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Technology Opportunities and Strategies towards Climate friendly trAnsport FP7-TPT-2008-RTD-1 Coordination and Support Action (Supporting) Deliverable D8 (WP 6.1 report) Scenarios of European Transport Futures in a Global Context Ecorys Robert Kok Konstantina Laparidou Adnan Rahman University of Cambridge Lynnette M. Dray Dissemination level Public PU X Restricted to other programme participants (including Commission Services) PP Restricted to a group specified by the consortium (including the Commission Services) PE Confidential, only for members of the consortium (including the Commission Services) CO

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Technology Opportunities and Strategies towards Climate friendly trAnsport

FP7-TPT-2008-RTD-1

Coordination and Support Action (Supporting)

Deliverable D8 (WP 6.1 report)

Scenarios of European Transport Futures in a Global Context

Ecorys

Robert Kok

Konstantina Laparidou

Adnan Rahman

University of Cambridge

Lynnette M. Dray

Dissemination level

Public PU X

Restricted to other programme participants (including Commission Services) PP

Restricted to a group specified by the consortium (including the Commission Services)

PE

Confidential, only for members of the consortium (including the Commission Services)

CO

Coordinator: Dr. Andreas Schäfer

University of Cambridge

Martin Centre for Architectural and Urban Studies, and

Institute for Aviation and the Environment

1-5 Scroope Terrace, Cambridge CB2 1PX, UK

Tel.: +44-1223-760-129 Fax: +49-341-2434-133 E-Mail: [email protected] Internet: www.toscaproject.org

Contact: Ecorys

Watermanweg 44, 3067GG, Rotterdam

Tel.: +31-10-453-8800 Fax: +31-10-453-0768 E-Mail: [email protected] Internet: www.ecorys.com

Robert Kok

Tel.: +31-10-453-8647 Fax: +31-10-453-0768 E-Mail:

Konstantina Laparidou

[email protected]

Tel.: +31-10-453-8570 Fax: +31-10-453-0768 E-Mail: [email protected]

Adnan Rahman

Tel.: +31-10-453-8796 Fax: +31-10-453-0768 E-Mail: [email protected]

Lynnette M. Dray

Tel.: +44-1223-760-124

Fax: +44-1223-332960

E-Mail: [email protected]

Date: 27.5.2011

3

Contents

Contents .......................................................................................................................................3

Abbreviations ................................................................................................................................5

Abstract ........................................................................................................................................6

1 Introduction ......................................................................................................................7

1.1 Background ................................................................................................................................... 7

1.2 Macro-economic transport-related trends in Europe .................................................................. 7

1.3 The TOSCA Project ......................................................................................................................10

2 Review of existing transport scenarios for Europe ............................................................ 12

2.1 EU transport GHG: routes to 2050 .............................................................................................12

Description ...................................................................................................................................... 12

Results ............................................................................................................................................. 13

2.2 TRANSvisions ..............................................................................................................................13

Description ...................................................................................................................................... 13

Results ............................................................................................................................................. 14

2.3 iTREN2030 Integrated transport and energy baseline ...............................................................14

Results ............................................................................................................................................. 15

2.4 EU energy trends to 2030 (Primes 2009 baseline) .....................................................................15

Description ...................................................................................................................................... 15

Results ............................................................................................................................................. 16

2.5 Roads toward a low carbon future .............................................................................................16

Description ...................................................................................................................................... 16

Results ............................................................................................................................................. 17

2.6 Transport, Energy and CO2 scenarios .........................................................................................17

Description ...................................................................................................................................... 17

Results ............................................................................................................................................. 18

2.7 Findings from scenario review ...................................................................................................18

3 Scenario formulation ....................................................................................................... 21

3.1 Definition of a TOSCA scenario ...................................................................................................21

3.2 Identification of scenario drivers ...............................................................................................21

4

3.3 Baseline scenario ........................................................................................................................24

3.4 Challenging scenario...................................................................................................................27

3.5 Favourable scenario ...................................................................................................................28

4 Projecting intra-European passenger and freight transport demand ................................. 29

4.1 European transport network model (Transtools) ......................................................................29

Transtools Demand Model .............................................................................................................. 29

Transtools Limitations ..................................................................................................................... 30

4.2 Intra- and intercontinental air transport and maritime transport .............................................33

Passenger aviation .......................................................................................................................... 33

Aviation Integrated Model .............................................................................................................. 33

Consistency ..................................................................................................................................... 35

Air Cargo .......................................................................................................................................... 35

Maritime Cargo ............................................................................................................................... 36

4.3 Aggregated Scenario Results .....................................................................................................37

Growth rates by scenario and mode of transport .......................................................................... 37

Total passenger and freight transport by scenario ......................................................................... 40

4.4 Detailed scenario results ............................................................................................................41

Baseline scenario results ................................................................................................................. 41

Challenging scenario results ............................................................................................................ 43

Favourable scenario results ............................................................................................................ 44

4.5 Conclusions .................................................................................................................................46

5 References ...................................................................................................................... 48

Annex A: Sensitivity Analysis - Disruptive Scenario ....................................................................... 53

Assumptions ...........................................................................................................................................53

Sensitivity analysis results ......................................................................................................................56

Conclusions .............................................................................................................................................59

5

Abbreviations

Abbreviation Description

AIM Aviation Integrated Modelling

bpkm Billion passenger kilometre

btkm Billion tonne kilometre

CO2 Carbon dioxide

eg. exempli gratia – for example

Eq. equation

etc. et cetera – and the rest

GDP Gross Domestic Product

GHG Greenhouse Gas

iTREN Integrated transport and energy

Mtoe Million tonnes of oil equivalents

Mton Million tonnes

pkm Passenger kilometre

SULTAN SUstainabLe TrANsport

tkm Tonne kilometre

TOSCA Technology Opportunities and Strategies toward Climate-friendly trAnsport

Transtools TOOLS for TRansport Forecasting ANd Scenario testing

TTW Tank-to-Wheels (downstream emissions, direct emissions)

WTT Well-to-Tank (upstream emissions, indirect emissions)

WTW Well-to-Wheels (fuel lifecycle emissions) = WTT+TTW

6

Abstract

The TOSCA project aims to identify promising technology and fuel pathways to reduce

transportation-related greenhouse gas emissions through mid-century. An important building block

of this project is the techno-economic specification of low-GHG emission transportation

technologies, which are input into a scenario analysis. TOSCA considers all major modes of passenger

and freight transport, along with transportation fuels and technologies capable of enhancing

infrastructure capacity. This report is thus one out of a number of such techno-economic studies.

TOSCA Work Package 6 (WP6) integrates the technology, fuel, and infrastructure studies carried out

in WP1-5 through a scenario and modelling analysis. The first step in the scenario analysis consisted

of a systematic review of existing European transport scenarios. Consequently, we determined a set

of scenario variables that affect future passenger and freight transport demand. These are used to

formulate four distinct scenarios (three detailed scenarios and one sensitivity case) that describe the

future levels of passenger and freight transport demand. The modelling stage includes the projection

of transport demand for each scenario under the assumption of no new policies. This part of the

modelling stage was carried out with the EU demand model Transtools (JRC, 2010). Due to the

limitations of that model, we complemented the Transtools model with other models such as the

Aviation Integrated Model (AIM). This report (WP 6.1) details the scenario generation and demand

modelling steps described above. A further report (WP6.2) investigates the fleet composition and

emissions which would result in each of these scenarios.

7

1 Introduction

1.1 Background

In December 2008, in order to combat climate change and increase the EU’s energy security, the

European Union adopted a climate and energy package. This package (‘3x20%’) includes the

following emissions and energy use reduction targets to be met by 2020:

• A reduction in EU GHG emissions of at least 20% below 1990 levels

• 20% of EU energy consumption to come from renewable resources

• A 20% reduction in primary energy use compared with projected levels, to be achieved by

improving energy efficiency.

EU Member States agreed to realise the emission reduction target (EC, 2008b) by the following

actions:

• A revision and strengthening of the Emissions Trading System (EU ETS) that sets a single EU-

wide cap on emission allowances. This cap will be cut annually, reducing the number of

emission allowances available to businesses by 21% below the 2005 level in 2020.

• An 'Effort Sharing Decision’ governing emissions from sectors, such as transport, housing,

agriculture and waste that are not covered by the EU ETS. Under this decision each Member

State has agreed to a binding national emissions limitation target for 2020 which reflects its

relative wealth. These national targets will cut the EU’s overall emissions from the non-ETS

sectors by 10% by 2020 compared with 2005 levels.

• The 10% reduction from the effort sharing decision, together with the 21% reduction from

the EU ETS during the same period, will accomplish the overall emission reduction goal of the

EU Climate and Energy package (20% cut below 1990 levels by 2020).

The transport sector, being strongly interconnected with many other sectors, currently accounts for

around 25% of EU27 GHG emissions and this share is increasing (EC 2010a), so changes in the

transport sector will be vital in achieving EU targets for total emissions. Thus, thorough planning

toward climate-friendly transport is fundamental. Before proceeding to the main subject of this

document, which is a discussion of the scenario generation process for the Technology Opportunities

and Strategies toward Climate-friendly trAnsport (TOSCA) project, a short description of the

transport sector is provided regarding the elements which are emphasized in the scenario analysis.

1.2 Macro-economic transport-related trends in Europe

Figure 1.1 depicts Final Energy Consumption in the EU27 countries to 2007, in Mtoe. One can clearly

observe that transport is one of the highest-consumption sectors (with a percentage of 32.6% for

2007). By 2000 transport energy consumption surpassed that of the industry sector (27.9% in 2007).

Accounting for almost one third of total energy consumption, transport is a strong driver for the

definition of current and future EU policies.

8

Figure 1.1 Final EU27 Energy Consumption by Sector

CO2 Emissions* by Sector: EU-27

0,70

0,80

0,90

1,00

1,10

1,20

1,30

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

1990=1

Energy Industries Industry ***Transport ** ResidentialCommercial / Institutional Other ****Total

Source: EU Energy in Figures 2010 DG TREN (EC, 2010a)

* Excluding LULUCF (Land Use, Land – Use Change and Forestry) Emissions and International Bunkers

** Excluding International Bunkers (international traffic departing from the EU)

*** Emissions from Manufacturing and Construction and Industrial Processes

**** Emissions from Fuel Combustion in Agriculture/Forestry/Fisheries, Other (Not elsewhere specified), Fugitive Emissions

from Fuels, Solvent and Other Product Use, Waste, Other

Figure 1.2 Indexed evolution of CO2 Emissions by Sector, EU27

Partly as a consequence of the large share of energy use, transport is responsible for generating a

significant share of EU27 CO2 emissions. At the moment, transportation produces the second largest

amount of CO2 emissions by sector (after households). This outcome is not surprising since the fuels

used are primarily petroleum products; transport in the EU27 mainly consumes diesel, gasoline and

kerosene (around 96% of the total consumption; EC 2011), with a small share of electricity and

alternative fuels (mainly biofuels and compressed natural gas). While other sectors in the EU have

9

shown a decreasing trend in CO2 emissions since 1990, transport continues to emit more carbon

dioxide (Figure 1.2).

Within the transport sector, civil aviation (domestic including international bunkers) shows the

largest growth in CO2 emissions since 1990 (Figure 1.3). However, road and shipping emissions have

also been increasing.

CO2 Emissions* from Transport, EU-27

0,500,600,700,800,901,001,101,201,301,401,501,601,701,801,90

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

1990=1

Civil Aviation Road Railw ays

Navigation Other Total

Source: EU Energy in Figures 2010 DG TREN (EC, 2010a)

* Excluding LULUCF (Land Use, Land – Use Change and Forestry) Emissions and International Bunkers

Figure 1.3 Indexed evolution of CO2 emissions by transport mode, EU27

The increase in transport sector CO2 emissions is strongly linked to economic growth. Figure 1.4

illustrates the link between socio-economic trends (GDP and population) and the volume of transport

(passenger and freight), and consequently the resulting transport GHG emissions. Economic growth

is a general trigger for transport demand because it means more goods are produced and

transported and people have more income available for transportation. This means that reducing

transport CO2 emissions will require either decoupling transport demand from economic growth,

decoupling transport demand from carbon atoms that are oxidized to CO2. TOSCA looks primarily at

the second of these options.

Although the TOSCA project has the goal of investigating how greenhouse gas emissions can be

reduced by new vehicle and fuel technologies, the likely future adoption and impact of these

technologies is fundamentally dependent on the future development of various exogenous factors.

For example, in a future world with low oil prices, the demand for energy efficient low carbon

technologies and fuels will be lower than in a future world with high oil prices. The high uncertainty

attached to future projections of these exogenous factors makes a scenario-based analysis

considering multiple different futures vital.

10

Source: EU energy and transport in figures (EC, 2009c)

Figure 1.4 Indexed evolution of GDP, population and GHG emissions from freight and passenger

transport demand

WP1:Road

WP2: Air

WP3: Rail

Marine

WP1:Road

WP2: Air

WP3: Rail

Marine

WP4: Fuels

WP5: Capacity

WP6/7:Modelling

WP7:Future Policies

WP6: Future Scenarios

Promising Technology/Policy combinations for reducing EU GHG Emissions to 2050

Figure 1.5 TOSCA Work Package structure

1.3 The TOSCA Project

The TOSCA project is an EU FP7 project investigating the potential of alternative vehicle technologies

to reduce the environmental impact of EU27 transportation to 2050. The structure of the project is

shown in Figure 1.5. Initially, WP1-5 estimate the techno-economic characteristics of current and

future vehicle, fuel and infrastructure technologies. WP6 then integrates these study results with

transport demand projections through a scenario and modelling analysis. The first step in the

scenario analysis consisted of a systematic review of already existing European transport scenarios.

