crude oil prices and renewable energy driving force in

CRUDE OIL PRICE AND RENEWABLE ENERGY

DRIVING FORCE IN EMERGING ECONOMIES

BY

CHAN JUN HONG

CHANG MEI CHEE

CHONG CAI XIN

LIM WEI JIE

TOH JIA NI

A research project submitted in partial fulfilment of the

requirement for the degree of

BACHELOR OF FINANCE (HONS)

UNIVERSITI TUNKU ABDUL RAHMAN

FACULTY OF BUSINESS AND FINANCE

DEPARTMENT OF FINANCE

AUGUST 2018

Crude Oil Price and Renewable Energy Driving Force in Emerging Economies

ii

Copyright @ 2018

ALL RIGHTS RESERVED. No part of this paper may be reproduced, stored in a

retrieval system, or transmitted in any form or by any means, graphic, electronic,

mechanical, photocopying, recording, scanning, or otherwise, without the prior

consent of the authors.


iii

DECLARATION

We hereby declare that:

(1) This undergraduate research project is the end result of our own work and

that due acknowledgement has been given in the references to ALL

sources of information be they printed, electronic or personal.

(2) No portion of this paper research project has been submitted in support of

any application for any other degree of qualification of this or any other

university, or other institutes of learning.

(3) Equal contribution has been made by each group member in completing

the research project.

(4) The word count of this research report is 10456 words.

Name of Student: Student ID: Signature:

1. CHAN JUN HONG 15ABB07696 _________________

2. CHANG MEI CHEE 15ABB07342 _________________

3. CHONG CAI XIN 15ABB07698 _________________

4. LIM WEI JIE 15ABB06971 _________________

5. TOH JIA NI 15ABB06943 _________________

DATE: 15 August 2018


iv

ACKNOWLEDGEMENT

Our study has been successfully completed with the assistance from various

authorities. We would like to express our special thanks of gratitude to everyone

who have helped us along the completing of our study.

First of all, we would like to thank Universiti Tunku Abdul Rahman (UTAR) for

giving us this golden opportunity to conduct this research project as partial

fulfilment of the requirement for the degree of Bachelor of Finance (HONS). This

has provided us an opportunity to learn on how to conduct a study and we have

gained a lot of knowledge and experience which could not be learnt from the

books.

Aside from that, we would like to express our deepest gratitude to our respective

supervisor, Mr. Lim Chong Heng for his continuous support and useful advice

throughout this research. His enthusiastic guidance and supervision ensured the

research is on its right path and being carried out smoothly.

Last but not least, we would like to extend our appreciation to our family

members and friends who had given us support and help when we were in need

for assistance. Not to forget, we would also like to thank our group members for

sacrificing their valuable time and their hard work in order to complete this study.

We have learnt, shared and experienced various memorable moments together

through the precious voyage of the completion of undergraduate research project.


v

TABLE OF CONTENTS

Page

Copyright Page……….……………………………………………………………ii

Declaration………………………………………………………………………..iii

Acknowledgement……………………………………………………………...…iv

Table of Contents………………………………………………………………….v

List of Tables……………………………………………………………………viii

List of Figures…………………………………………………………………….ix

List of Appendices………………………………………………………………...x

List of Abbreviations……………………………………………………………...xi

Preface…………………………………………………………………………....xii

Abstract………………………………………………………………………….xiii

CHAPTER 1 INTRODUCTION…………………………………………………1

1.0 Background of Research………………..…………………………1

1.1 Problem Statement………………………………………………...4

1.2 Research Objective………………………………………………...8

1.2.1 General Objective………………………………………….8

1.2.2 Specific Objectives………………………………………...9

1.3 Research Questions………………………………………………..9

1.4 Significance of Study……………………………………………...9

CHAPTER 2: LITERATURE REVIEW…………………………………….......12

2.0 Introduction………………………………………………………12


vi

2.1 Review of the Literature………………………………………….12

2.1.1 Crude Oil Price……………………………………….......12

2.1.2 Gross Domestic Product (GDP)………………………….14

2.1.3 Carbon Dioxide Emissions……………………………….15

2.1.4 Population Growth……………………………………….17

2.2 Review of the Relevant Theories………………………………...18

2.2.1 Environmental Kuznets Curve (EKC) Hypothesis……….18

2.3 Proposed Theoretical Framework………………………………..19

CHAPTER 3: METHODOLOGY……………………………………………….21

3.0 Introduction………………………………………………………21

3.1 Research Design………………………………………………….21

3.2 Data Sources……………………………………………………...22

3.2.1 Definition of Variables……………………………...........22

3.2.2 Empirical Model………………………………………….24

3.3 Data Analysis…………………………………………………….25

3.3.1 Pooled Ordinary Least Squares (POLS)………………….25

3.3.2 Fixed Effect Model (FEM)……………………………….25

3.3.3 Random Effect Model (REM)……………………………26

3.4 Diagnostic Test…………………………………………………...26

3.4.1 Multicollinearity………………………………………….26

3.4.2 Autocorrelation…………………………………………...28

3.4.3 Hausman Specification Test……………………………...28

3.4.4 Likelihood Ratio Test………………...….…………..…...29

3.4.5 Poolability F-Test………………………………………...29

3.5 Inferential Analysis………………………………………………30

3.5.1 T-test……………………………………………………...30


vii

3.5.2 F-test……………………………………………………...31

3.6 Conclusion………………………………………………………..32

CHAPTER 4: DATA ANALYSIS………………………………………………33

4.0 Introduction………………………………………………………33

4.1 Panel Data Analysis (BIO)……………………………………….34

4.1.1 Comparison Test (BIO)…………………………………..36

4.2 Panel Data Analysis (HYD)……………………………………...38

4.2.1 Comparison Test (HYD)…………………………………41

4.3 Diagnostic Checking……………………………………………..43

4.3.1 Autocorrelation…………………………………………...43

4.3.2 Multicollinearity………………………………………….44

4.4 Discussion on Major Findings………………………………........46

4.5 Conclusion………………………………………………………..46

CHAPTER 5: SUMMARY, IMPLICATION AND CONCLUSION…………...48

5.0 Summary on Implications………………………………………..48

5.1 Limitations……………………………………………………….48

5.2 Recommendations………………………………………………..49

5.3 Conclusion………………………………………………………..49

References………………………………………………………………………..50

Appendices……………………………………………………………………….56


viii

LIST OF TABLES

Page

Table 4.1: Model Comparison for POLS, FEM and REM for Bioenergy 34

Table 4.2: Model Comparison for Likelihood Ratio, Poolability F-test 38

and Hausman Test (Bioenergy)

Table 4.3: Model Comparison for POLS, FEM and REM for 38

Hydropower

Table 4.4: Model Comparison for Likelihood Ratio, Poolability F-test 42

and Hausman Test (Hydropower)

Table 4.5: Bioenergy Correlation Matrix 44

Table 4.6: Hydropower Correlation Matrix 45


ix

LIST OF FIGURES

Page

Figure 1.1: Global average annual net capacity additions by type 2

Figure 1.2: Clean energy asset finance in emerging markets. 2010-2016 3

Figure 1.3: Share of primary energy and growing oil demand in emerging 5

economies

Figure 1.4: Crude oil price ($ per barrel) as of May 2018 6

Figure 1.5: Global levelised cost of electricity from utility-scale renewable 7

power generation technologies, 2010-2017

Figure 1.6: Average key crude oil prices in USD/barrel 10

Figure 1.7: IEA total public energy research, development and 10

demonstration budget by technology

Figure 2.1: Environmental Kuznets Curve 18

Figure 2.2: Proposed theoretical framework 20

Figure 3.1: Typical hydroelectric dam 21


x

LIST OF APPENDICES

Page

Appendix 1: Bioenergy 56

Appendix 2: Hydropower 64


xi

LIST OF ABBREVIATIONS

CO2 Carbon dioxide emissions

OIL Crude oil price

EKC Environmental Kuznets Curve

EU European Union

FEM Fixed Effect Model

GDP Gross Domestic Product

kWh Kilowatt-hour

Mb/d Millions of barrels per day

MENA Middle East and North Africa

OECD Organization for Economic Cooperation and Development

POLS Pooled Ordinary Least Square

POP Population growth

REM Random Effect Model

IRENA International Renewable Energy Agency

UNFCCC United Nations Framework Convention on Climate Change

TOL Tolerance

USBR United States Bureau of Reclamation

VIF Variance inflation factors


xii

PREFACE

This research project is submitted in partial fulfilment of the requirement for the

degree of Bachelor of Finance (HONS) at University Tunku Abdul Rahman

(UTAR). This research paper is conducted under the supervision of Mr. Lim

Chong Heng. This study provides a detailed explanation of our topic completed

towards accomplishing our project goals.

The title for this report is “Crude Oil Price and Renewable Energy Driving Force

in Emerging Economies”. The variables included are renewable energy which

mainly focuses in bioenergy and hydropower, crude oil price, carbon dioxide

emissions, Gross Domestic Product and population growth.

The objective of this study is to investigate the relationship among the variables

and further examine the effect of crude oil price toward the renewable energy. The

study focuses in the emerging economiesformed by Brazil, Chile, China,

Colombia, Czech, Greece, Hungary, India, Indonesia, Malaysia, Peru, Poland,

Russia, Thailand, Turkey and South Africa over the period of 2000 to 2015.


xiii

ABSTRACT

Renewable energy plays a crucial role in today’s world; it can be regenerated

unlimitedly through natural processes, over a period of time without depleting the

Earth’s resources. However, there are different opinions toward the importance of

renewable energy. This study mainly investigates the relationship between

renewable energy (bioenergy and hydropower) and crude oil price, carbon dioxide

emissions, Gross Domestic Product (GDP) and population growth.

The general results of this study found that crude oil price provides a positive

reaction toward both bioenergy and hydropower regardless of the type of model

tested. It can indicate that when the crude oil price increases, the generation of

renewable energy increases as well. Thus, the consumers will be more preferred to

replace crude oil with renewable energy. Next, carbon dioxide emission has a

negative relationship with renewable energy whereas for GDP and population

growth, they are slightly insignificant towards the generation of renewable energy.

Furthermore, this study also intends to give a better understanding on whether the

pushing force of renewable energy generation is due to cost savings reason or to

achieve the goal of environmental protection.


Undergraduate Research Project Page 1 of 71 Faculty of Business and Finance

CHAPTER 1: INTRODUCTION

1.0 Background of Research

Renewable energy is defined as the energy generated from natural processes that

continuously restore, over a period of time without depleting the Earth’s resources.

