The ADB Transport Model – User Guide
The ADB Transport Model – User Guide
Table of content
1
Introduction
3
2
Overview of the model
3
2.1
Model scope and segregation
3
2.2
Input data to the model
4
2.3
The model calculation
5
2.4
The model operation
5
3
Definition of transport policy instruments
5
3.1
The transport policy framework
5
3.2
The AVOID and SHIFT policy instruments
6
3.2.1
Categories of policy instruments
6
3.2.2
Aggregation of AVOID and SHIFT policy impacts
8
3.3
4
IMPROVE policy instruments
8
3.3.1
Increase FE Improvements
9
3.3.2
Reduce fuel carbon intensity
9
3.3.3
Change road vehicle technology share
9
3.3.4
Railway electrification
9
Structure of the model Tabs
9
4.1
9
Input-data Tabs
4.1.1
Socio-economic data
10
4.1.2
Transport data
10
4.1.3
Vehicle technology data
10
4.1.4
Fuel specification data
11
4.1.5
Emission factors
11
4.2
Calculation Tabs
11
4.3
Policy input and impact Tabs
11
4.4
The model output result Tab
12
4.5
Supporting Tabs
12
4.6
Main data flow between Tabs
12
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Load country data – To do
12
6
Policy input method
13
7
AVOID Policy instruments
15
7.1
Transport demand based AVOID policies
15
7.2
Transport elasticity based AVOID policies
15
7.3
Transport load factor based policies
15
8
SHIFT Policy instruments
16
9
IMPROVE Policy instruments
17
10 The model output results – Section needed?
17
A.1 List of Excel model Worksheets or Tabs
19
A.2 List of Abbreviations
21
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1
Introduction
The ADB Transport Model estimates, at a country level, the total transport demand activity,
mode shares, road vehicle stock, fuel use, and emissions for years from 2000 to 2050, given
the exogenous socio-economic input data, and historical transport data, and transport
policies. The model can be used to assess the effects of transport policies, based on the
comparison of a policy scenario with a BAU (business as usual) scenario.
The ADB Transport Model is implemented in Microsoft Excel 2010. This user guide
document provides a user guide to the model. It will focus on the structure, operation, input
data, and so on, of the model. The modelling methodology and data collection will not be
discussed here; they can be found, respectively in the final project report (CAA, 2016a), and
the methodology document (CAA, 2016b).
This user guide is structured as follows. Section 2 provides an overview of the model.
Section 3 describes the approach used to forecast the effect of policies. Section4 describes
the structure of the spreadsheet model from a user’s perspective. Section 5.. 6
{to be finished}
Appendix 10A.1 sets out the list of worksheets (“Tabs”) in the spreadsheet model and
Appendix 10A.2 lists the abbreviations used in this manual.
2
Overview of the model
2.1
Model scope and segregation
The scope of the ADB Transport Model, including modes of transport, road vehicle types and
technologies, types of fuels, emissions, and so on, are summarised in Table 1.
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Table 1 Model scope and segregation
Data category
Geographical
countries and
regions
Model year periods
Type of transport
Mode of transport
Road vehicle type
Fuel types
Road vehicle
powertrain-fuel
types
Emission and
pollutants
Transport
scenarios
2.2
Scope and segregation types
In total 40 Asia countries may be modelled, although one country is modelled at a time.
The countries are grouped into 7 regions: China, India, Pacific, SE ASIA, Mountain,
Central Asia, and Other, with each region containing some common set of data.
Analysis is performed for years 2000 to 2050, with the base year being 2012, 2000-2012
being historical years and 2013-2050 being forecast years. All calculations are
implemented in one-year periods, and the main results at 5-year periods transferred to
the "Results" Tab.
Passenger and freight transport
Road, Rail, Water, and Air, each for both Passenger and freight transport
Car, Bus, Minibus, 3&4 Wheeler, Motorcycle, Light Commercial Vehicles(LCV), medium
Freight Trucks (MFT), and heavy freight trucks (HFT)
Gasoline, Diesel, Compressed natural gas (CNG), Liquid Petroleum Gas (LPG), Electricity,
Hydrogen, and Jet fuel.
Internal Combustion Engine (ICE) gasoline, ICE diesel, ICE CNG, ICE LPG, Hybrid gasoline,
Hybrid diesel, Plug-in gasoline, Plug-in diesel, BEV, and FCEV
CO2, CH4, N2O, NOx, CO, PM10, PM2.5, and BC
Benchmark scenario and an alternative (ALT) scenario. The model will have two
predefined ALT scenarios, although only one ALT scenario may be modelled as a time.
{Explain where to get the other scenario….}
Input data to the model
The ADB Transport Model has five main types of input data:
(1)
Socio-economic data
(2)
Transport data
(3)
Vehicle technology data
(4)
Fuel specification data
(5)
Emission data
(6)
Transport policy instruments data
(7)
Forecast model parameters
Most of the above data vary over the modelled years – historical data before 2013 and
exogenous forecast data after 2012. Some data such as data types (1) and (2) are countrydependent, while some data differ only by regions. These are listed section 4.1.
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2.3
The model calculation
The model firstly forecasts the total passenger and freight transport activities, in passengerkilometres (PKM) and ton-kilometres (TKM) respectively, for each motorised mode. Then,
secondly, fuel use and emissions are calculated for each mode separately.
