Fakulti Ekonomi dan Perniagaan
ANALYZING THE INFLUENCE OF SELECTED MACROECONOMIC VARIABLES
ON CO2 EMISSIONS IN MALAYSIA
Amir Aiman Bin Hamblie @ Awang Jamali
65410
BACHELOR IN ECONOMICS WITH HONOURS
(BUSINESS ECONOMICS)
2021
ANALYZING THE INFLUENCE OF SELECTED MACROECONOMIC VARIABLES ON
CO2 EMISSIONS IN MALAYSIA
AMIR AIMAN BIN HAMBLIE @ AWANG JAMALI
This project is submitted in partial fulfilment of
The requirement for the degree of Bachelor of Economics with Honours
(Business Economics)
Faculty of Economics and Business
UNIVERSITI MALAYSIA SARAWAK
2021
Statement of Originality
The work described in this Final Year Project, entitled
“ANALYZING THE INFLUENCE OF SELECTED MACROECONOMIC
VARIABLES ON CO2 EMISSIONS IN MALAYSIA”
Is the best author’s knowledge that of the author except where due to reference is
made.
08/07/2021
(Date Submitted)
(Student’s Signature)
Amir Aiman bin Hamblie @ Awang Jamali
65410
ABSTRACT
ANALYZING THE INFLUENCE OF SELECTED MACROECONOMIC
VARIABLES ON CO2 EMISSIONS IN MALAYSIA
By
Amir Aiman bin Hamblie @ Awang Jamali
This research was conducted to examine the relationship between renewable
energy consumption, foreign direct investment, gross domestic product (GDP) and
CO2 emissions in Malaysia. This study used time series data consist of 30 observations
which is from 1990 to 2019. Annual dataset is taken from World Bank Data. The data
was analysed using E-views software which consist of several method used in this
study. The method such as Ordinary Least Square (OLS), Augmented Dickey-Fuller
Unit Root Test, Johansen and Jeliues Cointegration Test and Vector Error Correction
Model (VECM) Causality Test were used in this study. The result shows that
renewable energy consumption, foreign direct investment, GDP have short-run and
long-run relationship with affecting the CO2 emissions in Malaysia. Thus, some
recommendations had been listed in this study to reduce the effect on the environment
such as empowering the green economy, education on environment and sustainable
development, tightening regulation on foreign investment, environmental policy and
tax enforcement.
Keywords: Renewable energy consumption, foreign direct investment, CO2
emissions, Malaysia.
i
ABSTRAK
MENGENAL PASTI PENGARUH PEMBOLEH UBAH MAKROEKONOMI
TERPILIH TERHADAP PELEPASAN KARBON DIOKSIDA DI MALAYSIA
Oleh
Amir Aiman bin Hamblie @ Awang Jamali
Penyelidikan kajian ini dijalankan bertujuan untuk mengenal pasti hubungan
antara penggunaan tenaga boleh diperbaharui, pelaburan langsung asing, KDNK dan
pelepasan karbon dioksida di Malaysia. Kajian ini menggunakan data siri masa yang
terdiri daripada 30 sampel dari tahun 1990 hingga 2019. Data tahunan diperolehi
daripada Bank Dunia. Data akan dianalisis menggunakan aplikasi E-views yang terdiri
daripada beberapa kaedah dalam kajian ini. Kaedah seperti analisis Ordinary Least
Square (OLS), ujian Augmented Dickey-Fuller Unit Root, ujian Johansen and
Julieues, dan Ujian Vector Error Correction Model (VECM) Granger Causality
digunakan dalam kajian ini. Keputusan menunjukkan penggunaan tenaga boleh
diperbaharui, pelaburan langsung asing, KDNK mempunyai hubungan jangka pendek
dan jangka Panjang dalam memberi kesan terhadap pelepasan karbon dioksida di
Malaysia. Oleh itu, beberapa cadangan telah disenaraikan dalam kajian ini untuk
mengurangi pengaruh terhadap persekitaran seperti memperkasakan ekonomi hijau,
pendidikan terhadap persekitaran dan pembangunan lestari, memperketat peraturan
terhadap pelaburan asing, polisi berkaitan persekitaran dan penguatkuasaan cukai.
Kata kunci: Penggunaan tenaga boleh diperbaharui, pelaburan langsung asing,
keluaran dalam negara kasar, pelepasan karbon dioksida, Malaysia.
ii
ACKNOWLEDGEMENT
First and foremost, praise and thanks to the God, the Almighty, For His shower
of blessings throughout my research work to complete the thesis successfully. I would
like to thank the Faculty of Business and Economics, University Malaysia Sarawak
(UNIMAS) for giving me the opportunity to conduct this study which is one of the
mandatory subjects in my program. I have gained a useful knowledge and skills
throughout the process of completing my thesis.
I would like to express my deep and sincere gratitude to my supervisor Miss
Audrey Liwan, for giving me a good opportunity to do this thesis and guideline
throughout the consultations for this thesis. She has given me a knowledge and ideas
that are very important and useful to help me complete my thesis. She also always
gives words of encouragement to be more motivated and enthusiastic throughout the
completion of my thesis. Without her, I would not be able to complete my thesis on
time.
I would like to thank my parents for their love, prayer, caring and sacrifices
for educating me and preparing for my future and the support that they give throughout
the difficult times of my journey to complete my thesis. Lastly, I would like to thank
my friends who have helped me throughout the process of completing my thesis.
iii
TABLES OF CONTENTS
Abstract……………………………………………………………………….
i
Acknowledgement…………………………………………………………….
iii
List of Table…………………………………………………………………..
vii
List of Figures………………………………………………………………… viii
CHAPTER 1 INTRODUCTION
1.0
Introduction………………………………………………..................
1
1.1
Background of Studies………………………………………………..
3
1.2
Problem of Statement…………………………………………….......
8
1.3
Research Question……………………………………………………
10
1.4
Research Objective…………………………………………………...
11
1.4.1
General Objective……………………………………………
11
1.4.2
Specific Objective…………………………………………...
11
1.5
Scope of Study……………………………………………………......
11
1.6
Summary……………………………………………………………...
12
CHAPTER 2 LITERATURE REVIEW
2.0
Introduction…………………………………………………………...
14
2.1
Theoretical Framework……………………………………………….
14
Environmental Kuznets Curve………………………………
14
Empirical Study………………………………………………………
16
2.2.1
Renewable Energy and CO2 emissions……………………...
16
2.2.2
Foreign Direct Investment and CO2 emissions.……………..
18
2.1.1
2.2
iv
2.2.3
2.3
Economic Growth and CO2 emissions………………………
19
Research Gap………………………………………………………….
21
CHAPTER 3 METHODOLOGY
3.0
Introduction…………………………………………………………...
22
3.1
Data Description……………………………………………………...
22
3.2
Estimation Model……………………………………………………..
23
3.3
Conceptual Framework……………………………………………….
23
3.4
Research Method……………………………………………………..
24
3.4.1
Ordinary Least Square (Double Log Model)………………...
24
3.4.2
Unit Root Test………………………………………………..
25
3.4.2.1 Augmented Dickey Fuller Test…………………….
25
3.4.3
Johansen and Juselius Cointegration Test…………………....
26
3.4.4
Vector Correction Error Model (VECM) Granger Causality
3.5
Test…………………………………………………………...
27
Summary………………………………………………………………
28
CHAPTER 4: RESULT AND DISCUSSIONS
4.0
Introduction…………………………………………………………...
29
4.1
Empirical Finding and Discussion…………………………………….
29
4.1.1
Ordinary Least Square (Double Log Model) …………………
29
4.1.2
Augmented Dickey Fuller Unit Root Test……………………
31
4.1.3
Johansen and Juselius Cointegration Test……………………
32
v
4.1.4
Vector Correction Error Model (VECM) Granger Causality
Test…………………………………………………………...
