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GREENHOUSE GAS EMISSIONSFORECAST FOR MUMBAI’STRANSPORTATION SYSTEM
ByArti Roshan Soni
CESE (IIT , Bombay)
1Theme : Transport and climate change
Outline of the study
• Motivation / Need of the study• Introduction• Data Source• Methodology• Results• Impacts of vehicle growth and CO2 emissions.• Conclusion
2
Motivation of the study• Greenhouse gases are the main causes of changing climate
(IPCC,2014)
• Vehicular emissions accounts for 60% of GHG in India (258.10 Tg ofCO2). Maharashtra share – 11.8% (Highest)
.(Ramachandra et al.,2009)
• Mumbai has a high exposure to risks especially rises in sea levels,due to its high density of population, low lying areas and industrialbuildings
• Forecasting results shall help in determining transport strategies infuture
• Implementation of transport mitigation policies 3
4
Introduction –Study area
• Mumbai Metropolitan Region(MMR) fastest growing metropolisesin India
• MMR comprises seven municipalcorporations, 13 municipal councilsand 996 villages
• Being surrounded by sea on threesides, the city had an averagedensity of approximately 20,000persons living per km2 in 2011
• There is a major reliance by most ofthe Mumbai’s inhabitants on publictransport to make the dailycommute to their workplace
Population trend in MMR (1971-2001)
02468
1012141618
1971 1981 1991 2001
Pop
ulat
ion
(Milli
ons)
Greater Mumbai Thane Kalyan - DombivaliNavi Mumbai Mira Bhayandar BhiwandiUlhasnagar
Source – CTS Report MMR 5
Per capita income of Greater Mumbai,Maharashtra and India
6Source – CTS Report MMR
Data Source
7
Historical data on different motor vehicles inMumbai metropolitan region
Source - Mumbai metropolitan region development authoritytransport report 2014
Year Total motor vehicleregistered
2001 10,29,5632002 10,69,4992003 11,23,5622004 11,99,4162005 12,94,9402006 13,93,6472007 15,03,4452008 16,04,7042009 16,74,3662010 17,67,7982011 18,70,3112012 20,28,500
8
Fuel consumption in Mumbai metropolitanregion
Source : RTI , IOCL, 2015 9
Year Diesel (MT) Petrol (MT)
2005-06 12,58,950 3,84,3132006-07 14,45,036 4,01,5092007-08 17,37,682 4,52,1542008-09 18,34,609 4,79,3712009-10 19,03,156 5,22,7002010-11 20,70,045 5,67,0652011-12 21,66,750 5,74,8492012-13 21,90,886 5,74,0552013-14 21,14,024 5,78,380
Methodology
10
Grey prediction model
• GM(1,1) implies a first-order single variable predictionmodel and it is used for time series forecasting purpose
• GM(1,1) can be used when the amount of input data islimited (four data is sufficient) (Hamzacebi and Es,2014)
• According to Deng (1989), GM(1,1) is based on threeessential steps: one is accumulated generation operation(AGO), another is grey modeling, and the last one isinverse accumulated generation operation (IAGO)
11
Step 1. Accumulated generating operation• The building of the AGO model is based on the accumulated generating
process of the original sequence,
• X(0) = [X(0)(1), X(0)(2), X(0)(3),…., X(0)(n)]
• =[1029563, 1069499, 1123562,…..,2028500]
• Where n represents total number of periods. The best part of GM model
is it does not require large data like linear regression and ANN.
• The AGO model is obtained as
• ( ) = ( ) , = , , , … (1)
• Where X1 is the one order accumulated generating sequence of X(0). That is
• X(1) = [X(1)(1), X(1)(2), X(1)(3),…X(1)(n)
• =[1029563, 2099062, 3222624….,17559751] (2) 12
Step 2. Grey difference equation( )( )+ = (3)
• Where a is the development coefficient and b is the grey controlledvariable.
• Usually, the expression of Eq. (3) is called the whitening model orshadow model of GM(1,1).
