quantifying transport...

Quantifying Transport

Emissions

Sudhir Gota Consultant (GIZ) Advisor (SLoCaT, Smart Freight Center, Urban Emissions) Preliminary Demand Side Management Study: Hands-on Training on Sustainable Transport Indicators for Malaysia 24/25 January 2017 Kuala Lumpur

1966 1983

Tools & Methodologies

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Nu

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GH

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eth

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To

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SLoCaT Partnership

Data and CO2 Quantifications

Needed at Multiple Analysis Scales

Project

Plan & City

Nation

Need to take care to evaluate system-wide impacts, induced demand

Optimal scale to consider system impacts for metropolitan plans/programs

Often best for evaluating large networks and system policies

Source – Michael Replogle



Project/Policy

Plan &

City Nation




1. Measurement approaches

Top –Down • Fuel sales

Bottom-up • Data gathered from more detailed sources – activity, mode share, fuel intensity etc.

Top-down

Volume of

fuel

consumed

Total Energy

consumed (TJ)

Carbon content

per energy unit

(ton Carbon/TJ)

Energy

contained in

fuel (MJ/kg of fuel)

Total CO2

emissions

(tonsCO2)

Carbon to CO2

converter

(44/12)

Fraction of fuel

oxidized (%)

Source : Alvin Mejia

Bottom-up

Bottom-up

Emissions Activity X Emission

“Intensity” =

Emissions Activity X “Brawn” of

Vehicle (energy intensity)

= X Carbon “ABC”

Framework

“AI”- Activity

Intensity

Framework

Lee Schipper “ASIF” Framework (1990’s)

Measurement approaches – ASIF

Framework

= A Si Ii Fi,j Fuel Use and

Emissions from

Transport

* * *

Occupancy/

Load Factor

Vehicle fuel

intensity Vehicle characteristics

Technological energy efficiency

Real drive cycles and

routing, driver behavior

Veh-km and

pass-km by mode

Modal Energy Intensity

Emissions per unit of

energy or volume or km

from fuel J in mode I

Total Transport

Activity

Source – Lee Schipper

Top-down approach

Bottom-up approach

+

• probability of data being

available

• consistency in data

collection

-

• low level of detail

• limitations in assessing

specific interventions

+

• more detailed information

allows better analysis of

interventions

• Enables analysis of other

co-benefits

-

• time and costs in data

collection

• standardized procedures

for collecting specific data

may not be available

Bottom-up approach

Top-down vs Bottom-up

Source –Alvin Mejia

TOP- Down based on

aggregate fuel

statistics

Bottom-up

?

Where did the fuel go?

Myth - “Top-down is accurate while bottom-up estimates unreliable

especially in countries with limited data”

Activity

Structure

Intensity

Fuel

1. Can’t tell good mitigation from poor

2. Miss long-term options for better transport and development

3. Risk of missing different drivers of behavior, technology, etc 4. Loss of good policy arguments that bring CO2 as a co benefit

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1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Tra

nspo

rt C

O2 E

mis

sio

ns (

MT

)

2011-44 MT Top-Down

Bottom-up

2005 Baseline

Malaysian - Danish

Environmental Cooperation

Programme

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

1960 1970 1980 1990 2000 2010 2020 2030 2040

Transport CO2 Kg/$

COP15, Malaysia has pledged to

voluntarily reduce carbon dioxide

(CO2) emission intensity of Gross

Domestic Product (GDP) up to 40%

by 2020 as compared to 2005

levels

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Tra

ns

po

rt (

as

su

min

g 1

99

0 =

10

0)

CO2 (IEA) MTOE (energy Statistics) CO2 (BUR)

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Ro

ad

Tra

nsp

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PM

10

/Ca

pit

a (

g/c

ap

ita)

GDP per capita (PPP constant 2005)

Data till 2008, European Commission, Joint Research Centre (JRC)/PBL Netherlands Environmental Assessment

Agency. Emission Database for Global Atmospheric Research

Philippines - Department of Energy claims ‘reduction’ due to “Impact of policies”

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Road Transport Fuel Consumption

Vehicles

GDP/Capita

Where did the fuel go? - Philippines

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1980 1990 2000 2010 2020 2030 2040

CO

2

“33%” of fuel sold in the Philippines is smuggled costing 1 billion USD loss of revenues

Top Down estimates are not realistic with ground conditions and aggressive

policies are needed to reach 2030 goals.