34
CHAPTER 5: CONCLUSION
5.0
Introduction……………………………………………………………
37
5.1
Summary of Study…………………………………………………......
37
5.2
Policy Implications and Recommendations…………………………...
38
5.3
Limitation of Study……………………………………………………
42
References
Appendix
vi
LIST OF TABLES
Table 4.1
Coefficient of Variation for Each Functional Form……...
30
Table 4.2
The Result for OLS Test………………………………….
30
Table 4.3
The Result for Augmented Dickey Fuller Test…………...
32
Table 4.4
The Result of Johansen & Juselius Test………………….. 33
Table 4.5
The Result of VECM Normalized Equation Test………...
34
Table 4.6
The Result of VECM Granger Causality Test……………
35
vii
LIST OF FIGURES
Figure 1.1
Trend of Gross Domestic Product at 2010 price (Billion
US Dollar)………………………………………………...
3
Figure 1.2
CO2 emissions (Kiloton) in Malaysia……………………..
4
Figure 1.3
Foreign Direct Investment in Malaysia (net Billion US
Dollar)……………………………………………………... 5
Figure 1.4
Renewable Energy Consumption % of Total Energy
Consumption………………………………………………. 6
Figure 2.1
Environmental Kuznets Curve (EKC)…………………….. 16
Figure 3.1
Dependent and Independent Variables……………………. 23
Figure 3.2
Frameworks Study of the Influence of Renewable Energy,
Foreign Direct……………………………………………... 23
viii
CHAPTER 1
INTRODUCTION
1.0 Introduction
Climate change, natural disasters and pollution are a growing phenomenon of
global warming, as well as increasing human needs to use less and more sustainable
natural resources have created challenges and competition in various fields such as
business, manufacturing and construction. This has led to high demand among the
wider community. Scientifically, this natural change has disrupted the well-being of
the people in Malaysia (Rahman, 2014). According to studies conducted by the
Ministry of Natural Resources and Environment, the impact of future climate change
will be a challenge in the effort to maintain the country's sustainability (Ministry of
Natural Resources and Environment, 2010)
The researchers Destek and Sarkodie (2019) stated that issues related to
climate change have in fact become an important topic of debate around the world and
have become a global concern as they are extremely dangerous to the environment and
sustainable development. Over the past few years, most of the countries in the world
has undergone tremendous economic development and growth due to expansion and
industrial development. In particular, nations such as Malaysia, Vietnam, India, China,
Russia, and South Africa, have seen historic growth in the Gross Domestic Product
rate due to rapid industrialization and urbanisation (GDP). (Begum et al., 2015). This
rapid development led to a widespread and uncontrolled increase in CO2 emissions.
1
Various countries have taken steps to reduce CO2 emissions including
developed countries such as the Japan, United States, England, France and China.
There are various policies such as Paris Agreement that have been introduced to aim
to measure the level of pollution caused by CO2 emissions and the impact on the
economic sector and the well-being of society (UNCC, 2015). Masouko, Kainuma and
Morita (1995) stated that the Asia-Pacific Integrated Model (AIM) was introduced
with the aim of studying the extent of greenhouse gas emissions and absorption in
Japan, Australia, Malaysia, Indonesia and countries in the Asian region and identifying
the impact of such CO2 emissions on the economy and environment in the member
states. The AIM model can assist and contribute in policy making and help to measure
the level of global warming. AIM consists of three models namely; the greenhouse gas
emissions model (AIM / emissions), the climate change impact model (AIM / impact),
and the global climate change model (AIM / climate.).
Paris Agreement are one of the most important steps in helping to reduce the
greenhouse gas emissions globally. This Agreement is an international agreement on
climate change that is legally binding. The agreement was approved by 196 Parties at
21st Conference of the Parties on 12 December 2015 that held in Paris and came into
force on November 4, 2016. The implementation of the Paris Agreement aims to
obtain cooperation from member states to lower the earth's temperature or global
warming by 2 degrees Celsius. Once the target has been reached, then member
countries will continue efforts to lower by 1.5 degrees Celsius compared to the level
of economic development (UNCC, 2015). To ensure that this goal is achieved, all
countries involved have tried to achieve the target in the near future in order to fulfil
2
the long-term temperature target and further preserve the earth and prevent extreme
climate change.
With rapid economic growth and the expansion of the built environment in
Malaysia, reducing energy intensity and CO2 emissions goals have led to a major
challenge. Kuala Lumpur is the capital of Malaysia among the cities that contribute to
the high rate of air pollution with a population of more than 2 million people. Malaysia
has introduced various efforts to reduce CO2 emissions caused by rapid development
especially in the capital city of Kuala Lumpur. The Malaysian government has worked
with other countries internationally to support efforts to address the issue of carbon
dioxide emissions extensively. The Paris 2015 Agreement is one of the measures taken
by the government to support efforts to reduce the intensity of greenhouse gas (GHG)
emissions by 45% by 2030 compared to the emissions intensity of the previous 25
years.
1.1 Background of Study
Figure 1.1: Trend of Gross Domestic Product at 2010 price (Billion US Dollar)
Source: World Bank Data (2015)
3
Figure 1.1 had summarised the trend of Gross Domestic Product (GDP) in
Malaysia (Billion US Dollar) at 2010 price from the year of 1990 to 2015. From the
figure 1.1, there had been a consistent annual growth of GDP. The highest value of
GDP recorded was 330.32 billion US dollar in the year 2015. There had always been
a strong relationship between the Country’s GDP and CO2 emissions where the rapid
development of Malaysia affected the total CO2 emissions in the country. Generally,
a country with uncontrolled rapid development will contribute to increasing in CO2
emissions.
Figure 1.2: 𝐂𝐎𝟐 emissions (Kiloton) in Malaysia
Source: World Bank Data (2015)
For the past 30 years, CO2 emissions in Malaysia had been growing
exponentially as demonstrated in Figure 1.2. From the late 1980s, the total of CO2
emissions in Malaysia had been increasing steadily until the 1997. During the period
of 1998 and 1999, the total of CO2 emissions had plunged for approximately 8.5%
and 13.5% compared to the year of 1997. However, it had risen for about 85.8% in the
following year compared to 1999 and continued to grow gradually until year 2015.
4
The lowest recorded CO2 emissions in Malaysia was in year 1990 that was 56,592.811
Kiloton and the highest was in the year 2014 that was 242,671.059 Kiloton.
Researchers Fulton, et.al (2017) stated that CO2 emissions are projected by AsiaPacific Economic Cooperation will show an upward trend of up to 300,000 kilotons
by 2030 compared to 200,000 kilotons currently.
Figure 1.3: Foreign Direct Investment in Malaysia (net Billion US Dollar)
Source: World Bank Data (2015)
Figure 1.3 had summarised the trend of Foreign Direct Investment in Malaysia
(Net Billion US Dollar) from the year of 1990 to 2015. From the figure 1.3, there is a
trend of instability on the value of FDI in Malaysia. During 1990 to 2006, FDI flow in
Malaysia was below 0 level or experienced a negative value. Malaysia recorded the
lowest FDI in 1992 at -5.183 Billion US Dollars during 1990 to 2015. This is due to
the declining world economy which causes a slowdown in FDI flow. in 2006, the flow
of FDI in malaysia began to recover by recording a value of more than 0 or a positive
value. This upsurge factor is due to the global economic recovery which accelerated
the flow of FDI in malaysia. FDI in Malaysia recorded the highest value in 2012 at 8
5
Billion US dollars and has increased by around 91.99% compared to the previous year
which recorded a total of 2.989 billion US dollars in 2011. there are several studies
conducted have linked the relationship between foreign direct investment and CO2
emissions where the two variables have diverse relationships depending on the flow
of foreign direct investment of a country.
