.
International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020 215
International Journal of Energy Economics and
Policy
ISSN: 2146-4553
available at http: www.econjournals.com
International Journal of Energy Economics and Policy, 2020, 10(1), 215-227.
The Impact of Energy Consumption based on Fossil Fuel and
Hydroelectricity Generation towards Pollution in Malaysia,
Indonesia and Thailand
Abdul Rahim Ridzuan1*, Aliashim Albani2, Abdul Rais Abdul Latiff3, Mohamad Idham Md. Razak4,
Mohd Haziq Murshidi5
1Faculty of Business and Management, Universiti Teknologi MARA, Kampus Alor Gajah, Km 26 Jalan Lendu, 78000 Alor Gajah,
Melaka, Malaysia, 2Universiti Malaysia Terengganu, Malaysia, 3Universiti Sains Malaysia, Malaysia, 4Universiti Teknologi MARA,
Malaysia, 5Universiti Malaysia Sabah, Malaysia. *Email: rahim670@staf.uitm.edu.my
Received: 16 May 2019 Accepted: 28 September 2019 DOI: https://doi.org/10.32479/ijeep.8140
ABSTRACT
This study investigated the effects of energy consumption (ENY) based on fossil fuels and alternative energy with hydroelectricity as its proxy upon
pollution, aside from ascertaining if the correlation between income and pollution determined the presence of Environmental Kuznets curve (EKC). In
addition, the functions of foreign direct investment (FDI) inflows and trade openness (TO) were probed into so as to generate more precise outcomes of
EKC hypothesis. Hence, in order to fulfil the objectives outlined in this study, the Bound estimation method was utilized to examine three developing
nations of the Association of South East Asian Nation (ASEAN), which are Malaysia, Indonesia, and Thailand. The main finding of interest retrieved
from this paper refers to the EKC hypothesis reflective of Malaysia and Thailand. It was discovered that hydroelectricity favourably lowered the release
of carbon emissions in the case of Malaysia, while it insignificantly influenced environmental degradation for Indonesia and Thailand. On the other
hand, as anticipated, per capita energy use displayed a significant long-run effect in raising the levels of carbon emission in Indonesia and Thailand.
Meanwhile, the FDI inflows seemed to improve the environmental quality only in Malaysia, while deepening in TO among ASEAN-3 nations appeared
to successfully minimize issues related to environmental degradation in these countries.
Keywords: Energy Consumption, Hydroelectricity, Real Output, Carbon Emissions
JEL Classifications: O1, Q2, Q4
1. INTRODUCTION
The deleterious effects of global warming have begun to affect
the human race due to the changes noted in the global climate, for
instance, acceleration in rising of sea level (Yi et al., 2017) and
increment in risks of wildfires (Kalabokidis et al., 2015). In fact,
the global climate system appears to be unambiguously warm
with the rising temperature at approximately 0.85 (0.65°C-1.06°C)
from 1880 to 2012, as reported by the Intergovernmental
Panel on Climate Change (IPCC) (2013). The climate change
can negatively influence the environment ecosystem and the
sustainability of socio-economic (Enríquez-de-Salamanca et al.,
2017; Zhang et al., 2017). The most significant driver of climate
change is the larger emission of Greenhouse Gases (GHG)
being released into the atmosphere. The GHG reabsorbs infrared
radiation and heat that is penetrated from the sun to the earth.
As such, GHG prevents the heat escaping from the earth, thus
causing the earth to become warmer; a phenomenon known as the
greenhouse effect. Since year 1750, the concentration of essential
elements that make up GHG in the atmospheres, which are carbon
dioxide (CO2), methane (CH4), and nitrous oxide (N2O), has
been reported to rise by approximately 144%, 256%, and 121%,
This Journal is licensed under a Creative Commons Attribution 4.0 International License
Ridzuan, et al.: The Impact of Energy Consumption based on Fossil Fuel and Hydroelectricity Generation towards Pollution in Malaysia, Indonesia and Thailand
International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020216
respectively. Nonetheless, the World Meteorological Organization
(WMO) (2016) reported that the concentration of these elements
for year 2015 appears to be approximately 400.0 ± 0.1 ppm (CO2),
1845 ± 2 ppb (CH4), and 328.0 ± 0.1 ppb (N2O).
The energy sector, especially the oil and coal-based fuel, has
contributed predominantly to the massive emission of GHGs
into the atmosphere (Bhanu et al., 2018). Coal and fossil fuel are
popular, particularly among developing countries for it is one of
the cheapest sources of energy, in comparison to other sources. In
addition, the International Energy Agency (IEA) has reported that
the Southeast Asia is one of the few regions in the world that has
an increasing share of coal in its energy mix (IEA, 2015). On top
of that, fossil fuel and coal have remained the larger contributor
for electric generation in Association of South East Asian Nation
(ASEAN)-3 countries, as reported by Suruhanjaya Tenaga (ST)
(2016) for Malaysia, Dewan Energi Nasional (DEN) (2016) for
Indonesia, and Electricity Generating Authority of Thailand
(EGAT) (2016) for Thailand.
In fact, numerous energy resources are available that are cleaner
than fossil fuel and coal; in which hydroelectricity is one of them,
which has been considered as one of the most environmental-
friendly energy forms (Tampakis et al., 2013; Melikoglu, 2017).
Hydroelectric energy has the potential to decrease GHG emission
into the atmosphere, as hydro plant is free from air pollution.
Moreover, the source of energy itself derives from natural resource;
kinetic energy from the flow of river water. In comparison to other
intermittent renewable energy resources, such as wind and solar
energy, hydroelectric energy appears to be the best alternative
energy that can compete with fossil fuel to generate huge volumes
of electricity. According to the Renewable Energy Policy Network
for the 21th Century (REN21) in year 2017, Thailand had set the
highest target on hydropower at a capacity of 6.1 GW to achieve
by 2021. While in Indonesia, the target capacities of hydropower
and mini-hydro by 2021 are 2.1 GW and 0.43 GW, respectively.
As for Malaysia, the target for micro-hydro is 2.1 GW of
generation capacity. Nevertheless, a recent report published by the
International Hydropower Association (IHA) (2018) states that the
present installed capacity of large hydropower in Malaysia seem
to be the highest among the three nations (6.094 GW), followed
by Indonesia (5.305 GW), and Thailand (4.51 GW).
Koutroumanidis et al. (2009) revealed that energy resource is the
dominant factor for the growth of socio-economic in any nation.
