Utrecht University

Student
Akmilatul Maghfiroh
Energy Science, Utrecht University
Student nr: 3745686 e-mail: a.maghfiroh@gmail.com
Examiner Utrecht University dr. Evert Nieuwlaar
Assistant Professor
Copernicus Institute of Sustainable Development - Energy & Resources,
Utrecht University e-mail: E.Nieuwlaar@uu.nl
Tel: +31 30 253 7607
Examiner Utrecht University prof. dr. Kornelis Blok
Professor
Copernicus Institute of Sustainable Development - Energy & Resources
Utrecht University e-mail: K.Blok@uu.nl
Tel: +31 30 253 7649
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Contents
1 Introduction and context of research
1.1 Background
1.2 Problem definition
1.2.1 Energy Consumption in transportation sector
1.2.2 Energy Subsidy
1.2.3 Low cost green car policy
1.3 Literature studies
1.4 Objective
1.5 Scope
1.5.1 Time frame
1.5.2 Passenger vehicles
1.5.3 Energy and GHG emissions
1.5.4 Fuel subsidies
1.6 Outline
2 Methodology
2.1 Vehicle fleet model development
2.1.1 Vehicle population
2.1.2 Vehicle sales mix scenarios
2.1.3 Market penetration of new types of vehicles
2.1.4 Vehicle kilometers traveled
2.1.5 Vehicle Fuel Consumption
2.1.6 Emission factor
2.1.7 Luxury tax reduction
2.1.8 Fuel prices and fuel subsidy
3 Results
3.1 Future trends in fuel use
3.2 Future trends in GHG
3.3 Luxury tax reduction
3.4 Fuel subsidy
3.5 Cost/Benefit of this policy
3.6 Sensitivity analysis
4 Discussion
5 Conclusion
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6 References
7 Appendix
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The Indonesian government introduced the low cost green cars policy since 13 May 2013 by imposing a regulation on tax reduction for luxury items particularly for low emission cars with price cap
(PP/41/13, 2013). This policy aims at increasing the share of low and zero emission vehicles in the national vehicle fleet. The policy is targeted at the passenger, commercial and public transport fleets and incorporates highly efficient vehicles, low emission vehicles, gas fueled vehicles, hybrids and grid-enabled electric vehicles. Apart from achieving targets of lower greenhouse gas (GHG) emissions, this program also attempts to reduce fossil fuel consumption of the transportation sector and to lessen the national budget share of energy subsidy.
From the report of Ministry of Energy and Mineral Resources 1 , the primary energy supply pattern in
Indonesia comes mainly from oil-based fuel with approximately 34%, the remaining consumption comes from biomass, coal, natural gas, liquefied petroleum gas, hydropower and others with composition of 27%, 13%, 11%,3%, 8% and 5% respectively.
Furthermore from the Figure 1, the transportation sector consumes almost a quarter of the final energy. At least 150x10 10 GJ are used to support the transportation system in Indonesia, of which
99,95% of them are fossil fuel based energy (ESDM, 2011). As reported in the fourth quarter 2011 of
Indonesia’s balance of payment report, oil-based fuel consumption in the transportation sector is up to
60% share, followed by industry and electricity production about 24% and 13% respectively (Bank
Indonesia, 2012)
30%
3% 3%
8%
23%
33%
Industrial
Transportation
Households
Non Energy Utilization
Commercial
Other
Figure 1. Final Energy Consumption 2010
(Summarized from Handbook of Energy & Economic Statistics of Indonesia (ESDM))
1 Handbook of Energy & Economic Statistics of Indonesia - 2011
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Of this oil-based fuel consumed in the transportation sector, road vehicles take almost 88% and of which the majority of vehicle types are the private fleet and freight trucks as can be seen in Figure 2.
Additionally the growth rate of vehicles in Indonesia is predicted to increase. The positive Indonesian population growth in conjunction with the increase of gross domestic product growth contributes to the increase of vehicles demand. In 2030 the national average number of vehicle per capita is expected to be up to 0.022 vehicles per capita (BPPT, 2011) from 0.007 vehicles per capita (ESDM, 2011) now.
Figure 2. Oil-based fuel consumption pattern of the transportation sector in Indonesia (summarized from DNPI, 2009)
The oil-based fuel price in Indonesia is subsidized as part of the energy subsidy budget. Because of these subsidies, retail fuel price and electricity tariffs are much lower than the cost of provision and in particular lower than price and tariffs in regional peers as can be seen on Figure 3. The retail gasoline price and electricity tariffs are regulated at a fixed price independent from the volatile international oil price. The counterpart is that oil-price volatility is transferred to public finances as energy subsidy.
Figure 3. Comparison of retail gasoline price and electricity tariffs in USD in 2008 (Mourougane,
2010)
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The amount of subsidy on energy has continuously increased, and also the realization of subsidy exceeds its planned national budget in every respective year, with an exception in 2009 due to crude oil price reduction. The comparison between Indonesia's national fuel subsidy budget and its realization can be seen in Figure (4). As the price of crude oil tends to increase and the growth of fuel consumption is rising, the government had topped up subsidy on energy in 2012 up to 305,9 trillion rupiah ( 25.49 M€ 2 ) or up to 20% of the total national budget of the year 2012 (Kompas, 2012).
250 000,0
200 000,0
150 000,0
100 000,0
50 000,0
Budget
Realization
0,0
2006 2007 2008 2009 2010 2011
Figure 4. The planning and realization of fuel subsidy from 2004 to 2011
(Summarized from Ministry of Finance, BUDGET STATISTICS 2006 – 2012)
Figure 5. Indonesia’s process of being net oil importer (Pallone, 2009)
Historically, Indonesia was an oil exporter country until 2004 as can be seen on Figure 5. The rapid decline in 
 Indonesia’s exports due to aging oil fields and lack of investment in new equipment and its rapid increase 
 in imports owed to higher energy consumption caused the shifting of Indonesia towards net importer of oil products (Palone, 2009). Moreover, the sign of this shifting has started from 1998 because the amount of fuel subsidy has then surpassed the annual income from crude oil
(Setneg, 2012) as seen on Figure 6.
2 Currency 1 EUR = 12.000 IDR
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50
40
30
20
8,79
11,28 11,45 11,41
9,47 9,31
11,96
13,36
8,43
10
0
-2,65
-10
-7,83
-20
Income from crude oil export Fuel subsidy Surplus/Deficit
Figure 6. The Surplus/Deficit of oil income – fuel subsidy (Setneg, 2012)
In addition to fuel subsidy, the electricity price is subsided as well. As can be seen on Figure 7, the electricity subsidies grow higher and higher into about half of oil-based fuel subsidy. This is likely the results of higher electricity demand and higher cost of production particularly due to higher price of fossil oil and coal as major energy resources.
Figure 7. Oil-based fuel subsidy and electricity subsidy (Abimanyu, 2010)
There are options to lessen the burden of fuel subsidy. The common option is increasing the retail price of fuel to diminish the amount of subsidy per unit fuel. It seems a simple measure, but on the contrary such policy has impacts on raising inflation rate as well as political escalation which could lead to chaotic riots. The retail price adjustments have been executed several times as depicted in
Table 1.
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Table 1. Historical gasoline and diesel price in Indonesia
Price of Subsidized Gasoline Price of Subsidized Diesel
Date From
(Rp)
To (Rp) % increase From to % increase
Remarks
1991
8-Jan-93
5-May-98
150
550
700
550
700
1.200
266,67%
27,27%
71,43%
53
300
380
Mr. Soeharto (2 nd President) era: the subsidy is mainly for
300 471,43% maintaining his regime.
However the domestic oil price increase in May 1998
380 26,67% accumulated with economic contraction at -7.6% (Suruji et al, 1998), currency depreciation
600 57,89% at 499% (setneg,2012) and very steep inflation rate at 77,63%
(inflation.eu, 2013) had lead riots and forced him to be
15-May-98
20-Apr-00
1-Oct-00
16-Jun-01
17-Jan-02
2-Jan-03
1.200
1.200
600
1.150
1.000
600
1.150
1.450
-16,67%
-50,00%
91,67%
26,09%
600
550
550
600 overthrown after 32 years as
550 -8,33% president
Mr. Abdurahman Wahid (4 th
550
President) era. The domestic fuel price was increased because of high burden fiscal due to deficit of crude oil
0,00% income surpassed by subsidy up to 9.5 trillion rupiah. The inflation expand into 9,35% after stabilized at 1,92%. In less than 2 year ruling the country
600 9,09% he faced an impeachment from parliament.
Ms. Megawati (5 th President)
900 50,00% era. Her policy to increase
1.450 1.550 6,90% 900 1150 27,78% gasoline price several times worsened poverty rate and it
1.550 1.810 16,77% 1150 1890 64,35% made her loose in election.
1-Mar-05
1-Oct-05
1.810 2.400 32,60% 1890 2100 11,11%
Mr. Susilo Bambang Yudoyono
(6 th President)
2.400 4.500 87,50% 2100 4300 104,76%
Every time the oil price increases, inflation rate rises.
24-May-08 4.500 5.500 22,22% 4300 5500 27,91%
The inflation rate in 2005 and
2008 increase up to 17,11%
1-Dec-08 5.500 6.000 9,09% 5500 5500 0,00% and 10,21% respectively.
However in December 2008
15-Dec-08
15-Jan-09
6.000 5.000 -16,67% 5500 4800 -12,73% and January 2009 prior to election and following the drop
5.000 4.500 -10,00% 4800 4500 -6,25% in international oil prices, the government reduced retail prices of gasoline and diesel
22-Jun-13 despite the subsidy spending
4.500 6.000 33,33% 4500 5500 22,22% was exceeding more than four
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times as its subsidy budget of given year (Figure 4) in order to gather votes for election.
It is apparent that there is a correlation between fuel price change and political circumstances as above mentioned, therefore, government attempts to reform energy subsidy by formulating other alternative policies. One of the policies is introducing the low cost green car policy.
In early 2012, the Indonesian Government introduced a policy to develop more massive automobile industry (National council, 2012) for enhancing automotive assembly and its auxiliary industries as well as introducing the use of electric vehicles particularly electric vehicles and gas converter engines that alter conventional cars into gas fueled cars, to alleviate the energy subsidy burden of the state budget. Additionally, this policy is claimed to have potential to gain additional benefit of reducing the
CO
2
emissions by the transportation sector and solving the problem of peak load electricity discrepancy (National council, 2012).
This policy also is empowered by the Low Cost Green Car (LCGC) policy in May 2013 with imposing
Government regulation number 41/2013. This policy involves a tax reduction for luxury items particularly low emission cars with a price cap. This regulation provides cuts on a luxury goods tax amounting to 25 percent for cars that can run 20 kilometers (12 miles) on a single liter of fuel, 50 percent for 28 kilometers per liter, and 100 percent for car managing more than that. Apart from fuel consumption criteria, the other eligibility criteria of tax reduction are that the car components are locally produced in Indonesia at minimum 60% and the off-the-road car price is limited to be less than
95 million rupiah (Setkab, 2013). This price can be increased by 10% if the car is given additional safety features. The car types include hybrid engines using dual gasoline and gas, electricity or biofuels. It is forecasted that low cost green cars could account for more than 35 percent of the 1.8 million passenger vehicles expected to be sold in Indonesia by 2020 (Thonkpak, 2013).
Using this program, the government attempts to shift the oil-based fuel consumption towards electricity consumption from the grid and gas consumption. However the electricity price itself is also subsidized particularly for business and residential user. Moreover, the natural gas is subsidized as well. Therefore the question rises what the extent of subsidy saving by this program will be.
Another consideration is that, the power plants built in Indonesia nowadays use several types of fuel.
The biggest resources are coal, oil and natural gas. Most of them are also subsidized. Less than 10 % of the electricity produced is harvested from renewable resources. Even the Agency for Assessment and Application of Technology (BPPT) predicts that the resources of electricity production likely will be more dominated by coal-power generation up to 80% in the future (BPPT, 2012).
Introducing new types of technology in a society like Indonesia is challenging. In a developing country with low-middle income, buying a vehicle needs such consideration since it is an expensive purchase for many people. People usually expect good experiences on any aspects of a new technology from other previous adopter, and then they will decide to imitate using the new technology. It commonly takes periods of time from 1 st innovative adopter who is willing to take the risk of using a new technology until the products are widely accepted in the society. For example the sales of electric cars in Chile, where the electric car is initially introduced at 2011, only reach 10 units in 1 st year
(Publinews, 2012). In Canada where has longer experience the total sale as of September 2013 has reached 4543 electric cars (Klippenstein, 2013).
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There are several studies on vehicle fleet projection, the energy use, cost and CO
2
emissions of emerging technology cars and fuel subsidy reform as depicted in following Table 2.
Table 2. The related studies
Author
Bandivadekar et.al. (2008)
Bodek &
Heywood
(2008)
Mourougane,
A. (2010)
Title
On the road in
2035: Reducing
Transportation’s
Petroleum
Consumption and GHG
Emissions
Method
Vehicle fleet model
Scope
USA
Europe’s Evolving
Passenger
Vehicle Fleet:
Fuel Use and
GHG Emissions
Scenarios through
2035
Vehicle fleet model
Europe
Phasing Out
Energy Subsidies in Indonesia
Policy analysis
Indonesia
Burniaux et al.
(2009)
Vliet et al.,
2010
The Economics of
Climate Change
Mitigation: How to
Build the
Necessary Global
Action in a Cost-
Effective Manner
Combining hybrid cars and synthetic fuels with electricity generation and carbon capture
Multi-country general equilibrium model
World,
Indonesia is categorized as net exporter
Markal linear programming problem – bottom up model
Netherlands
Finding
The scenarios and fleet models were used to evaluate the feasibility of proposed new vehicle GHG emission targets. It concludes that fuel consumption and
GHG emissions of our lightduty vehicle fleet can be reduced significantly. How rapidly that reduction occurs depends on the determination of the major stakeholder groups —vehicle and fuel suppliers, vehicle and fuel purchasers and users, and governments —to vigorously undertake the actions required.
The scenarios and fleet models were used to evaluate the feasibility of proposed new vehicle GHG emission targets, the evolution of the diesel to gasoline fuel use ratio, and the relative ability for changes in the sales mix,
ERFC and biofuels share to reduce fleet-wide fuel use and GHG emissions over the next 30 years.
Implementing reforms in a phased manner to soften the financial pain of those who will lose from the change and give them time to adapt.
Deliberative formulating reform to overcome oppositions
Effect on GDP: Unilateral subsidy removal:0.5% by
2050
Effect on greenhouse-gas:
Emissions Unilateral removal:
-20.2% CO
2
emissions by
2050 compared with BAU
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Author
Vliet et al.,
2010
Gao et al.,
2008
Hirte, 2013
Title and storage
Energy use, cost and CO
2 emissions of electric cars
Method
Life Cycle
Analysis
China Charged up: Electric vehicle opportunity
The optimal subsidy on electric vehicles in
German metropolitan areas: A spatial general equilibrium analysis
Diffusion of innovation analysis spatial general equilibrium analysis
Scope
Netherlands
China
Germany
Finding total cost of ownership (TCO) of current PHEV are uncompetitive with regular cars, unless. batteries cost
400 D kW/h. For BEV is competitive if cost of batteries drops to 150 D kWh −1
The market of EV in China could reach 150-400 billion renminbi by 2030 electric vehicles should not be subsidized but taxed
From literature reviews, it is found a niche that the CO
2
emission prediction due to changes on vehicle fleet model in Indonesia because of low cost green car policy has not been studied yet. There are in fact several studies on energy subsidy reduction in Indonesia that focus on the impact on economic welfare and politics qualitatively, however there has not been the specific analysis on energy subsidy budget after implementation of Government regulation no 41/2013 about luxury tax reduction yet.