Figure 1.4: Renewable Energy Consumption % of Total Energy Consumption
Source: World Bank Data (2015)
For the past 30 years, Renewable energy cunsumption shows a declining trend
as demonstrated in Figure 1.2. From the year of 1990, the total of renewable energy
consumption in Malaysia had been lessening steadily until the 1997. During the period
of 1998 and 1999, the total of renewable energy consumption had risen for
approximately 6.8% and 9.5% compared to the year of 1997. However, it had plunged
for about 46% in the year of 2010 compared to 1999 and continued to grow gradually
until year 2015. The lowest recorded renewable energy consumption in Malaysia was
in year 2010 that was 3.819% from total renewable energy consumption and the
highest was in the year 1990 that was 11.981% from total renewable energy
6
consumption. Renewable energy consumption and CO2 emissions in Malaysia
indicated a positive relationship where the decline in renewable energy consumption
has led to an increase in CO2 emissions in Malaysia. Renewable several studies have
been conducted stating that renewable energy consumption has a relationship with
CO2 emissions but the relationship is not very significant. renewable energy
consumption and CO2 emissions in Malaysia have a positive relationship where when
renewable energy consumption decreases then CO2 emissions increases, and vice
versa.
Malaysia has pledged to cooperate internationally in global efforts to address
climate change caused by widespread CO2 emissions even before the implementation
of the Paris Agreement. Malaysia's efforts to address the carbon emissions problem
began in 2006, 3 years before the public declaration by Prime Minister Razak. Since
then, the country has steadily been redirected to a less carbon-intensive future by a
variety of policies and strategic plans. Among the efforts made by the Government is
to establish the Sustainable Energy Development Board (SEDA) to encourage energy
supplier industries to use renewable energy in energy generation, the addition of palm
biodiesel to diesel fuel based on the National Biofuel Industry Act 2007. Furthermore,
supporting public transport while restricting the ownership of private vehicles on the
basis of the National Land Public Transport Master Plan and encouraging the use of
green technology (KeTTHA, 2017) through a programme initiated in 2017 by the
Ministry of Energy, Green Technology and Water (KeTTHA). Recently, the Green
Technology Master Plan for 2017 to 2030 has outlined multi-sectoral initiatives to
7
reduce the intensity of greenhouse gas emissions (GHG) by 45% by 2030 and promote
green technology-based economic development.
1.2 Problem Statement
Environmental issues such as climate change are hot topics being discussed
around the world as they can threaten the well-being of human life including nature.
Climate change such as global warming is becoming more apparent day by day as a
result of uncontrolled economic activity carried out by a handful of groups such as
widespread greenhouse gas emissions. There are several implications of global
warming among which has caused the rise of sea and earth surface temperatures to
cause the polar ice layer to melt, sea level to rise, and cause unstable and extreme
weather such as flash floods, landslides or heavy snowfall (Utinia, 2008). One of the
major GHGs that contribute significantly to global warming is carbon dioxide. With
the rapid economic development in Malaysia, large-scale CO2 emissions from
industry through the combustion of fossil fuels will increase air temperatures and
cause a worsening effect on the atmosphere (Begum et al, 2020). Based on the record
of pollution complaints received by the Department of Environment (DOE), there was
an increase in 2019 and 2020 compared to the previous year (Department of
Environment, 2015). This indicates an increase in public awareness among the
community to be more sensitive to environmental issues.
Malaysia is one of the most rapidly developing countries in terms of economic,
social, and land-use development (Sharif Ali, 2019). In line with this, there has been
an increase in the demand for electricity sources for transportation, industrial, and
8
domestic purposes. The increase was driven by an increase in population as well as an
increase in demand, particularly in the industrial, commercial, and residential sectors,
which all have an impact on the flow of energy consumption include electricity
consumption in Malaysia. While electricity is an important source of development in
the country, an increase in electricity consumption has resulted in an increase in
electricity flow which has resulted in the release of greenhouse gases. An increase in
uncontrolled energy consumption can have a negative impact on Malaysian urban
development. Recognizing this, the government has set a target of reducing
greenhouse gas emissions by up to 40% by implementing the concepts of sustainability
and energy generation (Susskind et al, 2020). The lack of information on energy
consumption, on the other hand, is a barrier to the sustainability and use of electricity
in Malaysia.
Studies on the relationship of foreign direct investment and renewable energy
consumption to CO2 emissions have been conducted by researchers in several
countries including ASEAN countries (Lee et al, 2019). However, studies on the
relationship between foreign direct investment and renewable energy consumption on
CO2 emissions in Malaysia are limited as a small number of researchers has conducted
the study on the relationship between these three variables in the context of Malaysia.
There are various opinions among researchers in interpreting the relationship between
foreign direct investment and CO2 emissions. This is because each country has a
different fluctuating trend in foreign direct investment flow which causes differences
in the results of studies and conclusions on the relationship between these two
variables. Study conducted by Broner, Salah, and Lienkamp 2013 have even argued
9
that there is a possibility that foreign industries opening their factories in Least
developed countries (LDCs) will produce cleaner production compared to domestic
industries. The study is supported by the theory from Grossman and Krueger (1991),
where foreign direct investment into LDCs is likely to result in cleaner production
technologies that do not have a high impact on CO2 emissions in the long-run.
The Sustainable Development Goals (SDGs), is a Global Goal formed as a
universal call to action to address the problem of poverty, protect the environment and
ensure that all people can achieve prosperity and peace by 2030. This charter has been
approved by all UN Member States and came into force on 2015. To achieve the 13th
objective of SDGs which is climate action, all countries including Malaysia play an
important role in formulating various policies to reduce air pollution including CO2
emissions. Cooperation from various parties including the government, policy makers,
citizens, and industry players is needed to preserve the environment and protect the
environment so that the 13th goal of the SDGs, namely Climate action can be achieved
and create a better world.
1.3 Research Question
The research question of this study is how economic variables such as
renewable energy, foreign direct investment and economic growth influences the CO2
emissions in Malaysia. To be more specific, the following are the questions?
1. What is the relationship in short run and long run between the renewable energy
consumption and CO2 emissions in Malaysia?
10
2. Is there a connection in the short run and long run between foreign direct
investment and CO2 emissions in Malaysia?
3. What is the correlation in the short run and long run between economic growth
and CO2 emissions in Malaysia?
1.4 Research Objectives
1.4.1 General Objective
The general objective of this research is to identify the factors that affecting
the CO2 emissions in Malaysia.
1.4.2 Specific Objectives
1. To determine the correlation in the short run and long run between the renewable
energy consumption and CO2 emissions in Malaysia;
2. To study the relationship in the short run and long run between foreign direct
investment and CO2 emissions in Malaysia;
3. To examine the connection in the short run and long run between economic growth
and CO2 emissions in Malaysia.
1.5 Scope of Study
The focus of this study was to investigate how renewable energy consumption,
foreign direct investment, and economic growth influence the carbon dioxide (CO2)
emissions in Malaysia. The data range used for this study was from year 1987 to year
2016. This paper was divided into five main chapters. Chapter one was about the
11
introduction, background of study, problem statement, research question, objectives
and summary.
While moving on to chapter two which was literature review, empirical study
that emphasized on the previous study and research papers that correspond to our topic
and reviewed closely. Other than that, the theoretical framework, empirical testing
procedures, and empirical findings was also included in this chapter. We would be
discussing a little deeper on the methodological approach used in Chapter 3. Data,
models and methods would be specified clearly in regard to our variables. Moving
further, in chapter 4, the methodologies that have established in previous chapter then
be implemented. This chapter will produce the empirical results for this research and
based on these results, it will understandable the estimated scenarios in the case for
Malaysia. Last but not the least, in chapter 5, the overall conclusion of this study and
recommendation will be given so that policy makers, researchers and governments
will have overall ideas how effectively should address the policies towards climate
change additional information will be given.
