Analysis of Green Economic Growth and Environmental Degradation

in Upper-Middle-Income ASEAN Countries

Fradya Randa and Mirza Ayunda Pratiwi

Faculty of Maritime Economics and Business, Maritim Raja Ali Haji University, Tanjungpinang, Indonesia

Keywords: Strategy, Promotion, E-Commerce Trends.

Abstract: The objective of this research is to examine the connection between green economic growth and

environmental degradation in upper-middle-income nations within the ASEAN region (specifically, Indonesia,

Malaysia, and Thailand) throughout the years 2000 to 2020, using a simultaneous panel model. The key

findings of this research can be categorized into two analytical models. First, cleaner energy has a positive

effect on green economic growth, while technological innovation has a negative effect. The positive impact

of trade openness and population on environmental degradation is evident. Green growth and environmental

degradation do not affect each other in upper-middle ASEAN countries. A different finding in this research

is that green growth and environmental degradation do not have any influence on each other in ASEAN upper

middle-income countries. The problem is believed to stem from the policy direction and focus on

technological innovation that has not been optimal for green economic development. The policy implication

that can be implemented is increasing the use of renewable energy in developing a green economy.

1 INTRODUCTION

The increase in CO2 emissions due to economic

activities in ASEAN member countries has an impact

on environmental degradation. Although there are

abundant natural resources and strong economic

performance. ASEAN countries face challenges in

creating environmentally sustainable economic

activities.

The rapid economic acceleration in ASEAN

nations is driving up the usage of fossil fuels,

resulting in elevated pollution levels and increased

emissions of CO2. Increasing energy consumption

has increased environmental degradation (Afridi et

al., 2019; Jian et al., 2019). Especially in ASEAN

upper-middle income countries, which are currently

spurring economic performance by increasing

economic growth. The heightened CO2 emissions

render the ASEAN region increasingly susceptible to

challenges posed by climate change (Sandu et al.,

2019).

The ASEAN region was found to contribute 3.6%

of global greenhouse gas emissions in 2013, partly

because of its strong economic growth and population

growth. (Chontanawat, 2018). The growth of the

economy has accelerated deforestation, resulting in a

swift exhaustion of natural resources. The 2017

edition of the fifth ASEAN Environmental

Economics Report affirms that the rise in energy

consumption is the primary driver behind the

escalating CO2 emissions, and it is projected to surge

by 61% between 2014 and 2025. (ASCCR, 2021).

According to a 2015 report from the Asian

Development Bank, it is projected that greenhouse

gas emissions from the energy sector in ASEAN

economies will see a threefold increase by the year

2050 (ADB, 2015).

This phenomenon is of particular concern in

ASEAN countries, especially upper-middle-income

countries (Indonesia, Thailand, and Malaysia). The

country is persistently striving to implement

measures aimed at sustaining natural resources in an

environmentally responsible way, with the aim of

boosting economic growth and curbing

environmental degradation. One of them is through

an agreement in the formulation of Sustainable

Development Goals (SDGs) (Alam et al., 2007;

Janoušková et al., 2018; Rosati & Faria, 2019).

Previous literature studies have proven that there

are many factors that influence the growth of green

economics and environmental degradation.

Analyzing the impact of energy consumption on

sustainable growth in China from 1997 to 2016, the

Randa, F. and Pratiwi, M.

Analysis of Green Economic Growth and Environmental Degradation in Upper-Middle-Income ASEAN Countries.

DOI: 10.5220/0012697100003798

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd Maritime, Economics and Business International Conference (MEBIC 2023) - Sustainable Recovery: Green Economy Based Action, pages 37-43

ISBN: 978-989-758-704-7

research revealed that green growth was primarily

driven by natural gas consumption and other forms of

energy use, while the utilization of coal and oil acted

as impediments to green growth (Hongxian, 2018).