Therefore this research may contribute to knowledge on this research subject.
The main objective of this master thesis was to provide further insight into the energy consumption,
GHG emissions, the fuel subsidies and opportunity loss due to luxury tax cut of transportation sector particularly passenger vehicles usage by considering details changes of vehicle fleet population. To fulfill the objective, a modeling tool was developed to overcome the present gap in analysis capacities caused by limited studies in this topic. The modeling tool developed in this thesis will be referred to in this document as the vehicle fleet model (VFM). The VFM provides opportunity to calculate the energy consumption, GHG emissions, the fuel subsidies and opportunity loss due to luxury tax cut of passenger vehicle fleet.
In this thesis, the VFM model was used to evaluate three market mix scenarios were conducted to assess and gave illustration on to what extend will the LCGC policy reduce the fossil oil consumption and CO
2
emission on light-duty vehicles usage and thereby reduce the amount of energy subsidy in
20 years implementation. Additionally, the sub questions to be answered are: a. How much is the oil consumption of at least three type of cars, namely more efficient gasoline car, gas fueled car and electric car, given that there is efficiency and technological improvement of all those car types during 20 years b. How many is the population of cars, its growth rate and their share in total population c. How much is the annual consumption of gasoline, CNG, and electricity consumed for those private passenger vehicles. d. Given that energy subsidy is assumed as a cumulative discrepancy between fuel price and international price of oil and considering that international oil price may increase and the price of gasoline, gas and electricity may incrementally be increased, how much is the energy subsidy spent for energy consumption of car fleet
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100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0% e. How much is the CO
2
emission and energy subsidy due to variation of market share scenarios
The timeframe of this model is capped at 20 years from 2013 to 2032. The rationales of this time span are tradeoff between prediction of vehicle technology developments and tentative future policies in
Indonesia. There are many limitations in term precisely projecting improvements in vehicle performances as well as market penetration of various powertrain vehicle types. There are some barrier such as high vehicle price, limited fuel storage and limited travel range, safety of battery and compressed gas chamber, fuelling cost of emerging vehicle technologies compared to gasoline and diesel vehicle in which their fuel are still subsidized, lack of refueling infrastructure, market diffusion of newly powertrain vehicle types, and lack awareness on energy efficiency.
Additionally, it is highly uncertain to predict the policy changes in Indonesia beyond this time period, since historically there were many policies changed in last 15 year as explained in Table 1. However, it is widely known that slow rate of fleet turnover as well as slow rate of adoption of new technology vehicle implies that it can sometimes take several decades to see obvious change in fleet fuel use and emissions. According to Schafer et.al (2006), the estimation time scales by which technologies can take quite long time to make significant impacts on fleet fuel use. For example, the time required for Gasoline-Hybrid engine car to penetrate market and fleet is about 25 to 30 years (Bandivadekar,
2008). It is not sensible to pick shorter timeframe because of it, therefore a 20 year time frame is used in this VFM model.
The focus of this modeling process has been on four wheeler passenger vehicles fleet. The motor bikes, bus, and truck are not considered in this model. The passenger vehicles types included in the case studies in this thesis, using developed modeling tool, are the most popular four wheeler ones.
Regarding to the car population pattern as it can be seen on Figure 8, the car types dominated the
Indonesian’s market are MPV and sedan with engine power below 3000 cc.
SUV MPV Sedan
Figure 8. Sales pattern of passenger vehicles in Indonesia
Furthermore, the car population in Indonesia has a specific pattern in which depicted in Appendix.
The car consumers in Indonesia tend to buy MPV car type. More than 94% car population in
Indonesia is 7-seater MPV cars (Gaikindo, 2013). The engine capacity lower than 1.500 cc, is being favorite of most car owners, along with the middle engine capacity, they are account for 99.63%.
Moreover, the powertrain types are mostly dominated by gasoline (Gaikindo, 2013). Therefore in this
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research, the vehicles are assumed consist of MPV and sedan with engine capacity less than 3000 cc.
Three market mix scenarios were examined in this master thesis. They are No Change, Low cost green car (LCGC) dominant and Emerging Alternative Technologies scenarios. These market mix scenarios represent the new vehicles sales mix starting in 2013. The No change scenario depicts reference sales mix scenario, the LCGC dominant scenario represents market respond due to LCGC policy limited to gasoline and diesel powertrains, while the Emerging Alternative Technologies scenario illustrates the market mix with penetration of new fuel technologies cars, namely gasoline hybrid, plug in electric and compressed natural gas (CNG).
This thesis only includes the 1 st order energy inputs to calculate the energy consumption and GHG emission, particularly CO
2
emission, of vehicles fleet operations. The energy consumption and the
CO
2
emission calculated in this thesis are the amount of each fuel types needed and the CO
2
emitted at 1 st order representation in which not only does it calculate them corresponding to tank to wheel scope of fuel needed during vehicle operation but also that of emission of electricity production particularly for electric fueled car. The 2 nd and 3 rd order energy inputs and CO
2
emission (for oil transportation and refining, for example, gas compression and transport, and the production of car assembling machines) are not considered. These 2 nd and 3 rd order data are very limited and very uncertain, particularly those related to the local car manufactures in Indonesia.
Both energy carriers, oil based fuel and electricity are sold to end-consumers at fixed subsidized price. However in this thesis , only diesel and gasoline used for car’s fuel are subsidized. The CNG and electricity for emerging technology vehicle types are sold at their economic prices as planned by government (setkab, 2013).
The fixed price of fuel is assumed to be incrementally increased once in every 5 years up to the level of their economical price, thus the fuel prices will change based on each of their economical price accordingly after the fixed prices surpass them. Meanwhile, the economical prices of fuels are estimated based on crude oil projection developed by US-Energy Information Administration (2013). It forecasts three scenarios of crude oil prices, namely reference, low price and high price scenarios.
The structure of this report is as follows. After the introduction in Section 1, the Section 2, methodology, covers the general approach and details on the development of vehicle fleet modeling tool in this thesis. Section 3, results, describes the details and the results of the three scenarios conducted using the VFM and the insights it provides. This section also includes the results of sensitivity analysis that have been performed using the VFM to assess the impact of main uncertainties of model parameter on the results. The benefits and limitations of the developed modeling approach are discussed in Section 4, Finally, the conclusions are presented in Section 5.
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This research attempts to answer the questions by developing a vehicle fleet model, which is a particular set of operations. In this model, six major components can be distinguished: vehicle fleet vehicle kilometers traveled, fuel usage, CO
2
emissions, luxury tax reduction and fuel subsidy. The required data collection was conducted by literature review and interviews.
The future passenger vehicle fuel use, CO
2
emissions and net cost/benefit due to tax reduction and fuel subsidy is analyzed using a Microsoft Excel-based fleet model. The model is developed with the insight gathered from the ways LDV fleet model developed in MIT’s Sloan Automotive Laboratory
(Bandivadekar, 2008). Thus the model in this research is adapted based on Indonesia’s regulation and market condition and is depicted schematically in Figure 9.
New vehicle sales Vehicle population
Total Km traveled
Purchase price of vehicle
Luxury tax Km traveled per vehicle
Loss of Luxury Tax
Revenue
Fuel consumption rate Fuel Use
Emission factor
1 st Order representation of
CO
2
emission
Fuel subsidy rate Fuel Subsidy Saving
Cost/Benefit
Adapted from LDV Fleet model
VFM – UU model
Figure 9. Vehicle Fleet Model
The model begins by using annual vehicle sales figures and their survival rate to determine the stock of vehicles in the fleet for any given model year: The number of vehicles in the fleet from each model year is then multiplied by the number of kilometers those vehicles traveled in that year. It is assumed that the number of kilometers traveled by an average car declines linearly with age. Third, the total kilometers traveled are multiplied by the corresponding fuel consumption of vehicles from that model year to yield the total amount of fuel used by the fleet in a given year. Finally, the amount of each type of fuel consumed is multiplied by its corresponding CO
2
emission factor at 1 st order representation to give the total CO
2
emissions emitted by the fleet.
While the net cost/benefit calculation starts by calculating the loss of luxury tax revenue of government due to luxury tax reduction. This cost is a discrepancies between luxury tax of each passenger vehicle sales in respective year at rate of Government regulation no 12/2006 and that of at rate of Government regulation no 41/2013. The earlier amount of tax is a baseline tax received by government. The later amount of tax is then multiplied by the number of vehicles in the fleet from each model year. Second, the total amount of fuel used by the fleet in a given year is multiplied by corresponding fuel subsidy to yield the total fuel subsidy. It is assumed that the domestic fuel price is incrementally increased once in every 5 year. The difference between the amount of baseline fuel
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subsidy and spent fuel subsidy is fuel subsidy saving. Lastly, calculation of net cost/benefit consider the opportunity tax income and fuel subsidy saving.
In this section the development of vehicle fleet model is elaborated. The VFM model includes the modeling of vehicle population, vehicle sales mix scenarios, market penetration of new types of vehicles, vehicle kilometers traveled, vehicle fuel consumption, emission factor, luxury tax reduction, and fuel subsidy. The correlation of each parameters developed in this VFM model are depicted as follows:
The current car population consists of survived cars that were purchased in the past years and the newly purchased cars in the current year. Therefore, to calculate the population of vehicles in year t, the initial steps is to estimate the population of survived old cars. In this step, the fraction of car’s population purchased at year i which was survived ( 𝑆𝑟 𝑖,𝑡
( 𝑡 ) ) at year t is calculated by using formula Eq
(2). This fraction is multiplied by the sales at year i (S i
) to result in the number of survived cars aged
(t-i) in year t ( 𝑃 𝑖,𝑡
). Thus, the survived cars purchased at year i will decrease logistically over time.
𝑃 𝑖,𝑡
= 𝑆 𝑖
∗ 𝑆𝑟 𝑖,𝑡
( 𝑡 )
𝑆𝑟 𝑖,𝑡
(𝑡) = 1 −
1
1 + 𝑒 −𝛽(𝑡 𝑖,𝑡
−𝑡
0
) where,
• P i,t
is the population of the car purchased at year i which survive at year t
• S i
is the new car sales at year i
• 𝑆𝑟 𝑖,𝑡
(𝑡) is the survival rate of vehicles at the present age of a given vehicle
• t
0
is the median age of vehicles when they are scrapped,
• 𝑡 𝑖,𝑡
is the present age of a given vehicle, and
• β is a parameter that expresses how quickly vehicles are retired around t
0
.
Eq. (1)
Eq. (2)
In this model, it is implied that the car purchased at year i is the car which is produced or assembled as well as sold in respective year. The indication of its production lot is usually obtained from Vehicle
Identification Number (VIN) in the car’s body which contains information about the vehicle production namely manufacturer identifier, vehicle’s characteristics, not to mention assembling vehicle model year.
The survived car calculation in this vehicle fleet model is assumed starting from 1976. Meanwhile the highest lifetime of car is assumed 50 years. This lifetime assumption is based on the oldest cars available in secondary cars market. There are apparently some cars, based on their VIN code, produced in 1964 which were traded in used car market in field observation Yogyakarta. Therefore the median of life time ( 𝑡
0
) used in this model to calculate the survival rate is 25 years.
Meanwhile, the research defining retirement parameter β has not been available yet for Indonesia case. As simplification, a value of 0.28 was used in the modeling adapted from parameter β fitted for cars in case of US vehicle fleet model (Bandivadekar, 2008). Although how this value determined is not well elaborated in LDV vehicle fleet model (Bandivadekar, 2008), it seems that by deriving eq. 3 the parameter β can be calculated using eq. 3,
β i
= ln (
𝑃
1 𝑖,1
− 1) − ln (
𝑃
1 𝑖,2
− 1) 𝑡 𝑖,2
− 𝑡 𝑖,1
Eq. (3)
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The parameter β at 0.28 means that the population of car purchased at any year will rapidly deteriorate from 90% to 25% of initial population during 11.75 years. As simplification, the retirement parameter of cars purchased at any year i are assumed having same rate.
It should be noted that in year t, the car population consists of old survived cars purchased from year
1976 to t-1 year and new cars purchased at year t. It is assumed that new car sales in year t will not scrapped at year t, it started to deteriorate at year t +1. Figure 10 shows the estimated survival rates of passenger cars.
1,00
0,90
0,80
0,70
0,60
0,50
0,40
0,30
0,20
0,10
0,00
1 5 9 13 17 21 25 29 33 37 41
Vehicle's age
Figure 10. Survival rate estimation
Data of car population is taken from Gaikindo (the Association of Indonesian Automotive Industries) instead of from BPS (Statistic Indonesia). Despite commonly used as formal references of Indonesian statistic, the BPS data of car population has some drawbacks, thus the model does not utilize this data to depict factual passenger vehicles population. There is inconsistent data regarding BPS data that the registered cars starting 2006 have far exceeded the cumulative sales of new cars regardless the scrappage factor. This is illogical since the rate of accidents in Indonesia is relatively high, according to BPS data on accident occurrences, for example there were 298 accidents per day in
2012 (BPS, 2013). Therefore, the registered cars should be less than cumulative new cars sales. The possible reasons of this inaccurate data are, first, the car data classifications. The BPS data, taken from Indonesian National Police, is different from that of Gaikindo classification. In BPS, data of cars are taken from total registered cars at certain years, in which the classifications consist of passenger vehicles, buses, trucks and two-wheeler motors, however the data is not differentiating the old cars from newly released cars from the manufactures. While the Gaikindo data is classified into sedan,
MPV, SUV, buses, pick-up and trucks, and double cabin. It may occur that the pick-up and double cabin is included in BPS’s passenger vehicle category along with sedan, MPV and SUV, although the numbers do not fit either. Second, there is a possibility of multiple registration of a car due to reregistration of stolen cars as a new entity whilst the legitimate data of the car is still recorded
(Kompas, 2012). Apart from that, the Gaikindo new cars sales data is classified in more detail, which it will be useful for further analysis particularly in analyzing the fuel consumption, CO
2
emission and luxury car tax reduction.
Meanwhile, the new vehicle sales growth rates were estimated using historical annual new vehicles sales trends (Gaikindo, 2013). The annual new car sales were projected based on historical growth of
Page 17

