0.81800 0.7940 -6.0940 0.0000 -5.5880 0.0000

itNRE -0.8850 0.1880 -0.7860 0.2160 -10.687 0.0000 -10.141 0.0000

itRE -1.2374 0.1080 -0.6620 0.2541

-13.068 0.0000 -12.564 0.0000

itINS -0.574 0.2830 0.7470 0.7730 -8.3580 0.0000 -7.2200 0.0000

itURB 1.2160 0.8880 1.8130 0.9650 -24.0520 0.0000 -26.139 0.0000

itP 1.3090 0.905 1.7660 0.9620 -5.1090 0.0000 -4.8120 0.0000

itTo 0.4050 0.6570 -0.489 0.3130 -11.303 0.0000 -10.549 0.0000

122

R≤3 10.810

R≤4 6.529

R≤5 1.912




coefficients of nonrenewable, renewable energy sources, urbanization and inflation are

positive and significant in all selected countries of South Asia except India here,

coefficient of NRE is negative and insignificant. All the positive coefficients suggest

that increase in nonrenewable, renewable energy sources, urbanization and inflation

leads to increase in economic growth. Institution development have positive and

significant impact on economic growth in Pakistan and India. However, the impact is

negative in the case of Bangladesh and Sri Lanka.



Country Variables Coefficients P-value

India

tNRE -0.029 0.252

tRE 0.140 0.001

tINS 0.083 0.000

tURB 3.954 0.014

tP 0.003 0.000

Pakistan

tNRE 0.030 0.000

tRE 0.071 0.007

tINS 0.001 0.037

tURB 1.133 0.000

123

tP 0.001 0.000

Bangladesh

tNRE 0.049 0.000

tRE 0.001 0.739

tINS -0.001 0.000

tURB 0.308 0.000

tP 0.007 0.000

Sri Lanka

tNRE 0.005 0.000

tRE 0.004 0.083

tINS -0.014 0.000

tURB 34.526 0.000

tP 0.004 0.000

4.4.1.2.3.2 FMOLS Estimates Panel

Table 4.22 shows that all coefficients are statistically significant and their signs are

according to economic theory. Results of FMOLS indicates that 1 percent increase in

renewable energy sources, nonrenewable energy sources, urbanization and inflation

increases economic growth per capita by about 0.026 percent, 0.226 percent, 0.351

percent and 0.006 percent respectively. There is a negative impact of institution on

economic growth. The estimated results regarding the link between energy and growth

are consistent with the results of Sharma (2010), Kasman and Duman (2015), Belke et

al., (2011), Shahbaz et al., (2013), Raza et al., (2015), Kahouli (2017,2019). Mitic et

al. (2017), Ramli et al. (2019, Natonas et al. (2018) and Bilan et al. (2019). As they

mentioned that energy is a major contributor in economic growth. In the production

process energy is an input as it is renewable or non-renewable Ahmed and Shimada

(2019). Our results are contradicts with the study of Lee (2005) as he concluded that

Energy conservation would be harmful to economic growth. Inflation measured as consumer

price index also contributes to economic growth. Our results are not consistent with the studies

124

of Sharma (2010), Omri (2013) and Omri and Kahouli (2014) as they reported that inflation

has negative impact on economic growth.




itNRE 0.026 0.076

itRE 0.226 0.000

itINS -0.006 0.052

itURB 0.351 0.036

itP 0.006 0.000



that there is a unidirectional panel causality running from economic growth to

nonrenewable energy sources and urbanization to economic growth. There is no

causality among growth and renewable energy sources, institutions and inflation.

Table 4.23 DH Panel Causality Test


HNC

N TW ,

HNC

N TZ P-Value

itNRE → itY 3.449 1.102 0.271

itY → itNRE 4.537 2.032 0.042

itRE → itY 1.697 -0.396 0.693

itY → itRE 3.775 1.379 0.168

itINS → itY 3.079 0.785 0.433

itY → itINS 1.103 -0.905 0.366

itURB → itY 4.534 2.029 0.043

125

itY → itURB 4.686 2.158 0.031

itP → itY 2.176 0.014 0.989

itY → itP 4.031 1.599 0.110


Energy, Financial Development and Economic Growth





the series at their level shape, however stationary at first difference or the selected all

variables in the sample are integrated at I(1) for individual country.


Country/ Variables



India

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

tRE -0.129 -2.465 -6.035* -6.065*

tPD 0.196 -0.521 -1.769*** -7.232*

tFD -1.528 -2.010 -2.613** -3.049***

tTo 0.721 -1.674 -5.086* -5.725*

Pakistan

tY 0.582 -2.287 -3.289* -3.704**

tNRE 0.163 -1.266 -1.782*** -3.083***

126

4.4.1.3.1.2 Johansen Co-integration Test



integrating vectors in India, six co-integrating vectors in Pakistan and four co-

integrating factors in Bangladesh and Sri Lanka. The existence of co-integrating vectors

confirms the presence of co-integration between the variables. This shows that


tRE -0.314 -2.169 -5.721* -5.926*

tPD -0.013 -1.449 -1.945*** -4.616*

tFD -1.453 -1.520 -2.094** -3.204**

tTo -1.881 -3.123 -1.208*** -5.078*

Bangladesh

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

tRE -4.963 -5.588 -9.152* -9.205*

tPD 0.277 -1.298 -1.847*** -6.669*

tFD -1.606 -1.289 -3.024* -5.241*

tTo -1.081 -2.364 -5.823* -6.181*

Sri Lanka

tY 0.719 -0.889 -3.406* -4.787*

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

tPD -0.065 -0.844 -4.445* -5.385*

tFD -0.635 -1.173 -3.798* -3.935*

tTo -1.157 -1.104 -5.962* -6.408*


127

(NRE), population density (PD), financial development (FD) and trade openness (To)

have long run relationship over selected period of time i.e. 1980–2014.


Country likelihood ratio 5% critical value p-value

India

R=0 242.762 95.754 0.000

R≤1 120.335 69.819 0.000

R≤2 70.453 47.856 0.000

R≤3 44.029 29.797 0.001

R≤4 22.754 15.495 0.003

R≤5 2.708 3.841 0.100

Pakistan

R=0 190.719 95.754 0.000

R≤1 122.698 69.819 0.000

R≤2 74.071 47.856 0.000

R≤3 43.322 29.797 0.001

R≤4 22.840 15.495 0.003

R≤5 7.348 3.841 0.007

Bangladesh

R=0 229.124 95.754 0.000

R≤1 140.843 69.819 0.000

R≤2 69.592 47.856 0.000

R≤3 31.920 29.797 0.028

R≤4 9.047 15.495 0.361

R≤5 0.166 3.841 0.684

128

Sri Lanka

R=0 155.511 95.754 0.000

R≤1 99.132 69.819 0.000

R≤2 59.129 47.856 0.003

R≤3 33.031 29.797 0.021

R≤4 9.919 15.495 0.287

R≤5 0.559 3.841 0.455


4.4.1.3.2.1 Panel Unit Root Test Results



found to be non-stationary at level. At first difference, all the series are integrated i.e. I

(1).



Variable


Intercept P-value

Trend &


Trend &

Intercept P-value

itY 0.8840 0.8120 -1.0460 0.1480 -1.7660 0.0380 -5.8490 0.0000

itNRE 0.7020 0.7580 1.9320 0.9740 -7.1990 0.0000 -5.9990 0.0000

itRE 0.7170 0.7630 0.9750 0.8350 -14.060 0.0000 -12.753 0.0000

itPD -2.1540 0.9370 -1.1130 0.1330 -2.0060 0.0220 -1.6140 0.0530

itFD -3.7290 0.2420 -4.7870 0.2490 -11.134 0.0000 -11.175 0.0000

itTo 1.8910 0.9710 0.1430 0.5570 -9.648 0.0000 -7.9210 0.0000


129




selected panel. Table 4.27 presents that r = 5 which is the highest rank. And values of

maximum likelihood ratio i.e. 27.444, 18.088, 12.064, 9.298 and 6.195 are greater than

the critical value at 1% level of significance. This leads us to reject the null hypothesis

of no panel co-integration between the variables. Hence, the result of Larsson et al.,

(2001) panel co-integration indicates the existence of at least four co-integrating vectors

in selected panel of South Asia. Finally, panel co-integrating results confirm a stable

long-run relationship between economic growth (Y), renewable energy sources (RE),

nonrenewable energy sources (NRE), population density (PD), financial development

(FD) and trade openness (To) in four South Asian countries.



R=0 27.444

2.45

R≤1 18.088

R≤2 12.064

R≤3 9.298

Variable


Intercept P-value

Trend &


Trend &

Intercept P-value

itY 0.0050 0.9880 0.8180 0.7940 -6.0940 0.0000 -5.5880 0.0000

itNRE -0.8850 0.1880 -0.7860 0.2160 -10.687 0.0000 -10.141 0.0000

itRE -1.2370 0.1080 -0.6620 0.2541

-13.0680 0.0000 -12.564 0.0000

itPD -0.8170 0.2070 1.6830 0.9540 -6.1520 0.0000 -6.7530 0.0000

itFD -1.0040 0.1570 -0.3940 0.3470 -8.9580 0.0000 -7.8570 0.0000

itTo 0.4050 0.6570 -0.4890 0.3130 -11.303 0.0000 -10.549 0.0000

130

R≤4 6.195

R≤5 2.095



According to table 4.28, the coefficients of nonrenewable, renewable energy sources

and financial development are positive and significant in all selected countries of South

Asia. All the positive coefficients suggest that increase in nonrenewable, renewable

energy sources financial development and trade openness leads to increase in economic

growth. The coefficient of renewable energy is very high for Sri Lanka which show

expressing the major increment in economic performance toward the development of

renewable energy sources. In Pakistan, the coefficient of renewable energy is also high

as compared to the coefficient of non-renewable energy. Population density increase

the economic growth in India. However, in the case of Pakistan, Bangladesh and Sri

Lanka, the coefficient of population density is found negative. The coefficient is not

only negative but also very problematic and alarming indicator for policymakers. Trade

openness increases economic growth in all three countries except Sri Lanka where the

coefficient is negative.



Country Variables Coefficients P-value

India

tNRE 0.008 0.086

tRE 0.024 0.021

tPD 1.188 0.000

tFD 0.005 0.000

tTo 1.551 0.000

Pakistan tNRE 0.028 0.000

131

tRE 0.150 0.000

tPD -2.809 0.000

tFD 0.002 0.000

tTo 0.145 0.039

Bangladesh

tNRE 0.009 0.000

tRE 0.002 0.054

tPD -2.212 0.000

tFD 0.000 0.000

tTo 0.012 0.076

Sri Lanka

tNRE 0.854 0.000

tRE 1.718 0.000

tPD -7.074 0.000

tFD 0.046 0.016

tTo -13.647 0.000


Results of FMOLS in table 4.29 indicates that 1 percent increase in renewable energy

sources, nonrenewable energy sources, financial development and trade openness

increases economic growth per capita by about 0.004 percent, 0.061 percent, 0.001

percent and 0.088 percent respectively. Although both the coefficients of renewable

and non-renewable energy are positive but magnitude of renewable energy is greater

which shows that renewable energy is the major contributor in enhancing economic

growth than the traditional energy. Population density shows the negative impact on

economic growth indicates that 1 percent increase in population density decreases

growth by 0.580 percent. Considering the escalating population in this panel the result

is very alarming and calls for prudent policy formulation. The estimated results

regarding the link between energy and growth are consistent with the results of Sharma

132

(2010), Kasman and Duman (2015), Belke et al., (2011), Shahbaz et al., (2013), Raza

et al., (2015), Kahouli (2017,2019). Mitic et al. (2017), Ramli et al. (2019, Natonas et

al. (2018) and Bilan et al. (2019). As they mentioned that energy is a major contributor

in economic growth. In the production process energy is an input as it is renewable or

non-renewable Ahmed and Shimada (2019). Our results are contradicts with the study

of Lee (2005) as he concluded that Energy conservation would be harmful to economic

growth. Trade openness also has positive impact on economic growth. These results are

consistent with the results of Ulasan (2012), Nasreen and Anwar (2014) and Kahouli

(2017), as trade promotes network of transportation which consumes more energy

through renewable or non-renewable sources. In the later study Eris and Ulasan (2013)

found no impact of trade openness on economic growth. Financial development has

positive impact on economic growth like the study of Beck and Levine (2004),

Bittencourt (2010), Jalil and Feridum (2011) and Destek (2018). Population density

also considered major contributor in the process of production (both agriculture and

industrial sector) as well as in economic growth here, our results are not consistent with

the results of Fredericksen (1981) and Shabani et al., (2011).




itNRE 0.004 0.018

itRE 0.061 0.000

itPD -0.580 0.014

itFD 0.001 0.001

itTo 0.088 0.013



that there is a unidirectional panel causality running from economic growth to

133

nonrenewable energy sources, economic growth to trade openness and financial

development to economic growth. There is bi-directional causality between economic

growth and population density. These results are in line with Chandio et al. (2019).



