Planning and Policy Direction for Utilization of Renewable Energy in
Sustainable Development in Indonesia
HERU WAHYUDI1,*, UKHTI CIPTAWATY2, ARIVINA RATIH3
1Economic Development, Faculty of Economics and Business,
University of Lampung,
Jln. H. Komarudin, Rajabasa Raya, Rajabasa, Bandar Lampung,
INDONESIA
2Economic Development, Faculty of Economics and Business,
University of Lampung,
Jln. Teuku Cik Ditiro No. 13, Kemiling, Bandar Lampung,
INDONESIA
3Economic Development, Faculty of Economics and Business,
University of Lampung,
Jln. Pulau Raya No. 5 Perumnas Way Kandis, Bandar Lampung,
INDONESIA
Abstract: - The Indonesian government through the National Energy Council (DEN) has a target for new
renewable energy to be increased, starting from 2025 with a target of 23 percent to 2060 with a target of 66
percent, but ,new renewable energy in Indonesia only increases 0.55 percent per year. Indonesia has great
potential, but can the potential be maximized by the government in the direction of a better and
environmentally friendly energy policy. This study analyzes the movement of renewable energy and CO2
emissions to the Indonesian economy from 1990-2021, using the Vector Error Correction Model (VECM)
statistical method by considering short-term and long-term results in the model. The results show that in the
long and short-term economy the role of GDP per unit of energy use for the economy is needed and has a
positive effect, the role of carbon emissions in the short and long term CO2 has a positive and significant
direction, non-renewable energy in the long term and short term is still moving negative and significant, this
indicates that renewable energy in Indonesia tends to be low, energy replacement must be carried out slowly
and gradually, shock response conditions conclude when GDP energy use and CO2 are affected by a negative
shock will disrupt economic development, meanwhile, if there is a negative shock on consumption Renewable
energy still tends to be stable and positive for the development of the Indonesian economy.
Key-Words: - Economy, Renewable Energy, CO2 Emissions, VECM, Indonesia.
1 Introduction
Indonesia is one of the developing countries with
increasing economic growth, this can be seen from
the continuous growth of Indonesia's GDP per
capita every year. However, this economic growth is
followed by an increase in carbon dioxide emissions
every year so it has an impact on reducing
environmental quality by the EKC hypothesis.
Indonesia has established Government Regulation
Number 79 of 2014 concerning the National Energy
Policy (KEN). KEN as a product of public policy
was determined after going through a "top-down"
debate since 2010. The target of KEN is only to
focus on energy supply, even though energy policy
has shifted to the side of energy demand. KEN does
not set greenhouse gas mitigation targets. To
support the achievement of the KEN target
regarding the mix of new and renewable energy
(EBT), the Government of Indonesia has stipulated
various regulations regarding the Feed-in Tariff for
the selling price of electricity for EBT generators
but it does not run optimally because the price is
more expensive, this is an obstacle to the problem of
using renewable energy in Indonesia, [1]. The
government through the National Energy Council
(DEN) has drawn up a roadmap for the energy
Received: July 6, 2023. Revised: February 7, 2024. Accepted: April 7, 2024. Published: May 2, 2024.
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transition towards net zero emissions in 2060. Every
year the target for new renewable energy will
increase, starting from 2025 with a target of 23
percent to 2060 with a target of 66 percent. The
Indonesian government is trying to reach the target
of 23 percent new renewable energy in 2025 but in
fact, new renewable energy in Indonesia only
increases 0.55 percent per year. The target that
should be achieved by the government for the
achievement of new renewable energy per year is
0.9 percent, [2].
Reviewing energy development in Indonesia is
presented by several important findings. The
increase in energy intensity in Indonesia is
represented by an increase in energy in 33 provinces
in Indonesia. Energy intensity at the provincial level
in Indonesia is largely determined by regional
economic activity. The role of the industrial sector
influences energy intensity quite dominantly and is
also supported by policies in the industrial sector
that will greatly influence the achievement of
government targets related to energy policy to
reduce energy intensity by 1% per year, [3].
The increase in non-renewable energy in
Indonesia cannot be simply minimized because the
economic sectors in Indonesia are very dependent
on the use of non-renewable energy usage which is
very high and continues to increase every year. The
following is an overview of data on CO2 carbon
emissions and the use of GDP per capita for energy
activities in Indonesia.