= (BTB)-1 BT Xn
Xn =
( ) (2)( ) (3)… . .( ) …( )
Step 3. Least square method.(4)
Where,
B =− (2) 1− 1 (3) 1… . .− 1 ( ) …1 And
Grey prediction model
13
=−1564313 1−2660843 1… . .− 1 ( ) …1 =
1069499 21123562 3… . .2028500( ) …( )
a = -0.063b = 973815.92
• Step 4. Inverse accumulated generating operation (IAGO)
• Next, the procedure of inverse accumulated generation is followedby Eq. (8) to get the predicated sequence of the primitive data.
(0) (k+1) = (1)(k+1) - (1) (k)
(1) (k+1) = ( (1) - ) +
(0) (1) = (1) (1),
(0) = [ (0) (1), (0) (2), ...., (0) (n)]
Is the estimated value of original sequence, X(0) and (0) (n+1) is the
predicted value of X(0).
(7)
(8)
Where,
Grey prediction model
14
To ensure the accuracy and efficiency of the forecastedresults, the evaluation of error analysis is made by Eq. (9)
Where X(0) (k) is the actual value and (0) (k) is thepredicted value.
Error examination
e(k) = (X(0)(k) - (0)(k)/X(0)(k))*100% (9)
Grey prediction model
15
CO2 emission estimation
• The emissions for each category of fuel is calculatedbased on the following equations:
E = (N X EF)
• Where, N is the amount of fuel (diesel or petrol )consumed in litres and EF is the CO2 emission factor forrespective fuel.
(10)
Sl. No. FuelCO2 emission
factor(kg CO2/litre)
1. Diesel 2.302. Petrol 2.66
Emission factor for fuels (Gilani, 2009)16
Results
17
Results of grey model forecasting
Actual and predicted motor vehicle in MMR 18
Year Actual PredictedLinear
regression
2002 1069499 1072720 1052348
2003 1123562 1142922 1143673
2004 1199416 1217717 1234999
2005 1294940 1297408 1326324
2006 1393647 1382314 1417650
Max Error GM = 2%& Reg. = 6%
0
1000000
2000000
3000000
4000000
500000020
0120
0320
0520
0720
0920
1120
1320
1520
1720
1920
2120
2320
25
Num
ber o
f veh
icle
s in
Mum
bai
Year
Number of actual and predictedvehicle in Mumbai
Predicted vehicle growth in MumbaiActual vehicle growth
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
Num
ber o
f mot
or v
ehic
les
Year
Bus3 Wheeler2 Wheeler4 Wheeler
Predicted vehicles in various categories in MMR
Results of grey model forecasting
19
Results of grey model forecasting(Diesel)
20
0500000
10000001500000200000025000003000000350000040000004500000
Die
sel C
onsu
mpt
ion
(MT)
Year
Actual and predicted consumption of diesel in GreaterMumbai
Actual diesel consumed Predicted diesel consumption
0
200000
400000
600000
800000
1000000
1200000Pe
trol
(MT)
Fuel Conumed till 2014 Fuel consumed predicted
Results of grey model forecasting(Petrol)
21
CO2 emission (Actual and predicted)
0
2000
4000
6000
8000
10000
12000
14000
CO
2em
issi
ons
(MT)
Predicted CO2 emissions from petrol(MT)Actual CO2 emissions from petrol(MT)Predicted CO2 emissions from diesel (MT)Actual CO2 emissions from petrol(MT) 22
Discussions
23
PopulationGrowth, 43%
Sub-urban traindaily trips, 35%
Bus daily trips(Main + Feeder),
9%
Registered Cars,137%
Registered twowheelers, 306%
Registered Auto,420%
Registered Taxi,125%
CommercialVehicle, 200%
AirportPassengers,
94%
0% 100% 200% 300% 400% 500%
Actual growth in vehicles and population (1991-2005)
Source - Mumbai metropolitan region development authoritytransport report 2014
24
Impact of vehicle growth
Congestion
25
Travel Time
Road Name
PeakHour
(Minutes)
Off – Peak(Minutes)
Lal bahadurShashtri Marg
32 18
Jogeshwari –Vikhroli linkroad
25 15
Easternexpress road
32 18
Source – Survey results
Average fuelconsumed whileidling gasoline
vehicle (litres/hour)2W 3W Cars0.5 1 1
Average fuel consumed whileidling from diesel vehicle
(litres/hour)Cars L.D.V H.D.T Bus
1 2 3 3
Impacts of vehicular idling
Source – Guttikunda, 2009
Impact of vehicle growth
• May demand parking space – Parking policyrequired– Increase in congestion– Reduces efficiency of public transport.– Improves safety– Smooth traffic flow reduces fuel consumption.
