Climate MRV for Africa – Phase 2

MRV of Mitigation Actions

TRANSPORT Sector

Project of the European Commission

DG Clima Action EuropeAid/136245/DH/SER/MULTI

Amr Osama Abdel-Aziz, Assen Gasharov, Mike Bess

and Laura Lahti

Team Leader and Key Experts

June 2017

Lead partner

Agenda

Emissions from Transport

Top Down Vs Bottom Up Modeling of Transport

Mitigation Opportunities in Transport Sector

Mass Rapid Transit (MRT) Systems

Emissions from Transport

gCO2/pKm

or

gCO2/VKT

#P or

#Vehicles

Km

X

X

=

gCO2

Transport Sector Overview

GHG

Disaggregated by:

Transport

use

Type of fuel

Type of emission gas

Rate:

GHG/fuel

unit

Fuel

Volume

Source: Baseline Compendium – Transport Sector

X

=

Transport Model – Top Down

Top-down analysis shows whether GHG emissions are

increasing or decreasing in the sector as a whole

Changes cannot be attributed with certainty to any

specific cause or variables

Use of national statistics for fuel and vehicles (similar

to the GHG Inventory)

Not sufficient to identify and estimate the effects of a

mitigation intervention; additional detail and variables

are required

Transport Model – Bottom Up

Individual person trips (or freight trips per unit of weight)

using motor vehicles are a basic unit of travel

Vehicle kilometres travelled (VKT) by type of vehicle is

key to estimating fuel volume

Information needed:

Number of trips

Length of the trip

Mode of the trip

Vehicle occupancy

Fuel efficiency of the vehicle

Transport Model – Bottom Up

Leakage and Rebound emissions

Leakage

Emissions outside the mitigation action boundary:

Upstream fuel production emissions (electricity)

Upstream vehicle production emissions

Downstream vehicle scrapping emissions

Rebound emissions (or lost savings)

Increase in emissions as a result of the project (unintended):

Growth in trips due to: increased capacity (new transit lane)

or lower cost (subsidy)

Approaches to estimate GHG

reductions

Travel demand modelling

Historical trends

Control group methods

Default or proxy data

Surveys

Travel Demand Modeling

Estimate important future variables

Trip length

Mode choice

Occupancy

Road speeds

Information used

Spatial interaction

Relationship between origin and

destination

Transport infrastructure

Historical Trends

Can be as simple as ‘drawing a line through data points’

Can involve complex regression analysis on multiple

parameters

Main weakness: future circumstances can change, i.e.

the trend can shift

Comparison Group

Select a similar local area to where the intervention is

implemented as control

Ensure similar boundary conditions are imposed

Measure key variables in both areas and compare

Default or Proxy Data

Data from another non-local area if readily available

Data for another time period as to ‘save time’ taking new

local measurements

Default data can be very accurate when based on a

large enough sample

Surveys

Transport modes used in the absence of project

Typical distance travelled

Daily/weekly variations

Warning: the respondent cannot identify or predict all

future changes in circumstances

Expert Opinion

Useful for questions involving future

policy changes

Fuel efficiency rules

Freight investment strategies

Less reliable when applied to

projections of human behaviour

Non-motorized mode choices

Policy scenarios:

Supply and Demand side

Mass Rapid Transit (MRT)

Baseline

scenario:

diverse &

individual

tran. modes

Mitigation

scenario:

new &

mass tran.

modes

CO2 savings = Fuel saving x Fuel CO2 factor

MRT – Causal Chain and MRV

Vehicle Efficiency Improvement

Programs

CO2 savings = Fuel saving x Fuel CO2 factor

Baseline scenario:

inefficient fossil fuel

use in existing fleet

Mitigation scenario:

more efficient use of

fossil fuel in existing

fleet (technical fix)

Vehicle Efficiency Improvement –

Causal Chain and MRV

Fuel Switching (higher- to lower-

carbon fuel)

CO2 savings = New fuel x change in CO2 intensity

Baseline

scenario:

oil products use

(petrol, diesel)

Mitigation

scenario: bio-

diesel, biogas,

LNG/CNG

Fuel switching - Causal Chain and MRV

Inter-Urban Rail

Baseline scen.:

existing modes

for passenger &

freight transport

Mitigation scen.:

inter-urban rail

for passenger &

freight transport

CO2 savings = Fossil fuel CO2 - Electricity CO2

Inter-Urban Rail – Causal Chain and

MRV

Freight Transport Modal Shift: Road to

Rail

Baseline

scenario:

trucks on road

(fossil fuels)

Mitigation

scenario:

rail replacing

trucks (electricity)

CO2 savings = Fossil fuel CO2 - Electricity CO2

Modal Shift of Freight Transport from Road

to Rail or Water – Causal Chain and MRV

GHG or Fuel Economy Standards

Baseline scenario:

NEW fleet – current

Fuel consumpiton /

CO2 emissions

Mitigation scenario:

NEW fleet – new

standard for Fuel

consumption / CO2

emissions

CO2 savings = OLD CO2 factor – NEW CO2 factor

National GHG or Fuel Economy

Standards – Causal Chain and MRV

Thank you!

Amr Osama Abdel-Aziz, Assen Gasharov, Mike Bess and Laura Lahti