CHAPTER VI EXTERNAL COST ANALYSIS
Introduction Influence of Biodiesel Fuel on Exhaust Emissions and Health Effect
In the United States, diesel engines are regulated for smoke opacity, total oxides of nitrogen NO
x
, total particulate matter 10 micro m PM-10 or PM, carbon monoxide CO, and total hydrocarbon THC according to the test
procedures defined by US Environment Protection Agency EPA in the Code of Federal Regulations. Since the magnitude of diesel engine emissions depends on
the fuel composition, emission certification testing is conducted along with the “certification of diesel fuel” that represents the US national average. Other
emissions from diesel engines such as aldehydes and polyaromatic hydrocarbons PAH may be regulated in the future in an attempt to control ambient levels of
toxic substances in the air [19]. Table 62 gives an overview of major diesel exhaust components and their suspected impacts on human health.
Table 62. Reported health impacts of major diesel engine exhaust component [16]
Emission component Examples
Health impact Gas-phase emissions
Carbon monoxide CO
Toxic due to blockage of oxygen intake Nitrogen oxides
NO
2,
N
2
O NO
2,
is an irritant for the respiratory tract and a major ozone precursor. Thus it is responsible for
the formation of photochemical smog and the induction of related disorders e.g. asthma and
bronchitis.
Sulfur dioxide SO
2
Respiratory tract irritation Alkanes and alkenes
1,3-butadiene Respiratory tract irritation. Ozone precursors.
Suspected mutagenic and carcinogenic potential. Aldehydes
Formaldehyde, acetaldehyde
Ozone precursors. Respiratory tract and eye irritation. Some compounds have carcinogenic
potential. Aromatic compounds
Benzene, PAH, nitro-PAH
Some compounds are known mutagens and carcinogens.
Particle-phase emissions
Element carbon C
carcinogenic potential Inorganic sulfate and
nitrate SO
4 2-
, NO
3 -
Respiratory tract irritation Aromatic compounds
PAH and nitro-PAH Some compounds are known mutagens and
carcinogens
103 Out of these impacts, toxicity and mutagenic behavior were chosen to be
treated in detail, as they are most frequently cited in the context of biodiesel research.
An important advantage of biodiesel is its ability to reduce total particulate emissions from an engine. Particulate emission is defined by the EPA as
condensed or solid material collected on an appropriate filter at a temperature 52
C. Particulate matter thus includes soot carbon, fuel and lubricating oil derivatives, and sulfuric acid aerosols. Particulate matter is often fractioned in
terms of sulfate, soluble organic fraction SOF, or volatile organic fraction VOF, and carbon soot. Biodiesel can affect soot and fuel-based SOF but not
lubricating oil based SOF. Diesel engines are significant contributors of NO
x
and PM to ambient air pollutant inventories. The quantities of CO and THC derived
from diesel engines are generally small compared to the emission from light duty gasoline vehicles. For that reason, the effect of biodiesel on PM and NO
x
emissions is usually the primary concern of the study. In the environmental economic science, environmental impact is known as
one form of the loss of externalities, which could effect to the uneffective source allocation and thus requires government interventions to overcome the problem.
The monetary value as the cause of the transportation sector environmental damage should be as a community responsibility and environment which still not
include in whether producer or consumer calculation. The largest contributor of external costs in the transportation energy sector is the impact of air pollutant to
human health. The study on externality in transportation sector in Indonesia is still very
limited and allmost of those study done by conventional fossil fuel base assumption, and none of them done by biodiesel case. The mapping of air
pollution level that contained SPM Suspended Particulate Matter, NO
2
, CO and SO
2
for Jakarta, Bandung and Surabaya city has been performed by Soedomo et al. 1991 [59]. In 1994, Kleeman [60] performed the study on the dispersion of
NO
2
, VHC Volatile Hydrocarbon, SO
2
and SPM in Java. Ostros study, 1994 [61] calculates the health profitability value as the impact of air pollution
reduction in Jakarta. Wilde et al. 2003 [15] calculated the external costs of several
104 type of power plant in Java. Shah and Nagpal, 1997 [62] from the World
Bank, have performed the study on the air quality of Jakarta. The study was meant to understand the impact of TSP Total Suspended Particle, PM10
10
and NO
x
in the period time of 1981 – 1992. Health impact is determined based on the dose
response function data taken from US research. ADB in 2002 [63] and Syahril et al. 2002 [4] have studied the air pollution problem for Jakarta with the data
collected for the period of 1990 – 2000 and their projection for year 2015. The measured emissions were CO, THC, NO
x
and PM
10
. The result proposed a recommendation to use the clean fuel such as CNG, LPG and biodiesel to reduce
the future air pollution in Jakarta. Samudro 2005 [64] performed a study on how to estimate the value of pollution tax as part of pollution effect compensation for
Kabupaten Sleman, Central Java. Numerous studies comparing biodiesel and fossil diesel fuels concerning
suspected health risk have been conducted worldwide, which provided both proponents and opponents of alternatives with arguments for their causes.