• Speed reduction – Average travel speed ofisland city fallen (18 to 8 kmph)
• Suburbs – 30 to 5 kmph (Max. travelspeed 40 to 45 kmph)
26
• Increase in number of trips – estimated results ( 4.75millions in 2005 to 10 million in 2031). (CTS report,2014)
• Fuel Consumption – GHG emissions
Impact of vehicle growth
27
Impact of CO2 emissions
Climate Change
28
Evidences of Mumbai’s changing climate
6900
6950
7000
7050
7100
7150
7200
1860 1880 1900 1920 1940 1960 1980 2000 2020
Trend in mean sea level data for Mumbai (1860 – 2000)
MSL Linear (MSL)
Source - http://www.psmsl.org/data/obtaining/rlr.monthly.data/43.rlrdata 29
25.5
26.0
26.5
27.0
27.5
28.0
28.5
1984 1991 1998 2005 2012Te
mpe
ratu
re (ᵒ
C)
YEAR
Trend in Mumbai's temperature(1984-2014)
0
500
1000
1500
2000
2500
3000
3500
4000
1980 1990 2000 2010 2020
Pre
cipi
taito
n (m
m)
YEAR
Trend in Mumbai's precipitation(1984-2014)
Source – Indian Meteorological Department
Evidences of Mumbai’s changing climate
30
Mitigation measures for Mumbai
• Railways, mass rapid transit and bus rapid transit aremore efficient than highways in terms of providingmobility per ton of CO2 emitted
• Suburban services should be increased without affectinglong distance services
• Extension of traffic networks – unsustainable effects ,like energy consumption & CO2 emissions
• Public Transportation first
31
• Restricting entry of polluting trucks and heavy duty goods vehiclesand banning of old commercial vehicles in the cities
• Need for comprehensive vehicle scrapping policy
• Identification of highly polluting areas as low emission zone
• Ensure that conversion to CNG/LPG is reported to authority forrecords
• Levying higher motor vehicle tax on old vehicles
• Random checking of CNG/LPG kits, any other emission controldevices or retrofit engines for emission performance
32
Mitigation measures for Mumbai
• Centralised I&M system where inspection and maintenanceare carried out independently
• Notification of fuel economy standards CO2 emission, fueleconomy yet to be implemented
• System to check emission warranty of new vehicles
• Linking of vehicle insurance with inspection and certification
• Comprehensive programme for zero emission vehicles toaccelerate development of alternative fuel vehicles (batterypowered, hydrogen and fuel cell)
33
Mitigation measures for Mumbai
Conclusion
• Grey model has good efficiency with maximum error of10%
• The vehicle growth, fuel consumption and CO2 emissionis expected to grow by 6.5%, 5% and 5%, respectivelyby 2025
• Vehicle growth is expected to reach 4.60 million in 2025with a maximum error of 2% in the analysis
• The CO2 emissions from the estimation are expected toreach 12.41 million tonnes(Diesel) & 3.36 million tonnes(petrol) in 2025
34
• Policies to control vehicle purchase andcongestion charging
• Dedicated lanes for high occupancy vehicles
• Alternate transport modes to be developed but notat the cost of public transport
• Need of one transport implementing authority
• Improvement of public transport
35
Conclusion
References
• Hamzacebi, C., and Es, H. A. 2014. Forecasting the annualelectricity consumption of Turkey using an optimized greymodel.Energy70:165–171
• Deng, J. L. 1989. Introduction to grey system theory.J. Grey Syst.1:1–24.
• Vivek Gilani, March 2012. Emission factor Ready Reckoner, India.Retrieved from(http://no2co2.in/admin/utils/internalresource/intresourceupload/EF_ready_reckoner_india_Mar2012_CC.pdf) on 29/09/15.
• T.V. Ramchandra and Shwetmala, 2009. Emission from India’stransport sector: state wise synthesis. Atmospheric Environment 43,5510 – 5517. 36
References
• Anjana Das and Jyoit Prakash,2004. Transport scenario in twometropolitan cities in India: Delhi and Mumbai. Energy conversionand management 45, 2603 -2625.
37