Transport DataBank Model Development

Authors: Xiaoyan Zhang and Paul Emmerson 1 18/08/2016

Technical Reviewers: Hayley Gilmour and Alex Koerner

Transport DataBank Model Development

2016

This document discusses the development of the Transport DataBank Model, as part of the outputs under a technical assistance of the Asian Development Bank

Technical Assistance 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 (45105-005)



Contents 1 Introduction 3

Scope of report 4

Overview of approach 4

Layout of report 4

2 Model design and development 5

Model structure 5

Model scope and limitations 6

Estimating historic travel activity 8

3 Passenger travel model structure 9

Forecasting motorized passenger demand 9

Forecasting energy use and emissions by mode 13

Forecasting travel by Non-Motorized modes 15

4 Freight model structure 16

Forecasting freight demand by mode 17

4.1.1 Rail 19

4.1.2 Road modes 19

Forecasting emissions by mode 21

5 Road safety modelling 21

6 Data collection using Thailand as an example 23

Data for Passenger demand (Benchmark Model) 24

Data for Freight demand (Baseline Model) 25

Data for fuel economy and emissions 25

Data for future forecasts 26

7 A brief description of Excel implementation 26

Overview of the Excel template model for producing the historic data and the benchmark forecasts 26

Main tabs 26

7.2.1 Input-data tabs 26

7.2.2 Calculation tabs 27

7.2.3 Policy input and calculation tabs 27

7.2.4 The model result tab 27

7.2.5 Supporting tabs 28

Main data flow between tabs 28

8 Validation 29

Back-forecasting 29

Comparison of estimates of total energy use in 2008 and 2015 30

Other validation data 31

9 Defining and running alternative Scenarios 32

The transport policy framework 32

The AVOID and SHIFT policy instruments 33

9.2.1 Categories of policy instruments 33

9.2.2 Demand-based policy instruments 34

9.2.3 Elasticity-based AVOID policy instruments 34

9.2.4 Elasticity-based pull policy instruments 35

9.2.5 Elasticity-based push policy instruments 35

9.2.6 Aggregation of AVOID and SHIFT policy impacts 35

IMPROVE policy instruments 36

9.3.1 Increase FE improvements 36

9.3.2 Reduce fuel carbon intensity 37



9.3.3 Change Road vehicle technology share 37

9.3.4 Railway electrification 37

10 Scenarios for the future 37

“Benchmark” scenario 37

Alternative scenarios 42

10.2.1 Backcasting – 1.5°C Scenario 43

10.2.2 Forecasting – Progressive adaption of best available technologies and policies leading to 2°C Scenario - 46

Particular assumptions for current scenarios 48

10.3.1 High and Low scenarios 48

10.3.2 Countries with no rail network or water transport 48

11 Known issues (as of January 20th 2017) 49

12 References 49

List of Figures

Figure 1: Structure of the motorized passenger demand forecasting methodology ........................................ 10

Figure 2: Structure of energy and emissions forecasting methodology for Road modes ................................. 14

Figure 3: Structure of the freight forecasting methodology ........................................................................... 17

Figure 4: Data flow in the Excel model .......................................................................................................... 28

Figure 5: The structure of the policy impact .................................................................................................. 32

Figure 6: Car Travel Forecasting under the Benchmark Scenario .................................................................... 40

Figure 7: Forecasting of other Land Travel Vehicles for the Benchmark Scenario ........................................... 41

Figure 8: Freight Forecasting for the Benchmark Scenario ............................................................................. 41

Figure 9: Avoid-Shift-Improve Concept to Reduce Energy Use and Emissions in the Transport Sector ............ 42

List of Tables

Table 1. List of Modes used in the Model ........................................................................................................ 6

Table 2: Gompertz parameters for land passenger transport ......................................................................... 12

Table 3: Assumptions for estimating demand for walking .............................................................................. 16

Table 4: Freight Intensity as a function of GDP1 ............................................................................................. 18

Table 5: Estimation of percentage of urban Road freight activity undertaken by different freight sub-modes – Thailand example ........................................................................................................................... 20

Table 6: Indicative elasticity estimates for fatality model ............................................................................... 23

Table 7: Performance of Transport DataBank Model in forecasting historic data using ICCT default parameters (Mtons CO2-eq) .......................................................................................................................... 29

Table 8: Comparison of the estimates of Energy consumption by transport mode 2015 with official estimates (Ktoe) ........................................................................................................................................... 31

Table 9. Definitions of gaining mode for losing modes ................................................................................... 33

Table 10. Property of elasticity-based model parameters .............................................................................. 34



1 Introduction

Scope of report

This note describes the structure and development of the Transport DataBank Model. The model was constructed using existing country-wide transport and energy models, and then was adjusted using data collected for one of the more data-rich countries in the transport database - Thailand. This ‘template’ version of the model was developed to test out the structure of the model with ‘real’ data as far as possible. It was found that Thailand is the most suitable country on which to develop the template model because not only were there a number of official government sources of data, but also there has been academic research on various aspects of passenger and freight travel and emissions in Thailand. Thailand also has significant amounts of travel on all four main modes of passenger and freight transport; namely Road, Rail, Water and Air, as well as a very important motorcycle component to its travel pattern.

This report describes the ‘Benchmark’ scenario model and the alternative scenarios of the future which are contained in the model. The user is free to change these scenarios and the policies they contain. Some information is also given on the data collection to set up the historic data for country, based on the example for Thailand as indicative of the data issues met in producing country-based models.

Overview of approach

The forecasting of changes in travel demand and subsequent emissions has been undertaken using different model structures for passenger travel and for freight travel. This arose because of the nature of the data and the previous models available at the start of the model’s development.

The model first forecasts the total passenger and freight transport activities, yearly up to 2050, in passenger-kilometers (PKM) and ton-kilometers (TKM) respectively, for each mode. Then, fuel use, energy and emissions are calculated for each mode separately. This provides a ‘Benchmark’ Scenario against which an alternative policy scenario can be modelled.

Layout of report

Section 2 discusses the general design and development of the model. Section 3 and Section 4 cover, for Passenger and Freight respectively, the structure and development of the demand models and the estimation of emissions and energy from the constituent modes. Section 5 considers the forecasting of accidents and fatalities from Road accidents and describes an outline model to use. Section 6 gives a brief description of the sources of data used to construct the data behind the template model, first for the Passenger modes, and then for Freight modes. Section 7 describes the implementation of the model as an Excel spreadsheet, while Section 8 discusses some validation results where the estimates from the model for Thailand are compared with largely independent estimates of total transport energy and the historic data itself. Sections 9-10 describes the way that the model allows the user to model alternative scenarios, based on sets of policies or measures.



2 Model design and development

The Transport DataBank Model was designed to

(1) Evaluate historic data to calculate energy use and emissions from the transport sector based on bottom-up data such as vehicle stocks, mileages, load factors, and fuel economies;

(2) Develop scenarios until the year 2050 to investigate energy use, carbon dioxide and pollutant emissions, costs and fatalities in the transport sector

(3) Evaluate a broad set of policy measures based on Avoid-Shift-Improve strategies to reduce future energy demand and emissions while providing greatly improved transport services.

Model structure

The model essentially has three parts:

(1) The description of the historic transport activity, energy use, emissions and fatalities for the historic period 2000 to 2012

(2) The production of a ‘Benchmark’ forecast scenario up to 2050 assuming business-as-usual scenario, pivoting off the 2012 transport data

(3) The forecasting of alternative forecast scenarios – the best available technologies and policies leading towards a 2°C scenario (called the Progressive Scenario) and a 1.5 °C scenario pivoting off the Benchmark forecasts.

The model has been developed after a literature search of existing climate and travel models, developed at a country/regional level. The current design is to have one Excel workbook file per country, containing all transport modes. A user will be able to download all the sheets of one Excel file required to model a given country. The data within the model for a given country, alongside the baseline data, represent societal and transport variables for that country over the period 2000 to 2012. Data were collected for the transport database and the transport model has been used to provide information for data items not directly available from published sources. Table 1 lists the modes used in forecasting of passenger and freight transport values.



Table 1. List of Modes used in the Model

Passenger Transport Freight Transport

1. Passenger car

2. Bus

3. Minibus

4. Bus Rapid Transit (BRT)

5. Motorized 3-wheeled vehicles

6. Motorcycles

7. Pedicabs (non-motorized 3-wheeled

vehicles)

8. Bike

9. Rail

10. Water (waterways and maritime)

11. Air (domestic and international)

1. Light Commercial Vehicles (LCVs)

2. Medium Freight Trucks (MFT)

3. Heavy Freight Trucks (HFT)

4. Rail

5. Water

6. Air

Mode types specifically found in a country were allocated to one of the modes listed above; for example, jeepneys in the Philippines were allocated to the ‘Minibus’ category. In the case of road freight, most countries did not have data on all 3 types of goods vehicle so, in these cases, the MFT class was used for any combination of goods vehicle classes available.

For some countries, it was not possible to obtain data for every required variable, for every year, so the spreadsheet model was used to synthesize data values where these were not available for a given data variate/year. These synthesized values were then uploaded to the transport data base.

Model scope and limitations

The scope of the model has been defined by both the project specification and the quality of data available from the DMCs in Asia. The model has been designed to forecast changes in transport activity and stock, emissions and fatalities up to 2050 at a country level. Whilst the specification required results at a 5-year time-interval, results are available on a yearly interval. These changes are disaggregated over several different categories, allowing the impact of changes or policies to be traced through the model. The model itself has been designed to make best use of existing official data sources and open-source material, all of which will be freely available for use by the public at the end of the project. Because of this last requirement, the model has been developed within a widely-available Excel spreadsheet format.



It has also been designed in such a way, that it will be possible develop the model further, either to add more detail or to make use of additional data. This has influenced the structure of the model and its implementation in Excel. The handling of policies, has been based on the Avoid-Shift-Improve-Fuel efficiency (ASIF) concept and the inputs of policies has been very flexible, allowing for individual policy inputs as well as more mechanistic input of modal changes. As part of the project specification, the development of alternative scenarios to assess the impact of low-carbon targets for all countries has been implemented using simplified policy clusters.

The form of data available in the DMCs and the form of data used in the modelling does set limitations to this type of model. It has not been possible to obtain comprehensive data for transport inputs in all countries as the model developed. More sophisticated models, with a more detailed feedback mechanism (e.g. congestion effects, and consumer vehicle-choice models) would be more reliant on default data values, and so have not been implemented in this version, although in the case of these two mechanisms, passive provision has been made for the inclusion of relevant mechanisms. The requirement to focus on open-source data means that it has not been possible to use proprietary data-sets or model relationships in the development of the model (and the database), which in turn means that we are more limited in the types of relationships that we can develop, or existing relationships that we can re-calibrate.

The modelling of the ownership and use of non-motorized modes posed a challenge as no data on a national level has been found for these modes. Models of ownership (where applicable) and use are derived from simple assumptions based on reviews of individual, usually city-based surveys, such as by Oke et al (2015), UC Davis, and the ADB Sustainable Partnership project.

The forecasting of public transport sub-modes was also limited by data availability and knowledge of travel behavior, while the impact of High-speed rail (HS Rail) is allowed for the estimate of travel has to be input exogenously and its impact on competing high carbon modes modelled.

The situation with Bus-Rapid Transit (BRT) is even less certain. While comprehensive historic data for BRT use is rare (apart from data from the People’s Republic of China), the model does track the impact on the Benchmark assumptions on BRT use and the impact of BRT-led policies on travel demand including by BRT. The modelling of the full impact of BRT on other modes is limited in the current version in that it only covers the impact on car travel of the introduction of BRT. The impacts on other modes such as other bus travel and non-motorized modes has not been modelled because this would require quite a complex organization of impact calculations since some of the modes affected are lower users carbon than BRT and some are probably higher users (other buses).

The model distinguishes between urban and non-urban transport and energy use and emission results can be disaggregated down to that level. Depending on the scope of the historic transport data, energy use, emissions, costs and fatalities of the transport sector can be modelled on a city level.