The investigation of different blends of biodiesel obtained from soybean, rapeseed or sunflower and diesel oil i.e. 100, 80, 70, 50, 30, 20 and
0 volume of biodiesel, respectively on six cylinder direct injection diesel engine which widely installed on local urban buses have been carried out by
Carraretto et al. 2004 [65]. The result shows that the performances are slightly reduced while Specific Fuel Consumption SFC is notably increased using
biodiesel. CO emissions are reduced but NO
x
are increased. Preliminary tests showed that by optimizing the injection advance both performances and emissions
can be improved. The influence of pure biodiesel and blends of biodiesel combined with
10 methanol on engine performance have been investigated by Laforgia et al. 1995 [66]. A remarkable reduction of smoke emerged from both solutions. The
addition of alcohol produced good results as well. When the injection timing was advanced 30 from the setting provided by the manufacturing, better results
were obtained, thus confirming the advantage of these fuels. The use of palm biodiesel mixed with fossil fuel for certain amount e.g. up
to 30 or B30 has significantly improved emission, as shown in the result of
105 20,000 km road test carried out by BPPT [33]. Reduction of emission is
shown in the Tables 63 and 64. Table 63. Regulated exhaust emission reduction of B30
Emission category Emission Reduction
CO gkm
2
25.35
NO
x
+ THC gkm 10.82
Particulate gkm 42.02
Opacity 23.50
Note: not regulated in Euro II
Table 64. Non-regulated emission of aromatic compounds from fossil diesel fuel FDF and B30
Parameter µggram
Distance 0 km Distance 20,000 km
FDF B30
∆ FDF
B30 ∆
Benzene 113
99
-12
186 168
-10
Toluene 83
56 -33
274 260
-5
Xylene 31
19
-39
113 96
-15
Ethyl Benzene 22
13
-41
86 73
-15
Note : negative sign in ∆ showing emission reduction
This study reveals the estimated health and economic impacts external costs of air pollution based on the calculated air pollution provided in chapter V.
Environmental impact can be estimated by the dose response function, also known as Concentration-Response or Exposure-Response Functions ERF. The
concentration of pollutant, which passed the limitation range, will have the impact on the various pollutant receptors such as human being, crops and building
materials. Impact to the human being could be from the asthmatic, bronchitis, medical treatment at the hospital and premature mortality. Mortality impacts are
quantified in terms of the reduction or loss in life expectancy, expressed as cumulative Years of Life Loss YOLL for the population at risk. EFR’s for health
impacts are derived from a survey of epidemiological studies. Simply stated, epidemiology is the study of the human health state and its related illnesses and
problems. Over the past three decades, numerous studies have been carried out in the attempt to establish a cause-effect relationship linking an increase in the
concentration level of a particular component in polluted air to an increase in the
106 nominal incidence rate of a particular health disease say, respiratory hospital
admission or mortality [67].
Materials and Methods
Each type of pollutants has a different impact to the human health. Environmental impact will increase by the increasing of the population density at
the polluted area. Due to the limitation of domestic dose response data, therefore in this research will use the data of dose response collected from several existing
dose response data as shown in Table 65.