HNC

N TW ,

HNC

N TZ P-Value

itNRE → itY 3.449 1.102 0.271

itY → itNRE 4.537 2.032 0.042

itRE → itY 1.697 -0.396 0.693

itY → itRE 3.775 1.379 0.168

itPD → itY 5.841 3.146 0.002

itY → itPD 7.631 4.675 0.000

itFD → itY 6.101 3.368 0.001

itY → itFD 1.099 -0.907 0.365

itTo → itY 3.542 1.181 0.238

itY → itTo 7.248 4.348 0.000

4.4.2 Impact of Renewable and Non-Renewable Energy Sources on

Environmental Quality


Energy, Population Density and Environment





the series at their level shape, however stationary at first difference. The results also

suggest that environment (CO2), economic growth (Y), renewable energy sources

134

(RE), nonrenewable energy sources (NRE), and population density (PD) are integrated

at I (1) for individual country.


Country/ Variables



India

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

tRE -0.129 -2.465 -6.035* -6.065*

2tCO 0.166 -2.429 -3.978* -4.272*

tPD 0.196 -0.521 -1.769*** -7.232*

Pakistan

tY 0.582 -2.287 -3.289* -3.704**

tNRE 0.163 -1.266 -1.782*** -3.083***

tRE -0.314 -2.169 -5.721* -5.926*

2tCO 0.603 -2.234 -7.677* -8.331*

tPD -0.013 -1.449 -1.945*** -4.616*

Bangladesh

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

tRE -4.963 -5.588 -9.152* -9.205*

135

4.4.2.1.1.2 Johansen Co-integration Test Results



integrating vectors in the case of India four co-integrating vectors in Pakistan and three

co-integrating vectors in the case of Bangladesh and Sri Lanka. The existence of these

co-integrating vectors confirms the presence of co-integration between the variables.

This shows that environment (CO2), economic growth (Y), renewable energy sources

(RE), nonrenewable energy sources (NRE) and population density (PD) have long run

relationship over selected period of time i.e. 1980–2014.



India

R=0 176.011 69.819 0.000

R≤1 72.049 47.856 0.000

2tCO 0.388 -2.209 -7.934* -8.179*

tPD 0.277 -1.298 -1.847*** -6.669*

Sri Lanka

tY 0.719 -0.889 -3.406* -4.787*

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

2tCO 0.049 -1.657 -4.943* -5.724*

tPD -0.065 -0.844 -4.445* -5.385*

*Denotes significant at 1% level. **Denotes significant at 5% level. ***Denotes significant at 10% level.

136

R≤2 33.747 29.797 0.017

R≤3 16.897 15.495 0.031

R≤4 4.922 3.841 0.027

Pakistan

R=0 207.385 69.819 0.000

R≤1 111.202 47.856 0.000

R≤2 65.270 29.797 0.000

R≤3 28.575 15.495 0.000

R≤4 1.894 3.841 0.169

Bangladesh

R=0 170.834 69.819 0.000

R≤1 82.668 47.856 0.000

R≤2 40.465 29.797 0.002

R≤3 14.404 15.495 0.073

R≤4 0.990 3.841 0.320

Sri Lanka

R=0 132.291 69.819 0.000

R≤1 67.448 47.856 0.000

R≤2 31.135 29.797 0.035

R≤3 12.559 15.495 0.132

R≤4 1.623 3.841 0.203

137


4.4.2.1.2.1 Panel Unit Root Results



found non-stationary at level. At first difference, all the series are integrated i.e. I (1).



Variable


Intercept P-value

trend &

intercept P-value Intercept P-value

trend &

intercept P-value

itY 0.884 0.812 -1.046 0.148 -1.766 0.038 -5.849 0.000

itNRE 0.702 0.758 1.932 0.974 -7.199 0.000 -5.999 0.000

itRE 0.717 0.763 0.975 0.835 -14.064 0.000 -12.753 0.000

2itCO -0.973 0.165 -0.135 0.446 -5.559 0.000 -4.312 0.000

itPD -2.154 0.937 -1.113 0.133 -2.006 0.022 -1.614 0.053


Variable


Intercept P-value

trend &


trend &

intercept P-value

itY 0.005 0.988 0.818 0.794 -6.094 0.000 -5.588 0.000

itNRE -0.885 0.188 -0.786 0.216 -10.687 0.000 -10.141 0.000

itRE -1.237 0.108 -0.662 0.2541 -13.068 0.000 -12.564 0.000

138




selected panel. Table 4.34 presents the results of Larsson et al., (2001) panel co-


case of panel, the maximum rank is r = 4. We find that the values of maximum

likelihood ratio i.e. 30.114, 16.532, 11.139 and 7.408 are greater than the critical value

at 1% level of significance. This leads us to reject the null hypothesis of no panel co-

integration between the variables. Hence, the result of Larsson et al., (2001) panel co-

integration indicates the existence of at least three co-integrating vectors in selected


relationship between environment (CO2), economic growth (Y), renewable energy

sources (RE), nonrenewable energy sources (NRE) and population density (PD) in four

South Asian countries.



R=0 30.141 2.45

R≤1 16.532

R≤2 11.139

R≤3 7.408

R≤4 1.641

2itCO -0.574 0.283 0.747 0.773 -8.358 0.000 -7.220 0.000

itPD -0.817 0.207 1.683 0.954 -6.152 0.000 -6.753 0.000

139



Table 4.35 displays the results of FMOLS at individual level. In this approach, the

coefficients of economic growth and population density are positive and significant in

all selected countries of South Asia. Both the positive coefficients suggest that increase

in economic growth and population density leads to increase in environmental

pollution. The coefficients of non-renewable energy sources are positive and significant

in Pakistan and Sri Lanka but insignificant in case of India and Bangladesh. Renewable

energy sources decrease the environmental pollution in India, Bangladesh and Sri

Lanka. However, in the case of Pakistan, the coefficient of renewable energy sources is

found to be insignificant which shows the no role of renewable energy sources in

Pollution. Results in model 6 with Kuznets curve shows the presence of the EKC

hypothesis. The results of variables economic growth, population density and non-

renewable energy, are consistent with the studies of Hossain (2011), Sadorsky (2009),

and Shafiei and Salim (2014), Rafiq et al., (2016) and Bilan et al., (2019) Ahmed and

Shimda (2019) that all these variables increase CO2 emissions in selected countries.

The tendency in the results of above reviewed studies are in the favour of renewable

energy consumption because it causes less carbon dioxide emission to the environment

Table 4.35: FMOLS Country Specific Results

( 2tCo : Dependent Variable)

Country

Variables

Model 4 Model 4 with Kuznets curve

Coefficients

p-value

Coefficients

p-value

India

tY 0.154 0.000 1.841 0.000

2

tY -------- -------- -0.134 0.000

tRE -0.086 0.000 0.033 0.035

tNRE 0.004 0.699 -0.068 0.000

140

tPD 1.845 0.000 1.756 0.000

Pakistan

tY 0.666 0.008 5.412 0.000

2

tY -------- -------- -0.355 0.000

tRE 0.007 0.445 -0.016 0.093

tNRE 0.013 0.000 -0.009 0.005

tPD 0.502 0.000 0.453 0.000

Bangladesh

tY 0.518 0.000 10.155 0.011

2

tY -------- -------- -0.781 0.014

tRE -0.111 0.017 -0.133 0.002

tNRE -0.021 0.429 -0.058 0.042

tPD 1.636 0.000 1.103 0.001

Sri Lanka

tY 0.469 0.033 22.536 0.000

2

tY -------- -------- -1.423 0.000

tRE -0.472 0.013 -0.321 0.000

tNRE 0.087 0.002 0.041 0.000

tPD 2.192 0.058 -6.297 0.000


Table 4.36 displays the results of FMOLS panel estimates taking CO2 as dependent

variable. Results shows that all coefficients are statistically significant and their signs

are according to economic theory. Results of FMOLS indicates that 1 percent increase

in economic growth, non-renewable energy sources and population density increases

141

CO2 emissions per capita by about 0.727 percent, 0.067 percent and 0.931 percent

respectively. However, the negative sign of renewable energy sources indicates that 1

percent increase in renewable energy sources will lead to decrease CO2 emissions per

capita by about 0.352 percent. Table 6 displays the results of FMOLS panel estimates

taking CO2 as the dependent variable in both models. Results of the model I shows

significant coefficients and their signs are according to economic theory. FMOLS

results indicate that 1 percent rise in growth, energy from non-renewable sources and

population density builds CO2 emanations per capita by around 0.727 percent, 0.067

percent and 0.931 percent individually. In any case, the negative indication of

sustainable energy sources demonstrates that 1 percent expansion in sustainable energy

sources will prompt decline CO2 discharges per capita by around 0.352 percent. The

existence of the EKC speculation have been additionally found in results of FMOLS in

table 4.34. In this examination, the results of factors like development, population (PD)

and non-sustainable energy source (NRE), are like the investigations of Chiu and Chang

(2009), Hossain (2011), Sulaiman et al. (2013), Sadorsky (2014), Shafiei and Salim

(2014), Boluk and Mert (2015), Jebli et al. (2016), Mitic et al. (2017), Ramli et al.

(2019) and Bilan et al. (2019) that populace, nonrenewable vitality and GDP adds to

CO2 emanations and sustainable energy source lessens CO2 outflows. Reduction in the

CO2 emissions due to the contribution of the renewable energy in the computed model

are related to the estimations of the study of Rafiq et al. (2016). The tendency in the

results of above-reviewed studies is in the favour of renewable energy consumption

because it causes less carbon dioxide emission to the environment.


( 2itCO : Dependent Variable)

Variables


Coefficients p-value Coefficients p-value

itY 0.727 0.000 2.078 0.001

2

itY -------- -------- -0.099 0.029

itRE -0.352 0.000 -0.250 0.000

142

itNRE 0.068 0.000 0.064 0.000

itPD 0.931 0.000 0.681 0.000



that there is a bi-directional panel causality running between CO2 and renewable energy

sources as well as between population destiny and CO2. Results provide evidence of

feedback relationship between CO2 and RE sources. There is unidirectional causality

running from CO2 to non-renewable energy sources. There is no causality between

growth and renewable energy sources.



HNC

N TW ,

HNC

N TZ P-Value

itY → 2itCO 2.365 0.175 0.861

2itCO → itY 3.344 1.012 0.312

itNRE → 2itCO 2.562 0.343 0.731

2itCO → itNRE 11.352 7.855 0.000

itPD → 2itCO 10.670 7.272 0.000

2itCO → itPD 8.781 5.658 0.000

itRE → 2itCO 6.382 3.608 0.000

2itCO → itRE 5.456 2.817 0.005

2

itY → 2itCO 2.2368 0.06564 0.9477

2itCO → 2

itY 3.31716 0.9889 0.3227

143


Energy, Urbanization, Energy Intensity and Environment




intercept both and only with intercept. The selected variables in this model are

environment (CO2), economic growth (Y), renewable energy sources (RE),

nonrenewable energy sources (NRE), and population density (PD). The outcomes

propose the non-stationery of all the series at their level shape, however stationary at

first difference. The results also suggest that all the series are integrated at I (1) for

individual country.