In fact, this notion is in agreement with Stern (2011) as he found
that the emerging and industrialized economies are driven by
both economic and better quality of energy inputs. Conversely,
the Asian Development Bank (ADB) (2016) highlighted that
climate change is putting pressure on food security and causing
a negative effect on the well-being of humans. Moreover, the
demand for total primary energy among Southeast Asia nations
has been projected to escalate by 80% from year 2013 until 2040
mainly due to increment of economic activities within the region
by triple and hike in shifts among the populations to urban areas
for better employment and lifestyle. Besides, vast industrial and
commercial facilities are available in urban areas due to increment
in population, thus contributing to the rising energy consumption
and demand IEA (2015) also reported that Indonesia has the largest
EN, followed by Thailand and Malaysia.
As such, this study investigated the effect of energy based on fossil
fuels and alternative energy with hydroelectricity as its proxy
upon pollution among selected three ASEAN countries, which
are Indonesia, Malaysia, and Thailand, over a period ranging
from 1980 and 2014. Furthermore, to shed light on the correlation
between growth and rate of pollution among these nations, this
paper probed into the presence of Environmental Kuznets curve
(EKC) hypothesis.
The EKC assumes that environmental degradation first increases
as income increases during the earlier stage of economic
development and then when income reaches a certain high
level, the level of pollution starts to decreases. Although this
issue has been studied by Adebola et al. (2017) and Jebli et al.
(2016), this paper offers a new view, in which cleaner energy via
hydroelectricity is introduced as one of the variables to determine
the existence of EKC. Hence, this paper appears to be a potential
study that adds value to the existing literature, especially from the
lens of ASEAN countries. The remaining sections of the paper
are organized as follows: Section 2 reviews the relevant literature
on several variables related to energy and the methodology
associated to EKC hypothesis. Next, Section 3 highlights the
sources of data and briefly explains the empirical methodology
applied in the study. After that, Section 4 presents the empirical
results and discussion. Finally, the paper ends with Section 5,
where the conclusion and several policy recommendations are
depicted.
2. LITERATURE REVIEW
The existence of EKC hypothesis has been a subject of interest
among many researchers over the years. Given that the theme
of this paper revolves around validation of EKC hypothesis and
source of energy (ENY), only papers that have utilized both gross
domestic product (GDP) and square of GDP (GDP2), as well as
all the types of energy sources tabulated in Table 1, have been
considered.
Overall, this area of study has varied spheres. The past literature
has employed a number of determinants to determine pollutions,
for example, real output (GDP), energy consumption (ENY),
industrial output, urbanization, population, financial development,
trade openness (TO), and foreign direct investment (FDI). Next,
some researchers have applied various indicators to identify
pollution in many nations across regions, namely East Asia and
Pacific (cf. Saboori and Sulaiman, 2013; Chandran and Tang,
2013), Europe and Central Asia (cf. Acaravci and Ozturk, 2010;
Pao et al., 2010; 2011; Kasman and Duman, 2015), Middle East
and North Africa (cf. Arouri et al., 2012; Ozcan, 2013; Shahbaz
et al., 2014), South Asia (cf. Shahbaz et al., 2012), and Sub-
Saharan Africa (cf. Kivyiro and Arminen, 2014; Shahbaz et al.,
2015). Third, some studies opted to use GDP solely to account
for the presence of EKC hypothesis. Nevertheless, in order to
gain better and accurate results of the inverted EKC hypothesis,
GDP and GDP2 should be incorporated as indicators for pollution.
Ridzuan, et al.: The Impact of Energy Consumption based on Fossil Fuel and Hydroelectricity Generation towards Pollution in Malaysia, Indonesia and Thailand
International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020 217
Authors Country Period Variable for energy Methodology EKC hypothesis
Acaravci and
Ozturk (2010)
European countries 1960-2005 Energy consumption ARDL and VECM granger
causality
Yes
Pao and Tsai
(2010)
Brazil, Russia, India and
China
1971-2005 Energy consumption Pedroni, Kao and Johansen
cointegration, ordinary least
square and VECM granger
causality
Yes
Pao and Tsai
(2011)
BRIC countries 1980-2007 Energy consumption Pedroni, Kao and Johansen
cointegration, OLS and VECM
granger causality
Yes
Pao and Tsai
(2011)
Brazil 1980-2007 Energy consumption Grey prediction model Yes
Wang et al.
(2011)
China 1995-2007 Energy consumption Pedroni cointegration and VECM
granger causality
No
Arouri
et al. (2012)
Middle East and North
African countries
1981-2005 Energy consumption The cross correlated effects and
CCE mean group
Yes
Jayanthakumaran
et al. (2012)
China and India 1971-2007 Energy consumption ARDL Yes
Pao et al. (2012) China 1980-2009 Energy consumption Grey prediction model No
Chandran and
Tang (2013)
ASEAN 1971-2008 Transport energy
consumption
Johansen cointegration and VECM
granger causality
No
Govindaraju and
Tang (2013)
India and China 1965-2009 Coal consumption Bayer and Hanck combine
cointegration test, and VECM
granger causality
No
Kohler (2013) South Africa 1960-2009 Energy consumption ARDL, Johansen cointegration
and VECM granger causality
Yes
Ozcan (2013) Middle east countries 1990-2008 Energy consumption Westerlund panel cointegration,
Full modified OLS and VECM
granger causality
Yes
Saboori and
Sulaiman (2013a)
ASEAN countries 1971-2009 Energy consumption ARDL, Johansen cointegration
and VECM granger causality
Yes, for Thailand
and Singapore
Saboori and
Sulaiman (2013b)
Malaysia 1980-2009 Electricity
consumption, oil
consumption, coal
consumption and
gas consumption
ARDL, Johansen cointegration
and VECM granger causality
Yes
Shahbaz
et al. (2013a)
Africa 1965-2008 Coal consumption ARDL, Johansen cointegration
and VECM granger causality
Yes
Bella et al. (2014) Organization for
Economic Cooperation
and Development
(OECD) countries
1965-2006 Electricity
consumption
Larsson, Lyhagen, and Lothgren
cointegration and VECM granger
causality
Yes
Farhani
et al. (2014)
Tunisia 1971-2008 Energy consumption ARDL, and VECM granger
causality
Yes
Kivyiro and
Arminen (2014)
Sub-Saharan countries 1971-2009 Energy consumption ARDL, and VECM granger
causality
Yes, in most
countries
Saboori
et al. (2014)
OPEC countries 1977-2008 Oil consumption ARDL, and
Toda-Yamamoto-Dolado-Lutkepohl
causality
Yes
Al-Mulali
et al. (2015)
Ninety-three countries
based on income
1980-2008 Energy consumption Panel fixed effects and the
generalized method of moments
Yes, for upper
and high income
countries
Al-Mulali
et al. (2015)
Vietnam 1981-2011 Renewable energy
consumption
ARDL, and VECM granger
causality
No
Baek (2015) Artic countries 1960-2010 Energy consumption ARDL Yes
Baek (2015) Nuclear producing
countries
1980-2009 Nuclear energy
consumption and
energy consumption
Pedroni cointegration, DOLS and
FMOLS
Yes
Begum
et al. (2015)
Malaysia 1970-2009 Energy consumption ARDL, DOLS and
Sasabuchi-Lind-Mehlum tests
Yes
Kasman and
Duman (2015)
European Union (EU)
countries
1992-2010 Energy consumption Pedroni and Kao cointegration,
FMOLS, and VECM granger
causality
Yes
Ozturk and
Al-mulali (2015)
Cambodia 1996-2012 Energy consumption Two-stage least square and GMM No
Table 1: Summary of EKC hypothesis and variables used for energy from 2010-2018
(Contd...)