An examination of green growth in Turkey from

1980 to 2017, employing the ARDL methodology,

indicated that sustainable growth in the long term is

predominantly steered by cleaner energy sources and

technological advancement. Conversely, long-term

green growth is negatively affected by militarization

(Sohag et al., 2019)

Other studies have been conducted to investigate

the impact of economic expansion, energy utilization,

and CO2 emissions in nations categorized as

developed, developing, and those in the MENA

region from 2001 to 2017. The finding is that in

developed and developing countries, economic

growth rises in tandem with heightened energy

consumption, whereas in MENA countries, it

experiences a decline (Muhammad, 2019).

An empirical study on the impact of energy

consumption and economic growth on environmental

deterioration in the Asian region between 1991 and

2013. The panel causality analysis using VECM

confirms the existence of a bidirectional causal

relationship between energy consumption, economic

growth, and environmental deterioration. (Jamel,

2016).

The primary cause of environmental degradation

is rapid industrialization due to the consumption of

natural resources to fuel economic expansion. (Burki

& Tahir, 2022). Environmental deterioration,

particularly in developing nations undergoing swift

industrialization, is driven by energy consumption

(Afridi et al., 2019; Al-mulali & Binti Che Sab, 2012;

Jian et al., 2019).

Environmental degradation is caused by many

factors (Jan et al., 2021; Shah et al., 2021). Increased

environmental degradation can also be caused by

trade openness. The impact of international trade on

environmental degradation is determined by the

volume, quantity, and production technique

employed. (Grossman & Krueger, 1991). A

substantial increase in the production of goods and

services resulted in a greater use of resources,

resulting in increased pollution (Liobikienė &

Butkus, 2019).

The influence of trade on heightened

environmental deterioration is a result of its impact

on the magnitude and composition (Halicioglu, 2009;

Nasir et al., 2021; Nguyen et al., 2021). However, the

effect of using production technology has a negative

impact on environmental degradation (Tachie et al.,

2020).

In Pakistan, environmental degradation is also

affected positively and significantly by population,

energy consumption, and industrialization.

Meanwhile, economic growth contributes negatively

to environmental degradation (Ur Rehman & Zeb,

2020). Other research has explored the causal link

between carbon dioxide emissions, energy usage, the

adoption of renewable energy, population expansion,

and economic growth in five ASEAN nations

(Indonesia, Myanmar, Malaysia, the Philippines, and

Thailand) from 1971 to 2014. The unidirectional

causal effect of economic growth on renewable

energy consumption, CO2 emissions, and energy

consumption in Indonesia is found (Vo, 2019)Click

or tap here to enter text.

At the beginning, economic growth has a negative

impact on the environment, but as time goes on, it

leads to environmental improvement. (Rahman et al.,

2020). The N-shaped Environmental Kuznets Curve

(EKC) demonstrates that as growth progresses, it

initially heightens environmental degradation,

subsequently diminishes it, and then exhibits a

renewed increase in degradation (Afridi et al., 2019;

Ahmad et al., 2019; Allard et al., 2018). This study

aims to contribute to new and comprehensive

literature on the determinants of green growth and

environmental degradation in the economies of

ASEAN upper-middle-income countries. This study

also discusses the possible consequences for future

generations due to environmental degradation and

policy measures to promote green growth and reduce

environmental degradation in ASEAN upper-middle-

income countries.

2 RESEARCH METHODOLOGY

The data used in this study consists of secondary data

that has been published by specific organizations or

authorities. The data employed in this analysis is

panel data, encompassing a time series spanning 21

years from 2000 to 2020, and covering three ASEAN

upper-middle-income nations, namely Indonesia,

Thailand, and Malaysia. The study encompasses both

internal and external variables. The variables that

control the economy are green economic growth and

environmental degradation. The variables that are

outside of the system include technological

innovation, clean energy, militarization, health

spending, population, and trade openness.

Each of the endogenous variables utilized in this

research also serves as an exogenous factor in other

equations. The connection between these variables is

illustrated in Figure 1, displayed below.

