INTERNATIONAL ENERGY AGENCY
TRANSPORTATION PROJECTIONS
IN OECD REGIONS
Detailed Report
May 2002
Michael Landwehr
Céline Marie-Lilliu
FOREWORD
Following the publication of the 2000 edition of World Energy Outlook, this report presents
additional details in IEA’s projected developments of energy use in the OECD from now to
year 2020, with a special focus on the transport sector.
A new methodology has been developed for this OECD Transportation Energy Outlook,
featuring a “bottom-up” analysis that incorporates explicit projections of transport activity,
structure, and energy intensity. It retains the basic approach used in the World Energy
Outlook, however, in that it does not try to predict the future, but to identify and analyse key
factors in transport energy consumption over the next two decades. It also provides a
quantification of the impact of a selection of transport energy policies in the context of the
World Energy Outlook projections. Policies to improve the fuel intensity of passenger
vehicles and the impact of the introduction of advanced vehicles and alternative fuels are
considered. A selection of measures that deal with demand are also examined in an attempt to
assess their impact.
Finally, the effect on transport energy demand of selected policies is presented for the OECD
area, pointing to the need for a package of measures to limit energy consumption and CO2
emissions.
This report is one of a growing series of International Energy Agency reports focusing on the
transport sector, identified by the IEA as the single most important sector accountable for
increasing oil demand and CO2 emissions in the OECD. The publication Saving Oil and
Reducing CO2 emissions in Transport Options and Strategies was released during 2001. It
identified in a more systematic way the potential of different strategies and options to improve
energy efficiency, reduce oil use and cut greenhouse gas emissions. This report focuses more
on providing a detailed structural assessment of potential future energy use in transport in
OECD countries, and estimates of how, and by how much, energy use may be reduced.
ACKNOWLEDGEMENTS
This study is the result of a collaborative work between two divisions of the Office of Energy
Efficiency, Technology and R&D and the Long-Term Office of the IEA: Michael Landwehr,
from the Energy Technology Policy Division (ETPD), designed the alternative transportation
cases, and drafted most of the material; Céline Marie-Lilliu, also from the ETPD, prepared the
base case model and was responsible for the preparation of the manuscript, and Laura Cozzi,
from the Economic Analysis Division, made important contributions throughout this analysis.
This work has been jointly supervised by Carmen Difiglio, Head of the Energy Technology
Policy Division, and Fatih Birol, Head of the Economic Analysis Division, that is responsible
for the publication of the World Energy Outlook.
The study also benefited from comments and suggestions of other colleagues from the IEA,
the European Conference of Ministers of Transport (ECMT) and the OECD: colleagues: Lew
Fulton, Lee Schipper, Clas-Otto Wene, François Cattier, Deborah White, Stephen Perkins and
Peter Wiederkehr.
TABLE OF CONTENTS
FOREWORD ............................................................................................................................2
ACKNOWLEDGEMENTS .....................................................................................................3
TABLE OF CONTENTS .........................................................................................................4
LIST OF ILLUSTRATIONS...................................................................................................6
EXECUTIVE SUMMARY ......................................................................................................9
CHAPTER 1. THE CONTEXT ............................................................................................16
ENERGY AND TRANSPORTATION .............................................................................................16
FROM ENERGY USE INDICATORS TO FUTURE ENERGY PROJECTIONS ........................................19
TERMINOLOGY, CONCEPTS AND MACRO-ECONOMIC ASSUMPTIONS ........................................21
CHAPTER 2. WHAT MAKES TRANSPORT ENERGY USE GROW?.........................25
A TRANSPORT SCENARIO FOR THE OECD TO 2020 ................................................................25
PASSENGER TRANSPORT ACTIVITY .........................................................................................27
FREIGHT TRANSPORT ACTIVITY ..............................................................................................32
REGIONAL TRANSPORT TRENDS ..............................................................................................37
HOW MUCH ENERGY TO MOVE A PASSENGER OR A TONNE OF GOODS?....................................46
SUMMING UP: TRANSPORT ENERGY PROJECTIONS FOR THE OECD .........................................53
CO2 EMISSION TRENDS IN THE BASELINE CASE .......................................................................60
CHAPTER 3. THE IMPACT OF SELECTED TRANSPORT ENERGY POLICIES ...63
POLICY PORTFOLIOS IN WESTERN EUROPE, JAPAN AND NORTH AMERICA.............................63
IMPROVING THE FUEL INTENSITY IN PASSENGER VEHICLES.....................................................69
MARKET INTRODUCTION OF ADVANCED VEHICLES AND FUELS ..............................................79
TRANSPORT DEMAND-SIDE POLICIES ......................................................................................90
FUEL TAXATION ...................................................................................................................101
CHAPTER 4. DEPARTURE FROM THE BASE TRENDS: WHAT DIFFERENCE DO
CURRENT AND NEAR-TERM TRANSPORT ENERGY POLICIES MAKE?..........104
THE COMBINED EFFECT OF THE REGIONAL POLICY PORTFOLIOS ...........................................104
TRANSPORT ENERGY TRENDS WITH “ADDITIONAL POLICIES” ...............................................105
CO2 EMISSION TRENDS WITH ADDITIONAL POLICIES .............................................................110
KEY FINDINGS – TRANSPORT ENERGY AND CO2 EMISSIONS TRENDS WITH ADDITIONAL
POLICIES ...............................................................................................................................113
REFERENCES .....................................................................................................................115
LITERATURE .........................................................................................................................115
LIST OF DATA SOURCES ........................................................................................................124
APPENDIX 1 DESCRIPTION OF THE FRAMEWORK ...............................................127
OBJECTIVES ..........................................................................................................................127
GENERAL DESCRIPTION OF THE FRAMEWORK .......................................................................128
GEOGRAPHIC BREAKDOWN...................................................................................................128
TRANSPORTATION MODE CONSIDERED .................................................................................128
THE GENERAL FRAMEWORK COMPONENT .............................................................................129
THE ESTIMATION OF ALTERNATIVE VEHICLE AND ALTERNATIVE FUEL INTRODUCTION ........134
A COMPONENT OF DEMAND POLICY IMPACT ESTIMATION .....................................................134
APPENDIX 2: MODELLING VEHICLE FLEET TURNOVER ...................................136
QUALITATIVE DESCRIPTION .................................................................................................136
MATHEMATICAL FORMULATION ..........................................................................................138
APPENDIX 3: TABLES FOR BASELINE AND SCENARIO PROJECTIONS...........141
LIST OF ILLUSTRATIONS
Executive Summary
Figure ES.1: OECD baseline energy demand by mode............................................................10
Figure ES.2: Driving factors to baseline energy demand growth.............................................10
Figure ES.3: Energy demand and policy impact in North America, Western Europe and Japan
(combined) .........................................................................................................................13
Figure ES.4: Incremental transport energy demand (1997-2020) by mode in the different
policy cases ........................................................................................................................14
Figure ES.5: Driving factors to demand growth in the different policy scenarios...................14
Chapter 1
Figure 1.1: Sectoral CO2 trends in Annex II countries (IEA 2000b) .......................................17
Figure 1.2: Accounting model framework ...............................................................................20
Table 1.1: Transport modes ......................................................................................................21
Table 1.2: Population growth and assumptions........................................................................23
Table 1.3: Economic growth and assumptions (Gross Domestic Product) ..............................24
Table 1.4: Fuel price assumptions ............................................................................................24
Chapter 2
Box 2.1: Factors affecting transport demand (Hecq 1995) ......................................................26
Figure 2.1: Passenger activity vs. income per capita (past trends 1970-1997, projections 1998-
2020) ..................................................................................................................................27
Table 2.1: Key parameters and projections of passenger transport activity.............................28
Table 2.2: Average price elasticity of modal activity...............................................................28
Table 2.3: Overview of aggregate growth trends and projections in passenger transport
activity................................................................................................................................29
Figure 2.2: Passenger vehicle ownership past trends and projections......................................31
Figure 2.3: Projections of aviation passenger activity..............................................................32
Figure 2.4: Freight activity vs. GDP.........................................................................................33
Table 2.4 Key parameters and projections of the freight transport activity .............................34
Table 2.5: Average fuel price elasticity of modal activity .......................................................34
Table 2.6: Overview of aggregate growth trends and projections in freight transport activity 35
Figure 2.5: Transport trends and projections –Western Europe...............................................37
Table 2.7: Transport activity growth trends and projections in Western Europe.....................38
Figure 2.6: Transport trends and projections –Central Europe and Turkey .............................39
Table 2.8: Transport activity growth trends and projections in Central Europe and Turkey ...40
Figure 2.7: Transport trends and projections –Japan................................................................40
Table 2.9: Transport activity growth trends and projections in Japan......................................41
Figure 2.8: Transport trends and projections –Australia and New Zealand.............................41
Table 2.10: Transport activity growth trends and projections in Australia and New Zealand.42
Figure 2.9: Transport trends and projections –North America.................................................44
Table 2.11: Transport activity growth trends and projections in North America.....................44
Figure 2.10: Fuel intensities of different modes over time (OECD average)...........................47
Table 2.12 Influences on modal energy intensity (MJ/passenger-km or tonne-km) ................48
Figure 2.11: Stylised behaviour of the energy intensity of new vehicles in a transport mode
and the corresponding fleet in the past ..............................................................................49
Table 2.13: Energy intensity trends and projections ................................................................50
Table 2.14: Average price elasticity of modal energy intensities.............................................50
Figure 2.12: OECD transport energy consumption by regions ................................................54
Figure 2.13: OECD transport energy consumption by transport mode ....................................54
Table 2.15: Energy growth in five OECD regions ...................................................................55
Figure 2.14: OECD incremental transport energy demand by mode .......................................56
Figure 2.15: Factors influencing transport energy use .............................................................58
Table 2.16: CO2 emissions trends and projections ...................................................................61
Chapter 3
Table 3.1: CO2 policies assessed for Western Europe..............................................................67
Table 3.2: CO2 policies assessed for Japan ..............................................................................68
Table 3.3: CO2 policies assessed for North America ...............................................................69
Table 3.4: Key assumptions on the fuel intensity of new cars and personal light trucks.........70
Figure 3.1: Test fuel intensity of new cars ...............................................................................71
Figure 3.2: Comparing the fuel intensity of new cars and the vehicle fleet (EU-9 countries) .73
Box 3.1: Estimating the test/on-road gap .................................................................................74
Table 3.5: Impact of fuel intensity policies in passenger vehicles: Reduction of energy
consumption compared to the base case ............................................................................76
Figure 3.3: Impact of fuel intensity improvement in new cars on energy consumption from
passenger cars ....................................................................................................................77
Box 3.2: The “learning curve”..................................................................................................80
Figure 3.5: The contribution of advanced vehicle technologies to tightened fuel intensity
standards in the aggressive production ramp-up scenario .................................................83
Figure 3.6: Cost reduction scenarios for the aggressive production ramp-up ..........................84
Figure 3.7: Production expansion scenario for low GHG-fuel.................................................86
Figure 3.8: Cost-reduction scenario for low-Greenhouse gas fuel...........................................88
Box 3.3: Policies favouring transport demand restraint and modal shift (extracted from IEA
2001, ECMT/OECD 1995, and EC 1999a) .......................................................................90
Table 3.6: Assumed demand-side measures and effects ..........................................................92
Figure 3.9: Modal structure and assumption on the intra-modal split for Western Europe
(1997).................................................................................................................................93
Figure 3.10: Differentiation of fuel intensities, Western Europe (base case, 2020).................94
Figure 3.11: Modal structure and assumption on the intra-modal split for Japan (1997) ........97
Figure 3.12: Differentiation of fuel intensities, Japan (base case, 2020) .................................98
Table 3.7: The effects of a carbon tax on transport-energy demand and activity compared with
the base case, 2020...........................................................................................................102
Chapter 4
Figure 4.1: Energy demand scenarios including the impact of the regional policy portfolios
..........................................................................................................................................107
Table 4.1: Transport energy trends and policy impact in three OECD regions .....................108
Figure 4.2: Policy impact of the different scenarios (North America, Japan and Western
Europe combined)............................................................................................................108
Figure 4.3: Modal breakdown of incremental energy demand in the different scenarios
between 1997-2020 (North America, Japan and Western Europe combined).................109
Figure 4.4: Factors contributing to energy demand growth: Comparison among the different
scenarios...........................................................................................................................110
Figure 4.5: CO2 scenarios including the impact of the regional policy portfolios .................111
EXECUTIVE SUMMARY
Transport energy demand is the single most important factor contributing to increases in oil
demand in the OECD over the past twenty years, and it is likely to continue to be so for the
next twenty. Transport policies already in place in IEA countries will not significantly
contribute to CO2 reduction efforts in the short term, and the transport sector is threatening to
offset any serious CO2 emissions reduction efforts in other economic sectors through its
emission increases. Hence, transportation is becoming central to the energy policy agenda,
with regard to oil security as well as climate change.
This study was first initiated to supplement the IEA's World Energy Outlook for its 2000
edition (IEA 2000b), which gave energy projections to 2020 and also presented alternative
cases in transportation and electricity generator sectors. The present analysis focuses on the
transport energy forecast to 2020 for OECD countries, and has two distinct objectives:
• Identifying the factors that shape transport energy demand, including the role of activity
growth, modal shifts and fuel intensity changes.
• Assessing and quantitatively estimating the impact of different policies and measures for
CO2 emissions reduction in transport either recently enacted or under consideration.
In order to maintain the objectives of this study, the IEA has developed a new framework
capable of describing energy use for transportation and estimating the impact of the measures
of interest in an OECD context: time series of detailed energy use indicators have been
assembled to address different aspects of energy demand growth. Each measure considered is
characterised and its impact is estimated. The macro-economic assumptions are similar to
those presented in IEA’s World Energy Outlook and have already complemented the World
Energy Outlook 2000 edition.
This study also draws on another recent IEA book, Saving Oil and Reducing CO2 Emissions
in Transport: Options and Strategies (2001), which assesses a variety of potential measures
for reducing oil use and CO2 emissions in transport. In Chapter 3 hereafter focusing on policy
impacts, a number of estimates are drawn from that volume.
A base case for transport energy demand until 2020 without policy action and stable fuel
prices
In the base case, total transport energy demand in the OECD is expected to grow by 44%
between 1997 and 2020 to a total of 1,519 Mtoe compared to 1,057 Mtoe in 1997 (Figure
ES.1). With a stable share of petroleum products in transport energy demand (at 97%), the
implications for supply dependency and CO2 emissions are quite clear. Transport baseline
trends are alarming.
The additional energy demand corresponds to an average growth rate of 1.6% p.a. until 2020
compared to an average of 2.2% p.a. between 1970 and 1997. This means some slackening in
Executive Summary 9
growth rates quite sharply in Europe, Japan and Australia & New Zealand, much less in North
America and Central Europe – but the growth in energy demand in transportation is still
substantial.
Figure ES.1: OECD baseline energy demand by mode
Note: other includes passenger and freight rail, bus and navigation transport
Figure ES.2: Driving factors to baseline energy demand growth
Passenger transport Freight transport
Executive Summary 10
Separating different drivers to energy demand growth (Figure ES.2) allows to weigh the
relative influence of the factors playing a role in the increases of total energy demand for
passenger and freight: transport activity growth, modal shifts and fuel intensity
improvements.
Activity growth has been and will remain the primordial factor to transport energy demand
increases. For passenger transport, growth rates are lower than in the past. Minor increases in
car use and massive expansion of aviation activity (40% of passenger activity increases until
2020) will drive this growth. In freight, trucking dominates the growth. Air freight volume
will triple over the projection period to represent over 5% of tonne-km transported in 2020.
Modal shifts, i.e. shifts towards more energy-intensive modes, have increased energy demand
significantly for freight, less so for passenger transport.
In the absence of significant fuel price increases and policy intervention, lower energy
intensity improvements are expected in the future. This is most pronounced for cars and light
trucks, where new technology would boost performance instead of improving intensity if
efficiency policies are not strengthened. As in the past, aviation remains the mode with the
strongest average energy intensity improvements, but the rate of improvement is also less
important than in the past. On average, fuel intensity improvements in passenger transport
will be negligible. In the case of freight transport, the importance of fuel intensity
improvements is expected to diminish compared to what was observed in the past.
Under the assumption of relatively stable fuel prices and no additional policy intervention in
the base case, fuel intensity improvements will not curb energy demand growth from activity
growth and modal shifts.
Transport energy demand and CO2 emissions with the impact of enacted measures and
climate change policies likely to be in place
Since the mid-1990s, some measures to tackle transport energy growth and CO2 emissions
have been enacted and others are under consideration. This study evaluates the impact of such
policies for North America, Western Europe and Japan1 (Box ES.1) reflecting the varying
level of policy implementation and debate in each region. The set of policies evaluated is not
meant to be exhaustive but covers the major measures in each region. The impact of each
policy is analysed and estimated relative to the baseline trends.
1
To make the study manageable, only Western Europe, Japan and OECD North America received detailed analysis.
Executive Summary 11
Box ES.1: Transport energy demand and CO2 emissions reduction: policies evaluated
Enacted policies
Western Europe:
Voluntary Agreement of European Automobile Manufacturers Association (ACEA VA)
(140g CO2/km in 2008).
Japan:
Top-Runner legislation for cars and small trucks (14.8 km/l in 2010).
North America:
Corporate Average Fuel Economy Standard (CAFE) legislation for cars and light trucks2.
Additional policies – likely to be enacted over the coming years
All regions:
Fuel tax increases equivalent to US$ 95 per tonne of carbon for all fuels.
Western Europe:
Further increased commitment until 2020 under the Voluntary Agreement of the European
Association Automobile Manufacturers Association.
Demand restraint and demand shift policies: urban car restraint; expansion of urban public
transport; high-speed rail expansion; and electronic charging of trucks per tonne-km.
Japan:
Sharpened requirements for car and light truck fuel efficiency under the Top-Runner
Programme until 2020.
Demand restraint and demand shift policies: urban road pricing and other car restraint
measures; improvement of public transport; mandatory city logistic schemes for small
commercial vans and trucks; and expansion of high-speed rail service.
North America:
Stricter fuel economy standards for cars and light trucks from 2005.
Introduction of “low-carbon” fuels after 2010 with widened regulation of alternative-fuelled
vehicle shares in fleets (“fleet mandates”) and fuel tax incentives.
The evaluation of current and likely near-term policies brings sobering results. Including
important policies not yet enacted (but under active consideration), further strong increases in
energy demand until 2010 have to be expected. After 2010, a stabilisation of energy demand
at its 2010 level appears in reach. In total, enacted policies (ACEA Voluntary Agreement in
Europe and Top-Runner regulation in Japan) are estimated to reduce energy demand in 2020
by 39 Mtoe (which represent 3% of the total transport energy demand in 2020). All additional
policies evaluated (enacted and envisaged) could curb demand from the base trends by 213
Mtoe or 15% in 2020. Compared to 1997, this still means an increase in transport energy
demand by 21% or 211 Mtoe for North America, Western Europe and Japan combined
(Figure ES.3). If one had as an objective to achieve energy demand and CO2 emission levels
similar to 1990, much stronger policy action would be required, and the stabilisation would
not be achieved by 2010.
2
The CAFE impact is not evaluated per se in this study, since it was chosen to focus on policy elements that were enacted recently. CAFE is
considered as a strong element of the base case. For the impact of CAFE, see especially Greene 1998
Executive Summary 12
Figure ES.3: Energy demand and policy impact in North America, Western Europe
and Japan (combined)
Three transportation modes account for the vast majority of energy consumption: cars, trucks
and aviation. However, only the car (light-duty vehicle) sector is seriously tackled by the
enacted and expected additional policies, which will serve to curb somewhat its expected
energy demand increases. No net reductions below 1997 levels are achieved in 2010 or 2020.
Stringent energy efficiency standards for cars and light trucks across OECD countries could
bring down car and light truck fuel consumption close to 1997 levels by 2020. New
technologies for vehicles (hybrids, fuel cell vehicles) and the introduction of alternative fuels
could also play a role in 2020, but the time needed for gaining further technological
experience, significant market penetration and finally stockturnover will limit their impact.
This issue is explored in some detail in IEA’s transport publication, Saving Oil (IEA 2001).
Other measures in the areas of reducing transport demand and encouraging modal shifts that
we consider likely to be implemented do not appreciably change the picture. In general, they
have a minor effect on the trends and will need time to take effect.
For freight and aviation, the enacted measures analysed have almost no impact on future
energy demand and CO2 emissions in these sectors (see Figure ES.4). With the additional
policy measures considered here, the energy demand in 2020 would be 30-40% below the
2020 base case – a substantial reduction – but still 20% above the 1997 level.
Executive Summary 13
Figure ES.4: Growth in transport energy demand (1997-2020) by mode
in the different policy cases
The set of measures evaluated here mostly provide reductions in energy intensity, especially
for passenger transport (Figure ES.5). The measures achieve the average pace of energy
intensity reduction experienced between 1970 and 1997, but not more. In this perspective, the
implementation of these additional policies is just "keeping up", but not going beyond the
pace of historical energy intensity progress in transport. This historical rate of improvement
typically has only been compensating about 20-30% of energy demand increases from activity
and shifts towards more energy-intensive modes. Only in the case of passenger transport
would our bundle of measures bring fuel intensity reductions at a faster rate than in the past,
but still not large enough to fully offset expected increases in activity. Modal shifts and
activity growth are relatively unaffected by these policy bundles.
Figure ES.5: Driving factors to demand growth in the different policy scenarios
Passenger transport Freight transport
Executive Summary 14
These findings are based on our analysis of a selection of the most prominent policies
currently under discussion. There are many other policies that are available and that could
provide much greater reductions in energy use than we consider here. In our conservative
approach, the difficulties to implement stringent measures widely are reflected, as
experienced in the past (IEA 2000d). Other effective policies for road freight and aviation
growth still need to be developed and implemented. Many potential options and strategies are
detailed in IEA’s Saving Oil book (IEA 2001). A more thorough debate within and between
countries on developing subsequent measures to decouple transport activity growth from
economic growth is strongly needed.
Chapter 1: The context 15
Chapter 1. The context
Energy and transportation
Transportation has long been associated with environmental and other problems: these include
safety, air, water and noise pollution, competition for urban space. From a perspective of
energy consumption and CO2 emissions, longer-term transportation trends and projections
give rise to serious concerns on how to ensure future economic and environmental
sustainability. A few key figures (IEA 2000b) may serve as illustration:
• With past average annual growth rates of 2.1% p.a. (1971-1997) and 1.5% in the future
(1997-2020), transport energy demand is growing significantly more than average energy
consumption. The share of OECD transport in final energy demand has been steadily
rising from 24% in 1971 to 33% in 1997 and is projected to account for 37% in 2020.
• CO2 emission trends follow the energy trends closely. Transportation emissions
(excluding upstream emissions from oil production and refining) accounted for 19% of
total OECD CO2 emissions in 1971 and for 27% in 1997. In 2020, they are expected to
reach 31%. Since 1971, CO2 emissions from transportation in the OECD have risen by
73%. Until 2020, they are expected to rise again by another 40% over 1997 levels.
• Transportation accounts for an increasing share in the total oil consumption of the OECD,
up from 61% in 1971 to 66% in 1997 and is projected to reach 69% in 2020. It is
responsible for all future increases in oil demand in the OECD. Despite two oil crises and
recurring government efforts to diversify energy supply, transportation relies to 97% on
petroleum products – a situation which has not changed and is not foreseen to change until
2020 under current policy conditions.
Hence, transportation has a large influence on oil dependence and oil supply security.
Transportation trends are decisive for the overall CO2 emissions reduction efforts, threatening
to “eat-up” reductions achieved in other economic sectors. Transportation is becoming central
to the energy policy agenda.
Chapter 1: The context 16
Figure 1.1: Sectoral CO2 trends in Annex II3 countries (IEA 2000c)
5
4
Gigatonnes of CO2
3
2
1
0
1971 1974 1977 1980 1983 1986 1989 1992 1995 1998
Electricity, CHP, heat Other energy industries Transport
Industry Other sectors
In a way, transportation has “successfully”’ resisted energy supply constraints and energy
policy efforts over the past decades. Transportation has gained a reputation as being rigid (in-
elastic) and difficult to be influenced by public policies – not only from the energy
perspective. With transport being closely linked to economic activity and life-style, almost
any private or public decision has implications for transportation and indirectly for its energy
demand, and any measure to change the transportation dimension in turn influences economic
activity and life-style.
While CO2 emissions reduction policies in transportation appear to become embedded in a
general framework for transport policy reform (IEA 2000d), energy policy’s direct influence
is (still) fostering efficiency, since measures geared to influence modal mix and activity
growth are in the realm of transport policy. What do energy policies achieve in this
framework and what roles have the other factors and the respective policies to play? In order
to understand the respective policy roles in transportation, a relatively detailed description of
the transport sector is needed beyond aggregate energy demand trends.
3
Annex II: countries that were OECD Members in 1992, i.e. Australia, Austria, Belgium, Canada, Denmark, the European Union, Finland,
France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden,
Switzerland, Turkey, the United Kingdom of Great Britain and Northern Ireland, the United States of America
Chapter 1: The context 17
Box 1.1: The alternative transportation case in the World Energy Outlook
2000 edition
For IEA’s World Energy Outlook 2000 several new approaches and submodels were developed, one of which
was the transportation model used in the “alternative transportation case”. The present report develops in much
more detail the richness of findings of which WEO 2000 gives an abridged version. Both share the same study
framework, in particular macro-economic assumptions (economic growth, fuel prices, etc.). WEO 2000's
perspective is wider in terms of sector and regions considered: it comprises all the energy sector at a world-wide
level. It actually allows to embed the present study which is focused on OECD transportation in the wider energy
context of the other energy-consuming sectors as well as the world-wide context.
Yet, there are a few differences the careful reader should keep in mind:
• In order to fit the bottom-up transportation model into the framework of WEO’s world energy model
(WEM), the bottom results were scaled to aggregate energy demand projections in the WEM. Both models
were iterated in a “soft-linking” procedure until a small discrepancy in energy demand projections remained
(±2% in 2010 and 2020). Since in other parts of the WEM the aggregate transport energy projections were
used and a consistent data set was needed throughout the publication, the bottom-up model results were
finally scaled to this envelope. In the present study, the unscaled results are presented, implying that
quantitative figures differ slightly from those presented in WEO 2000.
• WEO’s main energy projection scenario (reference scenario) already includes the impact of policies enacted
by mid-2000, which makes it different from a baseline case without any specific policies (business as usual
or BAU). The WEO reference scenario corresponds to the case “with enacted policies” in chapter 4.
• WEO's transport sector include pipelines, which is not the case in this study. Therefore, the WEO reference
scenario and the scenario with enacted policies as defined here are not quite comparable.
Moreover, since WEO 2000 publication, a few refinements were added to the transportation submodel: (1) the
Eastern Europe and Australia/New Zealand regional models have been developed in similar detail as the other
regions, (2) the modelling of aviation activity growth and of its price responsiveness has been refined.
Analysing past trends in more detail may highlight where policy can find the right levers to
influence transport energy demand and CO2 emissions. It also teaches where efforts might
remain ineffective or might be eroded by counter-effects. Doing such an analysis on an
OECD-wide basis gives insights with regard to overall oil demand trends and allows to learn
from experiences in different OECD regions.
Two sets of questions are underlying to the present study:
• Which factors shape transport energy demand? What is the past and likely future role of
activity growth, modal shifts and fuel intensity changes?
• What will be the impact of different policy measures for CO2 reduction in transport either
recently enacted or under consideration? To what extent do they change the overall trends
in the future? Will they be more successful than past efforts?
This study is structured along these two focal areas. Chapter 2 develops a projection of
transport energy demand for the OECD. For each region it provides a base case built on a
modally disaggregated transportation activity outlook to 2020 complemented by detailed fuel
intensity projections. By comparing the past with the projections, future developments can be
identified that are expected to depart from past trends.
Chapter 1: The context 18
Chapter 3 looks at the policy impact of enacted and possible short-term policies. The choice
of policies examined is geared to reflect the policy preferences in different OECD regions. It
is not meant to look at all kinds of policies and their potential impact but rather tries to reflect
the reality of the slow implementation process occurring in this policy area.
Chapter 4, finally, puts together base trends with the estimated future policy impacts and puts
forward two scenarios – one including those policies which were enacted by 1998, the so-
called reference case, and one including likely near-term policies that appear to represent the
next stage of policy action towards CO2 emissions mitigation in the different regions, the
alternative case.
From energy use indicators to future energy projections
This transport outlook relies on two pillars:
• Time series of energy intensity and activity indicators for the different transportation
modes, which provide the historic database with data from 1970 to 1997;
• An accounting model, which computes estimates and aggregates projections of the modal
indicators up to 2020. It is enhanced by more specific models for specific purposes and
policy areas (e.g. fleetturnover modelling, demand-side modelling) .
In the respective chapters and in the appendices, the modelling approaches are further
detailed. At this stage, only a brief overview of the methodology is given.
IEA has been collecting similar time series, called energy efficiency and use indicators, going
back to 1970 for all end-use sectors for some IEA countries (IEA 1997a, IEA 1997b). In
transport, they allow to constitute energy use in each of the main transportation modes from
indicators of the physical activity (tonne-kilometres or passenger-kilometres, and vehicle-
kilometres) and the modal fuel intensity (average energy use per tonne-km or passenger-km).
These indicators have been frequently used in retrospect to distinguish the factors that shape
energy demand growth in the past, namely:
• The growth in transport activity.
• Changes in shares of different transportation modes (modal shift).
• Changes in fuel intensity, of which changes in technical efficiency are just a part.
These indicators are collected from diverse national or regional statistical sources on transport
and energy. They are then consolidated and allow to reconstitute total transport energy
demand as published in the official IEA statistics to a large extent. Still, some data gaps
remain and need to be filled with estimates and interpolations in order to cover the entire
OECD. The database used here typically includes a discrepancy of a few per cent to the IEA
energy statistics, largely sufficient for the modelling purposes here4.
These disaggregated indicators are the core and the memory of the modelling work in the
OECD. They allow to calibrate the model and to directly compare the projected changes to
4
Some caveats remain specifically for the Central and Eastern European countries and Turkey, and for aviation.
Chapter 1: The context 19
the past trends. In Appendix 3, time series of all major indicators used are given for the past
and the projection periods. The study focuses on the projections and analyses past trends
mostly for comparison5.
The projections are made for each transportation mode in the framework of an accounting
model developed for the purpose of this study: activity and fuel intensity indicators are
projected for each transportation mode using simple econometric techniques see Chapter 2 as
well as Appendix 1. In particular, the influence of fuel prices, rebound of activity and fleet
turnover modelling techniques are integrated in the projection method (see Figure 1.2). The
accounting framework allows to add specific analyses and submodels and has been adapted to
integrate:
• Fuel efficiency modelling of cars and light trucks to reflect regulatory pressure.
• Market introduction of new vehicles and fuels.
• A selection of demand-side policies.