Estimating historic travel activity

In general, travel activity in the historic period (2000 – 2012) for Road passenger and freight transport by mode is estimated as the product of:

Pass-kms or ton-kms per year by mode =

Vehicle stock * Distance per vehicle per year * Load factor (persons per vehicle or tons per vehicle)

(1)

All four variables in the above equation are important within different aspects in the modelling of travel demand, energy consumption and emissions so that estimates of all four quantities were required for every year in the model. In forecasting the future, the relationship works from travel activity (in terms of passenger-km or ton-km) which is then disaggregated into stock, distance, and load factors.

For all countries, it was not possible to obtain national data on all four of these variables. In most cases, vehicle stock data were available sporadically over the years but consistent, reliable data on distance per vehicle and load factors were not available, even for a single year. Where estimates for a country were available for these quantities, they tended to be based on one-off surveys hosted for differing reasons and, as such, may not be representative of the country as whole. If local values were unavailable, default values were used based upon previous work in south-east Asia or other models.

In some countries, alternative estimates of transport activity could be obtained by:

Pass-kms or ton-kms per year by mode = Passengers carried or tons lifted * trip length (2)

This was especially useful for Rail, Water and Air travel. For non-road–based modes, the modelling only required total passenger and ton-kms per year, since the fuel consumption and emissions estimates were undertaken on a per pass-km and per ton-km basis, whereas for road-modes they were undertaken on a per vehicle km basis.

Where independent estimates of transport activity for a mode were available from national sources, then these were used to adjust the default values of vehicle kms per year and/or load factors, so that the relationship in Equation 1 held. For most countries and modes, it was possible to obtain or estimate the four variables for all modes, but the estimation of Heavy Freight Transport (HFT) or general road freight transport activity proved the most difficult to reconcile with national sources. Very few countries provide the full disaggregation of all road freight modes into the 3 available classes. Where only one category of ‘trucks’ were available they were classified as MFT and the default load factors adjusted accordingly.



Vehicle stock data, obtained for road transport, was the most readily available data item but in many countries a full time-series was not obtainable, the ‘missing years’ were estimated using either linear interpolation or extrapolation using GDP data.

Much of the required fuel consumption, energy and emission parameters required by the model were based on default values, as reliable local data were not comprehensive enough to be useful (but often proved useful as validation points).

The relationships used to translate changing societal and transport variables into forecasts of transport activity, in terms of pass-kms and ton-kms, although derived from other existing models, have been subject to some adjustment. As comparisons of model predictions for the historic data with ‘observed’ data have become available during the study, some of the parameters have been adjusted so that the models provide an improved back-prediction over the past 10-12 years.

3 Passenger travel model structure

The passenger transport model is made up of two components: (1) forecasting of overall passenger transport activities and mode share, and (2) a calculation of vehicle stock, fuel use, and emissions for each mode separately. Vehicle stock modelling is considered only for Road modes. More details of both approaches are given in the sub-sections below. The forecasting of non-motorized travel (NMT) has been handled in a different way reflecting the lack of country-specific data available, as well as existing model relationships. The modelling of non-motorized travel (NMT) modes is dealt with in section 3.3.

Forecasting motorized passenger demand

The passenger demand model closely follows the approach adopted by the International Council on Clean Transportation (ICCT) RoadMap model of travel and emissions (ICCT, 2011), in that it uses the Gompertz relationships for the forecast of different transport variables from socio-economic factors.

The forecasting of land-based passenger demand has been undertaken using a hierarchical structure, with totals at the higher level constraining forecasts lower down the hierarchy. Error! Reference source not found.1 shows the structure of the forecasting methodology. ater and Air Transport are estimated separately.



Figure 1: Structure of the motorized passenger demand forecasting methodology

Total land passenger travel activity, measured in PKM is forecast using a Gompertz

relationship of the form:

𝑇 = 𝑈𝐵𝐿 ∗ 𝑒𝐼𝑃∗𝑒𝐺𝑅𝑃∗𝑆𝐸𝑃 (3)

Where:

𝑇 = transportation parameter (e.g., per capita travel in kilometers/year),

𝑆𝐸𝑃 = intermediate socioeconomic parameter defined as:

𝑆𝐸𝑃 = [𝐺𝐷𝑃𝑃𝑃𝑃 × 𝑀𝑎𝑥(1 − 𝑉𝑃𝐶, 0)] + [

𝐺𝐷𝑃𝑃𝑃𝑃×(min (𝑉𝑃𝐶,1)

𝐹𝑢𝑒𝑙𝑃𝑟𝑖𝑐𝑒𝑟𝑎𝑡𝑖𝑜]

(4)

𝑉𝑃𝐶 (𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎) = 1 × 𝑒−3.206∗𝑒−5.4∗10−5×𝐺𝐷𝑃𝑃𝑃𝑃 (5)

Where:

𝑉𝑃𝐶 = Vehicles per capita

𝐺𝐷𝑃𝑃𝑃𝑃 = Gross Domestic Product measured in US dollars at Purchasing Parity in 2005

𝐹𝑢𝑒𝑙 𝑝𝑟𝑖𝑐𝑒 𝑟𝑎𝑡𝑖𝑜 = the local price of fuel relative to the current year US fuel price

𝑈𝐵𝐿 = Upper boundary limit for curve set at (30,000 kms/year) in RoadMap

IP and GRP are calibration parameters.

POP & GDP VPC (P1)

FPA_GDP

Land mode total PKM (P2)

Ave. fuel price

PKM by mode (P3)

VKM by modeLoad factor

VKT (Mileage) Stock by road vehicle type



In summary, the ICCT approach estimates vehicle ownership only as a way of estimating the impact that fuel prices have on total travel and then uses this effect to adjust the estimate of GDP per person.

In the absence of any data on which to calibrate the model relationships, we have adopted the ICCT Roadmap model approach and also used the ICCT Roadmap model parameters as default parameters. However, three changes have been made to the set of ICCT Roadmap Gompertz relationships:

1) Firstly, because the model’s price base has been advanced to 2010 USDPPP, these data have to be converted back to 2005 prices for use in the relationship, and

2) The calculation of SEP was modified slightly to reduce the impact of fuel price on the SEP index.

3) Finally, some recalibration of the parameter values was found necessary to better match the growth profiles of motorized land passenger transport in many countries.

The relationship in equation (1) cannot be expected to fit the observed data for a given country so the change in passenger travel from the base year position is assumed to be the difference of T, for the forecast year and the base year of 2012. This is the approach that ICCT used. It may seem more intuitive to use the ratio of the modelled and the observed however, the form of the Gompertz relationship means that ratios do not affect the final results.

Details of the data-set and calibration of the land travel relationship are not given in the published ICCT documentation but are assumed to be constant across all countries. However, once a number of country models had been created it became obvious that the relationship was not a good fit for some countries over the last 10 years, even as an approximate. A possible reason for this would be that the last 10 or so years (2000-2012) are out of the norm, and the ICCT data will have been calibrated over many more years and countries than what is currently available from the database. However, it was thought reasonable to allow for some re-appraisal of the parameter vales. As countries from different regions were built up, the estimated passenger kilometers for land transport were back-predicted to 2000 and the results compared with the data from the data-sets, and the Gompertz parameters adjusted to better match the historic growth profile of passenger kilometers per person.

The major change was to reduce, in all cases, the maximum passenger travel limit (UBL) from 30,000 kms per person to 15,000 (in some cases such as Fiji and the other Pacific islands, this value was further decreased1). In addition, the other two parameters were adjusted so that the growth profile of travel per person with GDP per person better matched that ’observed’ in the transport database. Where possible, the adjustment was undertaken on a regional basis (with groups of countries) but this was not always possible. The revised parameter values are provided below.

1 The ‘Upper Bound’ parameter has been adjusted to minimise the differences in historic growth profiles and

may not prove a useful estimate of the final saturation level at very high GDP/head levels where land travel is

physically restricted in, for instance, most of the Pacific islands.



Table 2: Gompertz parameters for land passenger transport

REGION Gompertz Upper Bound

(kms per person per year)

Gompertz Intercept Determinant

Gompertz Growth Rate Determinant

SE Asia 1 15000 -1.800000 -0.000060

SE Asia 2 15000 -4.100000 -0.000150

Indonesia 15000 -13.000000 -0.000480

Nepal 6000 -7.500000 -0.000500

Viet Nam 10000 -4.900000 -0.000415

PRC 9000 -2.800000 -0.000130

Mongolia 10500 -2.100000 -0.000250

Bangladesh 6000 -2.900000 -0.000155

India 12000 -2.450000 -0.000170

Fiji 11000 -1.700000 -0.000100

Kazakhstan 25000 -6.000000 -0.000120

Cambodia 15000 -2.100000 -0.000150

Group A 15000 -2.600000 -0.000046

Other 6000 -7.500000 -0.000500

Pacific 1000 -1.700000 -0.000100

ICCT 15000 -4.550454 -0.000068

These parameters provided a good fit to the profile of changes in land passenger for most countries. However, Viet Nam, Cambodia, Kazakhstan and Mongolia have seen passenger travel, based on available data, increase at a much faster rate than could be encompassed by any reasonable changes in the parameter values, so they have been modelled as individual countries.

Estimation of the sub modes, Rail, and then between the Road modes, are undertaken using similar Gompertz relationships based those in the ICCT Roadmap model. As with the ICCT model these estimates are not used in an absolute sense to derive forecasts but only to derive market shares, constrained by the total land passenger transport activity forecast in equation (1). Some recalibration of the individual mode relationships was attempted and although original car travel constantly overestimated car travel and motorcycle underestimated travel, the impact on the total travel activity, and then CO2 emissions, was surprisingly small (~1%).



In addition to the changes to the ICCT Roadmap model assumptions mentioned above, a number of adjustments to the model forecasts have been necessary due to the model producing unreasonable forecasts.

Because of the need to pivot off the base year market shares, it was found that the Rail passenger share tended to zero in the future, where the Rail mode share was small in the base (2012) year so the forecasts of Rail passenger activity in the far future are unreliable. As will be described later, a similar phenomenon occurred with Rail freight where the solution was to forecast Rail freight as an independent mode and simply take its total off the Land total to forecast Road activity. A similar approach could be undertaken for Rail passenger forecasts or the Road Gompertz curve could have a lower ‘Upper limit’ than 100%. In fact, the former method has been adopted. In both cases a unit elasticity (1.0) was chosen. This is likely to be an overestimate in most cases because, in the absence of significant investment, the growth profiles for most countries saw a gentle decline in Rail activity.

The Road travel activity was then divided again using Gompertz relationship to split the Road traffic into Car, motorized 3-wheelers, Motorcycles and Buses. Buses were then further split into standard buses and ‘minibuses’, and Bus Rapid Transit (BRT). In the latter case, historic data is very limited for many countries so the BRT historic estimates included in the ‘Transport_data’ worksheet are included in the Bus totals because national statistics do not identify BRT as a separate mode and the Bus relationship in the ICCT model is for all bus travel. However future incremental changes to BRT travel demand as a result of BRT-based policies are ascribed to the BRT mode.

The other main issue was with the motorcycle (and motorized 3-wheelers) forecasts. The motorcycle forecasting relationship in the ICCT model is actually a two-relationship curve, with motorcycle use increasing up to a given GDP per capita level, and then a separate curve giving rise to a decline occurring at GDP levels above this point.

Currently, Waterborne passenger activity and passenger Air travel are forecast by individual simple elasticity models, with travel increasing pro-rata with GDP. This is probably too elastic a value, i.e. should be much less than unity (and domestic Air and international Air travel sensitivities to national GDP are likely to be different). Waterborne travel, which includes Waterways ferries and maritime ferries, is poorly estimated in the base year (see section 8.2) and so its forecasts are particularly uncertain.

Forecasting energy use and emissions by mode

In order to accurately forecast changes in energy use and emissions, it is necessary to consider changes in the make-up of the vehicle stocks, by vehicle technology (fuel type, power train etc.).

Vehicle stock modelling has been considered only for Road modes. Road modes include Car, Bus, Minibus, motorized 3-Wheelers, and Motorcycles. Fitting a vehicle survival model like a Weibull function (as used in the ICCT Roadmap model), would need a suitable quantity and quality of historical data for vehicle sales and scrappage, which were not available for many countries, so a simpler stock model based on an ‘average lifetime’ was a more practical option for the ADB model, and has been adopted. The choice of this value does



impact on the speed of vehicle turnover as well as the speeds at which new vehicle technologies are adopted.