The passenger transport model is made up of two components: (1) forecasting of overall
passenger transport activities and mode share, and (2) calculation of vehicle stock, fuel use,
and emissions for each mode separately. Vehicle stock modelling is considered only for road
modes. The approach to modelling the demand for freight has been based on that used by
the ITF in its Transport Outlook 2015 (OECD/ITF, 2015). It considers all demand carried by
land flows - that is road and rail. Water and Air are modelled separately.
More details of modelling approaches can be found in the model document (CAA, 2016b).
2.4
The model operation
The ADB Transport Model is made up of the following parts
(1)
Transport Base Model (TBM), stored in a database,
(2)
Transport Scenario Model (TSM), i.e., the ADB Transport Model described in this
document.
Therefore, running the ADB Transport Model involves the following basic steps.
(1)
Load the exogenous input data from the database onto the “Socioeco data” ,the
“Transport_Data” and the “Veh_Tech” tabs;
(2)
Edit the transport policies in the “PolicyInputs” Tab
(3)
The model run results can be found in the “Results” Tab
3
Definition of transport policy instruments
Relevant Tabs: “PolicyInputs”, and “PolicyImpacts”.
3.1
The transport policy framework
The overarching sustainable objective may be achieved by a wide range of policy measures
or instruments. Therefore, the ADB Transport Model allows the user to specify policies at
individual levels of policies. These policies should not be imputed indiscriminately but as a
series of related policy instruments or “policy clusters.” If desired, the model structure also
allows the user to define a set of generic policies with aggregative impacts.
The structure of policies in an ASIF model framework follows naturally an Avoid-ShiftImprove (ASI) approach, with each of the ASI strategies affecting one or two aspect of the
transport energy or emission model, as shown in Figure 1. This structure has been adopted
in the ADB Transport Model.
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Demand
Activity
“AVOID” policy
instruments
X
Modal
Structure
X
Energy
Intensity
X
Emission
Factor
=
Total
Emission
“IMPROVE” policy
instruments
“SHIFT” policy
instruments
Figure 1. The structure of the policy impact
In effect, the AVOID and SHIFT policies reduce transport demand from High-carbon modes
(e.g., cars), and in the case of SHIFT, change the mode share structure by transferring the
demand to Low-carbon modes (e.g., public transport). The IMPROVE policy instruments
reduces fuel use and emissions by improving vehicle technology and fuel efficiency 1 (FE) of
different modes of transport, fuel carbon intensity, and transport infrastructure. The
policies may also include possible share structures in vehicle technology and alternative fuel
type.
The AVOID and SHIFT policies are described in the next subsection (Section 3.2), and the
IMPROVE policies in the subsequent subsection (Section 3.3).
3.2
3.2.1
The AVOID and SHIFT policy instruments
Categories of policy instruments
The AVOID and SHIFT policies reduce transport demand from High-carbon modes (e.g., cars),
and in the case of SHIFT, transfer the demand to Low-carbon modes (e.g., public transport).
An AVOID policy involves only one mode, for which transport demand is reduced and
removed from the model system2; while a SHIFT policy involves two modes: a “losing” mode
and a “gaining” mode, and will not cause any demand to be lost from the model system.
Transport demand may be reduced or shifted by addressing different aspects of transport.
Based on the element of transport they impact, the AVOID and SHIFT policies are further
divided into the following sub groups:
1
We use fuel efficiency (in Litre / km) rather than fuel economy (e.g., miles per Litre) in the ADB Transport
Model.
2
Strictly it is removed from the motorised transport system since it includes policies which shift high carbon
travel to Non-Motorised Transport (NMT) such as walk and cycle.
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(1) Transport demand (Passenger-kilometres per year (PKM) or Ton-kilometres per year
(TKM)) based, defined in terms of avoiding or shifting directly the PKM or TKM
demand.
(2) Transport elasticity based, defined in terms of percentage change in the cost of a
mode of transport, together with the corresponding elasticity of the demand with
respect to the costs.
(3) Transport load factor (passengers per vehicle or tons per vehicle) based, defined in
terms of increasing of load factors so as to avoid transport travel in terms of vehiclekms.
For the demand-based AVOID and SHIFT policies, each policy has associated with it a
maximum mitigation effect, and a time series of uptake rates. A ‘Maximum Mitigations arte’
is the change expect if the whole of the transport system is effect. Most polices will not
have a universal effect, for instance the implementation of Bus Rapid Transit (BRT) is not
likely the impact all the bus travel within an urban area so the maximum mitigation rate will
be less than 100%. Some polices, on the other hand can be expected to have 100%
geographical or population coverage such as fuel price rises and public transport fare
changes.
The elasticity-based policy instruments are further divided into three types, depending on
the type of elasticity used:
(a) The elasticity-based AVOID policies, using the direct elasticity of, say, car travel
demand with respect to car travel cost
(b) The elasticity-based SHIFT “pull” policies, using the cross elasticity of, say, car travel
demand with respect to the cost of a gaining mode of travel (e.g. car demand with
respect to bus fares)
(c) The elasticity-based SHIFT “push” policies, using the cross elasticity of the demand
for a gaining mode with respect to the cost of corresponding losing mode of travel.
(e.g. bus demand with respect to car costs)
For illustrative purposes, the properties of elasticity-based model parameters, including the
signs of elasticities, and the directions of changes in cost and demand, for Car and Bus travel
are shown in Table 2.