Fig. 1: Conditions for the use of GDP for energy
against Indonesia's CO2 emissions
Source: World Bank Indonesia Energy Data, 2023
Based on Figure 1, it can be seen that Indonesia
has a state of increasing energy GDP use, where the
use of energy GDP has a trend that continues to be
high from 1990 to 2021 in Indonesia, this is also
followed by an increase in CO2 which continues to
increase every year in Indonesia, according to
several studies, carbon emissions in Indonesia are
still contributed by non-renewable energy. Based on
[4], excessive consumption of fossil fuels, especially
for the combustion process, will increase emission
levels in the atmosphere and is very unfriendly to
the environment causing CO2 emissions. The
demand for fossil fuels is still high in several
countries, including Indonesia, given that the
manufacturing sector is the main consumer of fossil
fuels in Indonesia and the development of
Indonesia's manufacturing industry. Research from
[5], the positive influence of value-added
manufacturing and international trade on CO2
emissions in the long and short term in Indonesia.
High economic activity causes environmental
damage.
On the other hand, the potential for new
renewable energy in Indonesia is quite large,
including ocean, geothermal, bioenergy, wind,
water, and sunlight. This potential needs to be
developed. Energy policy in Indonesia is currently
following international energy policies, namely
reducing greenhouse gas emissions, transforming
towards new renewable energy, and accelerating the
economy based on green technology. Indonesia's
commitment to supporting international energy
policies includes increasing the use of new and
renewable energy, reducing fossil energy, increasing
the use of electricity in the household, industrial,
and transportation sectors as well as the use of
carbon capture and storage, [6]. In Figure 2, you
can see a comparison of renewable energy
consumption and non-renewable energy
consumption in Indonesia.
Fig. 2: Conditions for the use of renewable energy
and non-renewable energy in Indonesia
Source: World Bank Indonesia Energy Data, 2023
A comparison of energy trends is shown in the
following figure where non-renewable energy in
1990-2021 continues to experience an increasing
and higher intensity of use throughout the year, of
course, this is caused by an increase in the activity
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intensity of the production sector for the economy in
Indonesia. On the other hand, the consumption of
renewable energy has continued to decline from
1990-2021, where the trend has decreased, this is
allegedly because the capacity for renewable energy
in Indonesia is still minimal and has not been able to
greatly support Indonesia's sectoral economic
activity, so the intensity of its use tends to be low.
Research from [7], from 2000-2019, the
consumption of renewable energy did not have a
positive and significant effect on the movement of
GDP growth in Indonesia, due to the lack of
attention in the process of developing the
development as well as related to regulations and
Indonesia which is currently still meeting its energy
demand from non-renewable sources.
The other side of the process of developing
renewable energy in Indonesia is [8]. Indonesia has
experienced multi-aspect principal-agent problems
between PT PLN, the agent with the sole authority
to manage electricity transmission, and various
principals, namely the Ministry of State-Owned
Enterprises (BUMN), the Ministry of Energy and
Mineral Resources, the Ministry of Industry (MOI)
as intermediaries between the RE industry domestic
and foreign, and the Ministry of Finance. While
changes to the Ministry of Energy and Mineral
Resources' feed-in-tariff (FiT) policy send uncertain
policy signals, the Ministry of Finance's fiscal
incentive policies other than FiT to encourage RE
development in Indonesia remain suboptimal.
Indonesia's potential policy direction has natural
resources, especially abundant renewable energy
resources, which can potentially succeed in realizing
the energy transition. To create clean and renewable
resources, Indonesia is trying to optimize energy use
to ensure the availability of resources. Indonesia's
target is that in 2025 the use of renewable energy
will reach 23% and in 2050 it can reach 50%, [9].
Research from [10], In Indonesia all stakeholders
consider these factors in the development of
renewable energy and encourage the private sector
to invest and assist acceleration based on providing
an analysis of investment opportunities. Factors
influencing the development of the energy sector
include policies related to human capital,
environmental protection, and energy efficiency.
Based on [11], has large amounts of renewable
energy resources both on land and at sea. this
potential can enable a 100% renewable energy
electricity system and meet future demand with
limited impact on land availability.
Indonesia has great potential, but can the
potential be maximized by the government in the
direction of a better and environmentally friendly
energy policy. This study will lead directly to how
the direction of renewable energy policy in
Indonesia is by looking at the short and long-term
effects of CO2, renewable energy, and GDP spent on
economic activities on the sustainability of the
Indonesian economy and looking at how the
condition of CO2, renewable energy and GDP for
energy if a negative shock occurs, does it affect the
sustainability of economic activity in Indonesia.