For each model using an assumed load factor, and distance per vehicle per year, the total vehicle stock is calculated from the total passenger travel activity. New vehicle powertrain/fuel splits have been assumed to be exogenous input data, with data being provided by members of the project team.

Vehicle mileages are split into urban and rural mileages, as such energy and CO2 emissions are calculated for urban and rural areas separately. Laboratory to real-life, and urban congestion gap factors are included to account for, respectively, the difference between tested fuel economy and on-Road fuel economy, whilst considering the urban traffic congestion effects.

Vehicle emissions are calculated for pollutants CH4, N2O, NOx, CO, PM10, PM2.5, and BC (black carbon). Emission data, including emission rates, Euro standard introduction year and vehicle kilometer travelled (VKT) share, have been taken from the ICCT Roadmap model, for the area of ‘Asia-Pacific’.

The methodology is illustrated in Error! Reference source not found.2.

Figure 2: Structure of energy and emissions forecasting methodology for Road modes

Stock (y)

Sales (y)

Sales (y-TLT)

Sales by TECH (y)TECH shares

Stock by TECH (y)

Sales by TECH (y-TLT)

Mileage by urban & ruralPOP & Mileage

Urban share

Mileage

Stock veh FE by TECH (y)

New veh FE(y-TLT)

Stock veh FE (y-1)

Stock by TECH (y-1)

Sales (y-1)

TECH improvements

Stock veh FE by TECH & area (y)

VKM urban & ruralLab & urban gap factors

Total energy use by TECH and area

POP & Mileage Urban share

CO2 emission factors

Total CO2 emission



Forecasting travel by Non-Motorized modes

The modelling of the demand for non-motorized travel modes presented a particular problem. These modes are not considered in the passenger travel demand models used by the International Energy Agency (IEA), the International Transport Forum (ITF) or ICCT, which concentrate on motorized transport. In addition, the amount of data, collected at a national level and comparable to that collected for motorized modes is not available. Furthermore, some of the increase in demand for motorized travel demand will be at the expense of non-motorized travel but there is little evidence about how much, especially in the developing member countries in Asia. However, two aspects of forecasting sustainable development make the role of these modes important. Many of the sustainable policies that one might adopt to reduce the carbon transport footprint of a country will involve the transfer of travel from motorized modes to non-motorized modes such as walking and cycling, and in some countries, pedicabs. Secondly, the rise of electric bikes (e-bikes) and electric pedicabs means that these modes may not be totally non-motorized in the future but consume energy in the form of electricity.

In the absence of national data for any of these three modes, the travel demand for each was forecast separately and differently.

The ownership of bicycles and their use were estimated using data from two different sources using two sources of data. Work by Oke, et al (2015) looked at household cycle ownership and split the world into 4 groups of differing household cycle ownership all of them represented in the developing member countries in Asia. Work by Mason et al, (2015) and the Institute for Transportation and Development Policy (ITDP) database (ITDP_UCD Cycling Data master.xlsx) was used to estimate the use per bicycle and the impact of –e-bikes now and in the future. These studies only allowed a breakdown into 3 groups - India, PRC and Other Asia countries. The ownership assumptions were related to these 3 groups rather than the 4 groups from Oke’s work. (A consequence of this is that Bicycle ownership in countries in Central Asia and the Philippines is likely to be overestimated but should not affect forecasts of use. It will show up as low forecasts of use per bike.) For each group, the number of bicycles owned was estimated as a constant proportion of populations (assuming household size was 3.0 in all countries). Oke’s study showed no significant trends in ownership with time. In contrast, the bicycle use data suggested that the use per cycle declined gradually over time and an exponential function was fitted to the forecasts of use in the three groups – the decline in use in the PRC was predicted to be less than that in India and that country less than in the rest of Asia. With these assumptions, it was possible to forecast bicycle usage under the Benchmark scenario, and provide estimates of e-bike use in 2012, especially in PRC.

Evidence of the role of pedicabs on a national level was even rarer so a working assumption was made that pedicab ownership and usage was a proportion of that for motorized 3-wheelers. This proportion (18%) was based on the ADB Sustainable Partnership model (ADB Transport Model_2 Nov 2011.xlsx) and the same source estimated the distance per vehicle of 2,400kms and a load factor of 1.3 (This assumption may not hold as well for some countries where the ratio of motorized to human-powered 3-wheeler is suspected to be different, for example Bangladesh). Benchmark forecasts of pedicab travel were based on the change in forecasted demand for motorized 3-wheelers and the 2012 demand for pedicab travel.



In the absence of any national data, especially in terms of kilometers rather than trips, the demand for walking was assumed to be based on the TEEMP (Transport Emission Estimation Models for Projects) data-set aggregated to the same three groups with a constant distance per person per year across the years, even though this may overestimate Walk travel in the future by an uncertain amount.

Table 3: Assumptions for estimating demand for walking

REGION Bicycle distance

per person per

day

Intercept

Year

parameter

WALK km/per

person per year

Bike Ownership per

person (based on Oke

et al, 2015)

PRC 1.4 -0.000600 334.721 0.200

India 21 -0.010400 146.603 0.133

Other 67.16 -0.034200 268.798 0.133

4 Freight model structure

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. The ITF approach has been adopted for the Transport DataBank model. There are advantages and disadvantages for their approach for modelling freight in the ADB countries. For most countries, Water and Air can be considered as separate elements, with little interaction with land modes. However, there are two instances where this assumption may break down. In a number of countries there will be inherent competition between Water and land modes for some commodities – Water, in this case, could be internal waterways or coastal maritime transport. The second issue is in most of the Pacific countries, Air and Water are the primary means of moving goods around. In these cases, the primary modes will be Water and, to a lesser extent, Air in tonnage terms.

The modelling of the demand for freight by individual modes is done hierarchically, as set out in Error! Reference source not found.3. Thus, the total demand for freight by Land, ater and Air is estimated first. For Land freight, the demand of Rail is estimated and the rest assumed to be Road transport, the latter in turn is split into 3 classes of mode: light commercial vehicles (LCV), medium freight trucks (MFT) and heavy freight trucks (HFT). These models are described in more detail in the next sub-section.



Figure 3: Structure of the freight forecasting methodology

Forecasting freight demand by mode

Work by the ITF has corroborated a general finding from the classic econometric literature that Gross Domestic Product (GDP) and freight intensity are highly correlated, with an average long-term elasticity of about 0.98 found from a panel of countries. The form of model used in the Transport DataBank model is an elasticity model based on the figures in Table 2.3 in the publication (OECD/ITF, 2015 page 51), reproduced in Table 4.

POP, GDP/head

ITF parameters

Forecast Activity (ton -kms) LAND (Road + Rail)

Model F1

Split Road intoLCV, MCT & HCV

Model F3

Activity (ton -kms)

WATERModel F4

Activity (ton -kms)

AIRModel F5

Urbanisation, Mileage split,GDP/head?

Rail ActivityModel F2

LAND Activity(ton-kms)

Rail Activity (ton-kms)

Road Activity (ton-kms)


LCV

Road Activity(ton-kms)

MCT


HCT

SimplifiedEMISSIONSMODELLING

EMISSIONSMODELLINGwith stock turnover model





Load factors,Distance per vehicle Vkms and Vkms/stock

LCV

Vkms and Vkms/StockLCV

Vkms and Vkms/Stock

LCV



Table 4: Freight Intensity as a function of GDP1

Income group

(PPP, 2005 USD) Freight-intensity elasticity

0 - 3999 1.18

4000 - 19999 0.98

20000 - 39999 0.87

40000 + 0.82

The relationship used in the model to forecast freight demand takes the form of:

𝐹𝑦 = 𝐹𝑦−1 × (𝐺𝐷𝑃𝑦

𝐺𝐷𝑃𝑦−1)

𝐸

(6)

Where:

𝐹𝑦 = Freight demand (billion ton kms) in year y

𝐹𝑦−1 = Freight demand (billion ton kms) in year y-1

𝐺𝐷𝑃𝑦 = Gross Domestic Product (GDP) (2010 PPP USD) in year y

𝐺𝐷𝑃𝑦−1 = Gross Domestic Product (GDP) (2010 PPP USD) in year y-1

𝐸 = Elasticity parameter. For land freight this value varies with GDP per capita but for Air and Water it is assumed a constant. Non road passenger travel activity is forecast in a similar fashion with a constant value of ‘E’ set at 1.0.

The main issue with this relationship for Land freight is that for most countries modelled,

the change in freight activity with GDP over the historic period (2000 – 2012) has been

much less than that assumed by the elasticity values. Values of around -0.7, -0.8 are more

common for most countries, with GDP per head less than $20,000. The main exceptions to

this are Thailand and Laos, where freight estimates are further complicated by the

presence of a high numbers of pick-ups in the vehicle fleet, and whose usage probably

straddles both passenger and freight use. In these countries and in Nepal and the Maldives,

the pick-up stock was split 50:50 between passenger cars and Light Commercial Vehicles

(LCVs).



4.1.1 Rail

The modelling of the growth of Rail freight presented some problems. The work by IEA mentioned by ITF in their 2015 Outlook indicates that, in general, the proportion of a country’s freight carried by Rail declines as the GDP increases and this is incorporated into the IEA’s MoMo model that was used to provide Rail freight figures for the ITF. In the case of the country used to test this model, Thailand, the proportion of freight carried by Rail is already a very small proportion of all land freight, and the use of Gompertz curves, as in the ICCT work, led to no freight using Rail in the future. Instead, a simple elasticity model was used with a very inelastic (but positive) parameter value. This could be an over-estimate as the trend in Thailand appears to have been for a very slow decline over the last 10 years in Rail freight.

4.1.2 Road modes

The major issue to be faced is to breakdown the growth in Road freight into 3 different classes of vehicle: light commercial vehicle (LCV), medium freight truck (MFT) and heavy freight truck (HFT). The proportions of all Road freight traffic carried in these three classes will vary from country to country, based on differing classification systems (there is no MFT/HFT distinction in the Thailand transport statistics, and all are classified as Trucks and counted as HFTs for this study). In most countries, only one category – trucks, can be identified and in these cases, they have been identified with the MFT category.

Because we have forecast total ton-kms by Road (having removed the Rail share), we need to disaggregate ton-kms into the three Road modes. A number of existing approaches could potentially have been used, but each has significant disadvantages. The MoMo approach is based on forecasting stock and then estimating activity from stock and using that variable to divide the activity forecasts. Adopting this approach would mean developing relevant stock forecasting relationships (or access to the MoMo relationships).

Similarly, one could adopt the ICCT Gompertz relationships for the individual modes and again use them only for splitting the total Road freight activity, but these appear to have very low explanatory powers. What evidence there is suggests that, over time, the proportion of freight activity undertaken in LCVs would be expected to increase. There are a number of possible explanations for this increase. As GDP rises, the greater the proportion of the GDP arising from the Service sector, where freight loads tend to be smaller (and more frequent). This is the background to the approach adopted by the ForFITs model, which forecasts ‘Truck ‘travel and then adds LCV share as a function of GDP.

An alternative is that the role of LCV in Urban freight is more important than it is for rural freight, because of traffic congestion considerations, and the types of employment, so as urbanisation increases so the proportion of all freight traffic carried by LCVs will increase. This is the approach currently adopted in this ADB model.

In practice, both approaches lead to LCVs carrying an increasing amount of the freight over time, since GDP and urbanisation are both assumed to also increase over time. The approach adopted currently in the Transport DataBank model is to make an assumption about the amounts of traffic by each Road freight category and, given the known stock of each category in 2012, estimate the amount of ton-kms undertaken by each sub-mode, see Table 5.



Firstly, the split by traffic is assumed, based on the observed estimates for traffic in 2012 and using the assumptions about load factors and mileage per vehicles, an estimate of the split of freight activity by sub-mode is projected. The impact of this assumption is that the share of Road activity carried in LCVs in Thailand, is forecast to rise from about 25% of ton-kms to 30% of ton-kms by 2050.