Table 2 Property of elasticity-based model parameters
Type of Policy
instruments
AVOID
SHIFT pull
SHIFT push
Demand
mode
Car
Car
Bus
Cost mode
Car
Bus
Car
Cost change
Increase
Decrease
Increase
Elasticity
Negative
Positive
Positive
Demand
impact
Decrease
Decrease
Increase
The ADB Transport Model considers different modes as being high-carbon modes for AVOID
and for SHIFT policy instruments. For the SHIFT policies, the high-carbon modes are the
“losing” modes. For each “losing” mode, a group of “gaining” modes are defined. The mode
allowed for different type of policy instruments are shown in Table 3.
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Table 3 Definitions of gaining mode for losing modes
Type of policies
Mode allowed
The demand and elasticityCar, Domestic Passenger Air, HFT, MFT, LCV, Motorcycle, International
based AVOID policies
Passenger Air
Transport load facto- based
HFT, MFT, LCV
AVOID policies
The demand and elasticityCar, Domestic Passenger Air, HFT, MFT
based SHIFT policies
The “gaining” modes allowed for each “losing” mode for SHIFT policies
Losing mode
Gaining mode
Car
Bus, Minibus, 3&4 Wheeler, Motorcycle, Passenger Urban Rail, Passenger
Non High Speed (HS) Rail, Passenger HS Rail
Passenger Air
Passenger HS Rail, Passenger NonHS Rail, Domestic Passenger Water
HFT
Freight Rail, Domestic Freight Water
MFT
Freight Rail, Domestic Freight Water
Therefore, the AVOID / SHIFT policies are grouped hierarchically: firstly into Avoid / Shift
categories, and then, into subgroups policies. In addition, the SHIFT policies are grouped by
losing mode (Car / Passenger air / HFT / MFT), and then, into three types of policies with
different types of elasticities.
3.2.2
Aggregation of AVOID and SHIFT policy impacts
Relevant Tab: “PolicyImpacts”.
The impacts of various policy instruments need to be aggregated for each mode of travel.
Let
BM and DM be the transport demand by mode M in the BAU and ALT scenarios, respectively.
AM and SM be the transport policy impact factors for AVOID and SHIFT policies, respectively.
Then, the final demand in the ALT scenario is given by
DM  BM  AM  SM
(1)
Details on the calculations of the policy impact factors can be found in the methodology
document (CAA, 2016b).
3.3
IMPROVE policy instruments
In the ADB Transport Model, the following four types of IMPROVE policies are considered:
1. Increase ‘Fuel Efficiency (FE) Improvements (representing reduction in fuel
consumption)
2. Reduce fuel carbon intensity (or fuel de-carbonation)
3. Change road vehicle technology share
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4. Railway electrification
The methods for the calculation of impacts of the policy instruments are described in turn
below.
3.3.1
Increase FE Improvements
This is defined as the additional annual percentage FE Improvements (representing
reduction in fuel consumption), in addition to the Benchmark FE Improvements, for road,
rail, Water, and Air modes, and for relevant types of fuel used for each mode.
3.3.2
Reduce fuel carbon intensity
The fuel de-carbonation policy instruments reduce Well To Tank (WTT) Benchmark carbon
intensities. These policy instruments are defined for different types of fuel and sources of
energy, including biofuels, hydrogen and electricity.
3.3.3
Change road vehicle technology share
This policy instrument simply specifies an alternative set of road vehicle technology share,
replacing the Benchmark set of road vehicle technology share.
3.3.4
Railway electrification
This policy instrument simply specifies an increased (from Benchmark) proportion of railway
electrification in the total transport demand.
4
Structure of the model Tabs
The Excel Template Model is a one-country model, in that the model of Excel Workbook
contains all the data for one country. Within the Excel Workbook model, there are 44
Worksheets or tabs. They may be divided into 5 groups: Input-data tabs, calculation tabs,
output or result tabs, and supporting tabs. A full list of all Tabs in the Excel Template model
is shown in Appendix A. Here, each group of Tabs are described briefly below.
The user should edit only the data in the Input-data tabs.
4.1
Input-data Tabs
The Input-data Tabs hold input data to the model. This group of Tabs include the following
items:
(1) "Socioeco data" - containing socio-economic data
(2) "Transport_Data" - containing transport data
(3) "Parameters" - containing all the parameters in the model relationships used for
transport forecasts
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(4) "Veh_TECH" - containing vehicle technology data
(5) "Fuel_Spec" - containing fuel specification data
(6) "Emission_rates" - containing emission rate data
4.1.1
Socio-economic data
(1)
Historical and forecast population and urban proportion
(2)
Historical and forecast PPPGDP – Gross Domestic Product defined in terms of dollar
Purchasing Parity.
