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.

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