This approach, whilst eminently defendable, is cumbersome and needs calibrating for each country where more than one freight mode has been identified. However, at present, there are no data to estimate a more comprehensive version, based on GDP. An even more comprehensive relationship, based on GDP broken down by urbanisation, is likely to be impractical because of the difficulties in obtaining GDP below national level for most countries.

Table 5: Estimation of percentage of urban Road freight activity undertaken by different freight sub-modes – Thailand example

Assumed Vehicle-km mode share Calculated ton-km mode share

Urban Non-urban Urban Non-urban

Light 62% 25% 12% 2%

Medium1 18% 20% 22% 12%

Heavy 20% 55% 66% 86%

Sum 100% 100% 100% 100%

The forecasting of the gross Road freight activity proved to be the most difficult to match

with national values. Any minor differences were resolved by adjusting the distance

travelled and/or the load factors, so that the top totals matched. For a number of

countries, in particular Thailand and Laos, estimates of freight activity built ‘bottom-up’

from data on vehicle stock and reasonable estimates of kilometers per year, and load

factors produced forecasts much higher than independent estimates published by the

national authorities – beyond the range where simple adjustments could be made. PRC was

an exception to this finding, because for that country the bottom-up forecasts were

substantially below those published by the national authority. In both these scenarios the

bottom-up forecasts have been used to ensure consistency, so the forecasts from the

model do not necessarily compare with those from the national authorities over the

historic period.



Forecasting emissions by mode

The approach in estimating emissions from freight modes follows a similar pattern to that adopted for the individual passenger modes. For Road modes, emissions including energy and CO2 are estimated on a per vehicle-km basis however, for the other modes – Rail, Water and Air emissions are modelled on a per ton-km basis.

In addition, currently there are no pollutant calculations for Rail/Air and Water modes.

5 Road safety modelling

One of the requirements of the model was to forecast changes in accidents as a result of transport policies. Most previous modelling has only been concerned with forecasting Road accidents and casualties, which will be far higher than similar figures for other modes of transport.

Producing forecast models, even for road modes, is fraught with difficulties for developing countries. The information on road deaths, which is generally by far the largest category of deaths resulting from injury (with the exception of some countries with extreme violence and crime problems such as South Africa) is acknowledged to be poor, either from police crash reporting sources or morgue/medical sources. Information on modes that have fewer people killed than Road crashes i.e. Rail and Air, are even worse.

The World Health Organisation (WHO) undertakes a Bloomberg-funded task to gather data on fatal Road victim numbers, for all WHO member countries, every three years. This is the most comprehensive and systematically collected source of data that is available on the Road death toll in individual countries. It is, however, a problematic survey, and there were issues of consistency in the numbers between the first, second and third surveys for some countries. The process has recently abandoned collecting data on severities below fatality, as the quality of this was found to be very changeable between survey periods.

The task is undertaken by regional coordinators asking official road safety-led organisations (where there is one) or transport or health ministries to supply the required information. A local committee made up of key stakeholders validate, verify and approves the figures collected. The 2009 report gave 2007 fatalities, the 2013 report reported 2010 fatalities and the 2015 report reported 2013 fatalities.

There is significant underreporting of road fatalities in most Low and Middle Income countries. High Income countries generally achieve a high 90s percentage capture of Road deaths in official figures, with some notable exceptions e.g. Netherlands.

To account for underreporting, there is a regression model applied to improve the figures for countries that (a) have a reasonable population size, and (b) clearly have a significant underreporting issue. This uses factors such as population size, number of officially registered vehicles and other parameters to provide a “corrected” estimate.



Our conclusions arising from these circumstances are:

It is only worthwhile forecasting deaths (fatalities) as a result of Road accidents. Modelling of serious injuries and accidents would be susceptible to poor reporting.

It is recommended to only consider ’reported’ fatalities rather than the WHO ‘corrected’ estimates, as these are likely to be less politically sensitive and the focus is on an individual country through time rather than cross-country comparisons (although any calibrated model would need to cover many countries/year).

Work has been undertaken in the past on developing estimating relationships between Road accidents/fatalities as a function of national characteristics. These models tend to use accident/fatalities per 100,000 persons as the dependent variable (E.g., Jacobs & Hart, 1978). More recently accident models have been developed to estimate the impact on accident/fatalities as a result of changes in highway infrastructure at a Road link level, in connection with cost-benefit models such as HDM4.

Considering a general relationship the following form a relationship has been adopted:-

𝐹𝑦 = 𝐹𝑏𝑎𝑠𝑒 ∗ (𝑃𝑂𝑃𝑦

𝑃𝑂𝑃𝑏𝑎𝑠𝑒)

𝐸𝑃

∗ (𝐺𝐷𝑃𝐶𝑦

𝐺𝐷𝑃𝐶𝑏𝑎𝑠𝑒)

𝐸𝐺

∗ (𝑈𝑅𝐵𝑦

𝑈𝑅𝐵𝑏𝑎𝑠𝑒)

𝐸𝑈

∗ (𝑃𝑀𝐶𝑦

𝑃𝑀𝐶𝑏𝑎𝑠𝑒)

𝐸𝑀

∗ (𝑦

𝑏𝑎𝑠𝑒)

𝐸𝐸

(7)

Where:

𝐹𝑦 = Road fatalities in year y

𝐹𝑏𝑎𝑠𝑒 = Road fatalities in the base year (assumed to be 2012)

𝑃𝑂𝑃𝑦 = Population in relevant year

𝐺𝐷𝑃𝐶𝑦 = GDP per capita in relevant year

𝑈𝑅𝐵𝑦 = Proportion of population in urban areas >250,000

𝑃𝑀𝐶𝑦 = Proportion of travel by Motorcycle in relevant year

𝑦

𝑏𝑎𝑠𝑒 = Years from base - this issued to enable an assumption of an exogenous

improvement in fatality rate, not related to the other variables

All other parameters raised to the power, including EP, EG, EU, EM, EE, are the elasticities to the corresponding driving factors (see Error! Reference source not found. for the values

dopted in this model). Error! Reference source not found.



Table 6: Indicative elasticity estimates for fatality model

Indicator Variable in equation above Value

Population (included as original equation would

have predicted total fatalities rather than fatalities

per 100,000 persons)

EP 0.0

GDP per capita EG -0.5

Urbanisation EU -0.8

Proportion of passenger demand by motorcycle EM 0.5

Exogenous improvement EE 0.98

(a 2% per annum improvement)

All these variables have been used in previous models except for motorcycles, but this has been suggested as a useful variable in relation to accidents in Thailand2. Thus, fatalities per 100,000 people are expected to decline with increasing GDP per person and Urbanisation, and a reduction in the proportion of passenger traffic (veh-km) by motorcycle. In addition, there is assumed to be an exogenous reduction in fatalities per person caused by such effects as improved vehicle safety features, and other globally-generated improvements.

In the current version of the model, forecasts of road accidents changes by mode are included in the model but are not modelled as the split by mode is usually not available so the numbers in the tables are not to be considered reliable

6 Data collection using Thailand as an example

As part of the project specification, the transport model for a country is created with historic data from 2000 to 2012, for a large number of transport variables. These are coupled with variables or parameters that can be expected to be constant or stable across a number of countries. In addition, the model provides a Benchmark forecast as part of the set-up for a country (the user will then be able to construct alternative scenarios using the data). The definition of the Benchmark scenario is discussed in the Transport DataBank main report. The first country model to be developed was Thailand, this provided a ‘template’ model as the basis for the rest of the country models. A number of different sources and assumptions have been made to populate the data-set required. These are described in the following sub-sections.

In the current model, the historic transport data can be found within the ‘Transport_Data’ tab. Color coding is used to indicate the nature of data collected. Those data points based on official statistics or from survey data are shaded in Green, cells filled with interpolated/calculated data from known values are shaded orange and where data are unavailable at present, or where ‘proxy’ data used based on expert judgment, cells are shaded in yellow.

2 Vanhaleweyk, Guido. “Traffic accident statistics for Thailand – Car and Motorcycle Accidents.”

http://www.thaiwebsites.com/caraccidents.asp

http://www.thaiwebsites.com/caraccidents.asp



Data for Passenger demand (Benchmark Model)

As section 2 explains, the forecasting of passenger travel demand is undertaken at the Passenger demand ‘activity’ level, in relation to passenger-kilometers per year (Passenger-Kms). Estimates of passenger activity are usually built up from estimates of the constituent elements of Vehicle stock, Distance per vehicle per year and load factors, rather than being available directly from official data. Data was also collected, where available, for Road vehicles to assist with the development of stock models for Road modes.

Estimates of stock for Road vehicles for all relevant years (2000-2012) was obtained from a preliminary set of data collected for Thailand, both for this project and for an earlier project for ITPS, Japan.3 A complication in this data set is that in 2004 the Thai registration data was purged of vehicles not thought to be ‘on the Road’, this led to reductions in stock totals for many types of vehicle for that year. Data for historical new vehicle technology splitting has been provided by the team for this project.

In contrast to the stock data, there are no official estimates of the mileage driven by each type of vehicle in a given year (this is only required for Road modes). The only estimates available have been individual studies of particular places, which have been conducted at varying intervals since 1997. These are summarised in Tables IV-3 and 4 in GIZ (2014) and from the ATRANS (2010) report for the year 2000. Notably, most of the estimates are quite high by developed country standards, but this may be a product of the way the surveys were carried out. The lack of data makes it uncertain as to the trend over time in the mileage values.

In the case of load factors, we have been unable to obtain any valuable local estimates, and the values in the data-set have been chosen on the basis of values in the EU (ignoring instance of overloading in Heavy Freight Transport (HFTs) in many developing countries). Obtaining sales data has been problematic. Sales data for the whole of the historic period has been obtained for Cars and Motorcycles but data is only available from 2011 for Minibuses. We then assumed constant across the whole of the historic period. For buses and motorized 3-wheelers, sales have been assumed at a default value of 6% of the stock, for a given year.

Perhaps of more concern is the availability of data for the life expectancy of a vehicle, also needed for the stock models. This data is based on surveys undertaken in 1997 and 2008 reported in GIZ (2015) and ATRANS (2011). The life expectancy curves suggest that the average life of a vehicle in Thailand is very high, >30 years for a car. These numbers will have implications on the replacement rates of vehicles in the future. The current default is 18 years for cars and 21 years for freight vehicles.

The situation around data for the non-Road modes was much poorer. Some data was available for national Rail, especially since 2012 through the MOT data portal4, MOT (2016), including the actual amounts of diesel oil used for Diesel Multiple Units (DMUs) (passenger)

3 “Thailand Consolidated Road Data (partial).xls”

4 Ministry of Transport, Government of Thailand. Transport Statistics 2016 Edition.

http://www.news.mot.go.th/motc/portal/graph/index.html




and freight (diesel locomotives). However, the same level of information was unavailable for the Bangkok transit services, estimates of mean trip lengths have been used to convert ‘passengers carried’ estimates (from the company websites) into passenger kms. All these services were assumed to be electric.

For the two remaining modes (Water and Air) the availability of data was even scarcer. Recent data on passenger Air movements (international and domestic) from the Thailand MOT data-portal could be related to estimates of passenger-kms from the ICAO database (ICAO, 2013, 2014). For Water-borne passenger movements there was very little data available, although the existence of a graph of domestic passengers carried in the GIZ publication, coupled with an estimate of average trip length, was used to estimate the passenger-kms carried by Water-borne vehicles. Currently a mean trip length of 5km was assumed but, as the validation exercise suggests, this may be a severe underestimate, even if this were suitable for travel within the Bangkok region. (Water-borne passenger travel is not split between Waterways and maritime travel in the model).

Data for Freight demand (Baseline Model)

The Data for Freight demand has been consolidated from various sources, as follows.