Figure 1.2: Accounting model framework
Analysis of policies Analysis of policies
addressing demand growth to increase fuel efficiency
and modal shifts of new vehicles
Fuel price
Activity Energy Intensity Stock New vehicle
Base projections Rebound Base projections turnover fuel int.
effect
For cars and light trucks only
Fuel mix
Fuel demand
CO2 emissions Analysis of policies
to introduce (low-carbon) fuels
More details on the policy analyses and modelling are given in Chapter 2 and in Appendices 1
and 2. The approach is a convenient compromise between a more detailed modelling of the
transportation activity and fuel intensities as developed for narrower purposes (national
models or specific transportation modes) and the level of aggregation needed for the coverage
of five OECD regions, reducing data requirements and increasing transparency.
5
Detailed analyses of the same database focusing on past trends have been presented elsewhere for the most important OECD countries (IEA
1997a; Schipper and Marie-Lilliu, 1999a and 1999b).
Chapter 1: The context 20
Terminology, concepts and macro-economic assumptions
OECD regions
The study distinguishes five regions in the OECD:
The following two regions compose OECD Europe:
• Western Europe (European Union member states, Switzerland, Norway, Iceland).
• Central Europe and Turkey (Poland, the Czech Republic, Hungary and Turkey).
The following two regions compose OECD Pacific:
• Japan on the one side;
• Australia and New Zealand on the other.
OECD North America includes the United States and Canada.
In comparison with the regional breakdown available in the IEA's World Energy Outlook,
OECD Europe and OECD Pacific regions are disaggregated further, in order to reflect major
differences in the transport system and policy approaches. States with recent membership to
the OECD, such as Mexico and Korea, are not yet included, partly because of to the lack of
detailed transport data.
Transportation modes
The disaggregation of transportation modes used in this study depends primarily on the
availability of national/regional statistics (and hence indicators) for the different modes and is
not entirely homogeneous across the regions. Five or six modes are distinguished for
passenger transport, and four or five modes for freight. Light or small trucks can be separated
only in few regions (Table 1.1).
Table 1.1: Transportation modes
Modal
Passenger Freight
share
cars
light trucks for private heavier trucks
Road
use* small trucks and vans**
bus
100%
Rail passenger rail freight rail
Navigation freight navigation
intra-regional aviation
Aviation
international aviation freight aviation >100%
*Only for North America, Australia and New Zealand. For other regions, light trucks are included in “cars”.
**Only for North America and the Australia and New Zealand regions (light trucks in commercial use) and Japan (including
“Kei”-mini trucks), for other regions, included in heavier trucks (=trucks).
Chapter 1: The context 21
Some clarifications need to be given with regard to data quality and regional specifics of the
different modes:
• For North America and Japan, small/light trucks (below 2.5 tonnes) can be separated from
cars or trucks – though with some loss in data reliability, since their fuel consumption
cannot be easily separated from the other modes. Owing to their importance in the
respective regions, they are modelled separately.
• Bus transport includes scheduled bus services as well as chartered buses and school buses.
• Navigation freight transport only includes domestic or national transport, not trans-
boundary transport within one region.
• Aviation, often treated as a whole, is disaggregated into three categories, intra-regional
passenger transport, inter-regional passenger transport and freight transport. Such a
separation is convenient to describe the different trends in each mode, but requires certain
assumptions on the energy consumption side in order to derive an approximate database.
Intra-regional passenger aviation includes domestic flights and international flights within
each region. Inter-regional flights are mostly intercontinental. Freight aviation includes all
transport either within or between the regions. The activity data (passenger-kilometres or
tonne-kilometres) rely on statistics from the International Civil Aviation Organization
(source ICAO 1999), using the inter-regional activity of domestic carriers as a proxy for
the total inter-regional aviation activity of this region. On the other hand, the respective
regional energy consumption is derived from IEA statistics (IEA 2000f) without
distinguishing domestic and foreign carriers. Finally, since air freight is mostly carried in
passenger air-craft, 1 tonne-km is assumed to consume a similar amount of energy as 10
passenger-km (source ICAO 1999), which also serves as a basis for the splitting of energy
between freight and passenger aviation. With these assumptions and despite discrepancies
in conventions, an approximate picture of aviation fuel consumption can be drafted6.
Two transportation modes are not included in this study:
• Pipeline transport: the only region where pipeline fuel use is significant is North America
(3.8% of transport fuel demand in 1997); in the other regions, fuel use corresponds to less
than 1% of transport fuel use as described here.
• International maritime fuel use, or “international bunkers” (see IEA 2000f): in 1997, it
would have added about 6.5% of total fuel consumption to the transport energy use as
described here.
Convention on modal shares
Modal shares are defined as shares of each transportation mode in intra-regional transport, i.e.
excluding inter-regional aviation for passenger and for freight. This means that the total of
modal shares including these two modes is greater than 100%.
This seemingly odd convention has two reasons:
6
For a comparison of data sources and the problem with matching ICAO activity data with IEA fuel consumption, see Vedantham and
Oppenheimer 1998. The IEA statistics appear to offer the best fuel demand coverage for aviation.
Chapter 1: The context 22
• International aviation is rarely included in the national modal mix in most comparable
studies and statistics. Including these “within the 100%” would yield to very different
modal shares, making the data difficult to compare with other studies.
• The attribution of international (and inter-regional) passenger transport and freight
transport to a country is very approximate and the database is not as reliable as for the
other modes. Yet, international aviation shares in the modal mix is included in the factor
analysis and in the modal shifts component extracted there.
Macro-economic assumptions
Macro-economic inputs to the accounting model consist of population, gross domestic
product (GDP) and fuel prices. Population growth rates (Table 1.2), economic growth rates
(Table 1.3) and fuel price projections (Table 1.4) are those assumed in the IEA's World
Energy Outlook 2000 (see IEA 2000b for explanations). The assumptions for OECD Europe
and Pacific have been split into the respective sub-regions (Western Europe, Central Europe
and Turkey; Japan, Australia and New Zealand) according to different assumptions found in
the literature.
While crude oil prices are assumed homogeneous for all OECD States, end-use prices include
regional differences such as taxes, differing refining margins and transport costs. They are
expressed in a relevant currency for each region. Still, the comparison to prices experienced
by consumers is not straightforward. Values given are in 1990 currencies7 and represent a
sales-weighted mix of prices in the different countries in one region (IEA 2000b). Since the
model responds to price changes, only the relative changes in end-use prices have an
influence on energy demand, not the absolute price level.
Table 1.2: Population growth and assumptions
1997 1970-1986 1986-1997 1997-2010 2010-2020
[millions.] [% p.a.] [% p.a.] [% p.a.] [% p.a.]
Western Europe 386 0.3% 0.4% 0.2% 0.0%
Central Europe and
123 1.4% 1.0% 0.3% 0.3%
Turkey
Japan 126 1.0% 0.3% 0.1% -0.2%
Australia and New
22 1.3% 1.3% 1.0% 1.0%
Zealand
North America 297 1.0% 1.0% 0.7% 0.6%
7
Similarly to the WEO 2000 edition.
Chapter 1: The context 23
Table 1.3: Economic growth and assumptions (Gross Domestic Product)
1997 1970-1986 1986-1997 1997-2010 2010-2020
[bill. 1990
[%p.a.] [%p.a.] [%p.a.] [%p.a.]
US$ PPP]
Western Europe 6,767 2.5% 2.2% 2.2% 1.6%
Central Europeand
822 3.4% 2.3% 3.9% 2.8%
Turkey
Japan 2,613 4.0% 2.9% 1.6% 1.5%
Australia and New
402 3.1% 3.0% 2.2% 2.2%
Zealand
North America 7,222 2.7% 2.6% 2.3% 1.8%
PPP = purchasing power parity.
Table 1.4: Fuel price assumptions
Unit 1997 2000 2010 2020
IEA crude
US$ 1990 / barrel 16.0 13.9 16.5 22.5
import price
OECD Europe
Gasoline DM 1990/litre 1.41 1.55 1.55 1.68
Diesel DM 1990/litre 0.86 0.96 0.96 1.09
Kerosene DM 1990/litre 0.21 0.25 0.25 0.33
OECD Pacific
Gasoline Yen 1990/litre 121 125 123 147
Diesel Yen 1990/litre 67 77 76 91
Kerosene Yen 1990/litre 15 19 19 25
North America
Gasoline US$ 1990/gallon 1.19 1.46 1.46 1.88
Diesel US$ 1990/gallon 0.99 1.15 1.15 1.43
Kerosene US$ 1990/gallon 0.51 0.54 0.54 0.70
Chapter 1: The context 24
Chapter 2. What makes transport energy use grow?
This chapter focuses on the major trends in OECD transport energy demand and identifies
the major underlying drivers to its growth. It gives quantitative detail of a transportation
activity and fuel intensity “base case” up to 2020, which excludes major recent policy
initiatives departing from past trends. The chapter analyses the contribution of different
transportation modes to future growth of energy demand. It identifies in particular the role of
transport activity, modal shifts and energy intensity changes as drivers for transport energy
demand.
A transport scenario for the OECD to 2020
Over the last 25 years, the road vehicle stock grew by 130%, road vehicle-km grew by 160%,
passenger aviation increased more than four-fold and motorway infrastructure increased by
110% (OECD 1997c). Activity growth in passenger-km and tonne-km has been the key
constituent to the past energy demand growth in transportation. The role of other influences,
such as modal shifts and energy efficiency improvements, was much less important.
Projecting transport activity is therefore a key element to any future energy demand scenario
in transportation.
Projecting future transport activity
Forecasting transport levels is a complex task. Box 2.1 gives a qualitative overview of major
elements that play a role in shaping future transport activity. Few of these elements are easy to
capture, as data are sometimes unusable or inaccurate or even non-existent. Quantitative
models vary significantly depending on the purpose for which they are made8. As a result,
there are many transport scenarios for different countries and regions, established through
different methodologies and often leading to diverging results. Acknowledging these
variations, the transport activity projections made below are compared to results from major
regional studies, where possible.
In this study, the projections are obtained by prolonging the trends experienced in the past9.
The thus established base case serves as the backbone for the different scenario variations
analysed later on.
8
Transport models focusing on energy or greenhouse gas emissions usually differ significantly from transport models applied for transport
demand forecasting and infrastructure planning. A useful overview and categorisation of transport models with US examples can be found in
DoT 1999.
9
Where this method yields obviously misleading results compared to more elaborate studies, the modelling is adjusted.
Chapter 2: What makes transport energy use grow? 25
Box 2.1: Factors affecting transport demand (Hecq 1995)
From analysis performed on transport demand, a distinction has to be made between passenger transport and
freight transport. These two types of transport are not quite related to the same factors.
The factors that induce the passenger transport demand are numerous and relatively well known:
• Demography, age distribution, household composition (single-person family, family with children.);
• Economic factors: yearly growth rate of GNP, income or consumption; fuel and vehicle prices; public
transport prices; fiscal measures and structure of taxation...;
• Infrastructure facilities and spatial structure: road, geographical distribution of activities: school, stores,
public transport network, level of urbanisation...;
• Socio-cultural factors: car ownership, employment, part-time jobs, education level, recreation, foreign
trips...;
• Other: climatic conditions, technical and environmental concerns...
Factors related to freight transport can be summarised as follows:
• Demography, age,....;
• Economic factors: yearly GNP, income or consumption growth rate, fiscal measures and structure of
taxation, resources availability, internationalisation of trade...;
• Industrial structure and management: location of industries, division of production units, just-in-time
deliveries;
• Socio-cultural factors: labour mobility and availability...;
• Infrastructure facilities: road density and location...;
• Other: technical and environmental concerns...
Yet, like any model representing a complex reality, the approach neglects some, possibly
important, elements. Most notably, the complex link between infrastructure and transport
demand is not captured. No feedback, from e.g. increasing congestion on the transport activity
levels, is assumed. In other words, the predict and supply routine for planning and
constructing new infrastructure continues unchanged – an assumption not unlikely as a base
case.
The influence of fuel prices and transport cost changes is only captured in a simplified way
(see Appendix 2 for more details). Also, the competition between the different transportation
modes is not explicitly modelled.
Still, on the aggregate level of different OECD regions and compared to different national
studies, the model and its results serve well to describe and understand the current transport
activity trends.
In the following, transport trends are portrayed first by passenger and freight modes
neglecting regional differences, and specific features in the different regions are reviewed
afterwards.
Chapter 2: What makes transport energy use grow? 26
Passenger transport activity
Activity growth
Passenger transport measured in passenger-km has grown tremendously since 1970. In the
OECD it grew on average by 2.4% p.a. or 92% in total between 1970 and 1997. Figure 2.1
depicts the relationship between passenger-km and income on a per capita basis in the
different OECD regions. In 1997, passenger-km per capita are highest in North America at
24,400 km/cap, followed by Australia and New Zealand at 18,800 km/cap, Western Europe at
13,600 km/cap, Japan at 12,000 km/cap and Central Europe and Turkey at 4,400 km/cap. In
the past, most regions developed about parallel to the diagonal centreline, implying that per
capita growth of passenger transport is similar to the growth of income, with the exception of
North America where – a very high level of transport activity – this growth was lower. In
such an aggregate view, economic growth and population growth appear therefore to be key
determinants of total passenger transport activity. Also shown in Figure 2.1 are the
projections of total passenger– km until 2020. They imply that in most regions, future
transport growth per capita is expected to be significantly lower than over 1970-1997 – owing
to lower growth of per capita income, but also to a less strong link between transport growth
and income growth (see Table 2.1).
Figure 2.1: Passenger activity vs. income per capita
(past trends 1970-1997, projections 1998-2020)
Chapter 2: What makes transport energy use grow? 27
Table 2.1: Key parameters and projections of passenger transport activity
Average, aggregate
Passenger– km per capita
income elasticity*
1986-1997 1997-2020 1970 1997 2020
Western Europe 1.38 0.87 6,500 13,600 19,600
Central Europe + Turkey 1.17 0.85 2,600 4,400 8,100
Japan 1.29 0.91 6,600 11,900 16,700
Austr. + NZ. 1.26 0.72 11,100 18,900 23,000
North America 0.55 0.47 19,700 24,300 28,300
OECD average 0.99 0.72 10,000 15,700 20,800
Notes: Figures represent average across all passenger modes, * ratio of relative changes in passenger–km/cap and GDP/cap.
This feature of the projections coincides with a different outlook on demographics compared
to the past. The adult share of the population stagnates and the average age will be increasing
in many countries. Such factors have implications for a future transport demand growth in the
developed countries (TRB 1997, EIA/AEO 2000). In the model, the underlying economic
assumption of an increasing oil price after 2010 also contributes to a slower growth in
transport demand per capita.
In the base case projections, fuel price changes (after 2010) play a relatively minor role for the
growth trends in passenger activity. Table 2.2 gives the elasticities of modal activity to fuel
price changes. Combined with the price change assumptions in the base case (Table 1.4), they
lead to a small reduction in activity growth after 2010 (see regional trends, Figures 2.5-2.9).
Table 2.2: Average price elasticity of modal activity
Western Europe, North America,
Central Europe Australia and Japan
and Turkey New Zealand
Cars -0.15
-0.23 -0.12
Light trucks -0.19
Passenger rail 0 0 0
Bus 0 0 0
Intra-regional aviation -0.20 -0.19 -0.12
Inter-regional aviation -0.20 -0.23 -0.16
Notes: Each mode is being allocated one fuel, which serves as a reference for the calculation of the elasticities.
Price elasticities are subject to considerable uncertainties. The assumptions used here are consistent with the aggregate fuel
demand modelling in the World Energy Model (IEA 2000).
In terms of total passenger transport (passenger-km) in each, region, the assumptions on
population and economic growth add to the differences between past trends and the future
outlook (see macro-economic assumptions in Chapter 1). As a result, the future growth rates
of transport activity until 2020 are about half of the past growth rates since 1970 in Western
Europe and Japan, and 20% lower in North America. In OECD Central Europe and Turkey,
the average growth rate is expected to be more than twice as high as in the past. Table 2.3
Chapter 2: What makes transport energy use grow? 28
gives an overview of the aggregate activity trends and projections in each region10. Overall,
passenger transport activity will rise by more than 40% between 1997 and 2020 in the OECD.
Land transport will increase by about 30% as will the number of cars and light trucks.
Table 2.3: Overview of aggregate growth trends and projections
in passenger transport activity
Average annual growth rate of passenger-km [% p.a.]
1970-1986 1986-1997 1997-2010 2010-2020
Western Europe +3.3 +2.9 +1.8 +1.5
Central Europe and Turkey +3.8 +2.6 +3.8 +1.9
Japan +2.5 +3.7 +1.4 +1.4
Australia and New Zealand +3.3 +3.4 +1.9 +1.8
North America +1.8 +1.9 +1.5 +1.1
OECD average +2.4 +2.5 +1.7 +1.3
Note: Figures present averages across all transport modes. For underlying macro-economic assumptions, see Tables 1.2 – 1.4.
Modal shifts
With some regional differences – North America leading the trends, Western Europe and
Japan “lagging” somewhat – the past 25 years were characterised by the dominance of road
transport. Automobiles (cars and light trucks) command the largest share in passenger
transport with about 80% on average of the OECD total passenger-kilometres. Shares of
public transport modes such as bus and railways fell continuously. At the same time, domestic
and international passenger aviation has increased from very small levels in 1970 to now
accounting for 15% of passenger activity. Exceptions are the Central European countries as a
result of their different stage of economic development. Departing from a low activity level
and a still comparatively high share of public transport, they now follow rapidly the patterns
experienced elsewhere in the OECD in the past11.
Future modal shifts resulting from diverging growth trends in the different modes are unlikely
to be a simple extrapolation of what happened in the past. The modal share of automobiles
seems to stabilise and even diminish slightly in many OECD countries. Different long-term
analyses of modal shift patterns suggest a trend towards ever more rapid and comfortable
transport modes and longer distances. This implies that the past “dominance of automobiles”’
was just a phase. In the very long term, high-speed transportation modes such as aviation and
high-speed trains are expected to take the lead in shaping the next “growth wave’” of
transport infrastructures and equipment in the developed countries. Such broad conclusions
have been derived from different analytical approaches. From studying the long-term
evolution of transport technology and infrastructure, diffusion patterns of transportation
modes become apparent that show the introduction, expansion and saturation of a
transportation mode until it is superseded by a more performing one (Grübler and
10
While the total passenger-km are shown here, it should be kept in mind that all projections given are the sum of separate projections in
each of the passenger modes detailed further below.
11
Trends in this region will therefore be discussed separately further below and are neglected in the following discussion of modal trends
across the OECD.
Chapter 2: What makes transport energy use grow? 29
Nakicenovic et al. 1991). A different approach relies on the hypothesis of a fixed travel time
and travel money budget (Schafer and Victor 1997) over time. Assuming that per capita
transport demand grows roughly in line with per capita income12, the rising demand can only
be satisfied through the transition to ever-speedier transport means and allows to model
competition between modes and their shares in the very long term. How early and how
strongly this transition to high-speed modes takes place is difficult to foresee with precision.
The analytical approaches mentioned are generally too coarse and too long-term (2050) to
yield an outcome to 2020 consistent with the recent trends. Yet, the results from the model
approach do point to the same direction.
Trends by transportation mode
A major symptom from this transition is that automobiles (cars and light trucks) will no
longer take the lion’s share in the future growth of transport activity as in the past. The
projected growth rate of automobile passenger-km is lower than the average growth of
passenger transport in all regions (around 1% p.a.) implying that their modal share is
diminishing to about 76% on average in the OECD. The slowing growth is often identified
with the fact that ownership rates (vehicles per 1,000 inhabitants) follow an S-shaped curve
and many developed countries are now approaching a saturation level at the upper end of the
curve (Dargay and Gately 1999). Figure 2.2 shows the development of passenger vehicle
ownership in the different OECD regions. Owing to the paramount importance of the
automobile for passenger transport today, such a slowing ownership increase in the future is a
central hypothesis for the whole passenger transportation outlook.
12
A hypothesis which these projections depart from (see Table 2.1).
Chapter 2: What makes transport energy use grow? 30
Figure 2.2: Passenger vehicle ownership : past trends and projections
While the importance of bus and rail in passenger transport varies among the regions, their
modal shares have been continuously diminishing everywhere in competition with cars and
aviation. In North America, they now provide together less than 5% of total passenger-km. In
Western Europe, rail travel accounts for about 6% and bus travel for 10%. The shares in Japan
are respectively 24% and 6%. Rapidly expanding high-speed rail now accounts for 11% of
rail passenger-km in Western Europe and 19% in Japan taking some share from cars and
short-distance aviation. Yet, this regionally confined trend just compensates for reductions in
other rail segments and European regions. Over the outlook period, railways (high-speed and
conventional) and buses hold almost stable modal shares, growing just below average.
Air travel has more than quadrupled since 1970. Across the OECD, intra-regional travel
increased by about 5.3% annually; inter-regional travel by about 6%. In the projections, intra-
regional aviation increases by 3.0% p.a. and international by 4.3% p.a. with some regional
differences. North America, being the most mature market, shows slower growth than Europe
and Japan. Inter-regional aviation will reach a modal share of about 13% and interregional
aviation will attain a share of 12% in 2020, compared to 9% and 6% respectively today.
Figure 2.3 shows an index comparison of the projections with different studies. By 2020,
expectations are that activity will have increased by 220% to 260% over 1997, this outlook
being situated at the lower side of these projections, probably because of lower GDP growth
assumptions.
Chapter 2: What makes transport energy use grow? 31
Figure 2.3: Projections of passenger air travel
300%
250%
index 1997=100
200%
150%
100%
50%
0%
1970 1980 1990 2000 2010 2020
World Energy Outlook GDP growth 2.0% p.a.
Boeing GDP growth 2.3%p.a.
ANCAT 1998
OECD 1997
Freight transport activity
Activity growth
In the OECD, average freight activity – the movement of goods measured in tonne-km –
increased by 2.1% p.a. between 1970 and 1997. The absolute level of freight per unit of GDP
varies widely – depending on geography and industrial structure among other factors. Viewed
from an aggregate level, it follows closely GDP trends (Figure 2.4). The historical correlation
is actually strongest in Western Europe and Australia and New Zealand (elasticity close to 1),
and less so in Japan, North America and Australia and New Zealand (elasticity) 0.7 to 0.8,
Table 2.4).
Different factors play a role for shaping freight demand trends, mostly linked to structural
changes in the economy. The less material-intensive service sector has been growing more
than industrial production, leading to some “de-materialisation’” and less transport needs per
economic output. In other words, if this effect in the economic structure prevails, tonne-km
per unit of GDP would decline. On the other side, manufacturing production becomes more
vertically disintegrated and the sourcing and marketing of products is expanding
geographically. This translates into increases in the number of times that a (intermediary)
product is lifted and in the distance for which it is transported on each step in the transport
chain (McKinnon 1996, Schipper, Scholl and Price 1997, Vanek and Campbell 1999). This
Chapter 2: What makes transport energy use grow? 32
effect alone leads to increasing tonne-km per unit of GDP. All effects combined, the ratio of
tonne-km per unit of GDP has been slowly declining in all OECD regions since 198613.
Figure 2.4: Freight activity vs. GDP
Is the superposition of such effects in the future going to translate into less freight growth
relative to economic growth? Since freight activity is mostly a demand derived from
economic relations and activity, a pronounced slowing in freight growth trends would need to
be triggered by structural changes in the economy and the logistics chain. These could include
a “regionalisation” of demand and a supply for products instead of globalisation or a stronger
“de-materialisation” of economic activity than actually experienced. Few foresee such
developments – especially not as a base case. These projections of tonne-km are lower than in
the past mostly because of due to the lower economic growth assumptions. Depending on the
region, they include a decrease in tonne-km per unit of GDP but no trend-breaking “de-
coupling” of freight activity from economic growth (Table 2.4). The projections also include
the influence of the fuel price increase after 2010 via a set of modal elasticities (Table 2.5).
The price responsiveness of freight activity is usually lower than for passenger transport and
introduces a small dampening of activity growth in freight after 2010.
Table 2.6 gives an overview of trends and projections for total freight volume in the different
regions computed from the regional elasticities to domestic product and fuel prices. Compared
to the previous period 1986-1997, slower but still significant annual growth in freight tonne-
km is expected, between 1% and 2% p.a.
13
At the same time, it needs to be recognised that tonne-km are a very rough indicator for describing freight trends, especially when volume
becomes often more constraining to loading than weight.
Chapter 2: What makes transport energy use grow? 33
Table 2.4 : Key parameters and projections of the freight transport activity
Average, aggregate Tonne km per 1,000 US$
GDP elasticity 1990 PPP
1986-1997 1997-2020 1970 1997 2020
Western Europe 1.25 0.99 250 240 240
Central Europe and Turkey -0.10 0.77 650 400 340
Japan 0.80 0.72 340 210 190
Australia and New Zealand 0.67 0.66 870 850 720
North America 0.77 0.80 870 740 680
OECD average 0.83 0.85 540 460 430
Note: Figures represent average across all passenger modes,
Table 2.5: Average fuel price elasticity of modal activity
Western Europe, North America,
Central Europe Australia and Japan
and Turkey New Zealand
Heavier trucks -0.10 -0.14
-0.12
Small trucks and vans -0.05 -0.07
Freight rail 0 0 0
Navigation 0 0 0
Freight air -0.2 -0.17 -0.12
Note: Price elasticities are subject to considerable uncertainties. The assumptions used here are consistent with the aggregate fuel demand
modelling in the World Energy Model (IEA 2000);
Chapter 2: What makes transport energy use grow? 34
Table 2.6: Overview of aggregate growth trends and projections
in freight transport activity
Average annual growth rate of tonne-km [% p.a.]
1970-1986 1986-1997 1997-2010 2010-2020
Western Europe +2.0- +2.8 +2.1 +1.7
Central Europe and Turkey +2.1 -0.2 +2.8 +2.4
Japan +1.2 +2.3 +1.2 +1.1
Australia and New Zealand +3.6 +2.0 +1.3 +1.4
North America +2.1 +2.0 +1.8 +1.5
OECD average +2.1 +2.1 +1.9 +1.5
Note: Figures present averages across all transport modes. For underlying macro-economic assumptions, see Tables 1.2 –1.4.
Modal shifts
Modal shifts in freight from rail and navigation to trucking have been very pronounced
between 1970 and 1997 in all regions. Two factors are key to such shift: on the one hand,
there are changes in the mix and quality of the products transported (packaged and palleted
goods vs. bulk) and changes in the logistical features (lot size, short-term delivery, distance,
etc.) they require. Notably, bulk goods such as agricultural products and base materials have
lost in relative importance as a freight segment, and manufactured products have experienced
strong growth. On the other hand, there is the competitive situation of different freight modes
vis-à-vis the logistical requirements of the different product chains which is determined by
factors such as cost, speed, flexibility to adjust to changes and reliability. As a result, different
freight modes have characteristics that are best for a certain cargo. But also, changes in the
performance of each mode produce feedback on what logistic links become feasible and
competitive. Overall and similar to passenger transport, a trend towards more speedy and
flexible transportation means can be observed with the integration and globalisation of
different economies. As a result, road freight has gained significant modal shares. Freight
aviation – still at a very low absolute level – is expanding quickly. Carriers in trucking and
aviation can adapt quickly to changes in local demand, are less restrained by the
infrastructure than rail and waterways and are fast and reliable (McKinnon 1996, Komor
1995).
Trends by transportation mode
The strong growth in manufactured goods benefited most road freight i.e. trucking while rail
and waterways have kept their competitive advantage for bulk goods. Since the 1970s, the
activity of trucks has increased by 3%-4% annually and continuously gained market shares in
all regions. In Western Europe, road freight accounted for 76% in 1997. The values for Japan,
North America, Australia and New Zealand are respectively 47%, 43% and 37%. In the
projections, trucks are still growing at a faster pace than the total freight tonne-km, which
leads to further increases in modal shares, though at as lower pace than in the past.
Chapter 2: What makes transport energy use grow? 35
Although negligible from the point of view of tonne-km and hence accounting for a very
small modal share, smaller trucks and vans used in urban services and delivery have an
important share in vehicle-km and energy consumption. Data on small trucks and vans (also
mini-trucks in Japan) are not readily available in all regions and are often difficult to split
from passenger usage14. While the data do not cover all regions, this “freight” segment has
grown more than the heavier truck segment. The projections of vehicle-km show a
continuation of this trend and foresee higher growth than average freight - even higher than
heavier trucks.
Rail and navigation play different roles for freight transport in three regions. In North
America, rail shipping holds a 36% share in tonne-km and is projected to grow slightly faster
than average freight volume. This is due to the importance of agricultural, primary materials
and energy products and the very long distances (> 1,000 km) on which rail can compete well
with road freight (Komor 1995). In Japan, rail freight is marginal, accounting for only 5% of
tonne-km, and is projected to remain stable in share. In Western Europe, the railways have
continuously declined in share, from 33% of tonne-km in 1970 to 15% today. Old, slow-
rolling stock, inflexible service from segmented, state-owned national monopolies,
competition for infrastructure with passenger transport and a lack of modern freight terminals
have taken their toll. Within rail services, it is the inter-modal transport segment that is
growing most in all regions. This growth has compensated for some of the losses of rail in
other freight market segments. The outlook is uncertain. Market liberalisation and future
investment allocation in infrastructure allow some optimism European Environmental Agency
2000. The base case expects no further drop in this activity, but it will nonetheless suffer
further losses in market share.
Since the mid-1980s, navigation transport has fallen steeply in North America; it now
accounts for only 20% of total tonne-km. The projections assume a continued but slower
decline. Japanese coastal waterway transport is more significant with a 44% share owing to
the island geography but is slowly losing share. It shows a drop to 40% of tonne-km in 2020
from 44% in 1997. In Western Europe, navigation15 should remain relatively unimportant,
accounting for 5% of tonne-km in 2020.
Air freight has expanded very fast in the recent past – at close to 10% a year in some places,
on average 7.6% since 1970 in the OECD. It still claims under 2% of tonne-km in the OECD,
but, with projected growth at 5.5% per year, it will more than double its activity share to
around 5% by 2020. According to aircraft industry (Boeing 2000), inter-regional freight will
be outgrowing quickly domestic and intra-regional flows. For reasons of data availability (and
the difficulty to allocate energy consumption) the two parts are not separated, but projected
together16. The aircraft industry projects a world-wide average growth over the next 20 years
between 5.8% and 7.0% annually. Since more than 90% of the world-wide air freight is linked
14
In Western Europe and Central Europe and Turkey, small trucks and vans in commercial use are included in the heavier trucks category.
15
Excluding international sea transport within the region.
16
By convention, aviation tonne-km are not included in the intra-regional tonne-km total and are shown as a modal share above 100% in
each region.
Chapter 2: What makes transport energy use grow? 36
to OECD countries, these projections for the OECD can be compared and appear to be at the
lower boundary of the industry outlook.
Regional transport trends
Western Europe
Growth in passenger transport in Western Europe is expect to slow somewhat in the next two
decades to an average of 1.7% p.a. This implies that in 2020 (2010) travel activity will have
risen by 47% (27%). With a very low population growth, it is the growth in activity per capita
that expands. About 30% of the incremental passenger-km stem from intercontinental air
travel, another 20% from intra-European air travel, surface travel growing much more
modestly than in the past (+30% between 1997 and 2020). The modal share of cars has
peaked around 78% and is now retreating against aviation. Figure 2.5 and Table 2.7 give an
overview of the modal shifts in growth trends.