(3)
Historical and forecast population data
(4)
Historical and forecast Fuel prices
4.1.2
Transport data
(5)
Historical road vehicle stock and sales
(6)
Historical and forecast road vehicle sales power-train split
(7)
Historical road vehicle stock powertrain split
(8)
Historical road vehicle annual mileage and their urban proportions
(9)
Load factors for all transport modes
(10)
Historical Transport demand data by mode of transport, in PKM or TKM
(11)
Historical road vehicle activity, in VKM
4.1.3
Vehicle technology data
(12)
Road vehicle technology life time and average age, by vehicle type
(13)
Historical and forecast road vehicle sales fuel efficiency (FE) improvement per year
(%), by vehicle and powertrain categories
(14)
Historical road vehicle sales fuel efficiency (FE), by vehicle and powertrain categories
(15)
Historical and forecast road vehicle share of electricity driving, by vehicle and
powertrain categories
(16)
Historical and forecast road vehicle fuel efficiency (FE) lab-to-road gap factor, by
vehicle and powertrain categories
(17)
Historical and forecast road vehicle fuel efficiency (FE) urban congestion gap factor,
by vehicle and powertrain categories
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(18)
Historical road vehicle cost data, by vehicle and powertrain categories
(19)
For non-road modes, historical and forecast fuel efficiency (FE) improvement per
year (%), for rail, water and air transport modes, by fuel types
(20)
For non-road modes, historical and forecast fuel efficiency (FE) improvement per
year (%), for rail, water and air transport modes, in Lge/PKM or Lge/TKM, by fuel
types
4.1.4
Fuel specification data
(21)
Historical and forecast TTW CO2 Emission Factor, by fuel type
(22)
Historical and forecast WTT CO2 Emission Factor, by fuel type
(23)
Historical and forecast energy content, by fuel type
4.1.5
Emission factors
(24)
TTW Emission factors for each road vehicle type, main fuel type (Gasoline, Diesel),
and emission standard (e.g., Euro 1, Euro 2, etc)
(25)
Emission standard introduction and full implementation year, and VKT share in the
introduction year
4.2
Calculation Tabs
There are two Tabs, "Passenger_Travel_Demand" and "Freight_Travel_Demand", for
passenger and freight demand forecasts, respectively. The total PKM and TKM, and modal
splits are calculated in these two Tabs. The transport activities from these Tabs then feed
the mode-specific calculation Tabs.
The mode-specific Tabs contain calculations for PKM to VKM, vehicle stock (road mode only),
fuel use and emissions. For each mode considered in the model, there is a pair of Tabs,
"mode_BAU" and "mode_ALT", containing calculations for the Benchmark scenario and an
alternative scenario.
In the current version of Template model, 15 modes of travel and types of transport
(passenger and freight) are modelled.
All calculations are implemented in one-year periods, and the main results, at 5-year
intervals, transferred to the "Results" Tab.
4.3
Policy input and impact Tabs (in Purple)
The "PolicyInputs" tab allows the user to enter different policy instruments, and the
"PolicyImpacts" Tab aggregates and cumulates the policy impacts for each mode and vehicle
type. These impacts factors then feed the mode-specific "alternative" scenario Tabs.
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4.4
The model output result Tab
Currently, there is only one output Tab, the "Results" Tabs containing summary results of
main model outputs at 5-year intervals.
4.5
Supporting Tabs
This group of Tabs hold some global parameters for the model, such as Table of content,
lists of countries, modes of travel, and types of vehicle technologies, and unit conversion
factors. There are also Tabs containing information for model development, such as version
control, and colour coding, and so on.
4.6
Main data flow between Tabs
The main data flow between Tabs as represented by the Tab names is shown in Figure 2.
The solid line means that data from all Tabs in the upstream box feed all Tabs in the
downstream Tabs. The dashed line means that each mode-specific BAU Tab feeds its
corresponding mode-specific ALT Tab.
Passenger_Travel_Demand
Socioeco data
Transport_Data
Parameters
Veh_TECH
Fuel_Spec
Emission_rates
PLDV_BAU
Bus_BAU
Minibus_BAU
3_4W_BAU
2W_BAU
Passenger_Rail_BAU
Passenger_Water_BAU
Passenger_Air_BAU
PolicyInputs
Freight_Travel_Demand
PLDV_ALT
Bus_ALT
Minibus_ALT
3_4W_ALT
2W_ALT
Passenger_Rail_ALT
Passenger_Water_ALT
Passenger_Air_ALT
PolicyImpacts
LCV_BAU
MFT_BAU
HFT_BAU
Freight_Rail_BAU
Freight_Water_BAU
Freight_Air_BAU
Results
LCV_ALT
MFT_ALT
HFT_ALT
Freight_Rail_ALT
Freight_Water_ALT
Freight_Air_ALT
Figure 2 Data flow in the Excel model
5
Load country data – To do
All the country-specific data is loaded by selecting the country from the drop-down list in
the “Constants” Tab:
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Asian DMCs Country Region
Country
selected:
Thailand
SE ASIA 1
{Section to be added}
6
Policy input method
Relevant Tab: “PolicyInputs”.
For each sub group of policies, there are headings representing the categories of A/S/I type,
the losing mode, and subgroup type for clarity. The labels are also numbered using
hierarchical bullet numbering system.
A list of all policy instruments available in the model is given in Table 4.
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Table 4 List of categories of policy instruments
No.
1.
1.1
1.2
1.3
2.
2.1
2.2
2.3
3.
3.1
3.2
3.3
4
4.1
4.2
4.3
5
5.1
5.2
5.3
6.
6.1
6.2
6.3
6.4
7.
7.1
8.
8.1
9.
