CHAPTER III THE EFFECT OF BIODIESEL UTILIZATION ON
TRANSPORTATION SECTOR EMISSION IN JAKARTA
Introduction
Biodiesel has been proven as an environmental friendly alternative diesel fuel. Biodiesel is essentially sulphur free. Engines fueled by biodiesel emit
significantly fewer particulates, hydrocarbons and less carbon monoxide than the conventional diesel fuel. Many studies on the performances and emission of
compression ignition engines, fueled with pure biodiesel and blends with diesel oil, have been performed and reported in the literature. Although based on
different engine architectures that may influence the final results, all the tests showed a slight reduction in the performances e.g. 5 decrease in the power over
the entire speed range and a slight increase in the fuel consumption. The presence of oxygen in biodiesel led to more complete combustion processes, resulting in
lower emissions of CO, particulate and visible smoke. However, an increase in NO
x
emissions has been reported, due to higher temperatures in the combustion chamber.
The investigations of different blends of biodiesel fuel on exhaust emissions and health effect have been done by numerous studies conducted worldwide. They
provided both proponents and opponents of alternatives with arguments for their causes. Those studies, however, mostly used US and European countries raw
material based biodiesel such as soybean, rapeseed, sunflower and canola oil. Meanwhile, similar specific studies on the Indonesian raw material based
biodiesel such as palm oil were rarely found. One comprehensive study on air quality was performed by Syahril et al. [4].
They proposed interventions that will have a direct impact in reducing Jakarta’s air pollution problem. They include:
• A reduction in sulphur content in fuel • A switch in fuel type, i.e. from gasoline or diesel to CNG, LNG, and biodiesel
• Implementation of emission standards for new-type vehicles • Introduction of catalytic converters for taxis
31 • Improvements in IM programs for public vehicles
• Implementation of IM programs for passenger cars • Development of public transport, i.e. Improvements to rail-based transport and
the implementation of bus rapid transit In their study, Syahril and his co-workers investigated the impact of PM
10
, NO
2
and SO
2
ambient and suggested four policies that should be implemented in 2015; i the new vehicle emissions standard, ii catalytic converters for taxis,
iii public transportation management and iv the combination of these three policies. The study however did not include a policy to switch to the new fuel that
has lower sulphur content such as biodiesel. Table 7 shows the reduction in health costs associated with implementation of the four abatement policies as the result
of Syahril’s study.
Table 7. Reduction in health costs caused by the abatement policies
No. Proposed Action Plan
Jakarta North
East South
West Central
Total 1
New Vehicle Emission Standard
46,463 10.61
82,257 6.72
68,496 6.18
90,416 6.42
23,421 13.75
311,053 28.80
7.15 2
Catalytic Converter for taxis
3,363 0.77
6,064 0.50
5,139 0.46
6,698 0.48
1,675 0.98
22,940 2.12
0.53 3
Public Transportation Management
29,519 6.74
52,688 4.31
44,229 3.99
58,043 4.12
14,800 8.69
199,279 18.45
4.58 4
Combined Policy 63,491
14.49 113,416
9.27 94,674
8.55 125,143
8.88 32,353
18.99 429,077
39.73 9.87
Note :
•
Unit in million Rupiah and percentages. • For total Jakarta, the unit for the figures in the bracket is million USD USD 1 = IDR 10,800
The percentage numbers are reduction in health cost that might be saved due to the implementation of an abatement policy compared to the based condition;
i.e. No abatement policy. The figures demonstrate that the new vehicle emissions standard policy is more effective than the policies of installing catalytic converters
in taxis and improving public transportation management in reducing the health costs associated with PM
10
, NO
2
and SO
2
. Most health cost reductions were estimated to occur in west Jakarta. However, in terms of percentage health cost
32 reductions, central Jakarta should benefit most from the implementation of
these policies. The result also showed that the total health cost reduction that can be achieved in 2015 by implementing all three policies should be approximately
429 billion Rupiah 40 million US. This reduction would be equal to approximately 13 percent of the Jakarta government’s total revenue in 2000.