Table 65. Dose response
Dose Response Function Pollutant
Unit Value
Mortality long-term exposure PM
YOLLyr-person- μgm3 2.60x10-4
4.34x10-4 Restricted or limited activity days
PM casesyr-person-
μgm3 2.20x10-2
3.67x10-2 Long term chronic bronchitis
PM casesyr-person-
μgm3 4.51x10-5
7.53x10-5 Respiratory hospital admissions
PM casesyr-person-
μgm3 2.56x10-6
4.28x10-6 Lower respiratory symptoms adult
PM casesyr-person-
μgm3 9.62x10-2
16.1x10-2 Lower respiratory symptoms child
PM casesyr-person-
μgm3 4.10x10-2
6.85x10-2 Mortality short-term exposure
SO
2
YOLLyr-person- μgm3 2.30x10-6
Respiratory hospital admission SO
2
casesyr-person- μgm3
2.84x10-6 Mortality short-term exposure
NOx YOLLyr-person-
μgm3 1.70x10-6 Respiratory hospital admission
NOx casesyr-person-
μgm3 1.56x10-6
Cancer non-fatal HC
casesyr-person- μgm3
1.14x10-7 Mortality short-term exposure
CO YOLLyr-person-
μgm3 3.77x10-8 Source: Compiled from Bickel et. al. [66], Bickel et. al [67] , Wilde et. al. [15] and Spadaro [68]
Monetary value as the impact of the environmental deterioration is determined based on the quantification of physical impact. External costs can be
determined by multiplying the physical impact ex. Asthma attack with the cost unit Rupiah per asthma attack as represented in the following equation:
External cost = dose response function x health cost unit 18
107 Health cost estimation will be as an input, which should be considered
on the external cost determination. As developing countries including Indonesia have limited data on this aspect therefore, to fill the gaps, this study utilized
several relevant data from developed countries. Table 66 shows the estimated health cost collected from several existing sources to be used in this research.
Table 66. Health cost
Health Impact Unit
Value
Mortality short-term exposure US per YOLL
12,000 Mortality long-term exposure
US per YOLL 20,700
Restricted activity days US per incident
14 Long term chronic bronchitis
US per incident 21,000
Respiratory hospital admissions US per incident
540 Lower respiratory symptoms adult
US per incident 1
Lower respiratory symptoms child US per incident
1 Fatal cancer
US per incident 340,000
Non-fatal cancer US per incident
70,000 Severe hereditary effect
US per incident 490,000
- Source: Compiled from Bickel et. al. [68], Bickel et. al [69] , Wilde et. al. [15] and Spadaro [67] - Represented the damage cost for European unit case in the unit price of US in year 2000.
Some adjustment in Purchasing Power Parity Gross Domestic Product GDP
PPP
must be performed before used for Indonesian case. This adjustment is necessary to account for income differences between countries, and consequently,
the willingness to pay regarding valuation of health impacts by individuals in different parts of the world. The GDP
PPP
ratio may be raised to an exponent, the elasticity factor, which reflects an individual’s commitment to spend a larger
share of disposal income to protect against adverse health impacts as represented by equation below Markandya and Boyd, 2002 [70]:
19
Typ ical elasticity factors γ lay in the range 0.3 and 1. For example, γ1
means that individual’s in Indonesia commitment to spend more money than European individual’s to get the same protection against adverse health impacts.
Because individual in Indonesia has less commitment to spend more than European does, therefore the value γ of one will be used in this calculation.
γ
× =
Europe in
capita Indonesia
in capita
Europe in
unit Cost
Indonesia in
unit Cost
PPP PPP
GDP GDP
108 Table 67 represents the data of GDP
PPP
and GDP
PPP
capita for Indonesia and German case.
Table 67. GDP
PPP
and GDP
PPP
capita for Indonesia and German case
Year
GDP
PPP
Valid price Million USD
Population Thousand
GDP
PPP
Capita USDCapita
Indonesia German
Indonesia German
Indonesia German
1990 323,323
1,380,891 182,847
79,433 1,768
17,384 1995
532,793 1,737,156
197,411 81,661
2,699 21,273
2000 599,011
2,083,421 211,693
82,309 2,830
25,312 2005
847,609 2,429,644
226,063 82,652
3,749 29,396
Source: Proceed from World Bank 2007 [71] and United Nation 2008 [72]
The cost unit US year of 2000. From the money exchange value, GDP per capita and deflator data as listed in Table 68, therefore the correction factor for
Rupiah unit in year of 2005 can be estimated as follows: Cost unit in Indonesia USD 2000 = Cost Unit in Europe x 2,83025,312
= Cost Unit in Europe x 0.11180 Table 68. Indonesian GDP Deflator 1990 - 2005
Year GDP Trilion Rupiah
GDP Deflator Exchange value
Valid price Price constant