DOE 2030

Target

2-3X

Increase

Top Down

Bottom-up

Where did the fuel go? - Philippines

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140

PC Taxi HDT MDT LDT Mini T(G)

HDB LDB 3W 4W

Car Trucks Bus MC RV

Ch

ina

Th

ou

san

ds

VK

T

Maximum

Minimum

Average

Average VKT (China)

Source –Sudhir Gota (2014)

The Great Indian Mismatch

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315

161

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1960 1970 1980 1990 2000 2010 2020

M T

on

s o

f C

O2

Bottom-up

Top-down

Gasoline


Majority of variation in total CO2 emissions can be explained by variations in diesel

consumption.

India Passenger Road Transport

Efficiency

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20000

25000

0 20 40 60 80 100 120

PK

M (

Bil

lio

n)

CO2 g/PKM

ICCT RoadMap (2050) – RoadMap Tool

ADB/CAA Forecast (2035) Excel sheets

IEA SMP (2050) Excel sheets Govt

Includes NMT

Use a set of indicators to compare results and evaluate policy measures

WB Effect (2031) EFFECT TOOL

Source : Sudhir Gota – Crunching Numbers

For Mitigation - Decision 17/CP.8, paragraph 38:

“Based on national circumstances, non-Annex(NAI) Parties are encouraged to use whatever methods are available and appropriate in order to formulate and prioritize programmes containing measures to mitigate climate change and that this should be done within the framework of sustainable development objectives, which should include social, economic and environmental factors.”

For GHG Reporting - “There is no Tier 3 as it is not possible to produce significantly better results for CO2 than by using the existing Tier 2. In order to reduce the uncertainties, efforts should concentrate on the carbon content and on improving the data on fuel sold.” Section 3.2. of the 2006 IPCC Guidelines for

National Greenhouse Gas Inventories

Top-down vs Bottom-up

GHG Emissions from Transport

Transition

× × × A

Activity /

Transport

demand (VKT)

S Structure of

modes (VKT

by mode)

I Energy

intensity

(MJ/km)

F Fuel carbon

content

(CO2/MJ) Drivers

Economy

Technology

Demographics

Culture

Spatial

structure

Strategies and policies

National institutions

and stakeholders

Local institutions

and stakeholders

Avoid trips

or reduce

the distances

travelled

Shift

to

low carbon

modes

Improve

vehicle fuel economy

and

fuel quality

Measurement and accounting

Energy

consumption

(MJ by fuel type)

Fuel

carbon content

(CO2/MJ) ×

top-down

method

Source – Low Carbon Transport Handbook

2. ASI and ASIF : Mitigation Principle

3. Tools & Methodologies

Name of Tool/Methodology

Release

Year

Analysis period Co-Benefits Included

1 Year Multi-year CO2 PM NOx SLCP Fuel Other

International Vehicle Emissions (IVE) 2008 X X X X X

Methodology for calculating transport

emissions and energy consumption 1999 X X X X X

ForFITS Model 2013 X X X

GCAM - Transport Module (2013) 2013 X X X X

GHGenius model 2004 X X X

Global Transportation Roadmap Model 2012 X X X X X

Green Freight Asia 2014 X X

LEAP 2012 X X X X X

MOBILE 1978 X X X X X

MOMO 2009 X X X X X X

MOVES (Motor Vehicle Emission Simulator) 2010 X X X X

Policy and Action Standard Road Transport

Sector Guidance 2015 X X

STREAM Model 2008 X X X X

ITPS Model 2011 X X X

SULTAN 2012 X X X X X X

The 2050 Calculator 2014 X X X X X

TREMOD: Transport Emission Model 1993 X X X X X

TREMOVE model 1998 X X X X X

VIBAT 2007 X X

WB EFFECT Tool 2011 X X X X X

A S I F

Passenger

kilometre

Freight

kilometre

Different Modes:

Technology/ fuel

Vehicle

space per

passenger

or tonne

Fuel

efficiency NOx

HC

CO

PM

Sector Sub-sector End-use Device Energy

Intensity Emission

Factor

Personal

modes

IPT

modes

Public

modes

Goods

Pkm & Tkm % % 1/occupancy GJ/km gm/l or m3

CO2

Bottom-up – LEAP Model

Bottom-up – IVE Model

Bottom-up –

EFFECT Model

23-step modelling process for on-road

passenger and freight transport.