Country/ Variables



India

2tCo 0.166 -2.429 -3.978* -4.272*

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

tRE -0.129 -2.465 -6.035* -6.065*

tURB 0.43 0.053 -1.611*** -2.739***

tPT 0.196 -0.521 -1.769*** -7.232*

tEI 0.113 -1.983 -6.309* -6.464*

Pakistan

144

2tCo 0.603 -2.234 -7.677* -8.331*

tY 0.582 -2.287 -3.289* -3.704**

tNRE 0.163 -1.266 -1.782*** -3.083***

tRE -0.314 -2.169 -5.721* -5.926*

tURB 4.764 1.083 -1.610*** -2.876***

tPT -1.012 -1.44939 -1.94465*** -3.271**

tEI 1.225 -1.334 -4.973* -5.159*

Bangladesh

2tCo 0.388 -2.209 -7.934* -8.179*

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

tRE -4.963 -5.588 -9.152* -9.205*

tURB 0.414 -1.271 -1.815* -3.283**

tPT 0.277 -1.298 -1.847*** -6.699*

tEI 1.314 -2.397 -6.606* -6.894*

Sri Lanka

2tCo 0.049 -1.657 -4.943* -5.724*

tY 0.719 -0.889 -3.406* -4.787*

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

145

4.4.2.2.1.2 Johansen Co-integration Test Results


are presented in Table 4.39. According to the results null hypothesis is accepted which

indicate that there are 5, 4, 3 and 3 co-integrating vectors in India, Pakistan, Bangladesh

and Sri Lanka respectively. The existence of these vectors confirms the presence of co-

integration between the variables. This shows that all the selected variables in this

model have long run relationship over the period of 1980–2014.



India

R=0 451.377* 125.615 0.000

R≤1 288.278* 95.754 0.000

R≤2 175.926* 69.819 0.000

R≤3 113.790* 47.856 0.000

R≤4 56.719* 29.797 0.000

R≤5 21.742* 15.495 0.005

R≤6 0.120 3.841 0.729

Pakistan

R=0 495.032* 125.615 0.000

tURB 0.470 -1.011 -1.848*** -3.839*

tPT -0.065 -0.843 -4.445* -5.385*

tEI 1.431 -1.721 -3.483* -3.595**


146

R≤1 323.048* 95.754 0.000

R≤2 208.550* 69.819 0.000

R≤3 129.890* 47.856 0.000

R≤4 71.437* 29.797 0.000

R≤5 21.904* 15.495 0.005

R≤6 1.046 3.841 0.307

Bangladesh

R=0 398.061* 125.615 0.000

R≤1 272.755* 95.754 0.000

R≤2 179.811* 69.819 0.000

R≤3 114.479* 47.856 0.000

R≤4 69.431* 29.797 0.000

R≤5 34.141* 15.495 0.000

R≤6 9.140* 3.841 0.003

Sri Lanka

R=0 263.521* 125.615 0.000

R≤1 178.685* 95.754 0.000

R≤2 117.014* 69.819 0.000

R≤3 63.981* 47.856 0.001

R≤4 29.970* 29.797 0.048

R≤5 16.220* 15.495 0.039

R≤6 5.867* 3.841 0.015

147





found non-stationary at level. At first difference, all the series are integrated i.e. I (1).



Variable


Intercept P-value

trend &


trend &

intercept P-value

2itCO -0.973 0.165 -0.135 0.446 -5.559 0.000 -4.312 0.000

itY 0.884 0.812 -1.046 0.148 -1.766 0.038 -5.849 0.000

itNRE 0.702 0.758 1.932 0.974 -7.199 0.000 -5.999 0.000

itRE 0.717 0.763 0.975 0.835 -14.064 0.000 -12.753 0.000

itURB 2.0408 0.997 -4.423 0.746 -25.832 0.000 -28.235 0.000

itPT -2.643 0.1231 -3.422 0.110 -5.333 0.009 -6.591 0.007

itEI -1.009 0.946 -1.059 1.000 -15.732 0.000 -16.719 0.000


Variable


Intercept P-value

trend &


trend &

intercept P-value

2itCO -0.574 0.283 0.747 0.773 -8.358 0.000 -7.220 0.000

148


In table 4.41, results of Larsson et al., (2001) panel co-integration show that r = 4 is the

highest ranked value. The ratios of maximum likelihood are greater than the critical

value at 1% level of significance. This leads us to reject the null hypothesis of no panel

co-integration between the variables. There exists at least six co-integrating vectors in

selected panel of South Asia. Finally, panel co-integrating results confirm a stable long-

run relationship between environment (CO2), economic growth (Y), renewable energy

sources (RE), nonrenewable energy sources (NRE) and population density (PD) in four

South Asian countries.



R=0 57.035 2.45

R≤1 43.515

R≤2 33.779

R≤3 26.927

R≤4 20.431

R≤5 13.781

itY 0.005 0.988 0.818 0.794 -6.094 0.000 -5.588 0.000

itNRE -0.885 0.188 -0.786 0.216 -10.687 0.000 -10.141 0.000

itRE -1.237 0.108 -0.662 0.2541 -13.068 0.000 -12.564 0.000

itURB 1.216 0.888 1.813 0.965 -24.052 0.000 -26.139 0.000

itPT -0.776 0.219 1.516 0.935 -4.433 0.000 -6.847 0.000

itEI 0.496 0.691 -0.875 0.191 -12.066 0.000 -12.244 0.000

149

R≤6 5.437



In table 4.42, the coefficients of effluences and energy intensity are positive and

significant in all selected countries of South Asia. The coefficients of non-renewable

energy sources in all nominated countries are significant and show the positive impact.

Positive coefficients indicate that energy from renewable sources decrease the

environmental pollution. However, the non-renewable energy sources increases the

environmental pollution in India, Pakistan and Sri Lanka but shows no role of NRE in

the case of Bangladesh due to insignificant coefficient. The coefficient of urbanization

is positive and significant in India and Pakistan which shows that urbanization also

increases the Co2 emissions. However, in the case of Bangladesh and Sri Lanka, the

coefficient of urbanization is found insignificant which shows the no role of

urbanization in environmental Pollution. The coefficient of population is also found

positive and significant in all the selected countries except India. Model 4 with Kuznets

curve shows the presence of the EKC hypothesis in all the selected countries.

Table 4.42 FMOLS Country Specific Estimates


Country

Variables


Coefficients

p-value

Coefficients p-value

India

tAFL 0.444 0.002 15.504 0.000

2

tAFL ------- ------ -1.059 0.000

tNRE 0.030 0.079 -0.017 0.015

tRE -0.135 0.001 -0.031 0.026

150

tURB 6.071 0.000 4.869 0.000

tPT -0.156 0.277 -1.609 0.000

tEI 0.807 0.000 1.765 0.000

Pakistan

tAFL 1.152 0.000 3.648 0.414

2

tAFL ------ ------ -0.202 0.555

tNRE 0.041 0.000 -0.002 0.794

tRE -0.048 0.069 0.006 0.818

tURB 6.105 0.025 0.696 0.553

tPT 3.355 0.003 0.512 0.081

tEI 0.532 0.001 0.414 0.029

Bangladesh

tAFL 0.881 0.000 23.436 0.000

2

tAFL -------- --------- -1.875 0.000

tNRE -0.024 0.147 -0.040 0.086

tRE -0.107 0.000 -0.085 0.009

tURB -0.725 0.121 2.141 0.016

tPT 2.619 0.000 -0.390 0.617

tEI 0.518 0.023 1.181 0.001

Sri Lanka

tY 1.094 0.002 27.012 0.000

2

tAFL -------- ---------- -1.716 0.000

tNRE 0.058 0.000 0.049 0.000

151

tRE -0.273 0.003 -0.316 0.002

tURB -6.043 0.821 63.549 0.060

tPT 2.079 0.062 -2.898 0.043

tEI 0.906 0.000 0.346 0.192


Table 4.43 specify that 1 percent rise in affluences, non-renewable energy sources,

urbanization, population total and energy intensity rises CO2 emissions per capita by

about 1.070 percent, 0.068 percent, 0.931 percent, 0.419 percent, 0.910 percent and

0.661 percent respectively. However, the negative sign of renewable energy sources

indicates that 1 percent increase in renewable energy sources will lead to decrease CO2

emissions per capita by about 0.123 percent. Results in Model II shows the presence of

the EKC hypothesis. The results of variables affluences (per capita GDP), population

and non-renewable energy, are consistent with the studies of Chiu and Chang (2009),

Hossain (2011), Liddle (2013), Sulaiman et al. (2013), Sadorsky (2014), Shafiei and

Salim (2014), Boluk and Mert (2015), Alcantara (2001) and Sun (1999), Jebli et al.

(2016), Mitic et al. (2017), Ramli et al. (2019), Natonas et al. (2018) and Bilan et al.

(2019) that population, nonrenewable energy and GDP contributes to per capita CO2

in one side and renewable energy reduces CO2 emission in the other side. In this study,

the model also reveals that renewable energy reduces emissions. These outcomes are in

line with the studies of (Rafiq et al., 2016) and Baky et al. (2017).



Variables


Coefficients p-value Coefficients p-value

itAFL 1.070 0.000 11.740 0.000

152

2

itAFL -------- -------- -0.764 0.000

itNRE 0.068 0.000 0.046 0.000

itRE -0.123 0.025 -0.061 0.081

itURB 0.419 0.048 1.009 0.200

itPT 0.910 0.002 0.335 0.759

itEI 0.661 0.007 1.233 0.000


In table 4.44, results specify that there is a bi directional causality between renewable

energy and environment as well as between population and CO2 emissions. There also

exists unidirectional panel causality running from CO2 emissions to nonrenewable

energy sources and CO2 emissions to energy intensity, urbanization to CO2 emissions.

There is no causality between affluences and per capita CO2 emissions.



HNC

N TW ,

HNC

N TZ P-Value

itAFL → 2itCO 2.365 0.175 0.861

2itCO → itAFL 3.344 1.012 0.312

2

itAFL → 2itCO 2.237 0.066 0.948

2itCO → 2

itAFL 3.317 0.989 0.323

itNRE →

2itCO 2.562 0.343 0.731

2itCO

→ itNRE 11.352 7.855 0.000

itRE →

2itCO 6.382 3.608 0.000

153

2itCO

→ itRE 5.456 2.817 0.005

itURB →

2itCO 4.670 2.145 0.032

2itCO → itURB

2.986 0.706 0.480

itPT →

2itCO 10.670 7.272 0.000

2itCO

→ itPT 8.781 5.659 0.000

itEI →

2itCO 5.873 3.173 0.002

2itCO

→ itEI 2.121 -0.034 0.973

4.4.3. Demand for Renewable and Non-Renewable Energy Sources

4.4.3.1. Model 6: Demand for Renewable Energy Sources

4.4.3.1.1. Time Series Result

4.4.3.1.1.1. Unit Root Test Results




suggest that renewable energy sources (RE), economic growth (Y), population total

(PT), structure of the economy (IND), price of renewable and non-renewable energy

sources(P) and technological progress (T) are integrated at I(1) for individual country.


Country/

Variables



India

154

tRE -0.129 -2.465 -6.035* -6.065*

tY 0.793 -0.676 -4.294* -5.601*

tIND -1.23515* -2.52203* -6.86745* -6.77141*

tPT 0.195623 -0.52043 -1.7693*** -7.23161*

tP 1.175 -0.742 -2.283*** -2.955***

Pakistan

tRE -0.314 -2.169 -5.721* -5.926*

tY 0.582 -2.287 -3.289* -3.704**

tIND -1.115 -1.822 -3.854* -5.439*

tPT -1.012 -1.44939 -1.94465*** -3.271**

tP -0.043 -2.399 -2.094** -2.922***

Bangladesh

tRE -4.963 -5.588 -9.152* -9.205*

tY -0.718 -1.477 -1.782*** -5.331*

tIND 0.013883 -1.9462 -6.09165 -6.1578

tPT 0.277049 -1.2982 -1.84674*** -6.69959*

tP 1.172 -2.063 -1.966*** -3.0303*

Sri Lanka

tRE -1.145 -2.902 -7.563* -7.666*

tY 0.719 -0.889 -3.406* -4.787*

155

4.4.3.1.1.2. Johansen Co-integration Test Results


of demand for RE sources are presented in Tables 4.46. We find the acceptance of null

hypothesis i.e. three co-integrating vectors in the case of India, Pakistan and

Bangladesh. There are two co-integrating vectors in the case of Sri Lanka. The

existence of these co-integrating vectors confirms the presence of co-integration

between the variables. This shows that renewable energy sources (RE), economic

growth (Y), industrialization (IND), population total (PT), Price of renewable energy

(P) and Technological progress have long run relationship over selected period of time

i.e. 1980–2014.