MEBIC 2023 - MARITIME, ECONOMICS AND BUSINESSINTERNATIONAL CONFERENCE

Figure 1: The conceptual framework of the research.

In accordance with the research's conceptual

model presented in Figure 1, the study utilizes

specific indicators to measure the variables being

investigated.

Environmental Degradation (ED) CO2 emissions

resulting from the use of petroleum-derived fuel as an

energy source are expressed in kilotons.

Green Economic Growth (GEG) Renewable

energy supply, percentage of total energy supply

Technological Innovation (TI) Development of

environment-related technologies, percentage all

technologies

Cleaner Energy (CE) Renewable energy's share in

the overall final energy consumption is expressed as

a percentage.

Militarization (M) The capital spending allocated

to the military is measured as a percentage of the

Gross Domestic Product (GDP).

Health Expenditure (HE) The present healthcare

expenditure is represented as a percentage of the

Gross Domestic Product (GDP).

Population (PO) The total population is determined

using the de facto population definition, which

includes all residents, regardless of their legal status

or citizenship.

Using the conceptual framework depicted in

Figure 1, this research employs two analytical

models, encompassing green economic growth and

environmental degradation. The econometric

equations for these models are presented as Equations

(1) and (2) below:

GEG

= α

1.0

+ β

1.1

Log(ED

) + β

1.2

1.3

+ β

1.4

+ ε

1it

(1)

Log(ED

) = α

2.0

+ β

2.1

GEG

+ β

2.2

2.3

+ β

2.4

Log(PO)it + β

2.5

2it

(2)

In this context: α represents the parameter, i

signifies the cross-sectional dimension, t denotes the

time-series component, and ε signifies the error term.

This research employs a simultaneous panel

model methodology to accomplish the established

research goals. The description of the econometric

phases within this model approach consists of:

Selection of the appropriate model for the regression

model estimation method using panel data can be

done through three approaches, including:

First, the Common Effect Model (CEM) is the

most straightforward panel data approach, as it

simply merges time series and cross-sectional data. It

neglects the temporal and geographical dimensions,

assuming uniform data behavior within a country

across different time periods.

The Fixed Effect Model (FEM) argues that

differences in intercepts can be responsible for

variations between countries.

Third, the Random Effects Model (REM)

calculates panel data in which the disturbance

variables can related associations across time and

among different countries.

Furthermore, the panel analysis model that was

most suitable was chosen from the three models. The

model is selected through testing as follows:

First, the Chow test was performed to ascertain

the suitability of using either the CEM or FEM model.

The selection was carried out by comparing the p-

values obtained from the cross-sectional chi-square

test with a significance level of <unk> = 0.05.

In the second step, should the Chow test favor the

FEM as the preferable model, the Hausman test will

be conducted. This additional examination will

determine whether FEM or REM is the more suitable

choice. The decision will be based on comparing the

p-value of the random cross-section test at a

significance level of α = 0.05

Next, in the event that the Hausman test favors

REM as the preferred model, the Lagrange multiplier

test will be performed. Additional analysis will

determine whether REM or CEM is the more suitable

model. The decision will be based on comparing the

p-value of the cross-sectional test hypothesis for

Breusch-Pagan at a significance level of α = 0.05.

Simultaneous equation models are models that

have more than one equation that are interrelated and

have a causal relationship between endogenous and

exogenous variables. Obtaining the numerical value

for each parameter in each equation is unfeasible due

to the indistinguishable nature of the equations or

their strong resemblance to each other. Hence, it is

essential to perform an identification test utilizing the

order condition as depicted in Equation (3) below:

Analysis of Green Economic Growth and Environmental Degradation in Upper-Middle-Income ASEAN Countries

K – k ≥ m – 1 (3)

Where: M represents the quantity of endogenous

variables within the model, while m represents the

count of endogenous variables in the equation. K

indicates the total number of predefined variables in

the model, and k represents the number of variables

predetermined in the equation.

If K – k = m – 1, this equation is identified.