2 Literature Review
The assumption of Kuznets relates to the per capita
income of the country’s environment is known as
EKC. His assumption shows the attention will be
directed toward increasing the country’s income if it
is still relatively low, either through production or
investment that encourages income growth,
excluding issues of environmental quality Income
growth is followed by increasing pollution and then
it declines again if income growth conditions
persist. This assumption is from the amount for the
quality of the environment, which improves social
control and government rules therefore, people are
more prosperous, [12]. It will make a big
contribution to the national products if the country’s
income improves in line with economic
development, and manufacturing products. To
conclude, industrialization starts in small industries
and continues in large industries. The increment of
using the natural resources and degradation of
environment intensification degradation is the phase
of middle-income level, the development phase
dominates industrialization by increasing the share
of its internal social items when industrial activity
grows steadily. In this case, the utility of uncooked
materials will decrease, and the elimination of waste
per unit of production will increase.
The relationship between CO2 and the economy
from various studies has found the following results,
short-term two-way panel causality between CO2
emissions and employment and between available
energy and employment. The results show a one-
way causality of available energy and employment
to GDP. The results of a long-term causal
relationship show that the estimated ECT coefficient
meets the requirements in the short and long term
and has a close relationship between CO2 emissions
and GDP, [13]. The coefficient of economic growth
is positive and significant with CO2 emissions,
showing an increase of 1 % in economic growth is
associated with an increase in CO2 emissions, the
coefficient of renewable energy use is negative and
significant, implying that a 1% increase in
renewable energy use results in a reduction in CO2
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emissions in the long run. Empirical findings reveal
that economic growth and expansion of agricultural
land increase CO2 emissions while increased use of
renewable energy improves environmental quality
by reducing CO2 emissions. Research from [14],
energy consumption worsens environmental quality
through high carbon emissions, and economic
growth is a significant determinant of positive
carbon emissions. urbanization and population
growth as a factor of carbon emissions. In the causal
relationship in the middle of the series, there is a
two-way causality between economic growth and
carbon emissions, between energy consumption and
economic growth, between economic growth and
population growth, between energy consumption
and urbanization, and between economic growth
and urbanization, [15].
The relationship between renewable energy and
economic growth. Efficient energy use is a
prerequisite for economic development. However
the excessive use of fossil fuels is detrimental to the
environment. Because renewable energy emits no or
low greenhouse gases, more and more countries are
trying to increase their use of energy from
renewable sources. that renewable energy does not
hinder economic growth for both developing and
developed countries, while the consumption of
renewable energy (threshold level) is not so
important. On economic growth for developed
countries, [16], renewable energy consumption
triggers an acceleration of GDP, compared to other
energy variables considered in the model. Compared
to the standard econometric model, this experiment
can show and choose which input can produce the
best target. The best output is GDP per capita. The
positive variation, through the prediction process of
the four ITEs, is due to the acceleration of
renewable energy, policy measures intensifying the
process of changing the energy structure by
promoting a more intensive use of renewable
energy, [17].
3 Methodology
3.1 Types and Sources of Data and Variable
Operational Definitions
This research is in the form of descriptive
quantitative problem-solving based on data, by
presenting, analyzing, and interpreting it. The data
used is secondary data, this data is obtained
indirectly from various publications, publications of
official data platforms, and publications of various
data collection books. The data for the observation
area is the coverage of the territory of the State of
Indonesia, the data used is secondary data (time
series) with the 1990-2021 time series. The
following is a summary of the variables, units,
descriptions, and data sources in the study:
1. Economic Growth (PE): Growth and
development of income (GDP) from the value
of goods and services produced in Indonesia.
2. CO2 emission (ECO2): Carbon dioxide
emissions come from burning fossil fuels and
manufacturing cement. This includes carbon
dioxide produced by the consumption of solid,
liquid, and gaseous fuels and the combustion of
gases.
3. GDP per unit of energy use (GDP): GDP per
unit of energy use is PPP GDP per kilogram of
oil equivalent to energy use. PPP GDP is gross
domestic product converted to constant 2017
international dollars using the purchasing
power parity rate. The international dollar has
the same purchasing power over GDP that the
US dollar has in the USA.
4. Consumption of renewable energy (CET):
Renewable energy consumption is the share of
renewable energy in the total final energy
consumption.