India

R=0 119.042 83.937 0.000

R≤1 70.285 60.061 0.005

R≤2 40.967 40.175 0.042

R≤3 17.376 24.276 0.288

R≤4 5.429 12.321 0.509

R≤5 0.003 4.130 0.963

Pakistan

tIND -0.39541 -1.85571 -2.96369* -4.3633*

tPT -0.06494 -0.84338 -4.44505* -5.3848*

tP 0.987583 -0.59459 -2.52907** -4.8222*


156

R=0 162.388 83.937 0.000

R≤1 97.884 60.061 0.000

R≤2 53.141 40.175 0.002

R≤3 18.099 24.276 0.246

R≤4 3.734 12.321 0.749

R≤5 0.004 4.130 0.958

Bangladesh

R=0 112.860 83.937 0.000

R≤1 68.729 60.061 0.008

R≤2 40.637 40.175 0.045

R≤3 17.470 24.276 0.282

R≤4 7.231 12.321 0.303

R≤5 0.001 4.130 0.989

Sri Lanka

R=0 115.849 83.937 0.000

R≤1 64.747 60.061 0.019

R≤2 37.689 40.175 0.087

R≤3 14.706 24.276 0.479

R≤4 4.606 12.321 0.623

R≤5 0.096172 4.129906 0.7988



157



found to be non-stationary at level. At first difference, all the series are integrated i.e. I

(1).



Variable


Intercept P-value

trend &


trend &

intercept P-value

itRE 0.717 0.763 0.975 0.835 -14.064 0.000 -12.753 0.000

itY 0.884 0.812 -1.046 0.148 -1.766 0.038 -5.849 0.000

itPT -2.643 0.1231 -3.422 0.110 -5.333 0.009 -6.591 0.007

itIND 1.393 0.918 0.176 0.569 -1.415 0.078 -6.726 0.000

itP -1.639 0.982 -2.857 0.763 -7.667 0.000 -8.559 0.000


Variable


Intercept P-value

trend &


trend &

intercept P-value

itRE

-1.2374 0.108 -0.662 0.254 -13.068 0.000 -12.564 0.000

itY 0.005 0.988 0.818 0.794 -6.094 0.000 -5.588 0.000

itPT -0.776 0.219 1.516 0.935 -4.433 0.000 -6.847 0.000

itIND 1.459 0.928 1.301 0.903 -4.711 0.000 -4.118 0.000

itP 1.309 0.905 1.766 0.962 -5.109 0.000 -4.812 0.000

158


Table 4.48 present the results of Larsson et al., (2001). In the case of panel estimation

of renewable energy RE r = 5 is the highest rank. The values 16.349, 11.295, 8.369,

4.354 and 2.534 (i.e. maximum likelihood ratio) are greater than the critical value at

1% level of significance. This leads us to reject the null hypothesis of no panel co-

integration between the variables. Here in the selected panel at least three co-integrating

vectors are existed. Lastly, panel co-integrating results confirm a stable long-run

relationship between renewable energy sources (RE), economic growth (Y), population

total (PT), industrialization (IND), Price of energy (P) and technological progress (T)

in four South Asian countries.



R=0 16.349 2.45

R≤1 11.295

R≤2 8.369

R≤3 4.354

R≤4 2.534

R≤5 0.035



Table 4.49 displays that coefficients of economic growth are positive and significant in

all selected countries of South Asia. The positive coefficients suggest that increase in

economic growth leads to increase in demand for renewable energy sources. The

coefficient of energy price has negative impact on renewable energy demand in all

selected countries which is also according to the economic theory of demand. The

coefficient of population also have positive impact on renewable energy demand in all

selected countries. Technical progress show the negative impact on renewable energy

159

demand in all countries except Sri Lanka. Coefficient of industrialization shows the

positive impact on renewable energy in India and Pakistan. In the case of Bangladesh

and Sri Lanka industrialization shows the negative impact on renewable energy

demand.


( tRE : Dependent Variable)

Country Variables Coefficients p-value

India

tY 3.687 0.000

tPT 8.019 0.000

tIND 0.475 0.007

tP -0.007 0.000

tT -0.242 0.000

Pakistan

tY 2.614 0.000

tPT 0.318 0.000

tIND 0.477 0.000

tP -0.002 0.000

tT -0.017 0.000

Bangladesh

tY 2.432 0.012

tPT 0.111 0.000

tIND -0.029 0.002

tP -0.010 0.000

tT -0.294 0.001

160

Sri Lanka

tY 0.538 0.033

tPT 1.500 0.013

tIND -2.095 0.002

tP 0.001 0.513

tT 0.006 0.058


Results of FMOLS in table 4.50 indicate that 1 percent increase in economic growth,

industrialization and population total increases RE by about 1.228 percent, 1.230

percent, and 1.373 percent respectively. These results show that higher per capita real

income should result in greater economic activity which in turns accelerate the use of

renewable energy. These results are consistent with the studies of Cheng and Lai

(1997), Adjaye (2000), Kahsai et al., (2012), Bernstein and Madlener (2015) as they

concluded the positive relationship between growth and energy demand. Zhang and

Broadstock (2016) found rapid industrialization has given rise to massive energy

demand. Chaudhry (2010) found Positive income and negative price elasticities. The

degree of industrialization, as a measure of economic structure is also expected to

enhance the demand for renewable energy. However, the negative sign of P and T

indicates that 1 percent increase in energy price and technical progress will lead to

decrease renewable energy demand by about 0.004 percent and 0.020 percent

respectively. According to the study of Mountain et al., (1989) the negative sign of

technical progress show that the technology is energy saving.


( itRE : Dependent Variable)


tY 1.228 0.000

161

tPT 1.373 0.000

tIND 1.230 0.000

tP -0.004 0.005

tT -0.020 0.093


Table 4.51 specifies that there is a bi-directional panel causality running between

renewable energy and population total. There is unidirectional causality running from

growth to renewable energy sources, from renewable energy sources to industrialization

and technological progress to renewable energy sources. There is no causality between

price and renewable energy sources.



HNC

N TW ,

HNC

N TZ P-Value

itPT → itRE 4.150 3.848 0.000

itRE → itPT 6.833 7.201 0.000

itIND → itRE 0.698 -0.467 0.640

itRE → itIND 4.633 4.451 0.000

itY → itRE 3.533 3.077 0.002

itRE → itY 1.145 0.092 0.927

itP → itRE 1.480 0.511 0.610

itRE → itP 1.001 -0.088 0.930

itT → itRE 3.700 3.286 0.001

162

itRE → itT ------ ------ ------

4.4.3.1.5 Test of Forecasting Renewable Energy Demand

Demand for renewable energy sources for 20 years ahead has been forecasted by two

methods firstly by the method of variance decomposition and secondly by impulse

response function.

4.4.3.1.5.1 Variance Decomposition of RE Demand

The investigation of variance decomposition is processed through dispersing all factors.

Table C1 in appendices shows the forecasted renewable energy sources demand for 20

years ahead. Variance decomposition show that 1 percent change in renewable energy

demand is explained by price of energy. Similarly other factors as real income,

industrialization, technical progress and population are also affect the renewable energy

demand in future. Results show that 1 percent change in energy demand explained by

income, industrlization, technical progress and population total.

4.4.3.1.5.2 Impulse Response Function of RE Demand

Impulse response function portrays the reaction of the framework after some time to a

stun every one of the variable in the framework. Table C2 in appendices shows the

forecasted renewable energy sources demand for 20 years ahead. The values of price

show that energy demand for renewable sources is adversely affected by energy price

and positively affected by real income, population total, technical progress and

industrialization.

4.4.3.2. Model 7: Demand for Non-Renewable Energy Sources

4.4.3.2.1. Time Series Results





suggest that nonrenewable energy sources (NRE), economic growth (Y), population

163

total (PT), structure of the economy (IND), price of renewable and non-renewable

energy sources(P) and technological progress (T) are integrated at I(1) for individual

country in selected sample.


Country/ Variables



India

tNRE -0.872 -2.572 -1.953** -2.262***

tY 0.793 -0.676 -4.294* -5.601*

tIND -1.235 -2.522 -6.867* -6.771*

tPT 0.196 -0.521 -1.769*** -7.232*

tP 1.175 -0.742 -2.283*** -2.955***

Pakistan

tNRE 0.163 -1.266 -1.782*** -3.083***

tY 0.582 -2.287 -3.289* -3.704**

tIND -1.115 -1.822 -3.854* -5.439*

tPT -1.012 -1.44939 -1.94465*** -3.271**

tP -0.043 -2.399 -2.094** -2.922***

Bangladesh

tNRE -1.409 -2.362 -5.571* -5.597*

tY -0.718 -1.477 -1.782*** -5.331*

tIND 0.014 -1.946 -6.092 -6.158

164

.


In table 4.53 we find the acceptance of null hypothesis i.e. India has four co-integrating

vectors. Three co-integrating vectors in the case of Pakistan and Bangladesh and Sri

Lanka has two co-integrating vectors. The existence of these co-integrating vectors

confirms the presence of co-integration between the variables. This shows that non-

renewable energy sources (NRE), economic growth (Y), industrialization (IND) ,

population total (PT), Price of renewable energy (P) and Technological progress have

long run relationship over selected period of time i.e. 1980–2014.



India

R=0 131.821 83.937 0.000

R≤1 85.610 60.061 0.000

R≤2 50.073 40.175 0.004

tPT 0.277 -1.298 -1.847*** -6.699*

tP 1.172 -2.063 -1.966*** -3.0303*

Sri Lanka

tNRE -2.487 -4.344 -6.826* -7.105*

tY 0.719 -0.889 -3.406* -4.787*

tIND -0.395 -1.855 -2.964* -4.363*

tPT -0.065 -0.843 -4.445* -5.385*

tP 0.987 -0.595 -2.529** -4.822*

*Denotes significant at 1% level. **Denotes significant at 5% level. ***Denotes significant at 10% level

165

R≤3 26.920 24.276 0.023

R≤4 9.876 12.321 0.124

R≤5 0.002 4.130 0.974

Pakistan

R=0 161.505 83.937 0.000

R≤1 87.370 60.061 0.000

R≤2 50.686 40.175 0.003

R≤3 20.269 24.276 0.148

R≤4 3.671 12.321 0.758

R≤5 0.007 4.130 0.946

Bangladesh

R=0 132.784 83.937 0.000

R≤1 71.762 60.061 0.004

R≤2 45.318 40.175 0.014

R≤3 21.517 24.276 0.107

R≤4 8.590 12.321 0.195

R≤5 0.000 4.130 0.996

Sri Lanka

R=0 104.720 83.937 0.001

R≤1 67.172 60.061 0.011

R≤2 39.904 40.175 0.053

R≤3 16.240 24.276 0.362

R≤4 7.738 12.321 0.258

166

R≤5 0.135 4.129 0.762








Variable


Intercept P-value

trend &


trend &

intercept P-value

itNRE 0.702 0.758 1.932 0.974 -7.199 0.000 -5.999 0.000

itY 0.884 0.812 -1.046 0.148 -1.766 0.038 -5.849 0.000

itPT -2.643 0.1231 -3.422 0.110 -5.333 0.009 -6.591 0.007

itIND 1.393 0.918 0.176 0.569 -1.415 0.078 -6.726 0.000

itP -1.639 0.982 -2.857 0.763 -7.667 0.000 -8.559 0.000


Variable


Intercept P-value

trend &


trend &

intercept P-value

itNRE -0.885 0.188 -0.786 0.216 -10.687 0.000 -10.141 0.000

itY 0.005 0.988 0.818 0.794 -6.094 0.000 -5.588 0.000

167


In the case of panel estimation of non-renewable energy NRE in table 4.55, the

maximum rank is r = 4. We find that the values of maximum likelihood ratio i.e. 13.322,

7.956, 5.579 and 2.526 are greater than the critical value at 1% level of significance.

This leads us to reject the null hypothesis of no panel co-integration between the

variables. Results of panel co-integration indicate the existence of at least three co-

integrating vectors in selected panel of South Asia. Moreover the results confirm a

stable long-run relationship between non-renewable energy sources (NRE), economic

growth (Y), population total (PT), industrialization (IND), Price of energy (P) and

technological progress (T) in four countries.