Uses locally adjusted EURO COPERT

4 emissions factor

-GHG emissions, cost comparison of

scenarios

- Comparison of technologies over

lifetime

- Break even point of Carbon

- IRR of technologies

- Marginal abatement cost curves

4. Setting Baseline

“Do Nothing” baseline

“Do Minimum” baseline

“Do Something else” baseline

“Static“ baseline

“Dynamic” baseline

“Most Likely” dynamic baselines are most useful

Emissions Are Not Static

•Travel activity trends

•Mode share trends

•Travel Behavior trends

•Changing vehicle occupancy

•Changing vehicle economy

•Changing vehicle fuels

•Investment trends

•Economic activity trends

•Demographic changes

Parameter Importance

Total number of vehicles per class

Distinction of vehicle to fuel used

Distribution of cars/motorcycles to engine classes

Distribution of heavy duty vehicles to weight classes

Distinction of vehicles to technology level

Vehicle Kilometer Travel

Urban driving speed

Rural, highway driving speeds

Grade/Temperature/I&M/Age distribution

5. Data Quality over Quantity

3 “D” Rule – disaggregate, data for most “sensitive” mode & dynamic “most-likely”

baseline

Recipe to success



Project

Plan & City

Nation




1. City level Estimates - Boundary

Source – GIZ – Quantifying transport emissions in German Cities

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Average cycle speed [km/h]

[g/k

m]

PRE EURO 1

EURO 1

EURO 2

EURO 3

EURO 4

EURO 5

Fuel consumption

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Average cycle speed [km/h]

[g/k

m]

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EURO 1

EURO 2

EURO 3

EURO 4

EURO 5

NOx

2. Impact Of Speed

Source: Pischinger et al, Update of the emission functions for heavy duty vehicles in the handbook Emission factors road traffic, Bericht nr. Pi-55/01 d.d. 30.4.2002 Graz University of Technology

2. Impact Of Speed

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Cars

- C

O2

G/k

m

Speed (kmph)

China Green Transport India

Bangkok-PCD Copert

TEEMP QLD EPA

CDM TRL

Japan-JRI

Be consistent with the approach adopted in BAU, Scenario and

Evaluation


2. Crowdsourcing Speed

3) Consider Proposed Investment

-

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Project Scenario Revised ProjectScenario

Tau

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de

Total CO2 Savings (tons)

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2009 Baseline2030

MobilityPlan 2030

% of Motorized Trips

0

Metro/Monorail

BRT

Bus

Taxi

3W

2W

Car 5.069

5.070

5.070

5.071

5.071

5.072

5.072

5.073

5.073

5.074

Project Scenario Revised ProjectScenario

Total Cost (million)

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4.000

6.000

8.000

10.000

2009 2014 2019 2024 2029

VKT (millions) BAU VKT (millions) Mobility

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1.000

2009 2014 2019 2024 2029

Fatalities BAU Fatalities Mobility

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1.000

1.500

2.000

2009 2014 2019 2024 2029

Hours(millions) BAU

Hours(millions) Mobility

Source –Sudhir Gota

(2013)

In CO2 impact, 48% reduction in savings projected earlier due to delay

Measuring CO2 Emissions at City level

0,85 1,43

2,54

4,91

3,00 3,11

7,24

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0,83

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TS

(20

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nga

lore

-MO

UD

(20

08

)

SIM

-Air(2

00

8)

TE

RI (2

00

9)

CA

A (

20

08

)

IIS

C (

20

08

) (P

+F

)

KS

PC

B (

20

08

)(P

+F

)

Bo

se

(2

01

0)

(P+

F)

CA

A (

20

08

) (P

+F

)

ICL

EI (2

00

8)

(P+

F)

CO

2 (

MT

)

Data from Transport Model

Vehicle Registration Data

Both Methods

Data, Boundary and Capacity are critical issues.