Freight in tonne-km is expected to grow at 2%, mostly driven by road freight. With a growth
rate of average 2.4%, trucking increases its share further. The relative role of rail and
navigation is expected to diminish respectively to 11% and 5% in 2020, with virtually no
growth projected in the base case for both modes. Air freight, though still very small, at 2% of
tonne-km today, doubles its share to 4% in 2020.
Figure 2.5: Transport trends and projections –Western Europe
Passenger transport activity Freight transport activity
Passenger transport modal shares Freight transport modal shares
Chapter 2: What makes transport energy use grow? 37
These projections are somewhat higher than similar projections by the European Commission
(EC 1999b, DRI&KU Leuven 1999)17. With higher economic growth assumptions (2.2% and
2.3% p.a.) than here (1.9%), the Commission projects a passenger-km growth of 1.7% and
1.5% p.a. respectively over the next 20 years. The respective values for tonne-km are 1.6%
p.a. in both studies.
Table 2.7: Transport activity growth trends and projections in Western Europe
1970 - 1997- 1970- 1997-
Passenger-km Tonne-km
1997 2020 1997 2020
All modes +3.2% +1.7% All modes +2.3% +2.0%
Cars* +3.4% +1.2% Trucks** +3.6% +2.4%
Pass. rail +1.0% +0.8% Freight rail -0.6% +0.1%
Bus +1.4% +1.0% Navigation +0.5% +0.1%
Intra-reg. aviation +4.8% +4.3% Freight air +11.5% +5.3%
Inter-reg. aviation +4.9% +4.4%
*incl. light trucks; **incl. small trucks and vans
Central Europe and Turkey
The level of economic development and development of the transport system varies
significantly between Central Europe and Turkey18. Per capita transport activity in Turkey is
low compared to the other countries in the group and its infrastructure, especially rail, is much
less developed (ECMT 1988a). There is great uncertainty in the projections of transport since
the speed of economic development in those countries and the speed of their transition
towards transport patterns similar to those in Western Europe are uncertain. A strong
restructuring of the Central European economies and extreme shifts in their transport system
marked the last ten years.
The projections show a rapid growth of passenger activity by an average of 3% p.a. (Table
2.8). Average per capita transport activity will have almost doubled to 8,200 km/cap in 2020
compared to 1997 - equivalent to a level reached in Western Europe in the late 1970s.
Following the patterns elsewhere, cars will be the winners. They expand strongly and gain
important shares from rail and bus. In 2020 the share of cars in passenger transport will reach
80% - up from 60% today and 36% in 1986. This reflects the continuing, rapid motorisation
and road infrastructure development experienced in the Central European countries and also
Turkey (Figure 2.6). On average, the modal structure in 2020 will have changed dramatically
and resemble that of Western Europe during the 1980s.
For freight, strong growth in tonne-km is projected (an average of 2.6% p.a.). The freight
intensity of the economies (tonne-km/GDP) is expect to decrease, but it will remain
significantly higher than in Western Europe (Table 2.4). In 2020, total tonne-km will have
roughly doubled compared to 1997. Road freight is leading this with a growth rate of 3.5%
17
Although comparing Business-as-Usual projections should always be done with caution.
18
In 1997, Turkey accounts for about 50% of the population, 30% of passenger-km and 38% of tonne-km in the whole group of countries.
Chapter 2: What makes transport energy use grow? 38
p.a. Navigation and air freight19 are marginal, and rail continues to lose market share to road
freight. Overall, the modal shift towards road freight continues the trend started during the last
decade where it already lost most of its importance in freight (Figure 2.6).
Figure 2.6: Transport trends and projections – Central Europe and Turkey
Passenger transport activity Freight transport activity
Passenger transport modal shares Freight transport modal shares
The projections were difficult to do for this region. Recent work (commissioned by the
European Commission) tries to address this situation for the Central European countries
(NEA, 1999) with an exhaustive scenario approach. Still, being limited to projections of
passengers and tonnes carried, this exercise cannot be directly compared to the projections
given here. No comparable projections for Turkey are available (see ECMT 1998a).
19
Data on air freight were not separately available for the countries in this region.
Chapter 2: What makes transport energy use grow? 39
Table 2.8: Transport activity growth trends and projections
in Central Europe and Turkey
1970 - 1997- 1970- 1997-
Passenger-km Tonne-km
1997 2020 1997 2020
All modes +3.3% +3.0% All modes +1.1% +2.6%
Cars* +5.7% +4.2% Trucks** +5.1% +3.5%
Pass. rail -2.2% -2.3% Freight rail -2.0% +0.0%
Bus +2.5% -0.7% Navigation -2.4% +0.1%
Intra-reg. aviation +10.0% +4.0% Freight air n.a. n.a.
Inter-reg. Aviation +7.1% +5.7%
*incl. light trucks; **incl. small trucks and vans, n.a.: not available.
Japan
Growth in Passenger-km is expected to be the lowest among all OECD regions, at 1.4% p.a.,
partly depending on the low economic growth assumptions and the stagnant population. Cars
and passenger rail both grow at 0.9%, bus transport being even lower at 0.5% p.a. Slow
growth and relatively small growth differences between modes, apart from aviation, lead to a
relatively stable modal structure. With 27% rail and 5% bus shares in passenger-km in 2020,
Japan has the highest share of public transport among all regions. Almost half of the growth
in passenger-km will occur in inter-regional and intra-regional (domestic) aviation. They are
expected to attain respectively 12% and 9% in modal share in 2020 (Figure 2.7 and Table
2.9).
A similar picture, low growth and little modal shifts, is projected for freight in Japan. Overall,
Japan has the lowest freight intensity of all regions. Total tonne-km will grow at an average of
1.1% p.a. Trucking is expected to gain some shares, from 48% in 1997 to 53% in 2020,
mostly at the expense of waterway transport. Small trucks and vans (including. “mini”-trucks)
have little importance in terms of modal shares, but account for almost 70% of vehicle-km
and hence have an important role for energy consumption (see Hattla 94, Kiang/and Schipper
1996). With almost 40% of tonne-km in 2020, waterway transport remains very important.
Figure 2.7: Transport trends and projections – Japan
Passenger transport activity 120%
100%
80%
Modal share
60%
40%
20%
0%
1970 1986 1997 2010 2020
Cars Personal light trucks
Passenger rail Bus
Intra-regional aviation + general Inter-regional aviation
Passenger transport modal shares
Chapter 2: What makes transport energy use grow? 40
Freight transport activity Freight transport modal shares
These projections are similar to those in the Japanese Long-term Energy Outlook (Kibune/and
Ishida 1999). Using a slightly higher assumption of growth in GDP (+2% p.a. until 2010),
passenger-km are projected to grow by +1.7% p.a. and tonne-km by +1.1% p.a. until 2010.
The respective values in this outlook to 2010 are +1.4% and +1.2% p.a.
Table 2.9: Transport activity growth trends and projections in Japan
1970 - 1997- 1970- 1997-
Passenger-km Tonne-km
1997 2020 1997 2020
All modes +3.0% +1.4% All modes +1.7% +1.1%
Cars* +4.4% +0.9% Trucks +3.4% +1.2%
Pass. rail +1.2% +0.9% Small trucks/vans -2.8% +0.3%
Bus -0.4% +0.5% Freight rail -3.4% +1.1%
Intra-reg. aviation +8.1% +4.1% Navigation +1.7% +0.9%
Inter-reg. aviation +7.3% +5.0% Freight air +12.4% +5.6%
*incl. light trucks.
Australia and New Zealand
The growth projections of passenger transport in this region (+2% p.a.) are partly driven by a
relatively important population growth of 1% p.a. Aviation is responsible for around 50% of
the incremental activity, all other modes losing shares.
Figure 2.8: Transport trends and projections –Australia and New Zealand
Passenger transport activity 140%
120%
100%
Modal share
80%
60%
40%
20%
0%
1970 1986 1997 2010 2020
Cars Personal light trucks
Passenger rail Bus
Intra-regional aviation + general Inter-regional aviation
Passenger transport modal shares
Chapter 2: What makes transport energy use grow? 41
Freight transport activity Freight transport modal shares
For freight, with an average growth of 1.4% p.a., the modal structure remains relatively
balanced despite the higher growth for road freight. Road, rail and coastal shipping each carry
around a third of the tonne-km, with road and rail gaining share over shipping (Figure 2.8 and
Table 2.10).
Table 2.10: Transport activity growth trends and projections
in Australia and New Zealand
1970 - 1997- 1970- 1997-
Passenger-km Tonne-km
1997 2020 1997 2020
All modes +3.3% +1.9% All modes +3.0% +1.4%
Cars +2.8% +1.4% Heavier trucks +4.8% +1.7%
Light trucks +4.1% +1.3% Small trucks/vans +5.7% +2.3%
Pass. rail -1.4% +0.6% Freight rail +3.5% +1.8%
Bus +2.7% +0.5% Navigation +1.4% +0.7%
Intra-reg. aviation +8.0% +2.7% Freight air +10.8% +4.8%
Inter-reg. aviation +7.0% +3.4%
North America
North America is the region with the highest level of mobility per capita and the lowest shares
of public transport (rail and bus) among all regions. Over the outlook period, passenger-km
are expected to grow at 1.4% p.a., lower than in the past (1.8% p.a.). Population growth at 0.6
to 0.7% p.a. plays a role in sustaining passenger activity growth. Cars and light trucks
(personal use) together hold today a share of 84% in passenger-km, which is slightly
diminishing (81 % in 2020) owing to the expansion of aviation shares. These projections
assume a stabilisation of the light truck market share in new registrations at around 40% and
together with the lower mileage per vehicle, the overall modal share of light trucks in
passenger-km remains at 22%. The projections show thus a halt of the increasing past
penetration of light trucks. More than 40% of the incremental passenger activity takes place in
aviation. Inter-regional aviation is expected to double its shares from 5% today to 10% in the
future, while intra-regional aviation will increase from 12% to 15% (Figure 2.9 and Table
2.11).
Chapter 2: What makes transport energy use grow? 42
Table 2.11: Transport activity growth trends and projections in North America
1970 - 1997- 1970- 1997-
Passenger-km Tonne-km
1997 2020 1997 2020
All modes +1.8% +1.4% All modes +2.0% +1.7%
Cars +0.5% +1.0% Heavier trucks +3.6% +2.0%
Light trucks +7.5% +1.2% Small trucks/vans +6.0% +2.8%
Pass. rail +0.2% +0.5% Freight rail +1.4% +1.8%
Bus +1.4% +1.4% Navigation +0.4% -0.6%
Intra-reg. aviation +5.1% +2.3% Freight air +6.7% +5.5%
Inter-reg. aviation +6.6% +4.0%
Chapter 2: What makes transport energy use grow? 43
Figure 2.9: Transport trends and projections – North America
Passenger transport activity Freight transport activity
Passenger transport modal shares Freight transport modal shares
120%
100%
80%
Modal share
60%
40%
20%
0%
1970 1986 1997 2010 2020
Cars Personal light trucks
Passenger rail Bus
Intra-regional aviation + general Inter-regional aviation
Tonne-km are projected to grow at 1.7% p.a. Trucking is the mode with strongest growth. It
will increase its modal shares over time, at the expense of waterway transport. In contrast to
the other regions, rail freight has a high market share, which is expected to increase slightly
from 36% in 1997 to 38% in 2020. Air freight grows strongly reaching a total tonne-km of
5.5% in 2020.
The recent Outlook by the US Department of Energy/Energy Information Administration
(USDoE 2001, horizon 2015) assumes a somewhat higher total freight transport growth at 3%
p.a. than this outlook, which accounts for the difference with the projections made here20:
Light duty vehicle21 vehicle-km are expected to grow by 2% p.a. (this outlook: 1.4%), heavy
duty vehicle-km by 2.6% p.a. (1.8%) and rail and marine tonne-km by 1.6% p.a. (1.1%).
Key findings – activity trends and projections
Projections of transport activity are difficult to integrate into the perspective of sustainable
development. By 2020, passenger transport and freight transport will have expanded by 44%
and 48% respectively compared to 1997 across the OECD. These projections are marked by a
slower growth in passenger vehicle ownership, relatively modest rates of aviation growth and
20
It should also be noted that Canada is not included in the US Department of Energy’s Outlook
21
Cars and light trucks, either in personal or commercial use.
Chapter 2: What makes transport energy use grow? 44
a mostly stable correlation of freight traffic volume with economic growth. Despite these
partly optimistic assumptions, the difficulty to sustain such growth in the context of
infrastructure capacity, negative environmental impacts22 or urban attractiveness becomes
obvious.
Growth in transport is slowing but still important. In all regions a slow-down in the growth of
passenger transport activity is projected. Average growth in the OECD is expected to be 1.6%
p.a. until 2020 compared to 2.4% in the past. Land-based transport is growing less, by 1.2%
on average, mainly due to modest assumptions on passenger vehicle ownership increases.
Intra- and inter-regional aviation makes up about 40% of future growth in passenger transport
activity.
Freight activity growth is slowing somewhat, mostly because of the lower economic growth
projected than experienced in the past. No significant “de-coupling” from economic growth is
assumed. Tonne-km are expected to grow by 1.7% p.a. on average until 2020 across the
OECD, compared to 2.1% p.a. in the past since 1970. Road freight comprising small trucks
and vans leads most of the growth and increases its modal share further. In terms of vehicle-
km, small trucks and vans show strong growth, even stronger than heavier trucks. Air freight
is growing more than three times as quickly as average freight, at 5.5% p.a. on OECD
average.
The patterns of modal shifts are changing. Modal shifts as depicted in the projections are the
result of long-term differences in growth rates of different transportation modes. In passenger
transport, the past rise of cars and light trucks in modal shares levels off and their modal
shares will be retreating in favour of aviation. Total passenger aviation will command a share
equivalent to 25% of transport activity23. For freight, further modal gains of trucks are
projected in all regions, but much less dramatic than in the past. Air freight will expand
quickly and will comprise between 4 and 5% of freight activity in 2020.
The projected developments in Central Europe and Turkey are an exception in the sense that
the trends experienced elsewhere in the OECD countries over the last 30 years are expected to
appear over the projection period, leading to a dominance of cars and trucks for passenger and
freight transport respectively at close to an 80% modal share. The speed at which such
changes have already started to occur puts the society and the environment under extreme
strain.
22
For a discussion on Environmentally Sustainable Transport, see http://www.oecd.org/env/ccst/est/estproj/estproj1.htm
23
In relation to intra-regional transport activity (see “Convention on modal shares” in Chapter 1).
Chapter 2: What makes transport energy use grow? 45
How much energy to move a passenger or a tonne of goods?
“Energy intensities”: energy use per service unit
In order to move from the transport scenario to an energy scenario, modal energy intensities
come into play. They describe the ratio of energy used per “service unit” in each mode24.
They are, first of all, a statistical measure derived from transport activity statistics and energy
consumption statistics in the different countries25. They provide an average across many
different transportation means and situations in each mode in each country. Owing to
differences in the definition and collection of statistics, their comparison across countries is
often not simple. Yet, with these caveats in mind, energy intensities allow to analyse
differences between modes and countries (see Chapter 3).
Figure 2.10 gives the average energy intensity in each mode across the whole OECD.
Concealing all regional differences, it gives a first impression of the order of magnitude of
energy use per passenger-km and tonne-km and the trends over time. It shows the relative
magnitude between the average modes where public transport (rail and bus) for passenger and
rail and waterways for freight transport require much less energy than the road-based
modes26. Aviation is the mode that has improved most in the past owing to a combination of
strong technological and load factor improvements.
There are different “real life” influences that shape the energy intensity changes over time in
each region, namely:
• Technology (including energy efficiency in a technical sense);
• Vehicle mix (size, power, age and stock turnover);
• Usage (load factor, speed, driving behaviour) and also
• External factors such as quality of infrastructure, congestion, geography, climate,
environmental regulation, etc.
The modal energy intensity defined as energy consumption per passenger-km or tonne-km
can be separated into a load factor component (passengers or tonnes per vehicle) and a vehicle
energy intensity component, defined as energy consumption per vehicle-km.
24
Again, this is described in terms of passenger-km for passenger transport and tonne-km for freight.
25
See IEA’s energy indicator effort: IEA 1997a
26
While this is true for the average mode, the situation can be very different in the specific country and transport situation. See Chapter 3,
section on “Transport demand policies”.
Chapter 2: What makes transport energy use grow? 46
Figure 2.10: Fuel intensities of different modes over time (OECD average)
In the case of cars, light trucks and trucks – encompassing the bulk of energy consumption
and activity – the load factor in the historical time series is actually singled out. Load factor
and vehicle energy intensity (per vehicle-km) are projected separately. In all other modes,
load factor changes are implicit and the modal energy intensity is used. Table 2.12 gives an
overview of some key factors independent from the price signal with regard to the modal
energy intensities in each transportation mode and the general direction of influence.
The time series of energy use indicators that are used here are the superposition of all these
different influences, few of which can be explicitly traced. For example, technological
(vehicle) efficiency is just one, though important, component which cannot always be
separated from other influences. In other words, a low modal energy intensity is not
synonymous with “high efficiency” in a technological sense (e.g. energy input into a car and
mechanical energy as an output). In a policy context that aims at the reduction of energy use
per unit of service provided, it is convenient to have such a broad measure. It encompasses
many different policy levers to reduce energy per unit of service provided, e.g. energy
efficiency improvements from technology and use changes, load factors, modal shifts. On the
other hand, many influences remain undiscovered under the surface27.
27
See IEA’s work on indicators of energy use and efficiency (IEA 1997a and 1997b) which attempts to explain more of the different
components and influences.
Chapter 2: What makes transport energy use grow? 47
Table 2.12: Influences on modal energy intensity (MJ/passenger-km or tonne-km)
! increasing, " decreasing energy intensity, ↨ depends on context
! increased size and power28, general decrease in load factor with increasing vehicle
Cars,
ownership, higher speed, congestion, air conditioning and other equipment
light trucks
" fuel-efficient technology
! Speed,
Rail ↕ load factor, share of urban, inter-city and high-speed rail
" electrification, efficient technology (but slow turnover), traffic flow
! increased power, load factor often decreasing
Bus ↕ share of charter (long-distance) and scheduled bus services (urban, low-speed)
" fuel efficient vehicle technology, efficient driver behaviour (training)
! air traffic congestion, hubbing and routing
↕ average trip length
Aviation
" fuel-efficient technology, increases in load factor and seating capacity, increased
aircraft size
! more power, acceleration and speed, vehicle mix with more small and mid-sized trucks,
additional equipment (cooling, etc.), increased highway speed (long-haul), congestion
(trucks and vans in urban areas)
Trucks ↕ load factor (“cubing out” or increased volume constraints lead to decrease), increased
empty running (has been reduced in the past in the course of deregulation)
" fuel-efficient vehicle technology, maximum size increases, increased trip length for
inter-city trips, driver monitoring and training, maintenance
! increased share of more energy-intensive combined rail, increased speed;
Rail freight " efficient technology (but slow stock turnover); improved routing and logistics, less
empty running, switch from diesel to electric traction, reduction in stops and starts,
increases in trip lengths
Navigation " efficient technology (but slow stock turnover)
Note: the tendencies given reflect past changes and do not necessarily apply in each country and region.
With regard to the technology component of energy intensity, a certain pattern can be
discerned over time in different modes. While the period 1970 to 1986 was characterised by
two oil price hikes and a subsequent push for energy-efficient technology, the period 1986 to
1997 was characterised by relatively low and stable fuel prices. During the second period,
technological changes in new vehicles were often directed less towards energy savings than
towards improving performance. As a result, the energy intensity improvements of new
vehicles were slowing or even negative. Yet, vehicle energy intensity improvements in the
fleet continue during this period as a result of stock turnover that introduced more efficient
technologies, the development of which was triggered in the previous high price period. At
the end of this phase, the improvements “inherited” from the high price period came to an end
and levelled out since energy efficiency of new technology often did not progress as quickly
as before in absence of strong price signals. This stylised behaviour of the vehicle technology
component is depicted in Figure 2.11 and generally holds for cars, trucks and aviation (IEA
1997, Greene 1992, Vedantham and Oppenheimer 1998) and is decisive for this fuel intensity
outlook.
28
In North America, the shift from cars to light trucks in personal use is captured as modal shift since light trucks constitute a separate mode.
In the other regions, where this shift is less pronounced, light trucks are included in the car category. Here, the increase in light truck use
leads there to increased average weight and power within the car category.
Chapter 2: What makes transport energy use grow? 48
Figure 2.11: Stylised behaviour of the energy intensity of new vehicles in a
transportation mode and the corresponding fleet in the past
Energy intensity
in a vehicle class
Fleet
(vehicle stock)
New
vehicles
?
1970's to mid
1980s mid 1980’s to late 1990’s
time
Energy intensity trends and base case projections until 2020
Table 2.13 gives an overview of the modal energy intensity in 1997 and the projected trends
for three regions. The trend of the modal energy intensity between 1986 and 1997 is shown
for comparison.
Different “techniques” are used to project modal trends. They include namely the
extrapolation of the long-term base trend (“autonomous improvement”) that is experienced in
each region and the responsiveness to price changes. The base trend is assumed equivalent to
a third of the improvement rate experienced between 1986 to 1997. In the case of cars and
light trucks, a separate fleet turnover model is used (see Appendix 2 for a description of the
model) replacing this aggregate base trend hypothesis. The second component of the energy
intensity improvement is considered energy price-related. For determining the price
responsiveness (elasticity), values similar to what can be found in the literature are applied in
the different regions (Table 2.13). This approach is of course only a rough proxy for different
elements shaping the modal energy intensity trends. Some of the assumptions (e.g. future load
factor trends) depend necessarily on expert judgement and the regional context.
Chapter 2: What makes transport energy use grow? 49
Table 2.13: Energy intensity trends and projections
Western Europe Japan North America
MJ per passenger-km and tonne-km
1986-97 1997 97-2020 86-97 1997 97-2020 86-97 1997 97-2020
[% p.a.] [% p.a.] [% p.a.] [% p.a.] [% p.a.] [% p.a.]
Cars -0.8% 1.54 0.1% 0.6% 2.27 0.1% -0.7% 2.26 0.1%
Light trucks -0.8% 3.16 0.1%
Rail -0.5% 0.50 -0.5% 0.6% 0.19 0.6% -1.7% 0.87 -1.7%
Bus 1.3% 0.96 0.2% 1.9% 0.78 0.6% 0.3% 0.86 -0.1%
Intra-regional
-2.1% 2.42 -1.8% -1.4% 2.26 -0.7% -2.0% 2.54 -1.1%
aviation
Inter-regional
-2.5% 1.22 -0.8% -3.0% 1.75 -1.4% -3.8% 2.14 -1.7%
aviation
Heavier trucks 0.0% 3.22 -0.3% -1.7% 2.68 -0.2% -2.3% 1.81 -0.9%
Small trucks
n.a. 40.81 1.4% -3.7% 15.10 -0.1%
and vans
Rail -0.6% 0.41 -0.6% -0.6% 0.21 -0.6% -1.6% 0.27 -1.6%
Navigation -0.3% 2.25 -0.3% 0.9% 0.93 0.9% 0.9% 0.53 0.9%
Freight
-2.1% 17.19 -1.2% -2.8% 19.79 -1.3% -2.1% 8.33 -1.1%
aviation
Western Europe Japan North America
MJ per vehicle-km
86-97 1997 97-20 86-97 1997 97-20 86-97 1997 97-20
[% p.a.] [% p.a.] [% p.a.] [% p.a.] [% p.a.] [% p.a.]
Cars -1.0% 2.53 0.0% 1.0% 4.06 0.0% -1.3% 3.50 -0.1%
Light trucks -2.8% 4.92 -0.2%
Heavier trucks -0.2% 8.67 -0.1% -1.7% 8.44 0.0% -1.5% 12.33 -0.7%
Small trucks
n.a. 2.96 0.0% -2.1% 4.53 0.0%
and vans
Notes: large variations for energy intensities of one mode between the regions can be the result of varying definition and quality of data with
regard to passenger-km and energy consumption in each region.
Table 2.14: Average price elasticity of modal energy intensities
Passenger modes Freight modes
Heavier trucks, small
Cars and light trucks -0.2 to -0.3 -0.15
trucks and vans
Passenger rail 0 Freight rail 0
Bus -0.1 Navigation 0
Intra-regional aviation -0.3 Freight air -0.3
Inter-regional aviation -0.3
Note: price elasticities are subject to considerable uncertainties. The assumptions used here are consistent with the aggregate fuel demand
modelling in the World Energy Model (IEA 2000);
For the baseline case outlook, this implies that vehicle energy intensity improvements in the
fleet are expected to be generally lower than experienced since 1986 owing to relatively stable
prices and little policy intervention that could trigger strong improvements in new technology.
Later on, a different case is designed where certain already enacted energy efficiency policies
Chapter 2: What makes transport energy use grow? 50
are included (see Chapter 4, Figure 4.4). The impact of such policies is readily discernible in
comparison to this base case.
Cars and light trucks
The energy intensity of new cars improved between the mid 1970s and mid 1980s in all
regions triggered by high oil prices and fuel efficiency regulations, most markedly in North
America. Since 1986, new car energy intensity has been almost flat in Europe and North
America29. Power and weight increases counterbalanced the impact of technological
improvements on energy intensity (in MJ/vehicle-km). In Japan, new car energy intensity has
even deteriorated significantly since 1986. The fleet has followed with some time lag as a
result of gradual stock turnover and achieved significant improvements since 1986, except in
Japan. With unchanged new car energy intensity, the improvements of the fleet in Europe and
North America have levelled off in the mid 1990s – new cars are no longer more efficient
than the average fleet vehicle. Assuming no major policy initiatives in the base case and only
limited fuel price increases after 2010, the fuel intensity of new cars is projected to improve
only because of a small response to increasing fuel prices after 2010. Technological progress
continues to be translated into performance instead of fuel efficiency despite the cost-
effectiveness of various fuel efficiency improvements (IEA 2001). At the same time, the load
factor (passenger per vehicle or “occupancy rate”) is slowly decreasing along with increasing
car ownership in all regions. The on-road performance of vehicles is expected to deteriorate
slowly – a trend already experienced in the past. Both elements result in less improvement in
energy consumption per passenger-km than visible on a per vehicle-km basis. The fleet
energy intensity (per passenger-km) is therefore projected to stay flat through the outlook
period. A more detailed discussion (by region) is given in Chapter 3 where the detailed stock
modelling is presented (Figures 3.2 and 3.3).
Rail (passenger and freight) and waterways
The slow turnover of stock is limiting the rate of technological improvement. Load factor and
other influences (speed, vehicle mix, etc.) are difficult to extract and result in diverging trends
in the different regions. In the base case, the autonomous trend since 1986 is assumed to
proceed independently of fuel prices. Fuel intensity in buses often deteriorated in the past
owing to decreasing load factors. This trend is projected to continue countered only by a small
reaction to fuel price increases.
Aviation (passenger and freight)
Aviation is the mode with the strongest historical energy intensity improvements – on average
by 2 to 3% p.a. Only a small part of air freight is actually carried in dedicated freight planes,
and most of it uses spare capacity in passenger aircraft, which makes energy use difficult to
allocate. In terms of energy required, 1 tonne-km is considered equivalent to 11 passenger-km
(convention used by the International Civil Aviation Organization; ICAO 1992). Fuel
29
It has started to improve again only recently in Europe and Japan, at least partly as a result of the policies adopted since 1998.
Chapter 2: What makes transport energy use grow? 51
intensity projections are made using this convention and similar assumptions as for passenger
aviation. In Figure 2.10 it is striking that the average aviation passenger-km has improved to
the extent that an average aviation passenger-km now requires an amount of energy similar to
an average car30. Greene (1992) estimates that about a third of past improvements is due to
increases in load factor, the rest to technological improvements that enter the rapidly growing
fleet during stock turnover. In the future, further load factor improvements are becoming more
difficult to achieve and autonomous improvements are estimated in the range of 1 to 1.3% p.a.
up to 2020. At the same time, the technological improvement potential remains significant
and improvement rates higher than those observed are technologically feasible if sufficient
economic incentives exist (OECD 1997a). Reflecting this situation, the assumed price
responsiveness of the aviation energy intensity is the highest of all modes. The overall energy
intensity improvements projected are lower than in the past but still significant (1 to 2% p.a.).
Trucks
Energy intensity data for larger trucks are the most difficult to interpret. The truck category is
very diverse, ranging from smaller (2.5 tonnes) and medium-sized (10-20 tonnes) to large
trucks. The maximum size varies in each region from 27 tonnes in Japan to 44 in Europe and
varies by state in North America, being 120,000 lbs. (54 tonnes) in Florida, for example.
Changes in the vehicle class mix, additional vehicle equipment (e.g. cooling), different trunk
and trailer categories are an important factor in determining fuel intensity. The load factor
changes also. It depends on the vehicle mix and is not consistent over time. In general,
improvements in the load factor are expected because of improved logistics and the use of
information technologies. For the projections, load factors (including empty running) are
assumed to improve autonomously at about 0.25% p.a., which implies that over the coming
20 years, 30% to 50% of the technological potential as perceived today (10 to 15%) will be
autonomously realised. For the energy intensity (energy per vehicle-km) only North America
shows a clear trend over the last 20 years; in the other regions the energy intensity is basically
flat. In the projections, a significant autonomous change is assumed only in North America
(1/3 of past change rate) and a limited price responsiveness (see Tables 2.13 and 2.14).
For Japan (after 1992), Australia and New Zealand, and North America, small trucks and vans
can be separated from the general truck category. Their activity is best described by vehicle-
km because of the diverse nature of their use, which is not exclusively linked to goods
transport but rather to different services provided31. Their vehicle energy intensity improved
in the past following to some extent the technology trends in cars. Increased weight and
power as well as changes in the vehicle mix eroded some of the gains. The projections are
made with the same technique and assumptions as cars, assuming no autonomous
improvement and only a limited response to fuel price changes.
30
At the same time, this shows the limitations of such a comparison based on a homogeneous service unit “passenger-km” across all modes,
since air travel is not substitutable for car travel.
31
For convenience and to allow comparison, a fixed load factor has been assumed in each region in order to calculate a tonne-km value.
Chapter 2: What makes transport energy use grow? 52
Key findings – fuel intensity trends and projections
In the absence of significant fuel price increases and policy intervention, the trucking energy
intensity is expected to improve at a slower rate than in the past. This trend is expected to be
most pronounced for cars and light trucks, where new technology is assumed to boost vehicle
performance instead of efficiency. As in the past, aviation remains the mode with the
strongest average energy intensity improvements, even if slowing.
Summing up: transport energy projections for the OECD
From the projections made on transport activity demand and energy intensity in each
transportation mode, the total transport energy demand in each region can be computed. In the
following paragraphs the results are interpreted in different ways by looking at:
• future energy demand vs. past trends;
• transportation modes that contribute most to growth in energy demand, i.e. the additional
energy demand in each time period;
• different factors (activity growth, modal shift, energy intensity changes) that underlie the
energy growth patterns described.
Projections of transport energy demand are described here for a base case excluding the effect
of major policies recently enacted or potentially to be introduced in the future. It is thus a
theoretical case against which other scenarios will be compared. In particular, a reference
scenario is defined in Chapter 4, which includes certain policy measures enacted in 1998 – the
historical data set reaching only 1997.