Ridzuan, et al.: The Impact of Energy Consumption based on Fossil Fuel and Hydroelectricity Generation towards Pollution in Malaysia, Indonesia and Thailand
International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020218
Referring to the list of studies presented in Table 1, 80% of the
studies have accounted for the presence of EKC hypothesis by
embedding income and pollution. The studies that involved
high income nations, especially the Europe, did validate the
presence of EKC (cf. Acaravci and Ozturk, 2010; Kasman and
Duman, 2015), while contradicting results were found in most
underdeveloped countries (cf. Al-Mulali et al., 2015; Ozturk
and Al-mulali, 2015).
This study offers to bridge the existing gap of study concerning
EKC hypothesis. First, this study contributes to the study of
EKC hypothesis within the region of ASEAN-3 nations, namely
Indonesia and Thailand, which have experienced rapid economy
growth since the last three decades. Second, although the studies
mostly used source of ENY and fossil fuels, none has included
the aspect of alternative energy, such as hydroelectricity.
Electricity generated from hydroelectricity has been used in
these countries since the past four decades; however, it appears
that the econometric model has yet to be applied. Therefore, this
study examined the EKC hypothesis in Malaysia, Thailand, and
Indonesia by including both types of energy. Third, prior studies
outcomes may be inaccurate as a result of multicollinearity issue
due to the inclusion of both GDP and GDP2 within a regression.
Thus, in the attempt to address this problem, this study adhered to
the steps taken by Narayan and Narayan (2010), which is explained
in the analysis section.
3. METHODOLOGY
Initially, the model of environmental quality was developed by
presenting it in a broad format of EKC hypothesis, which can be
translated in the following equation:
CO f GDP GDP2
2= ( , ) (1)
Authors Country Period Variable for energy Methodology EKC hypothesis
Shahbaz
et al. (2015)
African countries 1980-2012 Electricity
intensities
Johansen cointegration, Pedroni
cointegration, and VECM granger
causality
Yes
Tang and
Tan (2015)
Vietnam 1976-2006 Energy consumption Johansen cointegration, and
VECM granger causality
Yes
Yin et al. (2015) China 1976-2006 Renewable energy
consumption
Panel random effects model Yes
Jebli et al. (2016) OECD countries 1980-2010 Renewable energy
consumption and
non-renewable
energy consumption
Pedroni cointegration, FMOLS,
DOLS and granger causality
Yes
Al-Mulali
et al. (2016)
Countries in 7 regions 1980-2010 Renewable energy
consumption
Pedroni and Fisher panel
cointegration, DOLS and VECM
granger causality
Yes, for East
Asia and the
Pacific, Western
Europe, East
Europe and
Central Asia and
The America
Shahbaz
et al. (2016)
African countries 1971-2012 Energy intensities ARDL, Bayer and Hanck
cointegration
Yes, for Africa,
Algeria,
Cameroon,
Congo Republic,
Morocco, Tunisia
and Zambia
Danish
et al. (2017)
Pakistan 1970-2012 Renewable energy
consumption and
non-renewable
energy consumption
ARDL, FMOLS, DOLS, and
canonical cointegration
Yes
Dong et al. (2017) 30 provinces in China 1995-2014 Energy and Natural
gas consumption
Panel FMOSL, panel DOLS Yes
Kharbach and
Chfadi (2017)
Morocco 2000-2011 Energy consumption
and diesel
consumption
Johansen cointegration Yes
Adebola
et al. (2017)
India and China 1965-2013 Hydroelectricity
used per capita
ARDL, and VECM granger
causality
Yes
Pal and
Mitra (2017)
India and China 1971-2012 Electricity generated
from coal as share
of total electricity
ARDL Yes
Shahbaz
et al. (2017)
US 1960-2016 Biomass energy
consumption
ARDL, and VECM granger
causality
Yes
Sinha and
Shahbaz (2018)
India 1971-2015 Renewable energy
generation
ARDL Yes
Adu and
Denkyirah (2018)
West Africa countries 1970-2013 Combustible
renewable Waste
Panel fixed and random effect No
Table 1: (Continued)
Ridzuan, et al.: The Impact of Energy Consumption based on Fossil Fuel and Hydroelectricity Generation towards Pollution in Malaysia, Indonesia and Thailand
International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020 219
where CO2 refers to carbon emissions per capita proxy for
pollution or level of environmental quality, GDP represents
economic growth, while GDP2 denotes the square of GDP.
Economic growth is achieved when a nation has the ability to
meet the demands of goods and services across a certain time
period. This is determined by examining the variance of GDP
between the target year and the previous year. The equation is
transformed from 1 to log-linear specification (LN) because it
can yield better and more accountable empirical outcomes, in
comparison to the alternative method of simple linear modelling
(Shahbaz et al., 2015), apart from allowing the value to convert
to elasticities. Thus, the logarithm form for the estimation model
is formulated as follows:
LNCO LNGDP LNGDPt t t t2 0 1 2
2= + + +a a a e( ) (2)
where t refers to time period, CO2 represents carbon emissions
per capita, GDP denotes per capita real GDP, and ɛ is standard
error term. The variance of the functional forms between
the variables of economic growth and carbon emissions is
portrayed in the values of income coefficients. When the result
shows α1=α2= 0, a level relationship is concluded, α1 < 0 and
α2 = 0 display the evidence of monotonically decreasing linear
relationship, α1 > 0 and α2 = 0 account for the presence of a
monotonically increasing linear relationship, α1 < 0 and α2 > 0
yield the evidence for U-shaped relationship, and α2 < 0 portrays
an inverted U-shaped relationship, which accounts for EKC in
relation to carbon emissions. In another instance, Saboori et al.
(2012) tested the EKC hypothesis by excluding other explanatory
variable, as displayed in equation 2. Nonetheless, the study
concluded that the outcome of EKC that appeared to exist in
the estimated model was insufficient to ascertain the presence
of inverted-U relationship between environmental degradation
and income.
