crude oil prices and renewable energy driving force in
TRANSCRIPT
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
x
LIST OF APPENDICES
Page
Appendix 1: Bioenergy 56
Appendix 2: Hydropower 64
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 2 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 3 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 4 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 5 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 6 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 7 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 8 of 71 Faculty of Business and Finance
(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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 9 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 10 of 71 Faculty of Business and Finance
Figure 1.6: Average key crude oil prices in USD/barrel
Source: World Energy Outlook 2017, International Energy Agency
Figure 1.7: IEA total public energy research, development and demonstration
budget by technology
Source: World Energy Outlook 2017, International Energy Agency
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 11 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 12 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 13 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 14 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 15 of 71 Faculty of Business and Finance
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,
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 16 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 17 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 18 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 19 of 71 Faculty of Business and Finance
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).
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 20 of 71 Faculty of Business and Finance
Figure 2.2: Proposed Theoretical Framework
Source: Developed for the research
Renewable Energy
Oil Price
Gross Domestic Product
Carbon Dioxide
Emissions
Population Growth
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 21 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 22 of 71 Faculty of Business and Finance
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)
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 23 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 24 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 25 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 26 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 27 of 71 Faculty of Business and Finance
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)
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 28 of 71 Faculty of Business and Finance
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)
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 29 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 30 of 71 Faculty of Business and Finance
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:
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 31 of 71 Faculty of Business and Finance
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)
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 32 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 33 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 34 of 71 Faculty of Business and Finance
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
Likelihood Ratio (Panel
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 35 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 36 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 37 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 38 of 71 Faculty of Business and Finance
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
Notes: *, ** and *** implies that the rejection of the null hypothesis at 10%, 5%and 1%
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
Likelihood Ratio (Panel
Cross Section Test) 422.8131
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 39 of 71 Faculty of Business and Finance
Continued from Table 4.3
Likelihood Ratio (Panel
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
Notes: *, ** and *** implies that the rejection of the null hypothesis at 10%, 5%and 1%
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 40 of 71 Faculty of Business and Finance
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 %
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 41 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 42 of 71 Faculty of Business and Finance
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
Notes: *, ** and *** implies that the rejection of the null hypothesis at 10%, 5%and 1%
significance level respectively.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 43 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 44 of 71 Faculty of Business and Finance
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).
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 45 of 71 Faculty of Business and Finance
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,
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 46 of 71 Faculty of Business and Finance
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).
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 47 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 48 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 49 of 71 Faculty of Business and Finance
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.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 50 of 71 Faculty of Business and Finance
REFERENCES
Agarwal, P. (2018, March 1). The Environmental Kuznets Curve. Retrieved April
1, 2018, from https://www.intelligenteconomist.com/environmental-
kuznets-curve/
Aguirre, M., & Ibikunle, G.(2014). Determinants of renewable energy growth: A
global sample analysis. Energy Policy, 69, 374-384. doi:
10.1016/j.enpol.2014.02.036
Akbostanci, E., Turut-Asik, S., & Tunc, I. G. (2009). The relationship between
income and environment in Turkey: Is there an environmental Kuznets
Curve? Energy Policy, 37(2), 861-867. doi: 10.1016/j.enpol.2008.08.088
Amadeo, K. (2018, July 31). The components of GDP explained. Retrieved May
20, 2018, from https://www.thebalance.com/components-of-gdp-
explanation-formula-and-chart-3306015
Antonie, M. D., Cristescu, A., & Cataniciu, N. (2010). A panel data analysis of
the connection between employee remuneration, productivity and
minimum wage in Romania. Recent Advances in Mathematics and
Computers in Business, Economics, Biology and Chemistry. Iasi, Romania:
G. Enescu University.
Apergis, N., & Payne, J. E. (2014). Renewable energy, output, CO2 emission, and
fossil fuel prices in Cental America: Evidence from a nonlinear panel
smooth transition vector error correction model. Energy Economics, 42,
226-232. . doi: 10.1016/j.enenco.2014.01.003
Apergis, N., & Ozturk, I. (2015). Testing Environmental Kuznets Curve
hypothesis in Asian countries. Ecological Indicators, 52, 16-22. doi:
10.1016/j.ecolind.2014.11.026
Bellakhal, R., Kheder, S. B., & Haffoudhi, H. (2016). Institutional and market
factors driving renewable energy development in MENA region: A panel
data approach. Retrieved from http://erf.org.eg/wp-
content/uploads/2017/03/Intl_ERF23AC_HoudaSoniaRihab.pdf
Bloomberg L.P. (2017, November 7). Clean energy investment in emerging
markets. Retrieved from Bloomberg New Energy Finance:
https://about.bnef.com/blog/clean-energy-investment-emerging-markets/
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 51 of 71 Faculty of Business and Finance
Bloomberg L.P. (2017). Climatescope 2017 – The clean energy country
competitiveness index. Retrieved from Bloomberg New Energy Finance:
http://global-climatescope.org/en/download/reports/climatescope-2017-
report-en.pdf
Bollen, K.A., & Brand, J.E. (2010). A general panel model with random and fixed
Effects: A structural equations approach. Social Forces, 89(1), 1–
34.doi:10.1353/sof.2010.0072
Boneau, C. A. (1960). The effects of violations of assumptions underlying the t
test. Psychological Bulletin, 57(1), 49-64. Retrieved from
https://pdfs.semanticscholar.org/86f9/1241e18df224372a01264069334972
cb7f34.pdf
Bonnie, K. S., Bruno, S., Cheryl, P., Cody, S., & Dawn, K. (2013, May 1). What
are the effects of multicollinearity and when can I ignore them? [Blog
post]. Retrieved June 2, 2018, from
http://blog.apastyle.org/apastyle/2016/04/how-to-cite-a-blog-post-in-apa-
style.html
Borenstein, M., Hedges, L., Higgins, J., & Rothstein, H. (2010). A basic
introduction to fixed-effect and random-effects models for meta-analysis.
Research Synthesis Methods, 1(2), 97-111.doi: 10.1002/jrsm.12
BP p.l.c. (2017). BP Energy Outlook 2017. Retrieved March 15, 2018 from
https://www.bp.com/content/dam/bp/pdf/energy-economics/energy-
outlook-2017/bp-energy-outlook-2017.pdf.
Carley, S. (2009). State renewable energy electricity policies: An empirical
evaluation of effectiveness. Energy Policy, 37, 3071-3081. doi:
10.1016/j.enpol.2009.03.062
Chang, T. H., Huang, C. M., & Lee, M. C. (2009). Threshold effect of the
economic growth rate on the renewable energy development from a
change in energy price: Evidence from OECD countries. Energy Policy, 37,
5796-5802. doi: 10.1016/j.enpol.2009.08.049
Cincotta, R. P., & Engelman, R. (1997). Economics and rapid change: The
influence of population growth. Washington, D.C: Population Action
International.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 52 of 71 Faculty of Business and Finance
Clark, T., & Linzer, D. (2015). Should I use fixed or random Effects? Political
Science Research and Methods, 3(2), 399-408. doi:10.1017/psrm.2014.32
Duguid, A. (n.d.). Renewable energy: An ethical decision or simple economics.
Retrieved on February 8, 2018 from Ethisphere:
https://insights.ethisphere.com/renewable-energy-an-ethical-decision-or-
simple-economics/
Gan, J. B., & Smith, C. T. (2011). Drivers for renewable energy: A comparison
among OECD countries. Biomass and Bioenergy, 35, 4497-4503. doi:
10.1016/j.biombioe.2011.03.022
Greg DePersio. (2018, January 29). Why did oil prices drop so much in 2014?
Retrieved March 2, 2018, from
https://www.investopedia.com/ask/answers/030315/why-did-oil-prices-
drop-so-much-2014.asp
Gujarati, D. N. (2004). Basic Econometrics. (4th Ed.). New York:The McGraw-
Hill Companies.
Gujarati, D. N., & Porter, D. C. (2010). Basic econometrics. (5th Ed.). Upper
Saddle River, NJ: Pearson/Prentice Hall.
Hausman, J. (1978). Specification tests in econometrics. Econometrica, 46(6),
1251-1271.https://www.jstor.org/stable/1913827
Ihtisham, M., Silya, G. A., Abdullah, A., Alam, A., Zaman, K., Kyophilavong, P.,
Shabaz, M., Baloch, S. U., & Shams, T. (2014). Turn on the lights:
Macroeconomic factors affecting renewable energy in Pakistan.