The major types of renewable energy resources such as sunlight, wind, rain,

biomass, geothermal, tides and waves are abundant and can be used to produce

electricity with fewer, if any, environmental damage as compared to conventional

energy technologies. The adoption of renewable energy systems helps to reduce

the emissions of carbon dioxide which leads to global warming and climate

change. It is therefore important to boost renewable energy innovation up and

create a sustainable energy ecosystem now and in the future.

Over the last decade, renewable energy driven electricity generation has now

become a fast-growing and opportunity-rich market worldwide. These fast-

growing emerging economies are overtaking the traditional centres of demand.

Renewable energy is expected to capture two-thirds of global investment in power

plants to 2040 as they become the least-cost source of new generation for many

countries.

Based on the Figure 1.1, renewable energy (renewables) grew strongly from 2010

to 2016 and it is predicted to rise more to 2040. It makes up around a quarter of

global energy demand growth, beating other energy sources which include coal,

gas and nuclear. It shows that the entire world is on the cusp of transition to clean

energy technology, at a large scale, to meet humankind’s changing energy

requirement.



Figure 1.1: Global average annual net capacity additions by type

Source: World Energy Outlook 2017, International Energy Agency

About two-thirds of global greenhouse gas emissions can be attributed from the

generation of energy from fossil fuels. Climate change is recognised as the most

serious and threatening global environmental problem in this modern era. It is in

urgent need to reduce these pollutant emissions and ensure the availability of

sufficient energy to satisfy energy demand and economic growth. By 2050,

renewable energy could supply four-fifth of the world’s electricity, massively

reducing carbon emissions and helping to mitigate climate change issue. The

urgency to take action on de-carbonization is obvious, as the temperature is

increasing steadily at 0.03oC each year, nearly 1

oC of global warming over the

past 25 years. The world will face the effects of a 2oC increase in temperature in

the next 30 to 40 years, if no corrective actions taken.

In December 2015, the representatives from 195 countries met at the 21st

Conference of the Parties of the UNFCCC in Paris adopted the Paris Agreement.

The Paris Agreement is to deal with greenhouse gas emissions, adaptation and the

finance starting in the year 2020. There are few highlights under the climate

agreement including the control of global temperature within maximum 2 degree

Celsius to mitigate climate risk, forest preservation to reduce carbon dioxide



emission from deforestation and developed country parties bear the cost. The Paris

Agreement also provides a more transparent framework for enhanced

transparency of action. Each party shall communicate a nationally determined

contribution every five years to minimize the loss in accordance with the adverse

effect of climate change.

Figure 1.2: Clean energy asset finance in emerging markets, 2010-2016

Source: Bloomberg New Energy Finance, 2017

In order to meet the climate goals of the Paris Agreement, more investments in

renewable energy is required. Based on the Figure 1.2, developing countries have

a larger proportion as compared to rest of the world in renewable energy

investment in wind, solar, geothermal, biomass and hydro projects since 2010.

Among them, China marks the lion’s share of renewable energy investment and

has attracted 63% of all such capital over the last decade. Nevertheless, Brazil,

India, Turkey, Mexico and South-Africa complete the top six emerging markets

nations in generating significant renewable energy.

According to Duguid (n.d.), moving toward renewable culture is an indispensable

part for the growth of society. His study shows collective moral principle is

always encountered by the media. Renewable market in United Kingdom (U.K.)

moving across to renewable technologies has been clearly supported by the



economics over recent years. There is a large number of users of renewable

energy have driven environmental decision allied with a goal to realize energy

independence. However, most of the uptakes have been influenced by economic

decision with government support to generate abnormal financial return. His study

also indicates what will happen if the economic stimulus disappears. As

mentioned, the upfront capital cost of moving from crude oil to renewables is still

prohibitive for numerous people. It is therefore required to develop a new system

for future generation as the crude oil is a limited resource. In order to achieve a

harmony environment, a close relationship between finance and fundamentals is

very important. However, his concept to move on and become part of the

consumers is considered as a long term energy viability of their property.

1.1 Problem Statement

The global energy landscape is changing rapidly in this modern world. Renewable

energy plays a vital role nowadays, emerged as possible alternatives to replace

traditional fuels. In order to reduce the dependence upon fossil fuels, more

government and organisation actively participate and make contribution to the

development of renewable energy sector recently.

The renewable energy is the fastest growing energy source with its share in

primary energy rising by 7.1% p.a. to 10% in 2035 (see Figure 1.3). Although

renewables continue to grow in the transition of energy mix, gas, oil and fossil

fuels such as coal still remain as the main energy sources. As stated in the 2017

report by International Energy Agency on key world energy statistics, oil is the

main fuel in the world economy, accounting for 41% of the total final

consumption by fuel. Even oil stands alone; its high share indicates the

importance of oil in energy demand of the world. It shows that although

renewable energy is growing fast but, oil still dominates.



Figure 1.3: Share of primary energy and growing oil demand in emerging

economies

Source: BP Energy Outlook 2017

Global liquids demand is expected to reach 110 Mb/d by 2035. All of this growth

in demand is derived from emerging economies, as rising prosperity leads to

increased oil demand. It can be seen from Figure 1.3 that China alone accounts for

half of the growth.We can indicate that emerging economies are having great

influence and control on the world development of energy in the future. The

researchers of many empirical studies have confirmed that demand and supply

curve of renewable energy are influenced largely by the oil price changes.

The global liquid fuels consumption is rising at a significant rate. The Energy

Information Administration (EIA) estimates global oil consumption-weighted

gross domestic product (GDP) growth for 2018 will be at its highest rate. The oil

consumption could increase above forecasted levels with a greater GDP growth. It

could then put upward pressure on crude oil prices. At the same time, the market

movement in equities, bonds and other commodities, which are in correlation with

the movement in crude oil price, would be driven systematically.

Volatility in oil price is one of the main driving forces to foster the adoption of

renewables technology, a way to lower the dependence on oil products in a

2035



country. Based on Figure 1.4, we can see that the oil price started to boom from

2006 and reached its peak in 2008. We can conclude that the fluctuation of oil

price is quite significant even excluding the financial crisis period (2008-2009).

However, in 2014, the price of crude oil has fallen significantly and the oil

industry is in a downturn. We can say that the oil industry is full of boom and bust.

Why did the oil price drop in 2014? According to Greg DePersio (2018), one of

the main reasons is the rapid growth of economies like China, the country with

world’s largest population, created an unquenchable thirst for oil. A slowdown in

its economy growth after 2010 affected the oil demand significantly and thus

drove down the oil price. Similar situations also faced by other emerging

economies such as Brazil, India and Russia, a fast growing during the first decade

and slow down after 2010. From this, we can comment that emerging economies

play a vital role in the oil industry and the economy of these countries has much

influence on the crude oil demand and its price globally.

Figure 1.4: Crude oil price ($ per barrel) as of May 2018

Source: U.S. Department of Energy, Energy Information Administration

Over the past few years, global oil prices have fallen sharply, indicating one of the

most unignorable dropping in crude oil price in recent history. Many researchers

have claimed that the main cause for the decline in oil price is the domestic oil



boom in the Iraq and United States. Muhammad et al. (2017) found that the long

term low crude oil prices may possibly threaten renewable energy. The sharp

dropping in the crude oil price could hurt the short-term outlook for certain

specific clean energy technologies like electric vehicles and bio-fuel which are

more competitive to oil-based transportation.

Figure 1.5: Global levelised cost of electricity from utility-scale renewable power

generation technologies, 2010-2017

Source: IRENA Renewable Cost Database

The renewable energy sources cost lower and will consistently cheaper than

traditional energy systems just in the next few years. As renewable energy

becomes less expensive, consumers will gain benefit from these investment in

green infrastructure.

According to Climatescope 2017, the renewable energy investments in developing

countries declined in 2016, due to its decreasing costs, unclear environmental

policy and market risk. However, the growth in renewable energy capacity has

been boost up. On the other hand, the International Renewable Energy Agency



(IRENA) report marked that the renewable energy cost will fall within the cost

range of fossil fuels by 2020. It signals a real paradigm shift, revolution in the

competitiveness of different power generation options is occurring now. Solar and

wind are becoming the victims of their own success, being much cheaper, beating

out the conventional fossil fuel source. The prices for solar photovoltaic and

onshore wind projects could be as low as $0.03 per kilowatt-hour (kWh) or even

less in the next two years.

The average costs of producing renewable energy projects have been competitive

with fossil fuels, based on the projects that have been auctioned and will be in

development in the future. Figure 1.5 shows that the average levelised cost of

electricity cost of electricity (LCOE) for utility-scale solar PV dropped to

$0.10/kWh in 2017. This decline in costs has been remarkable, marked about 73%

since 2010. Among them, hydropower was the cheapest at five cents per kilowatt-

hour. In the same timeframe, onshore wind has fallen by 25%, to six cents. Both

bioenergy and geothermal energy was at $0.07/kWh. In countries like Brazil,

Canada, Chine, Dubai, Germany and Mexico, auction prices for solar photovoltaic

and onshore wind projects have reached as low as $0.03/kWh in 2017.

1.2 Research Objective

1.2.1 General Objective

The objective of this research is to study the development of bioenergy and

hydropower in emerging economies in order to examine the relationship

between renewable energy (bioenergy and hydropower) with crude oil

price, gross domestic product, carbon dioxide emissions and population

growth as control variables.



1.2.2 Specific Objectives

1. To investigate the relationship between crude oil price and

bioenergy generation.

2. To investigate the relationship between crude oil price and

hydropower generation.

1.3 Research Questions

There are two research questions in this research:

1. Is there a relationship between crude oil price and bioenergy generation?

2. Is there a relationship between crude oil price and hydropower generation?

1.4 Significance of Study

The main objective of this study is to investigate and provide a better

understanding on the relationship between the renewable energy, namely

bioenergy and hydropower, and crude oil price, gross domestic product, carbon

dioxide emissions and population growth. However, this study will mainly focus

on what effect will crude oil price brings towards the renewable energy. The

significance of renewable energy is undoubted as compared to non-renewable

energy such as fossil fuel which those resources will eventually exhausted and

unable to recover.



Figure 1.6: Average key crude oil prices in USD/barrel


Figure 1.7: IEA total public energy research, development and demonstration

budget by technology




Based on Figure 1.6 and Figure 1.7, the rise of crude oil price in 2010 resulted an

increment of the budget alloacted to renewable energy sources. However, it

showed an inverse relationship in the year of 2015. Hence, this study also tends to

investigate the different direction of relationship between renewable energy and

the crude oil price.

Besides, this study also gives a brief understanding on the relationship between

renewable energy and the remaining independent variables: gross domestic

product, carbon dioxide emissions and population growth. In short, this study will

provide necessary information and platform for researchers to have further

understanding on crude oil price and other renewable energy driving force in

emerging economies.