Transport energy use in 2020 if trends persist
In the base case, total transport energy demand in the OECD is expected to grow by 44%
between 1997 and 2020, to a total of 1,519 Mtoe compared to 1,057 in 1997. Table 2.15 gives
an overview of the changes in each region, whereas Figures 2.12 and 2.13 give an overall
picture of energy projections, giving first a breakdown by region, then a decomposition by
transportation mode.
Chapter 2: What makes transport energy use grow? 53
Figure 2.12: OECD transport energy consumption by region
Figure 2.13: OECD transport energy consumption by transportation mode
Transport energy consumption is expected to grow at a much slower rate than in the past but
still significant compared to oil demand in other sectors of the economies. On average a
growth of 1.6% p.a. (1.9% until 2010, and 1.2% afterwards) is expected compared to an
average of 2.2% p.a. between 1970 and 1997. In all regions, growth rates are slackening, quite
Chapter 2: What makes transport energy use grow? 54
sharply in Europe, Japan, Australia and New Zealand, much less in North America, Central
Europe and Turkey.
Table 2.15: Energy growth in five OECD regions
Increase (%) Change rate (% p.a.)
1970-1997 1997-2020 1970-1997 1997-2020
Western Europe +114 +48 +2.9 +1.7
Central Europe and Turkey +145 +94 +3.4 +2.9
Japan +186 +42 +4.0 +1.6
Australia and New Zealand +105 +43 +2.7 +1.6
North America +58 +39 +1.7 +1.4
In terms of energy consumption, the share of cars and light trucks is slowly decreasing from
52% in 1997 to 48% in 2020 but remains by far the largest part of transport energy demand.
The growth rate of energy demand from these modes is estimated at 1.3% p.a. to 2020 (all
OECD), compared to 2% between 1970 and 1997. Road freight will account for a constant
share of around 27% in total transport energy consumption, growing at a rate of 1.7% p.a. to
2020 (1970 to 1997: 3.0% p.a.). In those regions where data allow to separate small trucks
and vans from heavier trucks, the small truck category stands for about 40% of the total road
freight energy demand in 2020 compared to around 30% today. The share of passenger and
freight aviation in energy consumption will increase from 14% in 1997 to 20% in 2020. By
that time more than 30% of the OECD aviation energy consumption will derive from air
freight. The future growth rate of energy demand in aviation is estimated at 3% p.a., very
close to the past. The importance of all other modes (bus, passenger and freight rail,
navigation) in terms of energy demand is expected to decrease from 6% today to 5% in 2020.
Which transportation modes contribute most to energy demand increases?
The total energy demand will increase by 462 Mtoe between 1997 and 2020, which compares
to a very similar value of the past 30 years (466 Mtoe). Identifying transportation modes that
contribute the most to the increase provides an interesting perspective. As shown in Figure
2.14, this allows to compare past (first bar) and future (second bar) trends.
The modal breakdown of additional energy demand reveals that the importance of passenger
cars and light trucks for energy demand growth is decreasing, except for North America and
Central Europe and Turkey, where passenger vehicles remain the main driver for energy
demand growth. In North America, which experienced the strongest energy intensity
improvements in passenger cars of all regions in the past, a continuation of this positive trend
is not expected under base case conditions, i.e. without policy intervention. Even with
slowing activity growth from cars and light trucks, energy demand growth remains therefore
strong. In Central Europe and Turkey, the dramatic increase in car activity is responsible for
the strong growth of energy demand in this mode. In all other regions, the slowing growth in
car activity translates into a slowing growth in energy demand despite stagnant fuel
intensities.
Chapter 2: What makes transport energy use grow? 55
Figure 2.14: OECD incremental transport energy demand by mode
250
1970-1997
200
1997-2020
150
Mtoe
100
50
0 y
nd
e
th
rn
pe
an
d
a
ke
op
ic
an
or
te
a
ro
p
er
r
ur
al
Ja
N
es
Tu
lia
Eu
Am
Ze
lE
W
ra
d
tra
st
an
ew
Au
en
N
C
Cars and personal light trucks Heavier trucks
Small trucks and vans Aviation (intra- and interregional, freight)
Other
The rest of the energy demand growth is equally shared between road freight and aviation.
Within the additional energy demand from road freight, as much as 50% could stem from
small trucks and vans. Aviation incremental fuel demand is massive, accounting for 146 Mtoe
between 1997 and 2020 in the OECD. It represents 30% of the energy demand increase in the
OECD. One-fourth of the additional energy demand in aviation is due to freight carried.
Factors driving growth in energy use
Figure 2.15 shows the decomposition of energy growth trends for the five OECD regions into
three basic constituents (“factors”): activity growth, structural change (i.e. in the case of
transport, modal shift) and energy intensity changes. Decomposing a variable into these
constituents is a technique widely used in many areas, including energy. Different
methodologies are used, which show some divergence in the result depending on how a
residual is treated that is not directly attributable to the three basic factors. The method used
here32 has the advantage of leaving a very small residual, i.e. the attribution is complete and
the three different factors almost add up to the total energy demand change.
The magnitude of each of the three decomposition factors denotes a hypothetical energy
demand growth rate which would have been experienced if the two other factors had
remained unchanged during that period. Each factor combines all modes in either passenger
32
The Adaptive Weighted Divisia (AWD) method used here. For a comprehensive overview and methodology of index decomposition
analysis, see Ang and Zhang 2000. Cross-country comparisons of transport trends can be found in Scholl, Schipper, Kiang 1995 (passenger),
Schipper, Scholl, Price 1997 and Greening, Ting, Davis 1999 (freight).
Chapter 2: What makes transport energy use grow? 56
or freight, weighted by their relative importance for energy demand. It therefore aggregates
and summarises effectively many of the trends observed before separately.
It is convenient to distinguish four periods:
• 1970-1986: including the two oil price shocks and the implementation of different policies
for improved energy efficiency;
• 1986-1997: the period after the counter shock of oil prices with generally stable energy
prices and slackening policy interest in energy efficiency;
• 1997-2010: continuing stable oil prices (at 17 US$1990/barrel or 21 US$2000/barrel);
• 2010-2020: gradual, limited increase in crude oil prices to 22.5 US$1990/barrel or 28 US$
2000/barrel
Figure 2.15 puts the magnitude of future activity changes, modal shifts and energy intensity
changes into the perspective of past trends and in relation to each other.
Chapter 2: What makes transport energy use grow? 57
Figure 2.15: Factors influencing transport energy use, 1970 to 2020
Passenger transport Freight Transport
Western
Europe
Central
Europe
and
Turkey
Japan
Australia
and New
Zealand
North
America
Chapter 2: What makes transport energy use grow? 58
Passenger transport
The projections show in all regions a gradual slowdown of activity growth, but activity will
remain the major constituent of energy demand increases in virtually all regions.
Modal shifts will play a minor role in energy demand, the major change occurring in
passenger transport. The increase in modal shares of aviation at the expense of cars does not
show up strongly, since the energy intensities are relatively similar (see Figure 2.10).
Energy intensity changes are unimportant over the projection period. Even during the phase
with the strongest energy intensity improvements (1986-1997), the change only compensated
for around 30% of the activity increases in Western Europe and about 50% in North America.
In the projections, a slight deterioration of car and light truck fuel intensity (per passenger-
km) will be balanced out by strong improvements in passenger aviation.
Freight transport
As is the case for passenger transport, the projections for freight show a slowing activity
increases in line with the underlying, lower economic growth rate.
For freight, gains in modal shares of small and heavier trucks are expected to continue, but at
a slower rate than in the past. The gains in modal shares by freight aviation, which has a much
lower fuel intensity than trucks, will become the most important component of energy
demand increase from modal shifts in the future.
The improvement of energy intensity (measured as energy per tonne-km) in trucks will
account for most of the increase in energy demand. Strong fuel intensity improvements in the
freight aviation segment will also play a role. After 2010, gradually increasing fuel prices will
lead to a slight reduction of fuel intensities for passenger and freight modes.
A few particularities in the different regions merit an additional explanation. In the Central
Europe and Turkey region, the 1986-1997 period was very disruptive for the formerly planned
economies in Central Europe. This is depicted by the strong modal shift effect towards cars
and trucks. For freight, total activity stagnated while it grew dramatically for passenger
transport. In the case of freight, the modal shift contribution to growth in energy demand is
partly balanced by reduced energy intensity from improved technology – mostly in trucks. In
North America and Australia and New Zealand, fuel intensity improvements in freight have
been particularly strong in the past. They stem to a large extent from fuel intensity
improvements in small and heavier trucks during a time of market deregulation, leading to
improved load factors and efficiency. At the same time, these improvements were balanced by
modal shifts towards road freight and aviation. The decomposition of energy demand
increases in Japan explains why the outlook shows energy demand increases much lower than
in the past: activity growth lower than in the past, linked to slow economic growth
Chapter 2: What makes transport energy use grow? 59
assumptions, will lead to lower energy demand projections in the future compared to past
trends.
Despite some slowing-down in transport activity compared to the past, the growth in energy
demand in North America is expected to remain rather strong, for passenger transport similar
to the past, for freight slightly below. The reason is the breakdown in fuel intensity
improvement in passenger transport and smaller fuel intensity improvements also in freight in
the absence of any policy action and with only small price signals. The factor balance is
shifting: in the past, high activity growth was at least partly compensated by fuel intensity
improvements. In the future, minor activity growth is expected but also less important fuel
intensity improvements – leading to total energy demand growth similar to the past.
CO2 emission trends in the baseline case
In 1997, gasoline, diesel and kerosene accounted for about 97% of all transport energy
consumption in Europe and Japan. This share is around 99% for North America. The rest is
mostly made up of liquified petroleum gas and electricity, other alternative fuels, such as
grain ethanol in North America (0.4% in 1997). There are few fuel substitution possibilities in
transportation, especially under the assumption of limited crude oil price increases. The fuel
mix does change in the base case since fuel demand is the result of changes in modal mix
(with different modes using different but stable mixes of fuels). But overall the fuel mix in
transportation remains quite stable over the projection period in the base case. Hence, CO2
emission trends follow closely the above-mentioned energy trends.
In the emission calculations, the focus is put on “tailpipe” emission neglecting the greenhouse
gas (GHG) emissions “overhead” from crude oil production, refinery and transport33.
Emissions from electricity production are therefore not included in the transportation
emissions total (IPCC 1996).
Table 2.16 gives the projected base case emissions in million tonnes of CO2 and relative to
the 1990 CO2 emissions level, the base year for the Kyoto GHG reduction target. Already by
1997, CO2 emissions had increased by 15%, 29% and 11% for Western Europe, Japan and
North America respectively (+14% for the OECD), compared to 1990. By 2010, the base case
projects a level of +50%, +60% and +40% respectively. Emissions are projected still
significantly higher by 2020. These levels provide a baseline for policy measures and
emissions reduction efforts. It becomes quite clear that under such growth dynamics, actual
reductions to or below 1990 levels, as the Kyoto Protocol prescribes them for the whole
economy34, would be particularly difficult to achieve in the case of transport, and that
transport will instead tend to off-set the CO2 reductions in other sectors of the economy.
33
Petroleum product emissions over the fuel life cycle are about 15 to 20% higher than tailpipe emissions (Delucchi 1997, IEA/AFIS 1999a).
34
Including effects from other mechanisms than domestic polices and measures.
Chapter 2: What makes transport energy use grow? 60
Table 2.16: CO2 emission trends and projections (megatonnes)
1990 1997 2010 2020
Western Europe 767 885 1154 1339
Central Europe and Turkey 65 84 130 157
Japan 214 277 343 384
Australia and New Zealand 73 84 106 125
North America 1593 1774 2225 2469
OECD 2714 3104 3957 4475
Changes over 1990 levels
1997 2010 2020
Western Europe +15% +50% +75%
Central Europe and Turkey +28% +93% +127%
Japan +29% +60% +79%
Australia and New Zealand +15% +45% +71%
North America +11% +40% +55%
OECD +14% +46% +65%
Note: tailpipe emissions according to IPCC guidelines (IPCC 1996).
Key findings – energy demand and CO2 emission projections in the base case
• Without policy intervention, total transport energy demand in the OECD is expected to
grow between 1997 and 2020 by 44% or 462 Mtoe to a total of 1,519 Mtoe.
• Passenger cars (and light trucks) will account for about half of total transport energy
demand in 2020, and road freight for more than a quarter. Aviation will consume about a
fifth of all transport energy demand by 2020.
• The importance of the different modes to energy demand increases will change. Aviation
is projected to drive almost 30% of all additional energy demand between 1997 and 2020.
Road freight and passenger cars account for about 30% and 40%.
• The separation of the factors activity, modal shift and fuel intensity shows that the
composition of energy demand growth is different in the base case projections compared
to the recent past. For passenger transport, activity growth rates are weakening, modal
shifts remain unimportant for energy demand, but fuel intensity changes are expected to
become almost negligible. For freight, activity growth is only slightly less than in the past,
modal shifts will continue to increase energy demand, and fuel intensity changes will be
rather small. The combination of these factors leads to projected energy demand growth
rates of 1.6% p.a. between 1997 and 2020.
• With only limited increase in crude oil price, the share of petroleum base fuels in total
transport energy consumption will remain around 98% over the projections period.
Alternative fuels are not expected to play a significant role in the base case.
Chapter 2: What makes transport energy use grow? 61
• Hence, CO2 emissions follow closely the energy demand trends. In 2010 and 2020, CO2
emission for the OECD will be higher by 46% and 65% respectively, compared to 1990
levels.
Chapter 2: What makes transport energy use grow? 62
Chapter 3. The impact of selected transport energy policies
This chapter provides the details of the impact analysis on energy and CO2 emissions for the
different policy bundles in the areas of fuel efficiency in passenger vehicles, alternative fuels,
transport demand management and fuel taxation. Only three regions have received a detailed
analysis: North America, Japan and Western Europe.
Policy portfolios in Western Europe, Japan and North America
During the 1990s, considerable research has been conducted on analysis of different policies
in response to rising oil use and greenhouse gas emissions in transport (e.g. OECD 1996). The
IEA recently published Saving Oil and Reducing CO2 Emissions in Transport: Options and
Strategies (IEA, 2001) which reviews the literature and provides a new assessment of a wide
range of potential options and strategies to improve vehicle fuel efficiency, reduce travel,
make greater use of alternative fuels, and move goods more efficiently. A variety of possible
instruments is at hand, using regulatory, economic/fiscal or public awareness measures as
well as the redirection of land use and urban planning. From these instruments, different
policy bundles can be designed to affect fuel demand and CO2 emissions, through:
• decreasing fuel intensity of vehicles (technology, operation);
• increasing the use of alternative fuels;
• decreasing transport demand levels and;
• modal shifts towards less energy-intensive transport means35.
OECD governments are gradually responding to the challenge of greenhouse gas emissions
reduction and have started to implement different policy instruments (IEA 2000d, ECMT
1998b). The goal here is not to evaluate all policies in terms of reduction potential, cost-
effectiveness, etc., but to estimate to what extent policies that are currently on some
governments' agenda will change the energy demand and CO2 emission trends portrayed in
the base case (see Chapter 2).
Quantifying the impact of diverse policies and measures enacted or about to be implemented
is not straightforward and requires a number of definitions, choices and simplifications.
1. Methodological issues. The variety of measures at hand requires a variety of approaches
in order to quantify their impact, especially when multiple measures are considered
together36. Many indirect effects can occur37. Measures taken together can reinforce or
hinder each other. Models to evaluate the measures comprehensively are very complex,
but the necessary data sets often do not exist to allow the quantification of policy bundles.
35
IEA 2000d (Table 2.1) gives a useful categorisation of instruments (energy intensity, modal structure, activity and fuel mix) and the way
they effect transport energy demand. IEA 2000b and 2000c give a succinct overview of the instruments and examples of their
implementation.
36
IEA 2001 provides estimates for many individual measures.
37
The debate around the impact of information technology in the transportation system can serve as an example. Certain telematics
technologies can help to keep traffic more fluid, avoid congestion and thus lower vehicle energy consumption. The side-effects on overall
energy consumption concern the effect of increased road capacity on transport activity or of additional traffic created, e.g. through alternative
routing.
Chapter 3: The impact of selected transport energy policies 63
As a result, sets of multiple measures to reduce emissions (e.g. in the national
communications to the UNFCCC) are very difficult to quantify reliably.
2. Selection of Policies. Policies can vary significantly from country to country and take
various forms. Some generalisation is required to evaluate policies at the OECD-region
level, but many questions remain. As virtually any transport policy has some impact on
energy consumption and CO2 emissions, which policies are representative of the whole
region? Which are those that can be considered a part of the base trend and those
significant for the climate change policy bundle? And, finally, how can those measures
“most likely” to be put in place in the near future be determined?
From these considerations, a relatively restrictive selection of policy measures emerges for
inclusion in our scenarios, in line with both the geographical approach by OECD region and
the required modelling capacity. Owing to uncertainties regarding both data and policy
impacts in many regions of the world, only Western Europe, Japan and North America
receive detailed study. Based on the policy debate in each region and relying on key policy
documents from different countries, a policy portfolio was developed and its direct effects
were estimated, reflecting regional circumstances. In particular:
• Attention was focused on those policies with potentially sizeable impact either already
enacted or on “top-of-the-list” for future implementation (“additional policies”). These
future measures are drawn from published government plans for CO2 emission mitigation
or appear to be a logical prolongation of policies already in place for this purpose.
• Isolated measures with locally confined effects are ignored (demonstration and pilot
schemes, policies in individual municipalities), even if they are significant to reduce local
pollution. General R&D support unconnected to particular market-deployment measures
is not taken into account. Awareness-raising measures trying to change behaviour are also
difficult to evaluate and are not explicitly included, although they could be an important
part of any policy package38.
• Some lead time for the implementation of additional policies is assumed, depending on
the type of measure.
• In line with the assumption of a carbon trading scheme among Annex-I countries as
simulated in the World Energy Outlook 2000 (IEA 2000b), a homogenous tax of US$95
per tonne of carbon phased in between 2001 and 2010 is applied to all fuels.
• Given the modelling approach used for the analysis (11 transportation modes and 5
regions), certain details of the transport system are not represented. Especially, spatial
transport patterns and vehicle usage (speed, urban/inter-city driving) for an individual
country or even a city are not depicted. Some measures would require such a detailed
approach for their evaluation, especially in the area of traffic and transport demand
management, that simplifying assumptions must be used to include these in this current
model. Relatively stringent restrictive measures are assumed (pricing, access restrictions)
38
Rather, R&D support and information campaigns are considered as a complementary part to measures with a more direct impact. For
example, labelling and fiscal incentives for low-consumption cars can be considered necessary ingredients to make the Voluntary Agreement
of the European Automobile Manufacturers Association (ACEA) successful.
Chapter 3: The impact of selected transport energy policies 64
since more modest changes are already occurring and can be considered part of the
baseline.
Tables 3.1 to 3.3 summarise these policy choices for the three OECD regions. Clearly, any
policy selection of this level of aggregation will be, to some extent, arbitrary. However,
quantitative analysis requires the definition of precise, quantitative measure levels (e.g. tested
fuel economy, in miles per gallon, of new cars in 2020), which can of course be debated. For
attaining the targets sketched out in Tables 3.1 to 3.3 (right column), many more supportive
measures (information, incentives, training, etc.), different from one region to another, are
needed. These are not exactly specified, but it is assumed that complementary measure
bundles are used to achieve these goals. Many different combinations of complementary
measures can be used.
These policy assumptions reflect political difficulties of achieving more ambitious goals
(IEA, 2000d). They draw a cautious, schematic picture of the most important measures.
Despite these restrictions, it should be noted that the policy selection made covers the bulk of
the currently envisaged emissions reduction policies in IEA Member countries39.
The European policy portfolio (Table 3.1, EC 2000a, EC 1998b as well as country policy
plans; see also IEA 2000d) features the strongest move towards fuel efficiency of passenger
cars compared to the other regions. This is the result of the Voluntary Agreement of the
European, Japanese and Korean Car Manufacturers. The agreement targets are assumed to be
reached and strengthened beyond 2008, supported by government measures that stimulate
demand for efficient vehicles (fiscal incentives, labelling). Demand-side policies play an
important role in European government plans. Modal shifts from road freight to rail are also
considered important and are expected to be triggered by km-charges for heavy trucks and
through the improvement and expansion of combined rail facilities. On the passenger side,
policies are less clear and often focused on the alleviation of (other) urban transport problems.
A proactive traffic restraining policy in the urban context (widespread parking and access
restrictions) is assumed, as well as substantial improvements in public transport. Regular
increases in fuel taxation have been occurring in a few European countries (for example in the
UK, Germany or Sweden) and may continue, although the scope for ongoing increases may
be limited (the UK recently suspended its automatic annual fuel tax increase). In this analysis
for Europe, as in other regions, increasing fuel taxation is assessed using a carbon value of
US$95 per tonne.
The Japanese policy portfolio (Table 3.2, MOT 1998 and 1999, Energy Conservation Center,
1999) features fuel efficiency regulation for cars and small trucks called “Top Runner”, since
today’s best performing vehicles set the standard for 2010. In addition, it relies heavily on
behavioural and organisational changes for which most triggering policy measures are yet to
39
At the same time, the impact of these measures on fuel demand and CO2 emissions from our independent analysis can depart significantly
from the effects projected by the governments.
Chapter 3: The impact of selected transport energy policies 65
be determined. For passenger transport, road pricing schemes are assumed in 30% of the
urban areas (which are currently covered by the NOx legislation) and the promotion of public
transportation and non-motorised transportation. Improving the physical distribution
efficiency of freight (higher load factor, optimised routing) is another focal point in the
Japanese approach. Mandatory urban logistic schemes bundling the goods distribution in
small trucks and improved load factors in larger trucks are assumed. The same amount of
carbon taxation as in other regions is also assumed.
In North America (Table 3.3, PDAC 1995 “Car talk” and Levelton, 1999), the policy debate
towards action to curb CO2 emissions is less advanced, and few measures explicitly designed
for GHG emissions abatement are visible. The current focus lies on R&D funding and
pollutant emissions abatement rather than proactive CO2 mitigation. A general move towards
transport demand-side measures with significant transport reducing and shifting effects, at
least from the national government, is considered unlikely in the North American policy
context. The retained portfolio is therefore mostly focused on technology improvements. As
additional measures in the future, a continuous tightening of fuel intensity standards for cars
and light trucks between 2004 and 2020 is assessed. Market introduction of low-carbon
biofuels after 2010 is also considered, once the production technology has been proven and
scaled up. Demand for efficient vehicles and low-carbon fuels is supported by fuel taxation in
line with the carbon value defined in the emissions trading scheme.
Chapter 3: The impact of selected transport energy policies 66
Table 3.1: CO2 policies assessed for Western Europe
Enacted by 1998 Additional (assumed)
Examples of measures Policy effect taken into account
Fuel efficiency of vehicles
− The Voluntary Agreement (VA) − Fuel intensity reduction − Further tightened VA: fuel
by the European Automobile Car of 25% by 2008 compared to intensity improvement by 46% by 2020
Manufacturer Association (and 1995, no change after 2008 compared to 1995
similarly the Japanese and Korean
Association)
− Support measures to the VA such
as the labelling scheme and fiscal
incentives for fuel-efficient cars, in
order to stimulate demand
Demand-side policies
Freight: modal shift towards rail Modal shifts freight
− Road pricing as part of the − Truck load-factor improvement by
internalisation strategy (see EC 1998c) 6% in 2020
− Expansion of combined rail − 5% of long-haul truck tonne-km
transport (railway reform, support to shift to combined rail by 2020
infrastructure and interoperability of − 3% of long-haul trucking is
different modes) suppressed by 2020
Passenger: modal shift towards non-
motorised and public transport Modal shifts passenger
− Urban car restraint: parking and − 3% of the urban car activity shifts
access restrictions, traffic calming to public modes (urban bus, urban rail)
− Improvements in public and non- − 4% is suppressed or shifts to non-
motorised transport motorised modes, ride-sharing, etc.
− High-speed rail (HSR) services are − HSR expansion after 2010 is twice
expanded as fast as in reference scenario; 30% of
additional high-speed rail activity shifts
from domestic aviation, 40% shifts
from motorways, The rest is genuine
growth due to expanded capacity
Taxation of fuels
Increased taxation of energy products US$95/tonne carbon levied on all
(minimal levels) and aviation fuel in fuels*, phased in between 2001 and
particular 2010
Similarly, the individual states are
expected to further increase their
current taxation levels
*
Similar to other OECD regions, assimilated to the carbon value derived from the emissions trading case of the World Energy Outlook (IEA
2000b).
Note: A concerted introduction of alternative fuels such as natural gas, methanol, biodiesel is not considered as likely in the next 20 years in
Europe. New fuels with relevant CO2 benefits are considered a long-term (R&D) issue by the European Commission and member
governments and not likely to have more than a marginal CO2 impact in the European context by 2020 (EC 1998a).
Chapter 3: The impact of selected transport energy policies 67
Table 3.2: CO2 policies assessed for Japan
Enacted by 1998 Additional (assumed)
Examples of measures Policy effect taken into account
Fuel efficiency of vehicles
− Reinforcement of fuel economy − Fuel intensity − Further tightened Top Runner
standards for cars and small trucks improvement in new cars of regulation: fuel intensity improvement
(Top Runner efficiency regulation) 17% until 2010 compared to of 28% compared to 1995
− Promotion of clean energy 1995, no change afterwards
vehicles; improved energy efficiency
of transportation equipment,
technological development
Demand-side policies
Freight: efficiency of physical goods Modal shifts freight
distribution − In 2020, 6% of urban small van
− City logistics(applied to 30% of vehicle-km is bundled through city
urban areas): efficient loading of logistics systems and terminals and
trucks, usage of trailers and trains, replaced by mid-sized, urban trucks on
mandatory trip bundling for small optimised routes
commercial trucks
Passenger: traffic control and Modal shifts passenger
transport demand management − Urban car activity in the reference
− Urban road pricing (applied to scenario is reduced by 10% in 2020:
30% of urban areas*) 40% shifts to public modes (urban bus
− Promotion of public transportation and rail), 60% is suppressed or shifts to
and non-motorised transportation non-motorised modes, ride-sharing,
− Public transport and high-speed etc.
rail (HSR) services are improved and − HSR expansion after 2010 is twice
expanded as fast as in reference scenario; 30% of
the additional high-speed rail activity
shifts from domestic aviation, 40%
shifts from motorways, the rest is
genuine growth due to expanded
capacity
Taxation of fuels
− US$95/tonne carbon levied on all
fuels**, phased in between 2001 and
2010
*
Areas which are covered by “NOx-legislation” and therefore assumed subject to additional pricing schemes;
**
Similar to other OECD regions, assimilated to the carbon value derived from the emissions trading case of the World Energy Outlook (IEA
2000b);
Chapter 3: The impact of selected transport energy policies 68
Table 3.3: CO2 policies assessed for North America
Enacted by 1998 Additional (assumed)
Examples of measures Policy effect taken into account
Fuel efficiency of vehicles
− Increasing the Corporate Average Fuel − Starting in 2004, the fuel intensity will
Economy (CAFE) standards be continuously lowered by 2% p.a. until
2020 (total change 2004-2020: 28%)
Alternative fuels
− Further strengthened R&D for low- − Gasoline sales in cars and light trucks
carbon biofuel production are displaced from biofuel use in 2020
− Widened regulation of alternative
fuelled vehicle shares in fleets
− Production incentives for low-carbon
biofuels
Taxation of fuels
− US$95tonne carbon levied on all
fuels*, phased in between 2001 and
2010
*
Similar to other OECD regions, assimilated to the carbon value derived from the emissions trading case of the World Energy Outlook (IEA
2000b);
Improving the fuel intensity in passenger vehicles
Future fuel intensity targets for new vehicles
During the 1990s, many studies have scrutinised the potential and the cost of fuel intensity
improvements in passenger vehicles40. The IEA recently conducted its own study of light duty
vehicle fuel economy for three countries (Denmark, Germany and the United States), which is
presented in IEA 2001. This study estimates that a considerable amount of cost-effective fuel
economy technology exists and that low-cost fuel economy improvements are possible in
these three countries and probably in all IEA countries. Major new policies have been
implemented in Europe and Japan that target a reduction in fuel intensity of new cars. This
analysis assumes that the targets set in Europe and Japan are reached and further strengthened
beyond 2010 at a similar improvement rate as before. North America, which has had no
significant change to its fuel economy standards since the mid-1980s, is assumed to follow
with gradually tightening standards after 2004.
The definition and units of the policy targets differ in the different OECD regions. In order to
allow a comparison, they are translated into a common fuel intensity unit (MJ/km) for each
vintage of new cars. In order to do so, a number of specifications need to be given (Table 3.4).
These concern mainly the diesel shares and vehicle size mix. Figure 3.1 gives the trajectory of
future fuel intensities for new cars in each region41. In addition, the data points in Figure 3.1
denote the performance of different technology concepts of mid-sized cars taken from the
40
See for example OTA 1996, DeCicco and Ross 1996, US DoE 1997, Enquete Commission 1995. For an overview of earlier cost-
effectiveness studies, see OECD 1996 (Appendix A).
41
No influence of increasing fuel prices after 2010 on fuel intensities – as assumed in the base case – is shown.
Chapter 3: The impact of selected transport energy policies 69
literature. These were adjusted on the time scale to correspond to mass-market vehicles
comparable to the fuel intensities of a whole car vintage.
Table 3.4: Key assumptions on the fuel intensity of new cars
and personal light trucks42
Western Europe: The target value of the ACEA Voluntary Agreement is 140g CO2 per km in 2008 compared
to 186g/km today (EC 1998a). For 2012 and 2020, respectively 120 and 100g/km are assumed as sales
weighted average across all cars (see similar assumption in EC 2000b).
% change
19972 2008 20203
1997-2020
Gasoline cars [l/100km1] 7.3 5.8 4.1
Diesel cars [l/100km1] 6.1 4.6 3.3
Diesel share [%] 22% 36% 36%
Japan: The Top Runner fuel economy regulation fixes improvements for gasoline and diesel cars and small
gasoline and diesel trucks (2 tons) concerned is assumed to increase by 20%
(Taniguchi and van der Heijden 2000). In return, urban tonne-km in heavier trucks would
increase by 0.1% only.
Impact on energy: The impact of the shifts and load factor improvements amounts to 50 PJ
(1.2 Mtoe) or 1% of the base case emissions in 2020.
75
Not all light and mini trucks transport goods; they are also used by small businesses for services. The energy consumed through this use is
usually also credited to the light trucks category without contributing to the tonne-km.
Chapter 3: The impact of selected transport energy policies 99
The total effect of the Japanese demand-side measure bundle is therefore estimated at 216 PJ
(5.2 Mtoe) or 4% of the transport energy demand of the 2020 base case.
Key findings – demand-side policies
The above analysis should be taken as a rough estimate of the impact of some transport
demand policies, not as an exhaustive analysis. Yet, the approach taken yields some
interesting points to consider:
Demand-side policies can address a specific transport segment (urban, long distance); rarely
do they change the use of a transport mode as a whole. Such a segmentation was portrayed
here only in a summary fashion. In reality, the segmentation goes much further and the reach
of many instruments is reduced to a small fraction of transport uses where it changes
behaviour and transport patterns. Clarifying this segmentation can bring some realism into an
evaluation of what certain measure bundles can achieve. As a result, the importance of a
certain transport segment and a measure bundle is limited in relation to country or region-
wide energy consumption and CO2 emissions. Few target transport segments can yield
reductions higher than 1 or 2% of the total transport emissions76. To achieve a sizeable
impact, many measure bundles need to be implemented in a concerted fashion targeting many
different transport segments.