Renewable and Sustainable Energy Reviews, 38, 277-284.doi:
10.1016/j.rser.2014.05.090
International Energy Agency. (2017, November 14). World Energy Outlook 2017.
Retrieved from https://www.iea.org/weo2017/.
International Renewable Energy Agency. (2018, January). Renewable power
generation costs in 2017. Retrieved from
https://cms.irena.org/publications/2018/Jan/Renewable-power-generation-
costs-in-2017
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 53 of 71 Faculty of Business and Finance
Jalil, A., & Mahmud, S. F. (2009). Environment Kuznets Curve for CO2
emissions: A cointegration analysis. Energy Policy, 37, 5167-5172. doi:
10.1016/j.enpol.2009.07.044
Jeeshim. (2003). Multicollinearity in regression models. Retrieved June 30, 2018,
from http://php.indiana.edu/~kucc625
Killingsworth, M. (1990). The economics of comparable worth. Kalamazoo,
Mich.: W.E. Upjohn Institute for Employment Research, 1-10. doi:
10.17848/9780880995528
Lehmann, E. (2006). On likelihood ratio tests. Institute of Mathematical
Statistics,49, 1-8.doi: 10.1214/074921706000000356
Lin, B, Nwakeze, N. M., Omoju, O., & Megbowon, E. (2016). Is the
environmental Kuznets curve hypothesis a sound basis for environmental
policy in Africa? Journal of Cleaner Production, 52. doi:
10.1016/j.jclepro.2016.05.173
Marques, A. C., Fuinhas, J. A., & Manso, J. R. P. (2010). Motivations driving
renewable energy in European countries: A panel data approach. Energy
Policy, 38, 6877-6885. doi: 10.1016/j.enpol.2010.07.003
Muhammad, I. K., Tabassam, Y., Abdul, S., Niaz, B. K., & Riaz, M. (2017). 2014
oil plunge: Causes and impacts on renewable energy. Renewable and
Sustainable Energy Reviews, 68, 609-622. doi: 10.1016/j.rser.2016.10.026
Nasir, M., & Rehman, F.(2011). Environmental Kuznets Curve for carbon
emissions in Pakistan: An empirical investigation. Energy Policy,39,
1857–1864. doi: 10.1016/j.enpol.2011.01.025
Omri, A., & Nguyen, D. C. (2014). On the determinants of renewable energy
consumption: International evidence. Energy, 72, 554-560. doi:
10.1016/j.energy.2014.05.081
Organisation for Economic Co-operation and Development (OECD). (2007,
April). OECD factbook 2007. Paris, France.
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 54 of 71 Faculty of Business and Finance
Pettinger, T. (2017, September, 11). Environmental Kuznets Curve. Retrieved
fromhttps://www.economicshelp.org/blog/14337/environment/environmen
tal-kuznets-curve/
Rafiq, S. & Alam, K. (2010). Identifying the determinants of renewable energy
consumption in leading RE investor emerging countries. Retrieved from
Rafiq, S., Bloch, H., &Salim, R. (2014). Determinants of renewable energy
adoption in China and India: A comparative analysis. Applied Economics,
46(22), 2700-2710. doi: 10.1080/00036846.2014.909577
Rajasekar, S., Philominathan, P. & Chinnathambi, V. (2006). Research
methodology. Retrieved May 29, 2018, from
http://arxiv.org/pdf/physics/0601009.pdf
Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and
renewable energy stock prices? Energy Economics, 48, 32-45. doi:
10.1010/j.eneco.2014.12.009
Sadorsky, P. (2009). Renewable energy consumption, CO2 emissions and oil
prices in the G7 countries. Energy Economics, 31, 456-462. doi:
10.1016/j.eneco.2008.12.010
Sadorsky, P. (2009). Renewable energy consumption and income in emerging
economies. Energy Policy, 37, 4021-4028. doi:
10.1016’j.enpol.2009.05.003
Salim, R. A., & Rafiq, S. (2012). Why do some emerging economies proactively
accelerate the adoption of renewable energy? Energy Ecconomics, 34,
1051-1057. doi: 10.1016/j.eneco.2011.08.015
Silva, P. P., Cerqueira, P. A., & Ogbe, W. (2017). Determinants of renewable
energy growth in Sub-Saharan Africa: Evidence from Panel ARDL.