Based on these findings, alternatives for significant variables
are proposed so as to exert influence pertaining to the presence
of EKC hypothesis in the model. An example of it is ENY,
which has been widely used in prior environmental quality
models based on studies carried out by Hossain (2011), Pao
and Tsai (2011), Al Mulali and Che Sab (2012), as well as
Saboori and Sulaiman (2013a,b), which considered ENY as a
vital determinant of carbon emissions. From the perspectives
of ASEAN-3 developing nations (Malaysia, Indonesia, and
Thailand), these countries heavily rely on dirty energy, such as
coal, to stimulate economic activities mainly because the cost
of using such energy is relatively cheaper. In precise, fossil
fuel combustions that yield higher ENY would end up causing
extensive damages due to higher release of carbon emissions
that contributes to the degradation of environmental quality. In
response to more call for alternative energy sources that stems
from the awareness of climate change, these countries have
begun utilising other cleaner sources, such as hydroelectricity,
as a substitute for fossil-type energy resources. Studies that have
depicted the use of hydroelectricity using the model are carried
out by Solarin et al. (2017) on India and China. Thus, the study
embedded hydroelectricity as a proxy for alternative ENY in the
model. The new equation is given as follows:
LNCO LNGDP LNGDP
LNENY LNAENY
t t t
t t t
2 0 1 2
2
3 4
= + + +
+ +
a a a
a a e
( )
(3)
Next, a study conducted by Lau et al. (2014) revealed an
increased dependency on FDI for growth among developing
countries in ASEAN. Nevertheless, the inflows of FDI in these
countries may have an adverse effect upon environmental
quality. Thus, similar to the prior model proposed by Al-Mulali
(2012) and Pao and Tsai (2011), the FDI had been incorporated
in this study as a crucial determinant of carbon emissions. In
other instances, Jensen (2006) and Acharyya (2009) concluded
that FDI can have a double-edged sword effect; which means,
it facilitates economic growth, but at the same time, causes
serious implication towards the environment through industrial
pollution and environmental degradation. Moreover, in order
to cut cost on environmental controls, the underdeveloped
regions become the safe haven for these polluting industries and
businesses as these regions have a more relaxed attitude towards
environmental standards, thus turning into pollution slums;
described as Pollution Haven Hypothesis (PHH). Meanwhile,
under the Halo Effect Hypothesis (HEH), more efficient and
cleaner production technology that commonly derives from
advanced countries is adopted as a result of FDI so as to enhance
the environmental quality (Stretesky and Lynch, 2008). Hence,
the new equation is stated as follows:
LNCO LNGDP LNGDP
LNENY LNAENY LNFDI
t t t
t t t
2 0 1 2
2
3 4 5
= + +
+ + +
a a a
a a a
( )
++et
(4)
From equation 4, the next variable that can generate a juxtaposition
effect upon the level of environmental quality (CO2) is TO. In
fact, many studies have employed TO as a determinant for carbon
emissions, for example, Halicioglu (2009) for Turkey, and Tiwari
et al. (2013) for India. According to Copeland and Taylor (2004)
and Baek et al. (2009), globalization may be the leading cause of
the rising active pollution from intensive industries among the
developing nations in ASEAN, which have severely affected the
quality of the environment. This implies that prior studies that have
omitted trade-related variables, such as FDI and TO, may portray
a hint of biasness. The new equation is listed in the following:
LNCO LNGDP LNGDP LNENY
LNAENY LNFDI
t t t t
t t
2 0 1 2
2
3
4 5
= + + +
+ +
a a a a
a a
( )
++ +a e6LNTOt t
(5)
where α denotes regression coefficient, while α1, and α3, are
predicted to display positive sign. Nonetheless, either positive
or negative sign can be expected for α2, α4, α5, and α6. Finally, μ
refers to error term that is assumed to be normally distributed
with zero mean and constant variance. In fact, the empirical
model employed in this study incorporated most of the vital
determinants for carbon emissions, thus clearing this study
from any concern associated to variable bias, primarily because
all the variables were regressed within the same multivariate
framework. Furthermore, by employing the unrestricted version
of Autoregressive Distributed Lag (ARDL) model initiated by
Pesaran et al. (2001), this study formulated the following error
correction models based on equation 5:
Ridzuan, et al.: The Impact of Energy Consumption based on Fossil Fuel and Hydroelectricity Generation towards Pollution in Malaysia, Indonesia and Thailand
International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020220
DLNCO LNCO LNGDP LNGDP
LNENY
t t t t
t
2 0 0 2 1 1 1 2
2
1
3 1 4
= + + +
+ +
- - -
-
b q q q
q q LLNAENY LNFDI LNTO
LNCO L
t t t
i t i
i
a
i
i
b
- - -
-
= =
+ + +
+å å
1 5 1 6 1
2
1 0
q q
b gD D NNGDP LNGDP
LNENY LNAENY
t i i
i
c
t i
i
i
d
t i i
i
e
-
=
-
=
-
=
+ +
+
å
å å
d
l J
D
D D
0
2
0 0
tt i i t i
i
f
i
i
g
t i t
LNFDI
LNTO
- -
=
=
-
+ +
+
å
å
y
r u
D
D
0
0
(6)
where ∆ represents the first difference operator and ut refers to
white-noise disturbance term. Besides, the residuals for unrestricted
error correction model (UECM) should be serially uncorrelated,
and the model has to be stable. Meanwhile, the null hypothesis of
no co-integration against the alternative hypothesis for the presence
of long-run co-integration is defined by the following:
H0: θ0 = θ1 = θ2 = θ3 = θ4 = θ5 = θ6 = 0 (absence of long-run
relationship)
H1: θ0 ≠ θ1 ≠ θ2 ≠ θ3 ≠ θ4 ≠ θ5 ≠ θ6 ≠ 0 (presence of a long-run
relationship).
Next, upon confirming the existence of long-run relationship via
F statistic, both long-run and short-run elasticity coefficients can
be determined.
The long-run relationship model is depicted in the following:
DLNCO LNCO LNGDP
LNGDP LNENY
t t t
t t
2 0 0 2 1 1 1
2
2
1 3 1 4
= + +
+ + +
- -
- -
b a a
a a a LLNAENY
LNFDI LNTO
t
t t t
-
- -+ + +
1
5 1 6 1a a u
(7)
Next, the short-run relationship model is presented as follows:
D D
D D
LNCO ECT LNCO
LNGDP
t t i t i
i
a
i
i
b
t i i
i
2 0 1 2
1
0
= + + +
+
- -
=
=
-
=
å
å
b j b
g d
00
2
0 0
1
c
t i
t i
i
d
t i t i
i
e
t i
i
i
LNGDP
LNENY LNAENY
å
å å
-
-
=
- -
=
-
-
+
+
+
l J
y
D D
==
- -
=
-å å+ +
0
1 1
0
1
f
i i
i
g
i tLNFDI LNTOD Dr u
(8)
where φ represents coefficient of error correction term (ECT). The
value of ECT must be significantly negative to reflect converges,
apart from displaying the rate of speediness of all the variables
towards equilibrium. The variable ECTt-1, which is a lagged value of
the estimated ordinary least square (OLS) residual (ʋt) from the long-
run model, is given based on equation 7.0. Moreover, it is essential
to ensure that the proposed model is absent from serial correlation,
normality, and homoscedasticity issues by performing a diagnostic test.