Year of 2000 USDRp
1990 210.9
875.0 24.1
1,905 1991
250.0 936.4
26.7 1,997
1992 282.4
999.7 28.2
2,002 1993
329.8 1,151.7
28.6 2,110
1994 382.2
1,238.6 30.9
2,208 1995
454.5 1,340.4
33.9 2,308
1996 532.6
1,445.2 36.9
2,383 1997
627.7 1,513.1
41.5 4,650
1998 955.8
1,314.5 72.7
8,025 1999
1,099.7 1,324.9
83.0 7,100
2000 1,389.8
1,389.8 100.0
9,595 2001
1,684.3 1,443.0
116.7 10,400
2002 1,863.3
1,506.1 123.7
8,940 2003
2,045.9 1,579.6
129.5 8,465
2004 2,303.0
1,656.8 139.0
9,290 2005
2,729.7 1,749.5
156.0 9,830
Source: PIE [73]
109 By using the exchange value, therefore the Rupiah in 2000 can be
estimated as follows: Cost unit in Indonesia Rupiah 2000 = Cost unit in Indonesia USD 2000 x
Exchange value RupiahUSD Cost unit in Indonesia Rupiah 2000 = Cost unit in Indonesia USD 2000 x
9,595 Since the calculation was based on the year of 2005, therefore the cost used
was constant in 2005. Cost unit in Indonesia Rupiah 2005 = Cost unit in Indonesia Rupiah 2000 x
GDP Deflator 2005GDP Deflator 2000 Cost unit in Indonesia Rupiah 2005 = Cost unit in Indonesia Rupiah 2000 x
156.0100.0 Cost unit in Indonesia Rupiah 2005 = Cost unit in Indonesia Rupiah 2000 x
1.56 Then, the correction factor will be as follows:
Cost unit in Indonesia Rupiah 2005 = Cost Unit in Europe x 0.11180 x 9,595 x 1.56
Cost unit in Indonesia Rupiah 2005 = Cost Unit in Europe x 1.673,44
Result and Discussion
External cost was estimated based on the sum of health impact of each type of emission to the individuals. According to the emission concentration input data,
therefore the external cost results were also divided by to cases low and high emission coefficient cases. External cost calculation result of both low and high
emission cases data can be seen in appendix 5. The value of external cost and external cost reduction compare to base
scenario as a calculation result for low emission coefficient case is summarized in Tables 69 and 70, while for high emission coefficient case is summarized in
Tables 71 and 72. Table 69 shows that the total external cost of health problem associated with SO
2
, NO
x
, HC, PM and CO from vehicles sources low coefficient emission case for the whole Jakarta except Pulau Seribu regency and its
surrounding area.
110 Table 69. Low emission coefficient external cost summary
External cost Billion Rupiah constant 2005 2005
2010 2015
2020 2025
Base 489.0
775.6 1060.8
1378.8 1754.9
B10 489.0
770.8 1055.5
1371.8 1745.1
B20 489.0
758.4 1039.8
1352.3 1723.1
B50 489.0
736.2 1012.6
1319.6 1682.9
B100 489.0
700.0 968.2
1265.8 1617.2
Table 70. External cost reduction compare to base scenario
External cost reduction Billion Rupiah 2005
2010 2015
2020 2025
Base-B10 0.0
4.8 5.3
7.0 9.8
Base-B20 0.0
17.2 21.0
26.5 31.8
Base-B50 0.0
39.4 48.2
59.2 72.0
Base-B100 0.0
75.6 92.6
113.0 137.7
Table 71. High emission coefficient external cost summary
External cost Billion Rupiah constant 2005 2005
2010 2015
2020 2025
Base 1,792.5
3,005.8 4,134.3
5,264.9 6,494.9
B10 1,792.5
2,992.4 4,114.2
5,243.4 6,469.7
B20 1,792.5
2,946.8 4,063.8
5,179.4 6,389.2
B50 1,792.5
2,872.1 3,973.6
5,073.3 6,264.6
B100 1,792.5
2,760.8 3,832.3
4,898.5 6,047.2
Table 72. External cost reduction compare to base scenario
External cost reduction Billion Rupiah 2005
2010 2015
2020 2025
Base-B10 0.0
13.4 20.1
21.5 25.2
Base-B20 0.0
59.0 70.5
85.5 105.7
Base-B50 0.0
133.7 160.7
191.6 230.3
Base-B100 0.0
245.0 302.0
366.4 447.7
The estimated cost in 2005 was 489 billion rupiah and it was expected to increase considerably to 1,754.9 billion rupiah in 2025 as the number of vehicles
and population grow as well. By introducing biodiesel fuel in 2010, the value of external cost decreases by the increasing of biodiesel content as shown in Table
70. In year 2010 case, the value of external cost reduction gradually increase from only 4.8 billion rupiah for B10 case, become 17.2 billion rupiah for B20, 39.4
billion rupiah for B50 case and reach to be 75.6 billion rupiah for B100. The
111
External Cost
200 400
600 800
1000 1200
1400 1600
1800 2000
Base B10
B20 B50
B100
In B
illio n
R u
p ia
h
2005 2010
2015 2020
2025
External Cost Reduction
20 40
60 80
100 120
140 160
2005 2010
2015 2020
2025
In B
illio n
R u
p ia
h
B10 B20
B50 B100
value increases by the time due to the continuously growing of fuel consumption and population density. The maximum external cost reduction when
B100 is used in 2025 is 137.7 billion rupiah. Figures 39 and 40 shows the trend of external cost and its reduction as a biodiesel fuel utilization Impact.