By implementing a new vehicle emissions standard such as utilization of biodiesel in Indonesia, this may improve the air quality level in major cities
especially in Jakarta. Thus, the objective of this study is to assess the effect of biodiesel utilization in transportation sector on the air pollution level in Jakarta.
The targeted emission are carbon monoxide CO, nitrogen oxides NOx, hydrocarbons HC, sulphur dioxides SO
2
and particulate matter PM from vehicle sources. The scenarios used are the utilization of B10 in 2010, B20 in
2015, B30 in 2020 and B50 in 2025 as an automotive diesel fuel substitution in Jakarta. The calculation was performed based on the emission measurement data
collected from the studies by Wirawan et al. [29, 37].
Materials and Methods
The study was performed through quantitative methodology based on the measurement and secondary data collection and qualitative methodology. The
data collection was carried out by surveying literatures related to the energy used in the transportation sector in Jakarta and its emission effect. Those data were
inputted into the model that was specifically developed to estimate the effect of biodiesel utilization on the reduction of emission from transportation sector in
Jakarta. The targeted emission pollutants are: • Carbon monoxide CO
• Nitrogen oxides NO
x
• Hydrocarbon HC • Sulphur dioxides SO
2
, and • Particulate matters PM
Figure 15 shows the methods to conduct the study.
33
Figure 15. Flowchart of study to estimate the Effect of Biodiesel Utilization on Transportation Sector Emission in Jakarta
Result and Discussion
Projection of RGDP and population of Jakarta
The population in DKI Jakarta Province was increasing from 8.4 million in 2000 to 9.04 million in 2005 or an average growth of 1.5 per year. Based on the
year of 2000-price constant, the growth rate of RGDP was estimated to around 5 per year. In 2000, RGDP reached 227.9 trillion rupiah and it increased to 295.3
trillion rupiah in 2005. The highest growth of RGDP was in 2005 with the value of 6 per year [38]. The growth of population and RGDP of Jakarta for the period
of the year of 2000 until 2005 is shown on Figure 16.
Number of Vehicles BPS Jakarta
Effective Operation yr ITB, 2000
Specific fuel consumption ltkm Source: BPPT – KFA, 1992
Milleage kmyr BPPT – KFA, 1992
Fuel Consumption UPMS III kl Source: PT. PERTAMINA
Base Data 2005
Emission Factor gkm BPPT – KFA, 1992
Emission Factor gkm Wirawan et. al. , 2008
Emission Factor gkm : - Per type of emission CO, HC, NOx,
SOx, PM - Per type of vehicle private, bus, truck
- Per type of fuel gasoline, diesel, biodiesel
RGDP, Number and growth of population, etc
Source: BPS, 2006 No. of Vehicle
projection 2005 - 2025
Fuel consumption 2005 Match
No Fuel demand kl
2005 - 2025 Milleage kmyr
Ok Study output
BAU Pollutant per type of
emission gryr 2005 - 2025
Effect of Biodiesel to the emission reduction
B10, B20, B30, B50
34
150 175
200 225
250 275
300
2000 2001
2002 2003
2004 2005
RG DP
T rillio
n Ru
p ia
h
8000 8200
8400 8600
8800 9000
9200
P opul
at ion
T housa
nds
RGDP constant 2000
Population
Figure 16. RGDP and population in Jakarta
Assuming that family planning program has been performed successfully, the growth of population should decrease gradually from 1.5 per year in 2005 to
0.85 per year in 2025. Further assumption is that urbanization process has been forcing the expansion of urban district development to the suburb areas of Jakarta
such as Bogor, Depok, Tangerang and Bekasi. Therefore, Jakarta’s population may be projected to increase from 9.04 million in 2005 become 11.21 million in
the year of 2025 [39]. In the case of RGDP, its value was expected to increase from 295.3 trillion
rupiah in year 2005 to be 965.0 trillion rupiah in year 2025 based on year 2000 constant price or increase in average 6.1 per year. That RGDP growth was
estimated based on the assumption of projected national growth rate of 6 to 6.5 set by the central government. Therefore, per capita income was also expected to
rise from 32.65 million rupiah in year 2005 to be 86.05 million rupiah in year 2025 or an average increase of 4.5 per year. Detailed projected growth of
population and RGDP from 2000 until 2025 is shown in Figure 17.