new car sales since 2000 to 2012. As it can be seen in Figure 11, by means of exponential regression, it is known that the growth is 10,15% per year. Given that the value of determination coefficient is about 0.81, the forecast of new sales vehicle starting from 2013 is assumed to be growing, rounded down to avoid an over optimistic forecast, at 10% per year.
900 000
800 000
700 000
600 000
500 000
400 000
300 000
200 000
100 000
-
1998 y = 1E-83e 0,1015x
R² = 0,8126
2000 2002 car sales
2004 2006 2008 2010
Экспоненциальная (car sales)
2012 2014
Figure 11. Passenger vehicle sales growth
Figure 12 illustrates the historical new vehicles sales since 1976 in Indonesia. It also shows the estimates of future new vehicles sales that grow at rate of 10% per year. It is depicted that the annual sales estimations exponentially increase in which the model year vehicles of 2032 can reach up to
5.885.854 units sold.
7 000
6 000
5 000
4 000
3 000
2 000
1 000
-
Proj. Vehicle sales Hist. Vehicle sales
Figure 12. Passenger vehicle sales projection
This projection was derived by extrapolating the new passenger vehicles sales trend-line developed as in Figure 12. It is implied that the projection of sales growth has consider the same inherent parameters that affect annual vehicle sales growth between 2000 and 2012. One of the parameter is the income per capita levels (DNPI, 2010).
The factors driving the growth of passenger car population in the world are namely economic development and population growth (DNPI, 2010; Dagnachew 2013). Various studies on relation
Page 18

between per Capita income, one of prominent indicators of economic development measure, and vehicle ownership in both OECD and non OECD countries (Dargay, et al., 2007; Ali & Dadush, 2012;
Sivak & Tsimhoni, 2008; Newman, 2000; Dargay & Gately, 1998; Currie & Delbosc, 2009;
Dagnachew, 2013) has suggested that the relationship between the growth of vehicle ownership and per capita income non linearly depends on level of GDP per capita of each countries. These studies show that among three GDP per capita group level, less than USD 3,000 (low level), between USD
3000 and USD 10.000 (middle level) and higher than USD 10.000 (high level) groups, the growth rate of vehicle ownership in the countries with low GDP per capita group is the lowest one. The second row is the high GDP per capita group and the highest growth rate is the countries with GDP per capita between USD 3000 and USD 10.000. These studies (Ali & Dadush, 2012 and Dargeu, et al., 2007) show that the relationship between the growth of vehicle ownership and per capita income is highly non-linear where vehicle ownership grows relatively slowly at the lowest levels of per capita income
(GDP per capita less than 3,000USD), then twice as fast as Per Capita income growth at middle income levels and as fast as per capita income growth at higher income levels (GDP per capita between 10,000USD and 20,000USD), before reaching saturation at the highest levels of income.
Additionally, Dargeu, et al. (2007) studies to project vehicle ownership of countries on the basis of assumptions concerning future trends in income, population and urbanization. The result shows relatively slow growth in vehicle ownership (0.6% annually) for most OECD countries because many of these countries are approaching saturation. Meanwhile, Non-OECD countries show a faster rate of growth in vehicle ownership (3.5% annually). According to the study (Dargay, et al., 2007), the most rapid growth is in the non-OECD economies with high rates of income growth, and Per Capita income at middle levels (USD 3,000 to USD 10,000) at which the income elasticity of vehicle ownership is the highest.
Figure 13. Relation among population, motorization and economic development in Jakarta
(Dagnachew, 2013)
In the context of Indonesia’s case, according to the study (Dagnachew, 2013) the motor vehicle population in Jakarta are collaterally increasing along with income per capita (GDP per capita) as illustrated in Figure 13. This study emphasizes the relation between new vehicle sales growth and income per capita growth.
Based on GDP per capita level, Indonesia is classified as lower middle income group with GDP of
USD 3,556.79 in 2012 and has GDP per capita growth 4.91 % in 2012 (World Bank, 2013). In regard to Ali & Dadush (2012) studies that the countries classified as middle income per capita level has growth rate of vehicle ownership as twice as fast as Per Capita income growth . Given that Indonesia’s income per capita growth is 4.91 % in 2012 (World Bank, 21012), therefore its vehicle ownership growth can be estimated at 9.82% growth rate. This growth rate is congruent with the estimation of future new vehicles sales that grow at rate of 10% per year as illustrated in Figure 11. Therefore this
Page 19

model assume the projection of new car sales based on growth rate of new car sales during 2000 –
2012 as presented in the proposal at about 10% per year.
As results, the projection of passenger vehicle population is depicted on Figure 14 that consists of survived cars from previous years and new sales cars of each year. By using Indonesia population projection based on United Nation estimation as numerator, the prediction of motorization trend (i.e. passenger vehicles numbers in every 1000 inhabitants) is depicted on Figure 14 as well. It projects the rapid growth of passenger vehicles population and motorization in Indonesia. It is depicted that the growth rate of motorization is slightly slower than that of vehicle population, since the UN estimate
Indone sia’s inhabitants will increase to grow at slower rate over time.
60,00
50,00
40,00
54,89
250,00
200,00
195,46
150,00
30,00
100,00
20,00
50,00
10,00 7,73
31,29
passenger vehicles population cars/1000 inhabitant
Figure 14. Projection of passenger vehicles population
In this model, the motorization rate soars from 31.2 to 195.6 passenger vehicles per 1000 people in which it far exceeds the assumption of BPPT which predicts there are 22 passenger vehicles per
1000 people in 2030. The BPPT forecast that the motorization rate in their model was built upon the assumption that the national motorization rate in 2030 will be at the same rate as Jakarta motorization rate in 2010. However, it is likely that this assumption has weaker literature base than the VFM model’s assumptions in this thesis. Meanwhile, another study by Indonesia National Council on
Climate Change estimates the penetration of personal vehicles will be 312 vehicles per 1000 inhabitants (DNPI, 2009). It is much higher than that of VFM model’s motorization rate. This study, however does not define what personal vehicles are. It is possible that the personal vehicle consists of passenger vehicles as well as two wheeler vehicles. Nevertheless, this model assumes the projection of new car sales based on growth rate of new car sales during 2000 – 2012 as presented in the proposal at about 10% per year. Because this represent the positive economic growth of
Indonesia during that period and it is assumed that economic growth of Indonesia has similar value as well.
Page 20

In summary, the vehicle population in this model was developed by considering scrappage factors, survival rate and new vehicle sales growth above mentioned. The detailed vehicle population can be seen in Appendix.
Several sales mix scenarios were examined, each of which included up to 5 different powertrain technologies: diesel, gasoline, gasoline hybrid, plug in electric and compressed natural gas (CNG).
These technologies were chosen because they are either currently sold in large numbers, in the case of gasoline, or because high sales trend of diesel car in many developed countries and emerging technology cars such as hybrid, plug-in electric cars and gas fueled cars, and additionally because
Indonesian Government imposes a Government regulation no 41/2013 to reduce the luxury tax of some type of cars that have low fuel consumption. The car’s types eligible for luxury tax reduction are shown in Table 4.
Table 4. Luxury tax reduction eligibility
No Category Engine capacity Fuel type Fuel consumption
1 MPV/SUV 0 - 1200 cc
2 MPV/SUV
3 MPV/SUV/Sedan
/station wagon
0 - 1500 cc all gasoline diesel advance diesel/petrol engine, dual petrol gas engine (converter kit CNG/LGV), biofuel engine, hybrid engine, CNG/LGV dedicated engine
Less than 5 l/100km eq.gasoline
The three sales mix scenarios that will be discussed are entitled No Change, Low cost green car
(LCGC) dominant and Emerging Alternative Technologies. They are represented quantitatively in
Table 5 and described qualitatively below.
Table 5. Hypothetical 2032 vehicle sales mix scenarios
Powertrain gasoline
LCGC gasoline
Diesel
LCGC diesel gasoline hybrid
Plug-in electric
2013
Today (%)
97
0
3
0
0
0
2032
No change (%) LCGC dominant
(%)
97 15
0
3
0
0
0
60
5
20
0
0
Emerging Alternative
Technologies (%)
15
52
5
10
5
3
CNG 0 0 0 10
The share of new vehicle sales in 2032 presented in Table 3 is the market potential of a certain type of powertrain at year 2032 which is denoted as m j
at equation Eq. (4). Those sales mix predictions on
Table 3 are based on several assumptions as follows.
2.1.2.1
No Change
The No Change scenario assumes that gasoline and diesel passenger vehicles continue to be sold in the future at 97 percent and 3 percent respectively, the same relative proportion as they were in 2012
(Gaikindo, 2013). According to Gaikindo, gasoline vehicles are dominating the vehicle market up to 76 percent and the remaining is diesel powertrain, in which the majority of diesel vehicles are truck and buses. Meanwhile the diesel passenger car is occupy a small fraction of diesel vehicle sales, in which
Page 21

most of diesel passenger car sold in Indonesia are MPV or SUV cars with engine capacity above
2500 cc (Gaikindo, 2012). It is depicted in Table 6 that in 2012 the market share SUV and MPV which have engine capacity higher than 2500 cars is assumed about 1.43 %. Nevertheless for this scenario, the diesel cars are assumed 3 percent, because there are some diesel cars that have engine capacity between 2000 and 2500 cc. This scenario is a business as usual scenario which is a market implicitly dominated by gasoline car as constantly similar market share as 2012 yet has better efficiency due to its learning curve.
Table 6. Market share of new car sales at 2012 based on car type and engine capacity
Type
Sedan
MPV
Engine capacity
CC < 1,5 lt
1.5 lt < CC < 3.0 lt(P) / 2.5lt (D)
CC > 3.0 lt (P) / 2.5 (D)
CC < 1,5 lt
1.5 lt < CC < 2.5lt
2.5 lt < CC < 3.0 lt
CC > 3.0 lt (P) / 2.5 (D)
SUV
CC < 1,5 lt
1.5 lt < CC < 3.0 lt(P) / 2.5lt (D)
CC > 3.0 lt (P) / 2.5 (D)
Note: * P is gasoline vehicle and D is diesel vehicle
Market share 2012
2,43%
1,89%
0,06%
74,68%
19,50%
0,40%
0,08%
0,00%
0,72%
0,22%
As a simplification, The No Change scenario assumes that the sales fractions for hybrid gasoline, plug in electric car and CNG vehicles was zero percent, since these vehicles captured less than 0.06 percent of new sales in total of the three markets in 2012 (Gaikindo, 2013).
2.1.2.2
LCGC Dominant
The LCGC Dominant scenario assumes that LCGC vehicles both gasoline and diesel start to capture a larger and larger share of new sales and that by 2035 they account for 80 percent of new sales in each of the seven markets. While the alternative technologies likewise hybrid gasoline, plug in electric car and CNG vehicles was assumed to account for zero percent. Under this scenario, the market share of diesel vehicles slightly increase its 2012 share and the growing LCGC share causes a decline in the share of gasoline vehicles as depicted in Table 4.
The factors that boost both LCGC gasoline and diesel vehicles in the market in this scenario are the infrastructure for both vehicle types is quite similar with the non-LCGC gasoline and diesel vehicles and they have relatively high acceptance from consumers. To be specific, the LCGC Gasoline has several positive factors likewise lower price & less fuel consumption per km compared to non LCGC gasoline. This car is likely being a favorite on the market because it meets the need of having a car as a transportation tool and a prestige item at relatively low price for people with average purchasing power at GDP/capita 3557 USD. There is a trend of consumer behavior in Indonesia to buy similar items particularly cars because the resale price of popular cars will not steeply decrease and their spare parts and maintenance services are easier to obtain.
Meanwhile, the LCGC diesel also has advantage factors for example the diesel machine delivers the torque surge of a much larger than gasoline engine, the maintenances are relatively easier, and the emerging trend in developed countries (EU) will increase awareness on diesel vehicle. In spite of those advantages, the LGCD diesel is assumed to grow less significant than LCGC gasoline since it has drawbacks in term of market penetration in Indonesia because the diesel car is less familiar and has bad image due to harsh pollution because the low quality of available diesel fuel which only complies euro 2 standard. The diesel technology until relatively recently has been significantly dirtier
Page 22