3.2 Vector Autoregression (VAR) Data
Analysis Method
The analysis used in this study is a non-structural
model using the Vector Autoregression (VAR)
method. Data analysis was carried out using a
descriptive quantitative approach. Quantitative is a
research method based on positivism, used to view
certain samples. The data used in this study is time
series data, so it is necessary to analyze the
interdependence between these variables. VAR is a
model that can analyze the interdependence
relationship between time series variables.
According to [12], VAR has several model
advantages:
1. There is no need to distinguish between
exogenous and endogenous variables. All
variables both exogenous and endogenous that
are believed to be related should be included in
the model. However, exogenous variables can
also be included in the VAR.
2. To see the relationship between variables in the
VAR requires several existing variable lags.
This lag is necessary to capture the effect of
this variable on other variables in the model.
This study uses the Vector Autoregressive
(VAR) model framework, used to find out how the
economic conditions are from various types of
Energy variables. In terms of the direct impact of
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energy use using carbon dioxide emissions,
conventional energy use uses GDP per unit of
energy use and renewable energy uses the total
final consumption of renewable energy. The
structure of this model begins with 1) The
relationship of energy conditions namely ECO2,
PDBE, and KET with Economic Growth. 2)
Response of economic growth to shocks from
energy conditions, namely ECO2, PDBE, and KET.
3) how big is the contribution of the energy
performance variables namely ECO2, PDBE, and
KET to Indonesia's economic growth. To answer
all questions in this study using the VAR analysis
method if not cointegrated, if cointegrated then
VECM analysis will be used. The Long-Term
Equation of Economic and Energy Growth in
Indonesia:
PEnt = α0 + β1 ECO2 t-j + β2 PDBE t-j + β3 KET t-j + Ɛt
Short-Term Equation of Economic and Energy
Growth in Indonesia:
∆PEt = α0 + ƛ1 ∆ ECO2t + ƛ 2 ∆ PDBE t + ƛ 3 ∆
KET t + ƛ 4 Ect + Ɛt
Where:
PE
:
Economic growth (%)
ECO2
:
CO2 Emissions (Metric Tonnes
Per Capita)
PDBE
:
GDP per unit of energy use
(USD)
KET
:
Renewable energy consumption
(%)
ƛ 1, ƛ 2, ƛ
3, ƛ 4,
:
Short term relationship
coefficient
α0
:
Constant Intercepts
β1, β2, β3
:
Long term relationship
coefficient
ECT
:
Error Correction Term
Ɛ
:
Error Term
j
:
Parameters (lag 1, 2,.... etc.)
3.3 Data Analysis Procedures
The unit root test was first developed by Dickey-
Fuller and is known as the Dickey-Fuller (DF) unit
root test, [18]. The DF unit root test assumes that the
disturbance variable et is bound with an average of
zero, so that the variance becomes constant and has
no relationship (non-autocorrelation). One of the
formal concepts used to determine stationary data is
through the unit root test. If a time series data is not
stationary at zero order I(0), then the stationarity of
the data can be searched through the next order so
that the level of stationarity is obtained at the nth
order (first difference or I(1), or second difference
or I(2). , etc. The hypothesis for this test is:
1. H0 : d = 0, there is a unit root, not stationary
2. Ha : d ≠ 0, there is no unit root, stationary
If the test results reject the hypothesis that there
is a unit root for all variables, it means that all are
stationary or in other words, the variables are
cointegrated at I(0), so the estimation will be carried
out using linear regression.
In determining the optimum lag, you can use the
criteria put forward by Akaike (Akaike Information
Criterion = AIC), [13]. These criteria can be written
as follows:
Ln AIC = 2 𝑘
𝑛 +ln 𝑆𝑆𝑅
𝑛
Where:
SSR = Sum of squares residual (sum of squares
residual)
k = Number of estimated parameter variables
n = Number of observations
The length of lag chosen is based on the
minimum AIC value by taking its absolute value.
VAR stability needs to be tested before conducting
further analysis because if the VAR estimation
results to be combined with the error correction
model are unstable, then the Impulse Response
Function and Variance decomposition will be
invalid. To test the stability of the estimated VAR
that has been formed, stability conditions are
checked in the form of roots of characteristic
polynomials and inverse roots of AR characteristic
polynomials.