(1) The Road model vehicle mileage is based on mean values of Bangkok and Nakhonratchasima (Thirayoot et al, 2009)

(2) For load factors for Road mode vehicles, a value of 0.5 tons per LCV has been assumed. The AJTP (American Journal of Trade and Policy) 2013 estimates an average Load factor for 'Road freight' of 7.35 tons per HFT (actually HFT & Medium Freight Transport (MFT))

(3) The sensitivity of total land transport activity in ton-km to GDP comes from Table 2 of the ITF Transport Outlook 2015 (note that the original data is in 2005 Purchasing Power Parity (PPP) units

(4) For the Rail transport activity, it is currently assumed that Rail freight activity is constant in the historical years before 2004 at the 2004 level for the Thailand model

(5) Mode share among Road modes is based on assumptions about the proportion of urban freight/non-urban freight carried by each type of vehicle, and is calibrated to 2013 data from GIZ report

Data for fuel economy and emissions

Fuel economy values (FE - in litre gasoline equivalent per 100km) are based on industry estimates, for example the GFEI (Global Fuel Economy Initiative). The associated FE annual improvement rates are available only for cars, and the data for cars has been adopted for other vehicle types. Other emission data have come from the ICCT Roadmap model.



Data for future forecasts

In order to provide a base case from which we can compare alternative visions of the future, the model forecasts a Benchmark scenario, in addition to presenting historic data. The Benchmark scenario represents the current trend continued, and so does not include policies that are expected to be implemented in the near future. In order to undertake forecasting, a limited set of socio-economic and vehicle technology variables were forecast through to 2050.

Population forecasts were taken from the United Nations forecasts and GDP forecasts were compiled using the World Economic Outlook (WEO) database up to 2020, ADBs forecasts for Thailand up to 2030 and the Price Waterhouse Cooper forecasts5 for Thailand up to 2050. Fuel prices were assumed to rise by 2% per annum up to 2050.

In addition to the socioeconomic data forecasts, assumptions were made by the study team on reasonable 2050 splits by technology for sales in the absence of major policy changes.

7 A brief description of Excel implementation

Overview of the Excel template model for producing the historic data and the benchmark forecasts

The Transport DataBank Model is an Excel-based one-country model, in that the Excel workbook for a specified country contains all the data for that one country6. Within the Excel workbook model, there are 55 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.

Main tabs

7.2.1 Input-data tabs

The Input-data tabs hold input data to the model. This group of tabs include the following items:

“Socioeco data” – containing socio-economic data

“Transport_Data” – containing transport data

5 Price Waterhouse Cooper (PWC) produced GDP forecasts for some 11 countries with the ADB data-set. For

the other DMC countries, ADB forecast growth-rates for the period 2016-2030 were halved for the period

2030-2050.

6 In future, it may be possible to repeatedly run and store the results for different countries perhaps using a

macro, and analyse on a region analysis Sheet.



“Parameters” – containing all the parameters in the model relationships used for transport forecasts

“Veh_TECH” – containing vehicle technology data

“Fuel_Spec” – containing fuel specification data

“Emission_rates” – containing emission rate data

7.2.2 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.

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_Benchmark" and "mode_ALT", containing calculations for the Benchmark scenario and an alternative scenario.

In the current version of 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 periods transferred to the “Results” tab.

7.2.3 Policy input and calculation tabs

The model forecasts a Benchmark scenario and an ‘alternative’ scenario. The current version of the model actually stores two different versions of the alternative scenario in separate ‘PolicyInputs’ worksheets. The "PolicyInput" sheet allows the user to enter different policy measures within a scenario, 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. The current version of the model stores two different alternative scenarios and the user chooses which one in the ‘PolicyToUse’ worksheet. Currently the ‘PolicyInput1’ worksheet holds policies relating to a progressive scenario aimed at roughly meeting a 2oC climate change, whilst the policies in the ‘PolicyInput2’ worksheet hold policies designed to meet a 1.5°C climate change. All the policies in the worksheets can be changed by the user and new policies added.

7.2.4 The model result tab

Currently, there is only one output tab, the "Results" tab containing summary results of main model outputs at 5-year periods. This worksheet contains summary data on travel demand, transport energy use and emissions by mode as well a series of illustrative graphs of the results.



PLDV_Benchmark PLDV_ALT

Bus_Benchmark Bus_ALT

BRT_Benchmark BRT_ALT

Minibus_Benchmark Minibus_ALT

3_4W_Benchmark 3_4W_ALT

2W_Benchmark 2W_ALT

Passenger_Travel_Demand Bike_Benchmark Bike_ALT

Pedicab_Benchmark Pedicab_ALT

Walk_Benchmark Walk_ALT

Passenger_Rail_Benchmark Passenger_Rail_ALT

Passenger_Water_Benchmark Passenger_Water_ALT

Passenger_Air_Benchmark Passenger_Air_ALT

Socioeco data Veh_TECH

Transport_Data Fuel_Spec PolicyInput1 Results

Parameters Emission_rates PolicyToUse PolicyImpacts

PolicyInput2

LCV_Benchmark LCV_ALT

Freight_Travel_Demand MFT_Benchmark MFT_ALT

HFT_Benchmark HFT_ALT

Freight_Rail_Benchmark Freight_Rail_ALT

Freight_Water_Benchmark Freight_Water_ALT

Freight_Air_Benchmark Freight_Air_ALT

7.2.5 Supporting tabs

This group of tabs hold some global parameters for the model, such as table of contents, lists of countries, modes of travel, types of vehicle technologies, and unit conversion factors. There are also Tabs containing information for model development, such as version control7, and country assumptions, color coding, and so on.

Main data flow between tabs

The main data flow between tabs, as represented by the tab names, is shown in Error! eference source not found.4.8 A solid line means that data from all tabs in the upstream box feed all tabs in the downstream tabs. A dashed line means that each mode-specific BAU tab feeds its corresponding mode-specific ALT tab.

Figure 4: Data flow in the Excel model

7 This tab will be omitted from the model version uploaded online.

8 Note that Tabs associated with the alternative scenario component are still under development and are

subject to changes.



8 Validation

There are a number of approaches to validate the constructed model, but so far these have been restricted to those involving the initial country – Thailand. Much of the data collected for Thailand, and discussed in section 6, has been used to construct the ‘observed’ data set from 2000 to 2012. The validation was based on model forecasts using the ICCT parameter values. However, two independent approaches to validating the model have been used, which are discussed in the following two sub-sections.

Back-forecasting

This approach consists of running the model back in time from 2012 to predict values of travel and emissions for the period 2011 to 2000, which can be compared with those ‘observed’. Given that the model, in essence, pivots off the 2012 value, the back-forecasting estimate is relatively independent of the observed data from 2000 to 2011.

The results can be seen in Table 7.

Table 7: Performance of Transport DataBank Model in forecasting historic data using ICCT default parameters (Mtons CO2-eq)

Mode 2000 2005 2010 2000 2005 2010 2000 2005 2010

Passenger Back-Forecast Historic data % difference

Car 4.6 9 14.5 9.8 11.9 16.2 -53% -24% -10%

Bus 2.1 3.2 4.4 4.8 4.8 5.2 -56% -34% -15%

Minibus 1.4 2 2.7 3.2 2.1 2.2 -56% -4% 24%

Motorized 3 -Wheeler

0 0 0 0.1 0.0 0.0 -100% -100% -100%

Motorcycle 5.3 9.4 14.8 6.6 6.8 7.9 -20% 38% 87%

Rail 0.1 0.1 0 0.22 0.21 0.19 -54% -52% -100%

Water 0.2 0.2 0.3 0.1 0.1 0.1 174% 165% 346%

Air 9.1 12.5 15.8 6.7 8.7 11.2 36% 44% 41%

Freight 2000 2005 2010 2000 2005 2010 2000 2005 2010

Back-Forecast Historic data % difference

LCV 3.5 4.2 4.8 5.6 6.1 7.9 -36% -32% -40%

MFT 4.0 4.5 4.8 9.1 9.6 10.6 -56% -54% -55%

HFT 7.6 8.0 9.4 5.6 5.9 6.5 37% 36% 44%

Rail 0.0002 0.0002 0.0002 0.0936 0.0799 0.0667 -100% -100% -100%

Water 0.0262 0.0377 0.0484 0.0458 0.0458 0.0458 -43% -18% 6%

Air 1.5 2.1 2.5 2.064 2.064 2.064 -27% 2% 21%

Total (Ave%)

43.4 65.2 88.5 91.2 97.3 101.5 -52% -33% -13%



What is obvious is that for most of the mode categories the difference between the back-forecast and the historic data increases the further back the data goes, being much worse in 2000 than in 2010. In the model, the historical data runs from 2000 to 2012 and the forecast starts in 2013. The forecast data series in 2012 and 2013 have been scaled such that the forecasts coincide with the 2012 observed data. This method of splicing the two series of data may be improved by considering more historical data points than just the final historical year (i.e., 2012).

It should also be noted that the historic data for 2000-2003 is not directly comparable with that of 2004, this is because of the change in the definition of vehicles ‘in use’ within 2004. Adjustments to the Gompertz relationship for passenger transport activity for Thailand were therefore based only on data from 2004 onwards, and a reasonable fit to this period of growth was obtained for passenger growth.

Another feature to note in the above table is that the Rail model forecast is the poorest amongst all the modes. This is because the mode-splitting relationships adopted underestimates for Rail mode share.

Comparison of estimates of total energy use in 2008 and 2015

Estimates of the total transport energy use in Thailand have been established from two separate sources. An estimate for 2000 from the 2nd National Communication, submitted to the UNFCCC, is quoted in GIZ (2012) and ATRANS (2010) as being 44.7 million tons of CO2-eq, this had risen by 2012 to 59 million tons of CO2-eq. In contrast, the model estimates that the CO2-eq production was 91.2 million tons in 2000, and rose to over 100 million tons by 2012. By 2005, official estimates of the CO2 emissions suggested that 59% were emitted by the freight sector (GIZ, figure 2.1) whilst the transport model estimates 44%.

A second validation point for 2015 was found in the Thailand MOT transport portal, where the total energy, defined in terms of ktoe, is given disaggregated by the main mode. A comparison of the model with this independent value is given in Table 8. Although the transport model estimates lack the Well-To-Tank (WTT) component, the figures for Road and Rail show a good correspondence with perhaps a slight over-estimate, Air a slight under-estimate and Waterways showing a significant difference. Despite trying a number of alternate assumptions for Water trip lengths, and fuel economy values we are yet to find an explanation for this major discrepancy. Work by Chunark et al (2015) using an AIM model for Thailand, produces estimates of Waterway ton-kms and an order of magnitude greater than the official Thai MOT transport portal figures and produces an energy output 4 times that of the MOT portal in 2015.



Table 8: Comparison of the estimates of Energy consumption by transport mode 2015 with official estimates (Ktoe)

Transport mode Ministry of Energy(1) ADB Transport model

Road 22190 25072

Rail 76 84

Waterway 1303 125

Air 4932 4454

Total 28501 29735(2)

Notes:

(1) From Table 0.4 1016 MOT Data Portal

(2) Excludes Well To Tank energy

The conclusion from the study of the template for Thailand is that the top totals for energy consumption look reasonable for three of the modes, but not for Waterborne travel. The trend over time since 2000 is also fairly well-captured by the transport model, with the official estimates of total fuel used not changing much over time, and the model predicting a 10% change in the decade 2000 to 2010.

Looking at the estimates of CO2 emitted, the predictions do not look as good. In terms of its forecasting ability, there is a discrepancy between back-forecasts of the historic data combined with the historic data itself, which tends to increase through time. There are three alternative explanations for this: either the model is overly sensitive to GDP changes over the period 2000 to 2012, or the estimates of travel/emissions in the earlier years of the historic data are over-estimates of the true situation (perhaps caused by the default assumption of a constant value before the earliest estimate). The third explanation is that the CO2 estimates from the 2000 work are incorrect. A consideration of the energy/CO2 ratios for our model, and that implied by the Thai 2000 estimates, suggests that this might be the case.

Other validation data

Validation data for other countries can be found from the ADB Energy Outlook database

where data in terms of energy use can be compared with those estimates produced by the

Transport DataBank model.



9 Defining and running alternative Scenarios

The transport policy framework

The overarching sustainability objective may be achieved via a wide range of policy measures or instruments. The Transport DataBank Model allows the user to specify policies either individually, or more usefully, as a series of related policy instruments or ’policy clusters’. 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 naturally follows an Avoid-Shift-Improve (ASI) approach, with each of the ASIF strategies affecting one or two aspects of the total transport energy or emissions, as shown in Figure 5. This structure has been adopted within the ADB Transport DataBank Model.