CHAPTER 2: LITREATURE REVIEW

2.0 Introduction

In this chapter, this study discussed the literature review on the relationship

between dependent variable (renewable energy) and independent variables,

namely crude oil price, gross domestic product, carbon dioxide emissions and

population growth.

This chapter will present the critical reviews on the past researchers’ findings on

these variables and the relevant theoretical frameworks on the renewable energy

will be discussed.

2.1 Review of the Literature

2.1.1 Crude Oil Price

Crude oil price indicates the price of substitutes. In the standard demand

and supply theory, the substitute product price has influence on the

demand or supply and hence the price of a commodity. An increase in

crude oil price will reduce the demand for crude oil and fossil fuel

generation and possibly increase the demand for renewable energy.

The recent plunge in oil prices has discouraged investment in oil and gas

exploration which could increase the development of renewable energy

sector. Reboredo (2015) concluded that high crude oil price promote the



development of renewable energy as the economic viability of renewable

energy is improved.

The study on the relationship between oil price and renewable energy

consumption was done by Sadorsky (2009a). The studies found out that oil

prices have negative but less significant impact on renewable consumption

as compared to GDP per capita and carbon dioxide. It concluded that there

is no effect of substitutability for G7 countries between 1980 and 2005.

The period of the study was covering years when oil price was falling

down steeply. The view is supported by Omri and Nguyen (2014). Their

study was focused on a global panel consisting of 64 countries over the

period 1990 to 2011 and divided into three subpanels according to income

level (high, middle and low income). The results indicated that oil prices

have negative impact on renewable energy consumption in middle-income

and global panels. The authors explained that in those countries, the

renewable energy does not substitutes, but only complements crude oil in

consumption.

According to Marques, Fuinhas and Manso (2010), there is a negative and

significant relationship between oil price and renewable energy in a model

which only included 24 European Union (EU) countries. In the absence of

environmental restrictions, the coal, other fossils and nuclear power are

used to be supplementary sources to oil, instead of renewable, among the

EU member countries.

Besides, analysis on drivers of renewable consumption using was extended

by Apergis & Payne (2014). The study was carried out for seven Central

American countries from 1980 to 2010 using non-linear panel smooth

transition vector error transition modelling. The results showed that the

variable of renewable energy consumption per capita was statistically

significant and positive coefficient in estimating the price of oil and

concluded that renewable is a potential substitute to oil and coal.



2.1.2 Gross Domestic Product (GDP)

Another significant driving force in the deployment of renewable is the

gross domestic product (GDP) or the GDP per capita. It is used commonly

to measure the income or wealth of a country. A wealthier country will

have more resources and potential to develop renewable energy

technologies and foster its growth.

In the long run, real GDP per capita has positive and significant effect on

renewable energy consumption (Sardorsky, 2009a; Sadorsky, 2009b). It

means that higher economic growth would need more renewable energy as

a share of the total energy consumption. The studies were further

corroborated by Chang et al. (2009), who investigated the impact of

energy prices on renewable development in OECD member countries

under different economic growth rate regimes. It concluded that countries

with high economic growth rates are more responsive to energy price

changes in their renewable energy use and vice versa.

Rafiq, Bloch & Salim (2014) carried out a comparative analysis on

determinants of renewable energy deployment in China and India. The

study implied that there is a unidirectional short-run causality from

renewable energy generation to output in India and bidirectional causality

between the variables in the long run. The scenarios were different for

China. The results revealed unidirectional causality from output to

renewable energy in both short-run and long-run.

The study by Gan & Smith (2011), attempted to identify the key factors

that may have driven the renewable energy in general and bioenergy in

particular among OECD countries from 1994 to 2003. From the results, it

revealed that GDP has statistically significant and positive impact on

renewable energy including bioenergy. It claimed that countries with

higher income level are generally more concerned on alternative energy

supply and environmental issues. They tend to emphasize more on the



development of renewable energy and bioenergy sectors as compared to

those with lower GDP.

Silva, Cerqueira & Ogbe (2018) and Bellakhal, Kheder & Haffoudhi

(2016), their study suggested that the GDP per capita has positive results

on renewable energy in Sub-Saharan Africa and MENA region

respectively. From the studies carried out for six major emerging countries

that are proactively accelerating the adoption of renewable energy, income

alone is the main determinant of renewable energy in both Philippines and

Turkey (Rafiq & Alam, 2010; Salim & Rafiq, 2012). Marques et al. (2010)

revealed that GDP has positive and statistically significant impact on

renewable energy for all European Union (EU) members but, negative

results for non-EU members.

In the contrast, Omri and Nguyen (2014) implied that economic growth is

not an important determinant of renewable energy consumption in the

countries under low income and global panels. However, for high and

middle income countries panels, the GDP per capita affect the renewable

significantly.

2.1.3 Carbon Dioxide Emissions

The phenomenon of climate change and global warming are closely related

to the emissions of carbon dioxide, methane, chlorofluorocarbons, and

nitric acid and ozone greenhouse gases. Carbon dioxide (CO2) has the

highest share and this variable is commonly used in previous literature.

Previous studies suggested that CO2 emission has positive and statistically

significant effect on renewable energy (Sadorsky, 2009a, Omri & Nguyen,

2014). Salim and Rafiq (2012) employ an autoregressive distribution lag

(ARDL) model along with fully modified least square and dynamic

ordinary least square models for six emerging countries (Brazil, China,



India, Indonesia, Philippines and Turkey). They implied that renewable

energy consumption is significantly determined by pollutant emission

besides income in these emerging countries in the long run. This is

supported by Rafiq & Alam (2010) who also analysed the drivers of

renewable energy in these six emerging economies covering the period

1980 to 2006.

However, Sisodia & Soares (2015) suggested that CO2 emissions affect

investment in both solar and wind energy sectors statistically significant

and negative. Marques, et al. (2010) found that there is a negative and

statistically relationship between CO2 emissions and renewable energy

across all countries including EU members and Non-EU members. This

result was consistent with the study by Bellakhal et al. (2016) among

countries in MENA region. It means that carbon dioxide emission did not

promote the deployment of renewable energy (Aguirre & Ibikunle, 2014;

Silva et al., 2018).

Another study by Rafiq et al. (2014), using a multivariate vector error

correction model to analyse dynamic relationship between output,

pollutant emission and renewable energy generation of China and India

over the period 1972-2011. The results for China and India revealed

unidirectional causality from carbon emission to renewable energy

generation in the short run whereas the variables have bidirectional

causality in the long run.

From the study by Gan & Smith (2011), CO2 emissions are statistically

insignificant but positive in terms of their influence on renewable energy

and bioenergy supply. However, this does not necessarily mean that CO2

emission is not a potential driving force to develop renewable energy. It

could be explained as the magnitude of the variable is not big enough to

significantly impact energy supply among OECD countries based on

limited historical data over the period 1994 to 2003.



2.1.4 Population Growth

Population and population growth are common indicators of a nation’s

energy demand. It is advisable that countries with more rapid growth

would tend to build power capacity to fulfil the growing demand for

electricity (Carley, 2009). Hence, renewable energy technology becomes

the viable path in order to satisfy the energy demand.

A study by Ihtisham et al. (2014), to examine the relationship between

macroeconomic factors and renewable energy in Pakistan from 1975 to

2012. From the analysis, there was a significant negative relationship

between population growth and renewable energy but, this has been

disappeared in the long-run. Overall, it indicated that macroeconomic

factors including population have positive impact on renewable energy

consumption in Pakistan. Moreover, Bellakhal, et al. (2016) also suggested

that population growth has a statistically positive impact on the share of

renewable energy in total energy production.

In contrast, according to Aguirre & Ibikunle (2014), there was a negative

relationship between energy consumption and renewable energy. The

study suggested that those countries with increasing energy needs are more

pressured to ensure sufficient energy supply. Therefore, in order to cover

the high energy demand, they tend to consume more fossil fuels and other

cheap alternative source instead of renewable.



2.2 Review of the Relevant Theories

2.2.1 Environmental Kuznets Curve (EKC) Hypothesis

Figure 2.1: Environment Kuznets Curve

The EKC hypothesis is used in the field of environmental economics to

examine the relationship between economic growth and the environment.

The concept of sustainable development has been a hot issue nowadays

thus, it is important to understand clearly the influence of economic

growth on the environment. Based on Figure 2.1, one will find an inverted

U-shaped curve. This hypothesis indicates that as the economic

development starts to develop, it contributes more damage to the

environment. After income exceeds the turning point, the level of

environmental degradation decline rises when GDP per capita rises

(Agarwal, 2018).

The study by Apergis & Ozturk (2015) for 14 Asian countries found the

presence of an inverted U-shape association between emissions and



income per capita. The view is shared Jalil and Mahmud (2009) and Nasir

and Rehman (2011) and Sinha and Shahbaz (2018) that yield empirical

support to the presence of an EKC hypothesis in China, Pakistan and India

respectively. However, Akbostanci, Turut & Tunc found that times series

and panel data analysis of Turkish data do no support the EKC hypotheses.

Regardless of whether economic development is driven by

industrialisation or agriculture, the data from Africa is not consistent with

EKC hypothesis (Lin et al., 2016).

From the past researches, we can say that there are different opinions

regarding the validity of EKC hypothesis. Although since 90th

centuries,

this theory had been used commonly in reviewing the environmental

policy but, there is still critics arguing its validity. Some argued that a

good economic growth does not guarantee the quality of environment. In

fact, there is more damage contributed to the environment when the

economy is growing (Pettinger, 2017).

2.3 Proposed Theoretical Framework

Based on the background of study and discussion of literature review between the

dependent variable, renewable energy and each of the independent variables

including crude oil price, gross domestic product, carbon dioxide emissions and

population growth, the theoretical framework can be developed (see Figure 2.2).



Figure 2.2: Proposed Theoretical Framework

Source: Developed for the research

Renewable Energy

Oil Price

Gross Domestic Product

Carbon Dioxide

Emissions

Population Growth



CHAPTER 3: METHODOLOGY

3.0 Introduction

Research methodology is a way for researcher to solve the research problem with

a scientific and systematic solution. Furthermore, this study discussed on the

collected variables and econometric model that were used and its advantage will

be made. In order to consolidate and clarify the results in this research, this

chapter involved different stages of process such as research design, data sources,

data analysis, diagnostic tests and inferential analysis.

In this study, the selected renewable energy included hydropower and bioenergy

as dependent variable and crude oil price, gross domestic product, carbon dioxide

emission and population growth as the independent variables.

3.1 Research Design

A research design plays an important role in enhancing the steadiness of the

research progress and to assess the progress of the research work (Rajasekar,

Philominathan & Chinnathambi, 2013). Quantitative research approach is the

technique applied in this study. This study statistically run the data and obtains

further interpretation on it by using this method.