Simultaneous equation estimation using the indirect

least squares (ILS) method

If K – k > m – 1, this equation is overidentified.

Simultaneous equation estimation using the two-

stage least square (2SLS) method.

If K – k < m – 1, this equation is not identified.

Equations that can be solved using a system of

simultaneous equations are equations that result in

identified and over-identified order conditions.

3 RESULTS AND DISCUSSION

3.1 Panel Analysis Results

Following the execution of the Chow and Hausman

tests to ascertain the most suitable model for this

study, the results show that the Fixed Effect Model

was selected as the best panel model. The analytical

findings have been presented in Tables 1 and 2.

Table 1: The results of the Chow test for the panel analysis

model.

Equation

Prob. Cross-Section Chi-Square

GEG

0.0000

Table 2: The results of the Hausman test for the panel

analysis model.

Equation

Prob. Cross-Section Random

GEG

0.0000

Table 1 shows that for all models, the probability

values are low, having a chi-square value of 0.05.

Consequently, the Fixed Effect Model (FEM) is

appropriate the most model use choice across all

analysis models. Next, the Hausman test was

conducted to determine the appropriate model

selection between the Fixed Effect Model (FEM) and

Random Effect Model (REM). The data presented in

Table 3 reveals that all models have probability

values with a small chi-square value of 0.05.

Therefore, the Fixed Effect Model (FEM) is the most

suitable choice for all analytical models, and there is

no need to proceed with the Lagrange Multiplier test.

3.2 Simultaneous Equation Analysis

Results

The necessary prerequisite test for conducting

simultaneous equation analysis involves performing

an identification test based on the order conditions

outlined in Equations (4) and (5) provided below.

Equation GEG 6 – 3 > 2 – 1

3 > 1 (overidentified)

(4)

Equation ED 6 – 4 > 2 – 1

2 > 1 (overidentified)

(5)

The identification test result indicates that all

analytical models employed in this study are

estimated using the two-stage least square (TSLS)

approach due to the over-identification of all

equations.

Drawing from the conclusive outcomes of both

panel analysis and simultaneous equation analysis

conducted in accordance with predefined steps, the

result of the simultaneous panel model analysis for

each analysis model is shown in Tables (3) and (4)

provided below:

Table 3: Results of simultaneous panel estimation of the

green economic growth model.

Variable

Coefficient

Prob.

-15.40643

0.6197

LOG(ED)

1.591296

0.4763

-0.077794

0.0474

0.643581

0.0000

1.024559

0.2584

Table (3) provides a summary of the outcomes

from estimating the simultaneous panel model for the

equation related to green growth.

This study revealed that environmental

degradation did not exhibit an impact on green

growth. Furthermore, the limited contribution of

renewable energy sources in clean energy

management, leading to environmental harm, does

not influence green economic growth (Panayotou,

1993).

Technological innovation was found to have a

negative and significant effect on green economic

growth in ASEAN upper-middle-income countries (

β1.4 = - 0.077794, P <0,05 ). This negative impact is

most likely caused by the type of technology being

MEBIC 2023 - MARITIME, ECONOMICS AND BUSINESSINTERNATIONAL CONFERENCE

developed that is not integrated with efforts to utilize

environmentally sustainable resources. Sometimes,

technological innovations can have undesirable side

effects, such as air pollution or hazardous waste,

which can harm the environment (Huesemann, 2011).

Technological innovation often enables greater

exploitation of natural resources. Overexploitation of

these resources can damage ecosystems and create

negative impacts on the green economy (Dernis,

2017).

The study revealed a noteworthy positive impact

of clean energy on green economic growth (β1.4 =

0.643581, P < 0.001). Clean energy involves the

utilization of renewable energy through efficient

technology. The effective deployment of clean energy

relies on a substantial contribution from renewable

sources, ensuring that an expansion of clean energy

leads to an enhancement of green economic growth.

The increased adoption of clean energy serves as a

key driver in fostering long-term green economic

growth (Sohag et al., 2019).