Figure 5: 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 efficiency9 (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 9.2), and the IMPROVE policies in the subsequent subsection (Section 9.3).

9 We use fuel efficiency (in Litre / km) rather than fuel economy (e.g., miles per Litre) in the ADB Transport

Model.

= Total Emission

X X X Demand

Activity

Modal

Structure

Energy Intensity

“AVOID” policy

instruments

“SHIFT” policy

instruments

“IMPROVE” policy

instruments

Emission

Factor



The AVOID and SHIFT policy instruments

9.2.1 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 system, while a SHIFT policy involves two modes: “losing” and “gaining”, and will not cause any demand to ‘disappear’ from the model system (which only considers motorized modes). A ‘SHIFT’ policy relating to encouraging a switch of travel from, for example, Car to walking or cycling would be added as an AVOID policy.

The ADB Transport Model considers three modes as being high-carbon modes: Car, (Passenger) Air, and HFT. For the SHIFT policies, the high-carbon modes are the “losing” modes. For each “losing” mode, a group of “gaining” modes are defined. See Table 9.

Table 9. Definitions of gaining mode for losing modes

Losing mode Gaining mode

Car Bus, BRT, Minibus, motorized 3 -Wheeler, Motorcycle, Passenger Urban Rail, Passenger Non-High Speed Rail, Passenger High Speed Rail, Bike, Pedicabs and Walk

Passenger Air Passenger High Speed Rail, Passenger Non-High Speed Rail, Domestic Passenger Water

HFT Freight Rail, Domestic Freight Water

Based on the element of transport they impact, the AVOID and SHIFT policies are further divided into two categories:

(1) Transport demand (PKM or TKM) based, defined in terms of avoiding or shifting directly the PKM or TKM demand

(2) Transport elasticity based, defined in terms of % change in the cost of a mode of transport, together with the corresponding elasticity of the demand with respect to the costs

The elasticity based policy instruments are further divided into three types, depending on the elasticity used:

(a) The elasticity based AVOID policies, using the direct elasticity of, for example, car travel demand with respect to car travel cost

(b) The elasticity based SHIFT “pull” policies, using the cross elasticity of, for example, car travel demand with respect to the cost of a gaining mode of travel, for example changing 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, for example increasing public transport as a result of increasing fuel prices



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 10.

Table 10. Property of elasticity-based model parameters

Type of Policy instruments

Demand mode

Cost mode Cost change Elasticity Demand impact

AVOID Car Car Increase Negative Decrease

SHIFT pull Car Bus Decrease Positive Decrease

SHIFT push Bus Car Increase Positive Increase

Therefore, the AVOID / SHIFT policies are grouped hierarchically: first, into Avoid/Shift categories, and then into subgroups by losing mode (Car/Passenger Air/HFT), and then into three types of policies with different types of elasticities.

In what follows, the methods for the calculation of impacts of all AVOID and SHIFT policies are demonstrated for Road passenger modes, passenger Air travel and HFT may be calculated in a similar manner. In addition, for SHIFT policies, we will consider the gaining mode ‘Bus’, the impacts for other modes such as Rail may be calculated using the same methods.

9.2.2 Demand-based policy instruments

Suppose a number of policy instruments are defined. Each individual AVOID policy reduces transport demand by Car for a given proportion, Pi, of the Benchmark demand. Similarly, each individual SHIFT policy shifts a given proportion, Pi, of Car travel demand to one other named gaining mode of transport. An aggregate policy impact factor, Lose

CarF , is given by the

products of individual policy impacts of all relevant policies:

i i

Lose

Car PF )1( (8)

The above calculation is applicable to both AVOID and SHIFT policies for mode Car.

9.2.3 Elasticity-based AVOID policy instruments

Suppose two elasticity-based policy instruments are defined, as the means to avoid Car travel, a change in fuel price of %FUEL, and a change in parking charges, %PARK. Then, the impact factor, Lose

CarG , for Car travel demand is given by:

vuLose

Car PARKFUELG %1%1 (9)

Where u, and v are the elasticities of Car travel demand with respect to fuel price and

parking charge, respectively.



9.2.4 Elasticity-based pull policy instruments

Suppose Bus is a gaining mode, percentage changes in three cost components are specified

so as to pull the demand from Car travel, bus fare (%FARE), bus journey time (%BJT) and

bus frequency (%FREQ). Then the impact factor, Pull

CarH , for Car demand is calculated as:

cbaPull

Car FREQBJTFAREH %1%1%1 (10)

Where a, b, and c are cross elasticities of Car travel demand with respect to the relevant

cost components of Bus transport.

9.2.5 Elasticity-based push policy instruments

Suppose Car is a losing mode, two policy instruments are defined for the purpose of pushing demand from Car to Bus, a change in fuel price (%FUEL) and a change in parking charges (%PARK). Then, impact factor, Push

BusK , for Bus travel demand is given by:

''%1%1

vuPush

Bus PARKFUELK (11)

Where u’, and v’ are cross elasticities of Bus travel demand with respect to Car travel costs,

i.e., fuel price and parking charge respectively.

9.2.6 Aggregation of AVOID and SHIFT policy impacts

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 Benchmark 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:

MMMM SABD (12)

The impact factor for AVOID policies represents collective impacts as the results of applying both demand-based, and the elasticity-based AVOID policy instruments. See equations (8) and (9). This factor is calculated as:

Lose

Car

Lose

CarCar GFA (13)



The impact factor for SHIFT policies for a losing mode, such as Car, is similar to that of the AVOID policy impact factor, in that it represents the collective impacts as the results of applying both demand-based and elasticity-based SHIFT policy instruments. This is given by:

Pull

Car

Lose

CarCar HFS (14)

The impact factor for SHIFT policies for a gaining mode, for example, Bus represents the impacts of only the push policy instruments and is simply:

Push

BusBus KS (15)

However, unlike the AVOID policies, the demand which is reduced from the losing mode in

the SHIFT policies, is not lost from the model system – it is transferred (or added) to the

gaining modes as specified within the policy instruments.

IMPROVE policy instruments

In the ADB Transport Model, the following four types of IMPROVE policies are considered:

1. Increase FE improvements (representing reduction in fuel consumption)

2. Reduce fuel carbon intensity (or fuel de-carbonation)

3. Change Road vehicle technology share

4. Railway electrification

The methods for the calculation impacts of the policy instruments are described in turn below.

9.3.1 Increase FE improvements

This is defined as further 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.

Suppose a number of policy instruments are defined, for a different mode and fuel type, with instrument (i) increase Benchmark FE Improvements (RBAU) by a percentage (Pi). Then, for each mode and fuel type, the annual percentage FE Improvements for the ALT scenario, (RALT) is calculated as:

i iBAUALT PRR 111 (16)



9.3.2 Reduce fuel carbon intensity

The fuel de-carbonation policy instruments reduce WTT Benchmark carbon intensities. These policy instruments are defined for different types of fuel and sources of energy, including biofuels, hydrogen and electricity.

Consider a particular type of fuel or source of energy. Let Pi be the percentage reduction of Benchmark carbon intensity (CBAU) for policy instrument (i). Then, the ALT carbon intensity (CALT) is given by:

i iBAUALT PCC 1 (17)

9.3.3 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 shares.

9.3.4 Railway electrification

This policy instrument simply specifies an increased (from the Benchmark scenario) proportion of Railway electrification in the total transport demand.

10 Scenarios for the future

This section sets out the assumption behind the benchmark scenario and the two ‘alternative’ scenarios represented by the policies in the ‘PolicyInputs’ tabs. As a rule the same policies have been set up for every country but section 10.3 sets out the circumstances where variations from this philosophy have been made.

“Benchmark” scenario

It is normal in a forecasting model to provide a base scenario with which to compare other, alternative, scenarios. This is often named ‘Business As Usual’ (BAU), ‘Do Nothing’ or ‘Do-Minimum’, depending on precisely what assumptions have been made in building the scenario of the future.

It would be unrealistic to forecast a state where there is expected to be change, but the issue is how many of the policies that national governments are committed to, or stated they aim to, implement should be included in such a scenario. Because the scenario would be implemented across all the countries it was decided that no individual policies would be included in the Benchmark scenario but ‘unavoidable’ changes would be implemented across all counties. This is why the term ‘Benchmark’ was chosen. It also has the advantage that any alternative policy could include all those policies that governments have stated they will implement as well as other they may implement.



The exogenous forecasts of population are derived from the UN population forecasts up to 2050. The GDP forecasts up to 2050 have been built up using a variety of sources. Using WEO forecasts to 2020, ADB forecasts to 2030, and then using PWC forecasts for a number of countries up to 2050. For countries not forecasted by PWC, a growth rate of 50% of that assumed from 2020- 2030 was assumed.

The changes from the existing conditions that were assumed can be divided into three classes: 1.) structural changes, 2.) technological changes, and 3.) behavioral changes.

(1) Structural changes – The model anticipates a range of structural changes to happen

between now and 2050 in the Benchmark scenario in the modelled countries,

namely:

a. the level of urbanization;

b. the shares of the different transport modes on total passenger and freight

transport demand.

The level of urbanization increases from around 40% (depending on each individual country’s level of urbanization) to around 70% (based on the United Nations Department of Economic and Social Affairs (UNDESA) population prospects)10. This effects various other variables in the model, since for instance, annual mileages of passenger cars driven in an urban environment are lower than those of passenger cars mainly driven in a non-urban environment. Similarly, the distribution of ton-kilometers of different road freight modes (such as light commercial vehicles [LCVs], medium freight trucks [MFTs] and heavy freight trucks [HFTs]) varies between urban and non-urban areas. Also, some of the policies which are introduced in the alternative scenarios only apply to urban (e.g. modal shift of passenger travel from passenger cars to urban public transport) or non-urban areas (e.g. modal shift of domestic air travel to high speed rail).

The shares of different transport modes on total passenger and freight travel change as functions of GDP per capita (passenger transport) or GDP and GDP per capita (freight transport). For instance, in case of passenger travel, the share of passenger travel using motorcycles grows until the personal income threshold of USD 22,700 is reached (based on the ICCT Roadmap model)11. Once this threshold is exceeded, the share of passenger travel using motorcycles declines. Similarly, in case of freight transport, the growth of road freight activity is based on the growth of GDP, whilst the elasticity linking both entities declines with increasing GDP per capita. On the other side, the share of rail freight increases with GDP per capita. Consequently, the shares of road and rail freight on total land transport change with increasing GDP and GDP per capita over time.

10 UN Economic and Social Affairs. 2015. World Population Prospects.

https://esa.un.org/unpd/wpp/publications/files/key_findings_wpp_2015.pdf 11 ICCT, 2011. Global Transportation Roadmap Model. http://www.theicct.org/global-transportation-

roadmap-model

https://esa.un.org/unpd/wpp/publications/files/key_findings_wpp_2015.pdf

http://www.theicct.org/global-transportation-roadmap-model

http://www.theicct.org/global-transportation-roadmap-model



(2) Technological changes – Technological progress is not assumed to be on hold in the Benchmark scenario. Technology continues to improve at rates seen in the historic past. The main changes are taking place:

a. in the field of energy efficiency improvements;

b. in the composition of new vehicle sales by powertrain technology.

Energy efficiency (i.e. fuel economy in case of road vehicles) improves at between 0.5% (heavy duty vehicles) to 1% per annum (light duty vehicles) for all road vehicle modes and powertrains except plug-in vehicles, such as battery electric cars and plug-in hybrids, which see an annual fuel economy improvement rate of only 0.3% in the Benchmark scenario. This well reflects historic average fuel economy improvement in the absence of dedicated policies (e.g. in the heavy freight vehicle sector, which is largely unregulated).

Hybrid vehicles continue to account for increased sales volumes, and reach shares of 10% (LCVs) to 30% (passenger cars) on new vehicle sales by 2050 also in the Benchmark scenario. New powertrain technologies other than hybrid vehicles are not projected to gain significant market shares by 2050 in the Benchmark scenario. The new vehicle sales shares of alternative-fueled cars such as CNG and LPG vehicles are assumed to stay constant at 2012 levels. Gasoline-fueled MFTs and HFTs are phased out.