in term of most criteria pollutant particularly particulates and NOX emission, noisier, and more expensive. This image is pinned in most of people because mostly the diesel vehicles are truck and buses that emits densely black fume polluting the air. For the passengers, the diesel cars is less comfortable due to its noise. Moreover the availability of diesel fuel is often scarce due to diesel sale limitation as imposed by Mineral sources and energy ministry (ESDM, 2013). In addition, diesel car purchases usually get highly taxed because mostly the diesel car have high engine capacity, more than 2000 cc, that is categorized in 75% luxury tax (PP no 12/2006).
2.1.2.3
Emerging alternative technologies
The Emerging alternative technologies scenario assumes that the sales share of gasoline hybrid, plug-in electricity and CNG vehicles grows significantly between 2013 and 2032, as detailed in Table
5. The assumptions that underlie this scenario are as follows:
Fuel cell vehicles were not considered in this model because it is not expected to account for a significant fraction of new vehicle sales (e.g. equal to or greater than 3 percent) in Indonesia by 2035.
This judgment is based on the fact that there are currently no announced plans to commercialize this technology even in developed countries (Bodek, 2008) and it is not even expected them to enter in passenger vehicle market in Indonesia by 2032, because cost premiums are projected to be high
(Park, 2011), and infrastructure challenges pose additional hurdles for adoption (Park, 2011).
The types of car are assumed consist of lower emission yet small engine capacity gasoline and diesel car, gas fueled car and electric car. The market penetration of electric car and gas fueled car are assumed to be fully endorsed through the LCGC policy as well as to be equipped with their supporting infrastructures.
It is assumed that the trend of LCGC gasoline vehicles comprising a large fraction of total vehicle sales will continue. Meanwhile diesel LCGC diesel will be approximately a half of its share in LCGC dominant scenario, because of the factors above mentioned in LCGC dominant scenario.
In 2032 there will be approximately 5 percent of Gasoline hybrids sold. Although its fuel consumption is significantly lower than both LCGC and non LCGC gasoline powertrains, its market share is highly limited by the high unit price of car because it remains taxed up to 75 % as luxury good.
Plug-in electric vehicles are assumed to account for 3 percent of all new vehicles sold in 2032. While seemingly hard to be achieved due to its steep price and limited supporting infrastructures, this target could be achieved if gasoline hybrids were able to reach 0.5 percent market share (similar to current
US hybrid sales share) by 2020 before it is rapidly accepted in the market.
The growth in the sales share of for CNG vehicles by 2032 will be modest (e.g. 10 percent). CNG vehicle is one of promising new technology vehicles because its price is relatively similar with gasoline and diesel vehicles. Moreover, however a significantly greater marketshare is limited by several factors, including the inconvenience associated with refueling, continued demand growth for natural gas by other sectors, and infrastructure limitations.
The prediction of the amount of each type of car population in the future will be projected by estimating the number of car is adopted in the market. The prediction of future market for vehicles particularly for passenger car uses market diffusion model for a new technology. The diffusion of new technology model is the most widely used model to estimate the purchase of new products because it can describe the S-shape penetration curve of new products adopted by consumers (Everett, 1983).
This logistic curve represents the important parameters of new technology adoption such as the innovation factor and imitation factor as well (Park et al, 2011).
Page 23

Figure 15 shows adoption stages of an innovative products entering the market that in early units of time, the adoption is slowly growing where only the innovators who willing to take risk of adopting a new items in the market. Later on, the adoption rate is going faster as the early adopters accept the new technologies based on, for example, good experiences of innovators. At the point of half potential market share, it is predicted that the adopter numbers is peaked, and also the number of innovators
(i.e. innovators, early and early majority adopters) and imitators (i.e. late majority and laggards) is equal. At the later unit of time, the imitators or laggards rate is slower until the market is going saturated.
Figure 15. Adoption stages (Rogers, 1983)
The electric car, hybrid and gas fueled car as a measure to reduce GHGs emissions in the transportation sector are especially considered as the new technology entering Indonesia vehicle market. Therefore the diffusion model can be used to forecast the market penetration of LCGC gasoline, LCGC diesel, hybrid gasoline, electric and gas fueled car in Indonesia case. The model predict that the sales of a certain type of car (S new
(t)) is the rate of purchase at time (t) (f(t)) multiplied by its optimal market potential (m). (Vijay et al, 1995 and Brandewinder, 2008) Meanwhile the market potential (m(t)) itself will increase year by year. The market potential (m(t)) is projected by calculating the new sales growth of car which is derived from historical data of light vehicles from 1990 to 2012.
S totali,t
(t) = ∑ S newj,i
(t) j
Eq. (4)
S newj,i
(t) = m j
(t) ∗ Purchase j,i
(t) Eq. (5)
S newj,i
(t) = m j
(t) ∗
1
1 + e
−  j
(t j,i
−T
0j
)
Eq. (6)
While cumulative sales of all types of car j purchased at year i ( S totali,t
) is equal to the new car sales at year i ( S i
). Later on this total sales at year i ( S totali,t
) will begin to be scrappaged as well in the later year. Therefore, started in 2013, the survived population of the car purchased at year 2013 and later
( P i,t
) is calculated by equation 7.
𝑃 𝑖,𝑡
= S totali,t
∗ 𝑆𝑟 𝑖,𝑡
( 𝑡 ) Eq. (7)
Where
Page 24

 S newj,i
(t) is the sales of a certain type of car j purchased at year i

Purchase j,i
(t) is the rate of purchase of a certain type of car j purchased at year i
 m j,i
is its market potential of a certain type of car j purchased at year i
  j
is market penetration growth rate of a certain type of car j
 t j,i
is time where a certain type of car j purchased at year i

T
0j
is time where the rate of innovator and imitator of a certain type of car j is equal
 P i,t
is is the population of the car purchased at year i which survive at year t
Furthermore the
 j
and T
0j
are calculated by using formula Eq (8) and (9)(Brandewinder, 2008)
 j
= ln ( 𝑓
1
1,𝑗
− 1) − ln (
1 𝑓
2,𝑗
− 1) 𝑡
2,𝑗
− 𝑡
1,𝑗
T
0j
= ln (
1 𝑓
1,𝑗
 j
− 1)
+ 𝑡
1,𝑗
Eq (8)
Eq (9)
Where 𝑓
1,𝑗
and 𝑓
2,𝑗
in this model are the assumptions of the fraction of market share of certain type of powertrain j at early stage 𝑡
1,𝑗
and at nearly saturated 𝑡
2,𝑗
respectively.
To determine the value of

and T
0
in the formula of every vehicles categories in each sales mix scenario, firstly the f
1,j
, f
2,j
, and should be defined. Where the f
1,j
and f
2,j
in this model are the assumptions of the fraction of market share of certain type of powertrain j at early stage t
1,j
and at nearly saturated t
2,j
respectively.
The ways to settle the value of f
1,j
and f
2,j
as well as the time prediction of t
1,j
and t
2,j
are based on some criteria of innovation diffusion. These criteria include relative advantage, compatibility, complexity or simplicity, triability and observatibility (Rogers, 1983). In this model those criteria are translated into (Table 7):
Table 7. Criteria of innovation diffusion
Criteria
Relative advantage
Measure
The amount of tax luxury cut, fuel economy,
Compatibility
Complexity or simplicity The user-friendliness of car in term of operating and maintenance
Triability
Observatibility
The availability and readiness of its infrastructure the experiences of the consumer itself as well as others
The degree of the information of certain car type which regards to advantage, compatibility and userfriendliness
Those criteria will be implied on how fast is certain car types to start rapidly grow at early stage and when its market will saturated near its potential market. The details about defining the criteria of innovation diffusion and market penetration criteria of each market mix scenarios are elaborated in
Table 8. The results of new vehicle sales compositions due to various criteria of innovation diffusion and market penetration of three marketing mix scenarios are depicted in details in Appendix.
Started in 2013, the future market share of each powertrains are changed overtime depends on each individual market penetration of powertrains. It changes from initial market mix share, 97 percent for gasoline vehicles and 3 percent for diesel one, thus particularly gasoline vehicles’s share may
Page 25

decrease accordingly from 97 % to its market potential in 2032 in respond to the sales growth of other powertrain vehicles. Meanwhile the diesel vehicles’ share may slightly increase from its initial market share, 3 percent, to its market share peak in 2032. The others will grow their market shares according to their market penetration criteria. The new vehicles market shares of each scenarios are depicted in
Figure 16 – 18.
Page 26

Table 8. The criteria of innovation diffusion and market penetration criteria of each market mix scenarios

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
CNG
Diesel
7,00
6,00
5,00
4,00
3,00
2,00
1,00
-
Plug-in electric
LCGC gasoline
LCGC gasoline
CNG
LCGC diesel
Gasoline gasoline hybrid
Gasoline
LCGC diesel gasoline hybrid
Diesel
Plug-in electric

4,00
3,00
2,00
1,00
-
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039
CNG
Diesel
Plug-in electric
LCGC gasoline gasoline hybrid
Gasoline
LCGC diesel
7,00
6,00
5,00
Gasoline gasoline hybrid
LCGC gasoline
Plug-in electric
Diesel
CNG
LCGC diesel
Page 29

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
3,00
2,00
1,00
-
6,00
CNG
Diesel
5,00
4,00
Plug-in Gasoline hybrid LCGC Diesel
LCGC Gasoline Gasoline
Gasoline
LCGC diesel
LCGC gasoline gasoline hybrid
Diesel
Plug-in electric
The vehicle kilometer traveled (VKT) behavior of all powertrain technologies will have significant effect on future fleet fuel use and GHG emissions.
The total vehicle kilometers traveled in year t is a result of the number of vehicles on the road and kilometers traveled per vehicle. The total vehicle kilometer travelled value reflects the fuel consumption regarding the fuel consumption of every vehicle.
The comprehensive data related to the annual passenger vehicle kilometers traveled (VKT) in
Indonesia is not available yet at the moment (Hirota, 2010). In this model, it is assumed that the baseline of VKT per vehicle in Indonesia is similar to that of Thailand that is 14.853 km/year. As mentioned by Bandivadekar et. al (2008) and Bodek (2010), the VKT per each car will decrease as it
Page 30

is aged. Thus, the average per-vehicle kilometers of travel (VKT) of a vehicle aged t years is calculated as (Bandivdekar et. al, 2008):
𝑉𝐾𝑇 𝑖,𝑡
= 𝑉𝐾𝑇 𝑛𝑒𝑤,𝑖 𝑒 −𝑟𝑡 𝑖
VKT total,i,t
= VKT i,t
∗ P i,t
Where:

VKT newi
is the distance travelled by new car purchased at year i
 VKT i,t
is distance travelled per vehicle by a new car j purchased at year i i
 r is annual rate of distance travelled decrease of car purchased at year i
 t i,t
is age of a car purchased at year i
 P i,t
is the population of the car purchased at year i which survive at year t
Eq. (10)
Eq (11)
Similar with annual passenger vehicle kilometers traveled (VKT) in Indonesia, the research defining distance travelled decay factor has not been available yet for Indonesia case. As simplification, a value of r in this model was adapted from parameter r fitted for cars in case of US vehicle fleet model
(Bandivadekar, 2008). After the first year, the average per-vehicle kilometer travel of light duty vehicles decreases at an annual rate (denoted r) of 4%.
The detailed results on distance traveled per-vehicle kilometers of travel (VKT) of each car that has model year i and age t can be seen in Appendix. The kilometers travelled by individual car of model year i at year t then multiplied by the car population at respective year results total vehicle kilometer traveled.
700,00
600,00
500,00
400,00
300,00
200,00
100,00
-
Gasoline
LCGC gasoline
Diesel
LCGC diesel gasoline hybrid
Plug-in electric
CNG
Figure 20. Total VKT per year of each powertrain in No change scenario
Page 31

400,00
350,00
300,00
250,00
200,00
150,00
100,00
50,00
-
Gasoline
LCGC gasoline
Diesel
LCGC diesel gasoline hybrid
Plug-in electric
CNG
Figure 21. Total VKT per year of each powertrain in LCGC dominant scenario
300,00
250,00
200,00
150,00
100,00
Gasoline
LCGC gasoline
Diesel
LCGC diesel gasoline hybrid
Plug-in electric
CNG
50,00
-
Figure 22. Total VKT per year of each powertrain in Emerging alternative technologies scenario
Page 32