Easy solution is to breakdown the estimates into different indicators and use a consistent

approach Understanding Freight is a key

Passenger Passenger Passenger + Freight




Project

Plan & City

Nation




Source – Michael Replogle

TEEMP Model Bus rapid transit

MRT

Railways

Walking infrastructure improvement

Bikeways

Bike sharing

Commuter strategies

Pricing strategies

Eco-driving

Vehicle Replacement

PAYD Insurance

TEEMP City

Credit: John Rogers, World Bank

1) Ex-ante & Ex-post

2) Avoid “Doing Nothing” in Projects

avoided road space for a BRT is 2 m2 per rider and Metro is 3 m2per rider.

Baseline - what would 'most likely' happen if this project is not executed

Impact of the project is compared with a 'no build' / 'without project'/ 'doing nothing'

baseline.

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MRT Costs (million USD) Avoided Infrastructure Costs (million USD)

Ahmedabad Cebu Guangzhou PimpriChennai Bangalore Ho Chi Minh Manila

Infrastructure Vehicle Fuel

Manufacture X X

Use X X

End-of-Life X X

1. Construction emissions for highways – few months to 2 years of operation

emissions, metro – 3-12 years of operation emissions, Railways – 10-20% of

lifecycle emissions

2. Maintenance emissions is less than 1% of lifecycle but high benefits

3. Fuel manufacture – ~14%-20% increase

4. Vehicle manufacturing &scrappage ~4 – 16%% of lifecycle emissions

5. Recommend to neglect scrappage emissions

3) Lifecycle emissions

-4000

-2000

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10000

CO2 saved (10 years) withconstruction

CO2 saved (10 years) withoutconstruction

To

ns/

Year/

km

Impact of Construction Emissions

Ho Chi Minh metro analysis for 10 years with and without construction

intensity gives opposite conclusions.

-4000

-2000

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14000



To

ns/Y

ear/

km

4) Impact of Project Life on Emissions

Emissions quantification based on shorter project lifecycles may

provide flawed results.

5) Impact of Induced Traffic

Emissions in a typical highway

projects are 17% to 58% as

compared to a scenario which

excludes induced traffic

considerations.

6) Impact of Speed in Projects

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s/km

/Yea

r

TEEMP

JICA

With Speed

Without Speed

7) Certainty Levels versus Costs

Costs

Certainty levels

Carbon Market

Governments with non-biding targets

Philanthropy

Governments with binding targets

Need to strike balance between “costs” and “certainty levels”

Need to get reasonable sense of direction for policy action

Wider Impact

Direct Impact

8) Prioritize Co-benefits

25$ of Fuel Savings

5$ of Travel Time Savings

15$ of Road investment savings

5$ of Health Benefits due to air pollution reduction

5$ of Road Safety Benefits

1000 KM of BRT

- 1200 reduced traffic fatalities/Year

- 300 tons of Black carbon/Year

- 2 Million tons of CO2/Year

- 175 deaths/year due to improved air

quality

- 90,000 short term jobs

- 128,000 New permanent jobs

- 500,000 $ avoided crop loses/Year

- 500 Million hours of time savings

For every 10 $ spent on BRT

8) Prioritize Co-benefits

IMPROVED BRANDING

GHG Measurement for Transport

Based on: • Traffic counts • Speeds • Costs • Lifecycle • 20 Years • Induced Traffic • Vehicle

disaggregation with age, fuel, emission standard

Based on: • Trip Rate • Links/Landuse • Mode Choice • Trip Distance • Vehicle Ownership • Speed • Vehicle

disaggregation with age, purpose, fuel, emission standard

Based on: • Fuel use • Vehicle ownership • Average km traveled • Vehicle disaggregation

with age, purpose, fuel, emission standard

• Region/road • Disaggregate

GHG mitigation

Air Pollution reduction

Energy savings

Quality of life

Reduced deaths

Reduced illnesses

Reduced external costs

Increased employment

Reduced congestion

Efficient investment

GHG Measurement for Transport

What is the purpose of your

quantification?

What is the scope of analysis?

What transport data is needed and

available?