Figure 17. Projection of population and RGDP in Jakarta
100 200
300 400
500 600
700 800
900 1000
2000 2005
2010 2015
2020 2025
T ri
ll io
n R
u p
ia h
8 9
10 11
12 13
P o
p u
la ti
o n
M il
li o
n
RGDP Population
35
Fuel consumption for transportation sector in Jakarta
Jakarta is highly dependent on oil fuel type of energy, especially for its transportation sector. In the year of 2005, the fuel consumption in Jakarta
including gasoline, diesel fuel and kerosene reached to about 68 of the total energy consumption. Since data on fuel consumption for the transportation sector
in Jakarta were not specifically available, therefore the consumption was estimated based on the data of fuel sold by PERTAMINA Marketing Unit III
UPMS III. The data that covered three provinces including DKI Jakarta, West Java and Banten are shown on Figure 18. The number of vehicles and their growth
in Jakarta for the year of 2000 up to 2005 are shown in Figure 19.
1000 2000
3000 4000
5000 6000
7000
Gasoline Kerosene
ADO IDO
MFO M
illio n
lit e
r
Electricity Sector Industrial Sector
Household Transportation
Figure 18. Fuel sold by UPMS III in the year of 2005
Figure 19. Number of vehicles in Jakarta 2001 – 2005
1.000.000 2.000.000
3.000.000 4.000.000
5.000.000
2001 2002
2003 2004
2005 N
um ber
of v
ehi c
les
Motor Cycle Passenger car
Truck Bus
36 The ratio between Regional Growth Domestic Product RGDP per capita
of Jakarta and RGDP per capita in other areas in UPMS III Banten and West Java were taken from statistic data issued by BPS DKI Jakarta [38]. RGDP per
capita of Jakarta in the year of 2005 was 48.25 million rupiah, whereas for Banten and West Java it was about 9.45 million rupiah. Fuel consumption in Jakarta was
assumed to be equal to the ratio of Jakarta’s RGDP to the total of RGDP of UPMS III region, which is 83 from the fuel consumption recorded in UPMS III data.
The fuel consumption by type of fuel in Jakarta for the year of 2005 is shown in Figure 20.
Figure 20. Estimated fuel consumption for transportation sector in Jakarta
The growth of vehicle population
Prediction of the future number of vehicles in this study was performed using four types of calculation model based on regression analysis approach. Such
an approach depends on the vehicle type and it has been applied in the BPPT- KFA study [40]. Each model depends on the RGDP, growth rate of the value of
goods, the correction factor of the growth of each type of vehicle. The correction factors are shown on the following equations [40]:
Model for Car
1
1 t
t t
KORC RVC
RVC +
=
−
1 Where:
CCA GC
t BCA
ACA KORC
t t
× +
− ×
− ×
=
−1
1 exp
2 RVC
: Registered cars per capita KORC
: Corrected growth factor for cars
Year 2005 Total: 7.612.302 kl
Kerosene 1,09
Gasoline 65,15
Industrial Diesel Oil
0,18 Automotive
Diesel Oil 33,58
37 t
: time GC
: RGDPcapita growth ACA
: Factor to adjust the starting value of growth BCA
: Speed factor to reach final growth CCA
: Multiplier for final growth
Model for Bus
1
1 t
t t
KORB RVB
RVB +
=
−
3 Where:
CBU GB
t BBU
ABU KORB
t t
× +
− ×
− ×
=
−1
1 exp
4 RVB
: Registered buses per capita KORB
: Corrected growth factor for buses t
: time GB
: RGDPcapita growth ABU
: Factor to adjust the starting value of growth BBU
: Speed factor to reach final growth CBU
: Multiplier for final growth
Model for Truck
1
1 t