Several factors were used to model the fuel consumption of future vehicles. Firstly, it was necessary to estimate the lowest level of fuel consumption that could reasonable be achieved by each powertrain. Some studies (Kasseris and Heywood 2007, Kromer and Heywood 2008) on future estimation of vehicle’s performance and fuel economy estimates that future advanced technology vehicles offer a number of feasible paths to greatly reduce petroleum consumption: the hybrid offers a
43% reduction over the 2035 NA-SI baseline, and a 63% reduction over the 2005 vehicle. The plug-in hybrid offers still greater potential for petroleum reduction, although the magnitude of this reduction depends upon the electric range of the vehicle, as well as the control strategy and degree of hybridization. The PHEV offers a 71% reduction in petroleum consumption over the NA-SI engine, and an 81% reduction over the 2005 vehicle (Bandivadekar, 2008). However, the calculations on their fuel consumption are based on vehicles’ parameters of US conditions. Meanwhile, another study by
CONCAWE, et, al (2007) simulated the projections of future vehicles’ performances for Europe, and the estimated 2035 fuel consumption for Europe as well.
However, the fuel consumption projection done by those studies (Kasseris and Heywood 2007,
Kromer and Heywood 2008, and CONCAWE, et, al,2007) has different values on fuel consumption estimation for same vehicle’s technology as well as the same model year as figured in Table 9 when they are extrapolated or interpolated. This difference is likely caused by several factors. Those are for example, the vehicle size, curb weight, aerodynamic drag coefficient, rolling resistance, gradient resistance of inclined road, driving cycle and driving style of drivers. Those factors may vary in Europe and US. The cars size for example, the US passenger vehicles are commonly larger, heavier and have higher performance than the average European vehicles (Bodek, 2008). Not to mention that US car owners tend to have driving cycle much different from European ones due to dissimilarities of traffic policies, geographic conditions and probably their lifestyles toward car usage.
Since the above studies focused on the US and European market, whose fuel consumption factors are possibly different from Indonesian market, they could not be used directly to estimate the future fuel consumption of vehicles in Indonesia. Rather, the future fuel consumption for vehicles in
Indonesia was determined by applying the relative improvement projected for the corresponding powertrain in the US and Europe to the fuel consumption of today’s gasoline, diesel, gasoline hybrid, etc. vehicles marketed in Indonesia.
The rationale of these discrepancies is that the above mentioned factors of fuel consumption in
Indonesia are different from US and Europe circumstances. For example, the vehicle fuel consumption data retrieved from many online automotive forums depicts that nowadays majority passenger cars consume about 8.9 – 12.5 l/100km depends on road and traffic condition , in which it is higher than that of both US’s and Europe’s. Therefore in this thesis, to construct the changes of fuel consumption in Indonesia case due to technological improvements in automotive sector during period of 1976-2012, the estimation were conducted by deducing the fuel consumption trend based on historical empirical fuel consumption data.
Despite limited reliable studies on fuel consumption of passenger vehicles in Indonesia case as one of the reasons to estimate the fuel consumption trend based on historical real fuel consumption data, this estimation method has some advantages. First, this method can evade the issue of determining the suitable driving cycle that fits for Indonesia case. Among driving cycle used in determining the fuel consumption, likewise New European Driving Cycle (NEDC) which is a common model used for estimating fuel consumption in Europeean countries (Bodek, 2008) and several driving cycle models used by The United States Environmental Protection Agency (EPA) namely Federal Test Procedure
(FTP-75), highway fuel economy test (HWFET) aggressive driving cycle (US06), a cold-start cycle
(cold FTP), and an accessories loading cycle (SC03). Those driving cycle models are formally used in estimating the fuel consumption and GHG emissions and they are developed throughout comprehensive research in their countries (Kromer and Heywood, 2008), for example CONCAWE
Page 33

et.al (2007) use NEDC driving cycle model to project fuel consumption of passenger vehicle fleet in
European counties case. Nevertheless these driving cycle models possibly cannot represent the driving cycle in Indonesia. But on the other hand, the studies about Indonesia driving cycle model as well as vehicles’s fuel consumption are limited.
Secondly, the results of common driving cycle used for estimating fuel consumption, NEDC, which is currently employed for vehicle certification purposes, is typically worse than real world’s value. The recent study (Smith, 2010) has discovered that the fuel consumption and CO2 emissions measure done by NECD, although, will underestimate those obtained under real world. Smith (2010) calculated that there is a difference of slightly more than 15% in the real world primary energy consumption of the Irish PC fleet compared to those of NEDC results. Additionally, the CONCAW E et al.’s (2007), who conduct recent well-to-wheels study on the estimated 2010-2035 fuel consumption for Europe, employs country specific New European Driving Cycle (NEDC) test to calculate vehicle fuel consumption and they also adjust the NEDC’s results upward by 10 percent to reflect the fact that
“real world” fuel consumption in European countries. Moreover, the range of NEDC adjustment needed for Indonesia case, has not being studied yet. Thus, in this thesis, estimating the fuel consumption based on fuel consumption reports from vehicles owners is the appropriate method so far.
The fuel consumption of today’s Indonesian vehicles was determined using the data collected in the online automotive forums, blogs and websites. These fuel consumption data are based on real fuel consumption calculated by vehicle’s owners, range from 1974 to 2012 model year cars. The gathered data of each certain model year cars consist of urban and extra urban fuel consumption in km/liter unit. After converted its unit into liter/100 km, the next steps is to make weighted average in which it is estimated that commonly a car will be driven in ratio of five times of urban driving to two times extraurban driving (urban driving : extra-urban driving ratio is 5:2). Then the weighted average data of fuel consumption were plotted in Figure 23 while the detailed data are presented in Appendix. These data is later on named as real fuel consumption of passenger vehicle (PV) in Indonesia. Unlike with survival car population, reliable estimates of fuel consumption degradation are not available and therefore the fuel consumption of older model vehicles in the fleet model was not degraded over time.
(Bodek, 2008). For simplification purposes, it is also assumed that the fuel consumption of vehicles purchased at year i in 2013 and above remains constant over the life of the respective vehicles.
25
20 y = -0,175x + 361,52
R² = 0,2957
15
10
5
0
1970 1980 1990 2000
Fuel consumption of car producted at year i
2010
Trendline
2020
Figure 23. Weighted average historical fuel consumption in Indonesia
The linear regression of weighted average fuel consumption data was used to estimate the historical fuel consumption of gasoline, since only fuel consumption data of gasoline vehicles gathered were
Page 34

available because data availability of diesel consumption is very limited as well as the gasoline vehicles were dominantly operated in Indonesia nearly 97 %. This gasoline fuel consumption data then were linearly extrapolated from 2012’s value to the 2032’s value to gives a simplified and approximate estimate of the fuel consumption of gasoline powertrain technology at any point during this period. Meanwhile the historical data for old type diesel vehicles were estimated by multiplying the ratio of diesel to gasoline energy content per liter. Additionally, the projection of future fuel consumption of diesel powertrain were conducted by linearly extrapolating from the results of the MIT simulations for the US (Bandivadekar, 2008) and adjusting these curves slightly upward or downward until the NA gasoline and diesel values approximately matched the ratio of MIT simulation’s result to
Indonesia PV’s real fuel consumption. Similar to projection method of diesel powertrain, the prediction of gasoline hybrid, plug-in and CNG powertrain employ the relative adjustments based on ratio of MIT simulation’s result (Bandivadekar, 2008)and CONCAWE’s results (Bodek, 2008) to Indonesia PV’s real fuel consumption . The fuel consumption projection of MIT simulation’s result (Bandivadekar,
2008) and CONCAWE’s results (Bodek, 2008) is presented in Table 9.
Table 9. Fuel consumption (Bandivadekar, 2008; Bodek, 2008)
As it can be seen in Figure 23, the average gasoline consumption of passenger vehicle of 2012 model year vehicles in Indonesia case is estimated at 9.42 liters/100 km while for the same model year in
US and Europe, the fuel consumptions are 7.88 liters/100 km and 6.37 liters/100 km respectively.
Apart from different driving cycle applied in each country, the rationales of high fuel consumption in
Indonesia are likely heating/cooling instruments and different popular types of car size. The energy requirement for cooling instrument in Indonesia is likely higher than that for in US and Europe due to climate condition, where it is observed that cooling air conditioner usually turned on year-around, not only in a seasonal time like sub tropical countries. In term of popular cars size, European car owners tend to have a smaller car size compared to US counterpart (Bodek, 2008). Therefore the fuel consumption in Europe is lower than that of US. Meanwhile Indonesia’s fuel consumption is higher than that of Europe and US, because the popular car types in Indonesia, van/MPV of 7 seater cars,
Page 35

are larger than popular cars size in Europe. Meanwhile, Indonesia’s fuel consumption for passenger vehicle remains higher than The US’s because US-EPA do not classify some 7 seater car types particularly van/MPV and SUV as passenger vehicle group but as light truck group. To project the fuel economy improvement during periof of 2013-2032, the adjustment ratio of gasoline drive trains among
MIT model’s result, CONCAWE results and Indonesia real fuel estimations, which represents all factors above mentioned, are presented in Table 10.
Table 10. the adjustment ratio of gasoline drive trains among
MIT model’s result, CONCAWE results and Indonesia real fuel estimations
Fuel Consumption of gasoline vehicles
(l/100 km gasoline eq)
MIT model - US real consumption Indonesia ratio MIT : real consumption Indonesia
Fuel Consumption of gasoline vehicles
(l/100 km gasoline eq)
CONCAWE model - Europe real consumption Indonesia (l/100 km) ratio CONCAWE : real consumption Indonesia
2005
8,8 5,5 7,876
9,42
1,196039
2010 2035 2012
6,57
2030 2012
4,11 6,3732
9,42
1,478064
Given that vehicle certification on fuel consumption for LCGC car types is conducted by employing
NEDC driving cycle model (Kompas, 2013), it is strongly assumed that the vehicle’s real fuel consumption is higher than that of stated in the vehicle certificate. So the assumption of LCGC gasoline fuel consumption in 2012 is approximately 7.39 liters/100 km as multiplication results of
LCGC policy treshhold, 5 liters/100 km, and ratio CONCAWE : real consumption Indonesia (Table
10). For other drive trains, the similar methods are employed to project their fuel consumption. The fuel consumption projection of Indonesia case is presented in Table 11.
Table 11. The fuel consumption projection of Indonesia case
Year Powertrain
Gasoline
Diesel
Gasoline
LCGC Gasoline
Diesel
LCGC Diesel
Gasoline hybrid
Plug-in electric
CNG
Gasoline
LCGC Gasoline
Diesel
LCGC Diesel
Gasoline hybrid
Plug-in electric
CNG
Fuel Consumption value Units
15,72 liters gasoline / 100 km
11,30 liters diesel / 100 km
9,25 liters gasoline / 100 km
7,27 liters gasoline / 100 km
7,01 liters diesel / 100 km
6,51 liters diesel / 100 km
5,82 liters gasoline / 100 km
4,53 liters gasoline / 100 km
31,43 kWh / 100 km
5,76 kg / 100 km
5,92 liters gasoline / 100 km
5,04 liters gasoline / 100 km
4,81 liters diesel / 100 km
4,28 liters diesel / 100 km
3,46 liters gasoline / 100 km
2,17 liters gasoline / 100 km
10,29 kWh / 100 km
4,02 kg / 100 km
Fuel Consumption
( l/100 km gasoline equivalent)
Relative to Todays
Gasoline
Relative to future
Gasoline
15,72
12,60
9,25
7,27
7,82
7,26
5,82
1,70
1,36
1,00
0,79
0,85
0,79
0,63
8,05 0,87
8,21
5,92
5,04
5,36
4,77
3,46
3,32
5,73
0,89
0,64
0,55
0,58
0,52
0,37
0,36
0,62
1,00
0,85
0,91
0,81
0,58
0,58
0,97
Page 36

45,00
40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
-
40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
-
Gasoline (liter gasoline) Diesel (liter diesel)
Figure 24. Fuel consumption of No change scenario
Gasoline (liter gasoline) Diesel (liter diesel)
Figure 25. Fuel consumption of LCGC dominant scenario
Page 37

40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
-
Gasoline (liter gasoline)
Electricity (kWh)
Diesel (liter diesel)
CNG (kg)
Figure 26. Fuel consumption of Emerging alternative technologies scenario
The CO
2
emission calculated in this research is the emission at 1 st order representation in which not only does it calculate the CO
2
emitted corresponding to tank to wheel scope of fuel needed during vehicle operation but also that of emission of electricity production particularly for electric fueled car
(Blok, 2008). Based on vehicle fuel consumption calculations discussed in Section 2.1.5, we may estimate the petroleum consumption and thus GHG emissions of different fuel of vehicles during vehicle operation. Note that compared to today’s average car, all future vehicles are expected to realize a significant reduction on fuel consumption as predicted in Table 11. The CO
2
emission factor of gasoline, diesel and CNG are 71, 76 and 52.8 g CO
2
/MJ delivered from tank to wheel respectively.
Meanwhile the CO
2
emission factor of electricity used in plug in car is the CO
2
emission produced during electricity production. In Indonesia case the emission factor of electricity production is about
890.55 g CO
2
/kWh in 2013 and is assumed linearly decreasing to 828.52 CO
2
/kWh in 2032. The emission factor reduction in electricity production is resulted from the projection of rapid development on geothermal power plants (Ministry of Mineral and Energy (ESDM). This prediction of emission factor of electricity production use data from state owned electricity company (PLN), Ministry of
Mineral and Energy (ESDM) and National Council on Climate Change - Indonesia (DNPI).
Every car purchased is levied by 2 types of tax namely VAT and luxury tax. Started in 2013, the low cost and green car policy provide luxury tax reduction for cars that conforms the requirements in
Government regulation no 41/2013. Luxury tax reduction scheme is provided in Table 12.
Page 38