Resources?

Capacity?

What if We Get it Wrong?

• Poor Forecasts and Scenarios of Transport (GIGO) • Miss long-term options for better transport and development

• Drive headlong into worse congestion, pollution, accidents

• Risk of missing different drivers of behavior, technology, etc

• Miss Opportunities to Mitigate Emissions • Can’t tell good mitigation from poor

• Can’t estimate costs of emissions mitigation

• Can’t discern low-carbon avoidance from other development strategies

• Little Chance of Co-Benefits, Avoidance • Cannot measure impacts until it may be too late

• Difficulties estimating counter-factual cases

• Loss of good policy arguments that bring CO2 as a co benefit

1. Very important to use a consistent approach

2. Know & understand the assumptions and numbers

3. ‘Dynamic – most likely’ baseline is the best baseline

4. Don’t stop quantification at absolute numbers, develop

different indicators to track and monitor results

5. Give importance to ‘quality’ over ‘quantity ‘of data

Prioritise - consistency, simplicity and co-benefits

Suggestions

Lee Schipper 1947-2011

Thank you!

Lee Schipper 1947-2011

Transport Data, Indicators

& “Best” Practices

Sudhir Gota Consultant (GIZ) Advisor (SLoCaT, Smart Freight Center, Urban Emissions) Preliminary Demand Side Management Study: Hands-on Training on Sustainable Transport Indicators for Malaysia 24/25 January 2017 Kuala Lumpur

High-Level Indicators

Detailed Data

Technical Level

The Public,

Policy makers

Experts, NGOs,

Policy advisors

Academics

Technicians,

Survey experts,

Indicator Pyramids

Henrik Gudmunsson, DMU

Which country has the most energy efficient transport

sector in the World?

How to Identify good Indicators?

“Cities, on average, are each

collecting in excess of 100

indicators, and in some cases,

annually collect 1,000

indicators. The eight pilot

cities were collecting over

1,000 various indicators,

___ of which were

common to all cities”

- Global City Indicators

Program Report (2008)

“3”


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1980 1985 1990 1995 2000 2005 2010 2011 2012 2013 2014

Energy Statistics 2011 andprevious

Energy Statistics 2012,2013, 2014,2015,2016

Transport Plantation Power Generation

Industry Misc Services Private Sales


“standardised” indicators – definition & methodology

Clean Air Scorecard

Air Pollution &

Health Index Clean Air Management

Capacity Index

Clean Air Policies

and Actions Index


Select indicators which can be effectively “communicated”

Select indicators which perform multiple functions


Policy Indicators – New Zealand

0 10 20 30 40 50 60 70 80 90 100

Access to the Transport System

Environmental Impact

Freight and the Transport Industry

Infrastructure and Investment

Network Reliability

Public Health

Safety and Security

Travel Patterns

Transport Price Indices

Transport Volume

% Complete Total Indicators

Tonkm

Vehicle km

Tonnes of CO2e emitted from domestic transport per vehicle km driven

Tonnes of CO2e emitted from domestic transport per tonne-km

Energy use (PJ) per vehicle kilometres travelled by domestic transport

Energy use (PJ) per tonne-km by domestic transport

Indicators on Energy Efficiency &

GHG Emissions

A good indicator should ideally meet the following standards:

1. The indicator has technical merit, easy to use & communicate.

2. It is feasible and “cost-effective” to measure the indicator.

3. The indicator has been field-tested or used operationally.

4. Mutually exclusive and collectively exhaustive.

5. Sensitive to the company’s classified information.

“Gold Standard” of Indicators

“if indicators are not selected carefully, they can consume extensive resources

and generate data with little or no value”

Core Indicators

TERM 01: Transport final energy consumption by mode

TERM 02: Transport emissions of greenhouse gases

TERM 03: Transport emissions of air pollutants

TERM 04: Exceedances of air quality objectives due to traffic

TERM 05: Exposure to, and annoyance by, traffic noise

TERM 12a/b: Passenger transport volume and modal split

TERM 13a/b: Freight transport volume and modal split

TERM 20: Real change in transport prices by mode

TERM 21: Fuel tax rates

TERM 27: Energy efficiency and specific CO2 emissions

TERM 31: Share of renewable energy in the transport sector TERM

34: Proportion of vehicle fleet by alternative fuel type.

