Mitigating Environmental Emissions from the Urban Transport System

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Mitigating Environmental Emissions from the Urban
Transport System
Ram M. Shrestha S.C. Bhattacharya Nazrul
Islam N. T. Kim Oanh
Asian Regional Research Programme in Energy,
Environment and Climate (ARRPEEC) Asian
Institute of Technology, Thailand
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Cities Covered
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City Profile
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Project Network
AITAsian Institute of Technology, Thailand
DOSTE Department of Science, Technology and
Environment, Vietnam Prof. Nguyen Thien Nhan
ERIEnergy Research Institute, China Dr. Zhou
Dadi IGIDRIndira Gandhi Institute of Development
Research Prof. Jyoti
Parikh ITBInstitut Technologi Bandung, Indonesia

Dr. Tatang H. Soerawidjaja SATMPSociety for the
Advancement of Technology in the Philippines,
Philippines
Dr. Joy V. Abrenica
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Study Objectives

• To analyze the demand for urban transport
  services and associated energy demand and
  environmental emissions
• To analyze and select the technical options for
  energy efficiency improvement and mitigation of
  GHGs and other harmful emissions from the urban
  transport system and
• To identify and rank the barriers to the
  introduction of selected technical options to
  mitigate environmental emissions from the urban
  transport system.

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Travel Demand, Energy Demand and Associated
Environmental Emissions
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Projection of Travel Demand
GDP Growth Rate for BAU Projection ()
GDP Growth Rate for Alternative Scenarios
Alternative Scenario 1 is 1.5 times BAU GDP
growth rate Alternative Scenario 2 is 1.25 times
BAU GDP growth rate Alternative Scenario 3 is
0.75 times BAU GDP growth rate Alternative
Scenario 4 is 0.5 times BAU GDP growth rate
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Demand for Transport Services (p-km) BAU
Projection

• Annual average growth rate of demand for
  transport services would be in the range of 3.3
  (Beijing) to 7.3 (HCMC) during 1998-2020.

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Vehicle share (of the total vehicles) in Bandung
and Beijing
Bandung
Beijing
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Vehicle share (of the total vehicles) in Delhi
and Hangzhou
Delhi
Hangzhou
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Vehicle share (of the total vehicles) in HCMC and
Jakarta
HCMC
Jakarta
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Vehicle share (of the total vehicles) in Manila
and Mumbai
Manila
Mumbai
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Change in Model Mix (2005-2020)
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Car Ownership in 1998 and 2020 (Units/1000
population)

• Beijing would have the highest car ownership
  among the cities (248 in 2020). However, the
  number would be still less than that in OECD
  countries.

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Bus Ownership in 1998 and 2020 (Units/1000
population)

• Beijing would have the lowest bus ownership
  during the planning horizon.

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Two-wheeler Ownership in 1998 and 2020
(Units/1000 population)

• 2-wheeler ownership would be relatively low in
  Beijing, Hangzhou and Manila

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Annual Average Growth Rate of Total Transport
Energy Demand (1998-2020) BAU Projection

• AAGR is above 5 in all Cities

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Share of CNG in Total Energy Demand in 2005 and
2020 ()

• The share of cleaner fuels, i.e. CNG, would
  increase in the future especially in the Indian
  cities of Mumbai and Delhi followed by Hangzhou
  Beijing and Jakarta.

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Average Annual Growth Rate of CO2 Emission During
1998-2020 ()

• Average annual growth rate in the range of 3.1
  (in Jakarta) to 12 (in Manila).
• Total transport CO2 emissions from the eight
  cities 53.8 million tonnes in 2020.

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Ratio of CO2 in 2020 to the Base Year (1998)
Emission BAU Projection
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Modal Share in CO2 Emissions in Bandung and
Beijing
Bandung
Beijing
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Modal Share in CO2 Emissions in Delhi and Hangzhou
Delhi
Hangzhou
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Modal Share in CO2 Emissions in HCMC and Jakarta
HCMC
Jakarta
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Modal Share in CO2 Emissions in Manila and Mumbai
Manila
Mumbai
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Changes in Modal Share in CO2 Emission
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Ratio of Local Pollutants in 2020 to the Base
Year (1998) Emission BAU Projection

• Among the cities, Mumbai would have the lower
  ratio due to the higher share of buses, use of
  CNG and penetration of 4-stroke 2-wheelers.
• HCMC would have the higher ratio due to the
  higher share of 2-wheelers.

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Technical Options for CO2 Emission Mitigation
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Technology Options Considered for Emission
Mitigation
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Least Cost CO2 Mitigation Options
ADOAdditive diesel oil
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Least Cost CO2 Mitigation Options Contd..

• MRTS is cost effective at 20, 40, 25 and 40
  CO2 reduction target in Bandung, Beijing,
  Hangzhou and HCMC respectively.

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Impact of CO2 Mitigation Target on Emissions of
Local Pollutants

• Local emission reduction objectives could still
  be served by focusing on CO2 emission reductions.
  
• In the case of Beijing and Hangzhou, the
  introduction of efficient diesel car would reduce
  the emission level of CO, NOx and NMVOC. However,
  it would increase the emission of TSP.
• TSP emission in Delhi would be reduced by 13
  under 10 CO2 reduction target.
• In Mumbai. TSP emission would be reduced 14 to
  10.
• In the case of Manila, CO emissions would fall by
  32 at 10 CO2 reduction.