t t
KORT TRU
TRU +
=
−
5 Where:
CTR GVTT
t BTR
ATR KORT
t t
× +
− ×
− ×
=
−1
1 exp
6 TRU
: Registered trucks per capita KORT
: Corrected growth factor for trucks t
: time GVTT
: Growth rate of value of goods per capita ATR
: Factor to adjust the starting value of growth BTR
: Speed factor to reach final growth CTR
: Multiplier for final growth
Model for Motor Cycles
1
1 t
t t
KORM RVM
RVM +
=
−
7 Where:
38 CMO
GM t
BMO AMO
KORM
t t
× +
− ×
− ×
=
−1
1 exp
8 RVM
: Registered Motor Cycles per capita KORM
: Corrected growth factor for Motor Cycles t
: time GM
: RGDPcapita growth AMO
: Factor to adjust the starting value of growth BMO
: Speed factor to reach final growth CMO
: Multiplier for final growth
The growth of RGDP, population and value of goods are parameters that will influence to the growth of vehicle number projection. The growth rates of
passenger car, bus and motorcycle are likely influenced by the growth of RGDP and population, while the growth rate of truck is influenced more by the growth
rates of value of goods and population. The growth of RGDP and population are presented in Figure 16 and 17. The annual growth rate of value of goods from
2006 until 2025 was assumed at a value of 2.06. Therefore, input data of the growth of vehicle number projection parameters can be listed as shown in Table 8.
Table 8. The list of the growth of vehicle number projection input data
Parameter Unit
2000 2001
2002 2003
2004 2005
Value of good
product Billion
Rupiah 79,769
90,063 101,684
107,047 126,256
140,686 RGDP
Billion Rupiah
227,856 238,656
250,331 263,624
278,525 295,270
Population Thousand 8,386
8,516 8,649
8,784 8,921
9,042 Passenger
car unit
1,065,121 1,130,496 1,195,871 1,529,824 1,645,306 1,766,801 Bus
unit 252,447
253,648 254,849
315,652 316,396
316,502 Truck
unit 328,665
347,443 366,221
464,748 488,517
499,581 Motorcycle unit
1,369,078 1,813,136 2,257,194 3,316,900 3,940,700 4,647,435 Note:
Value of gross output of large and medium scale manufacturing [38]
Based on 2000 – 2005 historical data shown in Table 8 and using the regression analysis method, the other parameters in the vehicle number projection
equations can then be determined as shown in Table 9.
39 Table 9. Parameters for vehicle number projection
Type of vehicle Parameter Value Passenger car
ACA 0.10
BCA 0.25
CCA 0.90
Bus ABU
0.05 BBU
0.78 CBU
0.45 Truck
ATR 0.10
BTR 0.34
CTR 0.18
Motorcycle AMO
0.36 BMO
0.19 CMO
0.16
Figure 21 shows the projection of vehicles population by type in Jakarta until 2025
2.000 4.000
6.000 8.000
10.000 12.000
14.000 16.000
2000 2005
2010 2015
2020 2025
Year V
ehi c
le num ber
T hous
and Motor Cycle
Passenger car Truck
Bus
Figure 21. Projection of vehicle number in Jakarta
Passenger cars and motor cycles are the type of vehicles that have highest growth rate with the value of 6.24 and 5.94 per year respectively, whereas bus
and truck are only 3.44 and 1.67 growth. Motor Cycles was expected to grow from 4.647 million in 2005 to 14.745 million units in 2025. Passenger cars
increased from 1.767 million in 2005 to become 5.933 million in 2025. The growth rate of buses and trucks were relatively small with the value of 317
thousand and 500 thousand vehicle sin the year of 2005 and they are expected to increase to 623 thousands and 696 thousand vehicles respectively in 2025.
