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IMPLICATIONS OF THE GLASGOW AGREEMENT (COP26)
PHASING DOWN OF UNABATED COAL POWER ON INDONESIA'S
TRADE BALANCE
Ivan Yulianto
1
, Heru Subiyantoro
2
Universitas Borobudur, Indonesia
Email : [email protected]
1
2
Abstract
At the 26th annual United Nations climate change conference (COP26) in Glasgow, Scotland, 197 countries
succeeded in reaching an agreement to address the “Phase-down of Unabated Coal Power”, i.e. the gradual
reduction of coal-fired power generation and to ends fossil fuel subsidies that are not in efficient. Its going with
the policy of Indonesia Government, in the General Draft of National Energy (RUEN), they will limit coal
production up to 400 million tons per year and the exports will be reduced gradually from year to year and will
be stopped on 2046. In another hand, China and India's dependence on coal energy for power generation and
industry is still very high. Both countries demanding will affect Indonesia's trade balance considering the
proportion of Indonesia's coal exports is 71% compared to domestic consumption. To determine this effect, this
study uses the Autoregression Vector (VAR) model with annual data from 2000 to 2021. The test results show
that Indonesia's coal exports have an effect on the trade balance by 13.17% in terms of the total export value.
International coal price will have a positive impact on the Indonesia’s trade balance by 17.91%. However, the
price of coal is very influential on the volume of Indonesia’s coal export. Momentum of Phase-down Coal is a
golden opportunity to maximize economic benefits while preparing renewable energy as a substitute for coal.
Keywords
: Coal, export, Consumption, price, Balance of trade, VAR.
INTRODUCTION
At the 26th annual United Nations (UN) climate change conference (COP26) in Glasgow,
Scotland, 197 countries reached an agreement on 13 November 2021 on reducing coal
consumption. Participants committed to accelerating the “Phasedown of Unabated Coal
Power”, namely the gradual decline of coal-fired power plants and the termination of
inefficient fossil fuel subsidies (Burki, 2022). This Glasgow Conference (COP26) highlighted
the need for a rapid transition to a clean economy to meet the goals of the Paris Agreement. A
crucial part of this transition is the energy transition from fossil fuels to renewable energy
since electricity and heat production account for a quarter of global greenhouse gas emissions
(Sharma et al., 2022). COP26 claims to “move away from coal and towards clean power
about five times faster than at present” (Nerlinger & Utz, 2022).
Coal is a fossil fuel from organic compound deposits formed naturally from plant remains
(Law No. 4 of 2009). According to, coal has more than 50 percent by weight (or 70 percent
by volume) of carbon material produced by the compaction and hardening of plant residues
called peat deposits. The varieties of coal differ due to differences in the type of plant
material (coal type), degree of coalification (coal rank), and range of impurity (coal grade).
Injuruty : Interdiciplinary Journal and Humanity
Volume 2, Number 2, February 2023
e-ISSN: 2963-4113 and p-ISSN: 2963-3397
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COAL POWER ON INDONESIA'S TRADE BALANCE
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Table 1. Indonesian Coal Production and Sales, 2013 - 2022
Source: MODI, 2022
Based on information obtained from the Ministry of Energy and Mineral Resources
(2021), Indonesia's coal resources as of 2021 amount to 143.7 billion tons and reserves of 38.84
billion tons. Kalimantan has 88.31 billion tons of resources and 25.84 billion tons of coal
reserves. Sumatra has 55.08 billion tons of resources and 12.96 billion tons of coal reserves.
With an average coal production of 600 million tonnes per year, Indonesia's coal reserves are
still 65 years old, assuming no new founding. Until now, most of Indonesia's coal is still
exported to various countries that rely on coal as a source of electrical energy, especially China
and India. In 2020, it was recorded that 71.9% (238.2 million tons) of coal produced was used
for export purposes (DEN, 2021).
The Glasgow COP26 agreement has the potential to impact Indonesian coal production,
consumption, and exports. In this paper, the author tries to describe the impact of implementing
the Phasedown of Unabated Coal Power Agreement on Indonesia's trade balance using the
Vector Autoregressive (VAR) approach and Game Theory (Putra & Damanik, 2017).
METHOD RESEARCH
Based on the previous explanation, the purpose of this study was to determine the effect
of production, consumption, price, and export of coal on the trade balance. (In this paper, the
trade balance uses a proxy for Indonesia's total export value). So it can be said that this research
belongs to associative research. According to Sugiyono (2015), associative research is research
that aims to determine the effect or relationship between two or more variables. In many cases,
the relationship between variables in a dynamic system cannot be explained using a single
equation model but must use dynamic equations (Mahyus & Riyanto, 2005).
In this study, the authors used the VAR (Vector Autoregressive) model to analyze the
relationship between variables using time series data. Implementation of the Government's
COP26 and RUEN coal pashing-down regarding reducing coal consumption gradually until
2046 is transmitted in the form of a domestic coal consumption shock/innovation of one
standard deviation or commonly referred to as the Impulse Response Function (Gujarati, 2003),
(Mahyus & Riyanto, 2005 ).
At the end of the discussion, the author adds a shock simulation obtained from the Game
Theory simulation payoff value. According to (McMillan, 2013), Game Theory is a
mathematical technique for analyzing situations where the utility of each agent does not depend
only on its own actions but also on the actions of others; all agents consider this
interdependence when deciding their actions. Von Neumann and Morgenstern explain the
difference between game theory problems and optimization problems that are usually solved
more simply by using economic theory, such as the consumer maximization problem. Game
theory has relatively little influence on international economic theory, but many policy issues in
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COAL POWER ON INDONESIA'S TRADE BALANCE
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the international trade economy have game theory characteristics, for example, joint tariff
reduction negotiations, FTAs , and others. Several game theory scenarios in economics can be
in the form of static & dynamic games of complete information and static & dynamic game of
incomplete information.
Figure 1: Framework
Source: Berbagai sumber, diolah (2022)
Source: HEESI 2022, diolah
RESULT AND DISCUSSION
European, American, and British coal embargoes from Russia have had a major impact on
world coal prices. The scarcity of coal experienced by importers requires them to look for
alternative sources of coal suppliers even though the price is higher. Power outages in India
occur for eight hours a day due to coal scarcity, the need for cooking coal from Australia is also
hampered by the high increase in coal prices in Australia, the United States, and Canada.
Factories experienced a slowdown, schools were closed, demonstrations occurred, and so on.
India is heavily dependent on coal, with as many as four million people directly and indirectly
employed in India's coal industry, according to a recent report from the Brookings Institution.
The majority of coal reserves are located in the east - the so-called Coal Belt - in the states of
Jharkhand, Chhattisgarh, and Odisha. In this region, coal also drives the economy. It is the
lifeline of the local communities, which are some of the poorest in India. Changing energy from
coal to cleaner energy sources still takes a long time so that no parties and interests are left
behind.
Based on research (Li et al., 2019), using two single forecasting models: Metabolic Gray Model
(MGM) and Back-Pro-Pagation Network (BP) to make predictions from India's coal
consumption data for 1995–2017. The forecast results show that coal consumption in India will
continue to increase on an annual basis at an average of 2.5% over the period 2018–2030.
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Figure 2: Indonesia's Coal Exports According to Destination Countries 2011-2021
Source: HEESI 2022, diolah
China is the largest coal importer of Indonesia, followed by India, Japan, Korea, the
Philippines, and Taiwan. In 1980 China's coal consumption amounted to 714.7 million tons
which were met by national production. Since 2005 China's coal production no longer meets
national coal consumption. For example, in 2016 China's coal consumption was 4.3 billion tons,
and only 86 percent of domestic production was met, the rest was obtained from imports, one of
which was from Indonesia amounting to 53.9 million tons, and in 2021 Chinese coal imports
from Indonesia amounted to 196.2 million tonnes (Ministry of Energy and Mineral Resources,
2022).
According to research (Lin et al., 2018), coal burning to power China's factories, generate
electricity, and heat buildings have steadily increased since China's energy use statistics were
first published in 1981. However, from 2013 to 2015, this trend reversed and coal use decreased
from 2810 million metric tons of coal equivalent (Mtce) to 2752 Mtce, leading to a reduction in
China's overall CO2 emissions. Some analysts have suggested that China's coal consumption
may have peaked . However, preliminary data suggest that coal consumption increased in 2017.
Lin finds that this projected increase will lead to near-term growth in China's coal use to levels
of around 2,900 Mtce to 3,050 Mtce in 2020, with a corresponding increase in CO2 emissions
associated with energy.
The Russian coal embargo caused a shortage of coal in India and China, which in turn led to
significant price increases. On the other hand, this price increase benefits Indonesia. According
to Kontan.co.id (2022), the price of Newcastle coal for the July 2022 trading contract is at
$343.9 per ton, this price has jumped 177.79% compared to early 2022. According to
Tradeindo.co-Founder, Wahyu (2022), the price of bricks will be around US $ 340-350 per ton
until the end of 2022. On the other hand, the high price of coal has a positive effect on the
Indonesian economy, especially Indonesia's trade balance. Indonesia's profits are obtained from
two aspects, namely the volume aspect and the price aspect (Zhongming et al., 2021).
Table 3: Export-Import of Indonesian Coal Products 2011-2021
Source: Handbook of Energy & Economic Statistics of Indonesia, 2022
Seeing that coal consumption tends to increase, national coal production will also continue to
increase because coal is still a cheap type of fossil fuel and is very much needed to meet the
needs of power plants and several industries such as cement, steel and other industries.
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Domestic coal consumption is dominated by the power generation sector (PLTU) with an
average of 61% of national coal consumption (Directorate of Energy Mineral Resources and
Mining BAPPENAS, 2016).
Table 4: Indonesian Coal Product User Industry 2011-2021
Source: Handbook of Energy & Economic Statistics of Indonesia, 2022
The basis for meeting domestic demand for coal is supported by regulations issued by the
government, namely: 1. In 2006, the role of coal in the country became increasingly important
since the issuance of Presidential Regulation Number 71 of 2006 regarding the assignment to
PT. State Electricity Company (Persero) to accelerate the development of power plants that use
coal to accelerate the energy diversification of power plants to non-oil fuels in order to meet the
demand for electric power.
Based on the Central Bureau of Statistics (2015, 2020), the value of GDP from 2000 to 2018
shows an increasing trend. In addition, according to the Ministry of Energy and Mineral
Resources (2016, 2018), domestic coal consumption and coal exports also experienced an
increasing trend. The increasing trend of these three variables indicates a unidirectional
relationship which will be tested using the vector autoregression (VAR) approach so that the
impact of coal exports and domestic coal consumption on economic growth can be identified.
1. Multivariate Data Processing Using the VAR-VECM Model
The data used in this study are as follows:
Table 5: Research Data
From the data above, the following research model equation is made:
=
+
+
+
+
+
(1)
=
+
+
+
+
(2)
=
+
+
+
+
(3)
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=
+
+
+
+
(4)
The above equation can be made in the form of a matrix equation as follows:
So that the equation in this study can be simplified to:
Y_t = A_0 + AY_(t-p) + ε_Yt (6)
Information:
Ex = Total Value of Indonesian Exports (billions of USD), as a proxy for the Trade Balance.
K = Indonesian Coal Consumption (Thousand tons)
H = Reference Coal Price (ICE NewCastle) (USD)
Ev = Total Coal Export Volume
The data collected in this study has a non-linear pattern, therefore the non-linear data is first
transformed into a natural logarithmic form (Ln) (Matondang & Nasution, 2022).
Transformations using natural logarithms are generally used when there is a non-linear
relationship between the independent and dependent variables. The logarithmic transformation
will make the relationship that was originally non-linear usable in a linear model. In addition,
logarithmic transformation can change data that originally had a skewed distribution or was not
normally distributed to become or at least close to a normal distribution (Benoit, 2011). Only
the price variable is maintained by the author without being converted into natural logarithms,
with the consideration that in this study prices will be more meaningful if they remain in the
form of nominal figures. From the results of the model transformation, a stationarity test is then
carried out to fulfill the VAR model requirements that the data must be stationary at the level
level.
By using the STATA 17 application, the stationarity test using the Dickey-Fuller Test method
obtained the following results:
Table 6: Unit Root Test Results
Data in Level
Variable
Test
statistic
DF Critical
Value 5%
Mac Kinnon
p-value
Information
Total exports
(Ln)
Domestic
Consumption
(Ln)
Coal price
Coal Export
(Ln)
-0.797
-1.227
-1.304
-1.937
-3.000
0.8200
0.6617
0.6275
0.3149
Not stationary
Not stationary
Not stationary
Not stationary
From the results of the statistical test, the value of Z(t) for all variables is below the critical
value of 5% (-3,000), or in another way the MacKinnon p-value for Z(t) is below the
probability value of 5%, so that it can be said that the variable Ex ( total exports), K (domestic
consumption), H(price), and Ev (export BB) are found to be non-stationary at levels. Thus, the
in-level model variables do not meet the requirements of the VAR model. The estimation
results of the VAR model can be seen below:
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Table 7: Regresi VAR
With a statistical description like this
The next step is to do a 1st order differential, the following results are obtained:
Table 8: Unit Roots Test Results
Data in
Differences
Variable
Test statistic
DF Critical
Value 5%
Mac Kinnon
p-value
Information
Total Exports (d.Ln)
Domestic
consumption (d.Ln)
Coal Price (d.H)
Coal Export (d.Ln)
-3.724
-4.026
-2.931
-3.556
-3.000
0.0038
0.0013
0.0419
0.0067
stationary
stationary
stationary
stationary
Source: STATA17 processing results
From the table above it can be seen that at the order 1 differentiation level all variables are
stationary, so that the model can be carried out a cointegration test to find out whether there is a
cointegration relationship in the four variables. The results show that there is a cointegration
relationship between these variables. Therefore, the model that will be used further in this study
is the VECM model.
In the causality test, the following facts are obtained:
Table 9: Causality Test
Next, Optimal Lag Test is performed to determine the best model. Determination of this
optimal lag is very important in the VECM model. According to (Alvyonita & Hidayat, 2013)
as cited by (Wikayanti et al., 2020), the variable lag length is used in the VECM model, it is
expected that it is neither too short nor too long. In this study to determine the optimal lag
length of the VAR model using Akaike Information Criteria (AIC) as follows: AIC_((p) )=
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p
ln det(∑▒(p)) + 2pK^2/T.
Table 10: Optimal Lag Test
Source: STATA17 processing results
(Wikayanti et al., 2020) The optimal lag is at the smallest (minimum) value obtained from the
AIC calculation. The minimum value was chosen because the greater the lag used, the more
parameters will be estimated so that it will reduce the degrees of freedom which will ultimately
reduce the efficiency of the estimated parameters (Sianipar et al., 2016). By using the STATA
17 Application the optimal lag is obtained at lag 4.
In the next stage, a cointegration test is carried out using the Johannsen Cointegration test with
the following hypotheses: Ho : Cointegration does not occur H1 : Cointegration occurs if the
trace statistic > critical value, then Ho is rejected and H1 is accepted, which means
cointegration occurs.
Table 11: Cointegration Test
Source: STATA17 processing results
From Table 11 it can be seen that the Cointegration Test results show that there are at least 2
cointegration equations (the * sign indicates cointegration limits). The test results show that
hypothesis H0 (no cointegration) is rejected. This research can use the VECM model.
Next is testing the stability of the VA model). According to (Firdaus & Hafidah, 2020) as
quoted by (Setiawan et al., 2020), if the VAR system is unstable, the results of IRF and FEVD
processing are invalid, the VAR estimation is stable if all roots have a modulus smaller than one
and located inside the unit circle.
Table 12: Model Stability Test
Residual Normality Test
From the table above it can be seen that the residual values are normally distributed, indicated
by the p-value (Prob>chi2) which is greater than the degree of freedom of 5%. The normal
distribution can be used to describe the average value and variance.
Autocollinearity Test
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Autocorrelation indicates a correlation between one member of the observation and another at
different times. In relation to Ordinary Least Square (OLS), autocorrelation is the correlation
between one residual and another. From the table above the Chi2 value is greater than the
critical value, according to the Lagrange Multiplier test presented by Bruesch and Godfrey, then
H0 = no autocorrelation is rejected, or it can be said that there is autocorrelation in the model.
In time series data it is suspected that this autocorrelation often occurs (Melliana & Zain, 2013).
Model Stability Test
According to (Firdaus, 2018: 182) as quoted by (Setiawan et al., 2020), if the VAR system is
unstable, the results obtained from IRF and FEVD will be invalid. From the stability test of the
Eigenvalue model above, it is known that the 3 unit-roots modulus is below 1, so it can be said
that the model is stable.
After testing the stability of the model, then the selection of the best decision-making model is
carried out. The best VAR model is obtained from the optimum lag. From Table 10 it is known
that the optimum lag is found in lag 4. From this optimum lag, a regression of the VECM model
is then carried out.
Table 13 Regression of the VECM Model with Optimum Lag
Source: STATA17 processing results
After obtaining the best VECM model, the next step is to provide shock/innovation to the
model using the Impulse Response Function (IRF) and Faorecast Errors Variance
Decomposition (FEVD) techniques. In addition to forecasting IRF and FEVD, it can be used to
see the impact of changes in one variable in the system on other variables dynamically. VAR
and VECM analysis is not focused on reading the model coefficients by looking at the optimum
lag, causality test, model stability test and others, because the VAR and VECM models are
quite difficult to interpret. The advantages of the VAR and VECM models lie precisely in the
analysis of the output Impulse Response Function (IRF) and Variance Decomposition (FEVD)
(Saskara & Batubara, 2015).
According to (Saskara & Batubara, 2015), IRF analysis describes the estimated impact
of a variable shock on the variable itself and other variables so that the duration of the shock or
innovation effect of a variable can be known, and which variable gives the greatest response to
the shock. Meanwhile, the FEVD analysis describes an estimate of how much a variable
contributes to changes in the variable itself and other variables in several future periods, whose
value is measured in percentage form.
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Figure 7 Graph of Impulse Response Function (IRF)
Source: STATA17 processing results
Based on the IRF simulation above, it can be explained that the previous total export
variable greatly influenced the total exports in the trade balance. It is estimated that there will
be a surge in Indonesian exports in 2023 and then it will gradually decline. The world market
shock as a result of the renewed demand for Indonesian goods post-covid-19 is expected to
have a momentary effect and at the same time form a pattern with a new intercept that is higher
than before. In contrast, the total export variable does not have much influence on the coal
price, coal consumption, and coal export volume variables. Likewise, the effect of total exports
does not have much effect on coal consumption and coal prices.
The coal export volume variable is strongly influenced by previous export volume data. It is
estimated that in 2022-2023 there will be a surge in coal exports, but after that, there will be
stagnation. This surge is thought to have occurred as a result of the Russian-Ukrainian war
(Nerlinger & Utz, 2022). The volume of coal exports will have a constant positive effect on
total exports even though the effect is sloping. This indicates that the portion of coal exports to
Indonesia's trade balance is still quite significant. A similar effect is experienced by domestic
coal consumption. On the other hand, total national exports do not have much influence on
Indonesia's coal demand.
The coal price variable relatively does not have too many shocks to consumption and
exports, except for a momentary shock. The effect of coal prices was responded to positively by
total exports although with relatively small changes. Likewise, the effect of coal prices on coal
consumption and exports. Meanwhile, the variable domestic consumption of coal is greatly
influenced by past consumption, but that only happens for a moment, after that it will gradually
decrease continuously. Coal consumption does not significantly affect the volume of coal
exports and total national exports. However, coal consumption has a significant impact on coal
prices, especially in the near future.
Figure 8 Graph of Forecast Errors Variance Decomposition (FEVD)
In general, the variable shocks of coal consumption, coal prices, and coal export volume
are not large enough to affect the trade balance. Based on the FEVD simulation above, in
general, the growth of total exports, domestic coal consumption, coal exports, and coal prices
are influenced by past performance. The interesting thing in this research is that all variables
experience significant shocks at the beginning of the forecasting. This is in line with the
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positive sentiment in 2022-2023 regarding the rise of the post-covid-19 commodity market and
the effects of economic sanctions on Russia.
In accordance with the objectives of this study, the implementation of reducing coal-
based energy industries is reflected in the consumption, price, and volume of coal exports,
which has a positive impact with a gentle growth in Indonesia's trade balance, which is
represented by total exports. From the FEVD simulation, the following results are obtained:
Table 16: Relationship Matrix between Variables
From Table 16 it can be seen that the Trade Balance (proxy of Total Exports) is most
influenced by past data of the Total Exports variable itself (49.76%), while the variables of
Domestic Coal Consumption, Coal Prices, and Coal Export Volume contribute to the Trade
Balance respectively by 15.31%; 17.91%; and 13.17%. Thus it can be said that the impact of
the Glasgow COP26 agreement for gradually reducing coal consumption will affect Indonesia's
trade balance through three transmissions namely coal prices, coal consumption, and coal
exports. To implement the Glasgow COP26 Agreement and the RUEN Policy, reducing
domestic coal consumption by gradually replacing environmentally friendly energy until 2046
will harm the trade balance by 15.31%.
Another finding in this study that is interesting to convey is that the impulse in the form
of changes in world coal prices will be responded to quite highly by Indonesian coal exports
with an influence of 39.95% (Suganda, 2018). This means that the volume of Indonesian coal
exports is strongly influenced by community coal prices. On the other hand, the response of
domestic coal consumption was not much affected by the impulse of coal exports (14.88%),
most likely this occurred due to the Domestic Market Obligation (DMO) policy implemented
by the Government, meaning that no matter how much the volume of coal exports does not
significantly affect the demand for coal national. Domestic consumption of coal responds
higher to impulse world coal prices (27.85%) (Pujoalwanto, 2014).
CONCLUSION
Based on the results of the VAR-VECM analysis regarding the effect of domestic
consumption, prices, and volume of coal exports on Indonesia's trade balance, it can be
concluded that the Trade Balance (Proxy of Total Exports) is most influenced by past data on
Total Exports (49.76%), while Domestic Consumption, Prices, and Coal Export Volume
contribute to the Trade Balance each by 15.31%; 17.91%; and 13.17%, implement the Glasgow
COP26 Agreement and the RUEN Policy, reducing domestic coal consumption by gradually
replacing environmentally friendly energy until 2046 will harm the trade balance by 15.31%,
and omestic coal consumption was not much affected by the fluctuation in coal exports
(14.88%), most likely this occurred due to the Domestic Market Obligation (DMO) policy
Response
Impulse
Total
Ekspor
Nasional
Konsums
i
Domestik
BB
Harga
BB
Vol
Ekspo
r BB
Total
Ekspor Nas
49.76%
9.17%
5.17
%
4.29%
Konsumsi
Dom BB
15.31%
44.26%
36.13
%
11.17
%
Harga BB
17.91%
27.85%
35.77
%
39.95
%
Vol Ekspor
BB
13.17%
14.88%
19.08
%
40.74
%
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implemented by the Government, meaning that no matter how much the volume of coal exports
does not significantly affect the national coal demand. Domestic consumption of coal is more
influenced by world coal prices (27.85%).
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