1.9 Summary
Alarming climate change is happening and because of climate pattern
alteration developing and under developing countries are enduring unexpected natural
disaster. As demonstrated an overall scenario of how the environmental degradation
impact the economic progress in Malaysia. Meanwhile, general background of the
Malaysia economy has elaborated in this chapter and trend of CO2 emissions in
Malaysia and the relationship of the selected macroeconomics on CO2 emissions. In
12
addition, the data that has relation with the influence of renewable energy
consumption, foreign direct investment, GDP towards CO2 emissions in Malaysia has
also been demonstrated as in various figures and tables.
The policy implementations by Malaysian government and from climate eras
how world’s leaders are striving together to mitigate greenhouse gas emission in future
has also been discussed. Moreover, motivation of this study by using CO2 emissions
as one of the primary variables lead this research more positive as because CO2
emissions become permanent in atmospheric level and the trend is constantly rising.
First six months of 2016 was recorded the hottest year ever globally. Malaysia has no
exception from this situation. In fact, in north-eastern region was all time record high
in last half a century. Thus, brings immense motivation and inspire to study this topic.
13
CHAPTER 2
LITERATURE REVIEW
2.0 Introduction
Chapter two was divided into three section; consisted of an introduction,
theoretical framework, empirical study. Section 2.1 would focus on the theoretical
framework which were the relevant theories and concepts applied in the study. In
section 2.2, the empirical testing method and findings used in the previous studies
would be reviewed as well as the theoretical model.
2.1 Theoretical Framework
2.1.1 Environmental Kuznets curve (EKC)
Kuznet's hypothesis identifies economic growth as a factor determining
changes in income distribution in the long-run. Kuznet believes that income inequality
rises with economic growth, but after reaching a certain point the inequality will
decrease along with better economic development. Therefore, the relationship
between income inequality and GDP per capita forms the Uterbalance curve (Kuznets,
1955). In 1991, Grossman and Krueger developed the concept of Environmental
Kuznet Curve (EKC) in which they applied the Kuznet hypothesis to find out the
relationship between economic growth and environmental quality. The EKC
hypothesis shows the contribution of economic growth to higher emissions but further
economic growth is then able to reduce environmental degradation. This is subject to
technological progress and a shift to a service-based economy (Grossman & Krueger,
1991).
14
Researcher Shafik (1994) stated in the study that The Environmental Kuznets
Curve (EKC) is a link between environmental quality and economic development
where this diagram has proven that environmental quality will deteriorate in line with
the economic development of a country. According to the EKC theory, it is expected
that the economic growth mechanism would inevitably restrict the environmental
degradation caused in the early stages of development. The theory of the EKC leads
many early 1990s researchers to conclude that each economy should concentrate on
its growth, and the economic growth process would ultimately eradicate any
environmental problems. EKC theory can be interpreted based on a U-shaped diagram
that is the relationship between the quality of the environment and the economic
development of a country.
Balado-Naves, Baños-Pino, & Mayor (2018) clarified that in Europe, Asia and
the world as a whole, there appears to be an inverted U-shaped relationship between
per capita income and national per capita emissions, neighbouring energy intensity
increases national per capita emissions, and economic growth is expected to accelerate
climate change. The EKC asserts that in response to economic growth, environmental
quality first decreases and increases only after per capita income reaches a crucial
threshold. During the course of economic growth and subsequent progress, this
combination of dropping then growing environmental quality results in an inverted
"U" shaped curve. Figure 2.1 below shows the stimulation of the EKC graph where
there is a U-shape in the graph represented the relationship between environmental
degration and economic development of a country.
15
Figure 2.1: Environmental Kuznets Curve (EKC)
2.2 Empirical Study
2.2.1 Renewable Energy and CO2 emissions
Researchers Bigili, Kocak and Bulut (2016) investigated the dynamic impact
of renewable energy consumption on CO2 emissions. The data used in this research
were taken from 17 countries in the Organization for Economic Co-operation and
Development over a period of 33 years from 1977 to 2010 using the panel Dynamic
Ordinary Least Square and the panel Fully Modified Ordinary Least Square
estimations. Studies indicated that there is a long-run negative relationship between
these two variables where the renewable energy consumption increases then CO2
emissions decrease, and vice versa.
16
Researchers Dong, Dong, and Jiang (2019) also agree that there is a negative
relationship between two variables; Renewable energy consumption and CO2
emissions but the effect between these two variables is not very significant. This study
was conducted based on 4 levels of income; low income, lower middle low income,
upper middle income and high income from 120 countries over a period of 21 years
from 1995 to 2015 and indicated that the increase in economic growth and nonrenewable energy consumption may obscure the mitigation effect.
Researchers Azilah Hasnisah, Azlina and Che Mohd Imran Che Taib (2019)
indicated that in 13 developing countries in Asia, renewable energy use is negligible
in contributing to less pollution with regard to CO2 emissions in long-run. The study
suggested that the countries sampled should develop a strategic planning to reduce the
rate of global warming and climate change, while at the same time stimulating
economic growth and encouraging energy from environmentally sustainable
resources, in order to better understand the potential factors influencing CO2
emissions. Moreover, researchers Natnaporn et.el (2020) indicated that there is an
inverse relationship between renewable energy and CO2 emissions in Malaysia in
long-run. Time series data for the period of 30 years from 1988 to 2017 has been used
by the researcher in this study. This study used ARIMA equation to examined the
impact of renewable energy on CO2 emissions.
In contrast, Researchers Farhani and Shahbaz (2014) found that the renewable
and non-renewable energy indicated a positive relationship on CO2 emissions where
17
the increase in the use of renewable and non-renewable energy will increase the CO2
emissions based on studies that have been conducted in the Middle East and North
Africa countries over the period of 30 years from 1980 to 2009 using the panel
Dynamic Ordinary Least Squares and panel Fully Modified Ordinary Least Squares.
2.2.2 Foreign Direct Investment (FDI) and CO2 emissions.
Researchers Blanco, Gonzalez, and Ruiz (2012) discovered that by using a
study of 18 Latin American Countries for the period 1980 to 2007, there is no robust
proof that FDI induces CO2 emissions. Researchers Haug and Ucal (2019) indicated
that changes in FDI had no statistically significant long-term impact on per capita CO2
emissions in Turkey. They used linear and nonlinear ARDL models in their analysis
and found important asymmetric effects of exports, imports and FDI on per capita CO2
emissions. FDI, however has no long-run effects that are statistically significant. In
the long run, export reductions minimise per capita CO2 emissions, but export
increases do not have any statistically significant impact. Import rises drive up CO2
emissions per capita, while import reductions do not have long-term consequences.
Researchers Omri, Nguyen and Rault (2014) found that there was bidirectional
casuality between FDI inflows and CO2 emissions in 54 countries over a period of 22
years from 1990 to 2011 using dynamic simultaneous-equation panel data models. The
researcher Acharyya (2009) found that there is a positive relationship between FDI
inflow and CO2 emissions in India for the long term. Time series data for the period
of 24 years from 1980 to 2003 has been used by the researcher in this study. This study
18
used co-integration regression and unit root test to examine the impact of FDI on CO2
emissions.
In addition, researchers Lee, Ramasamy and Yeung (2005) have studied on the
relationship between FDI and pollution by using Granger causality tests on 112
countries over 15-28 years. The findings of the study indicate that there is an
alternative causality relationship between FDI and CO2 emissions in long-run
depending on how fast a country is developing. Researchers Pao and Tsai (2011) stated
that the findings of causality indicate that for the period between 1980 and 2007,
except for Russia (1992-2007), there is bidirectional causality in Brazil, Russia, India
and China between FDI and emissions using a panel cointegration technique. CO2
emissions tend to be energy consumption elastic and FDI inelastic in long-run balance,
and the findings seem to support the hypothesis of the Environmental Kuznets Curve
(EKC). Moreover, the researchers Linh and Lin (2015) investigated the complex
causality correlation between depletion, economic development, FDI and energy
consumption in the 12 most populated countries in Asia. The studies indicated that
both short-term and long-term causality relationships occur between CO2 emissions,
economic development, FDI and energy consumption.