Figure 39. External cost, Low Emission Coefficient case
Figure 40. External cost reduction, Low Emission Coefficient case
By the same method, the external cost reduction for high emission coefficient case can be analyzed as shows in Figures 41 and 42. The total external cost for
non biodiesel base case in 2005 is 1,792.5 billion rupiah, and the value increases considerably by the growth of vehicle number and population to be 6,494,9 billion
rupiah in 2025, those values is approximately 3.7 times of the low emission coefficient case external cost value. The same trend with low emission coefficient
112 case, the value of external cost decreases by the increasing of biodiesel content
as shown in Figure 42.
Figure 41. External cost, High Emission Coefficient case
Figure 42. External cost reduction, High Emission Coefficient case In year 2010 case, the value of external cost reduction gradually increase
from only 13.4 billion rupiah for B10 case, become 59 billion rupiah for B20, 133.7 billion rupiah for B50 case and reach to be 245 billion rupiah for B100. The
value increases by the time due to the continuously growing of fuel consumption and population density. The maximum external cost reduction when B100 used in
2025 would be 447.7 billion rupiah. This means 3.2 times higher than the external cost reduction at low emission coefficient case. The data also shows that generally
PM emission has the biggest external cost contributor 79 from the total associated external cost value, followed by CO emission 15 and the rest is HC
External Cost
1000 2000
3000 4000
5000 6000
7000
Base B10
B20 B50
B100
In B
illio n
R u
p ia
h
2005 2010
2015 2020
2025
External Cost Reduction
50 100
150 200
250 300
350 400
450 500
2005 2010
2015 2020
2025
In B
illio n
R u
p ia
h
B10 B20
B50 B100
113 3 and NO
x
emission 3, while very small external cost contribution from SO
2
emission. Table 73 presents the comparison between low and high emission coefficient value estimated from emission coefficient data as shown in Tables 41
and 44. Table 73 shows that PM emission coefficient for high case is about 3.95 times of low case. This value is consistent with the one of external cost in high
emission coefficient case, which is also in the range of 3.2 to 3.7 for the value of low emission coefficient case.
Table 73. Comparison between low and high emission coefficient value
Emission Value of Emission Coefficient EC
SO
2
High EC case is 1.02 times of low EC case NO
x
High EC case is 7.55 times of low EC case HC
High EC case is 4.2 times of low EC case PM
High EC case is 3.95 times of low EC case CO
High EC case is 1.46 times of low EC case
• Sensitivity Analysis
To understand the influence of each variables to the result of model calculation, therefore some variable sensitivity analysis were performed. The
followings are variables, which are supposedly to have a high influence on the calculation result of emission dispersion see equation 9.
- Emission coefficient - Wind speed
- Fading function, that represented in the form of IGAV
a. Emission Coefficient Sensitivity Analysis The emission coefficient for all pollutants is assumed transformed in one
bundle with the value of c. The value of emission coefficient then changed by increasing and decreasing in percent unit the value of c. The emission
concentration as the result of changed emission coefficient is shown in Figure 43. Case to be analyzed is the base non-biodiesel case, year 2005, low emission
coefficient case.
114
Figure 43. Sensitivity of emission coefficient to each pollutant concentration Figure 44 presents the sensitivity of emission coefficient value to the external cost
estimation. The external cost value increase linearly with the increasing of emission coefficient.
Figure 44. The sensitivity of emission coefficient value to the external cost b. Wind Speed Sensitivity Analysis
With the assumption that the average wind speed in Indonesia is approximately in the range of 1 – 5 ms, therefore those ranges of wind speed will
applied for this sensitivity analysis. The emission concentration as the result of changed wind speed is shows in Figure 45. Case to be analyzed is the base non-
biodiesel case, year 2005, low emission coefficient case.
