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