3.1. Sources of Data
The annual data employed in this study are mostly in the form of
per capita and are derived from 1980 until 2014. CO2 emissions
are in metric tons per capita, real GDP is in constant 2010 US
dollar, ENY is based on per capita (kg of oil equivalent), alternative
energy with hydroelectricity generation as its proxy is divided by
the total population so as to incur billion kilowatt hours per capita,
while FDI and TO are based on their ratios over GDP. All data
used in this study were based on the World Development Indicator
(2017) generated by World Bank, except for hydroelectricity
generation that had been obtained from U.S Energy Information
Administration (2015).
4. RESULTS AND ANALYSIS
4.1. Testing the Stationarity of Data
The analysis was initiated by testing the data with Dickey-
Fuller (ADF) and Phillip Perron (PP) unit root tests, in which
the outcomes are depicted in Table 2. For all these tests, the
null hypothesis includes a unit root, whereas the alternative
hypothesis has no unit root. Unit root tests were performed
to determine the order of integration of each variable so as to
identify the best method of time series analysis suitable for
the proposed econometric model. The selection of lag for the
ADF unit root test was set based on Schwarz Info Criterion
(SIC), given a small number of observations carried out in this
study. In addition, all unit roots were estimated at level and
first difference.
Overall, the results showcased a mix stationarity of the variables at
level, I(0), and at first difference, I(1). To further clarify, based on
the Malaysian ADF unit root test outcomes at level, both LNAENY
and LNFDI appeared to be stationary, I(0) at 10% and 1% level,
respectively. On the other hand, based on PP test at level, LNFDI
was found to be significant at 1% level at both intercept and trend,
and intercept. Nevertheless, at first difference for the ADF test,
these variables seemed to be insignificant at trend and intercept for
LNAENY, but both intercept and trend, and intercept for LNFDI.
A more powerful property of unit root, which is the PP test, was
performed and exhibited that all variables were significant mostly
at 1% level. The mixed evidence for stationarity of the variables at
level and at first difference was also determined for Indonesia and
Thailand. Thus, the mixed stationarity of the unit roots favoured
the condition for implementation of ARDL estimation for all the
three countries.
4.2. Determining Long-run Relationship
In order to confirm the presence of long-run relationship between
the variables, the model of each ASEAN-3 had been tested by using
ARDL co-integration test, which revealed the F-statistic values,
as tabulated in Table 3. The null hypothesis cannot be rejected if
the F-statistic falls below the bound level, but if the F-statistic
exceeds the upper bound level; the null hypothesis is rejected, thus
signifying the existence of co-integration. The results showed that
the null hypothesis of no co-integration for Malaysia (4.14 > 3.61)
is rejected at 5% significant level, while Indonesia (7.00 > 4.43)
and Thailand (8.93 > 4.43) are rejected at 1% level, given that
their F-statistic values were greater than the upper bound critical
value, I(1), as given in Table 3. This implies a tendency for the
variables to move towards the long-run equilibrium for all the
proposed models.
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International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020 221
4.3. Diagnostic Test
Table 4 presents the outcomes of diagnostic statistics, such as
serial correlation test, misspecification test, heteroscedasticity
test, and normality test. All the tested models for the countries
selected in this study seemed to be absent from any diagnostic
test, given that the probability values are >10% significant level.
Table 2: Results of ADF and PP unit root tests
Country Variable ADF unit root test PP unit root test
Intercept Trend and intercept Intercept Trend and intercept
Malaysia
Level LNCO2 −1.25 (0) −1.57 (0) −1.28 (1) −1.61 (2)
LNGDP −0.66 (0) −1.69 (0) −0.66 (1) −1.87 (2)
LNGDP2 −0.49 (0) −1.83 (0) −0.50 (1) −2.01 (2)
LNENY −1.14 (0) −1.73 (0) −1.32 (5) −1.70 (1)
LNAENY −2.76 (1)* −3.11 (1) −2.05 (16) −2.17 (11)
LNFDI −4.93 (0)*** −4.98 (0)*** −4.93 (0)*** −4.99 (1)***
LNTO −1.56 (1) 0.42 (0) −1.34 (3) 0.13 (1)
First difference LNCO2 −6.46 (0)*** −6.49 (0)*** −6.42 (2)*** −6.46 (2)***
LNGDP −4.81 (0)*** −4.74 (0)*** −4.81 (0)*** −4.74 (0)***
LNGDP2 −4.89 (0)*** −4.81 (0)*** −4.89 (0)*** −4.81 (0)***
LNENY −6.28 (0)*** −6.32 (0)*** −6.39 (3)*** −6.82 (6)***
LNAENY −5.07 (1)*** −3.09 (3) −4.48 (15)*** −4.34 (15)***
LNFDI −1.63 (7) −1.84 (7) −24.13 (22)*** −23.16 (23)***
LNTO −3.41 (0)** −3.48 (2)* −3.44 (5)** −3.75 (15)**
Indonesia Level
LNCO2 −1.08 (0) −3.15 (0) −1.00 (6) −2.88 (5)
LNGDP 0.02 (0) −2.21 (1) 0.02 (0) −1.92 (2)
LNGDP2 0.25 (0) −2.19 (1) 0.25 (0) −1.86 (2)
LNENY −1.28 (0) −1.10 (0) −1.35 (5) −1.07 (1)
LNAENY −2.39 (2) −3.69 (0)** −2.42 (4) −3.68 (4)**
LNFDI −2.66 (0)* −4.29 (1)*** −2.44 (15) −3.46 (15)*
LNTO −2.94 (0)* −2.91 (0) −2.96 (3)** −2.94 (3)
First difference LNCO2 −5.57 (1)*** −5.43 (1)*** −6.67 (10)*** −6.43 (9)***
LNGDP −4.39 (0)*** −4.35 (0)*** −4.42 (1)*** −4.35 (2)***
LNGDP2 −4.34 (0)*** −4.34 (0)*** −4.37 (1)*** −4.33 (2)***
LNENY −5.91 (0)*** −3.97 (7)** −5.91 (1)*** −6.08 (5)***
LNAENY −7.39 (1)*** −7.70 (1)*** −10.50 (4)*** −10.73 (3)***
LNFDI −6.76 (2)*** −6.64 (2)*** −9.00 (11)*** −8.77 (11)***
Thailand Level LNTO −8.35 (0)*** −8.24 (0)*** −8.35 (0)*** −8.24 (0)***
LNCO2 −1.27 (0) −0.48 (0) −1.15 (2) −0.86 (2)
LNGDP −1.51 (1) −1.73 (1) −1.47 (2) −1.33 (3)
LNGDP2 −1.36 (1) −1.82 (1) −1.26 (2) −1.45 (3)
LNENY −0.51 (0) −1.30 (0) −0.54 (3) −1.75 (3)
LNAENY −3.88 (1)*** −4.86 (1)*** −5.73 (6)*** −12.38 (33)***
LNFDI −2.89 (0)* −3.26 (0)* −2.81 (3)* −3.34 (3)*
LNTO −0.85 (0) −1.61 (0) −0.86 (1) −1.87 (3)
First difference LNCO2 −3.89 (0)*** −4.24 (0)** −3.88 (1)*** −4.26 (6)***
LNGDP −3.15 (0)** −3.34 (0)* −3.15 (0)** −3.36 (1)*
LNGDP2 −3.31 (0)** −3.43 (0)* −3.31 (0)** −3.44 (1)*