The research revealed that militarization in upper

middle-income countries in ASEAN did not have a

significant effect on green economic growth. The

reason for this was the cautious and limited utilization

of natural resources for military purposes, which

minimized their broader environmental repercussions

and impact on green economic growth (Dincer,

2013).

Table 4: Results of simultaneous panel estimation of the

environmental degradation model.

Variable

Coefficient

Prob.

-33.21688

0.0000

GEG

-0.016726

0.6304

0.002958

0.8638

0.072542

0.0523

0.003174

0.0001

LOG(PO)

2.496564

0.0000

Table (4) provides an overview of the findings

from the simultaneous panel model estimation related

to the environmental degradation equation. The

outcomes of this investigation confirm the hypothesis

that trade openness contributes positively to

environmental degradation (β2.5 = 0.003174, P <

0.001). The findings reveal that the upper-middle-

income countries in ASEAN are strongly inclined

towards trade openness, resulting in an increase in

environmental degradation. Greater trade openness is

associated with a heightened level of global

environmental deterioration (Le et al., 2016; Yu et al.,

2019).

The study uncovered a positive correlation

between population and environmental degradation

in upper-middle-income ASEAN countries (β2.6 =

2.496564, P < 0.001). Environmental degradation

increases with population growth due to an inverse

relationship between population and the environment.

The combination of rapid population growth and

sustainable economic development is likely to create

significant environmental challenges. Consequently,

countries should establish measurable economic

development strategies to manage and mitigate the

environmental impacts stemming from economic

activities (Ur Rehman & Zeb, 2020).

Green economic growth does not contribute to

reducing environmental deterioration, as it exhibits a

detrimental pattern in upper-middle-income ASEAN

countries. The considerable expenses associated with

environmental harm pose a substantial challenge to

fostering environmentally sustainable economic

development (Kang et al., 2019). The limited extent

of green economic growth suggests that the utilization

of natural resources hasn't adequately considered the

environmental consequences when advancing

sustainable economic development.

The empirical findings indicate that despite the

expectation that cleaner energy would promote

environmental sustainability, this is not the situation

in upper-middle-income ASEAN countries. There is

a need for enhancements in the management of eco-

friendly energy to genuinely foster environmental

quality (Pata et al., 2023)

Health expenditure was found to have a positive

but statistically insignificant relationship with

environmental improvement in upper-middle-income

ASEAN nations. To effectively address carbon

emission reduction and promote a healthier

environment, a reevaluation of the health expenditure

sub-policy program is warranted (Ganda, 2021).

4 CONCLUSIONS

According to the analysis performed, this research

suggests that promoting green economic growth can

be accomplished by boosting the adoption of cleaner

energy sources and curbing environmental

degradation through the regulation of both trade and

population.

The innovation of technology will contribute to

the promotion of environmentally sustainable

economic growth. The direction of developing

innovative technology can balance the benefits of

exploitation and prevention of environmental damage

if institutional and financial commitments are

Analysis of Green Economic Growth and Environmental Degradation in Upper-Middle-Income ASEAN Countries

supported. Health expenditure needs to be increased

in overcoming environmental degradation. Although

it does not have a significant impact, it is effective in

reducing environmental degradation.

The government should consider policy measures

to maintain environmental quality and foster

sustainable economic growth by implementing a

clean development mechanism that focuses on

advancing renewable energy.

The growth of renewable energy not only helps in

reducing CO2 emissions but also offers multiple

benefits. These advantages encompass lowering

investment expenses for nations in the upper middle-

income bracket, such as those in the ASEAN region,

facilitating technology transfer, and gaining access to

sustainable technologies. Renewable energy has

prospects for development in ASEAN upper middle-

income countries, due to the availability of sufficient

natural resources. The government is expected to be

able to encourage the clean energy development

mechanism with various policies such as subsidies, in

order for renewable energy to become cost-effective

and competitive with the development of fossil fuels.

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