2.2.3 Economic Growth and CO2 emissions.
Researchers Arouri, Ben Youssef, M'henni and Rault (2012) indicated that in
12 Middle East and North African Countries (MENA) over the period of 25 years from
1981 to 2005, Real GDP shows a positive relationship with CO2 emissions. This study
19
used co-integration techniques and panel unit root test to examine the impact of real
GDP on CO2 emissions in 12 Middle East and North African countries (MENA). The
researchers Bengochea-Morancho, Higón-Tamarit, and Martínez-Zarzoso (2001)
examined the dynamic relationship in the European Union between economic growth
and CO2 emissions. The data of 10 European countries over the period of 15 years
from 1981 to 1995 has been used in this study. These findings do not tend to support
a uniform emissions control strategy, but countries in Europe can achieve the target of
reducing CO2 emissions by considering economic situations and some industrial
sectors that contribute significantly to the increase in CO2 emissions in those panel
countries.
Furthermore, the researchers Behnas Saboori, Jamalludin Sulaiman and
Saidatulakmal Mohd (2012) discovered an dynamic relationship between GDP and
CO2 emissions in both short and long-run in Malaysia, supporting by the hypothesis
of Environmental Kuznets Curve (EKC) over the time period of 30 years from 1980
to 2009. The analysis of granger causality based on vector error correction model
(VECM) has been used in this study. The study indicated that the absence of causality
relationship between GDP and CO2 emissions in the short-run while perceived unidirectional causality in the long-run. Researcher Omri (2013) investigated the dynamic
relationship between GDP and CO2 emissions over the period of 37 years from late
60s to 1996 using the Pool capacity test for a hundred states. Their findings showed
an upward leaning correlation between GDP and CO2 emissions for the one hundred
states. Simply stated, the study indicated that the economic growth has a long-run
positive relationship with CO2 emissions.
20
2.3 Research Gap
This study was conducted based on three selected macroeconomic variables
that have the potential to affect carbon dioxide emissions, which include renewable
energy consumption, foreign direct investment, and economic growth. There are few
previous studies have incorporated all of these variables in their research paper.
Therefore, this paper provides an update on studies related to economic degradation
in Malaysia for the reference of other researchers who want to conduct similar studies
in the future in addition to being used as a reference by policy makers in Malaysia to
further strengthen regulations and policies related to the environment. This study is a
continuation of previous studies where the data used in this study is the latest data that
covers up to 2019. Recommendations on policies and regulations listed in this study
are based on the current economic situation and environmental conditions in Malaysia.
In addition, this study was conducted to expand the study related to
environmental degradation due to the lack of studies related to CO2 emissions in
developing countries including Malaysia. This study to some extent will be able to
help other researchers to study in more depth so that the results found by researchers
in the future are more accurate and the recommendations on the solutions are more
effective and beneficial to policy makers in the country.
21
CHAPTER 3
METHODOLOGY
3.0 Introduction
This research aimed to study the influence of renewable energy consumption,
foreign direct investment, economic growth on the CO2 emissions in Malaysia from
the year 1987 until 2016. There is five parts in this chapter, consisting of an
introduction, data description, model of estimation, conceptual framework and
research method. The data used in this analysis was described in section 3.1, followed
by an estimation model in section 3.2. After that, we would discuss about the
conceptual framework and the research method for this analysis in section 3.4 was the
last part.
3.1 Data Description
There is various type of determinants to investigate the factors affection CO2
emissions in Malaysia and to study the relationships between CO2 emissions with the
three variables namely; renewable energy consumption, Foreign Direct Investment,
and Economic growth in a long run and short run based on the time series data.
indicators for the variables used in this study are RE consumption% of total RE
consumption, FDI in net US Billion, GDP at 2010 price (US billion dollars), and CO2
emissions (kilotons) in Malaysia. This data was obtained from World Bank data. This
research will structure theoretical framework for a comprehensive and potential
understanding. The dependent and independent variables are listed as below.
22
Figure 3.1: Dependent and independent variables
One dependent variable was used in this study as a measurement tool. The
framework study of the influence of renewable energy consumption, foreign direct
investment, economic growth on the CO2 emissions in Malaysia and to study the
relationship of renewable energy consumption, FDI, and GDP towards the CO2
emissions in Malaysia.
3.2 Estimation Model
The function form was express as:
𝐶𝑂2 emission= ƒ (RE, FDI, GDP)
3.3 Conceptual Framework
Figure 3.2: Frameworks Study of the Influence of Renewable Energy, Foreign Direct
Investment, Economic Growth on the CO2 emissions in Malaysia.
23
3.3 Research Method
There are several tests used in the study to identify the relationship between
dependent variables namely CO2 emissions and independent variables namely
renewable energy consumption, foreign direct investment, and economic growth in
short run and long run. Among the tests are Ordinary Least Square (Double log model),
Unit root test of Augmented Dickey Fuller, Johansen and Juselius Cointegration test,
and Vector Error Correction Model (VECM) Granger Causality Test. E-views has
been used in this study to help facilitate researcher to process accurate results based
on the tests performed.
3.4.1. Ordinary Least Square (Double Log Model)
Ordinary Least Squares (OLS) is the most common estimation method for
linear models to predict the relationship between the dependent (denote by Y) and
independent variable (denotes by X). This method does not base on one’s prediction
but applying statistical methods to analyze the relationship between these variables.
Usually, OLS is used to make predictions for models with 1 dependent variable and 1
independent variable. However, it is also a common use to predict multiple
independent variables and see what are their relationship, whether an increase in X
could bring what effect on Y. In this research, we will be using 3 independent variables
and 1 dependent variable to do analysis and see what relationship are they, and the
model that we are going to use in this research are as follow:
Y = α + β1X1 + β2X2 + β3X3+ εi
24
3.4.2 Unit Root Test
3.4.2.1 Augmented Dickey Fuller Test (ADF)
The augmented Dickey Fuller Test (ADF test) very helpful in analysing the
stationary of a series, as it is also one of the most commonly used statistical test as
present. The ADF test is an upgraded version of Dickey-Fuller test equation. More
differencing term and higher-order regression processes have been added, which
ultimately makes the test result of ADF test more convincing. The ADF test procedure
is derived from the following model:
∆𝑦𝑡 = 𝛼 + β𝑡 + 𝛾𝑦𝑡−1 +𝛿∆𝑦𝑡−1 + ⋯ + 𝛿𝑝−1∆𝑦𝑡−𝑝+1 + 𝜀𝑡
Where, 𝛼 is a constant term, β𝑡 is the coefficient of variable, p is the log order and
𝜀𝑡 , is an error term.
The following are null hypothesis and alternative hypothesis:
𝐻0 : Y = 0
𝐻1 : Y < 0
Where the null hypothesis, y = 1 is not rejected, that means the order 1. This means
the series is having a unit root and it is non-stationary or stochastic. But when the
alternative hypothesis, y < 1 is not rejected, then it indicates the order is 0, where the
series is stationary or there is no unit root in the series
25
3.4.3 Johansen and Juselius Cointegration Test
Johansen and juselius Cointegration Test (JJ Test) can help identify the existence
multiple cointegration vectors. Under the assumption that all variables are
endogenous, when two or more sequences are integrated separately, but some of their
linear combination have a lower integration order, the sequence is called cointegration. There are two test that can be proceeded to discover the number of
cointegrating vectors exist in the model. Those are Trace Test and Maximum
Eigenvalue test (Nkoro & Uko, 2016).