The objective of this study is to investigate the relationship between explanatory

variable and the independent variables by using the secondary data taken from

World Bank and International Renewable Energy Agency (IRENA). In order to

capture the impact of crude oil price toward the renewable energy (hydropower

and bioenergy), all the secondary data were extracted from various sources to



carry out the regression model.The data obtained are quantitative data covered the

period from 2000 to 2015 and 16 countries which in total 256 observations. The

countries chosen included Brazil, Chile, China, Colombia, Czech, Greece,

Hungary, India, Indonesia, Malaysia, Peru, Poland, Russia, Thailand, Turkey and

South Africa.

3.2 Data Sources

3.2.1 Definition of Variables

Hydropower

Hydropower, also known as hydroelectric power and it is a form of

renewable energy. The motion of the water is the initial form of energy

and transformed from potential energy to kinetic energy and in the end to

electrical energy. There is approximately 96% of the renewable energy are

generated from hydroelectric power among the renewable energy resources

in the United States (United States Bureau of Reclamation, 2018). Once

the electrical energy generated from dam, it will then transmitted to the

power plant for consumption purpose.

Figure 3.1: Typical hydroelectric dam

Source: United States Bureau of Reclamation (USBR)



Bioenergy

Bioenergy is a form of renewable energy generated from biological

material that can be used to produce heat, electricity, transportation fuels

and products. Most of the bioenergy produced from agriculture farm,

waste and farm. Chemical, thermal and biochemical are the three processes

to transform the bioenergy from raw sources. Today, bioenergy

contributed 10% of global primary energy consumption (Statham, 2013).

Crude Oil Price

Crude oil is one type of fossil fuel, can be used to produce petroleum,

diesel and various forms of petrochemical products. It is a limited resource

and non-renewable which means it is not replaceable after the

consumption. Crude oil price indicates the spot price of various barrels of

oil. The types of crude and average prices are the information to determine

the crude oil import price for each tariff position (OCED, 2007).

Gross Domestic Product

Gross Domestic Product (GDP) consists of the sum of consumption,

investment, government spending and net export of the country (Amadeo,

2018). GDP can delineate the standard of living of a nation and is also a

good way to measure the economy health of the country. To be more

simplified, the total monetary value of all the finished goods and services

produced within a country calculated within a certain period equal to the

GDP of the nation. It can be calculated on either annual basis or quarterly

basis.

Carbon Dioxide Emissions

Carbon dioxide (CO2) emissions indicate the release of carbon into the

atmosphere. The main contributor to the climate change, carbon dioxide

emission is considered a greenhouse gas and also known as carbon

emission when talk over global warming or climate change related topic.



Population Growth

The population growth is the rate of increment in the number of living

people in a population. All individual regardless of legal status or

citizenship in a nation is included in the size of population. According to

Cincotta and Engelman (1997), the population significantly influence

economic growth, employment and poverty and the management of assets.

Hence, it is chosen as one of the independent variable.

3.2.2 Empirical Model

The model adopted from Sadorsky (2009) is stated as below:

REit = β0i

+ β1t

Yit + β2i

CO2𝑖𝑡 + β

3iROPt +μ

it (Equation 1)

Then, the model is extended as below:

Renewable energy = f (crude oil price, gross domestic product, carbon

dioxide Emissions, population growth)

REit = β0 + β

1tOILit + β

2tGDPit + β

3tCO2𝑖𝑡

+ β4t

POPit +μit (Equation 2)

OIL: log of crude oil price by using Cushing, OK WTI Spot

Price FOB (Dollars per Barrel)

GDP: annual percentage of gross domestic product

CO2: carbon dioxide emissions in tonnes per capita

POP: annual percentage of population growth

The OIL has a positive relationship with the renewable energy. For the

rest of independent variables: GDP, CO2 and POP showing both positive

and negative impacts towards the renewable energy.



3.3 Data Analysis

In this study, software of EViews 10 is selected to run the regression analysis.

EViews 10 is chosen as we have sufficient knowledge and practical skills on how

to use it to run the data analysis.

3.3.1 Pooled Ordinary Least Squares (POLS)

In a linear regression model, Ordinary Least Squares (OLS) method was

probably the most widely used method to estimate the parameters. Since

the panel data is selected throughout this study, hence Pooled Ordinary

Least Squares (POLS) method can be used to estimate the parameters.

Panel data can enhance the coming empirical analysis rather than using

cross-sectional data or time series data only (Gujarati, 2004). According to

Killingsworth (1990), pooled OLS can estimate all the parameters in the

model consistently. Basically, pooled OLS estimation can be described an

OLS technique run on panel data. The estimation obtained from the OLS is

the optimal estimates from a broad class of possible parameter estimates

under the assumptions. In general, OLS makes very efficient use of the

data and good results can be obtained even with relatively small data sets.

It is said to be best linear unbiased estimator (BLUE) if it fulfils criteria: (1)

linear in parameters (2) unbiased, average value,𝐸(�̂�) = 𝛽 is equal toits

true value (3) efficient which means it has minimum variance and is

unbiased (Gujarati & Porter, 2010).

3.3.2 Fixed Effect Model (FEM)

Fixed effect model is a statistical model which treats all parameters as

fixed or non-random values. It can be assumed that there is one true effect

size that underlies all the studies in the analysis and all the observed effect



differences are caused by the sampling error (Borenstein, et al.,2009).

Since the time-invariant is allowed to correlate with the time-varying

variables (Bollen & Brand, 2010), one of the assumption of OLS is not

violated which stated that disturbances are not correlated with any

regressors. In addition, the high variance problem in fixed effect model

made the results lack of robustness (Clark & Linzer, 2012).

3.3.3 Random Effect Model (REM)

The random effect model, also known as error component model, is one of

the most popular models to be used in panel data. REM is allowed the

difference of true effect sizes. Hence, individual effect is assumed not

correlated with any regressor and estimate error variance specific to group

or times. It has a probability that all studies share a common effect size,

but also the effect size could be different from study to study (Borenstein

et al., 2009). Thus, the random effect model has higher efficiency than the

fixed effect model if the assumption holds.

3.4 Diagnostic Tests

3.4.1 Multicollinearity

Multicollinearity arises when all explanatory variables in the model are

highly correlated with one another. According to Jeeshim (2002), the

improper use of dummy variable, including a variable computed from

other variable in equation and the same variable twice will resulted the

multicollinearity problem.



However, there is no an explicitly way to evaluate the multicolliearity

problem of a linear regression model. The correlation coefficient of

independent variables can be computed to indicate the problem but high

correlation coefficient do not necessary mean that there is a

multicollinearity problem.

To get rid of multicollinearity problem, we can use the prior information or

transforming data, omit variable with high collinearity and combine cross-

sectional data and time series data. Variance inflation factors (VIF) is one

of the calculation to determine multicollinearity problem. The closer the

value of VIF to 10 and R-squared to 0.90, the higher the collinearity

between independent variables in the model (Gujarati, 2004). The formula

of VIF is stated as below:

𝑉𝐼𝐹𝑘 =1

1−𝑅𝑘2 (Equation 3)

The inflated amount of variance of the model and variance of coefficients

can be used to determine there is a multicollinearity problem. As a result,

any inference is not reliable and the confidence interval becomes wide and

some independent variable may found insignificant. Estimators remain

BLUE (as stated in POLS), same goes to R-squared. When the inversely

proportional relationship of VIF, which is tolerance value (TOL) gets close

to zero, the greater the degree of collinearity of that variables with the

other regressors and otherwise. A small value of TOL indicates that one of

the variables is highly correlated to the rest of independent variables

(Gujarati & Porter, 2010). The formula of TOL is stated as below:

𝑇𝑂𝐿𝑘 =1

𝑉𝐼𝐹𝑘 (Equation 4)



3.4.2 Autocorrelation

The autocorrelation function can be used detect non-randomness in data

and identification an appropriate time series model if the data are not

random. The autocorrelation or serial correlation expresses those situations

where observations of the dependent variable are not independently drawn.

This condition is usual at the time series data. In the other way round, this

does not happen in the case of cross-section data, for individual units are

independent with each other. In the case of time series data,

autocorrelation is a frequent phenomenon as the time dependence

associated with the inertia in economic data (Gujarati & Porter, 2010).

Breusch-Godfrey Serial Correlation LM test was used to detect correlation

between the error terms in the model. If p-value of χ2 is less than the

significance level (α) at 0.01, 0.05 or 0.1 then the null hypothesis will be

rejected. The null hypothesis of LM test is set as there is no autocorrelation

problem while the alternative assumption is set because there is an

autocorrelation problem in the model (Gujarati & Porter, 2010).

3.4.3 Hausman Specification Test

In this study, Hausman test is used to identify the predictor variables which

also known as endogenous regressors in a regression model. Predictor

variables sometimes referred as independent variable, determined by other

variables in the system to show its value. One of the assumptions of

Ordinary least squares (OLS) stated that there is no correlation between an

endogenous regressor and the error term (Hausman, 1978). Hence, OLS

will eventually fail by having endogenous regressors in a model.

The formula use for H-test is as below:

𝐻 = (�̂�𝐹𝐸𝑀 − �̂�𝑅𝐸𝑀)[(�̂�𝐹𝐸𝑀) − 𝑉𝑎𝑟(�̂�𝑅𝐸𝑀)] − 1(�̂�𝐹𝐸𝑀 − �̂�𝐹𝐸𝑀)



The hypothesis is stated as below:

𝐻0: 𝑅𝐸𝑀 𝑖𝑠 𝑏𝑒𝑡𝑡𝑒𝑟 𝑡ℎ𝑎𝑛 𝐹𝐸𝑀

𝐻1: 𝑅𝐸𝑀 𝑖𝑠 𝑛𝑜𝑡 𝑏𝑒𝑡𝑡𝑒𝑟 𝑡ℎ𝑎𝑛 𝐹𝐸𝑀

According to the rule, reject null hypothesis if the probability value of H-

test is less than the significant level. Otherwise, do not reject the null

hypothesis.

3.4.4 Likelihood Ratio Test

The likelihood ratio test (LR test) was introduced by Neyman and Pearson

in 1928. The test is used to compare the maximum likelihood under the

hypothesis testing (Lehmann, 2006). In short, it is a hypothesis test used to

identify which is a better model between the statistical models. In this

study, comparison between POLS and REM are used to examine which

models are more suitable in term of goodness of fit. The example model is

stated as below:

𝐻0 = 𝑃𝑂𝐿𝑆 𝑚𝑜𝑑𝑒𝑙 𝑖𝑠 𝑏𝑒𝑡𝑡𝑒𝑟 𝑡ℎ𝑎𝑛 𝑅𝐸𝑀

𝐻1 = 𝑃𝑂𝐿𝑆 𝑚𝑜𝑑𝑒𝑙 𝑖𝑠 𝑛𝑜𝑡 𝑏𝑒𝑡𝑡𝑒𝑟 𝑡ℎ𝑎𝑛 𝑅𝐸𝑀

P-value or a critical value is used to decide whether to reject the null

hypothesis as stated in the previous statistical model.