Energy. doi: 10.1016/j.energy.2018.05.068
Sinha, A., & Shahbaz, M. (2018). Estimation of Environmental Kuznets Curve for
CO2 emission: Role of renewable energy generation in India. Renewable
Energy, 119, 703-711. doi: 10.1016/j.renene.2017.12.058
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 55 of 71 Faculty of Business and Finance
Sisodia, G. S., & Soares, I. (2014). Panel data analysis for renewable energy
investment determinants in Europe. Applied Economics Letters, 22(5),
397-401. doi: 10.1080/13504851.2014.946176
Statham, B. (2013). World Energy Resources: 2013 Survey. London: World
Energy Council.
Ugoni & Walker. (1995). The T test: An introduction. Comsig Review, 4(3), 61-64.
United States Bureau of Reclamation. (2018). Managing water in the west:
Hydroelectric power. Retrieved from
https://www.usbr.gov/power/index.html
U.S. Department of Energy, Energy Information Administration, Independent
Statistics & Analysis. (2018, June 11).Crude oil (Cushing, OK WTI spot
price) in dollars per barrel. Retrieved from
https://www.eia.gov/dnav/pet/hist/RWTCD.htm
U.S. Department of Energy, Energy Information Administration, Independent
Statistics & Analysis. (2018, May 10).EIA raises crude oil, gasoline price
forecasts for 2018. Retrieved
https://www.eia.gov/todayinenergy/detail.php?id=36152
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 56 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 57 of 71 Faculty of Business and Finance
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:
Dependent Variable: LOG(BIO)
Method: Panel Least Squares
Date: 06/25/18 Time: 16:04
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
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
Adjusted R-squared 0.147063 S.D. dependent var 1.915063
S.E. of regression 1.768649 Akaike info criterion 3.997722
Sum squared resid 782.0295 Schwarz criterion 4.067158
Log likelihood -504.7095 Hannan-Quinn criter. 4.025652
F-statistic 11.94866 Durbin-Watson stat 0.089124
Prob(F-statistic) 0.000000
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 58 of 71 Faculty of Business and Finance
Autocorrelation
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 16
Cross-sections included: 16
Total panel (unbalanced) observations: 255
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
Dependent Variable: LOG(BIO)
Method: Panel EGLS (Cross-section random effects)
Date: 06/25/18 Time: 16:12
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
Total panel (unbalanced) observations: 255
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
LOG(OIL) 0.973887 0.096310 10.11197 0.0000 Positive & Significant
GDP -0.046753 0.014171 -3.299145 0.0011 Negative & Significant
POP -0.082384 0.199759 -0.412418 0.6804 Negative & Insignificant
LOG(CO2) 0.917921 0.269852 3.401568 0.0008 Positive & Significant
C 1.900955 0.665183 2.857794 0.0046
Effects Specification
S.D. Rho
Cross-section random 1.857956 0.8957
Idiosyncratic random 0.633843 0.1043
Weighted Statistics
R-squared 0.454418 Mean dependent var 0.591209
Adjusted R-squared 0.445689 S.D. dependent var 0.862980
S.E. of regression 0.642658 Sum squared resid 103.2523
F-statistic 52.05657 Durbin-Watson stat 0.387692
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared -0.299510 Mean dependent var 6.947375
Sum squared resid 1210.541 Durbin-Watson stat 0.033068
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 59 of 71 Faculty of Business and Finance
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
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 60 of 71 Faculty of Business and Finance
Normality test
0
4
8
12
16
20
-5 -4 -3 -2 -1 0 1 2 3 4
Series: Standardized Residuals
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
Jarque-Bera 5.550167
Probability 0.062344
Autocorrelation Test
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 16
Cross-sections included: 16
Total panel (unbalanced) observations: 255
Note: non-zero cross-section means detected in data
Test employs centered correlations computed from pairwise samples Test Statistic d.f. Prob. Breusch-Pagan LM 592.7902 120 0.0000
Pesaran scaled LM 30.51848 0.0000
Pesaran CD 10.29051 0.0000
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 61 of 71 Faculty of Business and Finance
Pooled OLS
log (BIO)it = 0 + 1 log (OIL)it + 2GDPit + 3POPit+ 4 log (CO2)it+ eit
Dependent Variable: LOG(BIO)
Method: Panel Least Squares
Date: 06/25/18 Time: 15:51
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
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
Adjusted R-squared 0.147063 S.D. dependent var 1.915063
S.E. of regression 1.768649 Akaike info criterion 3.997722
Sum squared resid 782.0295 Schwarz criterion 4.067158
Log likelihood -504.7095 Hannan-Quinn criter. 4.025652
F-statistic 11.94866 Durbin-Watson stat 0.089124
Prob(F-statistic) 0.000000
Normality Test
0
5
10
15
20
25
-4 -3 -2 -1 0 1 2 3 4
Series: Standardized Residuals
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
Jarque-Bera 7.007865
Probability 0.030079