R=0 13.322 2.45

R≤1 7.956

R≤2 5.579

R≤3 2.526

R≤4 0.852

R≤5 -1.481



itPT -0.776 0.219 1.516 0.935 -4.433 0.000 -6.847 0.000

itIND 1.459 0.928 1.301 0.903 -4.711 0.000 -4.118 0.000

itP 1.309 0.905 1.766 0.962 -5.109 0.000 -4.812 0.000

168

Table 4.56 displays that coefficients of economic growth, population total and

industrialization are positive and significant in all selected countries of South Asia. The

positive coefficients suggest that increase in economic growth, population and

industrialization leads to increase in demand for non-renewable energy sources. The

coefficients of energy price and technical progress show negative sign and significant

in all countries. So the coefficient of price is according to the economic theory.

However the negative sign of technical progress show that technology is energy saving

in all selected countries.


( tNRE : Dependent Variable)


India

tY 0.154 0.000

tPT 0.086 0.000

tIND 0.004 0.699

tP -1.845 0.000

tT -9.595 0.000

Pakistan

tY 0.666 0.007

tPT 0.007 0.444

tIND 0.013 0.000

tP -0.502 0.000

tT -7.672 0.000

Bangladesh

tY 0.518 0.000

tPT 0.111 0.016

169

tIND 0.021 0.429

tP -1.636 0.000

tT -13.166 0.000

Sri Lanka

tY 0.469 0.033

tPT 0.471 0.013

tIND 0.086 0.001

tP -2.192 0.058

tT -8.056 0.011


Table 4.57 display the results of FMOLS panel estimates taking demand for non-

renewable energy sources as dependent variable. All the coefficients of selected

variable are significant and have signs related to the economic theory. Results shows

that 1 percent increase in economic growth, population and industrialization increases

demand for non-renewable energy sources by about 4.889 percent, 9.760 percent and

1.514 percent respectively. However, the negative sign of P and T indicates that 1

percent increase in renewable energy sources will lead to decrease nonrenewable

energy demand by about 0.008 percent and 0.299 percent respectively.


( itNRE : Dependent Variable)


tY 4.889 0.000

tPT 9.760 0.000

170

tIND 1.514 0.056

tP -0.008 0.049

tT -0.299 0.000


Similarly, the results represented in table 4.58 specify that there is a bi-directional panel

causality running between non-renewable energy and population total as well as in price

and non-renewable energy. There is uni-directional causality running from growth to

non-renewable energy sources and industrialization to non-renewable energy sources

and as well as from technological progress to non-renewable energy sources. There is

also unidirectional causality between price and non-renewable energy sources.



HNC

N TW ,

HNC

N TZ P-Value

itPT → itNRE 5.546 5.592 0.000

itNRE → itPT 5.724 5.814 0.000

itY → itNRE 6.575 6.879 0.000

itNRE → itY 1.888 1.020 0.308

itIND → itNRE 3.115 2.554 0.011

itNRE → itIND 1.956 1.106 0.269

itP → itNRE 3.257 2.732 0.006

itNRE → itP 8.372 9.124 0.000

itT → itNRE 5.358 5.357 0.000

171

4.4.3.2.5 Test of Forecasting Non-Renewable Energy Demand

Demand for non-renewable energy sources for 20 years ahead has been forecasted by

two methods firstly by the method of variance decomposition and secondly by impulse

response function.

4.4.3.2.5.1 Variance Decomposition of NRE Demand

Table C3 in appendices shows the forecasted non-renewable energy sources demand

for 20 years ahead. Variance decomposition show that 1 percent change in non-

renewable energy demand is explained by price of energy. The other variable like real

income, population, technical progress and industrialization also explain the variation

in non-renewable energy demand.

4.4.3.2.5.2 Impulse Response Function of NRE Demand

Table C4 in appendices shows the forecasted non-renewable energy sources demand

for 20 years ahead. The values of price show that energy demand for non-renewable

sources is adversely affected by energy price. Similarly, it is positively affected by

population total, real income, technical progress and industrialization.

4.4.4. Impact of Renewable and Non-Renewable Energy Sources on

Energy Intensity


Energy, Economic Growth, Urbanization and Energy Intensity



Table 4.59 reports the unit root outcomes energy intensity (EI), economic growth (Y),

renewable energy sources (RE), nonrenewable energy sources (NRE), urbanization

(URB) and population density (PD). The outcomes propose the non-stationery of all the

series at their level shape, however stationary at first difference. The results also suggest

that all variables are integrated at I(1) for individual country in selected sample.

172


Country/

Variables



India

tEI 0.113 -1.983 -6.309* -6.464*

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

tRE -0.129 -2.465 -6.035* -6.065*

tURB 0.43 0.053 -1.611*** -2.739***

tPD 0.196 -0.521 -1.769*** -7.232*

Pakistan

tEI 1.225 -1.334 -4.973* -5.159*

tY 0.582 -2.287 -3.289* -3.704**

tNRE 0.163 -1.266 -1.782*** -3.083***

tRE -0.314 -2.169 -5.721* -5.926*

tURB 4.764 1.083 -1.610*** -2.876***

tPD -0.013 -1.449 -1.945*** -4.616*

Bangladesh

tEI 1.314 -2.397 -6.606* -6.894*

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

173


Table 4.60 shows that the null hypothesis is accepted that is there are five co-integrating

vectors in India, Pakistan and Bangladesh and three in the case of Sri Lanka. This shows

the existence of co-integration between energy intensity (EI), economic growth (Y),

renewable energy sources (RE), nonrenewable energy sources (NRE), urbanization

(URB) and population density (PD).



India

R=0 342.051 95.754 0.000

R≤1 183.258 69.819 0.000

tRE -4.963 -5.588 -9.152* -9.205*

tURB 0.414 -1.271 -1.815* -3.283**

tPD 0.277 -1.298 -1.847*** -6.669*

Sri Lanka

tEI 1.431 -1.721 -3.483* -3.595**

tY 0.719 -0.889 -3.406* -4.787*

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

tURB 0.470 -1.011 -1.848*** -3.839*

tPD -0.065 -0.844 -4.445* -5.385*

* Denotes significant at 1% level.** Denotes significant at 5% level.*** Denotes significant at 10% level.

174

R≤2 100.768 47.856 0.000

R≤3 47.538 29.797 0.000

R≤4 21.601 15.495 0.005

R≤5 2.282 3.841 0.131

Pakistan

R=0 313.176 95.754 0.000

R≤1 197.347 69.819 0.000

R≤2 116.653 47.856 0.000

R≤3 62.876 29.797 0.000

R≤4 21.541 15.495 0.005

R≤5 0.447 3.841 0.504

Bangladesh

R=0 301.210 95.754 0.000

R≤1 187.471 69.819 0.000

R≤2 121.280 47.856 0.000

R≤3 62.324 29.797 0.000

R≤4 25.170 15.495 0.001

R≤5 0.114 3.841 0.736

Sri Lanka

R=0 144.152 103.847 0.000

R≤1 79.522 76.973 0.032

R≤2 53.911 54.079 0.052

R≤3 29.777 35.193 0.171

175

R≤4 13.330 20.262 0.338

R≤5 4.406 9.165 0.355








Variable


Intercept P-value

trend &


trend &

intercept P-value

itEI -1.009 0.946 -1.059 1.000 -15.732 0.000 -16.719 0.000

itY 0.884 0.812 -1.046 0.148 -1.766 0.038 -5.849 0.000

itNRE 0.702 0.758 1.932 0.974 -7.199 0.000 -5.999 0.000

itRE 0.717 0.763 0.975 0.835 -14.064 0.000 -12.753 0.000

itURB 2.666 0.996 0.727 0.766 -23.689 0.000 -24.174 0.000

itPD -2.154 0.937 -1.113 0.133 -2.006 0.022 -1.614 0.053


Variable


Intercept P-value

trend &


trend &

intercept P-value

176


Table 4.62 presents the values of maximum likelihood ratio i.e. 41.330, 27.836, 20.935,

14.346 and 8.827 are greater than the critical value at 1% level of significance. Here

maximum rank is 5. This leads us to reject the null hypothesis of no panel co-integration

between the variables. There exists at least four co-integrating vectors and panel co-

integrating results confirm a stable long-run relationship between energy intensity (EI),


(NRE), urbanization (URB) and population density (PD) in four South Asian countries.



R=0 41.330 2.45

R≤1 27.836

R≤2 20.935

R≤3 14.346

R≤4 8.827

R≤5 0.908

itEI 0.496 0.691 -0.875 0.191 -12.066 0.000 -12.244 0.000

itY 0.005 0.988 0.818 0.794 -6.094 0.000 -5.588 0.000

itNRE -0.885 0.188 -0.786 0.216 -10.687 0.000 -10.141 0.000

itRE -1.237 0.108 -0.662 0.2541 -13.068 0.000 -12.564 0.000

itURB 1.829 0.966 0.127 0.551 -1.305 0.095 -25.499 0.000

itPD -0.817 0.207 1.683 0.954 -6.152 0.000 -6.753 0.000

177



Table 4.63 displays individual level results and shows that coefficients of economic

growth and population density are negative and significant in all selected countries of

South Asia. Both the negative coefficients suggest that increase in economic growth

and population density leads to reduce in energy intensity. The coefficient of non-

renewable energy sources is positive and significant in all selected countries. The

positive coeffiecnt shows that increase in non-renewable energy sources will lead to

increase energy intensity. Renewable energy sources decrease the energy intensity in

Pakistan, Bangladesh and Sri Lanka. However, in the case of India, the coefficient of

renewable energy sources is found to be negative. Urbanization shows the positive

impact on energy intensity in the case of India and Bangladesh but negative impact on

energy intensity in Pakistan. The coefficient of urbanization in Sri Lanka is insignificant

which implies that there is no role of urbanization.


( tEFI : Dependent Variable)


India

tY -1.491 0.000

tNRE 0.092 0.009

tRE 0.115 0.059

tURB 8.760 0.000

tPD -3.731 0.000

Pakistan

tY -0.279 0.000

tNRE 0.010 0.000

178

tRE -0.026 0.000

tURB -9.692 0.000

tPD -1.323 0.000

Bangladesh

tY -1.318 0.000

tNRE 0.024 0.030

tRE -0.078 0.000

tURB 3.155 0.000

tPD -3.007 0.000

Sri Lanka

tY -0.757 0.000

tNRE 0.006 0.000

tRE -0.159 0.000

tURB 1.138 0.769

tPD -1.023 0.000


Table 4.64 shows that all coefficients are statistically significant and their signs are

according to economic theory. Results of FMOLS indicates that 1 percent increase in

economic growth, renewable energy sources and population density decreases EI by

about 0.600 percent, 0.027 percent and 0.960 percent respectively. However, the

positive sign of non-renewable energy sources and urbanization indicates that 1 percent

increase in renewable energy sources will lead to increase EI by about 0.021 and 0.442

percent respectively.

179


( itEI : Dependent Variable)


itY -0.600 0.000

itNRE 0.021 0.000

itRE -0.027 0.037

itPD -0.960 0.000

itURB 0.442 0.000


Results in table 4.65 specify that there is a bi-directional panel causality between energy

intensity and economic growth as well as between energy intensity and population

density. Also uni-directional causality running from energy intensity to all other

remaining variables as renewable energy sources, non-renewable energy sources and

urbanization.



HNC

N TW ,

HNC

N TZ P-Value

itY → itEI 4.491 1.991 0.046

itEI → itY 4.921 2.359 0.018

itPD → itEI 4.382 1.898 0.057

itEI → itPD 4.221 1.761 0.078

itURB → itEI 4.221 1.761 0.078

180

itEI → itURB 4.422 1.933 0.053

itRE → itEI 3.949 1.529 0.126

itEI → itRE 5.201 2.599 0.009

itNRE → itEI 3.200 0.889 0.374

itEI → itNRE 5.154 2.559 0.011


Energy, Economic Growth, Trade Openness and Energy Intensity






suggest that energy intensity (EI), economic growth (Y), renewable energy sources

(RE), nonrenewable energy sources (NRE), trade openness (To) and population density

(PD) are integrated at I(1) for individual country in selected sample.