3.4.5 Poolability F-Test

One of the main objectives of pooling a time series of cross-sections is to

enlarge the database in order to obtain precise parameters of the model

(Antonie, Cristescu & Cataniciu, 2010). Besides, the main function of

poolability test is to determine either Pooled OLS preferable or Fixed



Effect Model preferable to explain the model. Commonly, Poolability F-

test has its null hypothesis as below:

𝐻0 = 𝑃𝑂𝐿𝑆 𝑚𝑜𝑑𝑒𝑙 𝑖𝑠 𝑝𝑟𝑒𝑓𝑒𝑟𝑎𝑏𝑙𝑒

𝐻1 = 𝐹𝐸𝑀 𝑖𝑠 𝑝𝑟𝑒𝑓𝑒𝑟𝑎𝑏𝑙𝑒

The test statistics for poolability test will be restricted as below:

𝐹 = (𝐸𝑆𝑆𝑅 − 𝐸𝑆𝑆𝑈) (𝑁 − 1)⁄

𝐸𝑆𝑆𝑈 ((𝑇 − 1)𝑁 − 𝐾)⁄

The decision rule indicates that if the p-value of F-statistic is lower than

significant level, the null hypothesis should be rejected. Or else the null

hypothesis should not be rejected. In this case, the FEM is more preferable

compared to Pooled OLS.

3.5 Inferential Analysis

3.5.1 T-test

T-distributions help us to decide if a mean is different from a known

standard value (Ugoni & Walker, 1995). First of all, there are few

assumptions needed to be made in order to carry out the t-test statistics.

First, the data should be collected randomly from a sample of a large total

population and the mean of the sample must be distributed normally. Next,

the standard deviations of the sample are approximately the same, thus the

variance will also be equal (Boneau, 1960).Generally, researcher is able to

identify the individual relationship between each independent variables

and the dependent variable by conducting a hypothesis testing.(Gujarati &

Porter, 2010) The hypothesis is stated as below:



𝐻0 = 𝛽1 = 0, 𝛽2 = 0, 𝛽3 = 0, 𝛽4 = 0 (𝑁𝑜𝑡 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡)

𝐻1 = 𝛽1 ≠ 0, 𝛽2 ≠ 0, 𝛽3 ≠ 0, 𝛽4 ≠ 0 (𝑆𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡)

Where,

𝛽1 = 𝐶𝑟𝑢𝑑𝑒 𝑂𝑖𝑙 𝑃𝑟𝑖𝑐𝑒 (𝑂𝐼𝐿)

𝛽2 = 𝐺𝑟𝑜𝑠𝑠 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 (𝐺𝐷𝑃)

𝛽3 = 𝐶𝑎𝑟𝑏𝑜𝑛 𝐷𝑖𝑜𝑥𝑖𝑑𝑒 𝐸𝑚𝑖𝑠𝑖𝑜𝑛𝑠 (𝐶𝑂2)

𝛽4 = 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝐺𝑟𝑜𝑤𝑡ℎ (𝑃𝑂𝑃)

T-test in this research is used to determine the significance of each

independent variable (Crude Oil Price, Gross Domestic Product, Carbon

Dioxide Emissions and Population Growth) individually to the dependent

variable (Renewable Energy). In addition, the P-value in a T-test also

plays an important role on whether to reject the null hypothesis or not. If

P-value less than 0.01, 0.05 or 0.1, it automatically indicate the rejection of

null hypothesis and proved that there is a significant relationship between

the individual independent variable and the dependent variable.

3.5.2 F-test

In contrast to the T-test, F-test concerns on several parameters in the null

hypothesis instead of only one parameter. F-test is used to determine the

overall significance of the estimated regression under the F-distribution.

𝐻0 = 𝛽1 = 𝛽2 = 𝛽3 = 𝛽4 = 0 (𝑁𝑜𝑡 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡)

𝐻1 = 𝛽𝑖 ≠ 0, 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 𝑜𝑓 𝑡ℎ𝑒 𝛽 𝑖𝑠 𝑛𝑜𝑡 𝑒𝑞𝑢𝑎𝑙 𝑡𝑜 𝑧𝑒𝑟𝑜 (𝑆𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡),

Where i = 1, 2, 3 and 4.

𝛽1 = 𝐶𝑟𝑢𝑑𝑒 𝑂𝑖𝑙 𝑃𝑟𝑖𝑐𝑒 (𝑂𝐼𝐿)

𝛽2 = 𝐺𝑟𝑜𝑠𝑠 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 (𝐺𝐷𝑃)

𝛽3 = 𝐶𝑎𝑟𝑏𝑜𝑛 𝐷𝑖𝑜𝑥𝑖𝑑𝑒 𝐸𝑚𝑖𝑠𝑖𝑜𝑛𝑠 (𝐶𝑂2)



𝛽4 = 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝐺𝑟𝑜𝑤𝑡ℎ (𝑃𝑂𝑃)

Both test statistic value and P-value are allowed to examine the

significance of the hypothesis testing. For test statistic value, the null

hypothesis will be accepted if the F-statistic value is fall in between the

upper critical value and the lower critical value. Or else, it will be rejected.

For the P-value approach, null hypothesis will be rejected if P-value less

than the significant level of 1%, 5% or 10%. Otherwise, the null

hypothesis will be accepted in this study.

3.6 Conclusion

All the data are obtained from World Bank and IRENA, total with 16 countries

from year 2000 to 2015. The results from 256 observations are generated through

EViews 10. Further interpretation and discussion on the result will be stated in the

following chapter.



CHAPTER 4: DATA ANALYSIS

4.0 Introduction

This chapter explained the descriptive statistic and the panel data analysis for 16

countries have been run between the years 2000-2015. It presented by using

various tests which comprise of multicollinearity test and autocorrelation test.

Lastly, it examined the crude oil price and clean energy driving force in emerging

economies.

To test the panel data with different assumption from different models, the several

models had been regressed as POLS, FEM and REM. The test conducted

separately for bioenergy and hydropower. The results are shown in Table 4.1 and

Table 4.3 respectively. Further tests had been carried out for the comparison

between POLS, FEM, and REM in Table 4.2.



4.1 Panel Data Analysis (BIO)

Table 4.1: Model Comparison of POLS, FEM and REM for Bioenergy

Dependent variable BIOENERGY

Independent variables Model OLS Model REM Model FEM

LOG(OIL) 1.265353*** 0.973887*** 0.901343***

(0.0000) (0.0000) (0.0000)

GDP 0.045722*** -0.046753*** -0.050277***

(0.1674) (0.0011) (0.0005)

POP -0.035650*** -0.082384*** -0.147178***

(0.8535) (0.6804) (0.4764)

LOG(CO2) -0.686814*** 0.917921*** 1.288411***

(0.0001) (0.0008) (0.0000)

R-squared 0.160495 0.454418 0.898648

Adjusted R-squared 0.147063 0.445689 0.890454

F-test 11.94866*** 52.05657*** 109.6660***

Breusch-Pagan LM 640.4048 592.7902 571.5400

Likelihood Ratio (Panel

Cross Section Test) 205.1335


Period Test) 9.731343

Hausman Test

11.001540***

(0.0265)

Poolability F-test

539.124415***

(0.0000)

Durbin Watson Test 0.089124 0.387692 0.414313

Jaquer-Bera 7.007865*** 5.550167*** 10.66975***

(0.030079) (0.062344) (0.004821)

Observations 255 255 255

Notes: *, ** and *** implies that the rejection of the null hypothesis at 10%, 5%and 1%

significance level respectively. **** indicate unbalanced observation, which do not affect the

nature of results.

OIL is crude oil price, GDP is gross domestic product, POP is population growth and CO2 is

carbon dioxide emissions.



Pooled Ordinary Least Squares (BIO)

From Table 4.1, POLS model has showed that the goodness of fit was 0.1604

which indicated the independent variable was 16.05% fit into the model. There

were two variables which have the different sign from theoretical expectation

which include of population growth (POP) and carbon dioxide emissions (CO2).

Moreover, the independent variable including gross domestic product (GDP) and

population growth (POP), were statistically insignificant as their p-value 0.1674

and 0.8535 were higher than their significance level of 10%, 5% and 1%

respectively.

On the other hand, crude oil price (OIL) and carbon dioxide emission (CO2) that

statistically significance as it showed the p-value of 0.0000 and 0.0001 which both

were lesser than significance level of 10%, 5% and 1%. The F-test showed that the

model is significant as p-value of F test is 0.0000 with a test statistic of 11.94866

which less than their significance level of 1%, 5% and 10%.

Random Effect Model (BIO)

Refer to Table 4.1, REM model showed that its adjusted R squared 0.445689 was

higher than POLS model R squared of 0.147063. However, in REM model, there

were two independent variables consists of different sign from theoretical

expectation which include of gross domestic product (GDP) and population

growth (POP). In addition, the significant variable has increased from two

variables to three variables. The independent variable of gross domestic product

(GDP) has changed from insignificant to significant at the level of significance of

10%, 5% and 1%. Its p-value of 0.0011 was less than its level of significance.

However, population growth (POP) was statistically insignificant as their p-value

0.6804 is less than the significance level of 10%, 5% and 1%. Crude oil price

(OIL) and carbon dioxide emissions (CO2) remained statistically significance as

their p-value 0.0000 and 0.0008 were less than the level of significant of 10%, 5%

and 1%. The F-test has the same result as POLS model that showed the model was



significant as p-value of F test 0.0000 less than their significance level of 10 %, 5%

and 1%.

Fixed Effect Model (BIO)

As shown in the Table 4.1, FEM had the highest adjusted R-squared compared

among the POLS model and REM model. FEM model consists of 0.890454 of

adjusted R-squared. In FEM model shown that gross domestic product (GDP) and

population growth (POP) remained different sign from the theoretical expectation.

Nevertheless, population growth (POP) remained statistically insignificant as their

p-value 0.4764 is less than its level of significant of 10%, 5% and 1% although the

model have the greatest adjusted R-squared among POLS and REM model.

Furthermore, crude oil price (OIL), gross domestic product (GDP), carbon dioxide

emissions (CO2) were statistically significance as their p-value 0.0000,0.0005 and

0.0000 respectively were lower than its significance level at 10%, 5% and 1%.

The F-test result was constant as its p-value, 0.0000 significant at the level of 10%,

5% and 1%.