Consequently, we determined a set of scenario variables that affect future passenger and freight

transport demand. These are then used to formulate four distinct scenarios (three detailed scenarios

and one sensitivity case) that describe the future levels of passenger and freight transport demand.

The modelling stage includes the projection of transport demand for each scenario under the

assumption of no new policies. This part of the modelling stage was carried out with the EU demand

11

model Transtools (JRC, 2010). Due to the limitations of that model, we complemented the Transtools

model with other models such as the Aviation Integrated Model (AIM). This report (WP 6.1) covers

the scenario analysis and demand modelling stages. A further report (WP6.2) covers technology

uptake and CO2 emissions by scenario.

The basic structure of the TOSCA modelling block is shown in Figure 1.6. In addition to the exogenous

factors (growth in GDP, oil price, and the CO2 intensity of electricity) which form part of the TOSCA

scenarios, the modelling stage uses several other major inputs. These include an updated form of the

European Transport Policy Information System (ETIS), which forms the input to the Transtools model.

This database contains a wide range of transport policy-relevant variables, as appropriate for a 2005

base year (NEA, 2005). TOSCA input consists of changes to the Transtools input database to reflect

the development of baseline scenario variables from 2005 for each year run (e.g. in terms of

population, urbanisation, fuel prices, carbon prices) and any development in the characteristics of

the reference technologies identified by WP 1-5 (e.g. in terms of cost, speed and fuel consumption).

In the case that the database contains a cost to end-users rather than operators (for example, rail

ticket prices rather than costs accruing to rail operators) it is assumed that all cost increases are

passed on to consumers.

Demand, fleet and emissions

Technology Characteristics

Demand Modelling

(Trantools/AIM)

PolicyCharacteristics

ScenarioCharacteristics

Reference only

Stock Model

Changes in Demand(Summa)

All technologies

Promising Technology/Policy combinations for reducing EU GHG Emissions to 2050

Figure 1.6 TOSCA Modelling approach

The remainder of this document is structured as follows. Chapter 2 presents a review of existing

transport scenarios for the EU. Chapter 3 outlines the framework for both passenger and freight

transport that is used to construct and evaluate the TOSCA scenarios. It describes the steps leading

to the definition of the scenarios, and describes a baseline and two internally consistent alternative

scenarios including the underlying assumptions. The scenarios are based on the most relevant and

uncertain exogenous drivers with a potentially large impact on the primary outcome-of-interest, GHG

emissions reductions in transport. Chapter 4 describes the projected demand for transport and

12

modal split by scenario. Finally, in the annex we discuss the effects that a disruptive event may have

on transport demand, and give results from a sample disruptive scenario.

2 Review of existing transport scenarios for Europe

Many scenarios for future EU27 transport demand already exist. Within the scope of this work

package a large number of studies, models and data sources have been reviewed. The review focuses

on quantified transport and energy scenarios, both ‘Baseline’ (no or limited emissions mitigation

policies) and decarbonisation or GHG mitigation scenarios. In analyzing the various studies, the

scope, underlying assumptions and the evolution of key system variables or outputs of the scenarios

were compared. Specifically, the main aspects considered for this review were:

• Timeframe of the scenarios, including a horizon up to 2030 or 2050.

• Geographical scope, including the EU27 or other regions.

• Scope of the transport sector, including all modes or only selected modes.

• Volume of transport, including explicit demand modelling or not.

• Scope of the emissions, including tank-to-wheel (TTW) emissions or well-to-wheel (WTW)

emissions.

• Definition of a scenario, including exogenous drivers, policy instruments or both.

• Key assumptions including GDP per capita, oil prices, and other drivers.

• Nature of the scenario drivers (e.g. linear/continuous or disruptive).

A representative selection of these scenario exercises are reviewed and described individually below.

2.1 EU transport GHG: routes to 2050

Description

The SULTAN Illustrative Scenarios Tool was developed as part of the EU Transport GHG: Routes to

2050? Project (Hill et al, 2010). The project as a whole reviewed the abatement options – technical

and non-technical – that could contribute to reducing transport’s greenhouse gas (GHG, WTW scope)

emissions, both up to 2020 and in the period from 2020 to 2050. SULTAN is a Microsoft Excel-based

tool that can be used to investigate GHG emissions, and some other outputs, associated with

transport from the EU-27 countries in the period 2010-2050. It allows users to create and edit “Policy

Scenarios” – illustrative scenarios for the EU transport system that make assumptions on how

policies might impact on the transport system – and then view the outcomes of the scenarios in

terms of GHG emissions, some other pollutant emissions and some abatement costs. The SULTAN

13

tool is a high-level scoping tool for quick appraisals, not for detailed transport policy impact

assessments.

Results

The baseline scenario shows that the total transport GHG emissions may increase from about 1,600

Mton CO2 equivalent emissions in 2010 to more than 2,000 by 2050. The combined reduction

potential from selected technical and non-technical abatement measures indicates that a reduction

of up to 80-90% over the 1990 level in total transport GHG emissions could be achieved by 2050.

2.2 TRANSvisions

Description

TRANSvisions (2009a) discusses a wide range of drivers related to transport. Three main categories

were identified. First, external drivers, that is, drivers external to the transport sector, for which five

main categories were identified (population, economic development, energy, technology

development and social change). Second, internal drivers, that is, drivers internal to the transport

sector, e.g., infrastructure, vehicles and fuel development and transport impact on environment and

society. Finally, policy drivers, that is, broad policy responses which affect the evolution of the

transport system, and in particular the governance of the transport sector.

A number of different exploratory scenarios for 2050 were formulated based on the identified

drivers. Each scenario was formulated as a different path towards a post-carbon society. A “meta-

model” was developed for the scenario analysis for the timeframe 2005-2050. The meta-model

applied in TRANSvisions was calibrated against scenario results from the Eurpean Commission’s

transport model Transtools for 2005 and 2030. The Transtools scenarios are established based on the

main inputs for the Transtools model. Such inputs include: socio-economic input (population, GDP

development, work places); transport policy input (change in vehicle operating costs, fares and

transport costs for different transport modes); and network input (links and nodes and data related

to these). Four main scenarios were evaluated:

1. The behavioural path: “Moving Less” or Reduced Mobility: Environmental concern and

changes in behaviour.

2. The technological path: “Moving Alone” or Induced Mobility: Exponential growth of

technological improvements, unlimited clean and cheap energy sources, high economic

growth.

3. The mandatory path: “Stop Moving” or Constrained Mobility: Very slow process of

technological implementation, high pensions and health cost, low productivity, strong

mobility regulation, legislation and taxation.

14

4. The organisational path: “Moving Together” or Decoupled Mobility: Strong decoupling

between economic development and growth of traffic volume is gradually achieved, changes

in behaviour.

Results

As both exogenous drivers like GDP per capita and policy assumptions like taxation are used, as well

as ‘wildcard’-type assumptions such as unlimited clean and cheap energy, the resulting demand for

transport varies considerably across the scenarios. Due to different levels of carbon intensity in each

of the scenarios, the resulting CO2 emissions show a smaller range around the baseline. The results in

terms of CO2 emissions trajectories to 2050 show that in all scenarios at least a 10% reduction in

emissions might be achieved by 2050 compared to 2005. The scenarios Decoupled and Reduced

Mobility might achieve over a 50% reduction in emissions by 2050 compared to 2005.

2.3 iTREN2030 Integrated transport and energy baseline

Description

The iTREN-2030 (iTREN, 2009) project contributes to the extension of the European policy

assessment toolbox providing improved tools as well as a consistent energy and transport baseline

scenario, called the Reference scenario, for the EU to 2030. iTREN-2030 applies updated versions of

four European models to generate the baseline scenario:

1. ASTRA (2000), an integrated and strategic transport-economy assessment model.

2. POLES (2006), a global energy model describing the supply- and demand-side of world

energy markets from a technological bottom-up perspective.

3. Transtools, a European transport model focusing on the medium- to long distance transport

flows on the European transport networks.

4. TREMOVE (2007), an environmental assessment model providing vehicle fleet, fuel

consumption and emission indicators for the European transport system.

The basic concept of the Reference scenario is “frozen policy 2008”, i.e. the scenario considers only

policies that were decided by the EU Council and/or EU parliament by mid 2008. It should be noted

that the global economic crisis that started in 2008 is not reflected in the iTREN-2030 Reference

scenario. The iTREN-2030 Reference scenario shows that transport demand will increase until 2030,

especially freight demand, which is projected to be 50% larger in the year 2030 than in the year

2005, while passenger demand is expected to grow more slowly. Therefore, for the EU27 as a whole,

some relative decoupling between economic growth and transport demand is expected, only for

passengers but not for freight. While the economy of the EU27 is expected to grow on average at

1.5% per year until 2030 (but with a decreasing level of employment), and transport increases at a

slightly lower rate, final energy demand is expected to grow less than 1% per year, which means that

the EU27 should become slightly more energy-efficient (iTREN, 2009).

15

Results

The Reference scenario developed in the iTREN-2030 project shows that transport CO2 emissions are

expected to increase from 1,268 million tons in 2005 to 1,485 million tonnes by 2030, an increase of

17% or 0.6% annually. Oil prices are expected to more than double between 2005 and 2030 from 44

to 90 € 2005 per barrel.

2.4 EU energy trends to 2030 (Primes 2009 baseline)

Description

PRIMES (PRIMES, 2010) is a general purpose energy system model supported by some more

specialised models, such as the GEM-E3 and PROMETHEUS. The GEM-E3 (World and Europe versions)

model is an applied general equilibrium model, simultaneously representing 37 World regions/24

European countries, which provides details on the macro-economy and its interaction with the

environment and the energy system. It covers all production sectors (aggregated to 26) and

institutional agents of the economy. GEM-E3 is specifically designed to provide high resolution

output by energy sector and GHG emission source (E3M-Lab, GEM-E3 model description). A fully

stochastic World energy model is used for assessing the uncertainties and risks associated with the

main energy aggregates including uncertainties associated with the impact of policy actions (R&D on

specific technologies, taxes, standards, subsidies and other supports). The model makes endogenous

projections of future energy prices, supply, demand and emissions for 10 World regions (E3M-Lab

PROMETHEUS model description).

Primes is designed for future projections, scenario building and policy impact analysis. It covers a

medium to long-term horizon to 2030. The PRIMES model simulates a market equilibrium solution

for energy demand and supply. It includes transport activity for both passengers and freight. The

model structure defines several technology types (car technology types, for example), which

correspond to the level of energy use. Within road transport, a further subdivision distinguishes

between public road transport, motorcycles and private cars. The model considers six to ten

alternative technologies for transport means such as cars, busses and trucks; the number of

alternatives is more limited for rail, air and navigation (PRIMES, 2010).

The “EU energy trends to 2030” study (E3M-Lab, 2009) is an update of previous trend scenarios, such

as the “European energy and transport - Trends to 2030” study published in 2003 and its 2005 and

2007 updates. Two scenarios, the “Baseline 2009” (finalised in December 2009) and the “Reference

scenario” (April 2010) are presented. The scenarios are available for the EU and each of its 27

Member States simulating energy balances for future years under current trends and policies as

implemented in the Member States by April 2009.

The Baseline 2009 scenario determines the development of the EU energy system under current

trends and policies; it includes current trends in population and economic development including the

recent economic downturn and takes into account the highly volatile energy import price

environment of recent years. Economic decisions are driven by market forces and technology

16

progress in the framework of concrete national and EU policies. Measures implemented before April

2009 are included. This includes the EU emissions trading scheme (ETS) and several measures for

energy efficiency but excludes the renewable energy target and the non-ETS targets. The Reference

scenario is based on the same macroeconomic conditions, price, technology and policy assumptions

as the baseline. In addition to the measures reflected in the baseline, it includes policies adopted

between April and December 2009 and assumes that national targets under the Renewables

directive 2009/28/EC and the GHG Effort sharing decision 2009/406/EC are achieved in 2020.

Results

Key assumptions for the 2009 Reference scenario include:

• a GDP growth rate of 2.2% annually between 2010 and 2020 and 1.7% between 2020 and

2030;

• Rising oil price to about 106 US$ 2008 per barrel by 2030;

• A decreasing carbon intensity of power generation to 179 grams CO2 per kWh by 2030;

• Increasing average after tax electricity prices from 110 to 144 €2005 per MWh between 2010

and 2030.

The total CO2 emissions from transport (excluding maritime and intercontinental aviation) in the

Reference scenario remain at about 1,000 to 1,100 million tonnes of CO2 between 2010 and 2030.

2.5 Roads toward a low carbon future

Description

The background of the study (McKinsey, 2009) is the increasing policy focus on carbon dioxide

emissions from passenger vehicles, due to the fact that these vehicles are a highly visible source of

greenhouse gases and total passenger vehicle emissions are projected to grow to 2030. Three

scenarios are defined assuming different rates and time frames for the diffusion of different

technology packages. These scenarios are:

• ICE scenario

This scenario assumes optimization of the fuel efficiency of ICE-powered vehicles. The sector

does not witness any meaningful global penetration of hybrid or electric vehicles.

• Mixed-technology scenario

A balanced mix of technological solutions reaches the market, including optimized ICEs,

hybrids, and electric vehicles.

• Hybrid-and-electric scenario

There is a rapid transition toward a world of electricity-based vehicle propulsion systems.

17

Results

The CO2 abatement curve that was constructed for Europe in this study includes a number of

abatement options ordered with increasing cost per tonne of CO2. The x-axis represents cost-

effectiveness levels at an oil price of US$ 60 WTI (West Texas Intermediate) or 45€. In addition, two

other dashed horizontal lines represent cost-effective abatement levels at oil prices of 100 (or 75€)

and 150 US$ (or around 110€) per barrel. The results show that over 200 million tons of CO2

equivalent emissions can be abated by abatement options at negative marginal abatement costs

assuming a 60 US$ per barrel oil price.