Table 12. The luxury tax based on
Government regulation no 12/2006 and Government regulation no 41/2013
No Category Engine capacity Fuel type Remarks
1 MPV
2 MPV
3 MPV/SUV/Sedan
/station wagon
4 MPV/SUV/Sedan
/station wagon
0 - 1200 cc
0 - 1500 cc all all
The Luxury Tax is reduced gasoline < 5 l/100km diesel < 5 l/100km dual petrol gas engine (converter kit l/100km
CNG/LGV), biofuel
3,57 - 5 eq.gasoline engine, hybrid engine, CNG/LGV dedicated engine dual petrol gas
CNG/LGV), biofuel engine, hybrid
< 3,57 engine (converter kit l/100km eq.gasoline engine, CNG/LGV dedicated engine
The Luxury Tax is not changed
1200 cc - 1500 cc gasoline
1500 cc - 2500 cc gasoline/diesel
2500 cc - 3000 cc gasoline
> 2500 cc diesel
5 MPV
6 MPV
7 MPV
8 sedan/station wagon
9 sedan/station wagon
10 sedan/station wagon
11 sedan/station wagon
12 SUV
> 3000 cc
1500 cc - 2500 cc diesel
1500 cc - 3000 cc gasoline
> 2500 cc gasoline diesel
14 SUV
15 SUV
16 SUV
> 3000 cc gasoline
1500 cc - 2500 cc diesel
1500 cc - 3000 cc gasoline
Remark : the city cars model is categorized as MPV.
Luxury tax (%)
Government Government regulation no 12/2006 regulation no 41/2013
10
10
100
100
10
20
40
75
75
40
40
75
75
40
40
0
0
75
50
10
20
40
75
75
40
40
75
75
40
40
2.1.7.1
The feedback of luxury tax reduction
The sales price reduction due to luxury tax cut may raise assumption that it will lead to an increase of new vehicle purchases as feedback impacts of fuel price increase. In this tax policy, the cars that eligible for luxury tax cut are the ones that meet the prerequisites as mentioned in Table 12. As of
December 2013, there are 3 car brands benefited from luxury tax cut. They are 5 seater city car types fueled by gasoline. The tax cut on car purchases may lead soaring sales. Apparently, since the rumor on this tax cut policy broadcasted until August 2013, the sales of similar car category has depleted as shown in Figure 27 below:
Page 39

25000
20000
15000
10000
5000
0 апр.13 май.13 июн.13 июл.13 авг.13 сен.13 окт.13 ноя.13
Figure 27. New vehicle sales of city car type
Started in September 2013, the sales of city car was rapidly soared due to the LCGC cars are available in the market. Regarding Gaikindo data, in November 2013 there are 22.273 unit of new city car sold, or increase by 34,30% from that of Oktober 2013. Previously, a rapid sale of city cars was also happened in September 2013 which there is 13.958 units, it increases almost four times than that of August
2013. (Kontan, 2013).
Based on this data, it seems that the LCGC cars perceived as cheaper car options tend to make people to postpone their car purchases and then to shift their choices from higher priced car to cheaper cars due to tax cut. The total sales of city cars at period January – September 2013 based on
Gaikindo data is at 76.064 units which is slightly higher than that of same period last year (Gaikindo,
2013). It seems that luxury tax cut do not give significant impacts on total sales, conversely it has a strong effect to shift the sales mix.
Nowadays majority of Indonesian consumers prefer a 7 seater MPV car type that counts approximately 84% (Gaikindo, 2012) to small cars types with less than 5 seats. Since the recent certified LCGC vehicles are city car type, 4 to 5 seater car, so the LCGC sales at year 2013 has not dominate the passenger car vehicle yet. If there are 7 seater MPVs which successfully get certified as
LCGC and therefore their selling price is cheaper, it is very likely that the LCGC car sales will started to soar.
The influence of luxury tax reduction on the sales mix has been represented on how the model determine the

and T
0
by using formula Eq (5) and (6) (Brandewinder, 2008) as it is elaborated in
Section 2.1.3.
2.1.7.2
New car prices
To calculate the amount of luxury tax reduction, the data of car prices is required. These detailed data was obtained from sales data of several brand car distributors. However in this research, the car prices is categorized by its power drive. As simplification, the price classification of cars is depicted on
Table 13. The calculation of luxury tax cut of each scenario is results of multiplication of new vehicle sales and the price of vehicles types of each scenario. This calculation uses assumption that car type composition will be based on car categories depicted on Table 5, Table 12 and Table 13. The car price particularly for LCGC gasoline and diesel is predicted increase by 10% because the cost production is not going to decrease as these vehicle types is in the mature stage of learning curve and additionally costumers still tend to increase comfort facilities that eventually increase the cost. For gasoline hybrid, plug in- electric and CNG, the sales price is lower in 2032 because these vehicle types is in the early stage of learning curve. Combining the effect of learning curve and technological
Page 40

improvement thus their cost reduction rate are vary due to the amount of their market adoption which are 5 %, 3 % and 10% respectively.
Table 13 . Car’s off the road price classification (IDR 2011)
Vehicle type
LCGC Gasoline
LCGC Diesel gasoline hybrid
2013
95.000.000
95.000.000
423.120.000
2032
104.500.000
104.500.000
401.964.000
Plug-in electric
CNG
524.720.000
172.250.000
498.484.000
146.412.500
The assumption of basis price as mentioned in the Table 13 is based on the basis price or the over the showroom price at neighbor exporter countries likewise in Thailand and India. This assumption took place because limited information about the basis price of new vehicle prices. The structure of car sales distribution from the manufactures to the end consumers in Indonesia consists at least manufacturer/importer, sole distributor, dealer, sub-dealer, and end consumer subsequently. In each selling flows from manufacturer to sole distributor, from sole distributor to dealer, and so on, there are price components of VAT tax and margin revenue of every seller. It is quite difficult to calculate the basis price of a new car because the data available is mostly the on the road price in which thoroughly consists of taxes, seller revenue and other administrative cost. Meanwhile the information about the seller revenue and the administrative cost is not easily provided.
The gasoline, diesel, gas and electricity in Indonesia are sold at fixed prices regardless the fluctuation of their real price. The discrepancies between real price and fixed price are subsidized. The majority fuel consumed oil-based fuel vehicles in Indonesia are gasoline RON88 and diesel. Both of them are subsidized by selling them at certain price defined by government and parliament and the difference between selling price and economic price of gasoline and diesel oil will be redeemed by government as part of energy subsidy budget. In fact, the fixed price for gasoline and diesel is Rp. 6,0 00 (€ 0,39
(2011)) and Rp. 5,500 ( € 0.35 (2011)) respectively.
2.1.8.1
The feedback of fuel price
The increase of fuel price due to fuel subsidy reduction might raise assumption that it will lead to a decrease of new vehicle purchases as feedback impacts of fuel price increase. Later on, it raises questions on is the assumption true in case of Indonesia. If the assumption is confirmed, how many is the sales reduction per percentage of fuel price increment? What determinants do represent the feedback influence on car sales? Is there substitute of passenger cars for transportation if people stop to buy car due to fuel price change? Assuming that public transportation can be a substitute for passenger car, How much is the elasticity of substitutability between cars and, for example, public transportation due to the fuel price changes?
Based on the historical data of annual rate car sales and the fuel price in Indonesia, it seems that there is no clear correlation between them. The fuel price reduction is not necessarily giving positive influence on car sales increase relatively from previous year, as depicted in Figure 28 that are elaborated that the car sales keep soared although the fixed fuel price were increased. Additionally, the annual sales rates are likely stable at range -40% to +40% regardless the change in fuel price, except an anomaly during severe economic crisis in 1998.
Page 41

Moreover, if the assumption of fuel price change has influence on number of car sales is kept valid, and then to determine the elasticity of substitution factor, we should take a look on Indonesian behavior toward public transportation. A study on sustainable mobility in Jakarta (Dagnachew, 2013) shows that: a. There is 58 % respondents who did not think that public transportation is better for environment b. Although they says that owning car (the purchasing cost and operational cost) is very expensive for their income rate, 83% respondents give opinion that owning personal passenger car is important because the public transportation is not good even though the government has policy to improve the public transportation infrastructure and system. Because, in the past the government projects on public transportation has not meet people expectation so far.
Due to those research results, the elasticity of substitution between passenger vehicles and public transportation is predicted at nearly zero value. In other word, the increase in fuel price that lead to increase in operational cost, is not necessarily lead to interchangeability of transportation mode and do not halt the growth rate of car sales.
Page 42

Other reason is that owning car is not only to be transportation tool but also as sign of social status and lifestyle. The increase in car price as well as fuel price, in my opinion, will not automatically halt people to buy a car, but it will make people to consider shifting their car choices to the cheaper car and to the better fuel economy one.
2.1.8.2
Fuel price
The amount of fuel subsidy depends on the Mean Oil Platt Singapore as reference price. In MOPS oil market, the fuel oils traded are the refined ones. Meanwhile, this reference price is predicted to fluctuate relatively similar with the crude oil price fluctuation. The crude oil price projection uses the data from Annual Energy Outlook 2013 released by US Energy Information Administration ’s crude oil projection. EIA project three oil price scenarios as it is depicted in Figure 29.
Figure 29. Crude oil price projections (EIA, 2013)
The crude oil price projections developed by EIA are used in this thesis regard on some reasons. EIA projection has considered the current laws and policies, anticipated new policies or regulations that possibly being implemented includes alternative oil price scenarios and impact on markets, global other energy carrier demand and global total energy demand.
Given that the data of Mean Oil Platt Singapore (MOPS) is quite difficult to be tracked as individual researchers because the data is only available to those who purchase it, it is assumed in this thesis that the Mean Oil Platt Singapore as reference price fluctuate in parallel with the changes of crude oil price projection. According to a study (Rahadi, 2008), the historical monthly average MOPS price during period 2005-2008 is higher than crude oil price by 10% due to production and refinery process cost. Therefore, in this thesis the MOPS price estimation is 10% higher than EIA crude oil projection at respective year.
2.1.8.3
Fuel Subsidy
The fuels for vehicles nowadays are subsidized and sold at fixed retail price, namely gasoline and diesel. The formula to calculate the amount of subsidy is elaborated in the next section. However in the scenario emerging alternative technologies, there are new developed technology vehicles penetrating in the passenger vehicle market in Indonesia which some drive train need alternative fuel likewise CNG and electricity. Unlike gasoline and diesel, these fuel are not subsidized (Setkab 2013;
BUMN, 2013).
Page 43

The subsidy on fuel oil is determined by formulas below. Calculation are based the adjusted price of a product at the nearest international hub, the cost of freight and insurance to the net importer, the cost of internal distribution and marketing and any value-added tax (VAT) (ICW, 2012)
𝑆 𝑓
(𝑡) = (𝑃 𝑓.𝑟𝑒𝑓
(𝑡) − (
1 + 𝑇
𝑃 𝑠
(𝑡)
𝑉𝐴𝑇
+ 𝑇
𝐹𝑇
)) ∗ 𝑄 𝑓
(𝑡)
Eq. (12)
Eq. (13)
𝑃 𝑓.𝑟𝑒𝑓
(𝑡) = ((
𝑀𝑂𝑃𝑆(𝑡) ∗ 𝐸
158.9872956
𝑟
) + 𝛼)
α denoted for the total cost of freight and insurance to the net importer and internal distribution, including the profit margin. Given that α is about 15 % of MOPS (Rp/liter) as mentioned in Blueprint of
National Energy Management 2006 – 2025 as product of President Decree 5/2006
Eq. (14)
𝑃 𝑓.𝑟𝑒𝑓
(𝑡) = ((1 + 0.15) ∗ (
𝑀𝑂𝑃𝑆 (𝑡) ∗ 𝐸
158.9872956
𝑟
))
Denotation:

𝑆 𝑓
= Subsidy

𝑃 𝑓.𝑟𝑒𝑓
= the reference price of fuel
 𝑃 𝑠
 𝑄 𝑓
= Subsidized oil price (Rp/liter)
= consumption of subsidized fuel (Kl)

MOPS = Mean Oil Platt Singapore as reference price ($US/barrel)
 𝐸 𝑟
= Currency ratio (Rp/$US)
 𝑇
𝑉𝐴𝑇
= Value-added tax (10 %)
 𝑇
𝐹𝑇
= tax of vehicle fuel (5 %)
The subsidized oil price 𝑃 𝑠
(𝑡) is assumed to be incrementally increased 30% per 5 years until the value of 𝑃 𝑠
(𝑡) before taxed is equal with 𝑃 𝑓.𝑟𝑒𝑓
Eq. (15)
𝑃 𝑠
(𝑡) = ((1 + 𝑇
𝑉𝐴𝑇
+ 𝑇
𝐹𝑇
) ∗ ((1 + 0.15) ∗ (
𝑀𝑂𝑃𝑆 (𝑡) ∗ 𝐸
158.9872956
𝑟
)))
Moreover, the subsidy saving is difference between subsidy paid in the No change scenario as business as usual scenario and subsidy paid during other scenarios. The Figure 27 and 28 show the gasoline and diesel price projection, the value of 𝑃 𝑠
(𝑡) before taxed, respectively in the three oil price scenarios.
Page 44

16 000,00
14 000,00
12 000,00
10 000,00
8 000,00
6 000,00
4 000,00
2 000,00
-
Pf high oil price
Pretax Ps high oil price
Pf reference
Pretax Ps reference
Pf low oil price
Pretax Ps low oil price
14 000,00
Figure 30. The pre taxed retail price and the reference price of gasoline.
12 000,00
10 000,00
8 000,00
6 000,00
4 000,00
2 000,00
Pf high oil price
Pretax Ps high oil price
Pf reference
Pretax Ps reference
Pf low oil price
Pretax Ps low oil price
Figure 31. The pre taxed retail price and the reference price of diesel.
Page 45

