The Effect of GDP per Capita, Population, and Income Inequality
on CO
2
Emissions in Indonesia
HERU WAHYUDI
Economic Development, Faculty of Economics and Business,
University of Lampung,
Jln. H. Komarudin, Rajabasa Raya, Rajabasa, Bandar Lampung,
INDONESIA
Abstract: - This study aims to see the effect of GDP per capita, income inequality, and population on CO2
emissions in Indonesia from 1990-2021. This research uses a descriptive quantitative method. The data used is
secondary data, in the form of annual data for 32 years. The analytical method used is the error correction
model (ECM) to see the short and long-term effects between the independent variable and the dependent
variable. The results of this study indicate that GDP per capita has a positive and significant effect on
Indonesia, both in the short term and in the long term. The income inequality variable has a positive and
insignificant effect on CO2 emissions in Indonesia in the short term. Meanwhile, in the long term, income
inequality has a negative and insignificant effect on CO2 emissions in Indonesia. The population variable has an
insignificant negative effect on CO2 emissions in Indonesia in the short term. However, in the long term, the
population has a significant positive effect on CO2 emissions in Indonesia.
Key-Words: - CO2 Emissions, ECM, GDP per Capita, Income Inequality, Indonesia, Population.
Received: April 25, 2024. Revised: September 15, 2024. Accepted: October 17, 2024. Published: November 18, 2024.
1 Introduction
Environmental degradation is a topic that is often
raised because it is a serious problem at the world
level. The most serious impact due to environmental
degradation is global warming. Global warming is
caused by the rise of Greenhouse Gases (GHG).
Indonesia continues to experience an increase in
GHG emissions from year to year. Based on data
from [1], shows that the energy sector is the largest
contributor to GHG emissions in Indonesia, which is
34%, followed by the waste sector (7%), agriculture
(6%), and IPPU (3%). This shows that the energy
sector contributes to national GHG emissions.
Based on IPCC GL 2006 guidelines, [2], gases from
the energy sector consist of CO2, CH4, and N2O. In
2019 in Indonesia the amount of CO2 emissions
amounted to 607,368 Gg CO2e, followed by CH4
27,181 Gg CO2e, and NO2 (3,903 Gg CO2e), [3].
Environmental degradation is a decrease in
environmental quality caused by natural and human
factors. The main factor that causes environmental
degradation is the human factor. Human factors that
cause environmental degradation include industrial
activities, land use change, the use of fossil energy,
and others. Environmental degradation is driven by
a country's need to promote economic growth and
development and meet human needs, [4].
Economic growth can be used to improve the
public welfare, [5]. Economic growth indicates an
increase in the country's productivity to produce
goods and services, [6]. Gross Domestic Product is
the main indicator characterizing economic growth,
[7]. To increase economic growth, carrying out
economic activities and energy consumption is
necessary. According to [8], energy is an important
parameter to fulfill basic human needs from the food
chain to carrying out various economic activities.
The biggest challenge for developing countries is
being able to maintain economic growth while
maintaining environmental quality, [9].
Increasing economic growth without improving
the structure of development causes problems of
inequality in society. Income inequality occurs due
to the gap in income distribution among community
groups. According to [10], income inequality is a
factor causing environmental pollution in
developing countries. Income inequality causes the
government to focus on economic growth policies
only, without regard to environmental aspects, [11].
Economic growth efforts to reduce income
inequality lead to increased resource use and energy
consumption. This is a factor causing the increase in
CO2 emissions.
As an effort to increase GDP, it takes people or
humans as development actors. The population
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becomes an economic actor, both as a producer and
a consumer. Currently, the population of Indonesia
continues to increase from year to year. According
to [12], an ever-increasing population will be
followed by an increase in demand for goods and
services which in turn increases the use of natural
resources. The increase in demand for goods and
services affects the increase in industrial activity. In
addition, the growing population also causes an
increase in the use of energy such as fossil fuels
which results in environmental degradation in the
form of CO2 emissions. Efforts to reduce
CO2 emissions can be realized through poverty
alleviation initiatives, which is highly prioritized in
developing countries, [13].
The focus of this study is the rapid increase in
GDP every year, income inequality, and increasing
population that causes CO2 emissions in Indonesia.