Supporting Indicators = 29

After 15 years of assessment, “The importance of monitoring and the

definition of targets against which progress can be measured has

increasingly been recognised, as well as the role of proper ex-post

evaluation of policies”

Transport Energy Efficiency & GHG

Indicators

Operating Conditions

Mode Distribution

Fuel Split

Engine Size

Technology classification (Euro)

Travel

[Fleet, Distance

travelled, Trips, Load

Factor, Fuel efficiency

and Speed]

Trip Purpose

Transport Energy Efficiency & GHG

Indicators

Indicators - “ASIF” + “DPSIR” Framework

Suggestions

Approach Parameter Data Tier 1 (First Priority) Tier 2 (Second Priority)

Top Down Energy Use Fuel sold Amount of Fuel sold/consumed (litre/MJ)fuel type in transport sector

Normalizing factors for Intensity

Population No of inhabitants

Economic

Development GDP/Capita or GDP

Infrastructure Km of infrastructure

Headline Indicators

Transport Activity

Total vehicle kilometre travel (VKT) per population

Freight Tonkm/GDP

Passenger kilometre travel/GDP

Energy

Consumption Transport energy consumption per GDP

GHG Emissions

GHG emissions from transport sector segregated by modes

Transport GHG per capita

Passenger GHG per PKM

Freight GHG per TKM

Air Pollutants PM Emissions from transport sector segregated by modes

NOx Emissions from transport sector segregated by modes

Fuel Type Proportion of vehicle fleet by alternative fuel type

Share of renewable energy in total transport fuel consumption

Road Accident Fatality/Million VKT

Accidents/Million vehiclekm

Motorization Passenger and Freight Motorization Index ( vehicles/1000 population)

Freight Rates Unit Price ($) per Tonkm for different modes

Fuel Subsidy Fossil Fuel Subsidy/Unit of GDP

Investment Transport Investments

Climate Finance share in transport investments

Indicators – ASIF + DPSI”R” + “tiers”


Data Tier 1 (First Priority) Tier 2 (Second Priority)

Fleet Number of vehicles by vehicle registration

type & fuel type

No of Vehicle in use/mode/fuel type/by

engine size/emission standard/operating

conditions

Distance Travelled

Vehicle kilometre by

vehicle type (in vkt) (mode & fuel)

Vehicle Kilometre/mode/fuel type/by engine

size/emission standard/operating conditions

Passenger Kilometre (pkm) (mode & fuel)

Passenger Kilometre/mode/fuel type/by


conditions

Ton Kilometre (tkm) (mode & Fuel) Ton Kilometre/mode/fuel type/by engine

size/emission standard/operating conditions

Trips Total Number of Trips/Mode/Fuel type

Total Number of Trips/mode/fuel type/by


conditions

Load Factor

Average Occupancy (No of persons/Vehicle)

(by mode & fuel type)

Average Occupancy (No of

persons/Vehicle)/mode/fuel type/by engine

size/ emission standard/age/operating

conditions/geographical area

Average Loading (Tons/Vehicle) (by mode &

fuel type)

Average Loading (Tons/Vehicle)/mode/fuel

type/by engine size/ emission

standard/age/operating

conditions/geographical area


Approa

ch Parameter Data Tier 1 (First Priority) Tier 2 (Second Priority)

Bottom-

up

Fuel Intensity

Fuel Efficiency

Fuel Efficiency (kmpl or

L/100km or MJ/km) (by

mode & fuel type)

Fuel Efficiency (kmpl or

L/100km or

MJ/km)/mode/fuel type/by

engine size/ emission

standard/age/operating

conditions/geographical

area

Speed Speed by mode/fuel type

Speed by mode/fuel

type/by engine size/

emission

standard/age/infrastructure

type/geographical area

Emission factor

Emission factors for air

pollutants in g/KM per

vehicle/fuel type

Emission factors for air

pollutants in Kg/KM per

vehicle/fuel

type/technology type

[email protected]

sudhirgota

Skype - sudhirgota

Thank you

mailto:[email protected]

quantifying transport...

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