Better Transport Data ADB Transport Database Model Training for Sustainable Transport Policies and Investment Planning TA- 8046 (REG) Alex Körner - Consultant 2017 March 31 ADB Headquarters Mandaluyong City, Philippines
Content of the Training I. General introduction to modelling principles 1. Introduction to the ASIF framework 2. Introduction to scenario modelling Avoid-Shift-Improve II. Exploring the model 1. Scope of the tool 2. Interactive walk-through the model J III. Introduction to tool debugging IV. Discussion
Introduction to the Modelling Principles
Session I General Introduction to Modelling Principles 1. Introduction to the ASIF framework A. The implementation of ASIF in the ADB Transport Database Model B. Projection of transport activity based on population growth, GDP trajectories and Gompertz functions C. Mode share and transport efficiency, technology improvement and energy intensity, fuel demand and fuel choice, emission factors and emissions 2. Brief introduction to Avoid-Shift-Improve Understanding the basics of scenario development
The ASIF Framework
Linking activity and fuel use - ASIF approach n n n n Vehicle Activity the Structure of the organization of vehicle across services, modes, vehicle classes and powertrain groups the energy Intensity of each of the vehicles in this structure the emission Factor of various à To calculate total Emissions
Data requirements The ASIF framework requires key parameters: 1. Vehicle activity (expressed in vkm) vkm are the product of number of vehicles and average mileage vkm are linked to pkm (passenger activity) and tkm (freight activity) by occupancy rates/load factors 2. Structure (percentages of vkm) Requires the knowledge of vehicle activity by mode and service 3. Energy intensity (expressed in energy units per vkm) Requires the knowledge of the specific energy consumption by mode and service To understand the penetration of new, less energy intense technologies in the vehicle stock the implementation of a stock turnover model is required 4. Carbon intensity (measured in mass of CO 2 per energy unit)
Drivers of demand for transport activity Transport activity (pkm, tkm), vehicle activity (vkm), and vehicle stock are largely determined by: Relationships linking GDP and population with transport activity and modal choice GDP per capita ßà personal vehicle ownership & modal choice Economic output (GDP) ßà tonnes transported Urbanization Changes in the cost of driving and moving goods Changes in the price of fuels and vehicles (either as a result of higher commodity prices and technology costs or higher taxes) Structural changes in the transport system Passenger: role of public transport in urban areas Freight: economic and trade structures Transport demand and modal choices are subject to travel time budget and travel money budget
ASIF in the ADB Transport Database Model The ASIF framework is a the heart of the ADB Transport Database Model
Projection of Transport Demand Linking GDP and Population to Passenger Kilometres Projection of annual travel demand based on Gompertz function PKM = POP UBL e,-./01_340 354 With PKM: POP: UBL: ID: FPA_GDP: GRD: Land transport pkm Population Upper boundary limit curve Intercept determinant Fuel price adjusted GDP_PPP Growth rate determinant Variation of GRD Variation of ID
Projection of Transport Demand Linking GDP and Population to Passenger Kilometres Comparison of per capita travel projections of the Philippines to selected 2010 data 25000 Group A Annual km per capita 20000 15000 10000 5000 Variant 1 Variant 2 Brazil USA China Mexico India Russia 0 0 10000 20000 30000 40000 50000 60000 Fuel price adjusted GDP PPP per capita South Africa EU
Modal Split & Energy Intensity Passenger activity - Benchmark Billion pkm 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Car Bus Minibus Motorised 3_Wheeler Motorcycle Rail Water Domestic Passenger Air BRT Public mass transport modes achieve much lower per passenger energy demand and GHG emission values Shifting individual motorized travel to public mass transport can save significant amounts of energy and CO 2
Technology Improvement & Energy Intensity Energy efficiency of the vehicle fleet improves over time: Due to improved energy efficiency of vehicle technologies Due to changes in the powertrain portfolio Million vehicles Passenger car stock by technology - Benchmark 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 ICE_gasoline ICE_diesel ICE_CNG ICE_LPG Hybrid_gasoline Hybrid_diesel Plug-in_gasoline Plug-in_diesel Lge per 100km 10 9 8 7 6 5 4 3 2 1 0 Road vehicle new tested FE - Benchmark 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Car Motorised 3_Wheeler Motorcycle BEV FCEV
Fuel Choice & Carbon Intensity Carbon intensity links fuel use and CO 2 emissions Carbon intensity varies by fuel type and over time Can vary substantially between tank-to-wheel and well-to-wheel