Trace test can be expressed as below:
𝜆𝑡𝑟 = −𝑇 ∑𝑝𝑖=𝑞+1 log⁡(1 − 𝜆𝑖 )
Where,
T = the number of observations,
𝜆𝑖 ⁡= the largest 𝑖 𝑡ℎ estimated eigenvalue
The following are null hypothesis an alternative hypothesis for Trace Test:
𝐻0 : 𝑟 − 𝑟0
𝐻0 : 𝑟⁡ ≥ 1
The regression of the Maximum Eigenvalue test is shown as following:
𝜆𝑚𝑎𝑥 = ⁡ −𝑇𝑙𝑜𝑔(1 − 𝜆𝑟−1 )
Where,
T = the number of observation,
𝜆𝑟−1 = the largest estimated eigenvalue at r-1.
26
The following are null hypothesis and alternative hypothesis for Maximum Eigenvalue
Test:
𝐻0 : 𝑟 = 𝑟0
𝐻0 : 𝑟 = 𝑟0 + 1
The null hypothesis for Cointegration test is rejected when the t-statistic greater than
the critical value for selected level of significance. Thus, it can be stated that the model
exists at most of r Cointegration vector at selected level of significance. According to
Johansen and Juselius (1990), the Maximum eigenvalue test is preferable to the Trace
Test.
3.4.4 Vector Error Correction Model (VECM) Granger Causality test
According to Granger (1998), once Cointegration vector is defined in
Johansen-Juselius cointegration test, the Vector Error Correction Model (VECM)
Granger Causality Granger Causality test id developed to determined short run
relationship among surveyed variables to avoid misspecification problem. Besides,
Greanger Causality test based VECM can detect the direction of causality between
surveyed variables from long run cointegrating vector.
The hypothesis of Granger Causality based VECM is specified as follow:
𝐻0 = The independent variable does not granger cause the dependent variable
𝐻1 = The independent variable granger causes the dependent variable.
27
The null hypothesis of Granger Causality test can be successful rejected when
the p-value is less than selected level of significance. Thus, it can be concluded as the
independent variable granger cause the dependent variable in short run at selected
level of significance. Assume the p-value is greater than selected level of significance.
Hence, there is insufficient evidence to reject the null hypothesis. Therefore, the
independent variable does not granger cause the dependent variable in short run at
selected level of significance.
3.5 Summary
In conclusion, there is numerous studies have conducted related to climate
change but there isn’t enough study has done using the exact variables. At the same
time, econometrics results may vary one case to another case. However, this study will
follow the most widely acceptable and available methods. Therefore, this study
combined with the several steps in the research method or known as econometric
methods. In this case, the first step is applied with the ordinary least square (OLS) to
compute the models between linear, log-linear, linear-log and double log equations.
Then, the second step applied with ADF unit root test. This step followed by lag criteria
selection to determine the number of lag for continuing the cointegration test. If there
is more than one cointegration vector, the model will choose multiple cointegration
tests to identify the long run association between one dependent variable to
independent variable as stated in this objective of the study. Finally, the last step is to
applied VECM based Granger causality test after the existence of a valid cointegration
result. The result and interpretation of result continue in next, which is chapter 4 of
empirical result and discussion.
28
CHAPTER 4
RESULT AND DISCUSSIONS
1.0 Introduction
This chapter will provide an in depth discussion as well as present the outcomes
of the data on the renewable energy consumption, foreign direct investment, and GDP
on the CO2 emissions in Malaysia from 1987 to 2016. The empirical analysis were
used in this study in order to gain insights into unit root test, Cointegration test and
vector error correction model (VECM) in granger causality. These tests will be applied
to determine the relationship and the influence of renewable energy consumption,
foreign direct investment, economic growth on CO2 emissions in Malaysia from year
1990 to 2019. All the analysis was carried out using EViews, version 11. The
stationary properties of the data were examined by using the unit root test which is
includes the Augmented Dickey Fuller test. The Johansen and Juselius Cointegration
Test is for testing the long-run relationship between the variables. Lastly, Granger
Causality then be applied to identify the existence of causality of variables.
4.1 Empirical Finding and Discussion
4.1.1 Ordinary Least Square (OLS)
The coefficient of variation (CV) is used to calculate the average error of the
sample regression function, also known as the standard error of regression (SE) of the
regression relative to the mean of Y, the dependent variable. Coefficient of variation
helps to compute the models between linear, log-linear, linear-log and double log
equations.
29
𝒄𝒗 = ⁡
𝒔𝒕𝒂𝒏𝒅𝒂𝒓𝒅⁡𝒆𝒓𝒓𝒐𝒓⁡𝒐𝒇⁡𝒓𝒆𝒈𝒓𝒆𝒔𝒔𝒊𝒐𝒏⁡(𝑺𝑬)
𝒎𝒆𝒂𝒏⁡𝒐𝒇⁡𝒅𝒆𝒑𝒆𝒏𝒅𝒆𝒏𝒕⁡𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆, 𝝁
Table 4.1: Coefficient of variation for each functional form
Function form
CV
Linear
508.669
Lonear-log
511.374
Log-linear
9.821
Double log
9.816
Based on the result of coefficient of variation from each functional form,
Double log model has the smallest Coefficient of variation and thus it will be chosen
as the model to continue the test for further analysis.
Table 4.2: The result for OLS test
Ordinary Least Square (OLS) test
Variables
Coefficient
Standard Error
T-statistic
Probability
LREC
0.866
1.883
0.460
0.650
LFDI
0.148
0.066
2.233
0.034*
LGDP
1.473
1.809
0.814
0.423
C
3.009
12.674
0.237
0.814
Note: Asterisks (*) donate statistically significant at 5%
Estimation Model:
Log(CO2) = 3.009 + 0.866REC + 0.148FDI + 1.473GDP
Where,
30
Log(CO2) = CO2 emissions (Kiloton)
REC = Renewable Energy Consumption % of Total Energy Consumption
FDI = Foreign Direct Investment in Malaysia (net Billion US Dollar)
GDP = Gross Domestic Product at 2010 price (Billion US Dollar)
The results of the ordinary least square (OLS) test is shown in Table 4.2. The
result indicates that there is a positive relationship between renewable energy
consumption, FDI, GDP with CO2 emissions. An increase in 1% or renewable energy
consumption of total energy consumption will increase 0.87% of environmental
degradation (CO2 emissions) in the same direction while the other variables held
constant. Secondly, an increase in 1 billion US Dollar of FDI will increase 0.15% of
environmental degradation (CO2 emissions) in the same direction while the other
variables held constant. GDP at 2010 price has a positive relationship towards CO2
emissions. Lastly, an increase in 1 billion US Dollar of GDP will increase 1.47% of
environmental degradation (CO2 emissions) in the same direction while the other
variables held constant.
4.1.2 Augmented Dickey-Fuller unit root test
Unit root approaches may be beneficial for discovering and characterizing the
cointegration test, as well as applying the VECM method based on empirical results.
Many academics utilize the unit root test to measure in empirical analysis the
stationarity and non-stationarity of either I(0) or I(1) variables. There are various
approaches for determining the unit root among variables. The ADF unit root test will
be employed in this regression model to understand the stationarity and nonstationarity
31
of the dependent (CO2 emissions) variable and the independent variables (REC, FDI,
GDP).
Table 4.3: Result for Augmented Dickey Fuller Test
Series
Level
Intercept
First Difference
Trend and
Intercept
Intercept
Trend and
Intercept
LCO2
-4.557(0.001)*
-5.324(0.000)*
-8.839(0.000)*
-8.685(0.000)*
LREC
-0.453(0.888)
-2.363(0.390)
-5.165(0.0002)*
-5.058(0.002)*
LFDI
-1.753(0.395)
-1.871(0.644)
-6.267(0.000)*
-6.235(0.000)*
LGDP
0.181(0.965)
-3.381(0.074)
-3.945(0.006)*
-4.131(0.016)*
Note: * referring to the rejection of null hypothesis at significant level of 5%
Table above shows the results of the dependent variable which is CO2
emissions and the independent variables, renewable energy consumption, foreign
direct investment and GDP in Malaysia by implementing Augmented Dickey Fuller
Test. The results show that the variables are non-stationary at level for renewable
energy consumption, foreign direct investment and GDP but all the variables are
stationary at first difference level. Thus, all the variables a stationary or does not have
unit root test at the first difference which shows the data for this study does not have
issue on the “random walk” for the data.