In addition, a null hypothesis of normal distribution, Jarque-Bera statistic is

distributed with 2 degree of freedom. Based on the Table 4.1, the probability of

Jarque-Bera statistic of 0.004821 lead to a rejection of the null hypothesis as the

probability value was lesser than the significance level of 10%, 5% level and also

1% significance level.

4.1.1 Comparison Test (BIO)

Several additional tests have been conducted to choose the best model to

give a better explanation on the relationship between crude oil price and

renewable energy. The additional test that has carried was likelihood ratio

test, poolability test and Hausman test have been carried out.



According to our methodology, likelihood ratio test are used to test among

POLS model and REM model. Based on the table below, Likelihood ratio

test statistic was 205.1335 with a p-value of 0.0000. The null hypothesis is

rejected since the p-value 0.0000 is significant at the level of 10%, 5% and

1%. This has proven that there is sufficient evidence to reject the null

hypothesis and proved that REM model have more suitability than POLS

model. Next, poolability F test are used to test the suitability among POLS

model and FEM model. The Chi squared result showed that the test

statistic of 539.124415 with a p-value of 0.0000. Therefore, this study can

conclude that there was a sufficient evidence to prove that FEM model

was better than POLS model as the p-value 0.0000 was lower than the

level of significant of 10%, 5% and 1%.

REM model and FEM model were more preferable when comparing with

POLS model. By knowing REM model and FEM model were better than

POLS mode, Hausman test has been carried out to determine the

suitability among REM model and FEM model. The test statistic of

Hausman test was 11.001540 with the p-value of 0.0265. The p-value was

insignificant as it was higher than its level of significant at 10% and 5%.

As a result, this study had insufficient evidence to conclude that FEM

model was better than REM model. In conclusion, REM model was the

best model for the panel data for bioenergy.



Table 4.2: Model Comparison for Likelihood Ratio, Poolability F-test and

Hausman test

Likelihood Ratio Poolability F-test Hausman Test

Test Statistic 205.1335*** 539.124415*** 11.001540**

P-value (0.0000) (0.0000) (0.0265)

Decision

Making

Reject null

hypothesis

Reject null

hypothesis

Do not reject null

hypothesis

Conclusion REM is preferred FEM is preferred REM is preferred

compared to

POLS

compared to

POLS compared to FEM


significance level respectively.

4.2 Panel Data Analysis (HYD)

Table 4.3: Model Comparison of POLS, FEM and REM for Hydropower

Independent variables Model POLS Model REM Model FEM

LOG(OIL) 0.395157* 0.107607 *** 0.101517 ***

(0.0896) (0.0002) (0.0004)

GDP 0.159795*** 0.001874*** 0.001511***

(0.0000) (0.6454) (0.7106)

POP 0.479577** -0.156431*** -0.165072***

(0.0133) (0.0081) (0.0054)

LOG(CO2) -0.620802*** 0.488511*** 0.517841***

(0.0005) (0.0000) (0.0000)

R-squared 0.220048 0.324121 0.99221

Adjusted R-squared 0.207619 0.313350 0.991583

F-test 17.70371 30.09209 1582.054

Breusch-Pagan LM 480.158 290.1316 294.9447


Cross Section Test) 422.8131



Continued from Table 4.3


Period Test) 4.15307

Hausman Test 0.0000***

(1.0000)

Poolability F-test

1179.234139***

Durbin Watson Test 0.107463 0.862311 0.953774

Jarque-Bera 6.181931***

(0.45458)

5.708550*

(0.05798)

11.07977***

(0.003927)

Observations**** 256 256 256


significance level respectively. **** indicate balanced observation. OIL is crude oil price,

GDP is gross domestic product, POP is population growth and CO2 is carbon dioxide

emissions.

Pooled Ordinary Least Square

From Table 4.3, it showed the model is statistically significant with the0.207619

goodness of fits. The four independent variables were statistically significant at

different significance level. There was only one variable that has different sign

and statistically significance which was the carbon dioxide emissions (CO2).

Carbon dioxide emissions with p-value of 0.0005 was significant as its p-value

was less than the significant level of 10%,5% and 1% respectively.

On the other hand, crude oil price, gross domestic product (GDP) and population

growth (POP) were having the same sign with dependant variable. Crude oil price

consists a p-value of 0.0896 was statistically significance at significance level of

10%. Furthermore, the population growth (POP) was statistically significant at the

level of significance of 5% and 10% as its p-value of 0.0133 was lesser than the

level of significant. Gross domestic product had a p-value of 0.0000 which was

less than the significance level of 10%, 5% and 1% respectively. Thus, there was a

sufficient evidence to conclude that, crude oil price, gross domestic product,

population growth, and carbon dioxide emissions had a significant relationship

with hydropower. The F-test showed that the model is significant as p-value of F



test 0.0000 less with the test statistic of 17.70371 than their significance level of

10 %, 5% and 1%.

Random Effect Model (HYD)

According to the REM model in Table 4.3, the adjusted R squared 0.313350 was

higher than POLS model. In REM model, the significance variable has reduced

from four variables to three variables. Furthermore, gross domestic product (GDP)

has changed from significance to insignificance variable. Gross domestic product

(GDP) with a p-value of 0.6454 were higher than the level of significance of 10%,

5% and 1%.Therefore, there was insufficient evidence to conclude that there was a

relationship between hydropower and gross domestic product (GDP).

However, the remaining three variable of crude oil price (OIL), population growth

(POP) and carbon dioxide emissions (CO2) remained statistically significant.

Crude oil price (OIL), population growth (POP), and carbon dioxide emissions

(CO2) were showing a p-value of 0.0002, 0.0081 and 0.0000 respectively. There

were sufficient evidence to conclude that the three variables are statistically

significance as their p-value was less than the significance level of 10%, 5% and

1%. Moreover, sign of carbon dioxide emissions (CO2) was negatively related to

hydropower as in POLS model. The F-test showed that the model was significant

as p-value of F test 0.0000 less than their significance level of 10%, 5% and 1%.

Fixed Effect Model (HYD)

For the FEM model, its adjusted R squared 0.991583 was higher than POLS

model and REM model. In addition, most of the variable remained the same but

except for the gross domestic product (GDP), was different from the theoretical

expectation. Crude oil price (OIL) and population growth (POP) and carbon

dioxide emissions (CO2) has remained significant at the level of significance of

10%, 5% and 1%. Their p-value of 0.0004, 0.0054 and 0.0000 respectively were

less than its level of significance of 10%, 5% and 1 %



In contrast, gross domestic product (GDP) remained statistically insignificant as

their p-value 0.7106 was higher than the significance level of 10%,5% and 1%.

The F-test showed that the model is significant as p-value of F test 0.0000 less

than their significance level of 1 %, 5% and 10%.

Moreover, under the null hypothesis of a normal distribution, the Jarque-Bera

statistic is distributed as with 2 degrees of freedom. The reported Probability is the

probability that a Jarque-Bera statistic exceeds (in absolute value) the observed

value 0.003927 under the null hypothesis—a small probability value led to the

rejection of the null hypothesis of a normal distribution. The hypothesis of normal

distribution was rejected at the significance level of 10%, 5% and also 1%.

4.2.1 Comparison test (HYD)

Several additional tests have been conducted to choose the best model to

explain on the relationship between oil price and hydropower. The

additional test including likelihood ratio test, poolability test and Hausman

test have been carried out as stated in Table 4.4.

Back to the methodology, likelihood ratio test are used to test among

POLS model and REM model. Based on Table 4.3, Likelihood ratio test

statistic was 422.8131 with a p-value of 0.0000. The null hypothesis is

rejected since the p-value 0.0000 was significant at the level of 10%, 5%

and 1%. This has proven that there is sufficient evidence to reject the null

hypothesis and prove the REM model have more suitability than POLS

model.

Next, the poolability test is used to compare between POLS model and

FEM model. The result shows the test statistic of 1179.234139 with a p-

value of 0.0000. This result rejected the null hypothesis since the p-value

0.0000 was less than the significance level of 10%, 5% and 1%. Therefore,

there was a sufficient evidence to prove that FEM model was better than



POLS model. Therefore FEM model is more preferable compared to POLS

model since the null hypothesis of poolability test has been rejected.

Knowingly both of the tests have shown FEM model and REM model

were more suitable for the panel data compared to POLS model, the

Hausman test was carried out the comparison between FEM model and

REM model to select the best model. With the p-value, the null hypothesis

is rejected since the p-value of 1.000 was more than the significant level of

1%, 5% and 10%. Therefore, it can be concluded that FEM model was the

best model for the panel data in dependent variable of hydropower.

Table 4.4: Model Comparison for Likelihood Ratio, Poolability F-test and

Hausman test

Likelihood Ratio Poolability F-test Hausman Test

Test

Statistic 422.8131 *** 1179.234139*** 0.0000***

P-value (0.0000) (0.0000) (1.0000)

Decision

Making

Reject null

hypothesis

Reject null

hypothesis

Do not Reject null

hypothesis

Conclusion REM is preferred FEM is preferred FEM is preferred

compared to POLS compared to POLS compared to REM


significance level respectively.



4.3 Diagnostic Checking

4.3.1 Autocorrelation

In order to detect autocorrelation problem, Durbin Watson test are used to

determine whether or not the model consists of autocorrelation problem.

This study had determine the type of autocorrelation problem and

transform the model from original to free of autocorrelation problem if

there is existence of autocorrelation problem (Gujarati, 2004).

Based on Table 4.1, the critical values of Durbin Watsons test were

dL=0.73400 and dU=1.93506 at 5% level of significance. Based on the

Durbin Watson result, POLS model, REM model and FEM model were

significant. As their test statistic result were fall at reject null hypothesis

region, less than 0.73400, thus there was enough evidence to conclude that

POLS model, REM model, and FEM model had autocorrelation problem.

According to Table 4.3 above, at 5% level of significance, the critical

value of Durbin Watsons test were dL=0.73400 and dU=1.93506. Based

on the Durbin Watson result above, POLS model were significant. As the

test statistic was fall at reject null hypothesis region, less than 0.73400,

there was enough evidence to conclude that POLS model 0.107463 had

autocorrelation problem. However, REM model and FEM model were

inconclusive. It could be easy to get confused by mis-specified dynamics

with serial correlation in the errors. In fact, it was the best to always start

from general dynamic models and test the restrictions before applying the

tests for serial correlation.

Autocorrelation was a problem due to its presence representing that useful

information is missing from the model. Autocorrelation problem could be

eliminated or reduced by adding more variables. After adding in the



variables, the results can be generated and determine whether

autocorrelation problem still occur. In addition, formulate the model

correctly in the first place was the best approach toward the autocorrelation

problem (Gujarati & Porter, 2010).