The change in the fleet-wide consumption of gasoline and diesel fuel between 2013 and 2032 was examined across each scenario. For example, Figures 32 to 33 depict fuel use under the No change,
LCGC dominant and Emerging alternative technologies scenarios. It shows, for example Figure 33, a clear increase in the fuel that is consumed by alternative technologies at the expense of fuel consumed by gasoline powertrains.
Table 14 excerpts some of information contained in Figure 32-33, namely the total gasoline, diesel, electricity and natural gas use in 2013 and 2032. An analysis of these graphs and Table 14 leads to the following conclusions:
1. All of scenario predict significant increase of fuel consumption in the next 20 years in which their fuel consumption are multiplied into up to 5 times
2. Under the No change scenario fuel consumption in 2032 is soaring up to 5 times higher than its initial period.
3. The diesel consumption multiplies more than three times in the CGCG dominant scenario, while its gasoline consumption is reduced by 20.11 percent. While the Emerging Alternative technologies scenarios results in 21.02 percent of gasoline fuel use.
4. The LCGC dominant and Emerging Alternative technologies scenarios produce similar increase of fuel consumption at about 4,52 times higher than that of their initial period. These results are slightly lower than no change scenario. In other word, the two scenarios can reduce the total fuel consumption by 9,77% and 9,70% respectively.
Table 14. Fuel consumption in 2013 and 2032
2013
Fuel type
Gasoline
Diesel
Electricity
CNG
Total
Gasoline
No Change
8,498116
Scenario
LCGC dominant
8,471801
Emerging Alt Tech
8,481954
0,219067 0,232369 0,225015
8,717183
42,452432
8,704170
33,916434
5,432719
0,000000
0,002205
8,709175
33,527881
2032
Diesel
Electricity
CNG
Total
Fuel consumption
2032 relative to 2013
1,157830
43,610262
5,00
39,349153
4,52
3,269228
0,153517
2,430909
39,381535
4,52
Relative change to
No change scenario
Gasoline
Diesel
Total
0
0
0
-20,11%
369,22%
-9,77%
-21,02%
182,36%
-9,70%
Page 46

30,00
25,00
20,00
15,00
10,00
5,00
-
50,00
45,00
40,00
35,00
CNG
Plug-in electric gasoline hybrid
LCGC diesel
Diesel
LCGC gasoline
Gasoline
Figure 31. Fuel consumption under no change scenario
45,00
40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
-
CNG
Plug-in electric gasoline hybrid
LCGC diesel
Diesel
LCGC gasoline
Gasoline
Figure 32. Fuel consumption under LCGC dominant scenario
Page 47

45,00
40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
-
CNG
Plug-in electric gasoline hybrid
LCGC diesel
Diesel
LCGC gasoline
Gasoline
Figure 33. Fuel consumption under emerging alternative technologies scenario
In the Figure 32 and 33, it can be seen that the gasoline fuel use, for gasoline and LCGC gasoline powertrains, remains dominate the total fuel consumption during 20 year. The share of fuel used in gasoline powertrain type is diminished by the growth of LCGC gasoline vehicle, yet the total fuel used for gasoline vehicles is still the highest among other drive trains. The rationale is that the survived older cars from year 1976 are still being operated in the vehicle fleet, although the new gasoline vehicle sales are continuously decreasing. Therefore the share of gasoline vehicles remains high compares to other vehicle drive trains.
Despite the huge increase of fuel consumption across the three scenarios, the specific GHG emissions of each scenario are going lower and lower during 20 years period. Figure 32 shows the predicted trend in specific GHG emissions from all scenarios. The specific GHG emissions decline at least by 33.54 % from 237.34 g CO
2
/km to 157.75 g CO
2
/km in 2032 for no change scenario.
Meanwhile the other scenarios decline even further by 38.48 % and 40.11 % for LCGC dominant and
Emerging Alternative technologies scenarios respectively, in other words, both LCGC dominant and
Emerging Alternative technologies scenarios produce similar reduction rate in specific GHG emissions, CO
2
emission in particular.
The feasibility of achieving the proposed target to reduce transportation sector emissions by 20 percent CO
2
emission reduction out of business as usual carbon emission (DNPI, 2010) was evaluated for passenger vehicle fleet development in Indonesia under each of the three new vehicle sales scenarios. This target is part of Indonesia voluntarily commitment to an ambitious roadmap for reducing carbon emissions by 26 percent affirmed in G-20 summit in Pittsburgh and additional 15 percent associated with the Copenhagen Accord in January 2010 against a business-as-usual estimate of emissions in 2020. The Indonesia government has planned to achieve the CO
2
emission abatement target by develop principal mitigation strategic levers, such as improvements to internal combustion engines and moving from gasoline-powered vehicles to hybrid and electric vehicles
(DNPI, 2010).
Page 48

260
240
220
200
180
160
140
120
100
No Change Scenario LCGC dominant Emerging Alt Tech scenario
Figure 33. Specific GHG emissions trends of three scenarios
120,00
100,00
80,00
60,00
9,06 % reduction
9,98 % reduction
20 % reduction
40,00
20,00
-
No change LCGC dominant Emerging Alt Tech Target Copenhagen commitment
Figure 34. Potential CO
2
emission reduction
However, the scenarios of LCGC dominant and Emerging Alternative technologies may be unsuccessful to achieve the target set by government. Figure 34 depicts the potential CO
2
emission of those two scenarios. However those scenarios cannot satisfy the reduction target required from
Page 49

transportation sector by approximately 20 percent, presumably by mean of these scenarios the CO2 emission can only be reduced by 9.98 % and 9.06 % from LCGC dominant and Emerging Alternative technologies scenarios.
In this research, the result of the luxury tax reduction represents the potential revenue loss of governments due to imposing the LCGC policy. The total potential loss revenue due to luxury tax reduction of each scenario is depicted in Figure 35. It can be seen that the Government potentially loses luxury tax revenue in two scenarios, LCGC dominant and emerging alternative technologies scenarios. In LCGC scenario, the government loses a potential revenue at amount of 10% out of the
LCGC vehicles prices, both gasoline and diesel. While the potential revenue loss in emerging alternative technologies scenario is rooted from luxury tax reduction of gasoline hybrid, Plug-in electric, and CNG at amount of 50%, 25%, and 25% out of each vehicle price respectively, not to mention losses from LCGC vehicles’ tax cut.
Figure 35. Total potential loss revenue due to luxury tax reduction of each scenario
The LGCG dominant and emerging alternative technologies scenarios make the government lose more than 250 x 10 12 rupiah (2011) and 700 x 10 12 rupiah (2011) respectively. This is explained by the percentage of luxury tax reduction as elaborated in Table 12. It shows that in the LCGC vehicles case, the government loss a luxury tax income at amount of 10% of new LCGC vehicle price.
Additionally, the luxury tax of other alternative technology cars likewise hybrid, plug-in and CNG powertrains is reduced by 50%, 25% and 25 % respectively. Meanwhile, the price of a new LCGC vehicle is capped at 95.000.000 rupiah (2011) and the price of hybrid and plug in powertrain are
Page 50

substantially higher than LCGC vehicles as mentioned in Table 13. Thus for example, the amount of potential revenue loss from an individual hybrid vehicles sale is approximately 22.27 times higher than that of LCGC vehicle in 2013 and is slightly lower at 19.24 times in 2032. Consequently, the sales of emerging alternative technologies vehicles give impacts on soaring potential revenue loss.
The amount subsidies of fuel consumption, gasoline and diesel fuel, for the three examined scenario are depicted in several figures in this section. First, the figure 36 shows the amount of fuel subsidy spent in No change scenario due to various subsidized gasoline fuel price change due to effects of three oil price scenarios, namely high oil price, reference price and low oil price projection scenarios.
Meanwhile the changes of subsidized gasoline fuel price change due to effects of three oil price scenarios are previously presented in Figure 31 in section 2.1.8.3.
60,00
50,00
40,00
30,00
20,00
10,00
-
16 000,00
14 000,00
12 000,00
10 000,00
8 000,00
6 000,00
4 000,00
2 000,00
-
No change High oil price
High oil price no change low oil price
Low oil Price
No change reference
Reference
Figure 36. Fuel subsidy on gasoline fuel in No change scenario due to three oil price scenarios
The fuel subsidy in the high oil price situation is soaring at the first five year and it decrease significantly in 2018 when the fixed retail gasoline is increased by 30 % higher than previous price due to energy subsidy reduction. Then, it starts climbing as the international oil price remains increasing in the next second five year period just prior the next energy subsidy reduction. The subsidy has decline serration saw pattern during 15 years period because the retail oil price (Ps) incrementally increase approaching the reference oil price (Pf).
The fuel subsidy in the reference oil price scenario has similar patter with that of in the high oil price situation above mentioned. However it decreases faster than in high oil price scenario.
Meanwhile the amount of fuel subsidy in the low oil price projection is plummeting approaching to zero in 2018. Later on, the government does not spend any subsidies on fuel consumption.
Page 51

180,00
160,00
140,00
120,00
100,00
80,00
60,00
40,00
20,00
-
16 000,00
14 000,00
12 000,00
10 000,00
8 000,00
6 000,00
4 000,00
2 000,00
-
LCGC dominant High oil price
High oil price
LCGC dominant low oil price
Low oil Price
LCGC dominant reference
Reference
Figure 37. Fuel subsidy on gasoline fuel in LCCG dominant scenario due to three oil price scenarios
180,00
160,00
140,00
120,00
100,00
80,00
60,00
40,00
20,00
-
16 000,00
14 000,00
12 000,00
10 000,00
8 000,00
6 000,00
4 000,00
2 000,00
-
EAT High oil price
High oil price
EAT low oil price
Low oil Price
EAT reference
Reference
Figure 38. Fuel subsidy on gasoline fuel in Emerging alternative technologies scenario due to three oil price scenarios
Page 52

Form the Figure 36-38, it can be excerpt that the amount of annual fuel subsidy is fluctuating, but all scenarios have decreasing patterns at different rate, because the retail fuel price is estimated to be increase at 30 % increments in every five years. It seems that three market mix scenarios has similar pattern on the amount of fuel subsidy spending. The detailed information on fuel subsidy is elaborated in Table 15
Table 15. Fuel subsidy saving
Fuel subsidy (10 12 rupiah (2011))
High oil price
2163,18
Reference price
487,30
Low oil price
74,02
Gasoline
Diesel
Electricity
CNG
Total
Gasoline
Diesel
Electricity
CNG
Total
Gasoline
Diesel
38,93
0,00
0,00
2202,11
1924,18
114,45
0,00
0,00
2038,63
1934,87
74,84
0,00
6,26
0,00
0,00
493,56
455,19
11,47
0,00
0,00
466,65
459,13
8,58
0,00 Electricity
CNG
Total
0,00
2009,71
0,00
467,71
* Fuel subsidy saving relative to the No change scenario
0,00
0,00
74,66
73,58
1,23
0,00
0,00
74,81
1,17
0,00
0,00
75,19
73,33
1,32
Fuel subsidy Saving (10 12 rupiah (2011)) *
High oil price
%
0,00
0,00
0,00
0,00
0,00
239,00 12,42%
-75,53 -65,99%
Reference price
%
0,00
0,00
0,00
0,00
0,00
32,11 7,05%
-5,21 -45,43%
Low oil price
%
0,00
0,00
0,00
0,00
0,00
0,68 0,93%
-0,15 -11,58%
0,00
0,00
163,47 8,02%
228,31 11,80%
-35,91 -47,98%
0,00
0,00
192,40 9,57%
0,00
0,00
26,90 5,77%
28,17 6,14%
-2,32 -27,06%
0,00
0,00
25,85 5,53%
0,00
0,00
0,53 0,71%
0,44 0,60%
-0,06 -4,95%
0,00
0,00
0,38 0,51%
The fuel subsidy saving at LCGC dominant and emerging alternative technologies scenarios is very small compared to the amount of fuel subsidy spent in the No Change scenario as reference scenario. The fuel subsidy saving in the high oil price projection is merely 8.02% and 9.57% for LCGC dominant and emerging alternative technologies scenarios respectively. Meanwhile in the low oil price projection, the implementation of luxury tax reduction policy has insignificant results on fuel subsidy saving in which the saving is merely 0,71% and 0,51% for LCGC dominant and emerging alternative technologies scenarios respectively.
The analysis on feasibility of achieving the proposed target to reduce transportation sector emissions as elaborated in previous sections raises question on the effectiveness of this policy. To begin with, the amount of potential loss due to tax cut as previously analyzed in section 3.3 gives insight that the amount potential revenue loss of government due to luxury tax reduction is significantly higher than the fuel subsidy saving. The Table 16 provides the information on the impact cost of this policy.
Mostly, the results of the impact of luxury tax revenue on the cost and benefit of each scenario over three oil price projections burden the Indonesia government spending. For example the situations of the emerging alternative scenario in all of oil price projection are burdening the government due to this policy at the highest cost among other scenario namely 551.85 x 10 12 , 714.93 x 10 12 and 689.47 x 10 12 at high, low and reference oil price projections respectively.
The specific GHG emission abatement cost of these impact of luxury tax revenue on the cost and benefit of each scenario over three oil price projections are at the range between 93,37 IDR(2011) /kg
CO 2 e and 2.537,31 IDR(2011) /kg CO 2 e
Page 53