The changes that can be achieved in each transport segment should not be overestimated.
There are many limiting factors. Public transport services often cannot be expanded quickly
and drastically. The relative magnitude of modes to restrain (e.g. cars and trucks) vis-à-vis the
compensating modes (e.g. bus or rail) needs to be recognised. Individual trip behaviour or
logistic patterns of companies are the result of many different criteria often unrelated to
transport and therefore not easy to change. Such changes are possible, but they can involve
longer time periods than portrayed here. Long-term effects beyond 2020, e.g. from the
relocation of houses, services and supply patterns, could be significant. The assumptions on
the measure impact do not reflect such a profound change.
Implementing stringent measure bundles to limit transport volume and to shift it to other
modes is politically difficult and unpopular (IEA 2000d). A widespread implementation as
assumed here is a great simplification, especially since transport policy is driven by concerns
other than just energy and CO2 concerns.
Modal fuel intensities are an average across a large range of values. Calculating the gains
from modal shifts requires a detailed knowledge of the substituting modes and their usage
patterns (speed, load factor, etc.). Often, the energy savings from modal shifts are not as great
as expected from the comparison of the fuel intensities of the (average) modes (see e.g. long-
haul trucking vis-à-vis combined rail for Europe).
76
See also IEA 2001 for more policy examples and impact evaluations.
Chapter 3: The impact of selected transport energy policies 100
Overall, the authors believe that a cautious extrapolation of current policy-making as
portrayed could limit energy demand by 5% at maximum. The time frame, the often
unfavourable balance between public and private transport (or the difficulty to expand the
first), and a rather heterogeneous and uncoordinated policy intervention will limit the effects
on energy through transport demand-side measures, which remain mostly motivated by other
policy concerns.
Fuel taxation
Fuel taxation and, more broadly, the pricing of transportation plays an important role in all the
policy variants analysed so far. It provides a signal to consumers for purchasing more efficient
cars, it can counter the rebound in activity from fuel intensity improvements in cars. It is
relevant to the competitiveness of high-efficiency vehicles and alternative fuels and plays a
role for the transportation demand restraint and shift to non-road modes.
At the same time, the role of the fuel price signal in transportation should not be overstated,
especially as an isolated measure. The response to fuel price increases (only) is limited in the
short term, although somewhat greater in the long term. It is important to realise that fuel
prices are a relatively small share of total cost of road transport – in the order of 10 to 20%77,
one major reason why fuel price elasticities are usually relatively low. Increasingly, fuel
taxation is considered too blunt a measure to reflect e.g. the externalities from different
transport modes and to steer transport in an economically efficient fashion (ECMT 1998c).
Other, more differentiated forms of pricing (e.g. road charges differentiated by vehicle class)
are therefore likely to be introduced in the future. In this way, price signals can become a
more effective, complementary part of policy packages.
Despite these caveats, using price elasticities to calculate the reaction of fuel intensity and
activity to fuel price changes provides a simple way of modelling the aggregated transport and
energy demand response. When using such an approach, two aspects need to be considered:
• Considerable uncertainty surrounds long-term price elasticities as a measure of price
responsiveness (for overviews, see Oum et al. 1990, Dahl 1995). They depend strongly on
the specific situation (trip purpose, congestion, availability of alternatives, etc. (see e.g.
Hague Consulting Group, 1999). An aggregate elasticity calculation is therefore always a
rough approximation and simplification. The values used throughout this study (also in
the base case projections) are given in Chapter 2 (Table 2.278).
• The calculated impact – a price response – overlaps partly with those resulting from other
policy measures. The effects from fuel taxation cannot therefore be simply added to the
effects of other measures. Chapter 4 addresses this issue when the different policy
approaches are combined.
77
This is a rough order of magnitude for car use as well as truck transport. The share of fuel cost in variable cost is much higher, especially if
time cost is excluded.
78
No cross-elasticities between transportation modes are used..
Chapter 3: The impact of selected transport energy policies 101
The level of taxation assumed in this analysis is 1990US$95 per tonne of carbon or $26 per
tonne of carbon dioxide79. It is derived from the emissions-trading case in the World Energy
Outlook 2000 (IEA 2000b) which establishes a cost at which the Kyoto commitments for
GHG emissions reduction can be fulfilled by the Annex I countries. This carbon price level
would be imposed on transportation as a tax phased in progressively between 2001 and 2010.
In this way, transport would contribute to the economy-wide emissions reduction efforts. Due
to stock turnover, the full effect would only be seen somewhat later.
The relative fuel price change depends on the carbon content of the fuel and its base case
price level, including existing taxes in each region. The highest relative price change for
gasoline and diesel would occur in North America, where the base case prices are the lowest
owing to the low taxation. Kerosene is assumed untaxed in all regions and is therefore most
affected by the carbon tax adder. The resulting energy demand reduction of 5.7% is similar
for North America and Western Europe. Higher relative fuel price changes in North America
are balanced somewhat by lower assumed elasticities than in Western Europe. The price
response estimate of energy demand for Japan is somewhat lower, at 4.1%. The overall
reduction in energy demand is estimated at around 5.5% across the three OECD regions
(Table 3.7).
Table 3.7: The effects of a carbon tax on transport energy demand and activity
compared with the base case, 2020
a) Relative long-term change (elasticity a posteriori) due to a 1% fuel price increase*
Activity (passenger-km
Energy demand
or tonne-km)
Passenger (cars, light trucks) –0.35 to –0.55 –0.15 to –0.25
Freight (trucks) –0.25 to –0.3 –0.05 to –0.15
Aviation –0.4 to –0.7 –0.2 to –0.5
b) Relative change of fuel prices in 2020 due to a carbon tax of $95/tC
Gasoline Diesel Kerosene
Western Europe +7% +13% +40%
Japan +5% +9% +35%
North America +12% +18% +35%
c) Relative change of energy consumption and activity in 2020 as a result of the carbon
tax
Energy demand Activity
Total Pass. Freight Pass. Freight
Western Europe –5.7 –5.8 –5.6 –1.4 –1.2
Japan -4.1 -4.4 -3.6 -2.1 -0.6
North America -5.7 -6.0 -5.3 -1.9 1.2
* A range of elasticities is given to reflect the different values by region.
Due to the higher elasticity, energy demand from car passenger transport is generally more
reduced than road freight energy demand. The most affected sector is aviation because of
79
5.9 US cents per litre gasoline and 6.8 US cents per litre diesel or kerosene.
Chapter 3: The impact of selected transport energy policies 102
relatively high price elasticities (of fuel intensity and activity) and the relatively strong
kerosene price increases in the order of 35 to 40%.
Key findings – fuel price increases
A carbon tax would change the price of fuels differently depending on their carbon content
and their initial retail price (including taxation) in each region. The relative changes of
gasoline and diesel prices from a carbon tax of $95/tonne of carbon would amount to 5% and
9% respectively in Japan, 7% and 13% in Europe, and 12 % and 18% in North America.
Kerosene, so far exempted from fuel taxation, would increase most in relative terms, at about
35% in all regions.
Typical long-term elasticity assumptions imply that fuel use from cars is more elastic to price
changes than fuel use from trucks. The effect is usually considered to be about 60% due to the
change in fuel intensity (vehicle choice, improved technology, etc.) and 40% from a different
transport behaviour (suppressed traffic, mode shifting, ride-sharing or load factor changes).
Not surprisingly, because of its high price elasticity, the mode reacting the most to the price
changes is aviation. A carbon tax of $95 (1990) per tonne of carbon leads to a reduction in
fuel use of about 4-6% in transport. This is small compared to the expected growth of energy
demand of 1.6% p.a. (OECD average) over the next 20 years in the base case. This is the
typical result for fuel price changes as an isolated measure. The fuel price changes are
nevertheless an important part of transport policy portfolios where they can yield important
synergies together with other measures.
Chapter 3: The impact of selected transport energy policies 103
Chapter 4. Departure from the base trends: what difference do
current and near-term transport energy policies make?
This chapter summarises the combined impact on energy demand and CO2 emissions of the
different regional policy portfolios. It provides scenarios to 2020 under the assumption that
all additional policies analysed before were implemented. The results are evaluated in
comparison to the base case with focus on the changes in the different transportation modes
and the underlying driving factors, i.e. activity, fuel intensity and modal shifts.
The combined effect of the regional policy portfolios
The impact of each measure group (Chapter 3) has been estimated separately before with
reference to the transport energy base case (Chapter 2). In reality, measures are taken
simultaneously and interfere with each other. Below, possible overlaps and synergies between
the measure groups are described as they are apparent in the model. The need for a co-
ordinated policy approach is underlined with regard to fiscal and regulatory as well as
local/regional and country-wide measures. Fuel taxation, and more generally transport
pricing, has a central role in this context overlapping with all other policy areas. To estimate
the combined impact in each regional policy portfolio, some assumptions have to be made.
Interferences and synergies with policies improving vehicle fuel intensity
Fuel intensity policies for cars and personal light trucks and the assumed increases in fuel
taxation (carbon tax) act in parallel on the vehicle fuel intensity. They stimulate
simultaneously manufacturer’s efforts for offering, and customer’s preferences for choosing,
more fuel-efficient vehicles. The estimated changes in fuel intensity attributed to each single
measure cannot therefore be added up. For the combined policy effect, the measure with the
stronger impact has been chosen, in this case the agreements or regulation on fuel intensity
improvement.
Both measures also affect the activity (kilometres driven in cars) in parallel: improved fuel
intensity from regulation leads to a rebound in activity due to reduced fuel cost per km, while
increased fuel taxation forces less driving as a result of increases in fuel cost. Increased fuel
taxation can therefore limit (or outweigh) the rebound. As a consequence, their impact on
activity is superposed in the combined impact estimate.
Interferences and synergies with policies for the market introduction of advanced
vehicles and fuels
The introduction of high-efficiency vehicles through niche market regulation and purchase
subsidies is of course affected by the retail price of fuels and hence by fuel taxation80. The
80
Though the impact of relatively limited fuel tax changes assumed here might be more psychological than actually biasing markets
dramatically.
Chapter 4: Departure from the base trends 104
impact of fuel taxation, as portrayed in the previous chapter, is likely to be much smaller than
that of broader policy interventions across the whole new vehicle market. The effects of such
programmes are therefore not considered separately, but as flanking for a general
improvement in fuel efficiency standards, since it pushes the “best end” of the vehicle market.
For the alternative fuels, carbon taxation of gasoline and diesel plays an important role since it
affects the competitiveness of the different fuels. Yet, the absolute magnitude of the additional
fuel taxation on gasoline and diesel assumed in this case does not change the competitive
balance dramatically, it rather underpins the optimistic assumptions in the production
expansion scenario. The calculated life-cycle CO2-displacement impact is credited without
changes to the North American CO2 balance. In the energy balance, the fuel substitution
impact does not show.
Interferences and synergies with transport demand-side policies
In the description of the policy bundles for changing transport patterns, pricing (parking
charges, road pricing) has a central role to achieve significant impacts in a transport segment.
Fuel tax increases, while less selective, play a similar role and can change the competitive
situation between certain transportation modes and segments. Their increase is considered as a
crucial element to the demand packages, which allows them to achieve the estimated impact.
This restrictive impact assumption is conservative – in the best case, both measures could
have synergies and achieve more than assumed here.
Of course, future fuel intensity changes triggered by other policy measures have an impact on
the relative energy savings that can be achieved from mode switching. In the “isolated”
analysis (Figures 3.10 and 3.12) fuel intensities are assumed similar to the base case. For the
combined policy impact, the fuel intensity changes resulting from other policies are
superposed.
Transport energy trends with “additional policies”
Table 4.1 and Figure 4.1 give an overview of the transport energy demand and the combined
policy impact for Western Europe, Japan and North America81: the patterns are relatively
similar for the three regions. The case with enacted policies82 shows only a limited reduction
of 21 Mtoe and 39 Mtoe compared to the base case in 2010 and 2020 respectively. This
means no real change in trends; rather, without further policy action, energy demand increases
will grow at a slower rate. The average growth rates between 1997 and 2020 in Western
Europe and Japan are then 1.5% p.a. and 1.0% p.a. respectively compared to 1.8% p.a. and
1.5% p.a. in the base case.
With all additional policies, the reduction compared to the base trend amounts to 83 and 214
Mtoe in 2010 and 2020. The increase over 1997 is still 18 % in 2010 and 21 % in 2020 (or an
81
Eastern Europe, Australia and New Zealand are not examined here, since no specific policies were considered.
82
Equivalent to the reference case in the WEO 2000; for North America no measures were taken into account.
Chapter 4: Departure from the base trends 105
average of +0.8 % p.a. across the whole period), implying that most energy demand growth
occurs until 2010 and very little additional growth will occur during the second period, when
the policies will have sufficient time to take effect. Compared to the base case, this means a
reduction by 6 to 8% by 2010 and by 14 to 16 % by 2020 depending on the region.
Whereas there is a trend break for passenger energy demand by 2010 to 2015, when energy
demand stops growing and is even reduced in all three regions, freight energy trends are little
affected by the policy portfolio retained. The modal breakdown (Figure 4.3) shows that fuel
demand from passenger vehicles is affected most (a reduction of 20 % or 141 Mtoe compared
to the base case). This transportation mode is affected simultaneously by most measures
analysed, the dominant being voluntary or regulated fuel intensity reduction, but also by
demand policies and increased taxation. Aviation fuel demand in 2020 is reduced by 42 Mtoe
or 16% primarily because of the fuel (carbon) taxation. The reduction of trucking energy
demand is less important than for the other modes because of the limitations of the policy
portfolios, about 33 Mtoe or 8% in 2020. In North America and Japan, small trucks and vans
contribute most to the reductions in trucking compared to larger, long-distance trucking, since
they are affected by fuel intensity improvement policies, and in the case of Japan, by urban
logistic policies.
Chapter 4: Departure from the base trends 106
Figure 4.1: Energy demand scenarios including the impact of the regional policy
portfolios
Western Europe
500
400
300
Mtoe
200
100
0
1970 1980 1990 2000 2010 2020
Total transport sector Passenger transport Freight transport
Base case Base case Base case
Incl. enacted policies Incl. enacted policies Incl. enacted policies
Incl. additional policies Incl. additional policies Incl. additional policies
Japan
150
100
Mtoe
50
0
1970 1980 1990 2000 2010 2020
Total transport sector Passenger transport Freight transport
Base case Base case Base case
Incl. enacted policies Incl. enacted policies Incl. enacted policies
Incl. additional policies Incl. additional policies Incl. additional policies
Chapter 4: Departure from the base trends 107
North America
1000
800
600
Mtoe
400
200
0
1970 1980 1990 2000 2010 2020
Total transport sector Passenger transport Freight transport
Base case Base case Base case
Incl. enacted policies Incl. enacted policies Incl. enacted policies
Incl. additional policies Incl. additional policies Incl. additional policies
Table 4.1: Transport energy trends and policy impact in three OECD regions
Western Europe Japan North America
1997 2010 2020 1997 2010 2020 1997 2010 2020
Base case (Mtoe) 300 390 451 94 117 131 605 758 842
With enacted policies
375 422 111 121 no policies considered
(Mtoe)
Relative change to base
-4% -7% -5% -8%
case
With additional policies
363 380 108 110 711 720
(Mtoe)
Relative change to base
-7% -16% -8% -16% -6% -14%
case
Growth rate [% p.a.] 1.5% 0.5% 1.0% 0.2% 1.2% 0.1%
Figure 4.2: Policy impact of the different scenarios
(North America, Japan and Western Europe combined)
by region by mode
1600 1600
1400 131 1400 71 71
121
1200 110 1200 263 263 73
451 422 222
1000 94 1000 61
380 142 392 390
Mtoe
Mtoe
800 300 800
359
262
600 600
400 842 842 400
720 698 660
605 535 557
200 200
0 0
1997 2020, base case 2020, enacted 2020, additional 1997 2020, base case 2020, enacted 2020, additional
policies policies policies policies
Cars and light trucks Trucks (incl. small trucks and vans)
North America Western Europe
Aviation (intra- and inter-regional) Other
Japan
Chapter 4: Departure from the base trends 108
As a result, energy demand from cars in 2020 is almost brought back to 1997 levels (only
+4% between 1997 and 2020), while truck freight and aviation still experience substantial
increases under the additional policy case of +37 % and +56 % respectively. The other modes
(passenger and freight rail, bus and navigation), though very small in total, increase their
energy demand by 20 % or by about 2 Mtoe as a result of modal shifts.
Figure 4.3: Modal breakdown of incremental energy demand in the different scenarios
between 1997 and 2020 (North America, Japan and Western Europe combined)
A factor analysis of energy demand in the different regions (Figure 4.4) reveals the changes
induced by the regional policy portfolios. The average annual change rates of each factor
contributing to transport energy demand are shown over the whole projection period.
For passenger transport, the major change consists in higher fuel intensity reductions
compared to the base case, where these where almost nil. They stem primarily from passenger
vehicles but also from aviation in the combined policy case. Depending on the region, they
reach an average rate of –0.9 % p.a. to –1.3 % in the case of combined additional policies.
This compares to an average rate of –0.9 % p.a. in Europe and North America for the past and
reveals that substantial efforts will be needed to achieve energy intensity improvement rates
just similar or slightly superior to past trends. In Japan, where energy intensity improvements
were minor in the past, the policy impact assumptions are breaking with these trends and
actually do yield significant progress in energy intensity improvement rates compared to the
past83.
83
Major shifts towards larger cars were not taken into account, which could erode the gains per weight class prescribed in the Top Runner
legislation.
Chapter 4: Departure from the base trends 109
Figure 4.4: Factors contributing to energy demand growth:
Comparison among the different scenarios
Passenger Freight
Western
Europe
Japan
North
America
In Europe, the activity growth is slightly increased under the additional policy case. This
results from the rebound effect due to the relatively strong fuel efficiency improvements in
cars which are not fully compensated by the other measures considered. Modal shifts play a
very minor role in the change in energy demand from the policy scenarios, partly because of
the limited shifts assumed, partly because of the very similar fuel intensities of the competing
modes.
As for freight, the changes in the contributing factors are limited, as are the overall changes in
energy demand trends triggered by the assumed policy portfolios. Yet, some higher fuel
intensity improvements (especially in trucking and aviation) as well as lower energy demand
increases from modal shifts characterise the policy impact.
CO2 emission trends with additional policies
Similarly to the base case (see Chapter 2), no major changes in fuel mix are expected.
Alternative, low-carbon fuels could play a certain role with strong political support as
portrayed for North America. There, low-carbon fuels are estimated to account for 4.7% of
Chapter 4: Departure from the base trends 110
total transport fuel consumption in 2020. Otherwise, fuel mix is changing as a result of modal
shifts and the specific fuel share in each mode. Hence, in Europe, increased diesel share in
passenger cars (as a consequence of the ACEA Voluntary Agreement) and increased share of
kerosene due to the gains in modal shares in aviation play some role. Overall, the impact on
(tail-pipe) CO2 emissions is marginal, since the carbon content of the different petroleum
fuels is relatively similar84.
In Figure 4.5, the results are shown for the different cases in million tonnes of CO2 and
relative to the 1990 CO2 emissions, the base year for the Kyoto GHG reduction target.
Already by 1997, the latest year with actual data, CO2 emissions have increased compared to
1990 by 15%, 29% and 11% for Western Europe, Japan and North America. By 2010, the
base case expects a level of +50%, +60% and +40% respectively. In Western Europe and
Japan, the enacted fuel efficiency policies for cars will reduce this level somewhat to 45% and
52% respectively. The additional policies retained would change the situation significantly
only after 2010. Yet, the CO2 emissions increase by 2020 is still expected at +47%, +52% and
+27% over 1990 under the additional policy case, or about 30 percentage points under the
base case case at this time.
Figure 4.5: CO2 scenarios including the impact of the regional policy portfolios
Western Europe
1500
1250
1000
Mtoe
750
500
250
0
1970 1980 1990 2000 2010 2020
Total transport sector Base case
Incl. enacted policies Incl. additional policies
84
The discrepancy between diesel and gasoline per unit of final energy provided is around 7%.
Chapter 4: Departure from the base trends 111
Japan
500
400
300
Mtoe
200
100
0
1970 1980 1990 2000 2010 2020
Total transport sector Base case
Incl. enacted policies Incl. additional policies
North America
3000
2500
2000
Mtoe
1500
1000
500
0
1970 1980 1990 2000 2010 2020
Total transport sector Base case
Incl. enacted policies Incl. additional policies
Chapter 4: Departure from the base trends 112
Key findings – transport energy and CO2 emissions trends with additional
policies
The findings below need to be seen in relation with the policy portfolio retained in the
analysis, which tries to reflect current priorities in the different regions. Other, more stringent
policies would of course lead to different results.
The additional policies studied here can bring stabilisation of transport energy demand and
CO2 emissions after 2010 – but not before. Until 2010, they have no significant effect, rather
energy demand and CO2 emission will increase further significantly. This poses a real
challenge to the attainment of the Kyoto targets.
The policy measures currently in discussion appear insufficient to achieve actual reductions
back to the energy demand and CO2 emission levels of 1990 and below.
Growth in passenger and freight transport demand remains a long-term problem. It is
slowing, but it appears not feasible to compensate for its effect on energy demand with fuel
intensity improvements alone. Nor would this approach be sufficient to achieve significant
fuel demand reductions in the longer term.
Policies are available for containing passenger vehicle energy demand at today's level. If
measures to hasten fuel intensity improvements in cars and personal light trucks continue to
be tightened beyond what is enacted today, fuel demand from this mode after 1997 can be
stabilised until 2020 at current levels, despite significant activity increases. In the scenario
with additional policies, such policies yield the biggest impact compared to the base case.
Road freight, in small and large trucks, provides a large share of the increase in transport
energy demand under the combined policies in the alternative case. Strong economic and
regulatory measures beyond those assumed here might be necessary to contain its growth and
achieve actual reductions. Fuel intensity policies similar to those for cars applied to small
commercial trucks and vans can lead to significant fuel demand reductions, due to the
relatively high portion of this transport segment in energy demand.
Fuel intensity changes triggered by the policies analysed outweigh by far the effect of modal
shifts and activity restraint. This primarily reflects not only the choice of policies retained but
also the current thrust of policy-making on transport and CO2.
Growth in aviation fuel demand is a major concern. The fuel tax assumed here would not
significantly affect it, as the impact of an increase in price does not counterbalance the
increase in activity. A tougher burden could be needed and potentially justified by the greater
climate impact from greenhouse gases emitted by aircraft.
Chapter 4: Departure from the base trends 113
References
Literature
ADEME 1999: Impact Comparé de la Mise en Place de l’Accord ACEA et de la Pénétration
de la Climatisation Automobile, Agence de l’environment et de la maîtrise de l’énergie,
Paris
Advisory Group on Best Practice and Charges in Freight Transport: Report to Neil Kinnock,
European Commissioner for Transport, March 1998
ANCAT 1998: ANCAT/EC2 Global Aircraft Emissions Inventories for 1991/1992 and 2015.
Report by the ECAC/ANCAT and EC working group. ECAC-EC publication
Ang, B.W. , Zhang F. Q., 2000: “A survey of index decomposition analysis in energy and
environmental studies”, Energy, Vol. 25, pp. 1149-1176
Banister, D., 2000: “Sustainable Urban Development and Transport – a Eurovision for 2020”,
Transport Reviews, Vol. 20, No.1, 113-130
Boeing 1998-2000: Current Market Outlook, www.boeing.com/commercial/cmo/
Bowman, D., Leiby P., 1998: Methodology for Constructing Aggregate Ethanol Supply
Curves, TAFV Model Technical Note, draft, revision 3, Oak Ridge National Laboratory,
Oak Ridge, TN, 37831-6205 August 24.
CE-Delft, 1995: Comparing Emissions and Energy Consumption from Road and Rail Traffic,
Oplossingen voor milieu, economie en technologie (Solutions for environment, economy
and technology), Delft, July
Committee on Advanced Automotive Technology Plan, 1998: Review of the Research and
Development Plan for the Office of Advanced Automotive Technologies, National Research
Council, National Academy Press, Washington D.C.
Cullinane, K., Toy, N., 2000: “Identifying Influential Attributes in Freight Route/Mode
Choice Decisions: Content Analysis”, Transportation Research Part E, 36, pp. 41-53
Dahl, C., 1995: “Demand for Transportation Fuels: A Survey of Demand Elasticities and their
Components”, Journal of Energy Literature I, 2, pp. 3-27
Dargay, J., Gately, D., 1999: “Income Effect on Car and Vehicle Ownership, World-wide:
1960-2015”, Transportation Research A, 33 (1999), pp. 101-138
Dargay, J., Gately, D., 1997: “Vehicle Ownership to 2015: Implications for Energy Use and
Emissions”, Energy Policy, Vol. 25. Nos 14-15, pp. 1121-1127
Davis, S.C. 1998: Transportation Energy Data Book, Edition 18, Oak Ridge National
Laboratory, Oak Ridge, Tennessee
DeCicco, J, Mark, J., 1998: “Meeting the Energy and Climate Challenge for Transportation in
the United States”, Energy Policy, Vol. 26, No 5, pp. 395-412DeCicco, J., 1997:
Developing a Market Creation Program to Promote Efficient Cars and Light Trucks,
American Council for an Energy-Efficient Economy (ACEEE), Washington, August 1997
References 115
DeCicco, J., Delucchi M., 1997: Transportation, Energy and Environment: How Far can
Technology Take Us? ACEEE, Washington D.C. DeCicco, J., Gordon, D., 1993: Steering
with Prices: Fuel and Vehicle Taxation as Market Incentives for Higher Fuel Economy,
American Council for an Energy Efficient Economy (ACEEE), Washington 1996
DeCicco, J., Lynd, L., 1996: Combining Vehicle Efficiency and Renewable Biofuels to Reduce
Light Vehicle Oil use and CO2 Emissions, American Council for an Energy-Efficient
Economy (ACEEE), Washington 1996
DeCicco, J., Ross M., 1996: Recent Advances in Automotive Technology and the Cost
Effectiveness of Fuel Economy Improvement, Transpn Res.-D, Vol. 1, No. 2. Pp. 79-96,
1996
DeLucchi, M., 1997: A revised model of Emissions of Greenhouse Gases from the Use of
transportation Fuels and Electricity, Institute of Transportation Studies, University of
California, Davis, UCD-ITS-RR-97-22, November 1997
Department of Transport and Regional Services, 2000: The Commonwealth’s Transport
Directions – Task and Outlook Booklet – Text version, Canberra, Australia
(www.dotrs.bov.au/xmt/ta%5Ftext.htm)
Difiglio, C., 1997: “Using Advanced Technologies to Reduce Motor Vehicle Greenhouse Gas
Emissions”, Energy Policy, Vol. 25, Nos 14-15, pp. 1173-1178
DOT, 1999: Assessment of GHG Models for the Surface Transportation Sector, prepared by
Energy and Environmental Analysis Inc and Cambridge Systematics, Department of
Transportation, Washington D.C., November
DRI& KU Leuven 1999: Auto-Oil II Cost-Effectiveness Study – Final Draft Transport Base
Case Report, presented to the European Commission, MarchDuke, R., Kammen, D., 1999:
“The Economics of Energy market Transformation Programs”, The Energy Journal; Vol.
20, No. 4, pp 15-64
EEA (Energy and Environmental Analysis), 2000: Performance and Cost of Electric Hybrid
and Fuel Cell Vehicles, final report, prepared for the IEA, Paris, October
EEA (Energy and Environmental Analysis), 1999: Canadian Transport Table Study 3: Road
Vehicles and Fuels Technology Measures Analysis, Final Report, Prepared for the
Canadian Transportation Issue Table, Vehicle Efficiency Technologies Category, Hull,
Quebec, September
Elzen, B., 1995: Towards Cleaner Cars and Transport, Country Study Japan, Centre for
Studies on Science, Technology and Society, University of Twente, The Netherlands, April
1995
Energy Conservation Center, 1999: Japan Energy Conservation Handbook 1999, Tokyo
Enquête Commission, 1995: Mobility and Climate – Developing Environmentally-sound
Transport Concepts, Enquête Commission “Protecting the Earth” of the German
Bundestag
References 116
EC (European Commission), 1997: Energy for the Future: Renewable Sources of Energy,
Communication from the Commission, White Paper for a Community Strategy and Action
Plan, COM(97)599 final (26/11/1997), Brussels
EC (European Commission), 1998a: On Transport and CO2 - Developing a Community
Approach, Communication from the Commission to the Council, the European Parliament,
the Economic and Social Committee and the Committee of the Regions, COM(1998)204
final, Brussels, March
EC (European Commission), 1998b: Proposal for a Council Decision establishing a Scheme
to Monitor the Average Specific Emissions of Carbon Dioxide from New Passenger Cars,
COM(1998) 348 final, Brussels, 12.6.1998
EC (European Commission), 1998c: Fair Payment for Infrastructure Use: A Phased
Approach to a Common Transport Infrastructure Charging Framework in the EU, White
Paper, COM(1998) 466 final, Brussels, 22.07.1998
EC (European Commission), 1998d: Implementing the Community Strategy to Reduce CO2
Emissions from Cars: an Environmental Agreement with the European Automobile
Industry, Communication from the Commission to the Council, the European Parliament,
the Economic and Social Committee and the Committee of the Regions, COM(1998)495
final, Brussels, 29.07.1998
EC (European Commission) 1998e: EUCARS: A partial equilibrium model of European Car
emissions (version 3.0), C. Denis, G. J. Koopman, Directorate General II
EC (European Commission), 1999a: Non-technical Measures, Final Report, Working Group
5, Auto-Oil Programme, European Commission, Brussels, 10.11.1999
EC (European Commission), 1999b: European Union Energy Outlook to 2020, European
Communities, special issue, Brussels, November
EC (European Commission), 2000a: Action Plan to Improve Energy Efficiency in the
European Community, Communication from the Commission to the Council, the European
Parliament, the Economic and Social Committee and the Committee of the Regions, TREN
D1 17/399, Brussels, February
EC (European Commission), 2000b: STREAMS Project, final report, European Commission,
Brussels
EC (European Commission), 2000c: Towards a European Strategy for the Security of Energy
Supply, Green Paper , European Commission COM(2000) 769 final Brussels, November
ECMT, 1995: Urban Travel and Sustainable Development, OECD/ European Conference of
Ministers of Transport, Paris
ECMT, 1998a: Transport Infrastructure in ECMT countries: Profiles and Prospects
(Monograph), OECD/ European Conference of Ministers of Transport, Paris, May
ECMT, 1998b: CO2 Emissions from Transport, OECD/ European Conference of Ministers of
Transport, Paris
ECMT, 1998c: Efficient Transport in Europe: Policies of Internalization of External Costs,
OECD/ European Conference of Ministers of Transport, Paris
References 117
ECMT, 1999a: Cleaner Cars: Fleet Renewal and Scrappage Schemes, Guide to good
practice, OECD/ European Conference of Ministers of Transport, Paris
EIA (Energy Information Administration) 2000: Annual Energy Outlook , DoE/EIA,
Washington DC
Ellwanger, G., Wilckens, Martin 1995: High Speed for Europe, UIC/JRTR, Paris/Tokyo
European Conference of Ministers of Transport, 1999 (ECMT 1999b): Motor Vehicle
Pollution Control, CEMT/CS/ENV/(99)12/REV2, OECD/ European Conference of
Ministers of Transport, Paris
European Conference of Ministers of Transport, 1999 (ECMT 1999c): 14th International
Symposium on Theory and Practice in Transport Economics, “Which Changes for
Transport in the Next Century?”, OECD/ European Conference of Ministers of Transport,
Paris
European Environmental Agency, 2000: “Are we moving in the right direction? – Indicators
on transport and environment integration in the EU”, TERM 2000, Environmental Issues
Series No 12, Copenhagen
Gately, D. 1988: “Taking-off: The US Demand for Air Travel and Jet Fuel”, The Energy
Journal, Vol. 9 No. 4., pp. 63-91
Gielen, D., De Feber M., et al., 2001: “Biomass for Energy or Material? A Western European
Systems Engineering Perspective”, Energy Policy 29, pp 291-302
Goldemberg, J., 1996: “The Evolution of Ethanol Costs in Brazil”, Communication, Energy
Policy, Vol 24. No 12, pp. 1127-1128
Gorham, R., 1998: Land-Use Planning and Sustainable Urban Travel – Overcoming the
Barriers to Effective Co-ordination, prepared for ECMT, Paris
Greene, D., 1992: “Energy Efficiency Improvement Potential of Commercial Aircraft”,
Annual Review of Energy and the Environment, 1992, 17: 537-573
Greene, D., 1996: Transportation and Energy, Eno Transportation Foundation
Greene, D., 1998: “Why CAFE Worked”, Energy Policy. Vol. 26, No 8, pp. 595-613
Greening, L. A., Ting, M., Davis, W. B. 1999: “Decomposition of aggregate carbon intensity
for freight: trends from 10 OECD Ccountries for the period 1971-1993”, Energy
Economics, Vol. 21, pp. 331-361
Grubb, M., 1997: “Technologies, Energy Systems and the Timing of CO2 Emission
Abatement: an Overview of Economic Issues”, Energy Policy, Vol 25, No 2, pp. 159-172
Grübler, A., 1993: The Transportation Sector: Growing Demand and Emissions, IIASA,
Laxenburg, Austria
Grübler, A., Nakicenovic N. et al, 1999: “Dynamics of Energy Technologies and Global
Change”, Energy Policy, 27, pp 247-280
Grübler, A., Nakicenovic, N. and Schaefer, A., 1993: Dynamics of Transport and Energy
Systems, IIASA, Laxenburg, Austria
Grübler, A., Nakicenovic, N., 1991: Evolution of Transport Systems, IIASA, Laxenburg
References 118
Hague Consulting Group et al., 1999: TRACE, Final Report, Contract No. RO-97-SC.2035,
commissioned by the European Commission, Brussels
Hatta, Mikito, 1994: “An Analysis of Energy Consumption of Road Vehicle Transport –
Actual Conditions of Regional Transport (Trucking) in Metropolitan Area and Effects of
NOx Law,” Energy in Japan No. 127
Hayashi/EST Committee of Japan, 1999: A Mesoscale Estimation of Future CO2 Emissions in
Transport, OECD, May
Hecq, W.J., 1995: Mobility Analysis and Models of Mobility, in Estimating of Pollutant
Emissions from Transport, COST 319 wokshop, EC, Brussels, 27-28 November, 5 pp.