9.1
Policy instrument category
AVOID: Reduce PKM or TKM demand for travel
AVOID: Reduction of PKM/TKM from BAU
AVOID: Changing high-carbon mode costs (Own-price elasticity based
AVOID: Change of Load Factor of Road Freight Transport
SHIFT: Shift Car travel to low-carbon modes
SHIFT: Shift demand for car travel
SHIFT: Changing low-carbon mode costs (cross-price elasticity based)
SHIFT: Changing Car-travel costs (cross-price elasticity based)
SHIFT: Shift Air travel to low-carbon modes
SHIFT: Shift demand for air passenger travel
SHIFT: Changing low-carbon mode costs (cross-price elasticity based)
SHIFT: Changing Air-travel costs (cross-price elasticity based)
SHIFT: Shift MFT travel to low-carbon modes
SHIFT: Shift demand for Heavy Freight transport (MFT)
SHIFT: Changing low-carbon mode costs (cross-price elasticity based)
SHIFT: Changing MFT costs (cross-price elasticity based)
SHIFT: Shift HFT travel to low-carbon modes
SHIFT: Shift demand for Heavy Freight transport (HFT)
SHIFT: Changing low-carbon mode costs (cross-price elasticity based)
SHIFT: Changing HFT costs (cross-price elasticity based)
IMPROVE: better fuel efficiency improvement
IMPROVE: Increase FE improvments: road modes
IMPROVE: Increase Fuel Efficiency improvements: Rail
IMPROVE: Increase Fuel Efficiency improvements: Water
IMPROVE: Increase Fuel Efficiency improvements: Air
IMPROVE: Reduce Fuel Carbon Intensity
IMPROVE: Reduction of fuel carbon intensity
IMPROVE: Road vehicle technology shares
IMPROVE: Improve technology shares of vehicle sales
IMPROVE: Rail electrification
IMPROVE: Change in demand Share by Diesel
Each spreadsheet row contains an Avoid / Shift / Improve instrument for which there are
respective columns for journey purpose, uptake rates, mitigation potential and overall
effectiveness of the instruments for urban and non-urban splits.
There are also some options for each policy, including a switch (ON / OFF) and impact area
(urban / rural / ALL).
The following columns are used for policy inputs, columns B, C, D, F to N, P to R.
For each type of policies, use only rows which have been set up for a policy, that
is, the rows that have columns A and E filled (policy type and units).
Columns C, D, Q, and R, have dropdown select boxes.
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7
AVOID Policy instruments
1. AVOID: Reduce PKM or TKM demand for travel
1.1 AVOID: Reduction of PKM/TKM from BAU
Policy
type
AVOID
AVOID
AVOID
AVOID
AVOID
AVOID
AVOID
AVOID
AVOID
Brief description
Cars: Avoided PKM due to better urban planning
Cars: Avoided PKM due to incentivising NMT and behavioural change
Motorcycles: Avoided PKM due to better urban planning
Motorcycles: Avoided PKM due to incentivising NMT and behavioural change
HFTs: Avoided TKM (e.g. due to better transport logistics, better urban planning)
MFTs: Avoided TKM (e.g. due to better transport logistics, better urban planning)
LCVs: Avoided TKM (e.g. due to better transport logistics, better urban planning)
Domestic air: Avoided PKM (e.g. due to behvioural change, teleconferences)
International air: Avoided PKM (e.g. due to behvioural change, teleconferences)
UPTAKE RATE
Select losing mode
Car
Car
Motorcycle
Motorcycle
HFT
MFT
LCV
Domestic Passenger Air
International Passenger Air
-
%
%
%
%
%
%
%
%
%
1.2 AVOID: Changing high-carbon mode costs (Own-price elasticity based
Policy
type Brief description
AVOID Cars: Avoid car PKM due to increasing Fuel Prices (excludes mode-shift)
AVOID Trucks: Avoid truck TKM due to increasing fuel prices (excludes Mode-shift)
2010
0%
0%
0%
0%
0%
0%
0%
0%
0%
2015
5%
5%
5%
5%
5%
5%
5%
5%
5%
2020
10%
10%
10%
10%
10%
10%
10%
10%
10%
Brief description
HFTs: reduce VKM due to improved load factor
MFTs: reduce VKM due to improved load factor
LCVs: reduce VKM due to improved load factor
7.1
2030
25%
25%
25%
25%
25%
25%
25%
25%
25%
2035
35%
35%
35%
35%
35%
35%
35%
35%
35%
2040
45%
45%
45%
45%
45%
45%
45%
45%
45%
2045
58%
58%
58%
58%
58%
58%
58%
58%
58%
2050
70%
70%
70%
70%
70%
70%
70%
70%
70%
ANNUAL PERCENTAGE COSTS INCREASE
Select losing mode
Car
HFT
2010 2015 2020 2025 2030 2035 2040 2045 2050
- % 0% 5% 10% 18% 25% 35% 45% 58% 70%
- % 0% 5% 10% 18% 25% 35% 45% 58% 70%
1.3 AVOID: Change of Load Factor of Road Freight Transport
Policy
type
AVOID
AVOID
AVOID
2025
18%
18%
18%
18%
18%
18%
18%
18%
18%
POLICY OPTIONS
UPTAKE RATE
Select losing mode
HFT
MFT
LCV
- %
- %
%
2010
0%
0%
0%
2015
5%
5%
5%
2020
10%
10%
10%
2025
18%
18%
18%
2030
25%
25%
25%
2035
35%
35%
35%
Maximum
Reduction Switched Impact
(%)
ON?
area
10.0%
TRUE Urban
20.0%
TRUE Urban
10.0%
TRUE Urban
20.0%
TRUE Urban
10.0%
TRUE ALL
10.0%
TRUE ALL
10.0%
TRUE ALL
50.0%
TRUE Rural
50.0%
TRUE Rural
POLICY OPTIONS
Elasticity
-0.10
-0.10
Switched Impact
ON?