LNENY −4.37 (0)*** −4.32 (0)*** −4.32 (2)*** −4.25 (2)**
LNAENY −5.89 (3)*** −6.10 (3)*** −12.40 (27)*** −15.85 (23)***
LNFDI −7.86 (0)*** −7.83 (0)*** −8.16 (2)*** −8.78 (4)***
LNTO −5.50 (0)*** −5.47 (0)*** −5.50 (1)*** −5.46 (1)***
(1) ***, **, and *are 1%, 5%, and 10% of significant levels, respectively. (2) The optimal lag length was selected automatically by using the SIC for ADF test, while the bandwidth was
opted by using the Newey–West method for the PP test. (3) Number in parentheses refers to standard errors
Table 3: Results of ARDL co-integration
ASEAN-3 Maximum lag SIC (a, b, c, d, e, f, g) F Statistic at SIC Result
Malaysia (4,2) (1,2,2,0,0,1,2) 4.14** Co-integration exists
Indonesia (2,2) (2,0,0,2,1,0,2) 7.00*** Co-integration exists
Thailand (3,3) (1,1,3,3,2,3,2) 8.93*** Co-integration exists
Critical values for F-statistics# Lower I (0) Upper I (1)
1% 3.15 4.43
5% 2.45 3.61
10% 2.12 3.23
#The critical values were obtained from Pesaran et al. (2001) based on case III: unrestricted intercept and no trend. *, **, and ***represent 10%, 5%, and 1% level of significance,
respectively
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International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020222
Therefore, the outcomes produced from all the three models are
indeed reliable. Besides, the size of the adjusted R2 indicated a
good fit for all the models.
Apart from diagnostic tests, it is compulsory to determine
the stability of each model via Cumulative Sum of Recursive
Residual (CUSUM) and Cumulative Sum of Squares of Recursive
Residuals (CUSUMSQ) stability tests. The diagrams illustrated in
Table 5 show that the plots of both CUSUM and CUSUMSQ, as
represented by blue line, appear to fall inside the critical bounds
of 5% significant level for all ASEAN-3 countries, except for
CUSUMSQ of Indonesia. The plot of CUSUMSQ for Indonesia
seemed to be out of the critical limits, hence suggesting some
instability in the model. Nevertheless, as the plot returned towards
the critical bands, the deviation was only transitory. Furthermore,
the outcomes of stability tests for Malaysia, Indonesia, and
Thailand suggest that policy changes, upon considering the
explanatory variables of carbon emissions embedded in this study,
did not cause any major distortion in the level of carbon emissions.
4.4. The Long-run Elasticities
The outcomes of long-run elasticities, as displayed in Table 6, are
briefly explained in this section.
4.4.1 Malaysia
Based on the estimation of long-run elasticities, this study validated
the EKC hypothesis, given that Malaysia’s both GDP and GDP2
have the correct expected sign and are significant at 1% level.
Furthermore, the use of varying sets of determinants in this study,
as opposed to prior studies conducted by Saboori et al. (2012),
Saboori and Sulaiman (2013b), Lau et al. (2014), and Begum
et al. (2015), aids in contributing in-depth knowledge on this
scope. The presence of EKC hypothesis, which is also known as
inverted U-shaped relationship between economic growth and
environmental quality with CO2 emissions as the proxy, displayed
that through the period of observation, Malaysia took several
active measures in minimizing pollution by joining in the efforts
that protect the environment, namely Kyoto Protocol that aims to
put a stop to greenhouse effect. Aside from that, Malaysia is also
Table 4: Results of diagnostic test
ASEAN-3 Serial correlation
X2(1) [P-value]
Functional form
X2(1) [P-value]
Normality
X2(2) [P-value]
Heteroscedasticity
X2(1) [P-value]
Adjusted
R2
Malaysia 0.38 [0.68] 1.14 [0.29] 0.05 [0.97] 1.43 [0.23] 0.98
Indonesia 1.01 [0.38] 2.48 [0.11] 0.91 [0.63] 1.51 [0.19] 0.97
Thailand 1.77 [0.22] 0.55 [0.47] 0.35 [0.83] 0.89 [0.60] 0.99
The numbers in brackets [ ] refer to P values
Table 5: Stability tests
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International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020 223
associated to millennium development goal (MDG) that assists in
overcoming environmental degradation through enforcement of
stringent environmental laws, such as Environmental Quality Act
1974 and Environmental Quality Order 1987, while simultaneously
boosting its economic development. Next, alternative energy
consumption (AENY) with hydroelectricity as its proxy resulted
in a significantly negative sign at 1%. This means; 1% increment
in hydroelectricity consumption could help to decrease 0.15% of
carbon emissions, thus suggesting control of air pollution. This
shows that the Malaysian government does not heavily depend
on single source of energy alone, but also places focus on cleaner
energy, which is in agreement with Ahmad et al. (2011). Besides
AENY, both FDI and TO seemed to be significantly negative at
10% and 1%, respectively. The negative coefficient of FDI appears
to be in line with the HEH, thus implying that higher FDI inflows
have helped Malaysia with cleaner technology spill-over. On top
of that, it was found that most multinational companies from
Japan and Korea have adopted more advanced technology that
focuses on cleaner energy, thus reducing reliance on dirty energy
and controlling the release of carbon emissions. For instance, 1%
increment in FDI led to 0.03% of decrease in carbon emissions.
Embracing (TO) has helped to cut the level of carbon emissions
in Malaysia, as 1% increment in TO decreases carbon emissions
release by 0.57, which seems greater when compared to AENY
and FDI. The negative sign of TO may imply that Malaysia has
opted for more clean-intensive products for exports and has chosen
to import pollution-intensive products from their trading partners.
Besides, the government has been actively encouraging the local
industries, particularly those involved in export activities, to select
cleaner technologies over those obsolete and dirty. Lastly, ENY
displayed a correct sign, which is positive, but it appears not to
influence the level of carbon emissions in Malaysia, as it was
insignificant at all levels. This result is opposed to the outcome of
most prior studies conducted for Malaysia, such as that obtained
by Shahbaz et al. (2013b).
4.4.2. Indonesia
As for the case in Indonesia, its economic progress has experienced
the U-shaped EKC, following the negative and positive signs
for GDP and GDP2, respectively. Such results are in line with
those retrieved from Lean and Smyth (2010) for Indonesia. This
particular scenario reveals that the economic progress in Indonesia
at both the initial and present stages has caused high environmental
degradation. The Indonesian economic development is currently
dealing with a rather critical environmental drawback, such as
thinning of forests, water pollution due to dumping of industrial
wastes into water, air pollution particularly in urban areas, and
haze produced from forest fires. Among the notable environmental
issue refers to the occurrence of massive and thick haze of smog
caused by human activity, whereby land is burnt annually to
make ways for the country to produce pulp, paper, and palm oil.
This activity is most commonly done on the island of Sumatra
located at the western Indonesia and Borneo, which does not only
portray a negative impact upon climate change, but also causing
a stir amongst its neighbouring countries, such as Malaysia and
Singapore. Furthermore, the high reliance among Indonesian
producers on dirty energy, which is based on fossil fuel type
of energy, has led to higher environmental degradation. Thus,
increase in ENY contributes to energy pollutants in a rather
significant manner after economic growth. The results infer that a
1% rise in ENY is linked with a 1.30% increment in CO2 emissions.
Next, the outcome of TO was found to be negative and statistically
significant at 5% level, which indicates that embracing TO has
managed to decrease environmental degradation due to carbon
emissions. This shows that TO offers access to Indonesia for
advanced technology that emits less CO2 emissions. Meanwhile,
the alternative energy with hydroelectricity generation as its
proxy (AENY) and FDI inflows failed to influence the level of
environmental quality in Indonesia for they appeared insignificant
at all levels.
4.4.3. Thailand
Similar to Malaysia, the validity of EKC hypothesis is also proven
for the case of Thailand as both its GDP and GDP2 displayed the
correct expected sign and statistical significance at 1% level. The
sustainable environmental quality, along with its progressive
economic growth achieved by Thailand is believed due to the success
of its 10th and 11th National Economic and Social Development Plan
implemented from 2007 until 2016 by its government. The policies
that emphasized on environmental governance, environmental
quality promotions, environmental-friendly production and
consumption, environmental responsibilities, as well as climate
and natural disasters resilience, were specifically designed for
the country to attain green and balanced growth. This outcome
suggests a new empirical finding that supports the validity of EKC
in Thailand, in comparison to all other past findings retrieved
by Narayan and Narayan (2010) and Lean and Smyth (2010),
who failed to support the validity of EKC for Thailand. Next,
fossil fuel ENY appears to intensify pollution by its significantly
positive effect upon carbon emissions. This particular outcome,
in general, is in agreement with Saboori and Sulaiman (2013a,b),
Shahbaz et al. (2014), and Cho et al. (2014). Briefly, 1% increment
in ENY leads to a hike in carbon emissions by 4.29%. Besides,
when compared to the outcomes derived from Indonesia, the
Table 6: Estimation of long-run elasticities
Country/ARDL Malaysia (1,2,2,0,0,1,2) Indonesia (2,0,0,2,1,0,2) Thailand (1,1,3,3,2,3,2)
LNGDP 28.36*** (4.27) −9.46*** (2.52) 10.04*** (1.25)
LNGDP2 −1.54*** (0.242) 0.61*** (0.15) −0.76*** (0.10)
LNENY 0.06 (0.22) 1.30*** (0.28) 4.29*** (1.03)
LNAENY −0.15*** (0.04) 0.04 (0.06) −0.04 (0.52)
LNFDI −0.03* (0.01) 0.02 (0.03) 0.09 (0.08)
LNTO −0.57*** (0.14) −0.29** (0.13) −1.67** (0.67)
Constant −127.62*** (18.43) 29.84*** (9.72) −53.12*** (6.44)
Dependent variable is∆LNCO2.*
,**,*** indicate significance at 10%, 5%, and 1% significant level, respectively. Numbers in brackets represent standard error. The ARDL estimation
outcomes were generated by using SIC
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International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020224
magnitude for this variable seems greater for Thailand. Its AENY,
on the other hand, displayed a negative impact upon pollution, but
insignificant at all levels. Therefore, alternative energy has failed
in becoming a key solution to decrease pollution. In addition, the
findings of FDI and TO for Thailand are similar to those obtained
for Indonesia, given that only TO displayed a significantly
negative correlation with pollution or environmental quality.
Along with other ASEAN nations (in this case, Malaysia and
Thailand), trade liberalization has helped the country to be more
particular with their export and import activities. Furthermore,
in the attempt to support sustainable development goal (SDG)
initiated by United Nation, Thailand and other ASEAN countries
have begun implementing several effective strategies, such as
imposing higher tax towards high pollution-intensive products
and providing subsidies to its local producers who adopt cleaner
technology. Thailand also may have comparative advantage in
cleaner intensive product that promotes environmental quality.
The outcomes further revealed that 1% increment in TO leads to
1.67% reduction in environmental degradation.
4.5. The Short-run Elasticities
The outcomes of short elasticities are as tabulated in Table 7.
Narayan and Narayan (2010) suggested that a different method
is required to determine if the tested countries have managed
to reduce their CO2 emissions over time with increment in
their economic growth by comparing short-run with long-run
elasticities. If the result shows smaller long-run income elasticity
than that of short-run over a period of time, income is assumed
to have contributed to less carbon emission. This appears to be a
response to issues related to collinearity or multicollinearity that
may exist between GDP and GDP2.
Based on the significance at the same lag for GDP and GDP2, it
was discovered that in the short-run, the development in Malaysia
resembled a U-shaped EKC, thus suggesting that development
has results in greater environmental degradation. Similar scenario
is reflected in Indonesia. Nevertheless, it was found that only
Thailand validated the presence of EKC hypothesis and its size of
magnitude seemed relatively greater in short-run, when compared
to long-run. Thus, between the three ASEAN countries analysed
in this paper, it can be concluded that only Thailand has managed
to achieve sustainable economic development both for short-run
and long-run, while sustainable economic development is only
achieved by Malaysia in the long-run.
The size of magnitude for AENY for Malaysia seemed relatively
greater in the short-run with a coefficient value of 0.19. This
implies that the generation of hydroelectricity energy in
Malaysia could effectively reduce carbon emission. Apart from
AENY, TO (at lag 1) in Malaysia has also reduced the release
of carbon emission. As for the case in Indonesia, increment in
ENY has the ability to reduce carbon emission, while increased
participation of Indonesia in international trade (TO) leads to
worsening of air quality with a positive sign of TO. On the other
hand, as for Thailand, higher ENY improves its environmental
quality through lower release of carbon emissions, which is
similar to the outcomes derived for Indonesia. Nevertheless,
AENY displayed a positive sign, which means that the use of
hydroelectricity generation in short-run could cause greater
pollution. On top of that, TO seemed to exhibit mixed expected
signs on varied lags.