Table 16 The Benefit or Cost of the implementation of PP/41/13 policy
Potential loss of luxury tax revenue (10 9 IDR 2011) Potential fuel subsidy saving (10 9 IDR 2011) Benefit (Cost) analysis (10 9 IDR 2011)
Gasoline
LCGC gasoline
NO change
-
Scenarios
LCGC dominant
Emerging
Alt Tech
- -
NO
High oil price projection change
LCGC dominant
Emerging
Alt Tech
- 598.363,20 598.302,64
NO
Low oil price projection change
LCGC dominant
Emerging
Alt Tech
- 1.610,30 1.069,20
Reference oil price projection
NO change
LCGC dominant
Emerging
Alt Tech
- 79.059,79 67.538,32
NO
High oil price projection change
LCGC dominant
Emerging
Alt Tech
- 598.363,20 598.302,64
NO
Low oil price projection change
LCGC dominant
Emerging
Alt Tech
- 1.610,30 1.069,20
NO
Reference oil price projection change
LCGC dominant
Emerging Alt
Tech
- 79.059,79 67.538,32
- 197.852,86 169.023,78 - -359.364,65 -359.330,81 - -925,58 -618,11 - -46.947,65 -37.368,73 - -557.217,51 -528.354,59 - -198.778,44 -169.641,89 - -244.800,51 -206.392,51
- - - - -3,85 -3,85 - -3,85 -3,85 - -3,85 -3,85 - -3,85 -3,85 - -3,85 -3,85 - -3,85 -3,85
Diesel
LCGC diesel gasoline hybrid
Plug-in electric
- 65.950,95 32.504,57
-
-
- 298.054,72
- 99.869,55
- -75.521,72 -75.504,22
-
-
-
-
-0,62
-0,00
- -149,49
-
-
-
-
-57,14
-8,13
-0,02
- -5.205,38 -2.317,48
-
-
- -1.669,82
- -329,84
- -141.472,67 -108.008,80
-
-
- -298.055,34
- -99.869,55
- -66.100,44 -32.561,71
-
-
- -298.062,85
- -99.869,58
- -71.156,33
-
-
-34.822,05
- -299.724,55
- -100.199,39
CNG
- - 115.863,93 - - - - - - - - - - - -115.863,93 - - -115.863,93 - - -115.863,93
Total
- 263.803,82 715.316,56 - 163.472,98 163.463,13 - 531,38 381,94 - 26.902,92 25.848,60 - -100.330,84 -551.853,43 - -263.272,43 -714.934,61 - -236.900,90 -689.467,96
Table 17. The specific CO2 abatement cost (IDR/g
Gasoline
LCGC gasoline
Diesel
LCGC diesel gasoline hybrid
Plug-in electric
CNG
Total
Benefit (Cost) analysis (10 9 IDR 2011)
High oil price projection
NO change
LCGC dominant
Emerging
Alt Tech
- 598.363,20 598.302,64
- -557.217,51 -528.354,59
Low oil price projection
NO change
LCGC dominant
Emerging
Alt Tech
- 1.610,30 1.069,20
- -198.778,44 -169.641,89
Reference oil price projection
NO change
LCGC dominant
Emerging
Alt Tech
NO
CO change
2
emission (Mt CO
2
)
LCGC dominant
- 79.059,79 67.538,32 992,77 709,57
- -244.800,51 -206.392,51 - 170,20
Emerging Alt
Tech
733,22
141,01
Specific abatement cost IDR/ kg CO
2
High oil price projection
NO change
LCGC dominant
Emerging
Alt Tech
-843,27 -815,99
3.273,98 3.747,00
Low oil price projection
NO change
LCGC dominant
-2,27
Emerging
Alt Tech
-1,46
1.167,94 1.203,07
Reference oil price projection
NO change
LCGC dominant
-111,42
1.438,35
Emerging Alt
Tech
-92,11
1.463,70
- -3,85 -3,85
- -141.472,67 -108.008,80
-
-
-
- -298.055,34
- -99.869,55
- -115.863,93
- -100.330,84 -551.853,43
-
- -66.100,44 -32.561,71
-
-
-
-3,85 -3,85
- -298.062,85
- -99.869,58
- -115.863,93
- -263.272,43 -714.934,61
-
-
-
-
-3,85 -3,85
- -71.156,33 -34.822,05
- -299.724,55
- -100.199,39
- -115.863,93
28,34
-
-
-
28,35
- 58,83
-
-
-
- -236.900,90 -689.467,96 1.021,11 966,95
28,35
28,10
7,79
6,65
20,57
965,68
0,14 0,14
2.404,76 3.843,90
38.263,88
15.026,66
5.631,53
103,76 571,47
0,14 0,14
1.123,58 1.158,83
38.264,84
15.026,67
5.631,53
2.537,31 1.251,06
0,14
1.209,52
93,37
0,14
1.239,28
38.478,17
15.076,29
5.631,53
551,11

The results on Figure 34, Table 16 and Table 17 shows that large share on the total fuel subsidy budget, energy consumption and GHG emissions of the vehicle fleet model in Indonesia are related to the distance travelled by vehicles population. Meanwhile the assumed percentage regular fuel subsidy reduction and new vehicle basis price also have significant impact on result of cost/benefit analysis and CO
2
emission abatement cost. The aim of this step is to estimate the rate of change in the prediction of CO
2
emission abatement and subsidy saving with respect to changes in some parameters, namely the annual distance of a new model year vehicle, the assumed regular fuel subsidy reduction and the new vehicle prices.
To determine how robust the conclusion is that the sensitivity analysis have been performed. The first sensitivity analysis were performed by additionally running the model for the low at 10% less and the high at 10 more of VKT estimation respectively. The other sensitivity analysis were executed by running the model for 15 % and 45 % fuel price increment and for the low at 10% less and the high at
10 more of new car prices respectively. All sensitivity runs were performed for the case of the emerging alternative technologies scenario in the reference oil price projection. The assumed the annual distance of a new model year vehicle, the assumed regular fuel subsidy reduction and the new vehicle prices can be found in Table 18. The results are summarized at Table 19.
Table 18. The annual distance of a new model year vehicle, the assumed regular fuel subsidy reduction and the new vehicle prices
VKT (km/year)
Retail oil price increment (% ) high
16.338
15% base
14.853
30% low
13.368
45%
Car price (IDR (2011) / unit (example: gasoline LCGC) 104.500.000 95.000.000 85.500.000
Table 19. The changes of parameters values
VKT high base low
Retail oil price increment Car price of gasoline LCGC high base low high base low
CO2 emission (Mt CO2e) 1062,25 965,68 869,11 965,68 965,68 965,68 965,68 965,68 965,68 benefit/cost (1012 IDR (2011)) -686,88 -689,47 -692,05 -742,78 -689,47 -618,48 -761,00 -689,47 -617,94 specific CO2 abatement cost IDR (2011)/kg CO2) 484,37 551,11 632,81 653,05 551,11 450,36 624,64 551,11 477,70

In this thesis, the impact of LCGC policy on CO2 emission and fuel subsidy has been investigated using a newly developed modeling tool built up with the insight gathered from the ways LDV fleet model developed in MIT’s Sloan Automotive Laboratory and adapted based on Indonesia’s regulation and market condition. Comparing the result of three new vehicles market mix scenarios provides insight in the vehicle population structure, potential market adoption, vehicle kilometer travelled, fuel price changes and luxury tax cut structure. It provides the opportunity to compare the performances
(in term of fuel consumption, CO2 emission, fuel subsidy saving and potential tax revenue loss) of three alternative new vehicles marketing mix scenarios.
Although with the detailed consideration in the model development process, there are still a number of important limitations to this analysis approach:
Accuracy of vehicle population of survived older model year vehicles: due to the lack of data about the exact historical number of vehicles that remains operated in Indonesia.
Accuracy on the number of vehicles kilometer travelled : the VKT have been highly simplified in the developed modeling tool. Because the comprehensive studies on traffic and transportation related to the vehicles kilometer travelled is very limited. Therefore it is intricate to determine the kilometer travelled by the whole vehicle population annually.
Accuracy on the basis price of new vehicles sales: the structure of car sales distribution from the manufactures to end consumers in Indonesia consist at least manufacturer/importer, sole distributor, dealer, sub-dealer, and end consumer subsequently. In each selling flow from manufacturer to sole distributor, from sole distributor to dealer, and so on, there are component of VAT tax and margin revenue of every seller. It is quite difficult to calculate the basis price of a new car because the data available is mostly the on the road price in which thoroughly consists of taxes, seller revenue and other administrative cost. Meanwhile the information about the seller revenue and the administrative cost is not easily provided.
Page 56

Future trends in fuel use
This analysis suggests that the gasoline fuel use, for gasoline and LCGC gasoline powertrains, remains dominate the total fuel consumption during 20 year. The share of fuel used in gasoline powertrain type is diminished by the growth of LCGC gasoline vehicle, yet the total fuel used for gasoline vehicles is still the highest among other drive trains.
Future trends in GHG
The scenarios of LCGC dominant and Emerging Alternative technologies may be unsuccessful to achieve the target set by government based on voluntarily commitment associated with the
Copenhagen Accord in January 2010. the potential CO
2
emission of two scenarios, LCGC dominant and Emerging Alternative technologies scenarios , cannot satisfy the reduction target required from transportation sector by approximately 20 percent, presumably by mean of these scenarios the CO2 emission can only be reduced by 9.98 % and 9.06 % respectively.
Luxury tax reduction
The LGCG dominant and emerging alternative technologies scenarios burden the government to lose the potential luxury tax revenue more than 250 x 10 12 rupiah (2011) and 700 x 10 12 rupiah (2011) respectively. The results suggest that the potential tax revenue loss of hybrid vehicles sales surpass that of LCGC gasoline vehicle in the emerging alternative technology scenario, despite its lower share of market mix. The rationale is that the luxury tax of other alternative technology cars likewise hybrid, plug-in and CNG powertrains is reduced by 50%, 25% and 25 % respectively. Meanwhile, the price of a new LCGC vehicle is capped at 95.000.000 rupiah (2011) and the price of hybrid and plug in powertrain are substantially higher than LCGC vehicles. Thus for example, the amount of potential revenue loss from an individual hybrid vehicles sale is approximately 22.27 times higher than that of
LCGC vehicle in 2013 and is slightly lower at 19.24 times in 2032. Consequently, the sales of emerging alternative technologies vehicles give impacts on soaring potential revenue loss.
Fuel subsidy
The fuel subsidy saving at LCGC dominant and emerging alternative technologies scenarios is very small compared to the amount of fuel subsidy spent in the No Change scenario as reference scenario. The fuel subsidy saving in the high oil price projection is merely 8.02% and 9.57% for LCGC dominant and emerging alternative technologies scenarios respectively. Meanwhile in the low oil price projection, the implementation of luxury tax reduction policy has insignificant results on fuel subsidy saving in which the saving is merely 0,71% and 0,51% for LCGC dominant and emerging alternative technologies scenarios respectively.
Cost/Benefit of this policy
The results of the impact of luxury tax revenue on the cost and benefit of each scenario over three oil price projections burden the Indonesia government spending. For example the situations of the emerging alternative scenario in all of oil price projection are burdening the government due to this policy at the highest cost among other scenario namely 551.85 x 10 12 , 714.93 x 10 12 and 689.47 x
10 12 at high, low and reference oil price projections respectively. Additionally, the specific GHG emission abatement cost of these impact of luxury tax revenue on the cost and benefit of each scenario over three oil price projections are at the range between 93,37 IDR(2011) /kg CO 2 e and
2.537,31 IDR(2011) /kg CO 2 e
Feasibility on achieving CO
2
emission reduction and fuel subsidy reduction through this policy
Given above mentioned analysis on the cost and benefit analysis and specific CO2 abatement cost, it raises a doubt on achieving aims on significantly reducing CO
2
emission and fuel subsidy through this policy. The Indonesia government has planned to achieve the CO
2
emission abatement target in the
Page 57

transportation sector by develop principal mitigation strategic levers, such as improvements to internal combustion engines and moving from gasoline-powered vehicles to hybrid and electric vehicles (PP
61/2011, 2011). This LCGC policy is imposed with published aim to reduce CO
2
emission and fuel subsidy by providing fiscal incentive to endorse the implementation of Presidential Regulation of The
Republic of Indonesia No.61 Year 2011. However, this policy conversely burden the government potential income and at the same time it only acquires a small fraction of fuel subsidy saving and of
CO
2
emission reduction.
It is necessary to develop other means to achieve the committed target of CO2 emission reduction, for example by developing mass public transportation. As depicted in Figure 2, the oil based fuel consumption for mass public transportation namely rail, bus and others, account for merely about
10% of national fuel consumption pattern. Despite the barriers and obstacles of Indonesian behavior toward public transportation as mentioned in section 2.1.8.1, it is essential to immediately develop the measures to increase the interchangeability between owning car and using public transportation as well as to overcome the obstacle factors of public transportation acceptance, likewise lack of availability information, distance between station and destination, total journey time taken at certain distance, poor integration, safety, fare and weather (Dagnachew, 2013).
It seems that the Government regulation number 41/2013 has other purpose that it is possibly projected to improve domestic automotive industries and to increase the Foreign Direct Investments in Indonesia PMA. Because the certification of luxury tax reduction eligibility for certain vehicles require proviso that the certified vehicles should be manufactured in Indonesia and contained at least
80 % of local components as mentioned in the Decree of the Ministry of Industry number 33/M-
IND/PER/7/2013. The development of automotive industries and its industrial support system may have impact on widening labor market and enhancing multiple effects on economics activities. If these are the intention of LCGC policy, it is interesting to further study on the automotive industries’ effects to different sectors.
Page 58

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