4.1.3 Johansen and Juselius Cointegration Test
The co-integration test is used in time series analysis to evaluate the long-run
relationship between variables. These variables are RE consumption, FDI, GDP, and
32
CO2 emissions in this study. The cointegration test is used to determine the
cointegration relationship among variables in equation (4) over the long run (Johansen
& Juselius, 1990).
Table 4.4: The Results of Johansen & Juselius Test
Null
Alternative
Trace
Max-Eigen
Critical Value (5%)
Statistic
Statistic
Trace
Max-Eigen
r-0
r>1
122.376*
82.760*
47.860
27.584
r<1
r>2
39.616*
24.720*
29.797
21.132
r<2
r>3
14.896
11.941
15.495
14.265
r<3
r>4
2.955
2.955
3.841
3.841
Note: * referring to the rejection of null hypothesis at significant level of 5%
𝐻0 : 𝑟 = 0
𝐻1 : 𝑟⁡ ≤ 0
The outcome of the Johansen-Juselius Cointegration test is shown in Table 4.4.
The results reveal that at a significance level of 5%, both the Trace test and the MaxEigen test are significant in rejecting the null hypothesis. There are two long run
cointegration relationships in the Trace test since the value of the trace statistic is
39.616, which exceeds the 5% threshold value of 29.797. Same goes to The Max-Eigen
test where The Max-Eigen test demonstrates two long run cointegration relationships
exist because the value of the trace statistic is 24.720, which above the 5% critical
value of 21.132. As a result, there is no full rank problem in this result.
33
4.1.4 Vector Error Correction Model (VECM) Granger Causality test
Moving forward in the procedure, the J-J test indicates that one cointegration
vector exists in the model; thus, this section elaborates the long-run normalized
coefficients and indicates the magnitudes of between dependent and explanatory
variables generated using VECM.
Table 4.5: The result of VECM Normalized Equation test
Variables
Coefficient
Standard Error
T-statistic
LCO2
1.000
LREC
3.541
0.690
5.129
LFDI
-0.182
0.020
-9.046
LGDP
6.424
0.570
11.268
C
27.946
Note: The estimated coefficients were obtained by normalizing the independent variables with respect
to their respective dependent variable (LCO2).
Table 4.5 presents the result for VECM Normalized Equation test. The
relationship between environment degradation and the determinants which are RE
consumption, foreign direct investment and GDP can be presented as below:
LCO2 = 27.946 = 3.541LREC – (-0.182)LFDI – 6.424
LGDPAs described in above, VECM normalized equation, the negative
coefficient of foreign direct investment (FDI), which is -0.182 has observed. The
34
finding indicates that foreign direct investment (LFDI) has a negative relationship with
the CO2 emissions at significant level of 10% (3.29), in other way it can be describe,
if 1 billion US dollar increase of foreign direct investment will lead to decrease of o.182 of environmental degradation. The other variables significant at 5% significance
level because t-statistic values are exceed the critical value at 1.96. It indicates that
null hypothesis is rejected and shows that existing a long-run relationship between the
dependent variable and independent variables namely; LCO2, LREC and LGDP. The
results of long-run relationship are support by Omri (2013); Bigili, Kocak and Bulut
(2016); Azilah Hasnisah, Azlina and Che Mohd Imran Che Taib (2019); Natnaporn
et.el (2020); Omri, Nguyen and Rault (2014); Lee, Ramasamy and Yeung (2005); Pao
and Tsai (2011); Arouri, Ben Youssef, M'henni and Rault (2012); and Behnas Saboori,
Jamalludin Sulaiman and Saidatulakmal Mohd (2012).
Table 4.6: The result of VECM Granger Causality Test
X2- Statistics (p-value)
Variables
∆LCO2
∆LREC
∆LFDI
∆LGDP
Coefficient
t-statistic
1.094
0.706
0.258
-0.232
-0.577
(0.579)
(0.702)
(0.879)
56.773
25.634
4.236
-0.462
-0.876
(0.000)*
(0.000)*
(0.120)
0.082
6.469
0.004
0.420
∆LCO2
∆LREC
∆LFDI
∆LGDP
ECT
7.327
0.560
1.045
(0.026)*
(0.756)
(0.593)
0.054
0.481
0.908
(0.973)
(0.786)
(0.635)
Note: Asterisks (*) donate statistically significant at 5%
35
Table 8 above shows the existence of short-run and long-run relationship
between the variables. Based on the result, the ECT coefficient for LCO2 is negative,
less than one and statistically significant at the 5% level. This shows the evidence of
a long-run relationship exists. Besides, LFDI and LCO2 does have a short-run
relationship with LREC, and the probability of LFDI and LCO2 is 0.000 for both
variables which is statistically significant at the 5% level and thus shows that LFDI
and LCO2 does granger cause toward LREC. Lastly, LCO2 does have a short-run
relationship with LFDI, and the probability of LCO2 are 0.026 which is statistically
significant at the 5% level. So, LCO2 does Granger cause LFDI. This granger causality
relationship had detected in another study has conducted by Pao and Tsai (2011);
Omri, Nguyen and Rault (2014); Linh and Lin (2015); and Lee, Ramasamy and Yeung
(2005).
LGDP
LREC
LFDI
LCO2
36
CHAPTER 5
CONCLUSION
5.0 Introduction
This chapter yet to provide an overall conclusion of this thesis, after being
completed the empirical analysis of data in the previous chapter. This chapter contains
3 section which the first sections. Summary of the study, second.section.is policy
implications that would be beneficial for policy maker and future researchers to assist
them in understanding about how the renewable energy consumption, foreign directi
investment and economic growth can influence CO2 emissions in Malaysia as well as
recommendations on how this study can be improved in the future. Last but not least,
this section will show the limitation of the study.
5.2 Summary of Study
Global climate change is toughest challenges faced in the 21st century
(Rahman, 2009). Most environmentalist agrees that it happened climate change is one
of the impacts from global warming (Ab-Rahim & Teoh, 2016). Although still not
fully understood for sure, increase in greenhouse gas concentration especially carbon
dioxide (CO2) in the earth atmosphere is believed be the cause of global warning.
Global warming happened because the presence of the greenhouse effect. Greenhouse
gas which is in the earth’s atmosphere can be equated with glass curtains on the farm
using a greenhouse. Hot sun which is in the form of incoming shortwave radiation to
earth by penetrating the greenhouse. Some of the heat is absorbed by the earth and the
rest are reflected back outwards space as longwave radiation. However, the heat is
37
supposed to be reflected back into outer space touching the surface of the Ozon and
trapped inside the earth. Without this greenhouse effect then the temperature at the
surface of the earth will be lower than that there is now so it is possible the existence
of life.
This paper examines the relationship between renewable energy consumption,
foreign direct investment, economic growth and environmental degradation (CO2
emissions), which is a part of sustainable development. this study offers an alternative
approach to achieve, which can lead to Malaysia’s sustainable economic growth. We
reviewed applicable theoretical and empirical studies performed by social scientists,
based on the environmental Kuznets curve (EKC) hypothesis, in order to produce the
most suitable model. This study confirms the inverted-U relationship between the
economic growth and environmental condition of the environmental Kuznets
hypothesis. Economics development at the early stage increases CO2 emissions, and
then reduces CO2 emissions after achieving a certain point. Based on the result shows
in chapter 4, it is shows that all variables includes renewable energy consumption,
foreign direct investment, GDP and CO2 emissions have short-run and long-run
relationship. GDP has positive impact towards CO2 emissions in short-run and also in
long-run.