Table 4.66 DF GLS Unit Root Test

Country/

Variables



India

tEI 0.113 -1.983 -6.309* -6.464*

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

181

tRE -0.129 -2.465 -6.035* -6.065*

tTo 0.721 -1.674 -5.086* -5.725*

tPD 0.196 -0.521 -1.769*** -7.232*

Pakistan

tEI 1.225 -1.334 -4.973* -5.159*

tY 0.582 -2.287 -3.289* -3.704**

tNRE 0.163 -1.266 -1.782*** -3.083***

tRE -0.314 -2.169 -5.721* -5.926*

tTo -1.881 -3.123 -1.208*** -5.078*

tPD -0.013 -1.449 -1.945*** -4.616*

Bangladesh

tEI 1.314 -2.397 -6.606* -6.894*

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

tRE -4.963 -5.588 -9.152* -9.205*

tTo -1.081 -2.364 -5.823* -6.181*

tPD 0.277 -1.298 -1.847*** -6.669*

Sri Lanka

tEI 1.431 -1.721 -3.483* -3.595**

tY 0.719 -0.889 -3.406* -4.787*

182


Table 4.67 shows that the null hypothesis is accepted which indicate that India has five

co-integrating vectors and Pakistan has four co-integrating vectors. Bangladesh and Sri

Lanka has three co-integrating vectors. This shows that energy intensity (EI), economic

growth (Y), renewable energy sources (RE), nonrenewable energy sources (NRE), trade

openness (To) and population density (PD) are co-integrated and have long run

relationship.



India

R=0 297.071 95.754 0.000

R≤1 173.205 69.819 0.000

R≤2 90.740 47.856 0.000

R≤3 53.117 29.797 0.000

R≤4 27.000 15.495 0.001

R≤5 6.555 3.841 0.011

Pakistan

R=0 237.138 95.754 0.000

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

tTo -1.157 -1.104 -5.962* -6.408*

tPD -0.065 -0.844 -4.445* -5.385*

* Denotes significant at 1% level. ** Denotes significant at 5% level. *** Denotes significant at 10% level.

183

R≤1 113.534 69.819 0.000

R≤2 64.627 47.856 0.001

R≤3 32.838 29.797 0.022

R≤4 14.607 15.495 0.068

R≤5 1.168 3.841 0.280

Bangladesh

R=0 228.661 95.754 0.000

R≤1 137.662 69.819 0.000

R≤2 81.494 47.856 0.000

R≤3 32.584 29.797 0.023

R≤4 10.415 15.495 0.250

R≤5 0.116 3.841 0.734

Sri Lanka

R=0 131.817 95.754 0.000

R≤1 73.893 69.819 0.023

R≤2 47.143 47.856 0.058

R≤3 27.442 29.797 0.091

R≤4 8.621 15.495 0.402

R≤5 0.176 3.841 0.675



184






Variable


Intercept P-value

trend &


trend &

intercept P-value

itEI -1.009 0.946 -1.059 1.000 -15.732 0.000 -16.719 0.000

itY 0.884 0.812 -1.046 0.148 -1.766 0.038 -5.849 0.000

itNRE 0.702 0.758 1.932 0.974 -7.199 0.000 -5.999 0.000

itRE 0.717 0.763 0.975 0.835 -14.064 0.000 -12.753 0.000

itTo

1.891 0.971 0.143 0.557 -9.648 0.000 -7.921 0.000

itPD -2.154 0.937 -1.113 0.133 -2.006 0.022 -1.614 0.053


Variable


Intercept P-value

trend &


trend &

intercept P-value

itEI 0.496 0.691 -0.875 0.191 -12.066 0.000 -12.244 0.000

itY 0.005 0.988 0.818 0.794 -6.094 0.000 -5.588 0.000

itNRE -0.885 0.188 -0.786 0.216 -10.687 0.000 -10.141 0.000

itRE -1.237 0.108 -0.662 0.254 -13.068 0.000 -12.564 0.000

185







ratio i.e. 31.208, 18.994, 12.864, 8.662 and 5.592 are greater than the critical value at



integration indicates the existence of at least five co-integrating vectors in selected


relationship between energy intensity (EI), economic growth (Y), renewable energy

sources (RE), nonrenewable energy sources (NRE), trade openness (To) and population

density (PD) in four South Asian countries.



R=0 31.208 2.45

R≤1 18.994

R≤2 12.864

R≤3 8.662

R≤4 5.592

R≤5 1.165


itTo

0.405 0.657 -0.489 0.313 -11.303 0.000 -10.549 0.000

itPD -0.817 0.207 1.683 0.954 -6.152 0.000 -6.753 0.000

186



coefficients of economic growth, renewable energy and population density are negative

and significant in all selected countries of South Asia. All the negative coefficients

suggest that increase in economic growth, renewable energy and population density

leads to reduce in energy intensity. The coefficient of non-renewable energy sources is

positive and significant in Pakistan and Bangladesh. The positive coeffiecnt shows that

increase in non-renewable energy sources will lead to increase energy intensity. Trade

openness shows the negative impact on energy intensity in the case of India and

Pakistan but positive impact on energy intensity in Sri Lanka and Bangladesh.


( tEI : Dependent Variable)


India

tY -0.301 0.000

tNRE -0.038 0.000

tRE -0.004 0.072

tPD -0.996 0.000

tTo -0.519 0.000

Pakistan

tY -0.279 0.000

tNRE 0.010 0.000

tRE -0.026 0.000

tPD -9.692 0.000

tTo -1.323 0.000

Bangladesh tY -2.441 0.000

187

tNRE 0.015 0.000

tRE -0.005 0.017

tPD -6.134 0.000

tTo 0.096 0.000

Sri Lanka

tY -0.459 0.000

tNRE -0.001 0.080

tRE -0.138 0.000

tPD -1.122 0.000

tTo 0.535 0.000


Table 4.71 displays the results of FMOLS panel estimates taking EI as dependent



in economic growth, renewable energy sources and trade openness decreases EI by

about 0.753 percent, 0.029 percent and 2.223 percent respectively. However, the

positive sign of non-renewable energy sources and population density indicates that 1

percent increase in renewable energy sources will lead to increase EI by about 0.008

and 0.283 percent respectively.




itY -0.753 0.000

188

itNRE 0.008 0.028

itRE -0.029 0.050

itTo -2.223 0.000

itPD 0.283 0.000



that there is a bi-directional panel causality running between economic growth and

energy intensity and as well between population density and energy intensity. It is also

found that there is a uni-directional causality running from energy intensity to

renewable energy sources and from energy intensity to non-renewable energy sources.

Table 4.72 DH Panel Causality Test


HNC

N TW ,

HNC

N TZ P-Value

itY → itEI 4.491 1.991 0.046

itEI → itY 4.921 2.359 0.018

itPD → itEI 4.382 1.898 0.057

itEI → itPD 4.221 1.761 0.078

itTo → itEI 1.680 -0.410 0.682

itEI → itTo 6.809 3.973 0.000

itRE → itEI 3.949 1.529 0.126

itEI → itRE 5.201 2.599 0.009

itNRE → itEI 3.200 0.889 0.374

189

itEI → itNRE 5.154 2.559 0.011


Energy, Economic Growth, Industrialization, Technological Progress and

Energy Intensity






suggest that energy intensity (EI), economic growth (Y), renewable energy sources

(RE), nonrenewable energy sources (NRE), industrialization (IND), population density

(PD) and technological progress (T) are integrated at I(1) for individual country.


Country/

Variables



India

tEI 0.113 -1.983 -6.309* -6.464*

tY 0.793 -0.676 -4.294* -5.601*

tNRE -0.872 -2.572 -1.953** -2.262***

tRE -0.129 -2.465 -6.035* -6.065*

tIND -1.235 -2.522 -6.867 -6.771

tPD 0.196 -0.521 -1.769*** -7.232*

190

Pakistan

tEI 1.225 -1.334 -4.973* -5.159*

tY 0.582 -2.287 -3.289* -3.704**

tNRE 0.163 -1.266 -1.782*** -3.083***

tRE -0.314 -2.169 -5.721* -5.926*

tIND -1.115 -1.821 -3.854 -5.440

tPD -0.013 -1.449 -1.945*** -4.616*

Bangladesh

tEI 1.314 -2.397 -6.606* -6.894*

tY -0.718 -1.477 -1.782*** -5.331*

tNRE -1.409 -2.362 -5.571* -5.597*

tRE -4.963 -5.588 -9.152* -9.205*

tIND 0.014 -1.946 -6.092 -6.158

tPD 0.277 -1.298 -1.847*** -6.669*

Sri Lanka

tEI 1.431 -1.721 -3.483* -3.595**

tY 0.719 -0.889 -3.406* -4.787*

tNRE -2.487 -4.344 -6.826* -7.105*

tRE -1.145 -2.902 -7.563* -7.666*

tIND -0.395 -1.856 -2.964 -4.363

191



are presented in Table 4.74. We find the acceptance of null hypothesis i.e. four co-

integrating vectors in the case of India, Pakistan and Bangladesh and three co-

integrating vectors in the case of Sri Lanka. The existence of these co-integrating

vectors confirms the presence of co-integration between the variables. This shows that

energy intensity (EI), economic growth (Y), renewable energy sources (RE),

nonrenewable energy sources (NRE), industrialization (IND), population density (PD)

and technological progress have long run relationship over selected period of time i.e.

1980–2014.



India

R=0 178.2164 111.7805 0.000

R≤1 120.166 83.937 0.000

R≤2 73.388 60.061 0.003

R≤3 43.272 40.175 0.024

R≤4 19.859 24.276 0.163

R≤5 7.301 12.321 0.296

R≤6 0.045 4.130 0.862

Pakistan

R=0 211.569 111.781 0.000

R≤1 128.722 83.937 0.000

tPD -0.065 -0.844 -4.445* -5.385*

* Denotes significant at 1% level. ** Denotes significant at 5% level. *** Denotes significant at 10% level.

192

R≤2 83.102 60.061 0.000

R≤3 44.911 40.175 0.016

R≤4 19.003 24.276 0.200

R≤5 6.785 12.321 0.347

R≤6 0.009 4.130 0.938

Bangladesh

R=0 190.904 111.781 0.000

R≤1 124.301 83.937 0.000

R≤2 75.367 60.061 0.002

R≤3 40.159 40.175 0.050

R≤4 19.347 24.276 0.185

R≤5 8.769 12.321 0.183

R≤6 0.011 4.130 0.931

Sri Lanka

R=0 140.197 111.781 0.000

R≤1 83.778 83.937 0.051

R≤2 54.989 60.061 0.125

R≤3 29.924 40.175 0.358

R≤4 12.295 24.276 0.681

R≤5 3.169 12.321 0.826

R≤6 0.028 4.130 0.892



193


constant and trend are reported in Table 4.75. The tests show that all variables as energy

intensity, economic growth, non-renewable energy sources, renewable energy sources,

industrialization, technical improvement and population density are found to be non-

stationary at level. At first difference, all the series are integrated.

Table 4.75 Panel Unit Root Test


Variable


Intercept P-value

trend &


trend &

intercept P-value

itEI -1.009 0.946 -1.059 1.000 -15.732 0.000 -16.719 0.000

itY 0.884 0.812 -1.046 0.148 -1.766 0.038 -5.849 0.000

itNRE 0.702 0.758 1.932 0.974 -7.199 0.000 -5.999 0.000

itRE 0.717 0.763 0.975 0.835 -14.064 0.000 -12.753 0.000

itIND 1.393 0.918 0.176 0.569 -1.415 0.078 -6.726 0.000

itPD -2.154 0.937 -1.113 0.133 -2.006 0.022 -1.614 0.053


Variable


Intercept P-value

trend &


trend &

intercept P-value

itEI 0.496 0.691 -0.875 0.191 -12.066 0.000 -12.244 0.000

itY 0.005 0.988 0.818 0.794 -6.094 0.000 -5.588 0.000

itNRE -0.885 0.188 -0.786 0.216 -10.687 0.000 -10.141 0.000

194







ratio i.e. 19.505, 13.735, 10.419, 7.316 and 3.308 are greater than the critical value at



integration indicates the existence of at least five co-integrating vectors in selected


relationship between energy intensity (EI), economic growth (Y), renewable energy

sources (RE), nonrenewable energy sources (NRE), industrialization (IND), technical

improvement (T) and population density (PD) in four South Asian countries.