This research combines economic, environmental,
and social aspects contained in the concept of
sustainable development. This study looks at the
effect of GDP per capita, population, and income
inequality on CO2 emissions in Indonesia both in the
short and long term.
Indonesia continues to experience rapid
economic growth. The indicator that characterizes
economic growth is Gross Domestic Product (GDP).
GDP can be defined as the sum of all value added at
every stage of production within a defined region,
[14]. Indirectly, efforts to increase GDP encourage
an increase in production and industrial activity.
Economic growth is a parameter that determines the
success of economic development, but on the other
hand, can cause environmental degradation in the
form of CO2 emissions. According to [15], exist a
trade-off between economic growth and
environmental preservation.
The increasing GDP figure does not guarantee
that Indonesia is free from social problems in the
form of income inequality. The richest 10% of
people in Indonesia control 75.7% of the national
wealth, and the richest 1% of people in Indonesia
control 49.3% of the national wealth, [16]. The data
proves that Indonesia still experiences income
inequality, where inequality can affect CO2
emissions.
According to Adam Smith's theory, one of the
most important components of economic growth is
population. The increase in population in Indonesia
is also accompanied by an increase in economic
growth. However, the increase in population causes
an increase in the use of natural resources that cause
pollution. The increasing population also has an
impact on increasing energy use which causes CO2
emissions. Based on this explanation, the researcher
determines the following problem formulation: (1)
how did GDP per capita affect CO2 emissions in
Indonesia in 1990-2021?; (2) how does population
affect CO2 emissions in Indonesia in 1990-2021?;
and (3) how did income inequality affect CO2
emissions in Indonesia in 1990-2021?
2 Methodology and Variables
The research method used is a quantitative method
with a descriptive approach. Quantitative methods
are research methods in the form of numbers
measured by statistical tests to provide conclusions.
The descriptive approach used serves to describe the
results of research by presenting, analyzing, and
interpreting them. The scope of this research is
Indonesia. The data used is annual data or time
series for 32 years starting from 1990-2021—
research data obtained from the World Bank and
Our World in Data.
This study consisted of three independent
variables and one dependent variable. The
independent variables used are GDP per capita in
units of US $, variables in population with units of
thousands of people and variables of income
inequality measured using the Gini ratio. The
dependent variable is CO2 emissions in tons. CO2
emissions taken into account in this study are only
emissions derived from fossil and industrial energy.
Time series data requires stationary data. So
before estimating data, it is necessary to perform a
stationary test. Data is said to be stationary if the
data does not have drastic changes. The first data
analysis carried out was a stationary test. The
stationarity test conducted in this study used the
Dickey-Fuller Augmented method by comparing the
t-statistical ADF with MacKinnon's critical value. If
the ADF value of t-statistics is greater than the
critical value of MacKinnon 5%, then the data is
stationary. If the stationary test data shows results
that are not yet stationary, then an integration test is
carried out. Integration tests are performed to see to
what degree the data will be stationary.
Furthermore, the cointegration test uses the Engel
Granger (EG) test. The Engel-Granger test can
determine the cointegration of stationarity in its
residuals.
Data estimation in this study uses an error
correction model (ECM). ECM estimation aims to
determine whether there are short-term and long-
term influences on the variables tested, [17]. The
data used in this study is time series data. The
advantage of using the ECM method is to overcome
the shortcomings of a common method, namely
Ordinary Least Square (OLS) which cannot be used
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when the variable is not stationary. Equation The
ECM in this study is as follows:
∆𝐶𝑂2 = 𝛼0 + 𝛼1∆𝑃𝐷𝐵𝑡+ 𝛼2∆𝐺𝑅𝑡+ 𝛼3∆𝑃𝑡+ 𝜀𝑡
For the regression equation in the long term, it is
written as follows:
𝐶𝑂2 = 𝛽0 + 𝛽1𝑃𝐷𝐵𝑡+ 𝛽2𝐺𝑅𝑡+ 𝛽3𝑃𝑡+ 𝜀𝑡
Information: CO2; CO2 emissions; PDB: GDP
per Capita; GR: income inequality (Gini ratio); P:
population; α0 and β0: constant; α1, α2, α3 and β1,
β2, β3: regression coefficient; and ε: error term.
The error correction model method is
characterized by the presence of an element of error
correction term (ECT). ECT is a residual that
appears in the ECM model. If the value of the ECT
coefficient < 1 and is significant at 5%, then the
specification model used is valid.