For non-road modes, annual fuel efficiency improvement is assumed to account for 0.5% per year. Rail is assumed to be entirely electrified by 2050.

The blend-shares of low carbon fuels such as biofuels in conventional petroleum fuels are assumed to stay at 2012 levels. The carbon footprint of electricity is reduced over time according to the business as usual scenario results.

(3) Behavioral changes – Behavioral changes are mainly assumed to take place in the

way vehicles are used. With respect to the model, these changes are translated into

changing annual vehicles usage (kilometers driven per vehicle per year, often called

‘mileage’). The distance driven per vehicle per year can be exogenously increased

(in case of low historic annual usage, especially in case of road freight vehicles) or

endogenously reduced (e.g. through an increasing ratio of fuel prices versus income

over time).



The forecasting of the Benchmark scenario is an amalgam of the approaches from the following models:

1. The ‘Sustainable Transport Evaluation with Policy, for the Association of Southeast Asian Nations’ (STEP-ASEAN) model, previously developed by the study team for the Institute for Transport Policies Studies, Japan

2. The ‘Global Transportation Roadmap’ model, by the International Council on Clean Transportation (ICCT) (ROADMAP 1.0, 2012)

3. The ‘Mobility Model’ (MoMo), by the International Energy Agency (2009)

4. The ‘Sustainable Fuel Partnership’ model, developed by the ADB

5. The ‘International Transport Forum’ (ITF) model, (OECD/ITF, 2015)

Motorized land passenger travel is forecast using a relationship derived from the ICCT Road map 1.0 model (ICCT, 2012). Flowcharts for the passenger car travel forecasting, ‘other road vehicles’, rail and freight under the Benchmark scenario are shown in Figures 6-8.

Figure 6: Car Travel Forecasting under the Benchmark Scenario



Figure 7: Forecasting of other Land Travel Vehicles for the Benchmark Scenario

Figure 8: Freight Forecasting for the Benchmark Scenario



Alternative scenarios

The alternative scenarios within this modelling approach are based on the ‘Avoid-Shift-Improve’ framework (Figure 9). Based on this concept, future travel demand can be avoided, shifted to more efficient modes or met using more efficient transport technologies and less carbon and pollutant emitting fuels. The current model framework allows for all the strategies to be applied on an urban and non-urban area level.

Figure 9: Avoid-Shift-Improve Concept to Reduce Energy Use and Emissions in the Transport Sector

Two alternative scenarios have been tested and evaluated:

1. a backcasting scenario - 1.5°C scenario;

2. a forecasting ‘Best available technologies and policies leading to 2°C’ scenario’ –

‘Progressive Scenario’

Backcasting scenarios are used to investigate how a certain energy or emission reduction target can be achieved using various policy levers of a simulation tool. In this case, the target is to achieve per capita CO2 emissions from the transport sector, which are in line with a carbon emissions scenario having at least a 50% chance to lead to a global temperature increase not higher than 1.5 degree Celsius by 2100.

Forecasting scenarios are used to estimate the impact of the application of policies and measures to reduce transport related GHG and pollutants emissions on transport energy use, emissions and costs.

It should be noted that the general principles of the two scenarios described below are subject to change and in the current version they have been modified so that the policies adopted in countries that currently (2010) have low carbon totals per capita (<1.0 tons per year) are adopted with less rigour than those for countries which have currently higher carbon usage (>1.0tons per year in 2010) (see section 10.3.1).

Avoid Shift Improve

Avoid the need to travelShift travel to more efficient

modes

Improve the energy efficiency of the

transportation technologies

• Integration of land use planning and transport

• Application of smart logistics

• Use of telecommunication technologies

• Shift of individual motorized passenger transport to public transport

• Shift of motorized transport to non-motorized transport

• Shift of freight transport to high efficiency/high load factor modes

• Use of intelligent transport demand management

• Improve the fuel efficiency of conventional technology

• Switch to new vehicle technology

• Shift to alternative and low carbon fuels



10.2.1 Backcasting – 1.5°C Scenario

The following section is intended to provide a brief overview of the main policies and measure applied to the transport sectors of the investigated countries within the two alternative scenarios. A comprehensive overview of all policy levers and measures available in the model is provided in section 9.

To stabilize global temperature increase at 1.5°C above pre-industrial levels by 2100, global energy related carbon emissions are needed to fall to zero somewhere between 2045 and 2060.12 The Emissions Gap Report recently published by the United Nations Environment Program suggests global greenhouse gas emissions to be in the range of 4 Gt to 14 Gt of CO2 equivalents by 2050, with the median being around 8 GtCO2e, to achieve a 1.5°C scenario. A study carried out by the Partnership on Sustainable Low-carbon Transport (SLoCaT) concludes that global annual tank-to-wheel (TTW) CO2 emissions from the land transport sector should not exceed 2 Gt by 2050.13 Assuming equal transport emissions per capita and a global population of around 9.5 billion people, this translates into a land transport TTW emission target of about 0.21 t CO2 per capita per year by 2050.

For this modelling exercise, a per capita emission target for the transport sector of 0.4 tCO2 per year is assumed to be in-line with a 1.5°C scenario, based on the assumption that the power as well as the residential sector would need to be fully decarbonized by that time, and that the remaining annual emissions of 8 GtCO2e would be distributed equally between the industry and the transport sector.

Since countries have very different historic starting points and since the level of development is assumed to still be different across countries by 2050, not every country will need to achieve these very low per capita CO2 emissions by 2050.

A very low carbon transport sector needed for a 1.5°C scenario can only be achieved through strong Avoid-Shift-Improve measures.

(1) Avoid – To realize the 1.5°C scenario, passenger car and motorcycle travel is reduced by 30% (in terms of pkm) compared to the Benchmark scenario by 2050. This overall reduction potential appears to be reasonable given various case studies evaluating the potential to avoid individual motorized transport based on measures such as better urban planning and land use densification, the use of telecommunication technologies and remote working as well as behavioral changes such as an increased use of cycling and walking.

In the road freight sector, about 10% of all ton kilometers using LCVs, MFTs and HFTs are avoided compared to the Benchmark scenario by 2050. Again, literature suggests the avoid potential (in terms of tkm) of measures such as the implementation of computerized transport logistics allowing for optimized routing and improved land use planning, which better combines zones with industrial

12 Nature Climate Change 5, 519–527, 2015. Energy system transformations for limiting end-of-century

warming to below 1.5 °C.

13 SLoCaT. November 2016. Implications of 2DS and 1.5DS for Land Transport Carbon Emissions in 2050.



activity, zones with a focus on services and residential areas, to be in that order of magnitude.

Domestic and international air travel are reduced by 30% compared to the Benchmark scenario by 2050. Although studies seem to indicate that the intensified use of telecommunication technologies for teleconferencing etc. might only account for a 10% reduction potential air travel needs to be dramatically reduced in order to achieve a 1.5°C scenario.

In addition to the above mentioned exogenously defined potentials, both passenger and freight travel are also endogenously reduced by estimating the impact of increasing costs for high carbon transport modes through the application of price elasticities. Therefore, it is assumed that per passenger kilometer and ton-kilometer travel costs of carbon intense modes increase by up to 70% by 2050 compared to the Benchmark scenario. Historic data suggests a price elasticity of demand of -0.1.

(2) Shift – Significant amounts of passenger and freight travel need to be shifted from carbon intense transport modes to low carbon or carbon free means of transport to achieve a 1.5°C scenario. Based on case studies, it is assumed that overall about 30% of urban passenger car travel can be shifted to public transport including urban buses, bus rapid transport (BRT) as well as urban metro/light rail once the relevant infrastructure is in place in the main urban centers within the respective country. In mainland countries, the development of high speed rail connections between main urban areas is estimated to provide an additional 5% potential to shift non-urban car travel to rail. These estimates are based on very broad assumptions and need to be verified on a country by country basis.

Similarly, the development of conventional and high speed rail connections between the main urban areas of land-based countries are estimated to account for a 10% and 20% potential to shift domestic air passenger kilometers to conventional and high speed rail, respectively.

Similar to the avoid strategy, in addition to the exogenously defined shift potentials, the impact of cost increases of carbon intense transport modes (e.g. passenger car and air travel) as well as the impact of cost reductions of low-carbon transport modes (e.g. reducing the fares for public transport) on the demand for carbon intense travel demand is evaluated.

In the case of freight transport demand, about 10% of MFT and 30% of HFT ton-kilometers are assumed to be shifted to rail in the 1.5°C scenario by 2050. In addition and where applicable, another 10% of HFT ton-kilometers are assumed to being shifted to inland water transport.

As for passenger transport, price elasticities are used to estimate the impact of increased per ton-kilometer costs of carbon intense transport modes as well as reduced costs of low-carbon transport modes on freight transport mode shares

(3) Improve – Technological change is the main pillar to achieve transport emissions in line with a 1.5°C scenario.

In the near term until 2030, fuel economy improvements of conventional technologies such as gasoline and diesel internal combustion engine (ICE) vehicles



play the central role. Annual fuel economy improvement rates need to be as high as 5% for passenger cars, values which have been previously observed in countries such as France and Denmark (GFEI WP11), which both have fuel economy standards as well as additional fiscal measures such as ‘feebate’ schemes in place.

For non-road modes, improving the fuel efficiency of air crafts is a major factor to reduce future carbon emissions. In the 1.5°C scenario, it is assumed that aircrafts achieve about a 50% reduction in specific fuel use compared to today’s state of the art airplanes. The same fuel efficiency improvement potential is assumed for ships – by 2050 ships consume on average 50% less fuel per ton-kilometer than today.

In the more distant future, a drastic shift towards alternative vehicles such as plug-in hybrids and battery electric vehicles is necessary. By 2050, sales of convention ICE passenger cars needs to be entirely phased out, hybrids have already passed their sales peak and plug-in hybrids and electric vehicles account for almost 90% of new vehicle sales. In case of road freight vehicles, conventional diesel powertrains account for only 25% and 50% of MFT and HFT sales, respectively. The share of CNG trucks is assumed to be as high as 20% for both MFTs and HFTs. While plug-in vehicles still have no significant market share among HFTs (due to the need for autonomy), about 30% of all new MFTs are assumed to be either plug-in hybrids or battery electric vehicles by 2050. About 50% of all new buses are assumed to be hybrids by 2050, and 20% of all new buses are fully electric. Fuel cell electric vehicles are not estimated to have a significant share on new vehicle sales in any of the investigated countries by 2050.

In addition to the uptake of alternative power train technologies, a shift towards low carbon transport fuels is equally important. All liquid and gaseous fuels are assumed to contain a 60% share of low carbon, sustainable biofuels, which itself are estimated to have a 70% carbon emission reduction potential compared to conventional petroleum fuels. Electricity needs to be almost fully decarbonized by 2050.

In addition to technological improve measures targeting the fuel efficiency of vehicles, additional measures to improve the load factor of road freight vehicles needs to be adapted. Better usage of the available payload of the freight vehicles reduces the amount of vehicle kilometers while providing the same overall transport activity in terms of ton-kilometers.



10.2.2 Forecasting – Progressive adaption of best available technologies and policies leading to 2°C Scenario -

As mentioned above, forecasting scenarios are not set up in a way to match a certain target but are used to assess the impacts of hypotheses on technological progress as well as avoid and shift policies, which are likely to be adopted in a future with strong commitment to climate change mitigation.

Similar to the 1.5°C scenario, avoid, shift and improve measures are introduced to reduce energy use, emissions and costs generated through transport activity. But in contrast to the 1.5°C scenario, these measures are not used to their maximum technologic and economic potential, but rather to an extent which appears to be perfectly doable.

The below points sketch the outline of an ambitious but achievable ‘Best available technologies and policies scenario leading to 2oC’ (2oC) scenario.

(1) Avoid – For the 2oC scenario, the maximum potentials to avoid passenger and freight travel of the various measures are similar to the 1.5°C scenario. The main difference is that they are only used to a certain extent. By 2050, it is assumed that only 70% of the actual travel reduction potential of each of the policy measures is exploited. This also means that policies are introduced at a slower pace, and changes over time are less drastic compared to the 1.5°C scenario.