ICAO (International Civil Aviation Organisation), 1999: Civil Aviation Statistics of the
World, ICAO Statistical Yearbook, Montreal
IEA/AFIS (AFIS,1999a): Automotive Fuels for the Future, OECD/IEA, Paris
IEA/AFIS (AFIS,1999b): Implementation Barriers of Alternative Fuels, Report for the
Advanced Motor Fuels Implementing Agreement, Innas BV, Breda, the Netherlands
IPCC (Intergovernmental Panel on Climate Change), 1996: IPCC Guidelines for National
Greenhouse Gas Inventories, revised guidelines
Inter-laboratory Working Group on Energy-Efficient and Low-Carbon Technologies, 1997:
Scenarios of U.S. Carbon Reductions: Potential Impacts of Energy Technologies by 2010
and Beyond (“5 lab study”)
IEA 1997a: Indicators of Energy Use and Efficiency – Understanding the Link between
Energy and Human Activity, OECD/International Energy Agency, Paris
IEA 1997b: The Link between Energy and Human Activity, OECD/IEA, Paris
IEA 2000a: Voluntary Agreements as Driver of Technological Change in the Transport (Light
Duty Vehicle Sector), OECD IEA project for the Annex 1 Expert Group on the UNFCCC,
Feb. 2000
IEA 2000b: World Energy Outlook 2000 Edition, OECD/IEA, Paris
IEA 2000c: CO2 emissions from Fuel combustion 1971-1998, OECD/IEA, Paris
IEA 2000d: The Road from Kyoto – Current CO2 and Transport Policies in the IEA,
OECD/IEA, ParisIEA 2000e: Experience Curves for Energy technology Policy,
International Energy Agency, Paris
IEA 2000f: Energy Statistics of OECD Countries, edition 2000, OECD/IEA, Paris
IEA 2001: Saving Oil and Reducing CO2 Emissions in Transport: Options and Strategies,
OECD/IEA, Paris
Keidanren (Japanese Federation of Economic Organisations), 1997: Keidanren Voluntary
Action Plan on the Environment (Final Report), 17 June
Kemp, R., 1997: Environmental Policy and Technical Change – A Comparison of the
Technological Impact of Policy Instruments, Edwards Elgar Pb. Ltd. Cheltenham UK
References 119
Kiang, N., Schipper, L. 1996: “Energy trends in the Japanese transportation sector”,
Transport Policy, Vol. 3, No ½, pp. 21-35
Kibune, H., Ishida, H. 1999: “Japan’s Long-Term Energy Supply and Demand Outlook and
its Implications”; Energy in Japan No. 156
Kirby, H. et al., 2000: “Modelling the Effects of Transport Policy Levers on Fuel Efficiency
and National Fuel Consumption”, Transportation Research Part D, 5, pp265-282
Kojima, F., Katsuki, S., 1999: Examination of Reform Measures of the Traffic Environment
by Applying the Latest Technology, The Subcommitee of Research on Environ- mental
Effects of ITS, 6th World Congress on Intelligent Transport Systems, Toronto, Canada,
November 8-12
Komor, P.,1995: “Reducing Energy Use in US Freight Transport”, Transport Policy, Vol.2,
No 2, pp. 119-128
Leiby, P., Rubin, J., 1997: Sustainable Transportation: Analyzing the Transition to
Alternative Fuel Vehicles, presented at the 1997 Asilomar Conference on Policies
Fostering Sustainable Transportation Technologies, 17-20 AugustLeiby, P., Rubin, J.,
2000: The Alternative Fuel Transition: Results from the TAFV Model of Alternative Fuel
Use in Light-Duty Vehicles 1996-2010, Oak Ridge National Laboratory,
ORNL/TM2000/168
Levelton (Engineering Ltd.) 1999: Alternative and Future Fuels And Energy Sources For
Road Vehicles, prepared for: Transportation Issue Table National Climate Change Process,
Hull, Canada
Lipman, T., Sperling, D., 1999: Forecasting the Costs of Automotive PEM Fuel Cell Systems
Using Bounded Manufacturing Progress Functions, IEA International Workshop on
“Experience Curves for Policy-Making: The case of Energy Technologies”, Stuttgart,
Germany, 10-11 May
Lynd, L., 1997: “Cellulosic Ethanol: Technology in Relation to Environmental Goals and
Policy Formulation”, in: Decicco and Delucchi 1997, pp.109-133
McDonald, A., Schrattenholzer, L., 2001: “Learning Rates for Energy Technologies”, in
Energy Policy, pp 255-261
McKinnon, A., 1996: Freight Distribution and Logistics: Fuel Use and Potential Savings,
Heriot-Watt University, Edinburgh, report prepared for ETSU, February
Michaelis, L., Davidson, O. 1996: “GHG mitigation in the transport sector”, Energy Policy,
Vol. 24, No 10/11, pp. 969-984
Minato, K., 1997a: Road Transportation CO2 Reduction in Japan, Seminar paper presented at
IEA, January
Minato, K., 1997b: Road Transportation and Global Environmental Problems, JARI,
Tsukuba, May
Minato, K., 1998: Road Transportation and Global Environmental Problems, JARI, Tsukuba,
August
References 120
Minato, K., 1999a: Road Transportation and Global Environmental Problems, JARI,
Tsukuba, SeptemberMinato, K., 1999b: Presentation at IEA on 20 September
Ministère de l’Equipement, des Transports et du Logement, 1999: Eléments d’Evaluation
Environnementale des Schémas de Services – Effets sur l’Environnement des Différents
Scénarios de la Demande de Transport, Paris, April
Ministry of International Trade and Industry, 1998a: Long-Term Outlook of Japanese Energy
Supply and Demand, Agency of Natural Resources and Energy, Tokyo
Ministry of International Trade and Industry, 1998b: Key Assumptions for the Outlook –
Long-Term Energy Supply and Demand Outlook (as of June 1998), Tokyo
MOT (Ministry of Transport), 1999: Environment and Transport: Towards an Environment-
friendly Next Generation Transportation System, Tokyo
MOT (Ministry of Transport) / Global Warming Prevention Headquarters, 1998: Guidelines
of Measures to Prevent Global Warming – Measures towards 2010 to Prevent Global
Warming, 19 June
Mintz, M., Singh M., 1996: “Issues and Cost associated with Transition to Alternative
Transportation Fuels and Vehicles”, Transportaion Research Record 1520, pp. 164-172
Nakicenovic, N., 1987: Transportation and Energy Systems in the US, IIASA, Laxenburg,
Austria
National Round Table on the Environment and the Economy, 1998: Greenhouse Gas
Emissions from Urban Transportation, Backgrounder, Renouf Publishing Co., Ottawa
OTA (Office of Technology Assessment), 1991: Improving Automobile Fuel Economy,
Washington D.C.
OTA (Office of Technology Assessment), 1996: Advanced Automotive Technology, Visions of
a Super Efficient Family Car, Washington D.C.
OECD, 1995: Motor Vehicle Pollution – Reduction Strategies beyond 2010, Organisation of
Economic Co-operation and Development, Paris
OECD, 1996: Policies and Measures for Common Action, Working Paper 1: Sustainable
Transport Policies: CO2 Emissions from Road Vehicles, Annex 1 Expert Group on the
United Nations Framework Convention on Climate Change, Organisation of Economic
Co-operation and Development /Laurie Michaelis, July
OECD, 1997a: Special Issues in Carbon/Energy Taxation: Carbon Charges on Aviation
Fuels, Annex 1 Expert Group on the United Nations Framework Convention on Climate
Change Working Paper No. 12 (OECD/ GD(97)78/Corr), Organisation of Economic Co-
operation and Development, Paris
OECD, 1997b: Policies and Measures to Encourage Innovation in Transport Behaviour and
Technology, Annex 1 Expert Group on the United Nations Framework Convention on
Climate Change Working Paper No. 13, Organisation of Economic Co-operation and
Development, Paris
OECD 1997c: OECD Environmental Data – Compendium 1997, Organisation of Economic
Co-operation and Development, Paris
References 121
OECD, 1997d: The World in 2020 – Towards a New Age, Organisation of Economic Co-
operation and Development, Paris
OECD, 1999: Environmentally Sustainable Transport, Report on Phase II of the OECD EST
Project, Volume 1: Synthesis Report (Second revised version) and Annex Volume:
Individual Project Case Studies for Phase II, Organisation of Economic Co-operation and
Development, Paris
Organization for the Promotion of Low-Emission Vehicles (LEVO(, 1998a: Low Emission
Vehicles in Japan – Current Diffusion Status and Promotional Measures for Diffusion,
Tokyo
Organization for the Promotion of Low-Emission Vehicles (LEVO), 1998b: Towards a
Sustainable Transport System, Tokyo
Oum T. H., Waters W.G., Yong J.S. 1990: Survey of Recent Estimates of Price Elasticities of
Demand of Transport, World Bank, Policy, Planning and Research, Working Papers
(Transportation), Washington DC
PDAC (Policy Dialogue Advisory Committee) 1995: Majority Report to the President to
Recommend Options for Reducing Greenhouse Gas Emissions from Personal Motor
Vehicles, October
Plotkin, S., Greene, D., 1997: “Prospects for Improving the Fuel Economy of Light-Duty
Vehicles”, Energy Policy, Vol 25, No 14-15, pp. 1179-1188
Replogle, M., 1993: Vital Strategies for Clean Air Attainment, Transport Conformity and
Demand Management, Environmental Defense Fund, Washington, D.C. Schafer, A., 1995:
Trends in Global Motorized Mobility - The Past 30 years and Implications for the Next
Century, IIASA, Laxenburg, Austria
Schafer, A., 1998: “The Global Demand for Motorized Mobility”, Transport Research A, Vol,
32, NO. 6, pp. 455-477
Schafer, A., Victor, D., 1997: “Past and Future Trends in Global Mobility”, Scientific
American, , pp. 58-61, October
Schipper L., et al., 1993: “Mind the Gap – the Vicious Circle of Measuring Automobile Fuel
Use”, Energy Policy, pp. 1173-1189, December
Schipper, L., Marie-Lilliu C., Fulton, L. , 2002: “Diesels in Europe: Analysis of
Characteristics, Usage Patterns, Energy Savings and CO2 Emission Implications”, Journal
of Transportation and Energy Policy, forthcoming.
Schipper, L., Marie-Lilliu, C., 1999a: Carbon Dioxide Emissions from Transport in IEA
Countries, Recent lessons and Long-Term Challenges, KFB, April
Schipper, L., Marie-Lilliu, C., 1999b: Transportation and CO2 Emissions: Flexing the link, A
Strategy for the World Bank, The World Bank, Washington, September 1999
Schipper, L., Scholl, L., Price, L., 1997: “Energy Use and Carbon Emissions from Freight in
10 Industrialised Countries: An Analysis of Trends From 1973 to 1992”, Transportation
Research – D, Vol. 2, No. 1, pp57-76
References 122
Schipper, L.; Tax, W., 1994: “New Car Test and Actual Fuel Economy: Yet Another Gap?”,
Transport Policy, 1, (4) pp. 257-265
Scholl, L., Schipper L. , Kiang, N. 1995: “CO2 Emissions from Passenger Transport – a
comparison of international trends from 1973 to 1990”, Energy Policy, Vol. 23, No 12
Sorrell, S., 1992: “Fuel Efficiency in the UK Vehicle Stock”, Energy Policy, August 1992,
p.p. 766-780
Standing Committee to Review the Research Programme of the PNGV, 1998: Review of the
Partnership for a New Generation of Vehicles, Fourth Report, National Academy Press,
Washington D.C.
Stead, D., 1999: “Relationships between Transport Emissions and Travel Patterns in Britain”,
Transport Policy, 6, pp. 247-258
Taniguchi, E., van der Heijden, R., 2000: “An Evaluation Methodology for City Logistics”,
Transport Reviews, Vol. 20, No. 65-90
TRB, 1997: Towards a Sustainable Future – Addressing the Long-term Effects of Motor
Vehicle Transportation on Climate Friendly Technology, Transportation Research Board
Special Report 251
U.S. DOE, 1997: Technology Opportunities to Reduce U.S. Greenhouse Gas Emissions (“11
lab study”), Prepared by National Laboratory Directors for the Department of Energy,
October
U.S. DOE (Department of Energy/Energy Information Administration), 1997: Alternatives to
Traditional Transportation Fuels 1996, DOE/EIA-0585(96) Washington D.C., December
U.S. DOE (Department of Energy/Energy Information Administration), 1998, Transportation
Sector Model of the National Energy Modelling System (Vol 1,2,3), Washington, D.C. U.S.
DOE (Department of Energy/Energy Information Administration), 2001: Annual Energy
Outlook 2001, Washington D.C.
Vanek, F.M., Campbell, J.B. 1999: “UK road freight energy use by product: trends and
analysis from 1985 to 1995”; Transport Policy 6, pp. 237-246
Vanejk, F., Morlok, E., 2000: “Improving the Energy Efficiency of Freight in the United
States through Commodity Based Analysis: Justification and Implementation”, Transport
Research Part D, 5 (2000) 1-29
Vedantham, A., Oppenheimer, M., 1998: “Long-term Scenarios for Aviation: Demand and
Emissions of CO2 and NOx”, Energy Policy, Vol. 26, No. 8, pp. 625-641
Wang, M., 1996: Greet 1.0 – Transportation Fuel Cycles Model, Methodology and Use,
Arbonne National Laboratory, Argonne, IllinoisWang, M., et al, 1999: Fuel Cycle Energy
and Greenhouse Gas Emissions Effects of Fuel Ethanol, Center for Transportation
Research, Argonne National Laboratory, prepared for the Office of Technology Utilisation,
Office of Transportation Technologies,. US DoE, JanuaryWohlgemuth, N., 1997: “World
Transport Energy Demand Modelling – Methodology and Elasticities”, Energy Policy, Vol
25, No 14-15, pp 1109-1119
References 123
World Energy Council (WEC) 1995a: Global Energy Perspectives to 2050 and Beyond,
London
World Energy Council (WEC) 1995b: Global Transport Sector Energy Demand towards
2020, London
World Energy Council (WEC) 1998: Global Transport and Energy Development, the scope
for Change, London
List of data sources
Apelbaum Consulting Group: The Australian Transport Task: Primary Energy Consumed and
Greenhouse Gas Emissions, Canberra, 1997
Australian Bureau of Transport and Communications Economics: Greenhouse Gas Emissions
for Australia Transport, 1991
Australian Bureau of Transport and Communications Economics: Transport and Greenhouse
Gases: Costs and Options for Reducing Emissions, 1996
Beca Carter Hollings and Ferner Ltd: Changing Energy Efficiency of Petroleum Products in
the Transport Sector, 1994
Beca Carter Hollings and Ferner Ltd: Energy Use in Transport, 1977 and 1986
Bureau of Transportation Statistics: Transportation Statistics Annual Report, Washington
D.C. , various editions
European Commission: EU Transport in Figures, Directorate-General for Energy and
Transport, in co-operation with Eurostat, various editions
European Conference of Ministers of Transport (ECMT): Statistical Trends in Transport,
OECD/ECMT, various editions
Federal Highway Administration: Highway Statistics, Washington D.C., various editions
International Air Transport Association (IATA): World Air Transport Statistics, various
editions
International Energy Agency: CO2 Emissions from Fuel Combustion, OECD/IEA, various
editions
International Energy Agency: Energy Balances of OECD Countries, OECD/IEA, various
editions
International Road Federation (IRF): World Road Statistics, various editions
Laboratory of Applied Thermodynamics, Aristotle University, Thessaloniki, Institut für
Umweltschutz und Energietechnik, TÜV Rheinland, Cologne, Germany and Institute of
Transport Sciences Ltd., Budapest, Hungary: Study on Transport-related Parameters of the
European Road Vehicle Stock, 1999
Ministry of Transport (New Zealand): Vehicle Fleet Emissions Model: New Zealand Vehicle
Fleet Database and Model Development, A Technical Report compiled by the Ministry of
Transport in support of the Vehicle Fleet Emission Control Strategy, 1998
References 124
Ministry of Transportation (Japan): Automobile Transport Statistics Yearbook, various
editions
National Resources Canada (NR Can): Energy Efficiency Trends in Canada, various editions
Oak Ridge National Laboratory: Transportation Energy Databook, Oak Ridge National
Laboratory (U.S.), various editions
The Energy Conservation Center, Japan: Handbook of Energy and Economic Statistics in
Japan, various editions
The Institute of Energy Economics, Japan: Energy in Japan, 1999
The International Civil Aviation Organization (ICAO): Air Transportation Statistics, various
editions
Union Internationale des Chemins de Fer (UIC): International Railway Statistics, various
editions
United Nations (UN): Annual Bulletin of Transport Statistics for Europe and North America,
various editions
References 125
Appendices
Appendices 126
APPENDIX 1 Description of the framework
Objectives
Since 1993, the IEA has provided long-term energy projections using the World Energy
Model (WEM). The WEM analyses the global energy supply and demand prospects. Climate
change, and CO2 emissions especially, have become a focus of the analysis in recent years.
Since the 2000 edition of the IEA's World Energy Outlook (WEO 2000) was also meant to
include an analysis of the effects of policy actions or technological changes in the OECD in
the context of GHG mitigation. It was decided to develop a new framework that would allow
for this type of study to be performed for the transport sector, with the main objectives to:
• Identify the factors that shape transport energy demand, including the role of activity
growth, modal shifts and fuel intensity changes;
• Assess and "quantify" the impact of different policies and measures for CO2 reduction in
transport either recently enacted or under consideration.
The framework is based on standard economic concepts and relationships, and incorporates a
variety of policy "levers" to allow comparison of a base case scenario with other scenarios
that involve implementation of specific transportation policy measures. For cars and personal
light trucks, the framework is capable of tracing the effects on the total stock of vehicles, and
provides estimates of CO2 emissions reductions. The framework is thoroughly described
below.
In this framework, the analysis has proceeded in 5 general steps:
• Development of a baseline case for each OECD region, reflecting future transportation
trends and CO2 emission levels in the absence of any initiatives.
• Analysis of the enacted measure to improve the vehicle fuel efficiency in Western Europe
and Japan and quantification of their impacts on the baseline projections. This allows the
computation of a Reference Scenario, as defined in the 2000 edition of the IEA's World
Energy Outlook.
• Comparison between the results of the reference scenario and the projections of the WEM.
The results from the two models were adjusted in iterations to yield a consistent picture.
• Analysis of a number of additional policy measures and evaluation of their individual
impact. Four policy types are defined: efforts to improve fuel efficiency of vehicles,
programmes to increase the use of alternative fuels, strategies to induce change in
transport demand and pricing measures, such as a tax policy.
• Development of a combined scenario, taking into account a package of policies as studied
in step 4.
Appendices 127
General description of the framework
An analysis framework has been developed for this study that integrates the following specific
features:
• Separation of the transportation modes (cars, light-duty trucks, railways, buses and
aviation for passenger transport modes, heavy trucks, small trucks and vans, freight
shipping by rail, inland navigation and freight aviation for freight transport modes) with
the capability to aggregate results across these sub-sectors to obtain a full estimate of
transport activity, energy and CO2 emissions.
• The baseline projections of activity and fuel intensity of each mode are based on
projections of population, GDP and fuel prices
• The fuel intensity modelling of cars and personal light trucks is computed in a stock-
turnover model. The simulation consists of evaluating the impact of new vehicle features
via a stock model on the fleet average and total fuel use.
• A similar approach is used to simulate the introduction of advanced vehicles and fuels.
The initial assumptions of the market expansion are evaluated in their consistency through
experience curve calculations.
• The transport demand-side management policies are evaluated using the impact shown in
different studies in terms of reduction of activity and modal shift. Transport modes are
decomposed into several sub-categories to allow this quantification.
The policy modelling framework is broken into 3 distinct components: a general framework
component, that includes a stock adjustment model in the case of cars and lights trucks (see
appendix 2); a framework for alternative vehicle and alternative fuel introduction; and a
component of demand policy impact estimation.
Geographic breakdown
The framework was developed to cover three OECD regions.
OECD North America is composed of the United States and Canada.
OECD Europe has been split into two distinct regions for the need of this study. Western
Europe is composed of the Member countries of the European Union, Norway, Iceland,
Switzerland, and alternative scenarios have been developed. The Central Europe and Turkey
region groups Poland, Czech Republic, Hungary and Turkey.
OECD Pacific is composed of Japan on one hand and Australia and New Zealand on another
hand. Only Japan has received a more detailed treatment regarding the policy analysis.
Transportation mode considered
The total transport sector has been decomposed in order to look at each transport mode.
Appendices 128
The first level of disaggregation is to differentiate passenger transport from freight transport,
since the demand of these two types of transport are not influence by the same factors (see
box 2.1) and react differently to economic variables.
The second level of details needed is to decompose both freight and passenger transport into
road, rail, navigation and aviation85.
And, when, as described further below, data allows, road transport has been split further into
cars, personal light trucks, bus, heavier trucks, small trucks and vans.
The disaggregation is presented in table A1.1.
Table A1.1: Disaggregation of transportation modes
Passenger Freight
− cars
− heavier trucks
Road − personal light trucks
− small trucks and vans
− bus
Rail − passenger rail − freight rail
− freight waterway and
Navigation
short-sea transport
− intra-regional aviation
Aviation − freight aviation
− international aviation
This decomposition definition needs to be adjusted according to data availability and national
or regional statistics are originated from different sources, originally collected for different
purposes and are therefore not homogenous across regions.
The general framework component
The development of this framework was initiated with the idea that the WEM should contain
more details to allow better analysis of some specific policy elements. The simple model,
called accounting model, is mainly based on the ASIF approach (See Schipper and Marie-
Lilliu, 1999b), which allows the decomposition of the CO2 emissions of the transport sector
into:
• Activity, measured in terms of passenger-kilometres (pass-km) or tonne-kilometres
(tonne-km);
• Sectoral structure (sub-sectoral shares of activity), characterizing the passenger or the
freight transport sector by its transport modal shares;
85
Pipeline transport, although included in the World Energy Model, is not included in this study, since the parameters influencing its energy
use are very different from the other transport modes.
Appendices 129
• Energy Intensity (energy used for each unit of Activity); For passenger, the energy
intensity is measured in MJ/pass-km (or Mtoe/pass-km) and for freight transport in
MJ/tonne-km (or Mtoe/tonne-km);
• And the carbon content of each Fuel, equal to the weight of CO2 released per unit of
energy, measured in kg CO2/MJ or kg CO2/Mtoe.
Figure A1.1: ASIF formulation
(1) Gi = ∑ Ai * S i * I i * Fi
where:
Gi is CO2 emissions in the transport sector i
Ai represents the activity level
Si is defined as the sectoral structure (sub-sectoral shares of activity)
Ii is the energy intensity (energy per activity unit)
Fi represents the carbon content of fuel k used (CO2 per energy unit)
Database of time series (past trends)
Databases have been developed for each region that include the activity level, energy intensity
and energy consumption between 1970 and 1997 for each transport mode (1995 for aviation).
The level of activity can generally be described in terms of passenger-kilometres or tonnes-
kilometres. However, in some cases, some further decomposition has been made.
In the case of private passenger vehicles (cars and personal light trucks, when separated), the
passenger-km have been decomposed into vehicle-km and a load factor (also called
occupancy rate), which is the average number of people per car. The vehicle-km data have in
turn been decompose in a number of vehicles (representing the fleet) and an average mileage
per vehicle per year. Finally, the number of vehicles can be translated into a vehicle
ownership rate, which is a convenient way to describe the private car sector which is often
described “close to saturation” levels. This decomposition allows the connection with the
stock model, by using the total fleet number, and is also used in the projection methodology
described below.
(2) Pass − km = load _ factor * veh − km
= load _ factor * nb _ vehicles * average _ mileage _ per _ vehicle _ per _ year
= load _ factor * nb _ vehicles _ per _ 1000 _ inh. * population * average _ milage
In the case of road freight vehicles (trucks, or heavier and small trucks when available), a
similar decomposition is made, into load factor (average tonne per vehicle) and vehicles-
kilometres. For small trucks and vans, the data available are measured in vehicle-km, and an
“artificial” load factor is applied to allow the computation of total freight transport activity
measured in tonne-kilometres. In fact, the unit describing in the most “correct” way their
Appendices 130
activity is vehicle-km, since the purpose of use of this type of vehicles is mainly the
accomplishment of services and only marginally the transport of goods.
(3) Tonne − km = load _ factor * veh − km
= load _ factor * nb _ vehicles * average _ mileage _ per _ vehicle _ per _ year
The energy consumption data are both described in Joule (J) and in tonne of Oil equivalent
(toe), as this unit is the one chosen in the WEO.
The fuel intensity of each transport mode is obtained by dividing the activity level by the
energy consumption of each mode. It is expressed in terms of MJ/pass-km for the passenger
modes and MJ/tonne-km for the freight modes. Again, in the case of road transport modes
(except for buses), the fuel intensity can be more conveniently described in terms of
MJ/vehicle-km, which is obtained by simply using the load factor. This vehicle intensity is the
one used in the stock model to account for the effect of a policy of improvement of fuel
intensity of vehicle. It allows some comparison with the value of other major regional studies,
very often expressed on vehicle intensity if they are focusing on road transport only.
(4) MJ / pass − km = load _ factor * MJ / veh − km
or
(5) MJ / tonne − km = loadfactor * MJ / veh − km
These databases are the core of the methodology, and an enormous effort was put in building
them (choosing the data sources and filling in missing data) and checking them as much as
possible. The result is not meant to be perfect, but has been “validated” as representing the
transport sector in a real way, keeping in mind the objective of this study.
Methodology for projections
The framework developed in this study uses the past data to “project” the activity levels and
fuel intensity between 1998 and 2020, by using relationships with a few macro-economic
indicators: total population, gross domestic product (GDP) and fuel prices.
The macro-economic indicator assumptions are given in tables 1.2, 1.3 and 1.4. They are
identical to those taken for the WEO 2000. Since for OECD Europe and OECD Pacific the
regions have been further split, the macro economic data have been split into the different
sub-regions taking literature sources into account.
This aggregated methodology is developed to get base case trends, i.e. with no specific policy
influences. Still, these projections are very useful to identify the factors influencing the total
transport energy demand, and pointing to the modes that would increase the energy use and
CO2 emissions.
Appendices 131
Activity projections
For each transport mode, simple econometric relations are tested and applied to the past
activity level. Three types of models have been applied to the different activity variables.
Log-log model with income and with or without fuel prices
In short, the variation of the input variables explain the variation of the output variable, and
the coefficients obtained the take the form of “elasticities” or “partial elasticities”.
(6) log(activity) = a + b * log(income) + c * log(fuelprices)
Two variables representing the income have been applied: GDP per capita for the passenger
modes, and GDP for the freight transport modes.
When fuel price elasticities are applied, i.e. in the case of trucking and aviation, the fuel prices
are chosen according to the transportation mode: diesel in the case of trucking, kerosene for
aviation. For the other some sectors, it was felt that either the model could not capture the fuel
price effect or it was assumed that it could be neglected in this simple approach: for collective
modes of transport, bus and passenger train, or for navigation and freight train.
Time trend model
In some case, it was felt that no macro-economic variables could be used to describe the
evolution of the output, for example in the case of load factor. Since all variables need to be
projected separately, some simple growth rates have been used. This methodology was also
used when no satisfactory relation could be found using the log-log approach.
The case of car ownership
In the case of car ownership, the model used in the 5 regions takes the form of a semi-log
model. Following Dargay and Gately, 1999, the ownership rate follows a S-shaped curve, and
most of the OECD regions are at the upper end of the saturation level, which can be described
by a semi-log approach.