area
TRUE ALL
TRUE ALL
POLICY OPTIONS
2040
45%
45%
45%
2045
58%
58%
58%
2050
70%
70%
70%
Maximum
Increase Switched Impact
(%)
ON?
area
50%
TRUE ALL
50%
TRUE ALL
50%
TRUE ALL
Transport demand based AVOID policies
For the Transport demand-based AVOID policies, the user specifys the following input data
(1)
A brief description of the policy
(2)
The mode to be impacted by the policy
(3)
Uptake rate at 5-year intervals
(4)
Maximum annual percentage reduction
(5)
Policy switched ON / OFF
(6)
The area to be impacted by the policy
(7)
The uptake rate and the mitigation potential
7.2
Transport elasticity-based AVOID policies
The inputs for this types of policies are the same as those to the demand-based AVOID
policies, except that the “Uptake rates” at 5-year intervals, are replaced by
‘Maximum annual percentage reduction ‘ from the Benchmark values for that year.
7.3
Transport load factor based policies
{To be completed}
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8
SHIFT Policy instruments
2. SHIFT: Shift Car travel to low-carbon modes
2.1 SHIFT: Shift demand for car travel
Policy
type
SHIFT
SHIFT
SHIFT
SHIFT
SHIFT
UPTAKE RATE
Select
losing
Brief description
mode Select gaining mode
Cars: Shift car PKM to bus due to BRT development
Car
Bus
Cars: Shift car PKM to bus due to pricing policies (e.g. congestion charging, parking
Carlevy) Bus
Cars: Shift car PKM to urban rail due to metro/light rail development
Car
Passenger Urban Rail
Cars: Shift car PKM to urban rail due to pricing policies (e.g. congestion charging, Car
parkingPassenger
levy)
Urban Rail
Cars: Shift car PKM to non-urban rail due to HS rail development
Car
Passenger HS Rail
%
%
%
%
%
2010 2015 2020 2025 2030 2035 2040
0% 5% 10% 18% 25% 35% 45%
0% 5% 10% 18% 25% 35% 45%
0% 5% 10% 18% 25% 35% 45%
0% 5% 10% 18% 25% 35% 45%
0% 5% 10% 18% 25% 35% 45%
%
%
%
%
2010 2015 2020 2025 2030 2035 2040 2045 2050
0% 2% 4% 6% 8% 10% 12% 16% 18%
0% 2% 4% 6% 8% 10% 12% 16% 18%
0% 2% 4% 6% 8% 10% 12% 16% 18%
0% 2% 4% 6% 8% 10% 12% 16% 18%
2.2 SHIFT: Changing low-carbon mode costs (cross-price elasticity based)
Policy
type
SHIFT
SHIFT
SHIFT
SHIFT
Brief description
Cars: Shift car PKM to bus due to reduced fares
Cars: Shift car PKM to rail due to reduced fares
Cars: Shift car PKM to bus due to improved frequencies
Cars: Shift car PKM to rail due to improved frequencies
Brief description
Bus: Shift car PKM to bus due to increased fuel prices
Bus: Shift car PKM to bus due to increased parking charges
Urban rail: Shift car PKM to urban rail due to increased fuel prices
Urban rail: Shift car PKM to urban rail due to increased parking charges
Rail: Shift car PKM to rail due to increased fuel prices
Bus: Shift car PKM to bus due to increased other car operating costs incl. taxes
2045
58%
58%
58%
58%
58%
2050
70%
70%
70%
70%
70%
ANNUAL PERCENTAGE COSTS REDUCTION
Losing
mode
Car
Car
Car
Car
Select gaining mode
Bus
Passenger NonHS Rail
Bus
Passenger NonHS Rail
2.3 SHIFT: Changing Car-travel costs (cross-price elasticity based)
Policy
type
SHIFT
SHIFT
SHIFT
SHIFT
SHIFT
SHIFT
POLICY OPTIONS
ANNUAL PERCENTAGE COSTS INCREASE
Losing
mode
Car
Car
Car
Car
Car
Car
Select gaining mode
Bus
Bus
Passenger Urban Rail
Passenger Urban Rail
Passenger NonHS Rail
Passenger NonHS Rail
%
%
%
%
%
%
2010 2015 2020 2025 2030 2035 2040 2045
0% 5% 10% 18% 25% 35% 45% 58%
0% 5% 10% 18% 25% 35% 45% 58%
0% 5% 10% 18% 25% 35% 45% 58%
0% 5% 10% 18% 25% 35% 45% 58%
0% 5% 10% 18% 25% 35% 45% 58%
0% 5% 10% 18% 25% 35% 45% 58%
2050
70%
70%
70%
70%
70%
70%
Maximum
Reduction Switched Impact
(%)
ON?
area
9.0%
TRUE ALL
11.0%
TRUE ALL
9.0%
TRUE ALL
11.0%
TRUE ALL
5.0%
TRUE Rural
POLICY OPTIONS
Elasticity
0.10
0.15
0.20
0.20
Switched
ON?
TRUE
TRUE
TRUE
TRUE
Impact
area
ALL
ALL
ALL
ALL
POLICY OPTIONS
Elasticity
0.30
0.20
0.30
0.20
0.30
0.20
Switched
ON?