The 2oC scenario foresees a reduction of passenger car travel compared to the Benchmark scenario of about 18% by 2050, due to better urban planning, a shift towards non-motorized travel, the uptake of telecommunication technologies as well as the cost increase of driving passenger cars.

Similarly, the travel of road freight is reduced by about 10% compared to the Benchmark scenario by 2050, mainly through better logistics, which allow for more intelligent routes, better land use planning and increased cost of road freight transport.

Air travel is reduced by 35% compared to the Benchmark scenario by 2050, still accounting for more than a 200% increase on average, compared to the year 2010. For comparison, air travel is estimated to increase by almost 400% in the Benchmark scenario.

(2) Shift – Reducing transport energy use, emissions and costs due to the adoption of shift strategies has a far higher impact than simply avoiding travel demand. More than 40% of passenger car person kilometers are shifted to more efficient public transport modes such as urban bus and bus rapid transport, urban metro and light rail as well as non-urban conventional and high speed rail (if applicable) by 2050 in the 2oC scenario compared to the benchmark scenario. In most of the countries this still means a remarkable passenger car travel growth of almost 500% compared to the year 2010.

By 2050, road freight transport is shifted by on average almost 60% to mainly rail freight, compared to the Benchmark scenario (this is only the case for countries with greater land mass). Such structural changes necessitate the development of



adequate rail infrastructure. Future versions of the model will contain a dedicated infrastructure module to estimates additional rail kilometers as well as the respective costs to build them.

Shifting air travel to far more efficient rail transport is also an important characteristic of the 2oC scenario. Due to this measure, by 2050 air travel is reduced by 20% compared to the Benchmark scenario (where applicable). On average, air travel accounts for about 500 kilometers per person per year in the Progressive Scenario, which is equivalent to about one international flight per person each four years (average trip length in 2012 is 1,830 km).14

While individual motorized passenger travel, road freight and air travel are reduced in the 2oC compared to the Benchmark scenario, other modes such as urban and non-urban rail, bus and bus-rapid transport see strong activity increases over time until 2050.

On average, passenger rail activity is increased by more than 170% by 2050 compared to the BATS and freight rail increases by about 50%.

Bus and bus rapid transport play an important role in urban areas. On average, they increase by 50% compared to the Benchmark scenario.

(3) Improve – While efficiency improvements for conventional technologies are assumed to be introduced at the same pace like in the 1.5°C scenario, the uptake rates of alternative very low carbon vehicles such as battery electric cars and plug-in hybrids are significantly less aggressive in the 2oC scenario.

For this scenario it is assumed, that by 2050 the sales of conventional gasoline and diesel ICE passenger cars are almost phased out, but compared to the 1.5°C scenario, the share of hybrid cars on new sales is still about 20%, whereas battery electric cars account for 20% and plug-in hybrids for 50% of all new sold cars (compared to BEVs and plug-ins accounting for 90% of all new vehicle sales by 2050 in the 1.5°C scenario).

Heavy duty vehicle sales still show a significant amount of conventional trucks by 2050 – about 30% of all new HFTs are diesel ICE vehicles.

Fuel decarbonization also takes place at a slower pace compared to the 1.5°C scenario. While blend shares of low carbon fuels need to reach levels of about 60% to reach per capita emissions in line with a 1.5°C scenario, the blend shares of low carbon fuels in liquid and gaseous fuels reach more realistic levels of about 40% by 2050. This means for example, that by 2020 blend shares would need to be around 5% in the 2oC scenario, whereas blend shares of almost 10% would be necessary to align with a 1.5°C scenario. The power sector is assumed to reach the same low carbon intensities in the Progressive Scenario as in the 1.5°C scenario – that is since the power sector has far lower mitigation costs on per ton CO2 basis compared to

14 International Civil Aviation Organization. 2012. Annual Report of the Council.

http://www.icao.int/publications/Documents/10001_en.pdf

http://www.icao.int/publications/Documents/10001_en.pdf



the transport sector, and that in any case a powerful mitigation scenario needs to include a deeply decarbonized electricity generation.

Particular assumptions for current scenarios

As a general rule the policies are implemented in the same fashion for a given scenario in each country. However, there are two circumstances where this has been changed.

10.3.1 High and Low scenarios

In the current version (Version Xa) the country models have been modified so that the policies adopted in countries that currently (2010) have low carbon totals per capita (<1.0 tons per year) are adopted with less rigour than those for countries which have currently higher carbon usage (>1.0tons per year in 2010)- that is their uptake rates are slightly less. This approach was adopted because it was felt more equitable for countries with currently low carbon use that they should not be subject to the full impact of policies designed for the higher carbon-using countries. The actually policies adopted for the ‘High’ and ‘Low’ countries can be inspected by comparing the two versions of the Model template available. ADBTransportModelTemplate_0XhaLOW.xlsb and ADBTransportModelTemplate_0XhaHIGH .xlsb. Both these templates use the same data from Thailand.

10.3.2 Countries with no rail network or water transport

The uniform adoption of the alternative polices in every country can lead to problems where countries do not have a rail network. Policies, within a scenario which switch travel (passenger or freight) from Road or Air to Rail will produce spurious results if the country in question has no rail network in 2012. A similar issue arises with policies which encourage switching to Water transport where a country is land-locked and does not have any navigable waterways. To allow for this additional model runs were made where, for those countries affected, mainly the Island nations, Bhutan, Laos and Afghanistan these policies were turned off i.e indicated FALSE. These models have the characters ‘Xb’ instead of ‘Xa’.



11 Known issues (as of January 20th 2017)

1) ‘Results’ Tab. The code in two summary tables, by fuel type, in the Results sheet have

not been updated to include the impact of NMT and BRT energy and fuel usage (rows

83-91 and 93-100) which is why the relevant check totals may give a FALSE value.

2) The current vehicle stock model works in the vast majority of cases but the model does

appear to breakdown where a combination of vehicle type and fuel type are changing

rapidly over time and the simplifying assumptions in the vehicle stock model (e.g.

constant lifetimes across all power-trains for a given year and vehicle type) can cause

negative stock totals for some power-trains and so can lead to negative fuel and energy

for some vehicle type/power-train combinations. Changing the assumptions about a

vehicle’s lifetime or the ratio between sales and stock in the historic data can mitigate

this in many cases but not in all. This is an issue known to affect buses in China in both

the alternative scenarios and some Pacific islands where the historic stock and sales

data are unstable.

12 References

ATRANS – Chollacoop, Dr N, Dr Y Laoonual, J Pongthanaisawan. 2010. A Case study on energy and CO2 intensities in Thai

freight transport by trucks. Final Report, Research grant 2010.

Puttipong Chunark, Panida Thepkhun, Kamphol Promjiraprawat, Pornphimol Winyuchakrit and Bundit Limmeechokchai.

2015. Low carbon transportation in Thailand: CO2 mitigation strategy in 2050. SpringerPlus (2015) 4:618

Esmael M O, K Sasaki, and K Nishhii. 2012. Road traffic accident trend in developing countries – The policy implications.

WCTR conference. Rio de Janerio, 2012

Global Fuel Economy Initiative, International Energy Agency, OECD. 2016. Technology and policy drivers of the fuel

economy of new light-duty vehicles. Comparative analysis across selected automotive markets. OECD/IEA, Paris, 2016

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ). 2012. Thailand Stocktaking Report on Sustainable

Transport and Climate Change. GIZ, Bangkok, Sept 2014.

International Transport Forum - OECD. 2015. ITF Transport Outlook 2015, OECD Publishing / ITF:

http://dx.doi.org/10.1787/9789282107782-en

The International Council on Clean Transportation (ICCT). 2012/ Global Transportation Roadmap. Model Documentation

and user Guide. Version 1-0, ICCT (The International Council on Clean Transportation), December 2012.

Jacobs G D, & W Hards (1978) Further research of Road accident rates in developing countries (Second Report). Transport

and Road Research Laboratory, DoE, Supplementary Report SR 434. 1978, Crowthorne, UK.

Mason, J Lew Fulton, Zane McDonald (2015). A Global High Shift Cycling Scenario: The Potential for Dramatically

Increasing Bicycle and E-bike Use in Cities Around the World, with Estimated Energy, CO2, and Cost Impacts. Institute for

Transportation & Development Policy and the University of California, Davis. November 2015

Ministry of Transport Thailand. 2016. MOT data portal: http://www.news.mot.go.th/motc/portal/graph/index.html

Oke, O, K Bhalla, D C Love, S Siddiqui. 2015. Tracking global bicycle ownership patterns. Journal of Transport & Health.

Vol 2, Issue 4, December 2015 pp 490-501. Elsevier, London.

http://dx.doi.org/10.1787/9789282107782-en




Appendix A - 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. It should be noted that the versions worksheet is missing from the individual country models and replaced by a ‘Country assumptions’ tab just before the ‘Constants’ worksheet.

No. Sheet name & link Content Functionality

1 TOC Table of contents Supporting

2 Flowchart Model structure: data flow between Sheets Supporting

3 Keys Keys used in the Workbook Supporting

4 Versions Version control Supporting

5 Constants Model global constants Supporting

6 Socioeco data Socio-economic data Input data

7 Transport_Data Transport Data Input data

8 Veh_TECH Vehicle Technology Data Input data

9 Fuel_Spec Fuel Specifications Input data

10 Emission_rates Emissions Rates Input data

11 Parameters Forecast Model Parameters Input data

12 PolicyInput1 Policy instruments inputs: Set 1 Input data

13 PolicyInput2 Policy instruments inputs: Set 2 Input data

14 PolicyToUse Policy measure inputs Calculation

15 PolicyImpacts Policy Impacts Calculation

16 Results Results Output

17 Passenger_Travel_Demand Passenger travel demand Calculation

18 PLDV_Benchmark Personal LDV Benchmark scenario Calculation 19 PLDV_ALT PLDV policy scenario Calculation

20 Bus_Benchmark Bus Benchmark scenario Calculation

21 Bus_ALT Bus policy scenario Calculation

22 BRT_Benchmark BRT Benchmark scenario Calculation

23 BRT_ALT BRT policy scenario Calculation

24 Minibus_Benchmark Minibus Benchmark scenario Calculation

25 Minibus_ALT Minibus policy scenario Calculation

26 3_4W_Benchmark 3&4 Wheeler Benchmark scenario Calculation

27 3_4W_ALT 3&4 Wheeler policy scenario Calculation

28 2W_Benchmark Motorcycle Benchmark scenario Calculation

29 2W_ALT Motorcycle policy scenario Calculation

30 Bike_Benchmark Bike Benchmark scenario Calculation

31 Bike_ALT Bike policy scenario Calculation

32 Pedicab_Benchmark Pedicab Benchmark scenario Calculation

33 Pedicab_ALT Pedicab policy scenario Calculation

34 Walk_Benchmark Walk Benchmark scenario Calculation

35 Walk_ALT Walk policy scenario Calculation

36 Freight_Travel_Demand Freight transport demand Calculation

37 LCV_Benchmark LCV Benchmark scenario Calculation 38 LCV_ALT LCV policy scenario Calculation

39 MFT_Benchmark MFT Benchmark scenario Calculation

40 MFT_ALT MFT policy scenario Calculation

41 HFT_Benchmark HFT Benchmark scenario Calculation

42 HFT_ALT HFTpolicy scenario Calculation

43 Passenger_Rail_Benchmark Passenger Rail Benchmark scenario Calculation

44 Passenger_Rail_ALT Passenger Rail Policy scenario Calculation

45 Freight_Rail_Benchmark Freight rail Benchmark scenario Calculation



46 Freight_Rail_ALT Freight rail policy scenario Calculation

47 Passenger_Water_Benchmark Passenger Water Benchmark scenario Calculation

48 Passenger_Water_ALT Passenger Water policy scenario Calculation

49 Freight_Water_Benchmark Freight water Benchmark scenario Calculation

50 Freight_Water_ALT Freight water policy scenario Calculation

51 Passenger_Air_Benchmark Passenger Air Benchmark scenario Calculation

52 Passenger_Air_ALT Passenger Air policy scenario Calculation

53 Freight_Air_Benchmark Freight Air Benchmark scenario Calculation

54 Freight_Air_ALT Freight Air policy scenario Calculation 55 Sheet name & link Content Functionality