The concept of cointegration is basically to find
out the possibility of a long-term equilibrium
relationship in the observed variables. In the
cointegration concept, two or more non-stationary
time series variables will be cointegrated if their
combination is also linear over time, although it is
possible that each variable is not stationary. As a
basis for decision making, the following testing
criteria can be used:
1. H0 : β = 0, The variables have no
cointegration
2. Ha : β ≠ 0, The variables have cointegration
After the cointegration test is carried out on these
variables, the next step is to form a VAR or VECM
model. If there is a cointegration relationship
between the research variables, then the estimation
is done with VECM, whereas if there is no
cointegration, the estimation is done with VAR. The
independent variable is said to be significant in
influencing the dependent variable. Meanwhile, to
find out a negative or positive relationship is to look
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at the sign on the variable coefficient.
Forecasting in VAR is an extrapolation of the
current and future values of all variables using all
the information in the past. Impulse Response
analysis is one of the important analyzes in the VAR
model. Impulse Response analysis tracks the
response of endogenous variables in the VAR
system due to shocks or changes in the disturbance
variable, [12]. Impulse Response analysis can see
how much the independent variable is affected by
shocks or shocks that occur in the dependent
variable sometime in the future (in units of each
variable), [19].
This test is used to measure the estimated error
variance of a variable, namely how big a variable is
in providing an explanation for another variable or
for the variable itself. Basically, this is another
method to describe the dynamic system contained in
VAR. It is used to measure the estimated error
variance of a variable. How big is the difference
between the variants before and after the shock,
both shocks originating from the variable itself and
shocks from other variables, [14]. This variance
decomposition will be used to help determine the
determinants of the dependent variable on the
independent variable because it can explain how
much the independent variable can affect the
dependent variable.
4 Result and Discussion
1. Stationarity test
The following are the results of the stationarity test
used in this study using the unit root test using the
Augmented Dickey Fuller Test (ADF test) (Table
1).
Table 1. Unit Root Test Test Results At Level
ADF T-
Statistic
Prob
Result
Conclusion
0.56033
0.9861
Accept
H0
Not Stationary
0.71265
0.0810
Accept
H0
Not Stationary
-0.78947
0.8082
Accept
H0
Not Stationary
0.71472
0.9906
Accept
H0
Not Stationary
Results of the unit root test by comparing the
value of the t-count with the critical value for each
α, namely 1 percent, 5 percent, and 10 percent, it
can be concluded that there are no variables that are
stationary at the level, so that the unit test will be
repeated root test on the first difference on each
variable and the results can be seen in the following
Table 2.
Table 2. Unit Root Test Test Results at Level 1
Variable
ADF T-
Statistic
Prob
Result
Conclusion
PE
-5.121783
0.0002
Reject H0
Stationary
KET
-6.967049
0.0000
Reject H0
Stationary
CO2
-5.679998
0.0001
Reject H0
Stationary
PDBE
-4.738180
0.0007
Reject H0
Stationary
The unit root estimation results at the first
difference level for all variables are stationary. This
means that the data used in this study are integrated
at order one or can be shortened to I (1) so that the
data is free from spurious regression problems.
Therefore, the stationary requirements have been
met, the next stage can be further data processing.
2. Optimum Lag Determination
Based on the Akaike Information Criterion (AIC)
value, the optimum lag length is 2. So the lag value
that will be used for further research is lag 2. The
results of determining the length of the lag are
shown in the following tables:
Table 3. Optimum Lag Determination Results
Lag
Akaike Information Criterion (AIC)
0
31.51178
1
16.98306
2
13.52191*
Based on Table 3 of determining the optimum
lag used in the economic growth model (PE), GDP
per unit of energy use (GDP), renewable energy
consumption (KET), and carbon emissions (CO2),
the VAR/VECM equation model is at lag 2.
3. VAR Stability Test
Stable or not the estimated VAR that has been
formed is checked for stability conditions using the
roots of characteristic polynomial and inverse roots
of AR characteristic polynomial.
Table 4. Results of Roots of AR Characteristic
Polynomial Test
Equation Model
Modulus Range
Equation Model
0.374938 - 0.987435
Based on Table 4, shows that the modulus
values of all equation models are less than 1 so it
can be concluded that the VAR model is valid.
Next, testing the stability of the VAR using the
inverse roots AR characteristic polynomial is shown
in the following Figure 3.
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Fig. 3: VAR Model Stability Test
VAR stability by using the inverse roots of AR
characteristic polynomial for all equation models
shows that the points on the circle or the distribution
of the data do not go outside the circle, this means
that the data is valid for further analysis using VAR.
Therefore, testing the stability of the VAR by using
roots of characteristic polynomial and inverse roots
of AR characteristic polynomial is valid, which
means that the results of the analysis of the impulse
response function and variance decomposition in the
VAR estimation are valid.