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
Impact
area
ALL
Urban
Urban
ALL
ALL
ALL
For the SHIFT policies, the user specifies the following input data
1. A brief description of the policy
2. The “losing” mode to be impacted by the policy
3. The “gaining” mode to be impacted by the policy
4. The area to be impacted by the policy
5. The uptake rate and the mitigation potential
Thus for the driver-based AVOID and SHIFT policies, the user needs to enter the following
input data.
1. A brief description of the policy
2. The size of % change in a demand driver (or, equivalently, a change factor), where
the “demand driver” will be anonymous and it may appear only in the policy
description.
3. The elasticity of the PKM or TKM demand with respect to the demand driver. This
may be either the direct (or own) elasticity, or the cross elasticity, depending on the
relationship of the mode and the driving factor.
4. The area to be impacted by the policy – Urban, non-urban or All areas.
5. The “gaining” mode to be impacted by the policy
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IMPROVE Policy instruments
6. IMPROVE: better fuel efficiency improvement
Select Tech Unit 2010 2015 2020 2025 2030 2035 2040 2045 2050
0% 30% 100% 100% 70% 50% 20% 20% 20%
ICE_gasoline %
Select mode
Policy typeBrief description
Car
Cars: Annual fuel efficiency improvement for ICE vehicles [Change Rate]
IMPRV
Select Tech Unit 2010 2015 2020 2025 2030 2035 2040 2045 2050
5% 10% 18% 25% 35% 45% 58% 70%
0%
%
Diesel
Select mode
Passenger HS Rail
Policy typeBrief description
Water: Higher Fuel Efficiency
IMPRV
Select mode
Domestic Passenger Water
Select Tech Unit 2010 2015 2020 2025 2030 2035 2040 2045 2050
5% 10% 18% 25% 35% 45% 58% 70%
0%
%
Diesel
Policy typeBrief description
Air: Higher Fuel Efficiency
IMPRV
Select mode
Domestic Passenger Air
Maximum
Reduction Switched
ON?
(%)
FALSE
20.0%
POLICY OPTIONS
UPTAKE RATE
6.4 IMPROVE: Increase Fuel Efficiency improvements: Air
Maximum
Reduction Switched
ON?
(%)
TRUE
5%
POLICY OPTIONS
UPTAKE RATE
6.3 IMPROVE: Increase Fuel Efficiency improvements: Water
Maximum
Reduction Switched
ON?
(%)
TRUE
4.0%
POLICY OPTIONS
UPTAKE RATE
6.2 IMPROVE: Increase Fuel Efficiency improvements: Rail
Policy typeBrief description
Rail: Higher Fuel Efficiency
IMPRV
POLICY OPTIONS
UPTAKE RATE
6.1 IMPROVE: Increase FE improvments: road modes
Select Tech Unit 2010 2015 2020 2025 2030 2035 2040 2045 2050
0% 30% 100% 100% 70% 60% 30% 30% 30%
%
Jetfuel
Maximum
Reduction Switched
ON?
(%)
TRUE
1.0%
7. IMPROVE: Reduce Fuel Carbon Intensity
POLICY OPTIONS
UPTAKE RATE
7.1 IMPROVE: Reduction of fuel carbon intensity
Policy typeBrief description
Reduce Gasoline carbon intensity (i.e use of Bio-fuels)
IMPRV
Reduce Diesel carbon intensity (i.e. use of Bio-fuels)
IMPRV
Reduce CNG carbon intensity (i.e. use of bio-methane)
IMPRV
Reduce LPG carbon intensity
IMPRV
Reduce Jetfuel carbon intensity (i.e. use of Bio-fuels)
IMPRV
Select Fuel Unit 2010 2015 2020 2025 2030 2035 2040 2045 2050
6% 10% 14% 20% 26% 33% 40%
3%
0%
%
Gasoline
6% 10% 14% 20% 26% 33% 40%
3%
0%
%
Diesel
6% 10% 14% 20% 26% 33% 40%
3%
0%
%
CNG
6% 10% 14% 20% 26% 33% 40%
3%
0%
%
LPG
6% 10% 14% 20% 26% 33% 40%
3%
0%
%
Jetfuel
Maximum
Reduction Switched
ON?
(%)
TRUE
70.0%
TRUE
70.0%
TRUE
70.0%
FALSE
70.0%
TRUE
70.0%
POLICY SCENARIO
POLICY OPTIONS
8. IMPROVE: Road vehicle technology shares
BAU SCENARIO
8.1 IMPROVE: Improve technology shares of vehicle sales
Policy typePolicy measure
Tech_Share_Sales
IMPRV
Tech_Share_Sales
IMPRV
Tech_Share_Sales
IMPRV
Vehicle type
Car
Car
Car
Power-fuel Unit
ICE_gasoline %
ICE_diesel %
%
ICE_CNG
2012 2050
67% 65%
0%
25%
5%
5%
2050
3%
0%
5%
3%
0%
5%
Switched
ON?
TRUE
TRUE
TRUE
9. IMPROVE: Rail electrification
BAU SCENARIO
9.1 IMPROVE: Change in demand Share by Diesel
Policy typePolicy measure
PKM Share by Electricity
IMPRV
PKM Share by Electricity
IMPRV
PKM Share by Electricity
IMPRV
TKM Share by Electricity
IMPRV
10
Vehicle type
Passenger HS Rail
Passenger NonHS Rail
Passenger Urban Rail
Freight Rail
Power-fuel Unit
2012 2050
100% 100%
0%
0%
100% 100%
0%
0%
POLICY SCENARIO
2050
100%
50%
100%
50%
POLICY OPTIONS
Switched
ON?