After obtaining the research model, the next
stage is to test classical assumptions. The classical
assumption tests used in this study are normality
tests, autocorrelation tests, heteroscedasticity tests,
and multicollinearity tests. The normality test uses
the Jarque-Bera test, if the JB value > α 5%, then the
residual is normally distributed. Autocorrelation test
using Durbin-Watson test, if DW value is between -
2 to +2, then there is no autocorrelation problem.
Test heteroscedasticity using the Breusch-Pagan-
Godfrey test, if the value of Prob. Chi-Square is
more than 0.05, so there is no heteroscedasticity
problem. Multicollinearity test using VIF test, if the
test result is below 10, then there is no
multicollinearity problem.
3 Result and Discussion
3.1 Result
Time series data requires stationary data. The
stationarity test using the Dickey-Fuller
Augmented method is shown in Table 1. Based on
the results of the stationary test, it shows that the
ADF test value on all variables is smaller than the
MacKinnon critical value and the probability value
is more than α 5%, so that all variables are not
stationary at the level. Furthermore, a
differentiation test is carried out to find out the
degree of integration to how much the data will be
stationary. Table 2, is the integration test in this
study.
Based on the results of the integration test (Table
2), it shows that the ADF test value on all variables
is greater than the MacKinnon critical value and the
Probability value is less than α 5%, so all variables
are stationary at the level of first difference.
Because all variables are stationary at the first
difference level, the next stage is to conduct a
cointegration test to be able to perform ECM
estimation. The cointegration test is shown in the
following Table 3.
Based on the results of the cointegration test, it
shows that the probability value is 0.0012 < α 5%
and the ADF test value is more than the critical
value. Thus, the equation tested has a long-term
equilibrium relationship. So that the estimation
model can be interpreted further.
This study uses the ECM Domowitz El-Badawi
estimation model to determine the short-term and
long-term effects of GDP per capita, income
inequality, and population on CO2 emissions. The
results of regression in the short term are shown in
Table 4.
Based on the results of estimates in the short
term, the regression equation is obtained as follows:
CO2=62045241+40630.62PDB+698885.5GR-
16.79596P-0.832239
The equation shows that the value of the constant
is 62045241, meaning that if the value of all
independent variables is zero, then the value of CO2
emissions is 62045241 tons. The value of the
coefficient in the variable GDP per capita is
40630.62, meaning that when GDP per capita
increases by 1 US$, CO2 emissions will increase by
40630.62 tons (cateris paribus). The coefficient in
the income inequality variable is 698885.5, meaning
that when inequality increases by 1%, CO2
emissions will increase by 698885.5 tons (cateris
paribus). The coefficient on the variable population
is -16.79596, meaning that when the population
increases by 1 million, CO2 emissions will decrease
by 16.79596 tons (cateris paribus). Meanwhile, the
value of the coefficient in the ECT variable is -
0.832239, because it has a negative sign (ECT < 1)
and is significant at α 5%, the model specification
used is valid. The R-square value has a coefficient
of 0.593224, meaning that GDP per capita, income
inequality, and population together can explain
59.3224% of CO2 emissions. While the rest is
explained by other variables outside the research
model.
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Table 1. Stationarity Test at Level
Variable
ADF Test
Scores
McKinnon Critical Values
Information
1%
5%
10%
CO2
-0.492203
-3.661661
-2.960411
-2.619160
Non-stationary
PDB
0.248447
-3.661661
-2.960411
-2.619160
Non-stationary
GR
-0.981690
-3.661661
-2.960411
-2.619160
Non-stationary
P
-1.889931
-3.737853
-2.991878
-2.635542
Non-stationary
Table 2. Integration Test on First Difference
Variable
ADF Test
Scores
McKinnon Critical Values
Prob.
Information
1%
5%
10%
Co2
-5.405547
-3.679322
-2.967767
-2.622989
0.0001
Stationary
.PDB
-4.314652
-3.670170
-2.963972
-2.621007
0.0020
Stationary
GR
-4.396564
-3.670170
-2.963972
-2.621007
0.0016
Stationary
P
-3.036626
-3.699871
-2.976263
-2.627420
0.0441
Stationary
Table 3. Cointegration Test
Augmented Dickey-Fuller test Statistics
t-Statistic
Prob.*
-4.507542
0.0012
Test Critical values: 1% level
5% level
10% level
-3.661661
-2.960411
-2.619160
Table 4. Short-Term Estimation Results
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
62045241
77047907
0.805281
0.4280
D(GDP)
40630.62
17211.78
2.360629
0.0260
D(INEQUALITY)
698885.5
3124697.
0.223665
0.8248
D(POPULATION)
-16.79596
25.13333
-0.668274
0.5098
ECT(-1)
-0.832239
0.195686
-4.252934
0.0002
R-squared
0.593224
Adjusted R-squared
0.530643
F-statistic
9.479298
Prob(F-statistic)
0.000073
Table 5. Long-term Estimation Results
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-3.19E+08
1.38E+08
-2.319187
0.0279
.PDB
50154.20
12143.09
4.130268
0.0003
INEQUALITY
-4716203.
2911658.
-1.619765
0.1165
POPULATION
3.344576
0.395793
8.450317
0.0000
R-squared
0.983160
Adjusted R-squared
0.981355
F-statistic
544.8930
Prob(F-statistic)
0.000000
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Table 4 also shows that based on the t-test the
variable GDP per capita has a significant positive
effect (Prob < 0.05), the income inequality variable
has a positive effect is not significant (Prob > 0.05),
and the population variable has a negative effect is
not significant (Prob > 0.05) on CO2 emissions in
Indonesia in the short term. Meanwhile,
simultaneously (test f) all variables together have a
significant effect on C2 emissions in Indonesia
(Prob f-statistics < 0.05).
Next is the classical assumption test which aims
to find out whether the estimated results violate
classical assumptions or not. The first classical
assumption test is the normality test. Based on the
results of the normality test, a Jarque-Bera value of
0.900505 > 0.05 was obtained, so that the data can
be concluded as normally distributed. Then based on
the results of the autocorrelation test showed that the
Durbin-Watson value in this study was 1.920450, it
can be concluded that there is no autocorrelation
problem in the regression model. Based on the
results of the heteroscedasticity test show that the
value of Prob. ChiSquare is 0.4816 > 0.05, so it can
be concluded that there is no heteroscedasticity
problem. Based on the results of the
multicollinearity test, a VIF value of less than 10 is
obtained on each variable, it can be concluded that
there is no problem with heteroscedasticity in
regression models.
Based on the results of the estimation in the long
term (Table 5), the regression equation is obtained
as follows:
CO2= -3.19+50154.20PDB -4716203GR+3.344576P
The regression equation shows that the
constant value is -3.19, meaning that in the long-
term if all independent variables are zero, the CO2
emission value is -3.19 tons. The value of the
coefficient in the variable GDP per capita is
50154.20, meaning that if GDP per capita increases
by 1 US $ then CO2 emissions will increase by
50154.20 tons (cateris paribus). The value of the
variable coefficient of income inequality is -
4716203, meaning that if income inequality
increases by 1%, CO2 emissions will decrease by
4716203 tons (cateris paribus). The value of the
coefficient on the population variable is 3.344576,
meaning that if the population increases by 1 million
people, CO2 emissions will increase by 3.344576
tons (cateris paribus). Meanwhile, the R-squared
coefficient of 0.983160 means that GDP per capita,
income inequality, and population together can
explain 98.3160% of CO2 emissions. While the rest
is explained by other variables outside the research
model. Table 4 shows that based on the t-test the
variable GDP per capita has a significant positive
effect (Prob < 0.05), the income inequality variable
has a negative effect is not significant (Prob > 0.05),
and the population variable has a significant positive
effect (Prob < 0.05) on CO2 emissions in Indonesia
in the long run. Meanwhile, based on simultaneous
tests (test f) show that all independent variables
have a significant effect on CO2 emissions in
Indonesia in the long term. This can be seen from
the statistical probability value f amounting to
0.000000 < 0.05.
3.2 Discussion
The Effect of GDP per Capita on CO2 Emissions
in Indonesia
The variable GDP per capita has a positive and
significant influence on CO2 emissions in Indonesia
in 1990-2021, both in the long and short term. The
results of this test are the same as the research
conducted by [18]. The research provides results
that in the short and long-term GDP per capita has a
positive effect on CO2 emissions in Indonesia.
Efforts to increase GDP require economic activities
such as consumption and production. The ever-
increasing GDP shows that people's purchasing
power is getting bigger. The higher the
consumption, the higher the production in industries
that require the use of fossil energy. This is a trigger
for CO2 emissions. So it can be concluded that the
increase in GDP per capita in Indonesia causes an
increase in CO2 emissions through increased
consumption of fossil energy and industrial
activities.
The Effect of Income Inequality on CO2
Emissions in Indonesia
The estimation results in this study show that
income inequality variables have a positive and
insignificant influence on CO2 emissions in
Indonesia in the short term. Meanwhile, in the long
run, income inequality has a negative insignificant
influence on CO2 emissions in Indonesia. The
results of this study are the same as the research
conducted by [19], [20]. The study showed that
there was no significant effect between income
inequality and CO2 emissions. The mechanism of
the effect of income inequality on CO2 emissions in
the short term can be explained through efforts to
increase economic growth. Income inequality drives
up GDP through increased production that requires
energy use. This is the main trigger for CO2
emissions. This reason is also supported by the
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focus on development that is only concerned with
economic growth rather than environmental
sustainability. The relationship between income
inequality and CO2 in the long run is negative. This
condition can occur due to efforts to reduce income
inequality and develop environmentally friendly
technological innovations.
The Effect of Population on CO2 Emissions in
Indonesia
Based on the results of the study, in the short term,
the population has an insignificant negative
influence on CO2 emissions. However, in the long
term, the population has a significant positive
impact on CO2 emissions. The results of the study
are the same as the research conducted by [21]. The
increase in population leads to an increase in
people's energy needs. In addition, it led to an
increase in production from various aspects. So if
not accompanied by environmentally friendly
policies, the population has a major influence on
increasing CO2 emissions. In the short term, the
population can be negatively affected due to efforts
to reduce fossil energy and reduce industrial
activities.
4 Conclusion
Economic growth, which in this case uses the
variable GDP per capita has a significant positive
influence both in the short and long term on CO2
emissions in Indonesia in 1990-2021. Based on this,
it is concluded that economic growth in Indonesia
causes an increase in CO2 emissions. The increase
can come from an increase in fossil energy
consumption and industrial activities.
Likewise, income inequality in the short term has
an insignificant positive influence on CO2 emissions
in Indonesia. Meanwhile, in the long run, income
inequality has a negative insignificant influence on
CO2 emissions in Indonesia. Income inequality
drives up GDP through increased production that
requires energy use. This is the main trigger for CO2
emissions.
The estimation results in this study show that
population has a negative insignificant influence on
CO2 emissions in the short term. While in the long
run, the effect becomes positive and significant. The
increasing population has led to an increase in
people's need for energy and increased production,
causing an increase in CO2 emissions. However, in
the short term, the influence of population on CO2
emissions becomes negative due to the effect of
decreasing economic growth which reduces the use
of fossil energy and reduced industrial activities.
Although GDP increases CO2 emissions, we
must still build the economy while considering the
environment One way is through green economic
transformation, which requires support from all
parties, including the government, private sector,
and the wider community Development should be
more evenly distributed and not concentrated in
certain areas specifically for areas that are lagging,
policies implemented must be different (acceleration
development strategies) This is done so that equality
can be achieved soon Not only that, the government
also socializes to the public to increase awareness of
the environment In addition, it can also be done
through policies that provide easy access to general
transformation for the public to reduce CO2
emissions.
The limitation of this research is not including
variables related to the industry, where it is known
that the industry is one of the contributors to CO2
emissions. Therefore, future research can include
the number of large and medium industries as well
as other significant variables This aims to identify
the policy interference needed.
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.59
Heru Wahyudi
E-ISSN: 2224-3496
622
Volume 20, 2024
Contribution of individual authors to the
creation of a scientific article (ghostwriting
policy)
Heru Wahyudi made a research framework and
collected literature reviews, wrote the research,
proposed policy recommendations, and collected
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Sources of funding for research presented in a
scientific article or scientific article itself
The research in this manuscript is supported by
Lembaga Penelitian dan Pengabdian kepada
Masyarakat (LPPM) Universitas Lampung.
Conflict of Interest
The author has no conflicts of interest to declare.
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.59
Heru Wahyudi
E-ISSN: 2224-3496
623
Volume 20, 2024