40
Projection of fuel consumption for each type of vehicle
The calculation of the projected fuel consumption for each type of vehicle started by collecting the data of fuel consumption at base case fuel consumption
2000 – 2005, specific fuel consumption, and mileage per year of each type of vehicle. A survey of the fuel consumption, for each type of vehicle in Jakarta and
Surabaya areas, was conducted by interviewing 150 respondents for private cars, 90 respondents for public transporter vehicles and 60 respondents for goods
transporter vehicles for each area has been carried out by RPC, 2006 [41]. BPPT- KFA study [12] also performed a survey to the fuel consumption, milleage and
specific fuel consumption for each type of vehicles. The studies showed that not all registered vehicles are operated everyday. The study performed by ITB, 2001
[42] showed that three wheels vehicles has a highest effective operation per year 82, whereas the big trucks has a lowest effective operation with the value of
23. Specific Fuel Consumption kmL, milleage kmyr and yearly effective operation data used in this study is shown on Table 10 and the estimated
projection of fuel demand for Business As Usual BAU case is shown in Figure 22. The figure shows that gasoline consumption keep its domination during the
analysis period, especially the gasoline for passenger car. Whereas the automotive diesel oil mainly is used for passenger cars and trucks.
Table 10. Specific fuel consumption, mileage and yearly effective operation
Vehicle type Milleage
kmyr Specific Fuel
Consumption kmlt
Yearly Effective
Operation
Passenger Car Private
Gasoline 15,379
8.48 55
ADO 20,429
8.80 41
Public Gasoline
101,307 9.19
55 ADO
113,400 16.00
41 Bus
Small and medium bus
Gasoline 39,979
8.81 60
ADO 39,338
8.45 60
Big bus ADO
42,985 5.92
29 Truck
Small Truck Gasoline
20,563 12.33
41 ADO
19,380 9.40
41 Medium Truck
ADO 69,800
6.60 27
Big Truck ADO
121,176 6.32
23 Motor Cycle
Gasoline 20,706
37.59 64
Source: Adapted from RPC, 2006 [41], ITB, 2001 [42] and BPPT-KFA, 1992 [43],
41
0,0 2,5
5,0 7,5
10,0 12,5
15,0 17,5
20,0 22,5
2000 2005
2010 2015
2020 2025
Year E
ner gy
C onsu
m pt
ion M
il li
on kl Bus Diesel
Truck Diesel Passenger car Diesel
Bus Gasoline Truck Gasoline
Passenger car Gasoline Motor Cycle Gasoline
Figure 22. Projection of fuel demand for transportation sector in Jakarta BAUnon biodiesel Scenario
Projection of emission BAU scenario
The number of emission load can be estimated based on the type of fuel, yearly milleage and emission coefficient for each type of vehicle. The emission
coefficient for gasoline and pure ADO was taken from the study performed by BPPT-KFA, 1992 [43], whereas the emission coefficient for ADO-biodiesel
blends fuel was taken from the study performed by Wirawan et al. [29, 33]. Direct measurement of emission coefficient was only performed on passenger car [29],
where the emission coefficient for the other type of vehicle ADO-biodiesel cases were assumed proportional to the ratio of passenger car ADO case.
42 The emission coefficient estimation determination method is shown in
appendix 1 and the estimated emission coefficient result is shown on Table 11. The total emission for each type of emission as the calculation result is illustrated
in Figure 23. CO is the main pollutant emitted from vehicles, followed by NO
x
and HC. Whereas the emission of SO
2
and PM Particulate Matter are relatively smaller but the effect to the human health is significant.