FALSE
TRUE
FALSE
TRUE
The model output results – Section needed?
Relevant Tab: “Results”.
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References
CAA (2016a) TA 8046-REG: Implementation of Sustainable Transport in Asia and the Pacific –
Better Transport Data for Sustainable Transport Policies and Investment Planning
(Subproject 1): Improving the Availability and Quality of Transport Data in the DMCs (45105005). Draft FINAL REPORT, November 2016. Clean Air Initiative for Asian Cities (Clean Air
Asia).
CAA (2016b) TA 8046-REG: Implementation of Sustainable Transport in Asia and the Pacific –
Better Transport Data for Sustainable Transport Policies and Investment Planning
(Subproject 1): Improving the Availability and Quality of Transport Data in the DMCs (45105005). Appendix F1. Description of the ADB Transport Scenario Model. November 2016. Clean
Air Initiative for Asian Cities (Clean Air Asia).
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A.1
List of Excel model Worksheets or Tabs
A full list of all Tabs in the Excel Template model is shown below. Note that the model is still
under development. In particular, Tabs associated with policy scenario calculations may be
changed.
N
o.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
Functionality
Sheet name
TOC
Keys
Assumptions
Constants
Socioeco data
Transport_Data
Veh_TECH
Fuel_Spec
Emission_rates
Parameters
PolicyInputs
PolicyImpacts
Results
Passenger_Travel_
Demand
Scenario_Maker_P
assenger
PLDV_BAU
PLDV_ALT
Bus_BAU
Bus_ALT
Minibus_BAU
Minibus_ALT
3_4W_BAU
3_4W_ALT
2W_BAU
2W_ALT
Freight_Travel_De
mand
Scenario_Maker_F
reight
LCV_BAU
LCV_ALT
MFT_BAU
MFT_ALT
HFT_BAU
HFT_ALT
Passenger_Rail_BA
U
Passenger_Rail_AL
T
Freight_Rail_BAU
Freight_Rail_ALT
Passenger_Water_
BAU
Passenger_Water_
ALT
Freight_Water_BA
U
Freight_Water_AL
T
Passenger_Air_BA
U
Passenger_Air_ALT
Author(s):
Content
Table of contents
Keys (e.g., colour coding) used in the Workbook
The specific assumptions made for that country when creating the historic data set.
Various global constants
Socio-economic data
Transport Data
Vehicle Technology Data
Fuel Specifications
Emissions Rates
Forecast Model Parameters
Policy inputs
Policy Impacts
Results
Supporting
Supporting
Supporting
Supporting
Input data
Input data
Input data
Input data
Input data
Input data
Input data
Calculation
Calculation
Calculation
Passenger travel demand
Calculation
Passenger transport scenario construction
Personal LDV BAU scenario
Personal LDV policy scenario
Bus BAU scenario
Bus policy scenario
Minibus BAU scenario
Minibus policy scenario
3&4 Wheeler BAU scenario
3&4 Wheeler policy scenario
Motorcycle BAU scenario
Motorcycle policy scenario
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Freight transport demand
Calculation
Freight transport scenario design
LCV BAU scenario
LCV policy scenario
MFT BAU scenario
MFT policy scenario
HFT BAU scenario
HFTpolicy scenario
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Calculation
Passenger Rail BAU scenario
Calculation
Passenger Rail Policy scenario
Freight rail BAU scenario
Freight rail policy scenario
Calculation
Calculation
Calculation
Passenger Water BAU scenario
Calculation
Passenger Water policy scenario
Calculation
Freight water BAU scenario
Calculation
Freight water policy scenario
Calculation
Passenger Air BAU scenario
Passenger Air policy scenario
Calculation
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44
44
44
Freight_Air_BAU
Freight_Air_ALT
Author(s):
Freight Air BAU scenario
Freight Air policy scenario
Calculation
Calculation
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A.2
List of Abbreviations
ABBREVIATIONS
ADB
CAA
CO2
DMC
GHG
ITS
NMT
RSDD
STI–OP
TOR
TA
BC
CH4
CNG
CO
CO2
CO2e
FPA
GDP
GHG
GVWR
HHDT
LDV
LHDT
LPG
MHDT
N2O
NOx
PM
ppm
PPP
RPKT
SECA
SO2
TTW
VKT
WTT
WTW
Author(s):
DEFINITION
Asian Development Bank
Clean Air Asia
carbon dioxide
developing member country
greenhouse gas
intelligent transport system
nonmotorized transport
Regional and Sustainable Development Department
Sustainable Transport Initiative–Operational Plan
terms of reference
technical assistance
Black carbon
Methane
Compressed natural gas
Carbon monoxide
Carbon dioxide
Carbon dioxide-equivalent
Fuel price-adjusted
Gross domestic product
Greenhouse gas
Gross vehicle weight rating
Heavy heavy-duty trucks
Light-duty vehicles
Light heavy-duty trucks
Liquified petroleum gas
Medium heavy-duty trucks
Nitrous oxide
Oxides of nitrogen
Particulate matter
Parts per million
Purchasing power parity
Revenue passenger kilometer traveled
Sulfur emission control area
Sulfur dioxide
Tank-to-wheels
Vehicle kilometer traveled
Well-to-tank
Well-to-wheels
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