3. Cointegration Test
Cointegration was carried out through the Johansen
Cointegration test with an optimum lag of 2
according to the determination of the optimum lag
based on the AIC that has been carried out
previously. The Johansen Cointegration Test
method is carried out by comparing trace statistics
with critical values using a significance level of 5
percent. If the trace statistic is greater than the
critical value, then there is cointegration in the
system of equations. Cointegration test results in
this study can be seen in the following Table 5.
Table 5. Cointegration Test Results Model Equation
1
Hypothesized
Eigenvalue
Trace
0.05
Prob.**
Statistic
Critical Value
None *
0.743201
64.07229
47.85613
0.0008
At most 1
0.513633
26.00741
29.79707
0.1285
At most 2
0.184002
5.825215
15.49471
0.7161
At most 3*
0.004689
0.131598
3.841466
0.0168
Based on the table above, shows that the trace
statistic value r = 0 is greater than the critical value
with a significance level of 5 percent, which is
64.07229 greater than 47.85613. This means that the
null hypothesis which states that there is no
cointegration in the variables used is rejected and
the alternative hypothesis which states that there is
cointegration is accepted. Thus, the results of the co-
integration test indicate that in the equation model
of the economic growth model (PE), GDP per unit
of energy use (GDP), renewable energy
consumption (KET), and carbon emissions (CO2),
have a stability/balance relationship and the same
movement in the long run. Therefore, in each short-
term period, all variables tend to adjust to each other
to achieve long-run equilibrium. Thus the correct
model to use in this study is the Vector Error
Correction Model (VECM) instead of Variance
Auto Regression (VAR) because the variables used
are cointegrated and stationary at the first difference
level.
4. Estimasi VECM
The results of first-order, stable and cointegration
difference tests in the long and short term are
estimated using an error correction vector model
(VECM), along with the results of the equation can
be seen in Table 6.
Table 6. Long-Term VECM Estimation Results
Variable
Coefficient
t-statistic
Description
PE (-1)
1.000000
PDBE (-1)
2.027177
[ 8.22251]
Significant
KET(-1)
-1.165004
[5.48567]
Significant
CO2 (-1)
21.91770
[ 22.8496]
Significant
C
-87.26952
[ ] : t-statistic
* : Based on confidence level 99% (α=1%) = 1.31370
** : Based on confidence level 95% (α=5%) = 1.70329
*** : Based on confidence level 90% (α=10%) = 2.47266
The estimation results on the research variables
obtained can be said to have a significant effect if
the t-count value is greater than the t-table value
with a significance level of (α=1%) = 1.31370,
(α=5%) = 1.70329 and (α=10% ) = 2.47266. In the
long-term model, there is a significant influence
between the variables per unit of energy use
(PDBE), Renewable Energy Consumption (KET),
and Carbon Emissions (CO2) on the Indonesian
economy in the 1990-2021 period.
Table 7. Short-Term VECM Estimation Results
Variable
Coefficient
t-statistic
Description
ECT
-0.623649
[-1.95734]
Significant
D(PDBE)(-1))
1.756400
[ 1.43908]
Significant
D(PDBE)(-2))
1.735481
[ 1.64131]
Significant
D(KET)(-1))
-0.766255
[-2.87565]
Significant
D(KET)(-2))
-0.245587
[-1.58332]
Signifikan
D(CO2)(-1))
9.602310
[ 1.27149]
Not significant
D(CO2)(-2))
15.02295
[ 2.15737]
Significant
C
-1.726701
[ ] : t-statistic
* : Based on confidence level 99% (α=1%) = 1.31370
** : Based on confidence level 95% (α=5%) = 1.70329
*** : Based on confidence level 90% (α=10%) = 2.47266
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The coefficient of Error Correction Term (ECT)
shows the speed of adjustment, namely the speed of
the residual/error in the previous period to correct
changes in variable Y towards balance in the next
period. Based on Table 7, it can be seen that the
ECT coefisein is negative (-). This indicates the
validity of the model specification. The VECM
short-term estimation results show a negative ECT
coefficient, which is -0.623649 and significant at a
significance level of 0.05. This means that the
conditions for short-term VECM estimation are met
and the VECM model is declared valid. The
negative sign in the coefficient indicates that the
error is corrected by 0.62 percent annually in the
equation for testing short-run balances.
5. Impulse Response Function (IRF)
the results of Impulse Response Function Analysis
(IRF) to analyze the impact of shocks (shock) on
economic growth on energy variables and carbon
emissions, shocks on PDBE, KET, and CO2. The
results of IRF analysis are used to show how a
variable responds to a shock in the variable itself
and other endogenous variables. The following are
the results of IRF.
Fig. 4: Impulse Response Function
The Figure 4 shows the positive response of
economic growth to GDP shocks per unit for energy
use (GDP) in period 1 to period 5, if GDP shocks
occur the economic growth tends to be stable and
positive, increasing or decreasing GDP per unit for
energy use (GDP) in periods 1- 2 is classified as
stable, whereas if there is a shock or a decrease in
GDP on economic growth in periods 3-5 it tends to
have a negative effect which will reduce or disrupt
the economy. Economic growth against shocks to
the consumption of renewable energy (PE) for
periods 1-5, if there is a shock to consumption of
renewable energy (PE) for economic growth in
periods 1-2 tends to have a negative effect that will
reduce or disrupt the economy, whereas if there is a
shock to renewable energy consumption (CET) on
economic growth tends to be stable and positive, the
increase or decrease in consumption of renewable
energy (CET) in the 3-5 period is relatively stable.
The response to carbon emissions (CO2) in periods
1-5, shows negative results, if there is a shock or
reduction in carbon emissions (CO2) on economic
growth in periods 1-5 it tends to have a negative
effect which will reduce or disrupt the economy.
6. Variance Decomposition
Variance decomposition is used to compile the
forecast error variance of a variable, namely how
big the difference is between the variance before
and after the shock, of other variables to see the
relative effect of the research variables on other
variables.
Table 8. Variance decomposition Results
Period
PDBE
KET
CO2
1
0.000000
0.000000
0.000000
2
0.011068
0.004354
7.315931
3
0.417272
0.160550
8.378486
4
0.715882
0.181632
8.574870
5
0.804131
0.253077
8.730641
Based on Table 8, it can be found that the
contribution of high carbon emissions is the same as
an indication of the improvement of the Indonesian
economy. The Variance decomposition figure in
periods 1-5 has the highest contribution to carbon
emissions with 8.37% influencing and contributing
to Indonesia's economic growth in 1990-2021.
4.2 Discussion
The direction of energy policy from several studies
on Indonesian energy is reviewed through several
findings, [20], energy consumption and economic
growth in Indonesia tend to have short and long-
term effects. energy consumption leads to economic
growth because industrial energy consumption tends
to increase output as well as unemployment which
adds to the value of a country's GDP and hence,
Indonesia has enjoyed increasing economic growth
over the last 19 years from 2000-2019. Research
from [21], The case in Indonesia of carbon dioxide
emissions and consumption of renewable energy-
hydropower is significantly positive for economic
growth in the long term and short term, while
consumption of renewable energy-thermal earth,
biomass, and other sources have a significant
negative effect on economic growth, during 1990-
2020. Research from [22], CO2 emissions encourage
Indonesia's economic growth because pollution
control is less than optimal in the short term.
Indonesia needs to implement development policies
that prioritize pollution control, emission taxes, and
energy conservation on electricity consumption,
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such as the use of renewable energy consumption.
can be an alternative for controlling CO2 emissions,
increasing Indonesia's carbon emissions cannot be
avoided because it is dependent on non-renewable
energy and this indicates Indonesia's economic
activity has increased from 1971-2020.
The results of the study show the direction of
energy policy in Indonesia seen from the results for
the 1990-2021 period showing statistical findings in
the sphere of influence of GDP per unit for energy
use (PDBE), renewable energy consumption (KET),
carbon emissions (CO2) on the Indonesian economy.
In the long term, the accumulation of GDP per unit
for energy use (GDP) has a positive and significant
effect with a 1% increase in value affecting and
increasing economic growth by 2.02%, renewable
energy consumption has a negative and significant
direction, every 1% increase will decrease economic
growth of 1.16% and carbon emissions have the
highest impact, every 1% increase will increase
21.9% of Indonesia's economic growth.
In the short-term accumulation, results show that
GDP per unit for energy use (GDP) in the short term
has a positive and significant effect on lags 1 and 2,
if there is an increase of 1% GDP per unit for
energy use (GDP) in the first lag it will increase
economic growth of 1.75% and in the second lag
economic growth will increase by 1.73%.
Consumption of renewable energy (CET) in the
short term has a negative and significant effect on
lags 1 and 2, so if there is an increase of 1%
Renewable energy consumption (CET) in the first
lag will reduce economic growth by 0.76% and in
the second lag economic growth will decrease by
0.24%. Carbon Emissions (CO2) in the short-term
estimate have a positive and insignificant effect on
lag 1, Carbon Emissions (CO2) on lag 2 have a
positive and significant influence with a coefficient
value of 15.02295, so if there is an increase of 1%
Carbon Emissions (CO2) in the second lag will
increase economic growth by 15.02%. Looking at
the trend graph between the 3 variables regarding
energy in Indonesia is presented through the
following Figure 5.
Fig. 5: Indonesia's Energy Consumption Trend in
1990-2021
The trend shows that throughout the year the
GDP per unit for energy use (GDP) for Indonesia
1990-2021 has fluctuated, but throughout 2010-
2021 it has continued to increase, this is due to
increasing economic activity in Indonesia, in terms
of renewable energy in Indonesia such as energy
Solar, water, wind, biomass, and geothermal trends
show a continuous decline seen in 2006-2021. It is
also alleged that Indonesia's renewable energy has
not been able to support domestic energy supply and
demand so Indonesia is still dependent on non-
renewable energy such as coal, natural gas, and oil,
thereby increasing the trend of Indonesia's carbon
emissions. From this trend, from 1990-2021 carbon
emissions have continued to increase in Indonesia.
Indonesia is still not able to lead the direction
of energy policy towards the use of renewable
energy, according to several studies also explain,
[23], The development of Indonesia's economic
potential in various sectors affects national energy
needs as a whole significantly. The economic sector
is mainly industry, services, and activities in the
transportation support sector. Indonesia's energy
needs for the next 40 years will adjust to
developments in population growth, increasing
GDP, technological developments, and people's
lifestyles according to the times. Energy needs will
increase until 2060 by almost 3. This is a challenge
for Indonesia to meet energy needs in the future by
considering green and environmentally friendly
trends. Research from [24], from 1990 to 2019.
Renewable energy consumption harms Indonesia's
economic growth. This is because the production of
renewable energy is still limited, but the level of
consumption continues to increase. Consumption of
non-renewable energy has a positive effect on
Indonesia's economic growth. In general, energy
consumption has a positive impact on Indonesia's
economic growth. When the two are combined, both
make a positive contribution to the national
economy, [25], energy consumption is dominated by
energy produced from fossils, namely oil, coal, and
0
2
4
6
8
10
12
14
90 92 94 96 98 00 02 04 06 08 10 12 14 16 18 20
PDBE KET EC02
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gas. Meanwhile, renewable energy has been used
since the early 2000s with a big jump in solar
energy consumption. Indonesia has the opportunity
to switch to geothermal energy which contributes
3.6% to the national energy supply but is still
classified as a low value.
5 Conclusion
Indonesia has great potential in developing
renewable energy due to its wealth of natural
resources and regional characteristics, the results of
the study show that in the long and short term, the
role of GDP per unit of energy use for the economy
is very much needed and has a positive effect, the
role of carbon emissions in the short and long term
CO2 has the positive and significant direction tends
to be quite high and indicates sectoral activity in the
Indonesian economy has increased quite a bit and
automatically the use of non-renewable energy tends
to be still high. The role of non-renewable energy in
the long term and short term is still moving
negatively and significantly. This indicates that
renewable energy in Indonesia tends to be still low,
when renewable energy is increased it tends to make
the economic sector temporarily have a temporary
shortage of energy intake if it is replaced directly
and will reduce the economy. Energy replacement
must be carried out slowly and gradually, the
condition of the shock response concludes that when
the GDP of energy use and CO2 is affected by a
negative shock it will disrupt economic
development, whereas if there is a negative shock
the consumption of renewable energy still tends to
be stable and positive for Indonesia's economic
development.
The direction of the policy transition from non-
renewable energy to renewable energy must be
carried out by the Indonesian government in stages.
The first step is that the Indonesian government
must be able to meet the needs of foreign
investment for renewable energy, which is
calculated for medium and long-term needs. The
economic sectors will temporarily be unstable when
the energy shift is due to fuel changes, of course, the
Indonesian government must prepare an economic
stability policy as the value of economic output
temporarily decreases due to a temporary decrease
in activity. Renewable energy use policy regulations
must be applied to all provinces in stages and
simultaneously, each region must be able to utilize
regional resources for renewable energy.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Heru Wahyudi made a research framework and
collected literature reviews, collects data, and
processes research data.
- Ukhti Ciptawaty wrote the research.
- Arivna Ratih proposes policy recommendations.
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 authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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