R=0 19.505 2.45

R≤1 13.735

R≤2 10.419

R≤3 7.316

R≤4 4.641

R≤5 3.308

itRE -1.237 0.108 -0.662 0.2541 -13.068 0.000 -12.564 0.000

itIND 1.459 0.928 1.301 0.903 -4.711 0.000 -4.118 0.000

itPD -0.817 0.207 1.683 0.954 -6.152 0.000 -6.753 0.000

195

R≤6 0.031




coefficients of economic growth and renewable energy sources are negative and

significant in all selected countries of South Asia. Both the negative coefficients

suggest that increase in economic growth and renewable energy leads to reduce in

energy intensity. The coefficient of non-renewable energy sources is negative and

significant in India and Pakistan and positive in rest of the two countries. The positive

coefficient shows that increase in non-renewable energy sources will lead to increase

energy intensity. Industrialization decrease the energy intensity in Pakistan and

Bangladesh. However, in the case of India and Sri Lanka, the coefficient of

industrialization is found negative. Population density shows the positive impact on

energy intensity in the case of Pakistan but negative impact on energy intensity in India,

Bangladesh and Sri Lanka. . Technological improvements show the negative impact on

energy intensity in the case of Pakistan but positive impact on energy intensity in India,

Bangladesh and Sri Lanka.


( tEI : Dependent Variable)


India

tY -1.027 0.000

tNRE -0.051 0.000

tRE -0.015 0.069

tIND 0.029 0.098

tPD -2.398 0.000

196

tT 0.049 0.000

Pakistan

tY -0.345 0.000

tNRE -0.042 0.000

tRE -0.070 0.001

tIND -0.131 0.002

tPD 1.185 0.000

tT -0.046 0.000

Bangladesh

tY -2.464 0.000

tNRE 0.012 0.000

tRE -0.007 0.000

tIND -0.020 0.011

tPD -6.101 0.000

tT 0.164 0.000

Sri Lanka

tY -1.009 0.000

tNRE 0.007 0.000

tRE -0.130 0.000

tIND 0.126 0.005

tPD -1.563 0.000

tT 0.026 0.000

197


Table 4.78 displays the results of FMOLS panel estimates taking EI as dependent



in economic growth, renewable energy sources, population density and technical

improvement decreases EI by about 0.557 percent, 0.096 percent, 0.51 percent and

0.003 percent respectively. However, the positive sign of non-renewable energy

sources and industrialization indicates that 1 percent increase in renewable energy

sources will lead to increase EI by about 0.008 and 0.271 percent respectively. These

results are in line with the study of Sheng et al. (2017).




itY -0.557 0.000

itNRE 0.008 0.004

itRE -0.096 0.000

itIND 0.271 0.000

itPD -0.510 0.000

itT -0.003 0.080



that there is a bi-directional panel causality running between economic growth energy

intensity and as well between population density and energy intensity. It is also found

that there is a uni-directional causality running from energy intensity to all other

198

remaining variables as renewable energy sources, non-renewable energy sources,

industrialization, and technical improvement.



HNC

N TW ,

HNC

N TZ P-Value

itY → itEI 4.491 1.991 0.046

itEI → itY 4.921 2.359 0.018

itPD → itEI 4.382 1.898 0.057

itEI → itPD 4.221 1.761 0.078

itIND → itEI 1.746 0.843 0.399

itEI → itIND 6.561 6.861 0.000

itRE → itEI 3.949 1.529 0.126

itEI → itRE 5.201 2.599 0.009

itNRE → itEI 3.200 0.889 0.374

itEI → itNRE 5.154 2.559 0.011

itT → itEI 5.396 5.405 0.000

itEI → itT 0.000 -1.339 0.181

199

CHAPTER 5

SUMMARY AND CONCLUSIONS

In this chapter, we present the summary and conclusions regarding the study. We, in

detail, summarize the stated research aims and important findings here. It provides

policy recommendations based on empirical findings. It concludes with remarks about

expectations for the future researchers.

5.1 Summary and Conclusions

The fundamental objective of this research was to explore the prospects of renewable

and non-renewable energy sources in South Asian economies by utilizing the latest time

series and panel data techniques over the period 1980-2014. In this context, the study

investigated the causal relationship between both energy sources and economic growth

and also between energies and environment. Moreover, we also calculated the demand

for energy and causality between energy intensity and energy sources.

The first chapter summarizes the plan and the purpose of the study. This chapter also

gives an introduction of the task and an overview of the rationale for the research work.

In the second chapter the relevant review of literature has been discussed which

provides the better understanding of the determinants of the energy demand. This

chapter also forms the foundation for important themes and ideas used throughout the

research process. The overview provides the key factors in South Asian countries

including economic growth, renewable and non-renewable energy sources,

industrialization, population total, population density, CO2 emissions and inflation.

Chapter four provides the methodological framework of the thesis including data and

econometric measures.

Fifth chapter provides detailed methodological issues employed in the estimation of the

empirical models. The empirical work has been done in four phases consisting of two

or three models in each phase. First three models in the study explore the relationship

between economic growth, renewable energy sources, nonrenewable energy sources,

institutions and trade openness taking real gross domestic product as a dependent

variable. Fourth and fifth models explain the impact of renewable and non-renewable

energy sources on environmental quality in the presence of environmental Kuznets

curve (EKC), energy intensity, urbanization and population. Sixth and seventh models

200

determine the demand for renewable and non-renewable energy. In these models, the

future demand for both energy sources has been also forecasted for twenty years. In the

last three models, the energy intensity has been used as dependent variable.

This thesis has applied both time series and panel unit root tests to examine the

integrating properties of the variables. To examine the co-integration between

variables, Johansen likelihood based panel co-integration approaches have been

applied. The Granger causality model is applied to examine the direction of causality

between variables in selected South Asian countries. Empirical results indicates that

all variables are integrated at I(1) confirmed by panel unit root tests and the same

inference is drawn about co-integration between economic growth, trade openness and

both energy sources ( RE, NRE). The FMOLS estimation analysis reveals a positive

relationship between economic growth (Y), renewable energy sources (RE),

nonrenewable energy sources (NRE), institutions (INS) and trade openness (To). The

causality analysis confirms the existence of unidirectional panel causality from

economic growth to nonrenewable energy sources, nonrenewable energy sources to

renewable energy sources and renewable energy sources to trade openness.

The analysis which explores the relationship between environment, economic growth,

renewable energy sources, nonrenewable energy sources and population destiny has

been estimated in models 4 and 5. Using available data we applied unit root tests to

examine the integrating properties of the variables. To examine co-integration between

variables, likelihood based panel co-integration approaches have been applied. The

Granger causality are applied to examine the direction of causality between variables

in selected South Asian countries. Empirical results indicates that all variables are

integrated at I(1) confirmed by panel unit root tests and the same inference is drawn

about co-integration between environment, economic growth, population destiny and

both energy sources ( RE, NRE). The FMOLS estimation analysis reveals a positive

relationship between CO2 emissions, economic growth (Y), and non-renewable energy

sources (NRE) and population destiny (PD). Causality results provide evidence of

feedback relationship between environment and both energy sources.

Model 6 and 7 explore the determinants of demand for renewable energy sources and

nonrenewable energy sources using data of 4 South Asian countries over the period of

1980-2014. Empirical results indicates that all variables are integrated at I(1) confirmed

by unit root tests Results of FMOLS in model 6 indicate that 1 percent increase in

201

economic growth, industrialization and population total increases renewable energy

demand by about 1.228 percent, 1.230 percent, and 1.373 percent respectively. These

results show that higher per capita real income should result in greater economic

activity which in turns accelerate the use of renewable energy. The degree of

industrialization, as a measure of economic structure, is also expected to enhance the

demand for renewable energy. However, the negative sign of energy price and technical

progress indicates that 1 percent increase in energy price and technical progress will

lead to decrease renewable energy demand by about 0.004 percent and 0.020 percent

respectively. The negative sign of technical progress show that the technology is energy

saving. Results in model 7 shows that all coefficients are statistically significant and

their signs are according to economic theory. Results indicate that 1 percent increase in

economic growth, population and industrialization increases demand for non-

renewable energy sources by about 4.889 percent, 9.760 percent and 1.514 percent

respectively. However, the negative sign of energy price and technical progress

indicates that 1 percent increase in renewable energy sources will lead to decrease

nonrenewable energy demand by about 0.008 percent and 0.299 percent respectively.

This analysis in model 8 explores the relationship between energy intensity, economic

growth, renewable energy sources, nonrenewable energy sources and population

destiny. This analysis also explores the relationship between energy intensity, economic

growth, economic growth squared, urbanization, population density and trade openness

in model 9. We also applied unit root tests to examine the integrating properties of the

variables. Empirical results indicates that all variables are integrated at I(1) confirmed

by panel unit root tests and the same inference is drawn about co-integration between

environment, economic growth, population destiny and both energy sources ( RE,

NRE). The FMOLS estimation analysis reveals a negative relationship between energy

intensity, economic growth (Y), renewable energy sources (RE) and population destiny

(PD). Causality results provide evidence of feedback relationship between energy

intensity and economic growth and population density.

According to the estimated results, the final conclusion shows the positive impacts of

renewable and non-renewable energy sources on economic growth in all South Asian

countries. It is commonly known that economic growth depends on production of goods

and services while production depends directly on input like energy whether it is

202

renewable or nonrenewable. Likewise, a country with more energy has comparatively

more economic growth as compare to energy insecure countries.

5.2 Policy Recommendations

On the basis of empirical investigations and obtained results, our recommendations

includes:

The use of energy from renewable sources should be increased while the energy from

non-renewable energy sources should be decreased particularly India and Pakistan

which are the larger consumer of the energy. For the advancement of environmental

quality, the implementation of renewable energy sources in each phase of production

process can be an important factor. It is suggested that this model can be used for long

term energy policy, formation and development of renewable energy sources in future.

South Asian economies which usually dependent on foreign sources for their energy

needs, can rely on renewable energy that reduces the inclusion of foreign energy

sources and hence can reduce their energy cost and dependence. Further, considering

the escalating population in Pakistan and India this result is very alarming and calls for

prudent policy formulation.

This study suggests that there is need to explore new and efficient energy sources to

accelerate economic growth and decrease in CO2 emissions. Renewable energy sources

are environment friendly and tend to decrease CO2 emissions. In order to keep

environment clean and pollution free south Asian economies not only at regional level

but at country level need to devise the policies which promote the use of renewable

energy sources to accelerate economic growth.

This study also suggests that all south Asian countries especially India, Pakistan and

Bangladesh which are the most populous countries in the region need to slow down the

rapid urbanization process to reduce energy intensity. Energy conservation policies

should be adopted to produce more income to improve energy efficiency. In this

context, non-renewable energy sources should be replaced with renewable energy

sources.

Results shows the positive impacts of renewable energy sources on economic growth

in all South Asian countries. To get the benefits of these energy sources on economic

growth, at country level, there is need to install, operate and maintain energy from

renewable sources and encourage energy efficiency technologies. South Asian

203

countries need to formulate those policies that encourage renewable energy

development, appropriate for local conditions of economic development, social

development and resource availability. Moreover, there is need to organize public

research and development programs for a concrete reduction of energy production cost.

These programs at central level can utilize intellectuals, research instruments,

knowledge and other resources across the country. It can also provide a big picture of

progress in technology for renewable energy recourse production cost reduction.

Each South Asian country need to target energy process which include study of sectoral

demand and supply of energy, forecasting trends of various energy sources based on

economic and technological models and strategies to diversify the energy sources. For

this purpose, south Asian countries need to publish their target and include renewable

energy sources in energy mix apart from adopting energy efficiency technologies.

For the special selected South Asian economies the findings suggested that the

Government should form a vigorous policy to diminish the oil, gas and fossil fuel

consumption for electricity production, as a replacement to depend on hydro, solar and

biomass energy sources. Subsequently, the government should promote more gas

concentrated projects to alleviate the contests of gas shortage and provide it to industrial

sector at cheap cost with easy access. These such activities like switching of non-

renewable energy resources to renewable energy resources will boost the growth level

of all countries in onside and will provide pollution free environment on other side. It

will also fulfil the required energy demand in all sectors. More research efforts should

be given to the development of the renewable technologies which will be great helpful

for improving renewable energy growth as well as economic growth in the case

Bangladesh.