5.2 Policy Implication and Recommendations
Effort to reduce CO2 emissions and create a low-carbon society is an effort
that needs to be carried out collectively by involving various strata of society includes
government, industry, NGO and the community. Industry plays a very important role
in reducing CO2 emissions in the country because the sector uses energy on large
38
scale. There are many industries in Malaysia have headed to be sensitive to the
important of green energy by making investment in energy efficiency, recycling and
reuse of materials, friendly technology nature, internal training of green economy as
well as the purchase of less detrimental materials environment. This investment has
been driven by concern industries on the impact of their operations and products on
the environment. The Malaysian government has developed a framework and
environment which is very conducive for all parties to play a role in the effort to reduce
the country’s CO2 emissions. Government assistance and guidance to the industrial
sector to empower the green economy has helped the sector to run operations and grow
in an environmentally friendly manner while reducing CO2 emissions. Relationships
and reputations both Malaysia at the international level allows the country’s nongovernmental organizations to creates networks with various international
organizations to share information and low-carbon mechanisms from other countries.
The progress and the prosperity of the country as well provide the public access to
information related to climate change, CO2 emissions, environmentally friendly
initiatives in daily life includes steps towards low carbon in the country.
Apart from government, Malaysia also has a coalition of non-governmental
organizations (NGOs) which plays a role to reduce CO2 emissions includes The
Malaysian Climate Change Group (MCGG). MCGG was established on December
1992 and consists of the Environmental Protection Society Malaysia (EPSM), Center
for Environment, Technology and Development Malaysia (CETDEM) and Malaysian
Nature Society (MNS). Perak Consumers’ Association (PCA) has joined the
organization in August 2002. The public in Malaysia is also believed to have the
39
potential to be involved in the effort to reduce CO2 emissions as it is seen to be
increasingly concerned about related issues climate change through increased sharing
of knowledge about nature environmental as well as encouragement to participate in
addressing environmental issues. Knowledge and awareness of the environment and
development has been inculcated since scold days through Education for Sustainable
Development (ESD) which covers three main pillars includes social, economic, and
environmental. Education on environment and sustainable development are also
pursued at tertiary level. In addition to the medium of formal education, non-formal
education is also actively carried out by various parties includes NGOs and business
sector.
Malaysia is one of the countries that rely on agricultural production to generate
national income. In Malaysia, a National Climate Change Policy has been
implemented to provide a framework that can be used as guidance for all government
agencies, industries, communities, and other stakeholders in order to face challenges
in a climate change scenario (National policy on climate change, 2019). The policy
was enacted to ensure climate-resilient development in order to meet the country's
aspirations for environmental sustainability. The policy is primarily intended to
control climate change through wise resource management and to improve
environmental
conservation,
resulting
in
increased
Malaysian
economic
competitiveness and improved national life quality (Rameli et al, 2018). As a result,
all policymakers must integrate and strengthen policies, plans, and programmes across
the country to reinforce resilience development while identifying ways to mitigate any
negative effects of climate change. To emphasise, improving institutional and
40
implementation capacity will result in better opportunities to mitigate the negative
effects of climate change.
Energy consumption inefficiency and dependence on fossil fuels as energy
sources have been identified as contributing factors to CO2 emissions in the country.
Thus there are practical steps that can be taken by the public as the largest energy
consumer, to reduce gas emissions greenhouses include carbon as well as using
renewable energy such as solar, hydro and wind energy and biomass for domestic
activities; reduce electricity consumption and promote the use of energy-saving
equipment for residential and commercial buildings; using sustainable transportation
includes non-motorized transport and public transport as well as promote consumption
and production energy efficient EEV vehicles such as hybrid cars; as well as practicing
consumerism prudent as well as implement the 3R concept in waste management
(reduce, reuse, and recycle). In addition, the public can also play a role through nonformal educational approaches such as dissemination information and knowledge on
the effects of climate change, steps need to be taken to reduce risk disasters and the
promotion of daily activities that can reduce CO2 emissions.
In order to further strengthen existing policies related to CO2 emissions, the
government can also take the initiative by tightening regulations for foreign
investment seeking to carry on their business in the country. Central and local
governments should levy an Energy Tax on FDI-invested high-pollution, high-energyconsumption, and high-emission businesses or projects. Corresponding laws and
regulations should be strictly enforced, and companies that violate these regulations
41
should face severe penalties. The regulations are intended to control the amount of
CO2 emissions in the country while encouraging the industry to use green technology
in their companies. The implementation of additional taxes to foreign investors plays
an important role in ensuring that the government can further strengthen the green
economy sector in the country.
Special policies can also be developed that are specific to addressing issues
related to foreign investment such as air pollution caused by foreign factories based in
Malaysia. This is crucial to ensuring that economic and environmental sustainability
can be achieved in line with developed countries such as China. The ongoing
development of China's modern economic system requires them to continue to insist
on introducing FDI to green technology and to encourage domestic and foreign
companies to engage in high quality, deep, and environmentally friendly cooperation
(Zhou et al., 2018). Foreign direct investment (FDI) can provide domestic firms with
multinational investment experience. At the same time, the technological spillover
effects of multinational corporations can boost the productivity of domestic firms.
Domestic enterprises can not only achieve “going global” and expand their business
scope through the technology spillover effect, but they can also use the reverse
technology spillovers of OFDI to upgrade Malaysian industrial structure.
5.3 Limitation of Study
Although there plenty of researchers has performed on climate change but
majority of them did not use time series regression. On the other hand, most of the
researchers used CO2 emissions as dependent variables in response of other
42
independents variables. Overall, there are several obstacle and hindrance faced
throughout this research conducted. First and foremost, the problem is regarding the
accuracy and availability of secondary data collected in this study due to the different
measurements in different sources. The secondary data in the World Bank is only up
to 2016. The data can be improved in the future by obtain the latest data and suitable
units of data in order to get more accurate empirical results.
Besides, there are more indicators can be examined or replaced in order to
contribute useful information and suggestion to the Malaysian government so that the
policy makers able to think of the most effective measures to improve the current
situation in that country. Moreover, different indicators used in the study have
different results to explain if it opposed from previous few findings and this could gain
more empirical evidence regarding the current environment issues in that country.
Lastly, the study regarding this topic is quite rarely done by the researcher.
Research on this study which concern the influence and relationship of renewable
energy consumption, FDI, GDP and CO2 emissions can be consider an interesting
topic in developing country including Malaysia. Thus, more study needs to be
conducted in order to strengthen the empirical findings and prove the related theories
that is valid in developing country.
43
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Appendix
ADF unit root test at level (intercept) LCO2:
ADF unit root test at level (trend and intercept) LCO2:
ADF unit root test at 1st difference (intercept) LCO2:
ADF unit root test at 1st difference (trend and intercept) LCO2:
ADF unit root test at level (intercept) LREC:
ADF unit root test at level (trend and intercept) LREC:
ADF unit root test at 1st difference (intercept) LREC:
ADF unit root test at 1st difference (trend and intercept) LREC:
ADF unit root test at level (intercept) LFDI:
ADF unit root test at level (trend and intercept) LFDI:
ADF unit root test at 1st difference (intercept) LFDI:
ADF unit root test at 1st difference (trend and intercept) LFDI:
ADF unit root test at level (intercept) LGDP:
ADF unit root test at level (trend and intercept) LGDP:
ADF unit root test at 1st difference (intercept) LGDP:
ADF unit root test at 1st difference (trend and intercept) LGDP:
Ordinary Least Square (OLS):
Johansen and Jeliues Cointegration result
VECM Normalized Equation:
VECM Granger Causality: