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The implication of the aggressive emission target on the Indonesian electricity sector

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Abstract

The pledge to reduce emission has reached a new height globally, and Indone-sia is no exception. According to Indonesia's latest NDCs, it vowed to reduce emission by 31.9% to 43.2%. One of the key for Indonesia to reduce its emission is electrification, which makes greening the grid a priority for the Indonesian government. However, Indonesia has been relying heavily on coal while the growth of renewable is slow. A linear optimization technique show that cleaning the indonesian electricity sector from coal leads to a potential increase in cost by 37%. The key to reduce this cost increase is by enabling the Indonesian independent power producers, which grow their electricity generation much faster than the state owned company. The government must improve the efficiency of procurement, improve its pricing, and forego local content requirements, if it wants to achieve its emission target.
The implication of the aggressive emission
target on the Indonesian electricity sector
Krisna Gupta
November 10, 2023
The pledge to reduce emission has reached a new height globally, and Indone-
sia is no exception. According to Indonesia’s latest NDCs, it vowed to reduce
emission by 31.9% to 43.2%. One of the key for Indonesia to reduce its emission
is electrication, which makes greening the grid a priority for the Indonesian gov-
ernment. However, Indonesia has been relying heavily on coal while the growth
of renewable is slow. A linear optimization technique show that cleaning the
indonesian electricity sector from coal leads to a potential increase in cost by
37%. The key to reduce this cost increase is by enabling the Indonesian inde-
pendent power producers, which grow their electricity generation much faster
than the state owned company. The government must improve the eciency
of procurement, improve its pricing, and forego local content requirements, if it
wants to achieve its emission target.
1 Introduction
The importance to ramp up eorts to decarbonize Indonesian energy sector just reached
a new height.The Indonesian government joined the global bandwagon of committing to
a more emission reduction target. Just before the 27session of the Conference of the
PRties (COP27), The Indonesian government comitted to a more aggresive reduction of
greenhouse gas (GHG) emission by 31.90% by 2030, or by 43.20% with the help of other
countries (Resosudarmo, Rezki, and Eendi 2023).
For the energy sector, the Indonesian government pledged to reduce the emission from energy
sector by 12.5% or by 15.5% with the global help. Energy sector remains an important
emitter of GHG in Indonesia. While the target is not as aggresive as the forestry and land
use sector, the energy sector is going to account for 58.17% of the total emission in 2030
under the business as usual (BAU) projection (Resosudarmo, Rezki, and Eendi 2023).
In the energy sector, electricity generation will play an even greater role going forward. The
latest data suggests the Indonesian electricity sector to provide around 20% of the Indonesian
total energy needs (Wahyuni 2022; MoEF 2021). This share, however, will increase as the
Indonesia nudge toward electrication of cooking and transportation (Resosudarmo, Rezki,
1
and Eendi 2023). Meanwhile, the Indonesian electricity sector aimed to have zero emission
by 2040, far quicker than energy and other sectors in general [Edianto (2022);iea]. Greening
the Indonesian grid, therefore, become one of the most important challenge the Indonesian
government must met to achieve the emission target (Burke et al. 2019; Resosudarmo,
Rezki, and Eendi 2023; IEA 2022; Edianto 2022).
This paper aims to discuss the cost of the Indonesian electricity transition toward renewable
under the government’s aggressive emission target. Linear optimization assume perfect sub-
stitution with constraints is chosen as the preferred method to project the cost of electricity.
Linear optimization is suitable for a perfect substitution grid (Cowell 2006; Sargent and
Stachurski, n.d.). Moreover, the method is simple enough to replicate and provides a useful
to make projection and plans given a proper parameterization and constraints.
Results suggests that the Indonesian government will be able to reach its NDC targets
without and additional cost to the consumer. This is true as long as Indonesia cover the
larger cost of renewables by replacing other fossil fuel source with coal. A grid without coal,
as envisioned by the Indonesian government in 2040 (Edianto 2022; MEMR 2020a), would
increase the cost of electricity by 37%, a non-trivial increase. This increase must be abated
by either a signicant reduction of renewable prices, or a subsidy.
Further discussion is made on the viability of this transition. The most promising renewable
in Indonesia so far is solar PV. However, the adoption speed is hindered by incompetent
bureaucrats and local content requirement imposed on government projects. Also discussed
is the role a newly regulated carbon market in Indonesia. This is, however, also have its
own challenges.
Enabling the growth of Independent Power Producers (IPP) is key since their renewable
growth far outpaced the state owned electricity company. The government must improve
its bureaucratic eciency so procurements can be conducted much faster with a better cer-
tainty. Electricity price needs to be made more exible to cater for niche o-grid consumers.
Lastly, nudging local industries with local content requirement hinders the adoption rate of
renewables, and one has to give.
The next section discusses the literature around the new emission target and Indonesia’s
electricity sector. Section 3 explains the method of choice. Section 4 discusses the results
and its implications toward greening the grid, and section 5 concludes.
2 Indonesia electricity outlook
Just prior the COP27, Indonesia submitted an optimistic document describing its updated
nationally determined contributions (NDCs) (Resosudarmo, Rezki, and Eendi 2023). In-
donesia pledged to reduce total emission by 31.9% under CM1 and 43.2% under CM21.
Meanwhile, the energy sector is pledged to reduce emission by 12.5% and 15.5% under CM1
and CM2 respectively.
1CM1 = Counter Measure 1 (without international support), while CM2 = counter measure 2 (with inter-
national support).
2
The strategy to achieve this target relies heavily on electrication of energy (IEA 2022).
According to the Indonesian government’s roadmap, Indonesia would supply 25% of the
total energy from renewables by 2030 (Resosudarmo, Rezki, and Eendi 2023). On the
demand side, it projects a 5.5 million of electric cars, 8.5 million of electric motorcycles, and
5 million households with induction cookers in in 2030 (Resosudarmo, Rezki, and Eendi
2023). Indeed, electrication is an important part of the Indonesian eort to fulll its
NDCs.
Indonesians are among a relatively smaller consumers of electricity, and its use is concen-
trated mostly in Java island (Burke and Kurniawati 2018). The electricity market is domi-
nated by the state-owned rm, Perusahaan Listrik Negara (PLN), in both distribution and
generation (Resosudarmo, Rezki, and Eendi 2023). While an Independent Power Producer
(IPP) is allowed to generate electricity, they must sell it to PLN as the sole distributor of
electricity.
Electricity pricing is highly regulated by the government (Burke and Kurniawati 2018). The
electricity tari schedule is layered and dierentiated between consumers. The tari schedule
is separated by households, business, and industry. For each group, tari is discriminated
further by its maximum volt-ampere. Moreover, households are often the highest receiver of
subsidy and consequently pay the lowest on electricity compared to businesses and industries
(Burke and Kurniawati 2018).
PLN is central to Indonesia’s electrication strategy. Renewable is projected to be the source
of 52% of PLN’s electricity generation (MEMR 2020a). However, the road toward greening
the greed seems slow. According to PLN (2021) statistics, the percentage of renewables
generated by PLN in 2021 is only 8% (see Table 1). A large majority of the renewable
electricity is sourced by hydropower and geothermal. Additionally, the growth of renewable
electricity since 1998 (31.2%) is dwarfed by PLN’s total capacity growth (144.08%). Indeed,
the majority of PLN’s capacity growth is from coal, defying the global trend (Lolla and
Yang 2021; Burke and Kurniawati 2018).
Table 1: The amount of renewable electricity by PLN (GWh)
source 1998 (GWh) 2021 (GWh)
Hydro 9,649.00 11,869.30
Geothermal 2,616.80 4,216.17
Solar - 5.63
Wind - -
The slow growth of renewables stem from the Indonesian’s reliance on coal (Resosudarmo,
Rezki, and Eendi 2023; Burke et al. 2019). Coal is cheap, abundant, and reliable, reducing
the incentives to look for alternative. Moreover, PLN’s pricing is highly regulated by the
government. Meaning, already relatively low, Indonesian electricity price cannot be raised
by PLN when it is needed. The Indonesian government also would like to limit subsidy,
leading to further reliance on coal (Resosudarmo, Rezki, and Eendi 2023).
3
Indonesia’s renewables are coming mostly from hydropower and geothermal. Unfortunately,
the two sources are growing very slowly, mainly due to a slow land acquisition, especially
geothermal (Burke et al. 2019). Indonesia’s wind potential is not promising, leaving solar
Photovoltaic (solar PV) as the best soource of renewable growth (Burke et al. 2019; IEA
2022). Indonesia has a good potential for solar energy and may utilize oating solar panels
above waters (Blakers and Silalahi 2023). The ecological impact of blocking the sun on
water may still need studies.
While the PLN’s renewable accounts for only 8% of total PLN generation, renewable ac-
counts for 17% of total general electricity generation (Lolla and Yang 2021). This majority
of the discrepancy may come from IPPs. In 2021, around 36.79% of the Indonesian elec-
tricity is generated by IPPs, which is only accounting for 3.78% of total generation in 1998.
Moreover, it is projected that IPP would provide 64% of electricity in 2030 (MEMR 2020a).
This shows the importance of IPPs for improving the Indonesian renewable electricity ca-
pacity.
The popularity of IPP rises in 2013 when the government announced reverse auction for solar
panel (Burke et al. 2019). In 2021, out of 225 megawatts solar energy capacity in Indonesia,
only 21.34 MW (9%) belongs to PLN. However, a limitation imposed on foreign involvement
in both investment and product components as well as low administrative capacity slow
down the growth (Burke et al. 2019). Moreover, some projects are too expensive for PLN.
Since it does not control price, it cannot impose a premium pricing for some expensive IPP
projects. Lastly, an overcapacity of coal electricity leads PLN to slow down its third-party
purchases of renewables. Direct competition with PLN’s own asset is also a known problem
in supporting renewable electricity (Burke et al. 2019).
In short, nancial capacity needs to be improved. PLN’s lack of pricing prower and subsidies
limit its capacity to invest and absorb energy invested by third party generators. It may need
to early-retire a large number of coal powerplant. The government also need to improve grid
infrastructure and general project infrastructure to lower the cost of building new renewable
generators and its transmission. According to MEMR (2020b), Indonesia needs Rp 3,500
trillion ($0.23 trillion) to achieve its NDCs targets.
Internal nancing is constrained considering the limitation of Indonesia’s domestic saving
and scal capacity (Gupta 2021; Resosudarmo, Rezki, and Eendi 2023). One way to
improve this situation is to nd alternative source of funds like the carbon tax. In 2021,
The Indonesian government issued regulations for implementing an emission trading system
(ETS) (Resosudarmo, Rezki, and Eendi 2023). The idea is the government will set a
carbon-cap for rms to emit. Firms who emit less than their allowance can sell those quota
to rms plan to emit more than its carbon-cap. If a rm emits too much without buying
an allowance, it will be taxed at the ETS market price.
A pilot project in 2021 results an average carbon price of 2 USD per tonne of carbon
dioxide and a proposed a 30 IDR/kgCO2 carbon tax (Putri 2022). This pricing translates
to around an extra 0.6 IDR/KWh addition to electricity price. The ocial ETS market
was implemented on September 2023 by IDXCarbon. The Resulting price was around 63
IDR/kgCO2 Wiku (2023), which translated roughly to 1.2 IDR/KWh. For comparison,
4
The Chinese ETS resulted in around 5 USD per tonne of carbon dioxide, while the price of
carbon in the European Union (EU) ETS is 96 USD in 2023 (World Bank 2023).
The government plans to reduce the carbon-cap every year. In a perfectly functioning
carbon market, The cap reduction will reduce the carbon-cap available to buy, which will
push carbon price (and consequently the carbon tax) upwards. If the carbon price is high
enough, it will incentive rms to either invest in green energy, reduce its energy consumption
altogether, or pay the taxes under the high ETS price. The latter would help the government
nance the desired investment in green energy.
Indonesia electricity consumption is only a quarter of the world average (Burke and Kurni-
awati 2018), many of them are poor2. As Indonesia aspires to grow faster, the consumption
of electricity needs to increase. A low carbon quota and an electricity price that’s too high
may discourage access to electricity and increase inequality (Pearse and Bohm 2015). Ac-
cording to a simulation by Eendi and Resosudarmo (2021), developing renewable electricity
may increase poverty incidence in Indonesia by 10-13 percentage points.
3 Simulation
This paper proposes a linear optimization method to simulate the state of Indonesian elec-
tricity given a changes in carbon quota and prices. The strength of linear optimization
method lies in its simplicity in execution and in explaining the resulting output (Sargent
and Stachurski, n.d.). A fully linear system also warrant a solution, and can be adjusted
easily by parameterization or additional equations.
A fully linear system may not represent well the industry’s electricity supply in general.
However, if direct competition among dierent supplier as in (Burke et al. 2019) is indeed
holds back renewable adoptions, it is likely the perfect substitution be feasible. In the
presence of additional contraints, we can poses a restrictions on the parameters.
This section focuses on building the system, parameterization, and scenarion descriptions.
3.1 The linear system
Let be a quantity produced by the Indonesian economy which is nested with a leontief
production function with energy. That is, min  󰄣 , where is a combination
of factors such as capital and labour. Let be the total energy required to produce in
one period. The economy produces 󰄣with a fully substitute sources:
󰄣
where is the amount of clean energy used, while and are coal and gas respectively.
if is a vector of prices of the three sources of energy and  , producers in
299.85% of PLN’s customers are low voltage (PLN 2021)
5
the economy are faced with a cost minimization problem to produce , and by extension,
󰄣.
min
subject to 󰄣
In this setting, 󰄣is taken as exogenous as the consequence of the Leontief production nest.
That is, factor of production is the driver of and consequently 󰄣. This assumption allows
the use of the cost minimization technique and observe the cost impact of idiosyncratic
shock to prices or 󰄣(and by extension) if one needs to change the total output (Cowell
2006).
We improve this setting by adding emission constraints. We limit total emission coming
from the use of each source of energy. Next, we limit how much the total combination
of emissions from these sources is allowed. This variable, then, can be set exogenously to
reect the government’s preference of emission.
Let   be parameters which reect emission generated per megawatt hour by
respectively. Let 󰄤be the total emission generated by the Indonesian electricity sector, Then
the total emission generated by these sources is:
   󰄤
With the above emission constraint, we have a complete linear system as follows:
min
subject to 󰄣
   󰄤
Cost minimization allows for the calculation of compensating variation, which is the com-
pensation for the consumer to maintain the same level of electricity consumption under
an economic shock (Cowell 2006). The shock of the model can come from two exogenous
variables which reects a carbon tax, or 󰄤which reects how much carbon quota is given
in the economy as a whole.
3.2 parameterization
The next step is to nd a representative parameter. PLN (2021) is the main source of 󰄣and
. Perusahaan Listrk Negara (PLN) statistics is reliable since it is the sole distributor of
electricity in Indonesia. According to PLN (2021), Indonesia generates 289,470.57 Gigawatt
hour (GWh) in 2021. From those, around 60% are produced using coal as its main source
6
and around 23% by some mixes of fossil fuels. Only 17% is generated by renewables, mostly
hydroelectric (Lolla and Yang 2021; PLN 2021).
PLN (2021) also contains data on prices per Kilowatt hour (KWh) of electricity based on
sources. The prices per KWh of solar is used for since solar PV has the most promising
renewable growth at the moment. The price for coal-based electricity is half as expensive
as accodring to PLN (2021). Meanwhile, other fossil fuel is priced very close to , as in
PLN (2021).
Lastly, emission factor   are calibrated from Febijanto (2010) and Steen (2001). The
number of emission factor varies between countries and in dierent reports, and emission
factor in this paper tries to balance those dierences. The emission factor is set to be very
low (from procuring the photovoltaic panels). The emission factor for coal is ten times from
the renewable, while other fossil fuels is set to be 70% of coal. Total emission generated by
the electricity sector is calculated based on the emission factor and how much energy source
is used by the sector.
3.3 Scenarios
Various scenarios can be tested on this model, but 7 scenarios stand out. Case 1 is the
status quo. That is, the source is restricted to t the current share of electricity by source
according to Lolla and Yang (2021). That is, 17% renewables, 60% coal and 23% from
other fossil fuels. This scenario serves as parameterization for total emission emitted by the
electricity sector given emission factor. This exercise also provides a baseline cost of the
status quo generation.
Case 2 is the cost optimization given the same electricity generated and the same total
emission from case 1. This scenario shows what the model would tell us how much share of
electricity would be if the sector is fully substitute with no switching cost. One can argue
the case for long-run generation if there is no emission targeting in place. That is, the
country adjust the electricity generation solely for accessibility. Looking at how the coal
progresses in Indonesia, this scenario would likely shows us a total coal domination if it is
not restricted.
The carbon tax scenario is the case 3. The 50 IDR/kgCO2 is imposed as a carbon tax.
This tax is extremely low, however, which translates to 0.6 IDR/KWh. Since the price of
electricity in 2023 is around 1,400 IDR/KWh, the proposed carbon tax is extremely trivial.
To show an actual impact of the carbon tax, a carbon tax equivalent to 50% of the electricity
price is imposed on coal.
Case 4 and 5 poses restrictions on the total emission generated. The goal from CM1 and
CM2 in the NDCs is implemented on the model. That is, case 4 and 5 imposes a restriction
on total emission to be 12.5% and 15.5% lower than case 2 respectively. The goal of these
exercise is to show how much more expensive electricity is under these scenarios, on top of
the cost to make the switch in the rst place.
Next, we would like to know how much the cost and emission generated under the gov-
ernment’s plan for the PLN. According to MEMR (2020a), Indonesia will have 52% of its
7
electricity to be sourced from renewables. Case 6 tests this scenario by limiting the lower
bound of renewable electricity use by 52% and see how much emission it reduce and how
much will it will cost to operate this distribution of electricity source.
The last 2 cases demonstrate the situation when Indonesia has successfully implemented its
strategy as explained by Edianto (2022). That is, case 7 shows the situation when Indonesia
is successfully reduced the use of coal to 0%, while case 8 shows the situation when Indonesia
implements 100% renewable electricity, as projected by 2040 and 2050 respectively (Edianto
2022). Case 7 is implemented by restricting the upper bound of by 0, while case 8 bounds
electricy by 100%.
All of these cases are summarized in Table 2. These 8 cases are exercised as a benchmark for
possible emission and cost of the Indonesian electricity generation. Moreover, these 8 cases
also show the exibility of the model in projecting cases by altering dierent exogenous
variables. These exercises provides a good comparative static which will be discussed in the
next section.
Table 2: scenario descriptions
case description model setting
1 status quo restricting the current share of generation.
2 current emission, optimized case 1 without source restriction.
3 carbon tax Same emission limit but with a carbon tax
4 CM1 same prices but a 12.5% emission reduction
5 CM2 same prices but a 15.5% emission reduction
6 New RUPTL case 2 with 52% renewables
7 Zero coal case 2 with 0% coal
8 Fully renewable case 2 with 100% renewables
4 Results and discussions
The 7 cases is shown below.
Case 1: status quo
The total cost is 262.29 trillion IDR or 906.11 IDR/KWh
The total emission is 225,208,103,460.00 kgCO2
Total electricity by renewables is 49,209,996.90 MWh (17.00 %)
Total electricity by coal is 173,682,342.00 MWh (60.00 %)
Total electricity by other fossil fuels is 66,578,231.10 MWh (23.00 %)
case 2: minimized cost, same emission (no target)
8
The total cost is 237.36 trillion IDR or 819.96 IDR/KWh
The total emission is 225,208,103,460.00 kgCO2
Total electricity by renewables is 71,402,740.60 MWh (24.67 %)
Total electricity by coal is 218,067,829.40 MWh (75.33 %)
Total electricity by other fossil fuels is 0.00 MWh (0.00 %)
case 3: carbon tax
The total cost is 310.18 trillion IDR or 1,071.53 IDR/KWh
The total emission is 225,208,103,460.00 kgCO2
Total electricity by renewables is 71,402,740.60 MWh (24.67 %)
Total electricity by coal is 218,067,829.40 MWh (75.33 %)
Total electricity by other fossil fuels is 0.00 MWh (0.00 %)
case 4: CM1
The total cost is 261.29 trillion IDR or 902.63 IDR/KWh
The total emission is 190,277,276,630.00 kgCO2
Total electricity by renewables is 110,214,770.41 MWh (38.07 %)
Total electricity by coal is 179,255,799.59 MWh (61.93 %)
Total electricity by other fossil fuels is 0.00 MWh (0.00 %)
case 5: CM2
The total cost is 265.75 trillion IDR or 918.07 IDR/KWh
The total emission is 183,753,484,288.40 kgCO2
Total electricity by renewables is 117,463,428.57 MWh (40.58 %)
Total electricity by coal is 172,007,141.43 MWh (59.42 %)
Total electricity by other fossil fuels is 0.00 MWh (0.00 %)
case 6: new RUPTL
The total cost is 286.14 trillion IDR or 988.49 IDR/KWh
The total emission is 153,998,343,240.00 kgCO2
Total electricity by renewables is 150,524,696.40 MWh (52.00 %)
Total electricity by coal is 138,945,873.60 MWh (48.00 %)
Total electricity by other fossil fuels is 0.00 MWh (0.00 %)
case 7: 0% coal
9
The total cost is 361.24 trillion IDR or 1,247.93 IDR/KWh
The total emission is 202,629,399,000.00 kgCO2
Total electricity by renewables is 0.00 MWh (0.00 %)
Total electricity by coal is 0.00 MWh (0.00 %)
Total electricity by other fossil fuels is 289,470,570.00 MWh (100.00 %)
case 8: 100% renewable
The total cost is 371.81 trillion IDR or 1,284.44 IDR/KWh
The total emission is 28,947,057,000.00 kgCO2
Total electricity by renewables is 289,470,570.00 MWh (100.00 %)
Total electricity by coal is 0.00 MWh (0.00 %)
Total electricity by other fossil fuels is 0.00 MWh (0.00 %)
In general, it is clear that coal is the main choice for cost reduction. The high price relative
to emission for other fossil fuels does not justify using it at all in the optimization process.
The cost-emission trade-o is basically a choice between coal and renewable sources. To
reect the status quo, some constraints need to be imposed on the variables.
The nal results of the simulation may not fully reect the real world price. However,
comparisons between cases is still valid since a dierence of a linear system are constant.
That is, while the prices of electricity in each cases may not exactly the same with the real
world, the dierence of prices between cases should still be useful. Additionally, the model
prediction can still be improved with parameterization.
Since case 1 is the status quo situation, we can use case 1 as the baseline. The electricity
mix of case 1 reects the mix from Lolla and Yang (2021). This combination, according
to the simulation, emits around 225,208,103,460 kgCO2 which is priced 906.11 IDR/KWh3.
Meanwhile, case 2 shows that the electricity mix in case 2 gives a 819.96 IDR/KWh, which is
9.5% cheaper given the same emission generated. This result is consistent with the long-run
trajectory of Indonesian electricity mix towards coal since the early 2000s.
The electricity mix from case 3 is consistent with case 2. It seems that in this model, a 25%
electricity generated from renewables is optimal to keep emission the same while dropping
the more expensive fossil fuel altogether. According to this case, a tax equivalent to a 50%
cost increase in electricity generated by coal translated into an 18.26% increase in the total
cost of electricity compared to the baseline. Indeed, as long as the tax is not high enough,
coal will still be preferable to other types of fossil fuel.
In the case 4 and case 5, the government is committed to the emission reduction by 12.5%
and 15.5% reduction respectively, obeying its NDCs. To fulll the CM1, Indonesia must
increase the renewable in the electricity mix by 21 percentage points from the baseline, and
2.5 percentage points more to fulll the CM2. Interestingly, the electricity cost in the case
4 is slightly lower compared to the baseline. Note that in case 4, the mix from coal diers
only slightly compared to the baseline.
3data from PLN (2021) suggests the average cost of electricity in 2021 is 1,083.30 IDR/KWh
10
Next, let us turn to the case 6 which is the situation where the government has success-
fully hit their 52% renewable targets. Under this case, electricity price increases by 9.09%
compared to the baseline. However, if the government is indeed able to hit this target, the
emission from this mix is reduced by 31.62%, far lower than CM2 in the NDCs.
Under the zero coal target in 2040 (case 7), the price of electricity jumps signicantly. The
price is increased by 37.72%. Without coal though, the electricity mix is sourced solely
by non-coal fossil fuels. This is already require less emission even without any renewable
necessary.
Meanwhile, case 8 is the most ambitious, which is a 100% renewable electricity. The cost of
electricity is virtually no dierence than case 7, but the emission is extremely small. This
emission comes from mainly manufacturing the solav panel and wind turbine.
It is clear from the result that coal is very important in minimizing cost. In fact, this
simulation shows that coal is the key to achieve emission target without a large increase in
electricity prices. A zero coal leads to an extremely high compensating variation of around
37%. The government needs to smoothen the increase by a subsidy, or repeat the success of
the communication strategy in the 2010s to avoid protests (Burke and Kurniawati 2018).
Note that the linear nature of this model leads to the use a constant cost of electricity. In
reality, the price of electricity may change as its production increases. The levelized cost of
electricity (LCOE) for solar PV has been declining by 88% from 2010 and 2021. IEA (2022)
projects Indonesia’s cost of solar electricity may be pushed down to 400-1,000 IDR/KWh in
the future. If the reduction of price continue, the compensating variation may reduce and
lower the cost of the energy transition.
However, the bottleneck may come from non-monetary cost. for example, land rights can
be a constraint as well. Securing land rights has already become a problem for geothermal
(Burke and Kurniawati 2018). If solar PV and wind has reached a certain level, land rights
may potentially be a bottleneck. While cost per KWh has been an important indicator
for planning electricity generation, how much area is needed per KWh may need to be
considered as well.
Additionally, administrative cost can also be a hindrance. It has been well known that the
Indonesian government’s procurement of solar PV projects has been slow and uncertain
(Burke et al. 2019; IESR 2022). Additionally, the local content requirement (LCR) imposed
on the government’s solar PV projects is also cited as a major bottleneck (IESR 2022).
While LCR for solar PV is imposed to help local manufacturing, it runs counter to the
emission reduction targets.
Can the carbon tax and the ETS system helps with nancing? According to Pearse and
Bohm (2015), ETS has often proved to be unsuccessful in reducing emission. While ETS
often modeled by the assumption of a perfectly functioning market, it is often not the case.
ETS is prone to political intervention by rms and the government often unable to correctly
punish over-polluters. In the early implementation of the EU ETS, carbon price leads to 0
amid a quota oversupply (Pearse and Bohm 2015). The jury is still out for Indonesia, but
looking at various corruption cases in import quota license in trade in goods (Amanta 2021;
Gupta, Pane, and Pasaribu 2022), it is not promising.
11
There are trade-o the government is facing. First is the cost-emission trade-o. If renew-
able can scale very quickly, then retiring coal-based power plant may be delayed, or even
desirable since it helps with lowering the cost. The incredible growth of IPP in the renew-
able electricity must be facilitated with more ecient bureaucracy and better certainties for
solar PV projects. Lastly, the government may need to forego the LCR rule on solar PV, if
it wants to reach the renewable goal as soon as possible.
It is also important to note the think of this results in the broader scheme of emission
reduction. The largest emission reduction according to the Indonesian NDCs will come
from the agriculture, forestry and land use sector. So far, forestry and land use in Indonesia
has covered by the REDD+ scheme under a limited success (Hermawan, Karim, and Rethel
2023; Indrajaya et al. 2016; Kim et al. 2018). The success of a program like REDD+ relies
on various issues like governance and evaluation(Pearse and Bohm 2015), which still need
a lot of work, especially for the Indonesian government. (Shinbrot et al. 2022; Gonçalves
2022; Goldstein 2021). If sectors outside of energy do not progress as expected, then the
energy sector may have to pick up the pace.
5 Conclusion
The Indonesian government latest NDCs shows a condent emission target. The Indonesian
electricity sector is targeted to reduce emission by 12.5% by its own eort or 15.5% with the
help of the international community. To achieve this, the Indonesian government vow to
increase its share of renewable electricity up to 52% in 2030, free its electricity grid from coal
by 2040, and become fully renewable in 2050. Considering how important electrication of
energy in the Indonesian net-zero strategy, greening the grid become extremely crucial.
Using an linear optimization technique, it is shown that freeing the grid from coal would
cost the Indonesian electricity consumer with 37% higher electricity tari. This increase
will really hurt Indonesian electricity consumption which already very low in international
standard. On the other hand, to ensure an aordable electricity, Indonesia should keep its
coal.
One option to compensate the increase in electricity price is to smoothen the price increase
by providing a subsidy or increase the price discriminately. This would add more nancial
burden on top of the necessary 3,500 Trillion IDR to green the grid. Carbon tax and ETS
can be one way to earn such funding. But judging from problems faced by other countries,
it may left to be desired.
The other option is to keep the progressive reduction of solar PV’s LCOE. The Indonesian
independent power supplier already greening much faster than the PLN. The Indonesian
government must keep up the momentum by facilitating them. Organize a better, more
ecient procurement and purchasing agreement. Uncertainty must be reduced, especially
in the contract design and land acquisition. Consider foregoing LCR, since the goal toward
zero emission may be more important than supporting indigeneous solar PV industry.
12
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