4.3.2 Multicollinearity

Table 4.5: Bioenergy Correlation Matrix

BIO OIL GDP POP CO2

BIO 1 0.2533 0.1123 -0.0188 -0.1495

OIL 0.2533 1 -0.0445 -0.0637 0.0713

GDP 0.1123 -0.0445 1 0.0395 -0.0991

POP -0.0188 -0.0637 0.0395 1 -0.5588

CO2 -0.1495 0.0713 -0.0991 -0.5588 1

Table 4.6: Hydropower Correlation Matrix

HYD OIL GDP POP CO2

HYD 1 0.0969 0.2977 -0.0474 -0.0594

OIL 0.0969 1 -0.0445 -0.0637 0.0713

GDP 0.2977 -0.0445 1 0.0395 -0.0991

POP -0.0474 -0.0637 0.0395 1 -0.5588

CO2 -0.0594 0.0713 -0.0991 -0.5588 1

In a model which consists of multiple factors that are correlated with

dependent variable and also to each other, it will cause a multicollinearity

problem. In other words, it results when there are factors that are a bit

redundant (Bonnie, et al., 2013).



In this study, Pearson correlation coefficient has used to examine

multicollinearity problem. It is used to examine the strength, direction of

the linear relationship between 2 continuous variables. Positive and

negative sign indicates the direction of the variables whether is same or

different direction. The correlation coefficient usually ranges from -1 to +1.

The closer the result to 1 indicates the stronger the relationship among the

variables. Moreover, if the correlation is near to 0, it proved that there is no

linear relationship among the variables (Bonnie, et al., 2013).

Refer to the Table 4.5, the relationship between bioenergy (BIO) with all

the independent variables including crude oil prices (OIL) and gross

domestic product (GDP) were moving in the same direction, while

different direction with population growth (POP), and carbon dioxide

emissions (CO2). Refer back to the Table 4.5, the independent variables do

not correlated to their dependent variable which is bioenergy (BIO). As the

correlation coefficient were small and near to 0. The correlation coefficient

between the independent variable and dependent variable which

specifically stated as: crude oil price = 0.2533, gross domestic product =

0.1123, population growth = -0.0188 and carbon dioxide emissions = -

0.1495. On the other hand, among the independent variables, there was a

slightly and moderate multicollinearity problem between population

growth and carbon dioxide emissions as the correlation coefficient

between this two variables were -0.559.

As shown in the Table 4.6, the relationship between hydropower with the

independent variable including crude oil price (OIL), gross domestic

product (GDP), were moving in the same direction, while hydropower

moving in different direction with population growth (GDP) and carbon

dioxide emissions (CO2). Based on the Table 4.6, the independent variable

do not correlated with the dependent variable which is hydropower. As the

correlation coefficient are small and near to 0. The correlation coefficient

of crude oil price was 0.0969, gross domestic product was 0.2977,



population growth was -0.0474 and carbon dioxide emissions was -0.0594.

On the other hand, among the independent variables, there is a slightly and

moderate multicollinearity problem between population growth and carbon

dioxide emission as the correlation coefficient between this two variable

were 0.5588 among the independent variable. There was a solution to

solve multicollinearity problem by dropping off the variable that highly

correlated to each other. However, the multicollinearity problem between

population growth and carbon dioxide emissions are not significant.

4.4 Discussion on Major Findings

Based on the result, this study showed a positive relationship between crude oil

price and renewable energy generation. Besides that, volatility of crude oil price

had a significant impact on the demand and supply of renewable energy. When the

crude oil price increased, the market forces of renewable energy rose in demand.

This indicated crude oil and renewable energy were substitute goods, when the

crude oil price increases, the generation of renewable energy will increase as well.

Thus, consumers will more preferable to replace crude oil with renewable energy

since lower cost incurred. Nevertheless, generation of renewable energy become

substitute good for crude oil due to both source of energy can meet the same

purpose.

4.5 Conclusion

From the results, crude oil price revealed a significant and positive relationship

with both bioenergy and hydropower. It is consistent with the previous research

stating that crude oil price has statistically significant impact on renewable energy

(Sadorsky, 2009a; Apergis & Payne, 2014).



On the other hand, gross domestic product (GDP) indicated a positive relationship

with hydropower according to the results. Population growth revealed a negative

relationship with bioenergy for three models used in this research. However,

carbon dioxide emissions showed different results for the models used in both

dependent variables (bioenergy and hydropower).

From the result above, this research can conclude that the FEM model is a more

appropriate model to fit the independent variables with the dependant variable

(bioenergy) when comparing to POLS model and REM model whereas REM

model is more appropriate to be used for hydropower dependent variable.



CHAPTER 5: SUMMARY, IMPLICATION AND

CONCLUSION

5.0 Summary on Implications

Based on the result shown in Chapter 4, this study shows that there is positive

relationship between crude oil price with the hydropower generation and

bioenergy generation. This is due to as the crude oil price increases, market

participants tend to demand more substitute of crude oil. Renewable energy can be

used to replace as an electricity generator by using hydropower and bioenergy.

The results tend to show that the country produce renewable energy are due to

cost saving. The increasing of price in crude oil will burden the company due to

increasing in their cost of production. Therefore, the country are targeting on

producing the renewable energy by natural resources such as hydropower and

bioenergy.

Besides crude oil prices, carbon dioxide emissions are one of the significance

variable throughout the bioenergy and hydropower model. There is a negative

relationship between renewable energy and carbon dioxide emissions. In another

point of view, the country that generates renewable energy is to reduce the carbon

dioxide emissions. However, population growth is insignificant in bioenergy

while gross domestic product does not affect the renewable energy as the result

showed that it is statistically insignificant.

5.1 Limitations

Nevertheless, the limitation was found in this study. This study concentrated in

only the emerging market, but not advanced countries and developing countries



which might narrow down the information available to other researchers. The

results from this study might not applicable to other region. Besides, the study

only focused on two types of renewable energy – bioenergy and hydropower due

to limitations of data assessable. Hence, it becomes the limitation to this study.

5.2 Recommendations

Future researches is suggested to have further research and investigation on the

effect of crude oil price towards the renewable energy in other region, such as

advanced countries and developing countries. Moreover, it is advisable to further

investigate on other types of renewable energy other than bioenergy and

hydropower to look on the potential of other renewable technologies.

5.3 Conclusion

Throughout the entire study, the primary objective is to carry out and investigate

the relationship between crude oil price and the renewable energy which consisted

of bioenergy and hydropower. Based on Chapter 4, the statistical testing were

successfully proved that crude oil price continuously provide a positive reaction

toward both bioenergy and hydropower regardless of the type of model tested.

Furthermore, population growth was found an insignificant relationship with

bioenergy while for GDP, it was insignificant with the generation of hydropower.

In order to provide a better understanding and explanation in this study, findings

on the significance of study, limitation and recommendation have been discussed

in this chapter as well.



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APPENDICES

Appendix 1: Bioenergy

Fixed Effect Model

Dependent Variable: LOG(BIO)

Method: Panel Least Squares

Date: 06/25/18 Time: 16:03

Sample: 2000 2015

Periods included: 16

Cross-sections included: 16

Total panel (unbalanced) observations: 255

Variable Coefficient Std. Error t-Statistic Prob.

LOG(OIL) 0.901343 0.099139 9.091717 0.0000 Positive & Significant

GDP -0.050277 0.014219 -3.535929 0.0005 Negaitive& Significant

POP -0.147178 0.206328 -0.713321 0.4764 Negative & Insignificant

LOG(CO2) 1.288411 0.295437 4.361031 0.0000 Positive & Significant

C 1.738754 0.483948 3.592853 0.0004

Effects Specification

Cross-section fixed (dummy variables)

R-squared 0.898648 Mean dependent var 6.947375

Adjusted R-squared 0.890454 S.D. dependent var 1.915063

S.E. of regression 0.633843 Akaike info criterion 2.001155

Sum squared resid 94.41303 Schwarz criterion 2.278902

Log likelihood -235.1473 Hannan-Quinn criter. 2.112877

F-statistic 109.6660 Durbin-Watson stat 0.414313

Prob(F-statistic) 0.000000



Normality Test

0

4

8

12

16

20

24

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Series: Standardized Residuals

Sample 2000 2015

Observations 255

Mean -3.82e-17

Median -0.023136

Maximum 1.780356

Minimum -2.194051

Std. Dev. 0.609676

Skewness -0.182856

Kurtosis 3.932988

Jarque-Bera 10.66975

Probability 0.004821

Poolability F-Test

Redundant Fixed Effects Tests

Equation: Untitled

Test cross-section fixed effects Effects Test Statistic d.f. Prob. Cross-section F 114.101394 (15,235) 0.0000

Cross-section Chi-square 539.124415 15 0.0000

Cross-section fixed effects test equation:



Date: 06/25/18 Time: 16:04

Sample: 2000 2015



Total panel (unbalanced) observations: 255 Variable Coefficient Std. Error t-Statistic Prob. LOG(OIL) 1.265353 0.233425 5.420815 0.0000

GDP 0.045722 0.033019 1.384715 0.1674

POP -0.035650 0.192911 -0.184800 0.8535

LOG(CO2) -0.686814 0.177017 -3.879931 0.0001

C 2.673966 1.009785 2.648054 0.0086 R-squared 0.160495 Mean dependent var 6.947375









Autocorrelation

Residual Cross-Section Dependence Test

Null hypothesis: No cross-section dependence (correlation) in residuals

Equation: Untitled




Test employs centered correlations computed from pairwise samples Test Statistic d.f. Prob. Breusch-Pagan LM 571.5400 120 0.0000

Pesaran scaled LM 29.14678 0.0000

Bias-corrected scaled LM 28.61345 0.0000

Pesaran CD 8.986401 0.0000

Random Effect Test


Method: Panel EGLS (Cross-section random effects)

Date: 06/25/18 Time: 16:12

Sample: 2000 2015




Swamy and Arora estimator of component variances



GDP -0.046753 0.014171 -3.299145 0.0011 Negative & Significant

POP -0.082384 0.199759 -0.412418 0.6804 Negative & Insignificant


C 1.900955 0.665183 2.857794 0.0046


S.D. Rho

Cross-section random 1.857956 0.8957

Idiosyncratic random 0.633843 0.1043

Weighted Statistics



S.E. of regression 0.642658 Sum squared resid 103.2523



Unweighted Statistics

R-squared -0.299510 Mean dependent var 6.947375

Sum squared resid 1210.541 Durbin-Watson stat 0.033068



Random Effect Test Correlated Random Effects - Hausman Test Equation: Untitled Test cross-section random effects

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 11.001540 4 0.0265 REM is preferred

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob. LOG(OIL) 0.901343 0.973887 0.000553 0.0020