115
Figure 45. Sensitivity of wind speed to each pollutant concentration Figure 46 presents the sensitivity of emission coefficient value to the external cost
estimation. The external cost value decrease exponentially with the increasing of wind speed. The external cost value decrease sharply at the average wind speed
ranges of 2 – 2.5 ms.
Figure 46. The sensitivity of wind speed to the external cost
c. Fading Function Sensitivity Analysis Fading function is represented by IGAV with the range value of 0 to 1. The
emission concentration as the result of changed fading function value is shows in Figure 47. Case to be analyzed is the base non-biodiesel case, year 2005, low
emission coefficient case.
116
Figure 47. Sensitivity of fading function to each pollutant concentration Figure 48 presents the sensitivity of fading function to the external cost. The
external cost value increase linearly with the increasing of IGAV value.
Figure 48. The sensitivity of fading function to the external cost
• External cost estimation result Validation
Validation of the external cost estimation result was performed by comparing the results to the same type of previous studies. Table 74 presents
such a comparison.
117 Table 74. Comparison of external cost estimation result with others existing
studies in Trillion Rupiah
1998 2005 2015 2025
Low Emission Coefficient case n.a.
0.49 1.06
1.75 High Emission Coefficient case
n.a. 1.79
4.13 6.49
Syahril, et al. [5] 1.80
2.60 4.30
n.a Samudro [64]
n.a. 1.48 n.a
n.a Proceed based on the ratio of GDP DKI JakartaSleman
The external cost of Kabupaten Sleman in year 2004 estimated by Samudro [64] was 16.3 billion rupiah. By using the ratio of GDP of Sleman 4.8 trillion
rupiah to the GDP of DKI Jakarta 436.25 trillion rupiah, the external cost of DKI Jakarta in 2005 based on Samudro model can therefore be calculated to a
figure of 1.48 trillion rupiah. In other study, Syahril’s [4] estimated the external cost of DKI Jakarta in 1998 was approximately 1.8 trillion rupiah and expected to
increase approximately by 2.4 times in 2015 to be 4.3 trillion rupiah. From Table 74, it can be seen that the estimation result of high emission coefficient case in
this study confirms similar findings from the studies of external cost value by Samudro and Syahril.
Conclusion
The external cost valuation for base case non-biodiesel in year 2005 and for biodiesel case B10, B20, B50 and B100 in year 2010, 2015, 2020 and 2025
has been performed and produced the following conclusions: 1. The total external cost of health problem associated with SO
2
, NO
x
, HC, PM and CO from vehicles sources in Jakarta for low coefficient emission case in
2005 non biodiesel case was 489 billion rupiah and the figure should increase to 1,754.9 billion rupiah in 2025. By introducing biodiesel fuel in
2010, the value of external cost reduction would gradually increase from merely 4.8 billion rupiah for B10 case to 17.2 billion rupiah for B20, 39.4
billion rupiah for B50 case and reach up to 75.6 billion rupiah for B100. The maximum external cost reduction when B100 used in 2025 would be 137.7
billion rupiah.
118 2. The total external cost for high emission coefficient case in 2005 non-
biodiesel case is 1,792.5 billion rupiah and it is expected to almost quadruple 3.7 times to 6,494.9 billion rupiah in 2025. Similar trend is also found in the
low emission coefficient case, in which the value of external cost decreases as biodiesel content blended with mineral diesel is raised. In year 2010 case, the
value of external cost reduction gradually increase from only 13.4 billion rupiah for B10 case to be 59 billion rupiah for B20, 133.7 billion rupiah for
B50 case and reach to be 245 billion rupiah for B100. The maximum external cost reduction when B100 is used in 2025 is 447.7 billion rupiah or about 3.2
times higher than the external cost reduction at low emission coefficient case. 3. The result of sensitivity analysis shows that the external cost value increases
linearly with the increasing of emission coefficient and fading function IGAV value. The cost however, decreases exponentially with the increasing
of wind speed. 4. By comparing the result from Samudro’s [64] and Syahril’s [4] studies, it can
be concluded that the external cost value of high emission coefficient case is more representative compared to the one from low emission coefficient case.
The result of the non-biodiesel case for 2015 is 4.13 trillion rupiah while Syahril’s study produced a figure of 4.30 trillion rupiah. Therefore, the
calculation result has confirmed similar finding from other studies.
CHAPTER VII GENERAL DISCUSSION
Kategori