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Selected Technical Options to Mitigate CO2
Emission

• Bandung LPG buses, bio-diesel buses and
  bio- ethanol buses
• Beijing CNG buses, diesel cars and MRTS
• Delhi CNG buses, CNG cars and 4-stroke
  2-wheelers
• Jakarta CNG buses, LPG buses, bio-diesel
  buses and bio-ethanol buses
• Hangzhou CNG buses, diesel cars and MRTS
• HCMC MRT, Diesel bus
• Manila CNG buses, alco-diesel buses and (coconut
  methyl ester) CME buses
• Mumbai CNG cars, CNG 3-wheelers and BOV
  3- wheelers

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Barriers to the Adoption of Efficient Options
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Barriers to the Adoption of Efficient Options

• Barriers varies from
• Country to country
• City to City
• Technology to Technology
• Technology specific barriers for each city were
  identified and the analysis of barriers are
  carried out using Analytic Hierarchy Process
  (AHP).

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Barriers to the Adoption of CNG Bus
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Barriers to the Adoption of Bio-fuel Buses in
Manila
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Barriers to the Adoption of CNG Cars
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Barriers to the Adoption of MRTS
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Barriers to the Adoption of 4-Stroke 2-wheelers
in Delhi
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• Thank you

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Additional Information
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Project Approach
Project Development (AIT, NRIs, Regional
Experts/Policy Makers)
Review of Methodology (NRIs)
Development of Methodology (AIT)
Country Case Studies (NRIs)
Review of Case Studies (AIT)
Cross-Country Synthesis (AIT)
Publications (AIT, NRIs)
Dissemination (NRIs, AIT)
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Methodology
Population
GDP
Emission Factor
Energy Intensity
Econometric Model
Vehicle Stocks
LEAP Model
Spread Sheet Model
Transport Demand (p-km)
Energy Demand
Utilization, Occupancy Rate
Emission
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Modal Mix in 2005 and 2020 under the BAU Case,
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Structure of the Projected Energy Demand in 2005
and 2020

• The share of cleaner fuels, i.e. CNG, would
  increase in the future especially in the Indian
  cities of Mumbai and Delhi followed by the
  Chinese cities of Beijing and Hangzhou.

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Ratio of Environmental Emission in 2020 to the
Base Year Emission
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Flow Chart of Methodology
Candidate Options
Costs
Vehicle Penetration Rate Fuel Availability Emissio
n target
Transport Demand Data
Vehicle-Mix Model
Vehicle-km by mode
Vehicular Mix
Emission Factor
Total Cost
Total Emissions
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Least-Cost Vehicle Options Bandung and Jakarta

• In the base case, the shares of gasoline vehicles
  in total passenger transport service supplied in
  both cities would be decreasing while that of
  additive diesel oil (ADO) and LPG would be
  increasing
• At 20 reduction target, LPG and bio-diesel
  vehicles (car, minibus, truck, and bus), and MRT
  would be cost effective options to meet the CO2
  reduction target in Bandung. The share of
  bio-diesel vehicles at 20 target would be 24.6
  in 2020.
• In the case of Jakarta, LPG car would be selected
  at 10 reduction target and bio-diesel vehicles
  (car, bus, jeep, minibus, bus, pick up and truck)
  and MRT would be selected at 40 reduction
  target. The share of bio-diesel vehicles and MRT
  at 40 reduction target would be 42.6 and 4.8
  respectively in 2020 in Jakarta.

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Least-Cost Vehicle Options Contd.. Beijing and
Hangzhou

• Under Base Case, the shares of gasoline car and
  diesel buses on total passenger kilometer
  supplied would increase in both cities.
• To achieve a reduction of 10 CO2 emission,
  gasoline cars needs to be replaced by diesel
  cars.
• At higher emission reduction target of 40 for
  Beijing and 25 for Hangzhou, the share of MRTS
  would increase substantially.
• The shares of diesel-buses in total passenger
  transport service supplied in different years
  would reduce significantly when the emission
  reduction target is increased from 30 to 40 for
  Beijing and 20 to 25 for Hangzhou.

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Least-Cost Vehicle Options Contd.. Delhi and
Mumbai

• Among the technologies considered, CNG buses
  would supply highest share of the transport
  services in passenger-km in Delhi while diesel
  buses would supply highest share of the transport
  services in Mumbai.
• In Delhi, at higher emission reduction target
  level of 25, diesel buses would replace the
  gasoline and diesel cars.
• In the case of Mumbai, CNG buses would replace
  diesel buses at the emission reduction target of
  5 while at higher emission reduction target of
  30 battery operated 3-wheelers (2.5 in 2020)
  would replace the diesel 3-wheelers.

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Least-Cost Vehicle Options Contd..

• Manila
• Alco-diesel bus, alco-diesel trucks and cars with
  catalytic converters would be cost effective
  technologies at the lower CO2 emission mitigation
  target of 5 in Manila.
• As the emission mitigation target is increased,
  the share of these three technologies would be
  increased, while the share of diesel buses and
  diesel trucks would decrease. At 15 reduction
  target, 99 of the trucks would be using
  alco-diesel in 2020.
• HCMC
• Vans would be a cost effective technology at
  lower CO2 emission reduction target of 3 in
  HCMC. At the emission reduction target of 3, van
  would replace diesel buses.
• At higher emission reduction targets of 12,
  electric 2-wheelers would also be cost effective
  technology. Its share would be 27 at 12 CO2
  reduction target

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Marginal Cost of CO2 Abatement (MAC), US/tonne
of CO2

• MAC would be relatively high for Manila (178
  /tonne of CO2 at 5 reduction target) and
  relatively low for HCMC (0.5 /tonne of CO2 at 6
  reduction target).
• The MAC values are relatively low in Beijing,
  Delhi and Mumbai.

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Three important Barriers in Beijing and Hangzhou
for the Selected Options
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Three important Barriers in Delhi and Mumbai for
the Selected Options
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Three important Barriers in HCMC for the
Selected Options
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Three important Barriers in Manila for the
Selected Options