(7) car _ ownership = a + b * log(GDP _ per _ capita )
The case of private vehicle use
Activity of private cars activity is decomposed into load factor, car ownership and average
vehicle mileage, and these three elements are projected in a almost independent way. The
influence of GDP per capita on private car activity is best captured by the car ownership
variable. To capture the influence of transport cost changes, fuel prices are simply used as an
indicator and applied in the modelling of the private vehicle use, together with a time
influence. It can in fact be observed from the past trends (since beginning of the 1980s) that
without price shocks, the average mileage of a car has a “natural” tendency to increase (like in
Europe or in North America) or to decrease (like in Japan). Some other factors, like the spatial
structure or congestion, cannot be captured in this simple approach.
Appendices 132
(8) car _ mileage = 10^ (a + b * log(fuel _ price * fuel _ int ensity) * time _ trend
The fuel cost (fuel_price*fuel_intensity) is used here as an simple indicator for the total cost
of using a private vehicle, and elements like purchase cost, insurance, maintenance etc... are
neglected.
The following table summarises the different approach used for each transport mode.
Table A1.2: Activity projections model
fuel fuel
time income
prices intensity
Private cars and personal light
trucks
Log
Ownership rate
GDP/cap
Log
Gasoline
Vehicle mileage Trend
“Combined” as fuel
cost
Load factor Trend
Log
Bus Log
GDP
Log
Passenger rail Log
GDP
Aviation (both intra-regional and Log Log
Log
international) GDP/cap Kerosene
Log Log
Heavier trucks Log
GDP Diesel
Log Log
Small trucks and vans Log
GDP Diesel
Log
Freight rail Log
GDP
Log
Navigation Log
GDP
Log Log
Freight aviation Log
GDP Kerosene
Fuel intensity projections
The modal fuel intensity described as the ration of energy used per unit of service in each
mode (passenger -km or tonne-km) is influenced by many factors (see Table 2.12). As
explained in the main text, the fuel intensity is modelled using a time change influence and a
fuel price influence.
The model to project modal fuel intensity has the following form:
(9) fuel _ int ensity = 10^ (a + b * log(fuel _ price) * time _ trend
Appendices 133
The time trend component is calibrated according to past trends, and the fuel price coefficient,
b, is based on what can be found in the literature.
In the case of private cars and personal light trucks in North America, a separate stock
modelling exercise has been performed, due to data availability and the need to quantify the
impact of fuel intensity improvement policies enacted in Europe and in Japan (see Appendix
2).
Calculating the Energy Consumption and CO2 emissions
Once the projections of activity and fuel intensity are made for each mode, they can be
multiplied to obtain the energy consumption. The total energy of the transport sector can be
obtained by adding all modal energy consumption.
With assumptions of shares of the respective fuels in each transport mode (gasoline, diesel,
electricity), and with values of the carbon content coefficient of each fuel, CO2 emissions can
also be calculated by transport mode and aggregated to obtain the total CO2 emissions for the
transport sector.
The estimation of alternative vehicle and alternative fuel introduction
A phase-in of advanced vehicles (vehicle sales share over time) and, separately, advanced
fuels (blending share and/or alternatively alternative fuelled vehicle share over time) is
assumed. In each case, the vehicle parameters are used as input into the stock model in order
to estimate the total effect on the fleet over time. This allows to calculate fuel and CO2
savings for the advanced vehicle introduction and fuel replacement (as the sum of blending
and dedicated vehicle demand) in the alternative fuel case.
The assumptions on production quantities are used as input into the experience curve
calculation in order to evaluate how likely such a phase-in could be from an economic
perspective. Slow cost reduction and long-range need for subsidies makes a phase-in unlikely,
and allows to conclude the the production quantities initially assumed are too optimistic.
Rapid cost reduction and rapid diminishing of subsidies with the volumes assumed point
would point at a realistic or even too pessimistic phase-in.
A component of demand policy impact estimation
A selection of policy elements is assumed for each region. In order to evaluate the impact of
each policy, which aims at tackling a specific sub category of transport activity, each transport
mode is further decompose to allow a distinction of activity and fuel intensity for each sub
category. The hypotheses taken for the quantification of each policy impact are shown for
each region in the main text.
Appendices 134
Table A1.3: Modal subcategories
Transport mode Sub category
urban
Cars regional
motorway
intercity
Bus
urban
urban (metro)
Passenger Rail normal
high speed
long haul
Trucks
others
bulk
Freight rail
combined/container
Appendices 135
APPENDIX 2: Modelling vehicle fleet turnover
Qualitative Description
In order to simulate the impact of efficiency changes in cars and personal light trucks (chapter
3.2 and 3.3) fleet-turnover is explicitly modelled. Such stock models often require detailed
information on the vintage composition (survival rates over time) (see for example the NEMS
Transportation Demand Module,
http://www.eia.doe.gov/oiaf/aeo/assumption/transportation.html). With the limitations in data
availability for the large set of countries covered in this study, a simpler approach is chosen,
relying on:
• data on new vehicle registration (yearly sales weighted average),
• total vehicle stock and
• new vehicle test fuel intensity performance
to determine fleet average on-road fuel intensity from year to year (with the initial fleet fuel
intensity being known).
The missing “scrappage” quantity of vehicles can be deduced from the above data (figure
A2.1) and the fuel performance of scrapped cars is assumed to lag behind the average fleet
performance by about half the fleet turnover time. This approach thus allows achieving a
sufficiently “dynamic” and precise stock model for different regions with relatively little data
requirements (such as the age distribution etc.). With these approach sales weighted fuel
intensity based on test performance can be calculated for the entire fleet. Actual on-road fleet
fuel intensities includes also a weighing by vehicle mileage, which means they reflect the fact
that different car types drive different distances per year.
Appendices 136
Figure A2.1: Fleet turnover from year to year
Most importantly, new cars are driven much more than older models. This implies that
changes in new car performance show more quickly in the average (km-weighted) fleet
performance than the (only) sales-weighted average would imply. We adjust for the fact in the
stock model and thereby simulate a km-weighted performance level.
Finally, the test/on-road gap (see Box 3.1) needs to be accounted for in order to make the
simulation above comparable to actual on-road fleet fuel intensities derived from total fleet
fuel consumption and vehicle-km estimates.
In absence of a detailed model for estimating the gap, we compare the values calculated by
the procedure above with the on-road fleet fuel intensity based annual fleet fuel consumption
and total vehicle km. This comparison over the period 1980 to 1997 allows to deduce an
absolute gap level as well as a historical trend.
The approach was tested and validated for different countries in comparison to historical data
between 1970 and 1995. Due to a time lag in the model (phase-in) and the general poor data
qualities, the model performs well after 1980 (see Figure 3.2).
Appendices 137
Mathematical Formulation
The mathematical formulation is given in full length, since important policy conclusions are
drawn based on this model (chapter 3.2 and 3.3).
Basic equations
Vehicle number equation (VN): constitution of annual vehicle stock (ST) from three cohorts
between two subsequent years: Carry-over (CO), Scrappage (SC) and New registrations (NR)
in the year (k) and the year (k+1) (see Figure AX.1):
(1) VN kST = VN kSC + VN kCO and VN kST1 = VN kCO + VN kNR
+ +1
Past time series of total vehicle stock VNSC and new registrations VNNR are used to determine
an average scrappage factor RSC. This factor is used to project VNCO and VNNR, the total
vehicle stock being known from the activity (passenger-km) projections (via load factor and
average annual mileage per vehicle).
VN SC VN SC
(2) VN ST
k
= const. = R = average
SC
k
ST
k k =1998...2020 k =1980...1997 VN k
The average past scrappage rate RSC for Western Europe, Japan and North America are 5.5%,
7.5% and 7% respectively.
From (1) and (2)
(3) VN kNR = VN kST1 − VN kST (1 + R SC )
+1 +
Fuel consumption equation using fuel intensity (FI) and vehicle kilometers (VKM)
(4) FI kST ⋅ VKM kST = FI kSC ⋅ VKM kSC + FI kCO ⋅ VKM kCO and
FI kST1 ⋅ VKM kST1 = FI kCO ⋅ VKM kCO + FI kNR ⋅ VKM kNR
+ + +1 +1
A priory, the fuel intensity of the annually scrapped cars is unknown. We posit that the fuel
intensities of the scrapped cars lags by l years behind the average stock fuel intensity.
(5) FI kSC = FI kSTl
−
The time lag l is adjusted “manually” in order to achieve a sufficient fit of the model results
FIST to the past time series (from statistics). It is about equivalent to half the fleet turnover
time (=1/scrappage rate).
Appendices 138
Yearly vehicle kilometre equation using an average annual kilometrage per vehicle (AKV)
for the different cohorts.
(6) VKM kST =VN kSC ⋅ AKVkSC + VN kCO ⋅ AKVkCO and
VKM kST1 = VN kCO ⋅ AKVkCO + VN kNR ⋅ AKVkNR
+ +1 +1 +1
Since the AKV for the different vintages are unknown, constant vehicle kilometrage
ponderation factors (PF) for new cars and old cars are defined:
AKVkCO
(7) PFkCO = and similarly PFkSC , PFkNR
+1
AKVkST
with PFkNN = PFkNN = PF NN , NN ⊂ {SC , CO, NR
+1 }
Typical US values (1996) for the ponderation factors are used in all regions:.(Davis 1998):
PF SC = 0.87, PF NR = 1.25
Stock model:
By solving equation (3) for FISTk+1 and inserting (1), (3), (5) and (7) it can be shown that
FI ST ⋅ VN kST − FI kSTl ⋅ R SC ⋅ VN kST ⋅ PF SC
(8) FI kST1 = k
+
−
⋅ ...
VN kST − VN kSC ⋅ PF SC
... + FI kNR ⋅ (VN kST1 − VN kST (1 + R SC )) ⋅ PF NR ⋅ 1 ST
+1 +
VN k +1
Equation (8) is the stock model that calculates the sales-weighted stock fuel intensity (based
on test performance) of a “target” year (k+1) after defining constant kilometrage weighing
factors and scrappage rates by using:
• fuel intensity values for the total vehicle stock of the previous year(s);
• fuel intensity values of the new registered vehicles of the “target” year (determined
exogenously, e.g. by policy measures); and
• total vehicle stock numbers.
Applying this model for the past and comparing the results between 1980 and 1997 to on-road
fuel intensity values derived from statistics allows to measure the test/on-road gap. The gap
for the year k is defined as
FI k ,or − FI k ,t
Gk =
FI k ,t
Appendices 139
In order to simulate an on-road equivalent fuel intensity, the gap factor is introduced in
1 + Gk +1 1 + Gk
1 + G ⋅ FI k − l ⋅ R ⋅ VN k ⋅ PF
⋅ FI kST ⋅ VN kST − ST SC ST SC
1+ G
k l
equation : (9) FI kST1
+ = ⋅ ...
VN k − VN k ⋅ PF SC
ST SC
... ⋅ (VN kST1 − (VN kST1 − VN kST (1 + R SC )) ⋅ PF NR ) + ...
+ +
... + (1 + Gk +1 ) ⋅ FI kNR ⋅ (VN kST1 − VN kST (1 + R SC )) ⋅ PF NR
+1 +
⋅ 1 ST
VN k +1
The results of model (8) and model (9) are shown in figure 3.2 (main text) as “fleet simulation
(test)” and “fleet simulation (on-road) after calibration” respectively.
Appendices 140
APPENDIX 3: Tables for baseline and scenario projections
General note to the tables
The analysis of energy demand in the transport sector is based on data up to 1997. Many
sources have been used in building the database containing past trends: a list of the
datasources is provided in the Reference section.
The tables in this section present detailed projections to 2020 of activity, energy intensity,
energy demand and CO2 emissions for the OECD regions.
The list of tables is as follows:
Baseline case:
OECD Europe
Western Europe
Central Europe and Turkey
OECD Pacific
Japan
Australia and New Zealand
North America
Comparison of cases
Western Europe
Japan
North America
Scenarios including all policies
Western Europe
Japan
North America
Both in the text and in the tables, rounding may cause some discrepancy between the total and
the sum of the individual components.
Appendices 141
Baseline case: OECD Europe
Shares (%)
Activity (pass-km) Growth rates (% per annum)
excluding inter-regional aviation
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total passenger transport 2498 5808 7430 8634 100 100 100 100 3.2 1.9 1.7
Cars
1656 4229 5210 5759 69 77 77 75 3.5 1.6 1.4
Personal light trucks
Bus 356 549 600 634 15 10 9 8 1.6 0.7 0.6
Passenger rail 299 337 362 381 12 6 5 5 0.4 0.6 0.5
Intra-regional aviation 93 349 602 917 4 6 9 12 5.0 4.3 4.3
Inter-regional aviation 93 344 656 943 5.0 5.1 4.5
Fuel intensity (MJ/pass-km) Growth rates (% per annum)
Average passenger transport 1.6 1.4 1.4 1.4 -0.5 0.0 -0.1
Cars
1.8 1.5 1.6 1.6 -0.7 0.2 0.1
Personal light trucks
Bus 0.6 0.9 1.0 1.0 1.4 0.4 0.3
Passenger rail 0.7 0.5 0.5 0.4 -1.0 -0.5 -0.6
Intra-regional aviation 4.2 2.4 2.0 1.6 -1.9 -1.7 -1.8
Inter-regional aviation 2.4 1.2 1.2 1.0 -2.5 -0.5 -0.8
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 97.4 199.9 256.1 289.9 100 100 100 100 2.7 1.9 1.6
Cars
72.8 153.7 192.4 213.4 79 81 81 80 2.8 1.7 1.4
Personal light trucks
Bus 5.3 11.9 13.5 14.7 6 6 6 6 3.0 1.0 0.9
Passenger rail 4.7 4.0 4.0 4.0 5 2 2 2 -0.5 0.0 0.0
Intra-regional aviation 9.2 20.3 28.1 34.8 10 11 12 13 3.0 2.6 2.4
Inter-regional aviation 5.3 10.0 18.1 22.9 2.4 4.6 3.7
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 291 607 805 922 100 100 100 100 2.8 2.2 1.8
Cars
212 450 564 627 77 78 75 74 2.8 1.8 1.5
Personal light trucks
Bus 21 58 89 106 8 10 12 12 3.8 3.3 2.7
Passenger rail 14 8 14 16 5 1 2 2 -2.1 4.4 3.1
Intra-regional aviation 28 61 84 104 10 11 11 12 2.9 2.5 2.3
Inter-regional aviation 16 30 54 69 2.4 4.6 3.7
Appendices 142
Shares (%)
Activity (tonne-km) Growth rates (% per annum)
excluding freight air
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total freight transport 1124 1967 2663 3221 100 100 100 100 2.1 2.4 2.2
Small trucks and vans
530 1450 2114 2629 47 75 81 84 3.8 2.9 2.6
Heavy trucks
Freight rail 472 353 356 359 42 18 14 11 -1.1 0.1 0.1
Navigation 120 135 137 138 11 7 5 4 0.4 0.1 0.1
Freight air 2 29 56 95 10.4 5.2 5.3
Fuel intensity (MJ/tonne-km) Growth rates (% per annum)
Average freight transport 2.0 2.7 2.8 2.8 1.1 0.1 0.1
Small trucks and vans
3.2 3.1 2.9 2.8 -0.1 -0.5 -0.4
Heavy trucks
Freight rail 0.5 0.4 0.4 0.4 -0.8 0.0 0.0
Navigation 2.3 2.2 2.2 2.1 -0.2 0.0 -0.2
Freight air 31.7 17.2 15.2 13.2 -2.2 -0.9 -1.1
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding freight air
Total freight transport 54.5 129.0 177.5 214.3 100 100 100 100 3.2 2.5 2.2
Small trucks and vans
41.1 106.7 146.8 174.5 77 91 93 95 3.6 2.5 2.2
Heavy trucks
Freight rail 5.6 3.4 3.2 3.0 11 3 2 2 -1.9 -0.4 -0.5
Navigation 6.6 7.2 7.1 7.0 12 6 5 4 0.3 -0.1 -0.1
Freight air 1.2 11.8 20.4 29.8 8.9 4.3 4.1
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding freight air
Total freight transport 169 427 620 751 100 100 100 100 3.5 2.9 2.5
Small trucks and vans
122 319 446 530 73 81 80 80 3.6 2.6 2.2
Heavy trucks
Freight rail 16 21 35 42 10 5 6 6 1.0 4.0 3.1
Navigation 28 52 78 90 17 13 14 14 2.3 3.2 2.4
Freight air 3 35 61 89 9.5 4.4 4.1
Appendices 143
Baseline case: Western Europe
Shares (%)
Activity (pass-km) Growth rates (% per annum)
excluding inter-regional aviation
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total passenger transport 2272 5263 6614 7670 100 100 100 100 3.2 1.8 1.7
Cars
1585 3912 4632 5050 73 79 77 75 3.4 1.3 1.1
Personal light trucks
Bus 277 397 462 504 13 8 8 7 1.3 1.2 1.0
Passenger rail 227 297 334 358 10 6 6 5 1.0 0.9 0.8
Intra-regional aviation 92 326 563 862 4 7 9 13 4.8 4.3 4.3
Inter-regional aviation 91 331 623 896 4.9 5.0 4.4
Fuel intensity (MJ/pass-km) Growth rates (% per annum)
Average passenger transport 1.7 1.5 1.5 1.4 -0.5 0.0 -0.1
Cars
1.9 1.5 1.6 1.6 -0.9 0.5 0.3
Personal light trucks
Bus 0.6 1.0 1.0 1.0 1.9 0.0 0.0
Passenger rail 0.7 0.5 0.5 0.4 -1.2 0.0 -1.0
Intra-regional aviation 4.2 2.4 1.9 1.6 -2.1 -1.8 -1.7
Inter-regional aviation 2.4 1.2 1.2 1.0 -2.5 0.0 -0.8
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 92.2 185.1 232.3 261.4 100 100 100 100 2.6 1.8 1.5
Cars
70.1 144.0 174.3 191.1 81 82 81 80 2.7 1.5 1.2
Personal light trucks
Bus 4.3 9.1 10.9 12.2 5 5 5 5 2.8 1.4 1.3
Passenger rail 3.5 3.6 3.7 3.8 4 2 2 2 0.1 0.4 0.3
Intra-regional aviation 9.1 18.9 26.2 32.5 10 11 12 14 2.7 2.6 2.4
Inter-regional aviation 5.2 9.6 17.1 21.8 2.3 4.5 3.6
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 268 536 676 763 100 100 100 100 2.6 1.8 1.5
Cars
204 421 511 561 81 83 82 80 2.7 1.5 1.3
Personal light trucks
Bus 13 28 34 38 5 6 5 5 2.9 1.5 1.3
Passenger rail 8 2 2 2 3 0 0 0 -5.0 0.0 0.0
Intra-regional aviation 27 56 78 97 11 11 12 14 2.7 2.6 2.4
Inter-regional aviation 16 29 51 65 2.2 4.4 3.6
Appendices 144
Shares (%)
Activity (tonne-km) Growth rates (% per annum)
excluding freight air
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total freight transport 879 1633 2180 2608 100 100 100 100 2.3 2.2 2.1
Small trucks and vans
472 1224 1738 2124 54 76 82 85 3.6 2.7 2.4
Heavy trucks
Freight rail 291 248 252 254 33 15 12 10 -0.6 0.1 0.1
Navigation 114 132 134 135 13 8 6 5 0.5 0.1 0.1
Freight air 2 29 56 95 10.4 5.2 5.3
Fuel intensity (MJ/tonne-km) Growth rates (% per annum)
Average freight transport 2.3 3.0 3.0 3.1 1.0 0.2 0.1
Small trucks and vans
3.3 3.2 3.1 3 -0.1 -0.2 -0.3
Heavy trucks
Freight rail 0.5 0.4 0.4 0.4 -0.8 0.0 0.0
Navigation 2.3 2.2 2.2 2.1 -0.2 0.0 -0.2
Freight air 31.7 17.2 15.2 13.2 -2.2 -0.9 -1.1
Shares (%)
Energy consumption (PJ) Growth rates (% per annum)
excluding freight air
Total freight transport 48.0 115.2 157.5 190.0 100 100 100 100 3.3 2.4 2.2
Small trucks and vans
37.0 94.1 127.9 151.2 79 91 93 94 3.5 2.4 2.1
Heavy trucks
Freight rail 3.5 2.4 2.3 2.2 7 2 2 1 -1.4 -0.3 -0.4
Navigation 6.3 7.0 6.9 6.8 13 7 5 4 0.4 -0.1 -0.1
Freight air 1.2 11.8 20.4 29.8 8.9 4.3 4.1
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding freight air
Total freight transport 144 348 476 575 100 100 100 100 3.3 2.4 2.2
Small trucks and vans
113 287 390 461 80 92 94 95 3.5 2.4 2.1
Heavy trucks
Freight rail 9 4 4 4 6 1 1 1 -3.0 0.0 0.0
Navigation 19 22 21 21 13 7 5 4 0.5 -0.4 -0.2
Freight air 3 35 61 89 9.5 4.4 4.1
Appendices 145
Baseline case: Central Europe and Turkey
Shares (%)
Activity (pass-km) Growth rates (% per annum)
excluding inter-regional aviation
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total passenger transport 226 544 815 965 100 100 100 100 3.3 3.2 2.5
Cars
71 317 578 709 32 60 74 77 5.7 4.7 3.6
Personal light trucks
Bus 79 152 137 130 35 29 18 14 2.5 -0.8 -0.7
Passenger rail 73 40 28 23 32 8 4 3 -2.2 -2.7 -2.3
Intra-regional aviation 2 23 38 55 1 4 5 6 10.0 4.2 4.0
Inter-regional aviation 2 13 33 47 7.1 7.2 5.7
Fuel intensity (MJ/pass-km) Growth rates (% per annum)
Average passenger transport 0.9 1.1 1.2 1.2 0.7 0.6 0.4
Cars
1.6 1.3 1.3 1.3 -0.8 0.2 0.1
Personal light trucks
Bus 0.5 0.8 0.8 0.8 1.4 0.3 0.3
Passenger rail 0.7 0.5 0.5 0.4 -1.0 -0.5 -0.5
Intra-regional aviation 4.0 2.6 2.1 1.7 -1.5 -1.7 -1.8
Inter-regional aviation 2.4 1.2 1.2 1.0 -2.5 -0.4 -0.8
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 5.1 14.8 23.9 28.5 100 100 100 100 4.0 3.8 2.9
Cars
2.7 9.7 18.1 22.3 55 68 79 81 4.8 4.9 3.7
Personal light trucks
Bus 1.0 2.8 2.6 2.6 20 19 11 9 3.9 -0.4 -0.3
Passenger rail 1.1 0.5 0.3 0.2 22 3 1 1 -3.1 -3.2 -2.8
Intra-regional aviation 0.2 1.4 1.9 2.3 3 10 8 8 8.4 2.4 2.0
Inter-regional aviation 0.1 0.4 0.9 1.1 4.5 6.7 4.8
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 23 70 128 158 100 100 100 100 4.2 4.8 3.6
Cars
8 29 53 66 36 42 42 43 4.8 4.9 3.7
Personal light trucks
Bus 8 30 55 68 35 43 44 44 5.0 4.9 3.7
Passenger rail 6 6 11 14 27 9 9 9 0.0 4.9 3.7
Intra-regional aviation 0.5 4 6 7 2 6 5 4 8.4 2.4 2.0
Inter-regional aviation 0.4 1 3 3 4.5 6.7 4.8
Appendices 146
Shares (%)
Activity (tonne-km) Growth rates (% per annum)
excluding freight air
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total freight transport 246 333 484 613 100 100 100 100 1.1 2.9 2.7
Small trucks and vans
58 225 376 505 24 68 78 82 5.1 4.0 3.6
Heavy trucks
Freight rail 181 104 105 105 74 31 22 17 -2.0 0.0 0.0
Navigation 6 3 3 3 3 1 1 1 -2.4 0.1 0.1
Freight air ND ND ND ND
Fuel intensity (MJ/tonne-km) Growth rates (% per annum)
Average freight transport 1.1 1.7 1.7 1.7 1.7 0.0 -0.2
Small trucks and vans
2.9 2.3 2.1 1.9 -0.8 -0.8 -0.8
Heavy trucks
Freight rail 0.5 0.4 0.4 0.3 -0.8 -0.7 -0.7
Navigation 2.3 2.1 2.0 1.9 -0.3 -0.5 -0.5
Freight air ND ND ND ND
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding freight air
Total freight transport 6.5 13.8 19.9 24.3 100 100 100 100 2.8 2.9 2.5
Small trucks and vans
4.0 12.6 18.9 23.3 62 92 95 96 4.3 3.1 2.7
Heavy trucks
Freight rail 2.2 1.0 0.9 0.8 33 7 5 3 -2.8 -0.7 -0.7
Navigation 0.4 0.2 0.2 0.2 5 1 1 1 -2.8 -0.4 -0.4
Freight air ND ND ND ND
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding freight air
Total freight transport 25 79 143 176 100 100 100 100 4.4 4.7 3.6
Small trucks and vans
9 32 56 69 38 40 39 39 4.7 4.4 3.4
Heavy trucks
Freight rail 7 17 31 38 28 21 22 22 3.3 4.9 3.7
Navigation 9 30 56 69 34 38 39 39 4.8 4.9 3.7
Freight air ND ND ND ND
Appendices 147
Baseline case: OECD Pacific
Shares (%)
Activity (pass-km) Growth rates (% per annum)
excluding inter-regional aviation
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total passenger transport 854 1918 2327 2694 100 100 100 100 3.0 1.5 1.5
Cars 395 1121 1306 1414 48 63 62 61 3.9 1.2 1.0
Personal light trucks 5 16 19 22 1 1 1 1 4.1 1.2 1.3
Bus 112 113 120 126 14 6 6 5 0.0 0.5 0.5
Passenger rail 304 405 454 495 36 23 22 21 1.1 0.9 0.9
Intra-regional aviation 15 124 197 274 2 7 9 12 8.0 3.6 3.5
Inter-regional aviation 22 138 232 364 7.0 4.0 4.3
Fuel intensity (MJ/pass-km) Growth rates (% per annum)
Average passenger transport 1.2 1.7 1.7 1.6 1.1 0.1 -0.1
Cars 2.0 2.3 2.3 2.3 0.5 0.2 0.1
Personal light trucks 1.9 2.1 2.2 2.2 0.4 0.3 0.1
Bus 0.6 0.8 0.8 0.9 1.1 0.5 0.5
Passenger rail 0.2 0.2 0.2 0.2 0.0 0.5 0.5
Intra-regional aviation 3.7 2.0 1.7 1.5 -2.3 -0.9 -1.1
Inter-regional aviation 3.3 1.5 1.3 1.1 -2.9 -1.3 -1.5
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 25.1 76.5 94.0 103.9 100 100 100 100 4.2 1.6 1.3
Cars 18.6 60.7 73.0 78.1 80 85 84 82 4.5 1.4 1.1
Personal light trucks 0.3 0.8 1.0 1.1 1 1 1 1 4.5 1.5 1.4
Bus 1.5 2.1 2.3 2.6 7 3 3 3 1.1 1.0 1.0
Passenger rail 1.6 2.1 2.5 2.9 7 3 3 3 1.1 1.4 1.4
Intra-regional aviation 1.4 5.9 8.2 10.1 6 8 9 11 5.5 2.6 2.4
Inter-regional aviation 1.8 4.9 7.0 9.2 3.9 2.7 2.7
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 66 211 248 288 100 100 100 100 4.4 1.2 1.3
Cars 49 169 191 217 80 86 84 84 4.7 1.0 1.1
Personal light trucks 0.7 2 3 3 1 1 1 1 4.5 1.5 1.4
Bus 5 6 7 8 8 3 3 3 1.1 1.0 1.0
Passenger rail 2 1 1 1 4 0 1 1 -3.3 1.4 1.4
Intra-regional aviation 4 18 25 30 7 9 11 12 5.5 2.6 2.4
Inter-regional aviation 5 15 21 28 3.9 2.7 2.7
Appendices 148
Shares (%)
Activity (tonne-km) Growth rates (% per annum)
excluding freight air
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total freight transport 500 879 1035 1174 95 98 98 98 2.1 1.3 1.3
Small trucks and vans 27 16 17 18 5 2 2 2 -1.8 0.5 0.5
Heavy trucks 136 368 440 498 27 42 44 44 3.8 1.4 1.3
Freight rail 104 128 158 187 21 15 16 17 0.8 1.7 1.7
Navigation 233 355 395 427 47 41 39 38 1.6 0.8 0.8
Freight air 1 12 26 44 11.9 5.9 5.7