GDP -0.050277 -0.046753 0.000001 0.0024 POP -0.147178 -0.082384 0.002668 0.2097

LOG(CO2) 1.288411 0.917921 0.014463 0.0021

Cross-section random effects test equation: Dependent Variable: LOG(BIO) Method: Panel Least Squares Date: 06/25/18 Time: 16:13 Sample: 2000 2015 Periods included: 16 Cross-sections included: 16 Total panel (unbalanced) observations: 255

Variable Coefficient Std. Error t-Statistic Prob. C 1.738754 0.483948 3.592853 0.0004

LOG(OIL) 0.901343 0.099139 9.091717 0.0000 GDP -0.050277 0.014219 -3.535929 0.0005 POP -0.147178 0.206328 -0.713321 0.4764

LOG(CO2) 1.288411 0.295437 4.361031 0.0000 Effects Specification Cross-section fixed (dummy variables) R-squared 0.898648 Mean dependent var 6.947375

Adjusted R-squared 0.890454 S.D. dependent var 1.915063 S.E. of regression 0.633843 Akaike info criterion 2.001155 Sum squared resid 94.41303 Schwarz criterion 2.278902 Log likelihood -235.1473 Hannan-Quinn criter. 2.112877 F-statistic 109.6660 Durbin-Watson stat 0.414313 Prob(F-statistic) 0.000000



Normality test

0

4

8

12

16

20

-5 -4 -3 -2 -1 0 1 2 3 4


Sample 2000 2015

Observations 255

Mean 0.010916

Median 0.040192

Maximum 4.117274

Minimum -5.283702

Std. Dev. 2.183069

Skewness -0.213873

Kurtosis 2.417418



Autocorrelation Test



Equation: Untitled




Note: non-zero cross-section means detected in data



Pesaran CD 10.29051 0.0000



Pooled OLS

log (BIO)it = 0 + 1 log (OIL)it + 2GDPit + 3POPit+ 4 log (CO2)it+ eit



Date: 06/25/18 Time: 15:51

Sample: 2000 2015



Total panel (unbalanced) observations: 255 Variable Coefficient Std. Error t-Statistic Prob. LOG(OIL) 1.265353 0.233425 5.420815 0.0000

GDP 0.045722 0.033019 1.384715 0.1674

POP -0.035650 0.192911 -0.184800 0.8535

LOG(CO2) -0.686814 0.177017 -3.879931 0.0001








Normality Test

0

5

10

15

20

25

-4 -3 -2 -1 0 1 2 3 4


Sample 2000 2015

Observations 255

Mean -9.44e-16

Median 0.298231

Maximum 4.372180

Minimum -4.563484

Std. Dev. 1.754667

Skewness -0.390678

Kurtosis 2.778539





Autocorrelation



Equation: Untitled







Pesaran CD 17.72975 0.0000

Likelihood Ratio

Panel Cross-section Heteroskedasticity LR Test

Null hypothesis: Residuals are homoskedastic

Equation: UNTITLED

Specification: LOG(BIO) LOG(OIL) GDP POP LOG(CO2) C Value df Probability

Likelihood ratio 205.1335 16 0.0000 LR test summary:

Value df

Restricted LogL -504.7095 250

Unrestricted LogL -402.1428 250

Unrestricted Test Equation:


Method: Panel EGLS (Cross-section weights)

Date: 06/25/18 Time: 16:01

Sample: 2000 2015




Iterate weights to convergence

Convergence achieved after 18 weight iterations Variable Coefficient Std. Error t-Statistic Prob. LOG(OIL) 0.912317 0.081953 11.13213 0.0000

GDP -0.035977 0.018552 -1.939268 0.0536

POP -0.556687 0.071301 -7.807586 0.0000

LOG(CO2) -1.089581 0.054783 -19.88907 0.0000

C 6.243481 0.350159 17.83040 0.0000 Weighted Statistics R-squared 0.658964 Mean dependent var 18.39795








Prob(F-statistic) 0.000000 Unweighted Statistics R-squared -0.093981 Mean dependent var 6.947375




Appendix 2: Hydropower

Fixed Effect Model

Dependent Variable: LOG(HYDRO)


Date: 06/25/18 Time: 16:08

Sample: 2000 2015



Total panel (balanced) observations: 256



GDP 0.001511 0.004069 0.371479 0.7106 Positive & Insignificant

POP -0.165072 0.058781 -2.808245 0.0054 Negative & Significant


C 8.720335 0.138739 62.85402 0.0000





S.E. of regression 0.181841 Akaike info criterion -0.496467

Sum squared resid 7.803588 Schwarz criterion -0.219500

Log likelihood 83.54775 Hannan-Quinn criter. -0.385072



Normality Test

0

4

8

12

16

20

24

28

32

36

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6


Sample 2000 2015

Observations 256

Mean -2.46e-17

Median -0.012974

Maximum 0.595462

Minimum -0.624436

Std. Dev. 0.174935

Skewness 0.154136

Kurtosis 3.971440





Poolability F-Test

Redundant Fixed Effects Tests

Equation: Untitled

Test cross-section fixed effects Effects Test Statistic d.f. Prob. Cross-section F 1559.509876 (15,236) 0.0000

Cross-section Chi-square 1179.234139 15 0.0000

Cross-section fixed effects test equation:



Date: 06/25/18 Time: 16:09

Sample: 2000 2015



Total panel (balanced) observations: 256 Variable Coefficient Std. Error t-Statistic Prob. LOG(OIL) 0.395157 0.231881 1.704136 0.0896

GDP 0.159795 0.032938 4.851450 0.0000

POP 0.479577 0.192437 2.492126 0.0133

LOG(CO2) -0.620802 0.176240 -3.522478 0.0005








Autocorrelation



Equation: Untitled



Total panel observations: 256

Cross-section effects were removed during estimation Test Statistic d.f. Prob. Breusch-Pagan LM 294.9447 120 0.0000


Bias-corrected scaled LM 10.75930 0.0000

Pesaran CD -0.390984 0.6958



Random Effect Test


Method: Panel EGLS (Cross-section random effects)

Date: 06/25/18 Time: 16:10

Sample: 2000 2015




Swamy and Arora estimator of component variances



GDP 0.001874 0.004067 0.460650 0.6454 Positive & Insignificant

POP -0.156431 0.058600 -2.669459 0.0081 Negative & Significant


C 8.729626 0.465462 18.75474 0.0000


S.D. Rho

Cross-section random 1.777492 0.9896

Idiosyncratic random 0.181841 0.0104

Weighted Statistics



S.E. of regression 0.185065 Sum squared resid 8.596529



Unweighted Statistics

R-squared -0.210731 Mean dependent var 9.748612




Hausman Test

Correlated Random Effects - Hausman Test

Equation: Untitled

Test cross-section random effects

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 0.000000 4 1.0000 FEM is preferred

* Cross-section test variance is invalid. Hausman statistic set to zero.

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob.

LOG(OIL) 0.101517 0.107607 0.000005 0.0062

GDP 0.001511 0.001874 0.000000 0.0006

POP -0.165072 -0.156431 0.000021 0.0607

LOG(CO2) 0.517841 0.488511 0.000129 0.0097

Cross-section random effects test equation:



Date: 06/25/18 Time: 16:11

Sample: 2000 2015





C 8.720335 0.138739 62.85402 0.0000

LOG(OIL) 0.101517 0.028324 3.584186 0.0004

GDP 0.001511 0.004069 0.371479 0.7106

POP -0.165072 0.058781 -2.808245 0.0054

LOG(CO2) 0.517841 0.084464 6.130880 0.0000





S.E. of regression 0.181841 Akaike info criterion -0.496467

Sum squared resid 7.803588 Schwarz criterion -0.219500

Log likelihood 83.54775 Hannan-Quinn criter. -0.385072





Normality test

0

4

8

12

16

20

-5 -4 -3 -2 -1 0 1 2 3 4


Sample 2000 2015

Observations 256

Mean -2.01e-16

Median 0.238053

Maximum 3.874829

Minimum -4.894187

Std. Dev. 2.180874

Skewness -0.242584

Kurtosis 2.452470



Autocorrelation Test



Equation: Untitled





Cross-section means were removed during computation of correlations Test Statistic d.f. Prob. Breusch-Pagan LM 290.1316 120 0.0000


Pesaran CD -0.244054 0.8072



Pooled OLS (Hydropower)

log (HYDRO)it = 0 + 1 log (OIL)it + 2GDPit + 3POPit+ 4 log (CO2)it+ eit



Date: 06/25/18 Time: 15:53

Sample: 2000 2015





LOG(OIL) 0.395157 0.231881 1.704136 0.0896 Positive & Significant at

10%

GDP 0.159795 0.032938 4.851450 0.0000 Positive & Significant

POP 0.479577 0.192437 2.492126 0.0133 Positive & Significant

LOG(CO2) -0.620802 0.176240 -3.522478 0.0005 Negative & Significant

C 8.098308 1.004387 8.062938 0.0000

R-squared 0.220048 Mean dependent var 9.748612 The model is significant.







Normality Test

0

5

10

15

20

25

30

35

-4 -3 -2 -1 0 1 2 3 4 5


Sample 2000 2015

Observations 256

Mean -2.18e-15

Median -0.213584

Maximum 5.074285

Minimum -3.988971

Std. Dev. 1.750413

Skewness 0.358347

Kurtosis 3.256719





Autocorrelation



Equation: Untitled





Cross-section means were removed during computation of correlations Test Statistic d.f. Prob. Breusch-Pagan LM 480.1580 120 0.0000


Pesaran CD 18.63540 0.0000



Likelihood Ratio

Panel Cross-section Heteroskedasticity LR Test

Null hypothesis: Residuals are homoskedastic

Equation: UNTITLED

Specification: LOG(HYDRO) LOG(OIL) GDP POP LOG(CO2) C Value df Probability

Likelihood ratio 422.8131 16 0.0000 LR test summary:

Value df

Restricted LogL -506.0693 251

Unrestricted LogL -294.6628 251

Unrestricted Test Equation:


Method: Panel EGLS (Cross-section weights)

Date: 06/25/18 Time: 15:57

Sample: 2000 2015




Iterate weights to convergence

Convergence achieved after 17 weight iterations Variable Coefficient Std. Error t-Statistic Prob. LOG(OIL) 0.269736 0.043342 6.223493 0.0000

GDP 0.012987 0.008521 1.524072 0.1287

POP 0.073153 0.037367 1.957654 0.0514

LOG(CO2) -1.272768 0.027603 -46.10988 0.0000

C 9.910729 0.193591 51.19429 0.0000 Weighted Statistics R-squared 0.916094 Mean dependent var 38.32820






Prob(F-statistic) 0.000000 Unweighted Statistics R-squared 0.065340 Mean dependent var 9.748612


crude oil prices and renewable energy driving force in

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