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 62 of 71 Faculty of Business and Finance
Autocorrelation
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 16
Cross-sections included: 16
Total panel (unbalanced) observations: 255
Note: non-zero cross-section means detected in data
Test employs centered correlations computed from pairwise samples Test Statistic d.f. Prob. Breusch-Pagan LM 640.4048 120 0.0000
Pesaran scaled LM 33.59199 0.0000
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:
Dependent Variable: LOG(BIO)
Method: Panel EGLS (Cross-section weights)
Date: 06/25/18 Time: 16:01
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
Total panel (unbalanced) observations: 255
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
Adjusted R-squared 0.653508 S.D. dependent var 18.18101
S.E. of regression 2.018989 Akaike info criterion 3.193277
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 63 of 71 Faculty of Business and Finance
Sum squared resid 1019.079 Schwarz criterion 3.262713
Log likelihood -402.1428 Hannan-Quinn criter. 3.221207
F-statistic 120.7652 Durbin-Watson stat 0.365365
Prob(F-statistic) 0.000000 Unweighted Statistics R-squared -0.093981 Mean dependent var 6.947375
Sum squared resid 1019.084 Durbin-Watson stat 0.038750
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 64 of 71 Faculty of Business and Finance
Appendix 2: Hydropower
Fixed Effect Model
Dependent Variable: LOG(HYDRO)
Method: Panel Least Squares
Date: 06/25/18 Time: 16:08
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
Total panel (balanced) observations: 256
Variable Coefficient Std. Error t-Statistic Prob.
LOG(OIL) 0.101517 0.028324 3.584186 0.0004 Positive & Significant
GDP 0.001511 0.004069 0.371479 0.7106 Positive & Insignificant
POP -0.165072 0.058781 -2.808245 0.0054 Negative & Significant
LOG(CO2) 0.517841 0.084464 6.130880 0.0000 Positive & Significant
C 8.720335 0.138739 62.85402 0.0000
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.992210 Mean dependent var 9.748612
Adjusted R-squared 0.991583 S.D. dependent var 1.982014
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
F-statistic 1582.054 Durbin-Watson stat 0.953774
Prob(F-statistic) 0.000000
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
Series: Standardized Residuals
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
Jarque-Bera 11.07977
Probability 0.003927
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 65 of 71 Faculty of Business and Finance
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:
Dependent Variable: LOG(HYDRO)
Method: Panel Least Squares
Date: 06/25/18 Time: 16:09
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
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
C 8.098308 1.004387 8.062938 0.0000 R-squared 0.220048 Mean dependent var 9.748612
Adjusted R-squared 0.207619 S.D. dependent var 1.982014
S.E. of regression 1.764305 Akaike info criterion 3.992729
Sum squared resid 781.3060 Schwarz criterion 4.061971
Log likelihood -506.0693 Hannan-Quinn criter. 4.020578
F-statistic 17.70371 Durbin-Watson stat 0.107463
Prob(F-statistic) 0.000000
Autocorrelation
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 16
Cross-sections included: 16
Total panel observations: 256
Cross-section effects were removed during estimation Test Statistic d.f. Prob. Breusch-Pagan LM 294.9447 120 0.0000
Pesaran scaled LM 11.29263 0.0000
Bias-corrected scaled LM 10.75930 0.0000
Pesaran CD -0.390984 0.6958
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 66 of 71 Faculty of Business and Finance
Random Effect Test
Dependent Variable: LOG(HYDRO)
Method: Panel EGLS (Cross-section random effects)
Date: 06/25/18 Time: 16:10
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
Total panel (balanced) observations: 256
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
LOG(OIL) 0.107607 0.028236 3.810999 0.0002 Positive & Significant
GDP 0.001874 0.004067 0.460650 0.6454 Positive & Insignificant
POP -0.156431 0.058600 -2.669459 0.0081 Negative & Significant
LOG(CO2) 0.488511 0.083699 5.836510 0.0000 Positive & Significant
C 8.729626 0.465462 18.75474 0.0000
Effects Specification
S.D. Rho
Cross-section random 1.777492 0.9896
Idiosyncratic random 0.181841 0.0104
Weighted Statistics
R-squared 0.324121 Mean dependent var 0.249244
Adjusted R-squared 0.313350 S.D. dependent var 0.223335
S.E. of regression 0.185065 Sum squared resid 8.596529
F-statistic 30.09209 Durbin-Watson stat 0.862311
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared -0.210731 Mean dependent var 9.748612
Sum squared resid 1212.833 Durbin-Watson stat 0.006112
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 67 of 71 Faculty of Business and Finance
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:
Dependent Variable: LOG(HYDRO)
Method: Panel Least Squares
Date: 06/25/18 Time: 16:11
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
Total panel (balanced) observations: 256
Variable Coefficient Std. Error t-Statistic Prob.