Fuel intensity (MJ/tonne-km) Growth rates (% per annum)
Average freight transport 1.9 2.2 2.4 2.5 0.6 0.6 0.5
Small trucks and vans 35.4 40.7 44.6 1.1 1.0
4.2
Heavy trucks 2.4 2.3 2.2 -0.3 -0.3
Freight rail 0.2 0.2 0.2 0.2 0.0 -1.6 -1.8
Navigation 0.9 0.7 0.8 0.8 -1.1 0.9 0.8
Freight air 39.4 19.8 17.0 14.5 -2.5 -1.2 -1.3
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding freight air
Total freight transport 22.7 46.8 59.3 69.9 100 100 100 100 2.7 1.8 1.8
Small trucks and vans 13.6 16.8 19.2 33 34 35 1.6 1.5
16.4 74 2.8
Heavy trucks 20.9 24.2 26.4 51 49 48 1.1 1.0
Freight rail 0.6 0.7 0.7 0.7 3 2 1 1 0.8 0.0 -0.1
Navigation 5.2 5.8 7.2 8.4 23 14 15 15 0.4 1.7 1.6
Freight air 0.5 5.7 10.4 15.2 9.1 4.7 4.3
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding freight air
Total freight transport 66 142 180 212 100 100 100 100 2.9 1.8 1.8
Small trucks and vans 41 50 57 33 34 35 1.6 1.5
47 3.0
Heavy trucks 65 75 81 73 52 50 49 1.1 1.0
Freight rail 1 1 1 1 2 1 1 1 1.0 -0.1 -0.2
Navigation 16 18 22 26 25 14 15 16 0.4 1.7 1.6
Freight air 2 17 31 45 9.1 4.7 4.3
Appendices 149
Baseline case: Australia and New Zealand
Shares (%)
Activity (pass-km) Growth rates (% per annum)
excluding inter-regional aviation
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total passenger transport 174 422 560 698 100 100 100 100 3.3 2.2 2.2
Cars 129 269 329 374 78 73 71 68 2.8 1.6 1.4
Personal light trucks 5 16 19 22 3 4 4 4 4.1 1.2 1.3
Bus 10 20 21 22 6 5 5 4 2.7 0.5 0.5
Passenger rail 15 10 11 12 9 3 2 2 -1.4 0.6 0.6
Intra-regional aviation 6 51 84 117 4 14 18 21 8.2 4.0 3.7
Inter-regional aviation 9 57 96 152 7.0 4.1 4.4
Fuel intensity (MJ/pass-km) Growth rates (% per annum)
Average passenger transport 2.3 1.9 1.8 1.7 -0.6 -0.4 -0.6
Cars 2.4 2.3 2.3 2.3 -0.2 0.1 0.0
Personal light trucks 1.9 2.1 2.2 2.2 0.4 0.3 0.1
Bus 0.7 0.7 0.7 0.7 -0.2 0.0 0.0
Passenger rail 1.3 1.3 1.3 1.3 0.0 0.2 0.2
Intra-regional aviation 3.5 1.6 1.3 1.1 -2.9 -1.3 -1.6
Inter-regional aviation 2.9 1.1 0.9 0.8 -3.5 -1.3 -1.6
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 9.4 19.5 24.5 28.1 100 100 100 100 2.7 1.8 1.6
Cars 7.4 14.6 18.0 20.4 84 81 81 81 2.5 1.6 1.5
Personal light trucks 0.3 0.8 1.0 1.1 3 5 4 5 4.5 1.5 1.4
Bus 0.2 0.3 0.3 0.4 2 2 2 1 2.5 0.5 0.5
Passenger rail 0.4 0.3 0.3 0.4 5 2 2 1 -1.4 0.7 0.7
Intra-regional aviation 0.5 1.9 2.7 3.0 6 11 12 12 5.1 2.6 2.1
Inter-regional aviation 0.6 1.5 2.2 2.8 3.2 2.8 2.7
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 27 53 67 77 100 100 100 100 2.5 1.8 1.6
Cars 21 39 48 54 85 81 80 80 2.2 1.6 1.5
Personal light trucks 1 2 3 3 3 5 5 5 4.5 1.5 1.4
Bus 1 1 1 1 2 2 2 2 2.5 0.5 0.5
Passenger rail 1 0.2 0.2 0.2 4 0 0 0 -6.4 0.7 0.7
Intra-regional aviation 2 6 8 9 6 12 13 13 5.1 2.6 2.1
Inter-regional aviation 2 5 6 9 3.2 2.8 2.7
Appendices 150
Shares (%)
Activity (tonne-km) Growth rates (% per annum)
excluding freight air
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total freight transport 156 342 413 480 100 100 100 100 3.0 1.5 1.5
Small trucks and vans 1 4 5 6 1 1 1 1 5.7 1.3 1.3
Heavy trucks 32 114 142 166 21 34 35 36 4.8 1.7 1.7
Freight rail 41 103 130 155 26 30 32 33 3.5 1.8 1.8
Navigation 82 118 129 138 52 35 32 30 1.4 0.7 0.7
Freight air 0.2 4 7 14 11.0 5.6 6.2
Fuel intensity (MJ/tonne-km) Growth rates (% per annum)
Average freight transport 1.2 1.2 1.2 1.3 -0.3 0.4 0.5
Small trucks and vans 30.5 20.0 20.2 20.2 -1.6 0.1 0.0
Heavy trucks 3.0 1.7 1.6 1.5 -2.1 -0.4 -0.4
Freight rail 0.2 0.3 0.2 0.2 0.8 -2.2 -2.2
Navigation 0.6 0.2 0.2 0.1 -3.4 -2.4 -2.4
Freight air 39.4 19.8 17.0 14.2 -2.5 -1.2 -1.4
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding freight air
Total freight transport 4.7 9.4 12.0 14.7 100 100 100 100 2.7 1.9 2.0
Small trucks and vans 0.7 2.0 2.4 2.7 16 26 26 28 4.0 1.3 1.3
Heavy trucks 2.3 4.6 5.5 6.1 52 60 61 62 2.6 1.3 1.2
Freight rail 0.2 0.6 0.6 0.6 5 8 7 6 4.1 -0.1 -0.2
Navigation 1.2 0.5 0.6 0.5 28 7 6 5 -3.1 0.5 -0.5
Freight air 0.2 1.7 2.9 4.8 8.2 4.4 4.7
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding freight air
Total freight transport 14 28 36 44 100 100 100 100 2.6 1.9 2.0
Small trucks and vans 2 6 7 8 15 26 26 27 11.0 1.3 1.3
Heavy trucks 7 14 17 19 52 61 62 63 4.0 1.3 1.2
Freight rail 1 1 1 1 4 6 5 4 3.1 -0.1 -0.2
Navigation 4 2 2 1 28 7 6 5 -3.1 0.5 -0.5
Freight air 1 5 9 14 8.2 4.4 4.7
Appendices 151
Baseline case: Japan
Shares (%)
Activity (pass-km) Growth rates (% per annum)
excluding inter-regional aviation
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total passenger transport 681 1496 1768 1996 100 100 100 100 3.0 1.3 1.3
Cars
267 853 977 1040 40 60 60 58 4.4 1.1 0.9
Personal light trucks
Bus 103 93 99 104 15 7 6 6 -0.4 0.5 0.5
Passenger rail 289 395 444 483 43 28 27 27 1.2 0.9 0.9
Intra-regional aviation 9 73 112 157 1 5 7 9 7.9 3.3 3.4
Inter-regional aviation 13 82 136 213 7.1 4.0 4.2
Fuel intensity (MJ/pass-km) Growth rates (% per annum)
Average passenger transport 1.0 1.6 1.6 1.6 1.9 0.2 0.0
Cars
1.8 2.3 2.4 2.3 0.9 0.3 0.1
Personal light trucks
Bus 0.6 0.8 0.8 0.9 1.3 0.6 0.6
Passenger rail 0.2 0.2 0.2 0.2 0.6 0.6 0.6
Intra-regional aviation 3.9 2.3 2.1 1.9 -2.0 -0.7 -0.8
Inter-regional aviation 3.6 1.7 1.5 1.3 -2.7 -1.3 -1.4
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 15.7 57.0 69.5 75.8 100 100 100 100 4.9 1.5 1.2
Cars
11.2 46.2 55.0 57.7 77 86 85 83 5.4 1.4 1.0
Personal light trucks
Bus 1.4 1.7 2.0 2.0 9 3 3 3 0.9 1.1 1.1
Passenger rail 1.1 1.8 2.2 2.5 8 3 3 4 1.8 1.5 1.5
Intra-regional aviation 0.9 3.9 5.6 7.0 6 7 9 10 5.8 2.7 2.5
Inter-regional aviation 1.1 3.4 4.8 6.4 4.2 2.7 2.8
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 39 159 194 211 100 100 100 100 5.3 1.5 1.2
Cars
28 130 155 163 77 88 87 85 5.9 1.4 1.0
Personal light trucks
Bus 4 5 6 7 12 4 3 4 0.9 1.1 1.1
Passenger rail 1 1 1 1 4 1 1 1 -2.1 1.5 1.5
Intra-regional aviation 3 12 17 21 7 8 9 11 5.8 2.7 2.5
Inter-regional aviation 3 10 14 19 4.2 2.7 2.8
Appendices 152
Shares (%)
Activity (tonne-km) Growth rates (% per annum)
excluding freight air
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total freight transport 344 537 623 694 100 100 100 100 1.7 1.1 1.1
Small trucks and vans 26 12 12 12 7 2 2 2 -2.8 0.2 0.2
Heavy trucks 104 255 298 332 30 48 49 50 3.4 1.2 1.2
Freight rail 63 25 28 31 18 5 5 5 -3.4 1.1 1.1
Navigation 151 237 266 288 44 45 44 43 1.7 0.9 0.9
Freight air 0.4 9 18 30 12.4 6.0 5.5
Fuel intensity (MJ/tonne-km) Growth rates (% per annum)
Average freight transport 2.4 2.9 3.2 3.3 0.7 0.7 0.6
Small trucks and vans 40.8 49.0 55.7 1.4 1.4
4.9 -2.2
Heavy trucks 2.7 2.6 2.5 -0.2 -0.2
Freight rail 0.3 0.2 0.2 0.2 -0.8 -0.6 -0.6
Navigation 1.1 0.9 1.1 1.2 -0.6 0.9 0.9
Freight air 39.4 19.8 17.0 14.6 -2.5 -1.2 -1.3
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding freight air
Total freight transport 19.7 37.4 47.3 55.1 100 100 100 100 3.4 1.8 1.7
Small trucks and vans 11.6 14.4 16.5 0 35 36 37 1.7 1.5
15.0 2.3
Heavy trucks 16.3 18.7 20.2 78 49 47 45 1.0 0.9
Freight rail 0.4 0.1 0.1 0.1 2 0 0 0 -4.2 0.4 0.4
Navigation 4.0 5.6 6.7 7.9 20 16 17 18 1.1 1.8 1.8
Freight air 0.3 4.1 7.4 10.3 9.6 4.8 4.2
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding freight air
Total freight transport 52 114 144 168 100 66 65 64 2.9 1.8 1.7
Small trucks and vans 35 43 49 34 35 36 1.7 1.5
38 75 3.0
Heavy trucks 51 58 63 50 48 46 1.0 0.9
Freight rail 0.5 0.1 0.1 0.1 1 0 0 0 -7.7 0.4 0.4
Navigation 12 16 21 25 24 16 17 18 1.1 1.8 1.8
Freight air 1 12 22 31 9.6 4.8 4.2
Appendices 153
Baseline case: North America
Shares (%)
Activity (pass-km) Growth rates (% per annum)
excluding inter-regional aviation
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total passenger transport 4456 7226 8908 9989 100 100 100 100 1.8 1.6 1.4
Cars 3776 4278 4949 5386 86 62 60 59 0.5 1.1 1.0
Personal light trucks 214 1507 1818 1992 5 22 22 22 7.5 1.5 1.2
Bus 147 214 262 296 3 3 3 3 1.4 1.6 1.4
Passenger rail 40 42 46 48 1 1 1 1 0.2 0.6 0.5
Intra-regional aviation 214 823 1168 1379 5 12 14 15 5.1 2.7 2.3
Inter-regional aviation 65 362 664 889 6.6 4.8 4.0
Fuel intensity (MJ/pass-km) Growth rates (% per annum)
Average passenger transport 2.7 2.4 2.4 2.3 -0.4 -0.1 -0.2
Cars 2.4 2.3 2.3 2.3 -0.3 0.0 0.0
Personal light trucks 3.9 3.2 3.3 3.3 -0.8 0.3 0.2
Bus 0.8 0.9 0.9 0.8 0.2 0.0 -0.1
Passenger rail 1.4 0.9 0.7 0.6 -1.7 -1.7 -1.7
Intra-regional aviation 6.7 2.5 2.3 2.0 -3.5 -0.8 -1.0
Inter-regional aviation 6.6 2.1 1.8 1.4 -4.1 -1.5 -1.7
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 288.7 418.4 509.2 552.2 100 100 100 100 1.4 1.5 1.2
Cars 220.3 231.0 268.3 292.2 79 58 56 56 0.2 1.2 1.0
Personal light trucks 19.9 113.6 142.6 157.0 7 28 30 30 6.7 1.8 1.4
Bus 2.9 4.4 5.4 6.0 1 1 1 1 1.6 1.5 1.4
Passenger rail 1.3 0.9 0.8 0.7 0 0 0 0 -1.5 -1.1 -1.2
Intra-regional aviation 34.1 50.0 64.1 65.8 12 13 13 13 1.4 1.9 1.2
Inter-regional aviation 10.2 18.5 28.1 30.5 2.2 3.3 2.2
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding inter-regional aviation
Total passenger transport 841 1221 1487 1613 100 100 100 100 1.4 1.5 1.2
Cars 639 671 780 849 79 58 56 56 0.2 1.2 1.0
Personal light trucks 58 330 414 455 7 28 29 30 6.7 1.8 1.4
Bus 9 13 16 18 1 1 1 1 1.6 1.5 1.4
Passenger rail 3 1 1 1 0 0 0 0 -2.9 -1.1 -1.2
Intra-regional aviation 102 150 192 197 13 13 14 13 1.4 1.9 1.2
Inter-regional aviation 31 55 84 91 2.2 3.3 2.2
Appendices 154
Shares (%)
Activity (tonne-km) Growth rates (% per annum)
excluding freight air
1970 1997 2010 2020 1970 1997 2010 2020 1970-1997 1997-2010 1997-2020
Total freight transport 3104 5368 6796 7857 100 100 100 100 2.0 1.8 1.7
Small trucks and vans 25 119 174 222 1 2 3 3 6.0 3.0 2.8
Heavy trucks 857 2249 2985 3512 28 43 46 47 3.6 2.2 2.0
Freight rail 1295 1880 2453 2859 42 36 37 38 1.4 2.1 1.8
Navigation 909 1011 937 883 29 19 14 12 0.4 -0.6 -0.6
Freight air 19.0 110 248 380 6.7 6.5 5.5
Fuel intensity (MJ/tonne-km) Growth rates (% per annum)
Average freight transport 1.4 1.5 1.5 1.5 0.2 0.4 0.2
Small trucks and vans 23.5 15.1 15.2 15.2 -1.6 0.1 0.0
Heavy trucks 1.9 1.8 1.6 1.5 -0.2 -0.9 -0.9
Freight rail 0.4 0.3 0.2 0.2 -1.9 -1.6 -1.6
Navigation 0.6 0.5 0.6 0.7 -0.5 0.9 0.9
Freight air 15.7 8.3 7.5 6.5 -2.3 -0.8 -1.1
Shares (%)
Energy consumption (Mtoe) Growth rates (% per annum)
excluding freight air
Total freight transport 103.5 186.8 249.0 289.3 100 100 100 100 2.2 2.2 1.9
Small trucks and vans 29.6 42.8 63.1 80.5 31 26 31 35 1.4 3.0 2.8
Heavy trucks 39.5 97.3 115.6 123.7 41 59 56 54 3.4 1.3 1.0
Freight rail 13.8 11.9 12.6 12.5 14 7 6 5 -0.5 0.4 0.2
Navigation 13.4 12.9 13.4 13.9 14 8 7 6 -0.2 0.3 0.3
Freight air 7.1 21.9 44.3 58.8 4.2 5.6 4.4
Shares (%)
CO2 emissions (Mt) Growth rates (% per annum)
excluding freight air
Total freight transport 280 566 753 873 100 100 100 100 2.6 2.2 1.9
Small trucks and vans 57 125 184 235 22 25 30 34 3.0 3.0 2.8
Heavy trucks 118 299 355 380 46 60 57 55 4.9 1.3 1.0
Freight rail 43 37 39 39 16 7 6 6 -0.5 0.4 0.2
Navigation 42 40 42 43 16 8 7 6 -0.2 0.3 0.3
Freight air 21 65 133 176 4.2 5.6 4.4
Appendices 155
Comparison of Cases: Western Europe
Passenger transport
Growth rates
Activity (pass-km)
(% per annum)
1997 2010 2020 1997-2010 1997-2020
Baseline 5263 6614 7670 1.8 1.7
Incl. enacted policies 6741 7929 1.9 1.8
Incl. Additional policies 6692 8035 1.9 1.9
Energy consumption Growth rates
(Mtoe) (% per annum)
Baseline 185.1 232.3 261.4 1.8 1.5
Incl. enacted policies 217.3 231.8 1.2 1.0
Incl. Additional policies 211.0 204.2 1.0 0.4
Growth rates
CO2 emissions (Mt)
(% per annum)
Baseline 536 676 763 1.8 1.5
Incl. enacted policies 635 680 1.3 1.0
Incl. Additional policies 616 597 1.1 0.5
Freight transport
Growth rates
Activity (tonne-km)
(% per annum)
1997 2010 2020 1997-2010 1997-2020
Baseline 1633 2180 2608 2.2 2.1
Incl. enacted policies 2179 2608 2.2 2.1
Incl. Additional policies 2170 2588 2.2 2.0
Energy consumption Growth rates
(Mtoe) (% per annum)
Baseline 115.2 157.5 190.0 2.4 2.2
Incl. enacted policies 157.5 190.0 2.4 2.2
Incl. Additional policies 152.1 175.8 2.2 1.9
Growth rates
CO2 emissions (Mt)
(% per annum)
Baseline 348 476 575 2.2 1.9
Incl. enacted policies 476 575 2.2 1.9
Incl. Additional policies 460 532 2.2 1.9
Appendices 156
Comparison of Cases: Japan
Passenger transport
Growth rates
Activity (pass-km)
(% per annum)
1997 2010 2020 1997-2010 1997-2020
Baseline 1496 1768 1996 1.3 1.3
Incl. enacted policies 1784 2025 1.4 1.3
Incl. Additional policies 1773 2022 1.3 1.3
Energy consumption Growth rates
(Mtoe) (% per annum)
Baseline 57.0 69.5 75.8 1.5 1.2
Incl. enacted policies 64.5 67.6 1.0 0.7
Incl. Additional policies 62.5 59.9 0.7 0.2
Growth rates
CO2 emissions (Mt)
(% per annum)
Baseline 159 194 211 1.5 1.2
Incl. enacted policies 184 193 1.2 0.8
Incl. Additional policies 178 169 0.9 0.3
Freight transport
Growth rates
Activity (tonne-km)
(% per annum)
1997 2010 2020 1997-2010 1997-2020
Baseline 537 623 694 1.1 1.1
Incl. enacted policies 623 694 1.1 1.1
Incl. Additional policies 621 692 1.1 1.1
Energy consumption Growth rates
(Mtoe) (% per annum)
Baseline 37.4 47.3 55.1 1.8 1.7
Incl. enacted policies 46.4 53.5 1.7 1.6
Incl. Additional policies 45.1 50.3 1.5 1.3
Growth rates
CO2 emissions (Mt)
(% per annum)
Baseline 114 144 168 2.2 1.9
Incl. enacted policies 141 163 2.2 1.9
Incl. Additional policies 137 153 1.5 1.3
Appendices 157
Comparison of Cases: North America
Passenger transport
Growth rates
Activity (pass-km)
(% per annum)
1997 2010 2020 1997-2010 1997-2020
Baseline 7226 8908 9989 1.6 1.4
Incl. enacted policies N/A N/A
Incl. Additional policies 8664 9994 1.4 1.4
Energy consumption Growth rates
(Mtoe) (% per annum)
Baseline 418.4 509.1 552.3 1.5 1.2
Incl. enacted policies N/A N/A
Incl. Additional policies 475.5 460.4 1.0 0.4
Growth rates
CO2 emissions (Mt)
(% per annum)
Baseline 1221 1487 1613 1.5 1.2
Incl. enacted policies N/A N/A
Incl. Additional policies 1380 1341 0.9 0.4
Freight transport
Growth rates
Activity (tonne-km)
(% per annum)
1997 2010 2020 1997-2010 1997-2020
Baseline 5368 6796 7857 1.8 1.7
Incl. enacted policies N/A N/A
Incl. Additional policies 6686 7759 1.8 1.7
Energy consumption Growth rates
(Mtoe) (% per annum)
Baseline 186.8 249.0 289.3 2.2 1.9
Incl. enacted policies N/A N/A
Incl. Additional policies 235..2 259.9 1.8 1.4
Growth rates
CO2 emissions (Mt)
(% per annum)
Baseline 566 753 873 2.2 1.9
Incl. enacted policies N/A N/A
Incl. Additional policies 698 772 1.6 1.4
Appendices 158
Scenarios including all policies: Western Europe
Comparison with baseline case
Activity (pass-km) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
1997 2010 2020 1997-2010 1997-2020 2010 2020
Total passenger transport 5263 6692 8035 1.9 1.9 99 101
Cars
3912 4735 5368 1.5 1.4 100 101
Personal light trucks
Bus 397 477 547 1.4 1.4 103 109
Passenger rail 297 340 400 1.0 1.3 102 112
Intra-regional aviation 326 517 823 3.6 4.1 92 96
Inter-regional aviation 331 623 896 5.0 4.4 100 100
Fuel intensity Comparison with baseline case
Growth rates (% per annum)
(MJ/pass-km) (base 2010 = 100) (base 2020 = 100)
Average passenger transport 1.5 1.3 1.1 -1.0 -1.5
Cars
1.5 1.4 1.1 -0.6 -1.3 99 86
Personal light trucks
Bus 1.0 1.0 1.0 -0.1 0.0 99 99
Passenger rail 0.5 0.5 0.4 -0.5 -0.5 100 101
Intra-regional aviation 2.4 1.8 1.4 -2.1 -2.4 94 87
Inter-regional aviation 1.2 1.1 0.9 -0.8 -1.3 94 87
Comparison with baseline case
Energy consumption (Mtoe) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total passenger transport 185.1 211.0 204.2 1.0 0.4 97 88
Cars
144.0 157.4 141.2 0.7 -0.1 99 87
Personal light trucks
Bus 9.1 11.2 13.0 1.6 1.6 103 107
Passenger rail 3.6 3.8 4.3 0.5 0.8 101 113
Intra-regional aviation 18.9 22.5 26.9 1.4 1.6 86 83
Inter-regional aviation 9.6 16.1 18.8 4.0 3.0 94 87
Comparison with baseline case
CO2 emissions (Mt) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total passenger transport 536 616 597 1.1 0.5 91 78
Cars
421 464 417 0.7 0.0 91 74
Personal light trucks
Bus 28 35 40 1.7 1.6 102 106
Passenger rail 2 2 3 1.2 1.3 117 133
Intra-regional aviation 56 68 80 1.4 1.6 87 83
Inter-regional aviation 29 48 56 4.0 2.9 94 87
Appendices 159
Comparison with baseline case
Activity (tonne-km) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
1997 2010 2020 1997-2010 1997-2020 2010 2020
Total freight transport 1633 2170 2588 2.2 2.0 100 99
Small trucks and vans
1224 1726 2083 2.7 2.3 99 98
Heavy trucks
Freight rail 248 259 280 0.3 0.5 103 110
Navigation 132 134 135 0.1 0.1 100 100
Freight air 29 52 91 4.6 5.1 92 96
Fuel intensity Comparison with baseline case
Growth rates (% per annum)
(MJ/tonne-km) (base 2010 = 100) (base 2020 = 100)
Average freight transport 3.0 2.9 2.8 -0.1 -0.2
Small trucks and vans
3.2 3.0 2.9 -0.4 -0.5 99 96
Heavy trucks
Freight rail 0.4 0.4 0.4 -0.5 -0.4 101 103
Navigation 2.2 2.2 2.1 -0.3 -0.3 100 100
Freight air 17.2 14.3 11.4 -1.4 -1.8 94 87
Comparison with baseline case
Energy consumption (Mtoe) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total freight transport 115.3 152.1 175.8 2.2 1.8 97 93
Small trucks and vans
94.1 125.2 141.9 2.2 1.8 98 94
Heavy trucks
Freight rail 2.4 2.4 2.5 -0.2 0.1 104 114
Navigation 7.1 6.9 6.8 -0.2 -0.2 100 100
Freight air 11.8 17.6 24.6 3.1 3.2 86 83
Comparison with baseline case
CO2 emissions (Mt) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total freight transport 348 460 532 2.2 1.9 97 93
Small trucks and vans
287 382 433 2.2 1.8 98 94
Heavy trucks
Freight rail 4 4 4 0.1 0.2 101 105
Navigation 22 21 21 -0.2 -0.2 102 100
Freight air 35 53 74 3.2 3.3 86 83
Appendices 160
Scenarios including all policies: Japan
Comparison with baseline case
Activity (pass-km) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
1997 2010 2020 1997-2010 1997-2020 2010 2020
Total passenger transport 1496 1773 2022 1.3 1.3 99 100
Cars
853 981 1042 1.1 0.9 99 97
Personal light trucks
Bus 93 101 111 0.7 0.8 102 107
Passenger rail 395 450 511 1.0 1.1 101 106
Intra-regional aviation 73 112 152 3.3 3.2 100 97
Inter-regional aviation 82 128 206 3.5 4.1 94 97
Fuel intensity Comparison with baseline case
Growth rates (% per annum)
(MJ/pass-km) (base 2010 = 100) (base 2020 = 100)
Average passenger transport 1.6 1.5 1.2 -0.6 -1.1
Cars
2.3 2.1 1.7 -0.7 -1.2 99 89
Personal light trucks
Bus 0.8 0.9 1.0 0.8 1.0 103 109
Passenger rail 0.2 0.2 0.2 0.7 0.9 101 106
Intra-regional aviation 2.3 1.9 1.6 -1.1 -1.4 94 88
Inter-regional aviation 1.7 1.4 1.1 -1.7 -2.0 94 88
Comparison with baseline case
Energy consumption (Mtoe) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total passenger transport 57.0 62.5 59.5 0.7 0.2 97 89
Cars
46.2 48.7 43.1 0.4 -0.3 97 87
Personal light trucks
Bus 1.7 2.1 2.6 1.5 1.8 106 117
Passenger rail 1.8 2.2 2.8 1.7 2.0 103 112
Intra-regional aviation 3.9 5.2 6.0 2.2 1.8 94 85
Inter-regional aviation 3.4 4.2 5.4 1.7 2.0 88 85
Comparison with baseline case
CO2 emissions (Mt) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total passenger transport 194 173 165 -0.8 -0.7 90 78
Cars 89 75
155 138 122 -0.9 -1.1
Personal light trucks
Bus 6 7 8 0.4 1.1 106 117
Passenger rail 1 1 1 0.2 1.1 103 112
Intra-regional aviation 17 16 18 -0.5 0.3 94 85
Inter-regional aviation 14 13 16 -1.0 0.5 88 85
Appendices 161
Comparison with baseline case
Activity (tonne-km) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
1997 2010 2020 1997-2010 1997-2020 2010 2020
Total freight transport 537 621 692 1.1 1.1 100 100
Small trucks and vans 12 11 11 -0.3 -0.5 99 98
Heavy trucks 255 298 332 1.2 1.2 100 100
Freight rail 25 28 31 1.1 1.1 100 100
Navigation 237 266 288 0.9 0.9 100 100
Freight air 9 18 29 5.6 5.4 95 98
Fuel intensity Comparison with baseline case
Growth rates (% per annum)
(MJ/tonne-km) (base 2010 = 100) (base 2020 = 100)
Average freight transport 2.9 3.0 3.0 0.3 0.2
Small trucks and vans 40.8 49.7 56.5 1.5 1.4 101 99
Heavy trucks 2.7 2.6 2.4 -0.4 -0.5 98 94
Freight rail 0.2 0.2 0.2 -0.6 -0.6 100 100
Navigation 0.9 1.1 1.2 0.9 0.9 100 100
Freight air 19.8 16.0 12.9 -1.6 -1.9 94 88
Comparison with baseline case
Energy consumption (Mtoe) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total freight transport 37.4 45.1 50.3 1.5 1.3 97 94
Small trucks and vans 11.6 13.4 14.3 1.1 0.9 100 96
Heavy trucks 16.3 18.2 19.0 0.9 0.7 98 94
Freight rail 0.1 0.1 0.1 0.4 0.4 100 100
Navigation 5.3 6.7 7.9 1.8 1.8 100 100
Freight air 4.1 6.7 8.9 3.9 3.5 90 86
Comparison with baseline case
CO2 emissions (Mt) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total freight transport 114 137 153 -6.9 -3.5 95 91
Small trucks and vans 35 40 43 1.1 0.9 93 87
Heavy trucks 51 56 59 0.9 0.7 98 94
Freight rail 0 0.1 0.1 0.4 0.4 100 100
Navigation 16 21 25 1.8 1.8 100 100
Freight air 12 20 27 3.9 3.5 90 86
Appendices 162
Scenarios including all policies: North America
Comparison with baseline case
Activity (pass-km) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
1997 2010 2020 1997-2010 1997-2020 2010 2020
Total passenger transport 7226 8664 9994 1.4 1.4
Cars 4278 4877 5459 1.0 1.1 99 101
Personal light trucks 1507 1792 2018 1.3 1.3 99 101
Bus 214 262 296 1.6 1.4 100 100
Passenger rail 42 46 48 0.6 0.5 100 100
Intra-regional aviation 823 1083 1327 2.1 2.1 93 96
Inter-regional aviation 362 604 847 4.0 3.8 91 95
Fuel intensity Comparison with baseline case
Growth rates (% per annum)
(MJ/pass-km) (base 2010 = 100) (base 2020 = 100)
Average passenger transport 2.4 2.3 1.9 -0.4 -1.0
Cars 2.3 2.2 1.9 -0.3 -0.9 96 82
Personal light trucks 3.2 3.2 2.7 0.0 -0.7 96 82
Bus 0.9 0.9 0.8 -0.1 -0.1 100 99
Passenger rail 0.9 0.7 0.6 -1.7 -1.7 100 100
Intra-regional aviation 2.5 2.2 1.8 -1.3 -1.6 94 88
Inter-regional aviation 2.1 1.7 1.3 -1.9 -2.3 85 84
Comparison with baseline case
Energy consumption (Mtoe) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total passenger transport 418.4 475.5 460.4 1.0 0.4 93 83
Cars 231.0 254.5 242.1 0.7 0.2 95 83
Personal light trucks 113.6 135.0 130.6 1.3 0.6 95 83
Bus 4.4 5.3 5.9 1.5 1.3 100 99
Passenger rail 0.9 0.8 0.7 -1.1 -1.2 100 100
Intra-regional aviation 50.0 55.9 55.5 0.9 0.5 87 84
Inter-regional aviation 18.5 24.0 25.5 2.0 1.4 85 84
Comparison with baseline case
CO2 emissions (Mt) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total passenger transport 841 1381 1342 3.9 2.0 93 83
Cars 639 732 699 1.1 0.4 94 82
Personal light trucks 58 392 381 15.9 8.5 95 84
Bus 9 16 18 5.0 3.3 100 99
Passenger rail 3 1 1 -7.1 -4.6 100 100
Intra-regional aviation 102 167 166 3.9 2.1 87 84
Inter-regional aviation 31 72 76 6.8 4.1 85 84
Appendices 163
Comparison with baseline case
Activity (tonne-km) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
1997 2010 2020 1997-2010 1997-2020 2010 2020
Total freight transport 5368 6686 7759 1.7 1.6 98 99
Small trucks and vans 119 174 220 3.0 2.7 99 99
Heavy trucks 2249 2892 3430 2.0 1.9 97 98
Freight rail 1880 2453 2859 2.1 1.8 100 100
Navigation 1011 937 883 -0.6 -0.6 100 100
Freight air 110 232 367 5.9 5.4 93 97
Fuel intensity Comparison with baseline case
Growth rates (% per annum)
(MJ/tonne-km) (base 2010 = 100) (base 2020 = 100)
Average freight transport 1.5 1.5 1.4 0.1 -0.2
Small trucks and vans 15.1 14.6 12.4 -0.3 -0.8 96 82
Heavy trucks 1.8 1.6 1.4 -0.9 -1.0 99 98
Freight rail 0.3 0.2 0.2 -1.6 -1.6 100 100
Navigation 0.5 0.6 0.7 0.9 0.9 100 100
Freight air 8.3 7.0 5.7 -1.3 -1.7 94 88
Comparison with baseline case
Energy consumption (Mtoe) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total freight transport 186.8 235.2 259.9 1.8 1.4 94 90
Small trucks and vans 42.8 59.7 65.3 2.6 1.9 95 81
Heavy trucks 97.3 110.6 118.4 1.0 0.9 96 96
Freight rail 11.9 12.6 12.5 0.4 0.2 100 100
Navigation 12.9 13.4 13.9 0.3 0.3 100 100
Freight air 21.9 38.9 49.8 4.5 3.6 88 85
Comparison with baseline case
CO2 emissions (Mt) Growth rates (% per annum)
(base 2010 = 100) (base 2020 = 100)
Total freight transport 566 711 786 1.8 1.4 95 90
Small trucks and vans 125 175 192 2.6 1.9 95 82
Heavy trucks 299 340 364 1.0 0.9 96 96
Freight rail 37 39 39 0.4 0.2 100 100
Navigation 40 42 43 0.3 0.3 100 100
Freight air 65 116 149 4.5 3.6 88 85
Appendices 164