Table 11. Emission coefficient for each type of vehicles
Type of vehicle Type of fuel
Emission Factor gkm CO
NOx HC
SO
2
PM
Passenger car Gasoline
4.373 3.939
0.448 0.060
0.000 ADO
0.876 1.167
0.121 0.860
0.176 B10
0.831 1.107
0.106 0.774
0.108 B20
0.790 1.140
0.060 0.688
0.095 B30
0.710 1.080
0.050 0.602
0.090 B50
0.660 1.030
0.040 0.430
0.070 Big truck
ADO 0.427
6.236 0.290
1.280 0.264
B10 0.405
5.916 0.255
1.152 0.162
B20 0.385
6.092 0.144
1.024 0.143
B30 0.346
5.771 0.120
0.896 0.135
B50 0.322
5.504 0.096
0.640 0.105
Big bus ADO
0.437 9.632
0.290 1.290
0.274 B10
0.415 9.137
0.255 1.161
0.168 B20
0.394 9.410
0.144 1.032
0.148 B30
0.354 8.914
0.120 0.903
0.140 B50
0.329 8.502
0.096 0.645
0.109 Small bus and
truck Gasoline
6.601 4.927
0.786 0.090
0.000 ADO
0.905 1.371
0.465 1.020
0.235 B10
0.858 1.300
0.409 0.918
0.144 B20
0.816 1.339
0.230 0.816
0.127 B30
0.733 1.269
0.192 0.714
0.120 B50
0.682 1.210
0.154 0.510
0.093 Motor Cycle
Gasoline 3.267
0.123 0.733
0.030 0.059
Sources: Wirawan et. al., 2008 [29], Wirawan et. al., 2005 [33], BPPT-KFA, 1992 [43]
Figure 23. Projection of emission BAU scenario
200 400
600 800
1000 1200
1400
2000 2005
2010 2015
2020 2025
E m
is s
ion Thous
a nd t
on y
r
CO NOx
HC SO2
PM
43
Projection of emission B10, B20, B30 and B50 scenarios
The utilization of biodiesel is an effort to reduce the emission emitted from the vehicles. This study assumed that B10 would be completely used in Jakarta by
the year 2010, B20 in 2015, B30 in 2020 and B50 in 2025. The projection of emission result shows that there are different value between B10, B20, B30 and
B50 biodiesel case with BAU non-biodiesel case. More significant emission reduction showed by SO
2
and PM emission for 2010 and 2025 as can be seen on Table 12.
Table 12. Comparison of emission value of BAU and biodiesel scenario
Emission 1000
tonyr BAU
B10 2010 B50 2025
B10 Decrease
BAU B50
Decrease
CO 591,64
590,71 0,16
1152,50 1145,06
0,65 NO
x
243,80 241,80
0,82 517,05
509,48 1,47
HC 108,87
108,11 0,70
202,47 196,59
2,91 SO
2
26,09 24,01
7,95 46,68
30,01 35,69
PM 10,73
9,05 15,62
18,79 14,43
23,21
Generally, the result shows that the utilization of biodiesel will reduce the exhaust gas emission consistently with the increasing of biodiesel content in
blending composition but depend on the characteristic each type of emission. Significant emission reduction occurred for SO
2
and PM emission. If all ADO in Jakarta substituted by B10 in 2010 scenario, the SO
2
will decrease around 7.90 2,070 ton and particle around 15.62 1,680 ton. If the B50 is used in 2025, the
SO
2
emission will decrease around 35.69 16,660 ton and particle around 23.21 4,360 ton. As a comparison, Syahril et al. [4] concluded that the load
of SO
2 and
PM emission in 1998 are 5,774 ton and 6,156 ton respectively. The value of reduction coefficient emission because of biodiesel utilization
used in this study is taken by a simple method based on the coefficient emission measured on passenger car. More measurements of coefficient emission on each
type of vehicles small bustruck and big bustruck should be done specifically if results that are more accurate were desired.
44
Conclusion
The study on the effect of biodiesel utilization on transportation sector emission in Jakarta has been performed and come up with the following
conclusion: 1. Generally the result shows that the utilization of biodiesel will reduce the
exhaust gas emission consistently with the increasing of biodiesel content in blending composition.
2. The rate of emission decrease depends on the characteristic of each type of emission. Significant emission reduction mainly showed by SO
2
and PM emission. If all ADO in Jakarta subtituted by B10 in 2010 scenario, will
reduce the SO
2
around 7.90 2,070 ton and particle around 15.62 1,680 ton. When the B50 used in 2025 will reduce the emission of SO
2
around 35.69 16,660 ton and particle around 23.21 4,360 ton. As
comparison, the load of SO
2
and PM emission in 1998 according to Syahril et al. 2002 [4] are 5,774 ton and 6,156 ton respectively.
CHAPTER IV BIODIESEL BLENDING SCENARIO