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
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.992210 Mean dependent var 9.748612
Adjusted R-squared 0.991583 S.D. dependent var 1.982014
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
F-statistic 1582.054 Durbin-Watson stat 0.953774
Prob(F-statistic) 0.000000
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 68 of 71 Faculty of Business and Finance
Normality test
0
4
8
12
16
20
-5 -4 -3 -2 -1 0 1 2 3 4
Series: Standardized Residuals
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
Jarque-Bera 5.708550
Probability 0.057598
Autocorrelation Test
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 16
Cross-sections included: 16
Total panel observations: 256
Note: non-zero cross-section means detected in data
Cross-section means were removed during computation of correlations Test Statistic d.f. Prob. Breusch-Pagan LM 290.1316 120 0.0000
Pesaran scaled LM 10.98195 0.0000
Pesaran CD -0.244054 0.8072
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 69 of 71 Faculty of Business and Finance
Pooled OLS (Hydropower)
log (HYDRO)it = 0 + 1 log (OIL)it + 2GDPit + 3POPit+ 4 log (CO2)it+ eit
Dependent Variable: LOG(HYDRO)
Method: Panel Least Squares
Date: 06/25/18 Time: 15:53
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
Total panel (balanced) observations: 256
Variable Coefficient Std. Error t-Statistic Prob.
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.
Adjusted R-squared 0.207619 S.D. dependent var 1.982014
S.E. of regression 1.764305 Akaike info criterion 3.992729
Sum squared resid 781.3060 Schwarz criterion 4.061971
Log likelihood -506.0693 Hannan-Quinn criter. 4.020578
F-statistic 17.70371 Durbin-Watson stat 0.107463
Prob(F-statistic) 0.000000
Normality Test
0
5
10
15
20
25
30
35
-4 -3 -2 -1 0 1 2 3 4 5
Series: Standardized Residuals
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
Jarque-Bera 6.181931
Probability 0.045458
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 70 of 71 Faculty of Business and Finance
Autocorrelation
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 16
Cross-sections included: 16
Total panel observations: 256
Note: non-zero cross-section means detected in data
Cross-section means were removed during computation of correlations Test Statistic d.f. Prob. Breusch-Pagan LM 480.1580 120 0.0000
Pesaran scaled LM 23.24810 0.0000
Pesaran CD 18.63540 0.0000
Crude Oil Price and Renewable Energy Driving Force in Emerging Economies
Undergraduate Research Project Page 71 of 71 Faculty of Business and Finance
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:
Dependent Variable: LOG(HYDRO)
Method: Panel EGLS (Cross-section weights)
Date: 06/25/18 Time: 15:57
Sample: 2000 2015
Periods included: 16
Cross-sections included: 16
Total panel (balanced) observations: 256
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
Adjusted R-squared 0.914757 S.D. dependent var 36.36935
S.E. of regression 1.931373 Akaike info criterion 2.341115
Sum squared resid 936.2808 Schwarz criterion 2.410357
Log likelihood -294.6628 Hannan-Quinn criter. 2.368964
F-statistic 685.1095 Durbin-Watson stat 0.309181
Prob(F-statistic) 0.000000 Unweighted Statistics R-squared 0.065340 Mean dependent var 9.748612
Sum squared resid 936.2828 Durbin-Watson stat 0.009247