As depicted in Table 7, the estimated lagged ECT in ARDL
regression for the three studied nations appear to be negative
and statistically significant. Based on the ECT value, the highest
speed of adjustment was obtained by Indonesia (−1.39), followed
by Malaysia (−1.29), and Thailand (−0.95). As for Indonesia
and Thailand, given their ECT value >−1, Narayan and Smyth
(2006) suggested that instead of monotonically converging to
the equilibrium path directly, the error correction process for
these two countries fluctuates around the long-run value in a
dampening manner. Nonetheless, once this process is complete,
convergence to equilibrium path becomes rapid. For instance,
more than 139%, 129%, and 95% of adjustments were completed
within less than a year for both Malaysia and Indonesia, whereas
a year for Thailand due to short-run adjustment, which is
considered as very rapid.
Table 7: Estimation of short-run restricted error
correction model
Variables Malaysia Indonesia Thailand
∆LNCO2 - - -
∆LNCO2-1 - 0.77***
(0.14)
-
∆LNCO2-2 - - -
∆LNGDP 18.52
(12.41)
−13.23***
(4.03)
26.98**
(8.83)
∆LNGDP−1 −31.76**
(13.41)
- -
∆LNGDP−2 - - -
∆LNGDP2 −0.96
(0.70)
0.86***
(0.25)
−1.68***
(0.53)
∆LNGDP2−1 1.83**
(0.76)
- −0.06*
(0.03)
∆LNGDP2−2 - - 0.11***
(0.02)
∆LNENY 0.07
(0.28)
0.13
(0.36)
1.91***
(0.40)
∆LNENY−1 - −0.94**
(0.06)
0.04
(0.23)
∆LNENY−2 - - −1.42***
(0.29)
∆LNAENY −0.19***
(0.06)
−0.09
(0.06)
0.14**
(0.04)
∆LNAENY−1 - - 0.11***
(0.03)
∆LNAENY−2 - - -
∆LNFDI −0.02
(0.01)
0.04
(0.04)
0.03*
(0.02)
∆LNFDI−1 - - −0.01
(0.01)
∆LNFDI-2 - - −0.03
(0.02)
∆LNTO −0.28
(0.25)
0.27**
(0.10)
−0.62***
(0.16)
∆LNTO-1 0.56**
(0.26)
0.21**
(0.09)
0.41**
(0.16)
∆LNTO-2 - - -
ECT −1.29***
(0.23)
−1.39***
(0.00)
−0.95***
(0.29)
Dependent variable is∆LNCO2. *
,**, and *** indicate significance at 10%, 5%, and 1%
significant level, respectively
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International Journal of Energy Economics and Policy | Vol 10 • Issue 1 • 2020 225
5. CONCLUSION AND POLICY
RECOMMENDATIONS
This paper has bridged the gap found in the literature pertaining
to environmental economics studies within the context of
selected ASEAN-3 nations regarding correlations between
carbon emissions, economics growth, energy based on fossil fuel,
alternative energy based on hydroelectricity generation, FDIs, and
TO so as to generate a comparison between the emerging economies
of Malaysia, Indonesia, and Thailand on the said terms. The study,
hence, used annual time series data over the period of 1980 until
2014. The following conclusions are drawn from this exercise:
• All the models have passed the diagnostic test settings,
i.e., normality test, stability tests, heterogeneity test, and
stability tests, thus producing reliable outcomes.
• Overall, evidence for the existence of EKC hypothesis has
been established for both the cases of Malaysia and Thailand.
Relatively, economic growth in Indonesia appears to cause
higher influence on carbon emissions and its policymakers
should, therefore, pay more attention on this situation.
• The fossil fuel type of energy harms the environmental quality
in Indonesia and Thailand, while insignificant impact of
energy was discovered upon Malaysian environment.
• Hydroelectricity generation has successfully improved
the environmental quality in Malaysia, but it has failed to
influence carbon emissions level in Indonesia and Thailand.
• Higher FDI inflows may aid in decreasing issues related to
environmental degradation in Malaysia, thus validating the
HEH.
• Embracing TO has successfully improved environmental
quality for all the studied ASEAN-3 countries.
Overall, the ASEAN-3 nations should devise effective strategies
so as to ascertain the quality of sustainable environmental in their
region. The strategies may consist the following:
• First, it is highly suggested that ASEAN-3 countries should
initiate an economic model based on sustainable development
goals. For example, Malaysia has made a commitment
to reduce carbon emissions by 40% by 2020 and actively
promoting green economy should be noted as a valuable
lesson. Besides, intensifying green economy initiatives could
allow decoupling economic growth and carbon emissions.
However, proper implementation of these strategies is
necessary so as to ensure the success of such initiatives.
For example, the Malaysia government has introduced the
Green Technology Master Plan 2017-2030 in the attempt to
slash carbon emissions from the present eight metric tonnes
(MT) per capita to six MT per capita in 2030. Based on the
outcomes of this paper, Malaysia and Thailand could share
their individual experiences on sustainable development
practices to their neighbouring country, Indonesia.
• Generally, the policymakers of ASEAN-3 countries should
develop a concreate policy framework that promotes long-
term value to reduce GHG emissions, aside from constantly
supporting the progress of new technologies that lead to less
carbon-intensive economy.
• It is suggested for Indonesia and Thailand to increase the
volume of investment by injecting more capital into projects
that utilise alternative energy, as practiced in Malaysia. This
strategy could cut the consumption of fossil fuels and facilitate
the role of alternative energy, such as hydroelectricity, as
highlighted in this research paper.
• Policymakers should impose stringent environmental laws,
particularly regarding energy-intensive and polluted foreign
industries. The design of new environmental policies that
improve regularity framework and enforcement activity can
also help to mitigate environmental damages, thus leading
towards sustainability in environmental quality among these
nations.
• Additionally, given the positive impact of trade towards
environmental quality among the studied nations, it is
advisable for these countries to maintain trade-related actions
and strategies so as to heighten environmental protection,
which is crucial to successfully lift environmental pressure
induced by trade, in precise.
ACKNOWLEDGEMENT
This paper was initially presented at The International Conference
on Innovative Applied Energy (IAPE’19) at the Oxford City 14-
15 March 2019, Oxford, United Kingdom. The source of fund
is provided by IRMI, UiTM, under the code grant of 600 IRMI/
Dana 5/3/GOT (003/2018). We also wish to extend our gratitude
to InQKA UiTM for financing the publication fees of this paper.
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