2.6 Transport, Energy and CO2 scenarios

Description

This study (IEA, 2009) shows how the introduction and widespread adoption of new vehicle

technologies and fuels, along with some shifting in passenger and freight transport to more efficient

modes, could result in a 40% reduction in CO2 emissions below year-2005 levels. This analysis uses

the same basic set of scenarios originally developed for the Energy Technology Perspectives (ETP,

2008) publication. These cover various futures through 2050, including several alternative routes to

achieve very low CO2 emissions for transport. Specific scenarios include:

• Baseline: follows the IEA World Energy Outlook 2008 (WEO 2008) Reference Case to 2030

and then extends it to 2050. It reflects current and expected future trends in the absence of

new policies.

• High Baseline: considers the possibility of higher growth rates in car ownership, aviation and

freight travel over the period to 2050 than occur in the Baseline.

• BLUE CO2 reduction scenarios: these scenarios update those presented in the IEA Energy

Technology Perspectives 2008 report. The BLUE variant scenarios are developed based on

achieving the maximum CO2 reductions achievable from transport by 2050 using measures

costing up to USD 200 per tonne of CO2. These scenarios will require significant policy

intervention if they are to be achieved.

• BLUE Map: this scenario achieves CO2 emissions by 2050 that are 30% below 2005 levels. It

does this via improvements in vehicle efficiency and the introduction of advanced

technologies and fuels such as plug-in hybrids (PHEVs), electric vehicles (EVs), and fuel cell

vehicles (FCVs). It does not envisage significant changes in travel patterns.

• BLUE EV Success: Similar to BLUE Map and achieving a similar CO2 reduction, but with electric

and plug-in hybrid vehicles achieving greater cost reductions and better performance to the

point where they dominate light-duty vehicle (LDV) sales by 2050, to the exclusion of fuel cell

vehicles.

18

• BLUE Shifts: this scenario focuses on the potential of modal shift to cut energy use and CO2

emissions. Air and LDV travel grow by 25% less than in the Baseline to 2050, and trucking by

50% less. The travel is shifted to more efficient modes and (for passenger travel) to some

extent eliminated via better land-use planning, greater use of information technology, and

other measures that reduce the need for motorised travel. Compared to the Baseline in

2050, BLUE Shifts results in a 20% reduction in energy use and CO2.

• BLUE Map/Shifts: this scenario combines the BLUE Map and BLUE Shifts scenarios, gaining

CO2 reductions from efficiency improvements, new vehicle and fuel technologies, and modal

shift. It results in a 40% reduction in CO2 below 2005 levels by 2050.

Results

The oil price is assumed to remain at 120 US$ per barrel from 2030 to 2050 in 2006 real US$. In

nominal prices this means the oil price will increase to over US$ 300 per barrel by 2050. In the

baseline for OECD Europe transport GHG emissions are expected to remain at the 2005 level of about

1,500 Mton CO2 eq. to 2050. Worldwide transport sector GHG emissions in the BLUE Map/Shifts

scenarios are 40% below year-2005 levels.

2.7 Findings from scenario review

• Based on the review of scenarios in this chapter, which are summarised in Table 2.1 below,

we draw the following conclusions:

• Scenario timeframes vary:

O Some studies focus on the end results by 2030 or 2050, while others focus on the

trajectory to the time horizon as well.

• Geographical scope varies:

O Some studies include the EU27, whereas others include world regions, OECD areas or

Western Europe only.

• The investigated scope of the transport sector varies:

o Some studies include all modes; others include selected modes.

o Some studies include main transport corridors/network; others include the full

transport network.

• The volume of transport varies:

o Some studies include explicit demand modelling, others do not. In the latter case,

fixed demand assumptions are applied in all scenarios.

• The scope of emissions varies:

o Some studies include only TTW emissions, where others include WTW emissions.

19

• The definition of a scenario varies:

o Some studies include only exogenous drivers (external forces), while others include

policy instruments or both.

• Key determinants vary:

o Some studies include only GDP and population, whereas others include oil prices and

other variables.

o Few studies include the carbon intensity of electricity as a scenario variable for

transport scenarios.

• The nature of the scenarios varies:

o Most scenarios are linear/ continuous (growth rate extrapolations).

o Few scenarios include disruptive developments/events.

Table 2.1 Review of existing transport scenarios

Scenario exercise:

Review element:

Hill et al., 2010

TRANSvisions, 2009a

iTREN, 2009 E3M-Lab, 2009

McKinsey, 2009

IEA, 2009

Timeframe 2050 2030 and 2050

2030 2030 2030 2050

Geographical scope

EU27 EU27 EU27 EU27 Europe World region, OECD Europe

Transport sector scope

All modes All modes (only intra-EU)

All modes (only intra-EU)

All modes (only intra-EU)

Passenger vehicles

All modes

Transport demand scope

No demand modelling, fixed assumption

Demand modelling

Demand modelling

No demand modelling, fixed assumption

No demand modelling, fixed assumption

No demand modelling, fixed assumption

Scope of emissions

WTW WTW Not specified Not specified WTW WTW

Scenario definition

Policy scenarios (technical and non-technical)

Exogenous and policy scenarios (mixed)

Baseline exogenous and policy assumptions (mixed)

Baseline exogenous and policy assumptions (mixed)

Policy assumptions (abatement options)

Policy assumptions (transport system assumptions)

Nature of scenarios

Continuous Continuous Continuous Continuous Continuous Continuous

Key assumptions

Based on TREMOVE, ExTremis, MARKAL-ED

Based on Transtools (metamodel)

Based on Transtools, TREMOVE, POLES, ASTRA

Based on Primes energy baseline 2009

Based on Global GHG abatement cost curve v2.0

Based on Mobility Model (MoMo)

20

In combination, these factors mean that it can be difficult to compare the results and projected

reductions in emissions from different scenario studies. For example, a study on a scope which

includes only emissions sources which have a relatively large range of mitigation options (e.g.

passenger cars) will project greater reduction potentials than one which includes sources which have

fewer options (e.g. intercontinental aviation). Based on the observations made in this review, the

next chapter clearly defines the approach used in TOSCA for the scenario formulation.

21

3 Scenario formulation

3.1 Definition of a TOSCA scenario

As mentioned in section 2, many scenario exercises use a mix of policy instrument and exogenous

factors to define scenarios. In TOSCA scenarios are defined as follows:

• Scenarios include exogenous variables only

• These exogenous variables are both uncertain and relevant for reducing GHG emissions

• The scenarios include a set of consistent assumptions about the future development of these

variables

• The scenarios are not forecasts, but represent a range of plausible futures

• The scenarios are intended to describe the uncertainty about the future collection of

consistent assumptions

3.2 Identification of scenario drivers

In TOSCA we focus exclusively on exogenous scenario drivers. A framework is used to select variables

that are both relevant and uncertain in terms of reducing GHG from transport. Relevant and

uncertain variables form one block of the evaluation framework used in this project (Table 3.1).

Variables having a low relevance are ignored. Variables which are relevant but not highly uncertain

are included in the Baseline scenario. The Baseline scenario is the reference development used to

evaluate the impact of alternative exogenous scenario developments. Variables with a high relevance

and high uncertainty are used as key scenario variables for the definition of the TOSCA scenarios. The

variables with the highest relevance and largest uncertainty include oil prices (and other energy

prices as a derivative of oil prices), GDP growth and the carbon intensity of electricity generation. The

reasons for these choices are described below.

EU transport depends on oil products for 96% of its energy needs. Transport accounts for 73% of all

oil consumed in Europe (EC, 2011). Oil prices depend on many factors, which are difficult to predict.

Oil prices generally have a large impact on the total volume of passenger and freight transport and

on mode/technology choices. Higher oil prices improve the business case of low carbon alternative

technologies and fuels.

Economic growth, measured as GDP, is another important driver of the volume of transport. Lower

economic growth (or an economic crisis) leads to lower transport demand, which may require less

stringent policy measures for reducing GHG emissions to a certain desired level. In addition, lower

economic growth may also lead to different fuel/technology choices by transport users. Similar

responses are found in certain transport service operations like in shipping; reduced demand for

consumer goods leads to a lower volume of freight transport which leads to overcapacity in (for

instance) container shipping. In response to this, shippers implement slow steaming, leading to

longer voyages (in days) but lower emissions.

22

Table 3.1 Evaluation framework for selecting exogenous drivers in TOSCA.

Uncertainty about development

Low High

Rele

vanc

e fo

r EU

27 G

HG

em

issi

on r

educ

tion

Low IGNORE

IGNORE

High Include on baseline: - Aging population - Population size - Urbanisation - Motorisation rate

Exogenous drivers: - Oil prices - Economic growth (GDP) - Carbon intensity of electricity

The carbon intensity of electricity generation is defined as the amount of CO2 equivalent emissions

per kilowatt hour (kWh) electricity generation (gram CO2 per kWh). This variable will become highly

relevant to transport as many future projections include electricity becoming a significant energy

source for EU transport (via plug-in hybrids, battery electric vehicles or a mode shift to rail). If

transport becomes highly electrified, but electricity does not improve considerably in terms of

carbon intensity, EU transport may not be able to significantly reduce its emissions.

Based on our review of a long-list of existing transport scenarios and models, of which a selection is

presented in chapter 2, we formulated a set of scenarios which include a wide range of the evolution

of the three scenario drivers. The table below depicts four scenarios. The first three scenarios are

defined as follows:

• A reference for the evolution of exogenous factors (Baseline)

• Challenging evolution of exogenous factors in terms of GHG emissions (Challenging)

• Favourable evolution of exogenous factors in terms of GHG emissions (Favourable)

Table 3.2 Overview of the key scenario assumptions which define the three TOSCA scenarios

Scenario assumptions:

Oil prices (US$2008/bbl)

GDP (p.a. % growth)

Carbon intensity (p.a. % growth)

Scenario 0: ‘Baseline’ Increasing 76 to 157 (+1.8%)

Increasing +1.7%

Decreasing -1.7%

Scenario 1: ‘Challenging’

- 76 to 76 (0%)

+ + +2.5 %

+ -0.5%

Scenario 2: ‘Favourable’

+ 76 to 200 (+2.5%)

- +0.7%

- - -3.0%

Scenario 3: ‘Disruptive’ (Annex A)

0 76 to 157 (+1.8%)

0 +1.7%

0 -1.7%

23

0

50

100

150

200

250

1850 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050

Oil

Pric

es U

SD'0

8 pe

r bar

rel

BP Historic Prices Scenario 0: 'Baseline'

Scenario1: 'Challenging' Scenario 2: 'Favourable'

Figure 3.1 Assumptions for Oil prices in all scenarios

The fourth scenario, Disruptive, mainly functions as a sensitivity analysis. The disruptive scenario

explores the behaviour of the TOSCA framework conditions under the occurrence of an extreme

event in the year 2020 (see Annex A). The development of the identified drivers for the other

scenarios is described below. The assumptions as mentioned in the table above are shown in the

following graphs. In the following sections, each scenario is described in detail. Figure 3.1 above

shows the historic development of yearly average oil prices from 1861 to 2009 and three alternative

oil price scenarios from 2009 to 2050.

0

5000

10000

15000

20000

25000

30000

35000

40000

1990 2000 2010 2020 2030 2040 2050

EU27

GD

P in

bill

ion

EUR

'09

Scenario 0: 'Baseline' Scenario 1: 'Challenging'

Scenario 2: 'Favourable' Eurostat

Figure 3.2 Assumptions for GDP in all scenarios

24

Figure 3.2 shows the historic development of GDP in the EU27 from 1990 to 2005 and the three

alternative GDP scenarios from 2009 to 2050.

0

200

400

600

800

1000

1200

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Car

bon

Inte

nsity

g C

O 2 /

kWh

World energy balances (IEA, 2010) and Emission Factors (ETC/ACC, 2003)Scenario 0: 'Baseline' Scenario 1: 'Challenging'Scenario 2: 'Favourable'

Figure 3.3 Assumptions for Carbon Intensity of electricity generation in all scenarios

Figure 3.3 above shows the historic development of the carbon intensity of electricity generation in

the EU27 from 1960 to 2007 and the three alternative carbon intensity scenarios from 2009 to 2050.

3.3 Baseline scenario

The Baseline scenario assumes a continuation of the existing socio-economic trends for EU transport

demand drivers. It is assumed that current policies1

will be successfully implemented and that no

other significantly different policies are introduced. In this scenario, as for all scenarios, the EU is

committed to reduce total GHG emissions by at least 20% relative to the 1990 level by 2020, as

specified in COM (2007), with a further tightening beyond 2020. Table 3.3 presents the evolution of

the relevant and uncertain scenario drivers and baseline variables which are relevant and less

uncertain.

1 This includes, for example, maintaining existing levels of fuel excise duty and VAT, and including aviation in

the EU emissions trading scheme from 2012.

25

Table 3.3 ‘Baseline’ scenario drivers and baseline variables

Scenario 0: ‘Baseline’ Unit 2010 2020 2030 2040 2050 Growth Rate

2010-2050

Relevant and uncertain scenario drivers:

Oil price US$ per bbl, 2008

76 102 118 136 157 1,8%

Carbon intensity electricity generation (DBFZ forecast /EURelectric)

g CO2 / kWh 380 262 145 117 89 -3,6%

GDP EU27 Billion €, 2009

12811 15747 18621 21999 25965 1,8%

GDP per Capita, EU27 €, 2009 PPP2 25674 30636 35810 42510 50417 1,7% Relevant and low-uncertainty baseline variables:

Population size EU27 Million, EU27 499 514 520 518 515 0,1% Urbanisation (EU27) Million, EU27 369 392 413 426 438 0,4% Urbanisation Rate (EU27) % 74 76 79 82 85 0,3% Motorisation rate (EU27) cars/1000

inhabitants 451 512 566 626 692 1,1%

Electricity prices € 2009/MWh 118 150 155 151 147 0,5%

Oil prices3 are shown in figure 3.1. This graph depicts the development of crude oil prices from BP

since 1861. One can here observe the price fluctuations which contribute to the high uncertainty in

the future development of this variable4

. From 2010 on, these prices steadily increase in the baseline

scenario with an average annual growth rate of 1.8%. By 2020 oil price is expected to rise beyond

100 US$’08 per barrel, originally due to growing demand but, later on, also because of being a finite

resource. Gas prices are assumed to be highly positively correlated to oil prices.

GDP growth is expected to increase moderately in the EU27 to 2050. The average EU27 growth rate

for GDP in the baseline scenario is projected to be 1.8% per annum for the EU27 countries (Table

3.3). GDP per capita demonstrates a slightly smaller growth rate of 1.7% per annum, due to a small

growth in population.

2 Note that some of the models within TOSCA used require input in the form of market exchange rate (MER)

GDP. In this case, the study of Manne, Richels & Edmonds (2005) was used to provide a plausible conversion

factor.

3 Oil prices affect transport costs. The effect though may be limited as a high proportion of gasoline and diesel

price is tax which is assumed to remain at present-day levels. In addition, for some transport modes energy

costs represent only a low proportion of operating costs (e.g. around 10% for rail).

4 Some of the main influencing factors of the oil price are economic growth, car ownership and in general fuel

demand, the existing –known and unknown- fuel reserves and the producing countries’ relationships to the

rest of the world.

26

The carbon intensity of electricity is projected to decrease due to cleaner, more efficient electricity

generation techniques and different primary energy sources. This expected technological evolution,

combined with the EU ETS regulation applicable to stationary emission sources such as power plants,

is projected to reduce the carbon intensity of electricity generation by 3.6% annually from 2010 to

2050.

Other relevant but less uncertain factors were studied for the Baseline scenario. Population is

projected to grow moderately for both the EU27 as a whole and the EU15 countries (table 3.3), but

not for the EU12 countries due to expected emigration. Urbanisation follows an increasing trend,

with 85% of the population living in urban areas by 2050. The motorisation rate is expected to grow

on average by 1.1% annually. Motorisation is expected to increase due to increasing GDP and income

levels, especially in Eastern Europe where motorisation rates rapidly catch up with those of Western

European countries. The baseline electricity prices are projected to reach their highest level around

2030 and drop slightly afterwards to 2050. The baseline electricity prices are based on the PRIMES

energy baseline 2009 for the EU (EC, 2010). Furthermore, assumption are made about the GDP

growth in non-EU world regions as these are relevant and required to determine intercontinental

transport flows. The economically developing countries (China, India, Brazil) are projected to lead

GDP growth with more than double the growth factors observed in Europe.

Table 3.4 ‘Baseline’ non-European GDP projections (adapted from Duval & de la Maisonneuve 2010).

Scenario 0: ‘Baseline’

Relevant and less uncertain scenario drivers: Non_EU27 GDP growth

Growth Rate

2010-2050

USA + Canada 2,3%

Japan 1,2%

China + India 5,8%

Brazil 4,0%

Russian federation 2,7%

Rest of World 4,8%

World total 3,6%

Even though major technological breakthroughs are not assumed in the Baseline, there is a moderate

efficiency improvement of current technologies in all modes of transportation. These autonomous

efficiency improvements are taken from the background trends in reference technology

development from WP1-5 of TOSCA, with the exception of aviation for which the evolutionary

replacement technology is specifically included. They are used as baseline developments in TOSCA.

In addition, because TOSCA WP3 anticipate significant changes in future maximum rail speeds for

both passengers and freight, an increase in maximum speed of 5% to 2020 and 15% to 2030 from

base year levels is assumed in all scenarios.

27

For the following two scenarios (favourable and challenging) only the uncertain and relevant

parameters (Oil price, GDP and the carbon intensity of electricity) are examined. In addition, a

further scenario for sensitivity testing (disruptive) is described in Annex A.

3.4 Challenging scenario

This scenario, representing an era of economic prosperity in the EU27 countries, leads to a challenge

in terms of emissions reduction. GDP is assumed to increase in the EU27 at a faster pace than in the

Baseline scenario. Energy-wise, this scenario assumes a situation in which sufficient oil and gas fields

are explored and recovered by conventional as well as unconventional but affordable techniques and

in both OPEC and non-OPEC countries. Hence, the oil price continues below the oil price trajectory in

the Baseline scenario (except for the starting year) and remains constant at the 2010 price level to

2050 (76 US$/bbl).

The availability of resources and the low fuel prices combined with high economic growth is likely to

increase demand for transport in Europe. In addition, the carbon intensity of electricity generation

only decreases by 0.5% per annum, leading to an overall 18.2% decrease in carbon intensity to 2050

where the Baseline projects an almost 50% decrease. These factors in combination are likely to lead

to high emissions from transport. The table below summarises the main indicators for the

‘challenging’ scenario.

Table 3.5 ‘Challenging’ Scenario Assumptions

Challenging Scenario 2010 2020 2030 2040 2050 Growth Rate p.a.

(2010 – 2050)

Oil Price ($’08 per bbl) 76 76 76 76 76 0.0%

Difference from Baseline 0% -25% -36% -44% -52%

GDP (billion €’09) 12492 15990 20469 26202 33540 2.5%

Difference from Baseline 0% +5% +13% +19% +29%

Carbon Intensity Electricity Generation

(gCO2/kWh)

380 361 344 327 311 -0.5%

Difference from Baseline 0% +13% +27% +44% +63%

As depicted above, the growth profile for key drivers differs significantly from the Baseline. The oil

price reaches a relative difference from the Baseline level of 52% in 2050. GDP also increases at a

faster pace and, in 2050, is higher by 29% than in the Baseline scenario. Finally, the carbon intensity

of electricity generation is assumed to only moderately decrease, ending up being 63% higher than

the baseline in 2050. These factors in combination mean that it will be difficult to reduce CO2

emissions in this scenario.

28

3.5 Favourable scenario

The ‘favourable’ scenario in terms of emission reductions reflects a period of economic vulnerability

in Europe. The annual GDP growth in the EU27 countries is only 0.7% throughout the whole 2010-

2050 period.

The oil price rises by 2050 up to 200 US$’08 per bbl. This may be a consequence of, for example,

limited oil supply. Due to this rise, transport costs also increase, reaching higher levels than in the

Baseline scenario. Low GDP growth and high oil prices are likely to lead to less demand for transport

and an improved business case for consumer adoption of alternative low carbon technologies and

fuels. The carbon intensity of electricity is expected to decrease by 3.0% per annum, achieving an

overall decrease of 70.4% from 2010-2050, 40% lower than the in the Baseline scenario.

The oil price demonstrates an annual growth rate of around 6.4% for the first decade, followed by a

growth rate of 1.2% from 2020 to 2040 and of 1.1% until 2050. GDP has an almost linear growth of

2.5% annually. Finally, the carbon intensity of electricity generation declines at 3% per annum5

.

These assumptions are summarised in Table 3.6.

Table 3.6 ‘Favourable’ Scenario Assumptions

Favourable Scenario 2010 2020 2030 2040 2050 Growth Rate

p.a. (2010 –

2050)

Oil Price ($’08 per bbl) 76 141 159 179 200 2.4%

Difference from Baseline 0% +38% +35% +32% +27%

GDP (billion €’09) 12492 13394 14362 15399 21999 1.4%

Difference from Baseline 0% -12% -21% -30% -15%

Carbon Intensity Electricity Generation

(gCO2/kWh)

380 280 207 152 112 -3.0%

Difference from Baseline 0% -13% -23% -33% -41%

5 The TOSCA scenarios do not consider in detail how carbon savings are made in electricity generation.

However, it should be noted that other calculations within TOSCA depend on the available biomass supply. In

these calculations it is assumed that there is not a significant use of biomass by electricity generation.

29

4 Projecting intra-European passenger and freight transport demand

In order to estimate the impact that each scenario described in Section 3 has on EU27 transport

emissions, we need to determine how transportation demand will develop in each case. Different

scenario assumptions are likely to have varying effects depending on the mode, country, population

segment and existing transportation system looked at. Therefore demand modelling that considers

these effects as far as possible is needed. This section describes the demand modelling process in

TOSCA.

4.1 European transport network model (Transtools)

As explained in chapter 1, the scenario assumptions described above are implemented within the

European transport demand model Transtools, to simulate the intra-EU demand impact for

passenger and freight transport by mode. The Transtools model input consists of a detailed database,

based on the European Transport Policy Information System (ETIS) database of transport policy-

relevant European variables, as appropriate for a 2005 base year (NEA, 2005). TOSCA input consists

of changes to the Transtools input database to reflect the development of baseline scenario variables

from 2005 for each year run (e.g. in terms of population, urbanisation, fuel prices, carbon prices) and

any development in the characteristics of the reference technologies identified by WP 1-5 (e.g. in

terms of cost, speed and fuel consumption). In the case that the database contains a cost to end-

users rather than operators (for example, rail ticket prices rather than costs accruing to rail

operators) it is assumed that all cost increases are passed on to consumers. A description of the

model itself is given below.

Transtools Demand Model

Transtools (DTU, 2010) is a large and complex demand model which includes several different,

interacting modules to calculate the end-results of passenger and freight demand, as shown in figure

4.1. These are:

• The regional economic model, which models interdependencies in economic processes

(using a multiregional computable general equilibrium model), incorporating transport costs

and other cost components.

• The freight models (mode and logistics). This module uses a top-down approach

incorporating global economic trends and national GDP, and calculates the trade volume

between countries and regions (NUTS 26

6 The EU Nomenclature of Territorial Units for Statistics (NUTS) system splits Europe into a number of

geographical regions for modelling purposes. Transtools uses NUTS2 and NUTS3-level data. These

classifications divide the EU27 countries into 271 and 1303 regions respectively.

level). The logistics module ranks the regions

30

according to their attractiveness for freight transhipment and assigns the freight demand to

the logistics chains. The output consists of trip matrices at a NUTS2 geographic level.

• The passenger demand model. This module is implemented as stored procedures in a

database. The inputs to the model are cost matrices, rail ticket prices, and zone data (e.g.

population, GDP, employment). It is specified for four trip purposes (e.g. business) for both

short and long trips (short trips do not include the air mode). The output consists of NUTS3-

level Matrices7

• The network model (for all modes). The network uses stochastic processes and calculates the

demand for each mode of transport. The road network consists of 50,000 links and 30,000

nodes, the air network 450 airports, the rail freight network from 6,000 links and 4,500

nodes and the rail passenger network 5,500 links and 4,500 nodes. The network model is

used to calculate routing and congestion on major routes, which in turn may impact demand.

.

• The air journey model. This is a stochastic model which uses the link times and the costs to

calculate the air passenger demand.

• The impact models. These are the environmental and the transport impact models. The first

includes indicators for energy consumption, emissions and external costs for road, rail and

air. The second includes the safety impact and the fatalities for road and rail.

Because network effects may impact on demand, an iterative run structure is used. Initially, the

network model is run, based on the model input variables and a starting set of demand inputs. After

this, the full passenger and freight demand models are run. Finally, the network model is run again

with the updated demand values. These steps can be iterated as necessary.

Transtools covers the whole of the EU27 and a number of other countries (e.g. the Ukraine). For the

present study, only EU27 results were extracted.

Transtools Limitations

Transtools simulates all major European networks (air, road, train and inland waterways) at NUTS2

(271 provinces/regions) and NUTS3 (1,303 districts/ groups of municipalities) geographical levels. The

output, in terms of network passenger and freight flow for a scenario year, is represented at a NUTS2

level for freight and at NUTS3 for passengers. Output is provided in vehicles-km for road transport,

tonnes-km for inland navigation and rail freight transport and passenger-km for aviation and rail

passenger transport. As well as demand flows, Transtools calculates detailed matrices for travel

costs, speed and travel times by mode as part of its demand and modal split calculations.

7 The air mode is calculated per airports (specified regions are included).

31

Figure 4.1 Transtools modules

However, Transtools has a number of limitations that means it is primarily suitable only for baseline

demand modelling within TOSCA. TOSCA has two broad requirements for demand modelling (Figure

1.6). The first is for scenario-dependent projections of demand by mode to 2050 in the case without

significant changes to vehicle technology. The second is for projections of how that demand may

change in response to developments in technology characteristics or changes in policy. Whilst the

first requirement may be satisfied with a small set or model runs per scenario, the latter case

requires a far greater number of model runs. Transtools allows a comprehensive, network-based

investigation of future transportation scenarios which includes many important effects, including

congestion and population heterogeneity. Some elements within the model are stochastic and

require several iterations for the results to stabilise. However, these factors result in a high

calculation time (approximately a week per run, where one run generates demand output for a single

year). This makes it infeasible in terms of time to run large grids of Transtools runs.

In addition, Transtools has a limited scope of the European network (e.g. covering mostly highways,

national roads, and interregional/provincial roads for the road network, and not covering tram,

metro and regional lines for railways). Whilst the network modelling carried out in Transtools is

valuable for projecting congestion effects, it is only carried out for this network scope, and modelling

outside this network is more limited. It does not model intercontinental air or marine transport, or

32

air freight. The rail modelling is limited, resulting in rough estimates of travel time, cost and flows.

Vehicle stock is also not modelled in detail.

Because of these limitations, some elements of the TOSCA demand modelling are calculated outside

the main model using alternative models. First, some demand calculations were made to cover areas

not included in Transtools. This includes estimates of intercontinental marine and air freight demand,

for which simple models from the literature were used. Intercontinental passenger aviation was

modelled using the Aviation Integrated Model (Reynolds et al. 2007). These models are described in

Section 4.2 below. In each case care was taken to ensure that the input assumptions were consistent

with those used in the Transtools input. Second, Transtools output is calculated until 2030. As not all

of the components of Transtools are designed to function after 2030, the long-term effects (beyond

2030) of the scenarios are simulated by meta-models and trend extrapolations. Besides the regular

runs for each scenario in Transtools (2010, 2020, 2030), two additional runs were used for the

Disruptive scenario (2021 and 2025) so as to fully depict the disruptive effect.

As Transtools is a network model, some of its network calculations exclude passengers and freight

travelling on minor roads or rail routes. For example, for rail transport, Transtools covers only the

main stations, excluding regional rail transport, metros and trams. This leads to underestimates

which need to be corrected for when using these calculations to estimate aggregate demand, as

discussed in TENconnect (2010). Therefore it was also necessary to calibrate the remaining data,

both rail and road transport, to Eurostat data for the base year (Eurostat, 2010) to account for these

excluded trips (see Table 4.1).

Table 4.1 Comparison of Transtools to Eurostat results

Mode Eurostat Transtools

output Divergence Rail Freight 443 461 4%

Rail Passenger 409 292 -29%

Passenger Cars 4725 4073 -14%

Trucks 1878 1897 1%

Finally, the long runtime of Transtools made it necessary to make runs before the final versions of

the results of TOSCA work packages 1-5 were available. In a few cases, the change from intermediate

to final values resulted in changes to technology characteristics which had been used as input to

Transtools. To account for these changes, the elasticity model developed as part of the stock

modelling (See WP6.2 Report, section 2.7) was used to adjust the total demand. For the scenarios

discussed here, changes were typically under 1%.

33

4.2 Intra- and intercontinental air transport and maritime transport

Passenger aviation

Transtools does not include a model for intercontinental aviation. However, there are several

reasons why having an estimate of intercontinental aviation demand is useful for the TOSCA project.

First, many intercontinental air passengers make multi-segment journeys which include an intra-EU

segment. Projections of intercontinental aviation demand typically show faster growth than for intra-

EU demand (e.g. Boeing 2010, Airbus 2009), so intra-EU journeys by intercontinental passengers will

also grow in importance in the future. Second, intercontinental and intra-EU flights share the same

airports and airspace. This means that any consideration of airport or airspace capacity is incomplete

if only one type of flight is considered. Third, although TOSCA does not consider specific alternative

technologies for long-haul aircraft, this is due to an anticipated lack of new technologies for these

aircraft which could make a significant fleet impact before 2050, not because they are unimportant

in terms of emissions. In fact, long-haul aviation is one of the fastest-growing sectors both in terms of

demand and emissions, with growth in pkm in excess of 5% per year predicted on some route groups

(Airbus 2009, Boeing 2010) and it is already included in EU climate policy via the EU’s inclusion of

aviation from 2012 in its emissions trading scheme (EU 2009). Therefore estimating intercontinental

aviation emissions is important in terms of estimating the relative importance of different future

transport emissions sources.

As Transtools excludes intercontinental transport, estimates of intercontinental aviation demand are

made using an existing model at the University of Cambridge, the Aviation Integrated Model. Since

this is a global model of aviation, it also provides an alternative forecast for intra-EU aviation. This

model, and its use within TOSCA, are described below.

Aviation Integrated Model

The Aviation Integrated Model (AIM) is a systems model of global aviation, covering around 95% of

global air pkm (Dray et al. 2010a). The future demand estimates provided by AIM are scenario-based

and require GDP, population, fuel and carbon prices as input. Output in terms of demand and

emissions is provided on a city-pair basis, allowing the change in demand over arbitrary geographical

regions to be calculated. These factors make the model suitable as a method of estimating

intercontinental aviation demand to and from the EU27 countries within TOSCA. A summary of the

model structure, inputs and outputs and use in TOSCA is given below.

Figure 4.2 shows the basic structure of AIM. AIM consists of seven interconnected modules,

programmed in Java and Matlab. Of these, only the first four are relevant for TOSCA demand

estimation.

The Air Transport Demand module projects true origin-ultimate destination demand for air travel

for a set of 700 global cities, served by 1,127 airports, which accounts for around 95% of global

34

scheduled air pkm. For use in TOSCA, population and GDP per capita from the TOSCA scenarios by

world region are provided as inputs8

.

Figure 4.2 Aviation Integrated Model structure

The Airport Activity Module assigns passenger routing, a flight schedule, and aircraft types by

flight segment, calculates the resulting flight delay and airport capacity requirements to maintain

future flight delays close to existing levels. This approach assumes that airport regions will be able to

expand runway capacity to meet demand requirements (if necessary by utilising secondary airports).

The Aircraft Movement Module calculates the location of emissions, accounting for en-route

inefficiencies.

The Aircraft Technology and Cost Module computes costs and emissions by aircraft type, and fleet

turnover rates, including airline decisions to invest in new technology. For the purposes of the TOSCA

baseline runs, it was assumed that no new technologies were available to airlines other than air

traffic management improvements provided by the SESAR project, and evolutionary replacement

narrowbody aircraft. The changes in costs resulting from these technologies were taken from TOSCA

WP2 data (Vera-Morales et al. 2011), in combination with TOSCA scenario data about fuel and

carbon prices. Airline costs are used in AIM to estimate average airfares, which are input to the Air

Transport Demand module, for the estimation of passenger demand. These modules are therefore

run iteratively until equilibrium between demand and supply is reached. For the purposes of TOSCA,

8 As noted in section 3.2, GDP scenarios for regions outside the EU27 countries are taken from Duval & de la

Maisonneuve (2009). Where population for regions outside the EU is needed, it is taken from the UN’s medium

population projection for all scenarios (UN 2009).

35

this represents the final output. More details about AIM, its previous use in modelling EU and global

policy scenarios, and the details of the individual modules may be found in Reynolds et al. (2007) and

Dray et al. (2010a, 2010b).

Multiple options are available in terms of scope when considering intercontinental pkm and tkm. For

the purposes of TOSCA, we report totals which are 50% of total intercontinental demand to and from

Europe. This effectively assigns responsibility for emissions to the country of origin for any given trip,

and allows a direct comparison of fuel use with bunker fuel totals.

Consistency

The use of different models for different modes raises potential consistency issues. In particular, two

different projections of intra-EU air demand were generated as part of the simulation process, one

by AIM and one by Transtools. Because intercontinental air passengers often include an intra-EU

flight as part of their journey (for example, flying from New York to London, then from London to

Rome), it is desirable to have projections for intercontinental and intra-EU passenger demand which

are linked, as is the case in AIM. Because intra-EU air passengers have the option of driving or taking

the train instead, it is also desirable to have projections of intra-EU demand by mode which are

linked, as is the case in Transtools.

In choosing which intra-EU projection to use within TOSCA, a comparison was made with industry

projections for future aviation demand (e.g. Boeing 2010; Airbus 2009; ICAO 2007). These

projections suggest a growth rate of 2-4% per year in intra-EU aviation pkm over the next 20 years is

anticipated by the aviation industry. AIM projections (1.6-4.5% per year) most closely matched these

growth rates, so they were used in TOSCA.

Air Cargo

Air cargo transportation is strongly linked to air passenger transportation. Around 58% of air cargo is

carried in the holds of passenger aircraft (ICAO 2008). The remaining cargo is carried in dedicated

freighter aircraft. Worldwide, these account for less than 10% of the global aircraft fleet (Morrell &

Dray 2009). Whilst dedicated freighter aircraft can be bought new, many are aircraft which were

retired from the passenger fleet and then converted to freighter usage. This results in a freighter

fleet which is typically older than the passenger fleet and has higher per-plane emissions.

Modelling air cargo demand on a network basis is a complex task, as data on current air cargo

movements is relatively sparse and can include goods which were actually shipped by truck (e.g.

Boeing 2004). The future locations of production facilities for goods which are commonly shipped by

air, and of transhipment points, can be difficult to predict. In addition, because air cargo typically

transports small amounts of low-weight, high-value goods, total tkm is almost negligible compared to

other modes (e.g. Eurostat 2010). For these reasons, air cargo is usually excluded from transportation

models. Neither AIM nor Transtools explicitly model air cargo demand or emissions. However, freight

36

carried in the holds of passenger aircraft is implicitly included via its effect on individual aircraft

weight.

To estimate the additional impact of dedicated cargo aircraft on emissions, a very simple elasticity-

based model is used here to generate demand projections. Intra-EU27 total air freight demand in

2007 was 3.1 billion tkm (Eurostat, 2010). This value is assumed to also be representative for 2009,

given the recessionary conditions in between. The corresponding base year intercontinental demand

was then estimated from a comparison of intra-EU and intercontinental freight from airline schedule

data (OAG 2005). The proportion of this freight carried in cargo aircraft was assumed to remain

constant at present-day values over the time period to 2050 (ICAO, 2008). As for air passenger

demand above, these values were then halved to obtain an estimate corresponding to bunker fuel

usage.

Secondly, GDP and price elasticities as appropriate for all-cargo carriers from Oum et al (1990) are

applied to this total to generate an idea of the total change to 2050 under different scenarios. It is

assumed that the proportion of freight carried in the holds of passenger aircraft will remain roughly

constant to 2050. These assumptions result in projected growth rates of intra-EU air cargo of 1-3%

per year, and intercontinental air cargo of around 4-5% per year. These values are low compared to

industry forecasts (e.g. Boeing 2007 predict 6%/year for global air freight tkm growth) but give a

reasonable idea of the general trend expected.

Maritime Cargo

Cargo shipping between the EU27 countries may be divided into three broad categories depending

on the geographical scope: inland waterways, short sea shipping and intercontinental shipping. In

addition, some passenger transportation is carried out by ship. Of these, by far the largest share of

emissions comes from intercontinental shipping. For example, in 2006 total EU27 shipping CO2

emissions were estimated at 195 million tonnes, of which 171 million tonnes were from

intercontinental freight transport (EC 2010). Therefore the focus of the maritime modelling in TOSCA

is on intercontinental freight shipping. This sector is not covered by Transtools so, as with aviation, a

simple alternative model based on TOSCA scenario inputs was utilised.

There exist relatively few projections of regional shipping demand on a tonne-kilometre basis. Total

seaborne trade has historically been very strongly dependent on GDP (Eyring et al., 2005a). Eyring et

al. (2005b) develop simple models for total seaborne trade in million tons and the number of ships

required as a function of GDP. In combination with TOSCA data on the typical utilisation by ship,

these models enable estimates of the growth in seaborne tkm by scenario. It should be noted,

however, that as TOSCA does not include trends in ship size over time, that the demand growth rates

here are probably underestimates. Baseline demand is estimated from bunker fuel totals, assuming

the reference ship technologies specified in TOSCA are representative of the shipping fleet. As we

exclude small ships, this also means that baseline tkm estimates will typically be higher than actual

values, since fuel use per tkm is higher for smaller ships. However, despite these simplifications,

37

these estimates result in similar growth rates in TOSCA for the numbers of ships required and CO2

emissions as in the set of projections used in Eyring et al. (2005b).

The Eyring et al (2005b) model does not include fuel price as an input variable. Fuel costs are a high

and increasing component of shipping costs (e.g. Buxton 1985). However, estimates of both the

elasticity of maritime freight rates to oil prices and the elasticity of demand for ship tkm to freight

rates are typically low. UNCTAD (2010) estimate that the elasticity of container freight rates to oil

price is between 0.19 and 0.36, whilst the corresponding value for crude oil is around 0.28. Oum et

al. (1990) give estimates of demand elasticities to freight rates for ocean shipping which range from

0.06-0.31 for commodities other than General Cargo, and 0.00-1.10 for General Cargo. In

combination, these figures suggest that GDP is a much stronger influencing factor on total maritime

shipping demand than fuel price. However, there is some evidence that the influence of oil price on

freight rates may be larger at times when the oil price is volatile (UNCTAD 2010). This may affect the

Disruptive scenario (Annex A) in particular, in which the oil price development follows that in the

baseline scenario apart from a strong peak and decline in the period 2021-2030. To account for the

effect of volatile oil price on demand in the Disruptive scenario, we calculate the relative change in

fuel prices from the Baseline scenario and apply elasticities from Hummels, Lugosky & Skiba (2007).

4.3 Aggregated Scenario Results

The following graphs depict the trends of the passenger and freight transport in passenger km and

tonne km respectively, expressed in billions. Road and Rail results come from Transtools calculations.

As discussed above, aviation demand is calculated by the AIM model and an approximate calculation

for maritime demand is made using literature values. The results do not include inland navigation

demand. This section presents the results for the three main scenarios described in Section 2:

Baseline, Favourable and Challenging. The Disruptive results will be presented in Annex A.

Growth rates by scenario and mode of transport

Demand growth rates by major intra-EU27 mode and time period are shown in Figures 4.3 – 4.5.

According to our findings, the highest growth rates in demand (excluding intercontinental transport

and air freight) occur in air passenger transport (intra-EU27). Air transport accounts for a 10% share

of passenger-km in 2010 and this increases to 26% by 2050 (Challenging scenario). At the same time,

there is a small decline in rail passenger mode share (1-2% depending on the scenario) and some

shift from road to air travel.

Another observation is that the Challenging scenario presents the highest growth rates. This is

expected as oil and electricity prices are low, and GDP growth is high. The Baseline scenario

demonstrates in general low growth in demand, which does not surpass 1.5% annually (with the

exception of aviation). In most cases the largest growth is observed in the 2020-2030 period. The

Favourable scenario is the only scenario demonstrating negative growth, primarily for rail freight

transport. It also has a high divergence between decades (for freight transport and passenger

38

aviation). This arises in part because oil price growth differs the most between decades in this

scenario. Most growth rates here remain in line with the average, within the 0-1% interval. This is

due to limited fuel resources and high fuel prices (both oil and electricity).

Figure 4.3 Baseline scenario annual intra-EU27 demand growth by mode.

Figure 4.4 Challenging scenario annual intra-EU27 demand growth by mode.

39

Figure 4.5 Favourable scenario annual intra-EU27 demand growth by mode.

Growth in demand is in general consistent with that expected from the scenario definition, with the

Challenging scenario experiencing high growth and the Favourable scenario only limited growth.

Passenger km for all scenarios

0

2000

4000

6000

8000

10000

12000

14000

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Billi

on p

asse

nger

-km

EC Pocketbook 2010 Scenario 0: BaselineScenario 1: Challenging Scenario 2: Favourable

Figure 4.6 Model results (intra-EU27): Total passenger-km

40

Total passenger and freight transport by scenario

Figure 4.6 depicts the total intra-European projected passenger demand for each scenario. The

overall trend is influenced mainly by the road transport demand, which also maintains the highest

share in the modal split. According to these models, by 2050, the Challenging scenario displays a

relative (to baseline) growth of almost 33%, with the total passenger-km reaching almost 13,000

billion. On the other hand, the Favourable scenario displays a relative (to baseline) decrease of

25.5%, slightly exceeding 7,000 billion passenger-km. Compared to 2010, the Favourable scenario still

shows an increase in demand to 2050 by 28%, while for the Challenging scenario that increase rises

to 128%. By maintaining the existing conditions (baseline scenario) the increase in passenger demand

will be around 72%.

Figure 4.7 shows the corresponding results for freight. Here freight displays lower growth than that

of passenger demand, but growth rates are still significant. Here the discrepancy between the

baseline and the challenging scenarios for 2050 is smaller (16%), while for the favourable scenario it

is greater (-29%). The difference between 2010 and 2050 values is as follows: the baseline results

depict a 51% increase, the challenging, a 76% increase and the favourable, a modest 8% increase.

Tonne km for all scenarios

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Billi

on to

nne-

km

EC Pocketbook 2010 Scenario 0: BaselineScenario 1: Challenging Scenario 2: Favourable

Figure 4.7 Model results (intra-EU27): Total tonne-km

The effect of scenario on passenger demand seems to be significantly higher than that on freight.

This is mainly due to the presence of air passenger transport, which is sensitive to GDP changes, in

41

the above figures. Comparing only the road and rail transport modes, the differences shrink

significantly.

Passenger

Split:

Freight

Split:

Figure 4.8 Modal splits for the baseline scenario (intra-EU27)

4.4 Detailed scenario results

Baseline scenario results

This section describes the results by scenario, starting with the baseline. The output under

examination is the transport demand and how this is distributed between the different modes of

transportation. The modal split is examined for road and rail (for both freight and passengers), and

for aviation (only for passengers). In addition, the results for intercontinental aviation, air freight, and

maritime transport, are included separately. It should be noted that these latter results are

approximate only and represent the results of much less detailed models than for the other modes.

For the baseline scenario, the passenger modal split demonstrates the largest changes. This is

demonstrated in Figure 4.8. Road transport undergoes a decline of 7% in modal share which is all

absorbed by aviation (with an additional 1% in modal share from rail transport). Given that the total

passenger km in 2050 is almost 10,000 billion pkm, the shift to aviation corresponds to about 800

billion pkm. The shift to aviation can be explained through socio-economical trends such as the

increase in demand for high-speed transport with increasing GDP per capita (Schäfer & Victor, 2000).

Together, rail and air transport account for almost one fourth of the total demand. In comparison to

the passenger sector, the freight transport modal split remains almost unchanged, with an

insignificant increase of 1% for road transport, which reaches almost 3,000 billion tonne km in 2050.

42

As mentioned before, the highest demand growth rate for the baseline scenario comes from air

passenger transport (almost 3% p.a.). The other modes have a modest growth of around 1%. More

specifically, for road transport pkm increases by 1.1% p.a. and rail pkm by 0.8% p.a., while in freight

transport road tkm demand increases by 1.1% p.a. and rail tkm demand by 0.9% p.a.. Due to the high

total proportion of road transport, total passenger demand increases by 1.4% p.a. and the freight

demand by 1.0% p.a. Table 4.2 below provides the results for the baseline scenario by mode and

their average annual growth rates.

Table 4.2 Baseline scenario results (intra-EU27)

Baseline Scenario 2010 2020 2030 2040 2050 Growth 2010-2050

Rail Freight (btkm) 443 503 540 584 633 0.9%

Rail Passenger (bpkm) 409 439 469 513 561 0.8%

Passenger Cars (bpkm) 4725 5477 6198 6779 7414 1.1%

Trucks (btkm) 1878 2172 2454 2657 2878 1. %

Aviation intra-EU27 (bpkm) 558 742 1003 1322 1791 3.0%

PKM (bpkm) 5692 6658 7670 8614 9766 1.4%

TKM (btkm) 2321 2675 2994 3241 3511 1.0%

Complementary demand

Aviation intercontinental (bpkm) 801 1307 2327 3673 6018 5.2%

Air freight intra-EU27 (btkm) 1 2 2 3 3 2.2%

Air freight intercontinental (btkm) 18 28 44 63 92 4.1%

Maritime (btkm) 24258 26479 29959 33894 39505 1.2%

As well as the Transtools results, we present the calibrated AIM model results for airfreight and the

Eyring model results for maritime. Intercontinental passenger demand increases with slightly more

than 5% annual growth. As noted above, this is consistent with industry forecasts. The airfreight

demand for freight carried by all-cargo aircraft increases for both intra and outside of EU27 by 2.2%

and 4.1% respectively. This is a slightly lower rate than predicted by industry forecasts, but still

captures the expected strong growth trend. Finally, the maritime results have a small positive growth

rate of 1.2%. As noted above, this is likely an underestimate as it does not take into account

increasing ship size; however, emissions projections are still plausible.

43

Challenging scenario results

The challenging scenario simulates the behaviour of a financially booming system, where overall

transport demand is constantly growing, driven by strong GDP growth and low fuel prices. The

development of modal split is shown in Figure 4.9. The challenging scenario follows the same basic

trends as the baseline freight demand but differs with regards to the passenger split in two ways:

first, the rail demand share decreases by 3% by 2050; secondly, the aviation share penetrates the

transport demand more than in the baseline, reaching 26% by 2050. Road transport is still the

leading mode but other modes now account for more than one fourth of the total.

Passenger

Split:

Freight

Split:

Figure 4.9 Modal splits for the challenging scenario (intra-EU27)

As already mentioned, total passenger transport demand is considerably greater than in the Baseline

scenario (by almost 33%). This is a result of the growth in road and aviation. In terms of passenger

km, by 2050, the pkm difference with the baseline is 1638 bpkm for road and 1559 bpkm for

aviation; the two modes contribute equally to the total demand. These developments are consistent

with the scenario assumptions about oil price and GDP. In contrast to road and aviation, rail demand

increases with a slower rate; by 2050 the passenger rail demand share is smaller than the in the

baseline scenario. This may result in part from the constant oil price at 76$/bbl (remaining until 2050

at Baseline levels), which means that the low energy costs currently enjoyed by rail are less of an

advantage.

The same conclusion arises from the freight demand results as well. This time the modal split

remains almost constant, decreasing only in 2050 by one percentage unit. Nevertheless the annual

growth in road is greater than the rail. Overall, the passenger demand is more sensitive to the

44

Challenging conditions than the freight demand. By 2050, the difference between Challenging and

Baseline reaches 32.7% for passenger and 17.5% (almost half) for freight

Table 4.3 Comparison of Challenging to Baseline Scenario (intra-EU27)

Scenario 1: Challenging

Scenario Output Comparison to Baseline (%)

p.a.

Growth

2010 2020 2030 2040 2050 2020 2030 2040 2050

2010-

2050

Rail Freight (btkm) 443 457 583 657 732 -9.1% 8.0% 12.5% 15.6% 1.3%

Rail Passenger (bpkm) 409 442 487 523 558 0.7% 3.8% 1.9% -0.5% 0.8%

Passenger Cars (bpkm) 4725 5703 6885 7953 9052 4.1% 11.1% 17.3% 22.1% 1.6%

Trucks (btkm) 1878 2321 2662 3024 3393 6.9% 8.5% 13.8% 17.9% 1.5%

Aviation intra-EU27 (bpkm) 558 867 1370 2117 3350 16.8% 36.6% 60.1% 87.0% 4.6%

PKM (bpkm) 5692 7012 8742 10593 12960 5.3% 14.0% 23.0% 32.7% 2.1%

TKM (btkm) 2321 2778 3245 3681 4125 3.9% 8.4% 13.6% 17.5% 1.5%

Complementary demand

Aviation intercontinental (bpkm) 814 1517 3230 5907 11191 20.2% 38.8% 60.8% 86.0% 6.8%

Air freight intra-EU27 (btkm) 1 2 2 3 4 3.5% 13.2% 23.8% 35.5% 3.0%

Air freight intercontinental (btkm) 18 30 51 78 121 7.4% 15.2% 23.2% 31.2% 4.8%

Maritime (btkm) 24258 26986 31462 36947 45065 1.9% 5.0% 9.0% 14.1% 1.6%

Intercontinental aviation demonstrates the highest average growth rate (6.8% p.a.), followed by air

freight demand (intra-EU27 3.0% p.a. and intercontinental 4.8% p.a.) and, finally, maritime demand

(1.6% p.a.). These rates remain the highest within the other scenarios. Compared to the baseline, the

main discrepancy is in the intercontinental passenger aviation with a difference of 86%. This

primarily reflects the relatively high sensitivity of aviation demand to GDP growth assumed within

AIM.

Favourable scenario results

The favourable scenario simulates the lowest growth (in terms of GDP) and the highest average oil

prices. The oil prices directly influence the transport costs, which are in this case the highest for all

scenarios and all modes of transport. Therefore it is expected that the total demand is lower than the

baseline case.

Figure 4.10 below shows modal split for the years 2010, 2030 and 2050. For passenger demand one

can observe the obvious dominance of the road mode, maintaining almost 80% mode share in 2050.

45

Here, rail transport behaves similarly to the baseline scenario, maintaining 6% of the total demand in

2050. The shift to aviation is smaller here than in other scenarios (3% less than the baseline in the

year 2050) in line with the cost and GDP assumptions. As in the challenging scenario, there are

limited changes in the modal split. The favourable scenario is affected more in terms of passenger

split and in the total of the demand values (both passenger and freight).

Passenger

Split:

Freight

Split:

Figure 4.10 Modal splits for the favourable scenario (intra-EU27)

Table 4.4 clearly depicts the differences in the demand volumes compared to the baseline. In all

cases the comparison to the baseline is negative. As mentioned in figure 4.3, for the passenger

demand, besides aviation, the growth rates do not fluctuate considerably around the annual average.

In the case of aviation, the growth increases from 0.5%p.a. to 2%p.a. (for the decades of 2010-2020

and 2020-2030) and then fluctuates around this rate. This is likely a response to a couple of aviation-

specific trend discontinuities: the introduction of the replacement aircraft technology in 2025,

reducing fuel costs, and the addition of aviation to the EU emissions trading scheme in 2012. Aviation

demand demonstrates the highest growth. For the other passenger modes growth does not exceed

1% (annually).

In the favourable scenario, the rail freight has a negative growth for the first decade. Nonetheless the

total demand for freight tonne-km has a slightly increasing trend due to the higher growth of road

freight transport. In year 2030, the situation is reversed with the rail freight having a negative

growth, inducing also the decrease in total demand. After 2030 the freight demand follows a positive

growth. In total, by 2050, the total tonne-km is increased by 7.6%.

46

In other demand modes, the results remain modest, with growth rates not exceeding 5% p.a. for

intercontinental passenger and freight aviation demand, and 2% p.a. in all other cases. Compared to

the baseline, the results as expected decline in significantly in all modes.

Table 4.4 Comparison of Favourable to Baseline Scenario (intra-EU27)

Scenario 2: Favourable

Scenario Output Comparison to Baseline (%)

p.a.

Growth

2010 2020 2030 2040 2050 2020 2030 2040 2050

2010-

2050

Rail Freight (btkm) 443 391 387 416 447 -22.3% -28.3% -28.8% -29.4% 0.0%

Rail Passenger (bpkm) 409 417 424 438 449 -5.0% -9.6% -14.6% -20.0% 0.2%

Passenger Cars (bpkm) 4725 4966 5314 5507 5742 -9.3% -14.3% -18.8% -22.6% 0.5%

Trucks (btkm) 1878 1928 1994 2024 2051 -11.2% -18.7% -23.8% -28.7% 0.2%

Aviation intra-EU27

(bpkm) 558 589 718 861 1082 -20.6% -28.4% -34.9% -39.6% 1.7%

PKM (bpkm) 5692 5972 6456 6806 7273 -10.3% -15.8% -21.0% -25.5% 0.6%

TKM (btkm) 2321 2319 2381 2440 2498 -13.3% -20.5% -24.7% -28.9% 0.2%

Complementary demand

Aviation intercontinental

(bpkm) 801 1086 1862 2877 4710 -16.9% -20.0% -21.7% -21.7% 4.5%

Air freight intra-EU27

(btkm) 1 2 2 2 2 -16.3% -26.0% -34.6% -42.1% 0.8%

Air freight

intercontinental (btkm) 18 26 40 57 83 -5.3% -8.2% -9.8% -10.4% 3.8%

Maritime (btkm) 24258 26127 29161 32592 37604 -1.3% -2.7% -3.8% -4.8% 1.1%

4.5 Conclusions

The TOSCA scenarios provide a set of self-consistent projections of exogenous factors which have an

impact on EU27 transportation demand. Policy measures are excluded from these scenarios, as they

are considered separately in TOSCA WP7. Following an extensive literature review of existing

scenarios, GDP growth and oil prices were identified as the most important uncertain driving forces

for changes in demand for transport, and the carbon intensity of electricity generation as a further

important and uncertain factor affecting future transport emissions. Trends in these variables were

projected for a Baseline scenario, a Challenging scenario (representing a future in which it will be

particularly hard to reduce transport emissions) and a Favourable scenario (representing a future in

which emissions reductions from transport will be relatively easy). These scenarios are designed to

47

cover the full range of likely variation in future EU27 transportation emissions. They were used as

input to demand modelling using existing transport demand models such as Transtools and AIM.

Based on the output of these models we conclude that:

• Demand for transportation increases in all scenarios, even in the Favourable scenario. The

projected intra-EU27 passenger-kilometres travelled in 2050 varies between around 13000

billon (in the Challenging scenario) and around 7000 billion (in the Favourable scenario), in

comparison to a year-2010 value of 5692 billion. Similarly, freight tonne-kilometres travelled

in 2050 varies between 4000 billion and 2500 billion , in comparison to a year-2010 value of

2321 billion.

• The variation around the baseline demand (passenger and freight) in the challenging (+33%,

+18%) and favourable (-26%, -29%) scenarios seems plausible and wide enough for their

purpose as exogenous scenarios in TOSCA.

• In all scenarios the modal shares of freight demand (intra-EU27) per mode are similar (82%

for road and 18% for rail, excluding maritime and air freight demand). Changes in the input

assumptions as used in TOSCA do not have a strong effect on the output for the freight

model in Transtools.

• The modal shares of passenger demand (intra-EU27) show greater differences across

scenarios, with road transport remaining the dominant transport mode. The greatest trend

change concerns the Challenging scenario which shows a considerable stronger growth of

the modal split in aviation compared to the Baseline (26% of the total demand in 2050, while

the Baseline grows up to 18%).

Table 4.5 provides a numerical description of the last two findings.

The scenarios and demand projections generated in this report are used to estimate

technology uptake and transportation emissions in the second TOSCA WP6 Report. The

impact of changes in policy on demand emissions and technology utilised is then considered

in the final report of TOSCA WP7.

Table 4.5 Modal split in pkm (road, rail and aviation) and tkm (road and rail) by scenario in 2050

2050 Modal Split (intra-EU27) Road Rail Aviation

TKM

Baseline 82% 18% Challenging 82% 18% Favourable 82% 18%

PKM

Baseline 76% 6% 18% Challenging 70% 4% 26% Favourable 79% 6% 15%

48

References

AEO, 2009. American Energy Outlook, with Projections to 2030. Energy Information Administration

Office of Integrated Analysis and Forecasting, U.S. Department of Energy, Washington, DC.

<www.eia.doe.gov/oiaf/aeo/>

Airbus, 2009. “Global Market Forecast 2009-2028,” Toulouse: Airbus Industrie.

http://www.airbus.com/en/myairbus/global_market_forecast.html

Astra, 2000. Schade, W., Martino, A., Roda, M. Assessment of Transport Strategies, Milano, 2000.

Brock Blomberg, S., Hess, G. D., & Orphanides, A., 2004. “The macroeconomic consequences of

terrorism”. Journay of Monetary Economics, 51, 1007-1032.

Boeing, 2010. “Current Market Outlook 2010-2029”. Seattle: The Boeing Company.

http://www.boeing.com/commercial/cmo/

Boeing, 2004. “World Air Cargo Forecast 2004-2023”. Seattle: The Boeing Company, 2004.

Buxton, I. L., 1985. “Fuel costs and their relationship with capital and operating costs”. Maritime

Policy and Management, 12, 47-54.

DECC, 2010. Updated Energy and Emissions Projections. Department of Energy and Climate Change.

UK. June 2010.

Dray, L., Evans, A. & Schäfer, A., 2010a. "The Impact of Economic Emissions Mitigation Measures on

Global Aircraft Emissions", 10th ATIO Conference, Fort Worth, 13-15 September 2010,

http://www.arct.cam.ac.uk/aim/Documents/ATIO2010Dray.pdf

Dray, L. Evans, A., Reynolds, T., Schafer, A., 2010b. "Mitigation of Aviation Emissions of Carbon

Dioxide", Transportation Research Record, 2177, pp. 17-26.

DTU, 2010. Tras-Tools overview. Nielsen, O. A., Technical University of Denmark, Department of

Transport.

Duval, R., & de la Maisonneuve, C., 2010. “Long-run growth scenarios for the world economy”.

Journal of Policy Modeling 32, 64-80.

European Topic Centre (ETC) on Air and Climate Change (ACC), 2003. “Comparison of CO2 emission

factors for fuels used in Greenhouse Gas Inventories and consequences for monitoring and reporting

under the EC emissions trading scheme”, Technical paper, 2003.

Eurelectric. Power choices, Pathways to carbon-neutral electricity in Europe by 2050.

http://www.eurelectric.org/PublicDoc.asp?ID=63875

European Commission, 2011. White Paper: Roadmap to a Single European Transport Area – Towards

a competitive and resource efficient transport system. Com(2011), 144 final, Brussels.

European Commission, 2010a. EU Energy and Transport in Figures-Statistical Pocketbook 2010.

Publications Office of the European Union, Luxembourg.

49

European Commission, 2010b. EU Transport GHG: Routes to 2050? "Towards the decarbonization of

the eu‘s transport sector by 2050, Brussels.

European Commission, 2009a. “Directive 2008/101/EC of the European Parliament,” Official Journal

of the European Union, January 13, 2009, pp. 3-21.

European Commission, 2009b. Directive 2009/28/EC of the European Parliament and the Council of

23 April 2009 on the promotion of the use of energy from renewable sources and amending and

subsequently repealing Directives 2001/77/EC and 2003/30/EC, 5 June 2009, pp. 16-62.

European Commission, 2009c. EU Energy and Transport in Figures-Statistical Pocketbook 2009.

Publications Office of the European Union, Luxembourg.

European Commission, 2008a. Proposal for a Directive of the European Parliament and of the

Council on “the promotion of the use of energy from renewable sources”, 23 January 2008.

European Commission, 2008b. The EU climate and energy package,

http://ec.europa.eu/environment/climat/climate_action.htm

European Commission, 2007. Communication from the Commission to the European Parliament, the

Council, the Economic and Social Committee and the Committee of the Regions “Limiting Global

Climate Change to 2 degrees Celsius The way ahead for 2020 and beyond”, Brussels, 10 January

2007.

European Commission, 2006. Communication from the Commission “An EU Strategy for Biofuels”, 8

February 2006.

European Commission, 2005. Communication from the Commission “Biomass Action Plan”, 7

December 2005.

European Commission, 2003. “Directive 2003/30/EC of the European Parliament and of the Council

of 8 May 2003 on the promotion of the use of biofuels or other renewable fuels for transport”, 8 May

2003, pp. 42-46

European Commission, 2001. Communication from the Commission to the European Parliament, the

Council, the Economic and Social Committee and the Committee of the Regions on “alternative fuels

for road transportation and on a set of measures to promote the use of biofuels”, 7 November 2001.

E3M-Lab,GEM-E3. URL:

<http://www.e3mlab.ntua.gr/e3mlab/index.php?option=com_content&view=category&id=36%3Age

m-e3&Itemid=71&layout=default&lang=en>

E3M-Lab,PROMETHEUS. URL :

<http://www.e3mlab.ntua.gr/e3mlab/index.php?option=com_content&view=category&id=37%3Apr

ometheus&Itemid=72&layout=default&lang=en>

E3M-Lab, 2010. EU energy trends to 2030, Update 2009. Luxembourg. August 2010

Eurostat, 2010. Eurostat online database: transport statistics.

<http://epp.eurostat.ec.europa.eu/portal/page/portal/transport/data/database>.

50

Eyring, V., Köhler, H. W., van Aardanne, J. & Lauer, A., 2005a. “Emissions from international

shipping: 1. The last 50 years”. J. Geophys. Res., 110, D17305.

Eyring, V., Köhler, H. W., Lauer, A. & Lemper, B., 2005b. “Emissions from international shipping: 1.

Impact of future technologies on scenarios until 2050”. J. Geophys. Res., 110, D17306.

Glick, R., & Taylor, A. M., 2005. “Collateral Damage: Trade Disruption and the Economic Impact of

War”. Federal Reserve Bank of San Francisco Working Paper Series, 2005-11.

Hill, N., Morris, M. and Skinner, I. (2010). SULTAN: Development of an Illustrative Scenarios Tool for

Assessing Potential Impacts of Measures on EU Transport GHG. Task 9 Report VII produced as part of

contract ENV.C.3/SER/2008/0053 between European Commission Directorate-General Environment

and AEA Technology plc; <www.eutransportghg2050.eu>

Hummels, D., V. Lugovsky, and A. Skiba, 2007. “The Trade Reducing Effects of Market Power in

International Shipping”. NBER Working Paper 12914. Cambridge, MA: National Bureau of Economic

Research.

ICAO, 2008. ICAO Global Database. <http://icaodata.com>.

ICAO, 2007. “Outlook for Air Transport to the Year 2025,” ICAO Publications, Clr 313 AT.134,

September 2007.

International Energy Agency (IEA), 2009. Transport, energy and CO2, 2009, Paris.

International Energy Agency (IEA), 2008. Energy technology perspectives 2008, Paris.

International Energy Agency (IEA), 2010. Energy Balances pf OECD countries 2010, Paris.

iTREN, 2009. Integrated transport and energy baseline until 2030, Deliverables 1-4. 2009. <

http://www.tmleuven.be/project/itren2030/home.htm >

JRC, 2010. Transtools website in: <http://energy.jrc.ec.europa.eu/transtools/TT_model.html>

McKinsey & Company, 2009. Roads toward a low-carbon future: reducing CO2 emissions from

passenger vehicles in the global road transportation system.

NEA, 2005. Core Database Development for the European Transport policy Information System

(ETIS), Final Technical report to the European Commission.

Tetraplan/MCRIT, 2009. TRANSvisions: Mobility scenarios toward a post-carbon society: Transvisions

Task 2 Quantitative scenarios.

Manne, A. S., Richels, R. G., Edmonds, J. A., 2005. Market Exchange Rates or Purchasing Power

Parity: Does the Choice Make a Difference to the Climate Debate? Climatic Change, 71, 1-8.

Morrell, P., & Dray, L. M., 2009. Environmental Aspects of Fleet Turnover, Retirement and Life Cycle.

Final Report for the Omega Consortium, 2009.

OAG, 2005. OAG Global Schedule Database. <http://www.oagdata.com>.

Oum, T. H., Waters, W. G., Yong, J. S., 1990. “A Survey of Recent Estimates of Price Elasticities of

Demand for Transport”, Infrastructure and Urban Development Department, The World Bank,

Washington, DC.

51

Poles, 2006. Enerdata. Prospective Outlook on Long-term Energy Systems, A World Energy Model.

2006

PRIMES, 2010. E3M-Lab. PRIMES Model, E3Mlab of ICCS/NTUA, Version used for the 2010 Scenarios

for the European Commission including new sub-models. 2010

Reynolds, T., Barrett, S., Dray, L., Evans, A., Köhler, M., Vera-Morales, M., Schäfer, A., Wadud, Z.,

Britter, R., Hallam, H. & Hunsley, R., 2007. "Modelling Environmental & Economic Impacts of

Aviation: Introducing the Aviation Integrated Modelling Project", 7th ATIO Conference, Belfast, UK,

18-20 September 2007. <http://www.arct.cam.ac.uk/aim/Documents/ATIO2007_AIMIntro.pdf>.

Schäfer, A., & Victor, D., 2000. The future mobility of the world population. Transportation research

part A, 34(3), 171-205.

TENconnect, 2010. TRANS-TOOLS version 2; Model and Data Improvements, Final Report, DG TREN,

Brussels, 2010.

TRANSvisions, 2009a. Report on Transport Scenarios with a 20 and 40 Year Horizon, Final Report,

Tetraplan A/S, Project funded by DG TREN. March 2009

TRANSvisions, 2009b. TRANSvisions Task 2 Quantitative Scenarios, Mobility scenarios toward a post-

carbon society. MCRT. March 2009.

TREMOVE, 2007. Transport & Mobility Leuven . Service contract for the further development and

application of the transport and environmental TREMOVE model Lot 1 (Improvement of the data set

and model structure), Final Report, DG TREN, Brussels, 2007.

UN, 2009. World Population Prospects: the 2008 Revision Population Database.

http://esa.un.org/unpp/

UNCTAD, 2010. “Oil Prices and Maritime Freight Rates: An Empirical Investigation”. United Nations,

New York and Geneva.

Vera-Morales, M., Graham, W., Hall, C. & Schäfer, A., 2011. Techno-Economic Analysis of Aircraft:

TOSCA WP2 Final Report.

52

List of Figures

Figure 1.1 Final EU27 Energy Consumption by Sector ............................................................................ 8

Figure 1.2 Indexed evolution of CO2 Emissions by Sector, EU27 ............................................................ 8

Figure 1.3 Indexed evolution of CO2 emissions by transport mode, EU27 ............................................. 9

Figure 1.4 Indexed evolution of GDP, population and GHG emissions from freight and passenger

transport demand ................................................................................................................ 10

Figure 1.5 TOSCA Work Package structure ........................................................................................... 10

Figure 1.6 TOSCA Modelling approach .................................................................................................. 11

Figure 3.1 Assumptions for Oil prices in all scenarios ........................................................................... 23

Figure 3.2 Assumptions for GDP in all scenarios ................................................................................... 23

Figure 3.3 Assumptions for Carbon Intensity of electricity generation in all scenarios ........................ 24

Figure 4.1 Transtools modules .............................................................................................................. 31

Figure 4.2 Aviation Integrated Model structure ................................................................................... 34

Figure 4.3 Baseline scenario annual intra-EU27 demand growth by mode. ......................................... 38

Figure 4.4 Challenging scenario annual intra-EU27 demand growth by mode. ................................... 38

Figure 4.5 Favourable scenario annual intra-EU27 demand growth by mode. .................................... 39

Figure 4.6 Model results (intra-EU27): Total passenger-km ................................................................. 39

Figure 4.7 Model results (intra-EU27): Total tonne-km ........................................................................ 40

Figure 4.8 Modal splits for the baseline scenario (intra-EU27) ............................................................. 41

Figure 4.9 Modal splits for the challenging scenario (intra-EU27) ........................................................ 43

Figure 4.10 Modal splits for the favourable scenario (intra-EU27) ....................................................... 45

Figure 0.1 Oil prices including the Disruptive Scenario (the three stages are apparent) ..................... 54

Figure 0.2 GDP values including the Disruptive Scenario (the three stages are apparent) .................. 55

Figure 0.3 Carbon Intensity values including the Disruptive Scenario (same as the Baseline) ............. 55

Figure 0.4 Evolution of pkm in scenarios and sensitivity case .............................................................. 58

Figure 0.5 Evolution of tkm in scenarios and sensitivity case ............................................................... 59

53

Annex A: Sensitivity Analysis - Disruptive Scenario

Assumptions

Future scenarios tend to assume smooth trends in projected scenario values over time. However,

effects may arise from fluctuations in scenario values which would not be predicted in a scenario

with the same average growth rate. For example, volatility in fuel prices affects risk perception in

user and policymaker decision making. A peak in fuel prices may result in technology investment with

benefits that are observed even after the fuel price has decreased. Conversely, it may result in a

decrease in demand and an oversupply in the vehicle fleet, delaying the penetration of new vehicle

technology. In this section we define a Disruptive scenario which, studies the occurrence of an

extreme event having a disruptive and challenging impact on transport GHG emissions. This scenario

serves as a sensitivity analysis in the TOSCA scenario building framework. Extreme events may have a

catalyst effect and structurally change the transport system or economy/society as a whole. Between

2000 and 2010, the world has witnessed several extreme events having an impact specifically on the

transport sector. The SARS outbreak and the 9/11 attack in New York affected the air transport

sector in particular. However, a couple of years after 9/11 the demand for air transport had returned

to the pre-9/11 growth rates. The oil crises in the 1970s led to structural changes in many countries.

Switzerland, for example, structurally reduced its dependence on oil as a primary energy source. The

2011 nuclear crisis after the earth quake and the following Tsunami in Japan highlights how the

public opinion towards nuclear energy may change drastically. As nuclear power is a zero carbon

energy source, changing attitudes and the abandoning of nuclear programmes may lead to more

coal-fired electricity generation and resulting carbon emissions.

For the Disruptive scenario, assumptions regarding the three main uncertain and relevant scenario

variables are equal to those in the baseline scenario until 2020. After this point, the following

assumptions are made:

• Around the year 2020 an extreme event will take place, with a mixture of short-term and

continuous effects afterwards.

• The impacts of this event are similar to those of a major terrorist attack or highly infectious

disease outbreak.

• Transport modes/locations that appear most vulnerable to this continuous threat are (mass)

public transport, air transport and transport infrastructure hubs (main railway stations and

airports).

• This will result in more decentralized land use and transport planning and a modal shift of

passenger transport from (mass) public transport towards more individual modes of

transport.

• These broad impacts are specifically implemented by an oil price peak in 2021 (US$ >300),

longer waiting times and higher ticket prices for aviation and rail due to intensified security

procedures, a lower urbanisation rate than the baseline and a short-term decrease in GDP.

54

There are three stages following the event: the first phase of regression lasts for around two years

and dramatically affects the European economy. In this period the oil price triples within one year

and maintains its high price for the following year as well. GDP in parallel to the oil price decreases by

2.7% in these two years (and 4% in the first year only). The second phase, from 2022 to 2030 is the

resuscitation period, where the economic conditions slowly return to the Baseline trends. From 2030

and onwards, the third phase of regularity, this GDP growth rate is similar to that in the Baseline, but

the urbanisation rate is lower. These trends are broadly based on analyses of historical disruptive

events and the economic impacts of wars and terrorist attacks in Glick & Taylor (2005) and Brock

Blomberg et al. (2004).

A better depiction of the Disruptive scenario can be found in Figures 0.1-0.3 below.

0

50

100

150

200

250

300

350

1850 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050

Oil

Pric

es U

SD'0

8 pe

r bar

rel

BP Historic Prices Scenario 0: 'Baseline' Scenario1: 'Challenging'

Scenario 2: 'Favourable' Scenario 3: 'Disruptive'

Figure 0.1 Oil prices including the Disruptive Scenario

55

0

5000

10000

15000

20000

25000

30000

35000

40000

1990 2000 2010 2020 2030 2040 2050

EU27

GDP

in b

illio

n EU

R'09

Eurostat Scenario 0: 'Baseline'

Scenario 1: 'Challenging' Scenario 2: 'Favourable'

Scenario 3: 'Disruptive'

Figure 0.2 GDP values including the Disruptive Scenario

0

200

400

600

800

1000

1200

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Car

bon

Inte

nsity

g C

O 2 /

kWh

World energy balances (IEA, 2010) and Emission Factors (ETC/ACC, 2003)Scenario 0: 'Baseline' Scenario1: 'Challenging'Scenario 2: 'Favourable' Scenario 3: 'Disruptive'

Figure 0.3 Carbon Intensity values including the Disruptive Scenario (same as the Baseline)

56

A major additional assumption is that travel costs and times9

will double for rail and air passengers

engaged for security requirements. This very large increase is applied partly as a proxy to simulate

the demand-reducing effects of the 2020 event itself. In combination with fuel price increases, this

leads to very big jumps in the travelling cost by air and rail (as a passenger) and consequent large

decreases in demand. One further assumption in the Disruptive scenario is the urbanisation rate,

which differs from all the other scenarios. For the first three scenarios, the urbanisation rate

increases by 1% per annum from 2020. This is not the case for the Disruptive scenario. Here, de-

urbanisation takes place as the population moves away from densely-populated areas that are

perceived as being high-risk. By 2050, the EU27 population living in urban areas has decreased from

380 million in 2020 to 282 million. A summary of assumptions for the main uncertain and relevant

variables is given in Table 0.1.

Table 0.1 Sensitivity Assumptions (Disruptive Scenario)

Sensitivity Analysis

(Disruptive Scenario)

2010 2020 2021 2025 2030 2040 2050 Growth Rate

p.a. (2010 –

2050)

Oil Price ($’08 per bbl) 76 102 311 218 118 136 157 1.8%

GDP (billion €’09) 12492 15267 14661 15439 17372 21999 25965 1.8%

Carbon Intensity

Electricity Generation

(gCO2/kWh)

380 320 315 294 270 227 191 -1.7%

Sensitivity analysis results

The reason that a disruptive scenario is defined in TOSCA is so that the modelling can be repeated

looking at the impact of substantial input changes which are not linear over time (particularly

concerning fuel prices and GDP). Linear variation in input variables is a common scenario assumption.

As discussed above, however, there are many outcomes which may arise from volatility in transport

drivers which may not be observed if smooth trends corresponding to the same average change are

imposed instead. The abnormality due to the disruptive event occurs in 2020 and its effects were

simulated in Transtools for the years 2021, 2025 and 2030. In year 2030, it is expected that the

immediate effects of the disruptive effect will have faded out, returning back to the baseline scenario

apart from long-term differences in public transport costs and urbanisation. Year 2020 was chosen

for this event as it is preferable to study the effects over a relatively long time period.

9 For intercontinental air transport, journey time is increased by only 25% as the total journey time is much

greater.

57

Table 0.2 Disruptive scenario results

Scenario 2: Favourable

Scenario Output (in pkm/ tkm & Modal split) p. a. Growth

2010 2020 2021 2025 2030 2040 2050

2010 –

2050

Rail Freight (btkm)

4725

(83%)

5477

(82%)

4596

(89%)

5101

(89%)

6829

(90%)

7768

(90%)

8809

(90%)

1.6%

0.2%

Rail Passenger (bpkm)

409

(7%)

439

(7%)

295

(6%)

290

(5%)

297

(4%)

272

(3%)

219

(2%)

-1.5%

-3.1%

Passenger Cars (bpkm)

558

(10%)

742

(11%)

248

(5%)

314

(6%)

458

(6%)

552

(6%)

719

(7%)

0.6%

-0.9%

Trucks (btkm)

443

(19%)

503

(19%)

604

(28%)

575

(25%)

538

(18%)

597

(18%)

648

(18%)

1.0%

-0.1%

Aviation intra-EU27 (bpkm)

1878

(81%)

2172

(81%)

1577

(72%)

1762

(75%)

2398

(82%)

2664

(82%)

2901

(82%)

1.1%

0.0%

PKM (bpkm) 5692 6658 5139 5705 7584 8592 9747 1.4%

TKM (btkm) 2321 2675 2181 2337 2936 3261 3549 1.1%

Complementary demand

Aviation intercontinental (bpkm) 801 1306 461 674 1210 1886 3101 3.4%

Air freight intra-EU27 (btkm) 1 2 1 1 2 2 3 2.8%

Air freight intercontinental (btkm) 18 24 11 19 44 62 91 4.1%

Maritime (btkm) 24258 26479 24662 26399 29891 33733 39222 1.2%

The results from the sensitivity analysis are shown in Table 0.2. For the years after the disruptive

effect both passenger and transport demand are significantly influenced in terms of modal split and

in total amount of pkm/ tkm. The lowest values are indicated for the year after the event (2021).

Passenger demand is slightly more affected than the freight demand. Nevertheless, both show a

positive average annual growth for the 2010-2050 time-period.

For the passenger demand, each mode behaves differently. Road demand is recuperating after 2021

and by 2030 it already exceeds the 2020 levels. This is in part due to the impact of de-urbanisation

and mode shift due to high rail and air costs. Aviation demand, on the other hand, is affected by the

assumed long-term price increases and even though it increases between 2021 and 2030, it does not

return to year-2020 levels. Finally, the rail demand demonstrates a negative slope until 2050. This is a

response to the assumed cost increases. The modal split is also affected. The rail percentage is

almost eliminated by 2050 with only 2% of the total. Aviation also decreases in mode share to 2050.

This increases the road share which reaches 90% by 2050.

Freight demand also changes radically. The road freight follows the trend of road passenger demand

and recuperates by 2030, demonstrating the highest per annum growth. This is again in part due to

58

de-urbanisation. Rail freight behaves differently. Contrary to all other modes, it increases in 2021 and

then starts dropping until 2030. By 2050, rail freight has returned to baseline growth rates. The

modal split also changes for the years 2021 and 2025, indicating a shift to rail. This is explained

through the interplay between the oil price increase and the GDP decline. The oil price in 2021 is very

high, making the price of transporting freight by road go up (more so than for rail, where a smaller

proportion of costs are fuel), so there is a shift to rail freight. GDP has also gone down, leading to a

decline in total tkm, but the shift to rail is big enough that rail sees an increase in total. By 2025, oil

prices have decreased significantly from their peak, but the GDP reduction from the baseline is

similar to that in 2021. Therefore total demand is still depressed, but there is less incentive to switch

to rail transport, so some of the 2025 demand shifts back to road. The other modes display similar

effects for the years 2021 and 2025. An overview of results is given in Figures 0.4 and 0.5.

Passenger km for all scenarios

0

2000

4000

6000

8000

10000

12000

14000

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Bill

ion

pass

enge

r-km

EC Pocketbook 2010 Scenario 0: BaselineScenario 1: Challenging Scenario 2: FavourableSenisitivity Analysis (Disruptive Scenario)

Figure 0.4 Evolution of pkm in scenarios and sensitivity case

59

Tonne km for all scenarios

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Billi

on to

nne-

km

Scenario 0: Baseline EC Pocketbook 2010Scenario 1: Challenging Scenario 2: FavourableSenisitivity Analysis (Disruptive Scenario)

Figure 0.5 Evolution of tkm in scenarios and sensitivity case

The effects of the disruptive event are obvious also for the other transport modes. For the years

2021 and 2025 all modes decline, especially intercontinental aviation (both passenger and freight).

Maritime transport is the least affected.

Conclusions

Based on this disruptive scenario exercise we conclude that it is generally difficult to reliably estimate

the impact of extreme events because:

• The timing of the disruptive event is arbitrary

• The number of the disruptive events is arbitrary

• Existing models are not suitable to predict extreme changes. Consumer preference dynamics

are often not covered by transport models like Transtools and TREMOVE. The underlying

elasticities in these models are often based on existing (or historical) price elasticities or

utility functions. These are only valid for moderate changes from this baseline reference

situation.

• The existing models cannot cope with memory effects after a disruptive event.

• Therefore this scenario is used only as a sensitivity test, rather than as one of the main

TOSCA scenarios.