Finally, at regional level renewable energy can attract investment, provide energy

security through diversification, super technological research and enhance stable

economic growth. Apart from these, increasing the cost effective penetration of

renewable energy requires Considerable Corporation among decision makers of the

energy sector. Renewable energy resource exploration, evaluation, related information

system and construction of independent renewable energy system in remote areas and

islands should be encouraged in India, Pakistan and Bangladesh.

204

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APPENDECIES

Table A1 Variable Description

Variables Variable Description Source

Y GDP per capita (constant 2005 US $) is a proxy for

Economic Growth

WDI developed by

World Bank(2014)

RE Electricity production from renewable sources

(kWh) is proxy for Renewable Energy sources

WDI developed by

World Bank(2014)

NRE Electricity production from oil sources (kWh) is

proxy for Non-Renewable Energy sources

WDI developed by

World Bank(2014)

INS Institutions (polity 2 score obtain from polity IV is

used to measure institutions) Institutions (polity 2

score obtain from

polity IV is used to

measure institutions)

IND Industry, value added (% of GDP) used to measure

industrialization. The degree of industrialization

used as a measure of economic structure.

WDI developed by

World Bank(2014)

URB Urban population (% of total) used to measure

urbanization WDI developed by

World Bank(2014)

CPI Consumer Price Index is used to measure inflation

WDI developed by

World Bank(2014)

239

FD Financial Development (Broad money to total

reserves ratio is used to measure FD) WDI developed by

World Bank(2014)

PD Population Density (People per square Km of land

area) WDI developed by

World Bank(2014)

TO Trade openness (export (US$) plus imports (US$)

divided by GDP is used to measure trade

openness)

WDI developed by

World Bank(2014)

CO2 CO2 = CO2 emissions (metric tons per capita)

used to measure environmental quality

WDI developed by

World Bank(2014)

EI Energy Intensity measured by ratio of total energy

consumption to GDP

WDI developed by

World Bank(2014)

PT Population Total WDI developed by

World Bank(2014)

P Energy Price (Price of energy consumption) since

energy prices were not available, this variable was

proxied by the consumer price index (Akinlo,

2008; Galindo, 2005; Hondroyiannis et al., 2002;

Adjaye, 2000).

WDI developed by

World Bank(2014)

T Technical progress or efficiency improvement

which is proxed by a deterministic time

trend

deterministic time

trend

240

Table B1 Summary Statistics of the variables (Panel data 1980-2014)

Variable Mean

Maximum Minimum Std. Dev.

itY

6.327 7.603 5.495 0.492

itRE

22.765 25.935 19.734 1.832

itNRE

21.931 24.313 15.425 1.768

itURB

3.200 3.634 2.698 0.245

itINS

4.007 9.000 -7.000 5.264

itEI

-19.262 -17.078 -21.622 1.251

itIND

3.234 3.500 3.006 0.106

itP

46.093 132.000 4.570 33.392

itTo

0.413 0.749 0.127 0.158

itPT

18.665 20.948 16.507 1.426

itPD

5.844 7.093 4.642 0.650

Note: tY = Economic Growth, tRE = Renewable Energy, tNRE = Non-Renewable Energy tURB =

Urbanization, tINS =, tEI = Energy Intensity, tIND = Industrialization, tP = Prices, tTo Trade

openness, = tPT = Population Total, tPD = Population

241

Table B2 Summary Statistics of the variables (Time series data 1980-2014)

Variables Mean

Maximum Minimum Std. Dev.

India

tY

6.274 7.082 5.676 0.427

tRE

25.109 25.935 24.564 0.396

tNRE

23.330 23.952 22.667 0.422

tURB

3.298 3.466 3.140 0.094

tINS

8.559 9.000 8.000 0.504

tEI

-20.974 -20.378 -21.623 0.389

tIND

3.287 3.500 3.190 0.070

tP

48.685 132.000 10.100 33.119

tTo

0.269 0.551 0.127 0.147

tPT

20.688 20.948 20.365 0.177

tPD

5.783 6.043 5.460 0.177

Pakistan

tY

6.359 6.672 5.982 0.196

tRE

23.706 24.196 22.888 0.378

tNRE

23.115 24.313 18.928 1.268

tURB

3.477 3.634 3.335 0.087

tINS

0.941 8.000 -7.000 6.438

tEI

-18.969 -18.421 -19.528 0.303

tIND

3.150 3.300 3.006 0.072

242

tP

42.962 132.000 9.110 34.080

tTo

0.336 0.449 0.282 0.034

tPT

18.665 19.020 18.197 0.248

tPD

5.110 5.465 4.642 0.248

Bangladesh

tY

5.826 6.432 5.495 0.292

tRE

20.388 20.716 19.734 0.233

tNRE

20.689 21.466 18.799 0.574

tURB

3.109 3.489 2.698 0.221

tINS

1.412 6.000 -7.000 5.663

tEI

-19.503 -19.089 -19.925 0.253

tIND

3.177 3.319 3.027 0.099

tP

52.438 126.000 14.300 30.698

tTo

0.434 0.555 0.313 0.069

tPT

18.602 18.869 18.228 0.197

tPD

6.825 7.093 6.452 0.197

Sri Lanka

tY

6.849 7.603 6.298 0.375

tRE

21.859 22.470 20.920 0.368

tNRE

20.590 22.532 15.425 1.942

tURB

2.917 2.933 2.907 0.007

tINS

5.118 6.000 4.000 0.591

243

tEI

-17.603 -17.078 -18.248 0.307

tIND

3.324 3.480 3.242 0.064

tP

40.286 123.000 4.570 35.602

tTo

0.612 0.749 0.495 0.081

tPT

16.707 16.854 16.507 0.104

tPD

5.660 5.807 5.460 0.104

Note: tY = Economic Growth, tRE = Renewable Energy, tNRE = Non-Renewable Energy tURB =

Urbanization, tINS =, tEI = Energy Intensity, tIND = Industrialization, tP = Prices, tTo Trade

openness, = tPT = Population Total, tPD = Population

244

Table C1 Variance Decomposition of RE Demand

Variance Decomposition of itRE 20 years Ahead

Period S.E. itRE itY

itPT itIND

itP itT

2015 0.197 100.000 0.000 0.000 0.000 0.000 0.000

2016 0.266 99.432 0.056 0.012 0.481 0.004 0.000

2017 0.315 98.594 0.175 0.031 1.133 0.019 0.000

2018 0.351 97.732 0.351 0.053 1.720 0.052 0.000

2019 0.381 96.929 0.585 0.075 2.156 0.113 0.000

2020 0.406 96.188 0.878 0.097 2.431 0.209 0.000

2021 0.426 95.478 1.236 0.118 2.563 0.354 0.000

2022 0.443 94.752 1.668 0.138 2.585 0.559 0.000

2023 0.458 93.957 2.182 0.158 2.528 0.840 0.000

2024 0.471 93.038 2.785 0.178 2.427 1.213 0.000

2025 0.482 91.938 3.487 0.198 2.317 1.700 0.000

2026 0.493 90.602 4.293 0.217 2.232 2.320 0.000

2027 0.502 88.974 5.208 0.236 2.208 3.097 0.000

2028 0.511 87.004 6.230 0.255 2.280 4.052 0.000

2029 0.521 84.650 7.354 0.273 2.486 5.207 0.000

2030 0.531 81.884 8.564 0.290 2.858 6.576 0.000

2031 0.543 78.697 9.840 0.306 3.426 8.168 0.000

2032 0.556 75.108 11.147 0.319 4.209 9.980 0.000

2033 0.571 71.162 12.449 0.330 5.217 11.996 0.000

2034 0.589 66.935 13.698 0.338 6.444 14.187 0.000

245

Table C2: Impulse Response Function of RE Demand

Response of itRE 20 years ahead

Period itRE itY

itPT itP

itT itIND

2015 0.197 0.000 0.000 0.000 0.000 0.000

2016 0.179 0.006 0.003 -0.002 0.000 -0.019

2017 0.164 0.012 0.005 -0.004 0.000 -0.029

2018 0.152 0.016 0.006 -0.007 0.000 -0.033

2019 0.142 0.020 0.007 -0.010 0.000 -0.033

2020 0.132 0.024 0.007 -0.013 0.000 -0.031

2021 0.123 0.028 0.007 -0.017 0.000 -0.028

2022 0.114 0.032 0.008 -0.021 0.000 -0.024

2023 0.105 0.036 0.008 -0.026 0.000 -0.019

2024 0.096 0.040 0.008 -0.030 0.000 -0.013

2025 0.086 0.044 0.008 -0.036 0.000 -0.007

2026 0.077 0.048 0.008 -0.041 0.000 -0.001

2027 0.067 0.052 0.008 -0.047 0.000 0.006

2028 0.057 0.056 0.008 -0.053 0.000 0.013

2029 0.047 0.061 0.009 -0.059 0.000 0.020

2030 0.036 0.065 0.009 -0.067 0.000 0.027

2031 0.025 0.069 0.009 -0.074 0.000 0.035

2032 0.014 0.074 0.009 -0.082 0.000 0.043

2033 0.003 0.078 0.010 -0.091 0.000 0.051

2034 -0.009 0.083 0.010 -0.100 0.000 0.059

246

Table C3 Variance Decomposition of NRE Demand

Variance Decomposition of itNRE 20 years ahead

Period S.E. itNRE itY

itPT itIND

itP itT

2015 0.671 100.000 0.000 0.000 0.000 0.000 0.000

2016 0.838 99.377 0.005 0.017 0.583 0.017 0.000

2017 0.924 98.273 0.018 0.049 1.600 0.061 0.000

2018 0.975 96.986 0.040 0.088 2.753 0.133 0.000

2019 1.008 95.722 0.071 0.128 3.840 0.238 0.000

2020 1.030 94.592 0.113 0.167 4.750 0.379 0.000

2021 1.045 93.634 0.164 0.201 5.442 0.559 0.000

2022 1.056 92.842 0.225 0.231 5.919 0.783 0.000

2023 1.063 92.182 0.295 0.257 6.208 1.058 0.000

2024 1.069 91.609 0.375 0.277 6.348 1.391 0.000

2025 1.074 91.072 0.463 0.294 6.384 1.788 0.000

2026 1.078 90.520 0.559 0.306 6.357 2.257 0.000

2027 1.082 89.907 0.660 0.316 6.309 2.809 0.000

2028 1.087 89.187 0.766 0.322 6.274 3.451 0.000

2029 1.092 88.322 0.876 0.325 6.283 4.194 0.000

2030 1.099 87.279 0.986 0.326 6.363 5.046 0.000

2031 1.107 86.029 1.095 0.325 6.535 6.016 0.000

2032 1.116 84.550 1.201 0.322 6.813 7.115 0.000

2033 1.128 82.826 1.301 0.318 7.207 8.348 0.000

2034 1.142 80.848 1.393 0.311 7.723 9.724 0.000

247

Table C4 Impulse Response Function of NRE Demand

Response of itNRE 20 years ahead

Period itNRE itY

itPT itIND

itP itT

2015 0.671 0.000 0.000 0.000 0.000 0.000

2016 0.498 0.006 0.011 -0.064 -0.011 0.000

2017 0.376 0.011 0.017 -0.098 -0.020 0.000

2018 0.288 0.015 0.020 -0.112 -0.027 0.000

2019 0.223 0.019 0.022 -0.113 -0.034 0.000

2020 0.175 0.022 0.022 -0.107 -0.040 0.000

2021 0.139 0.024 0.021 -0.095 -0.046 0.000

2022 0.111 0.027 0.019 -0.081 -0.051 0.000

2023 0.088 0.029 0.018 -0.065 -0.057 0.000

2024 0.070 0.031 0.016 -0.049 -0.063 0.000

2025 0.054 0.032 0.015 -0.032 -0.069 0.000

2026 0.042 0.034 0.013 -0.016 -0.075 0.000

2027 0.031 0.035 0.012 -0.001 -0.082 0.000

2028 0.021 0.036 0.010 0.015 -0.089 0.000

2029 0.012 0.037 0.009 0.029 -0.096 0.000

2030 0.005 0.038 0.008 0.043 -0.104 0.000

2031 -0.002 0.039 0.007 0.057 -0.113 0.000

2032 -0.008 0.039 0.006 0.070 -0.122 0.000

2033 -0.013 0.040 0.005 0.082 -0.133 0.000

2034 -0.018 0.040 0.004 0.095 -0.143 0.000