Energy Use and Emissions Road energy use by mode - Benchmark Road WTW CO2 emissions - Benchmark Million Lge 35000 30000 25000 20000 15000 10000 5000 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Car Bus Minibus Motorised 3_Wheeler Motorcycle LCV MFT HFT Million T CO2 160 140 120 100 80 60 40 20 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Car Bus BRT Minibus Motorised 3_Wheeler Motorcycle LCV MFT HFT BRT Bike Pedicab Energy use and emissions are the main results from the ASIF steps
Brief Introduction to Scenario Modelling
Brief Introduction to Avoid-Shift-Improve Avoid Shift Improve Avoid the need to travel Integration of land use planning and transport Application of smart logistics Use of telecommunication technologies Shift travel to more efficient modes 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 energy efficiency of the transportation technologies Improve the fuel efficiency of conventional technology Switch to new vehicle technology Shift to alternative and low carbon fuels The model contains more than 100 individual policy measures They are implemented based on a maximum reduction potential (travel demand, energy intensity, emission intensity) and can be gradually introduced over time
Avoid-Shift-Improve and ASIF Different ASI-Strategies act on different components of the ASIF framework In the ADB Transport Database Model, all policy levers are assigned to one of the strategies of the Avoid-Shift-Improve framework Policy levers can be: Purely exogenous: What if a certain action could lead to x% less transport, specific energy use, carbon intensity Based on price elasticity of transport demand: What is the change in transport demand subject to a change in a certain price (e.g. fuel price, fare, another goods price [cross-elasticities])
Forecasting vs. Back-casting Scenarios Research question: Forecasting: What is the effect of a set of policies on energy use, emissions and costs? Backcasting: How can a certain target (e.g. emission trajectory) be reached? Forecasting - BATS Back-casting 1.5DS Target No target 0.4 tco 2 per cap per year Avoid Shift Achieve 70% (60%) of max potential per policy option Achieve 70% (60%) of max potential per policy option Achieve 70% (60%) of max potential per policy option Achieve 70% (60%) of max potential per policy option Improve PC sales: 0% ICE, 10% hybrids, 60%PHEV, 30% BEV 40% bio fuel blend share Higher penetration of electric vehicles than in BATS Higher blend share of low carbon bio-fuels than in BATS
Results 1200 1000 Passenger travel demand - Philippines 70000 60000 Energy demand by mode - Philippines Billion PKM 800 600 400 200 Billion Lge 50000 40000 30000 20000 10000 0 BM BM 2DS 1.5DS BM 2DS 1.5DS 2015 2030 2050 Car Bus Minibus 3&4 Wheeler Motorcycle Rail Water Domestic Passenger Air 0 BM BM 2DS 1.5DS BM 2DS 1.5DS 2015 2030 2050 Road Rail Water Air Scenario modelling allows to compare impacts on travel, energy use, emissions and costs of different consistent story lines
Scope of the Model
Session II Exploring the Tool 1. Scope of the model 2. Interactive walk-though the model J
Scope of the Model General Purpose The model should be: open source an Excel based spreadsheet model able to interact with the Transport Database easy to use Main utility: To analyze the historic development of the transport sector starting with the year 2000 To analyze future transport scenarios out to 2050, with a focus on travel demand, energy use, emissions and costs
Scope of the Model Coverage of Modes & Analytical Capabilities Coverage: All modes road, rail, water, air, NMT Passenger/freight Domestic/international Urban/non-urban (simplified) Single countries, with ability to aggregate regional and sub-regional results Quantification by service/mode/vehicle type/fuel: Travel demand (pkm/tkm); energy use; CO 2 emissions; pollutant emissions; costs (investment&life time fuel); workforce impacts; fatalities
Policy Coverage Exogenously defined potential Urban planning Behavioural change Improved transport logistics Incentives for NMT Increased vehicle load factors Congestion charging & parking levy BRT development Endogenously calculated potential (elasticities) Fuel taxation Reduced bus fares Increased bus frequency Reduced rail fares Increased rail frequency Increased vehicle taxation Improved rail journey times Avoid Shift Avoid & Shift Improve Metro & light rail development High speed rail development Enhanced biking infrastructure Enhanced walking infrastructure Shift air to rail & HS rail Improved intermodality Improved rail depot times Improved handling facilities for water transport Efficiency improvement New vehicle technology Fuel switch Decarbonisation of fuels All policies need to be assigned to: A specific vehicle mode In case of shift: loosing mode and gaining mode An area: urban/non-urban/all
Coverage of vehicle technology and fuels Vehicle drivetrains Gasoline/diesel/LPG/CNG internal combustion engine (ICE) Hybrids Plug-in hybrids (PHEV) Battery electric vehicle (BEV) Fuel cell electric vehicle (FCEV) Fuels Gasoline Diesel CNG LPG Biofuels Electricity Hydrogen
Let s Start!