Indicator Assessment

Passenger transport demand - outlook from WBCSD

Indicator Assessment
Prod-ID: IND-35-en
  Also known as: Outlook 017
Published 08 Jun 2007 Last modified 11 May 2021
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Assessment created in 2007

If present policies and technological trends continue*, passenger transport will continue to grow worldwide, but more rapidly in the fast-growing economies of Eastern Europe, China and India. 

The modal shares are also expected to shift in a less sustainable direction. Air passenger transport is projected to be the fastest-growing mode. This and road passenger transport together will continue to be the biggest contributors to transport-related CO2 emissions.

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Passenger-km per year per capita in 2000 and projected for 2050 and Projected car ownership rates in 2050

Note: International comparison

Data source:

Data provenance info is missing.

Projected percentage change in passenger transport by mode and car ownership rate from 2000 to 2050

Note: International comparison

Data source:

World Business Council for Sustainable Development (WBCSD) "The Sustainable Mobility Project".

The increase in passenger-km per capita per year in non-EU Europe by 2050 is projected to be around 260%. This is more than in the EU and OECD North America and less than in China and India. In the former Soviet Union countries, passenger-km per capita per year will remain lower than in the OECD Europe and OECD North America (which is the highest with 26 000 passenger-km per capita per year). It is projected almost to triple in Eastern Europe, which include SEE and some EU 10 compared with 2000 and  by 2050reach the OECD Europe level (about 20 000 passenger-km per capita per year).

Although  passenger travel per capita in China will remain rather low compared with countries in OECD Europe and OECD North America, it is expected to have the second biggest share of transport volumes in the world (11 608 billion passenger-km per year), after OECD North America  (15 111 billion).

In terms of modal shifts, air passenger transport is projected to be the fastest-growing mode in all world regions (ranging from 1 167 % increase in India to 337 % in OECD Europe). Passenger rail will be the second most rapidly growing mode of personal transport, with the biggest increase in China and India. Road transport is expected to continue increasing at moderate rates, but loosing its share in the total due to increased air transport, which is projected to increase from around 10 % to one third of total passenger transport.

*Projections are based on the reference case scenario. The reference case projects one possible set of future conditions, based on recent trends. Adjustments are made for expected deviations from recent trends due to factors such as existing policies, population projections (UNSTAT), income projections (IEA) and expected availability of new technologies. Expectations of other future changes in trends, such as saturation of vehicle ownership, are also incorporated. In general, no major new policies are assumed to be implemented beyond those already implemented in 2003, and no major technological breakthroughs.

Supporting information

Indicator definition

Definition: This indicator is presented in two ways: (i) The number of kilometres travelled by persons in a given year by all modes of public transport (taxis, buses, trolleybuses, trams, underground, trains, inland water transport, maritime transport and airplanes) and by private transport. (ii) A breakdown of total passenger transport demand by mode (modal split: the share of each mode in total transport demand).
Model used: IEA/SMP

Ownership:  World Business Council for Sustainable Development 

Temporal coverage: 2000 - 2050

Geographical coverage: OECD Europe: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, the United Kingdom; OECD North America: USA, Canada, Mexico; Former Soviet Union: Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan; Eastern Europe: Albania, Bosnia and Herzegovina, Bulgaria, Croatia, the Former Yugoslav Republic of Macedonia, Poland, Romania, Slovakia, Slovenia, Serbia and Montenegro; India; China


The volume of the passenger transport is measured in the passenger-kilometre traveled (pkm), which represents one passenger travelling a distance of one kilometre.

GDP unit is billion US dollars.


Policy context and targets

Context description

Pan-European policy context

The large number of non binding policy instruments have been developed under fora such as Environment for Europe process, the European Council of Ministers of Transport (ECMT) and the UNECE/WTO Transport, Health and Environment Pan-European Programme (The PEP). The PEP was set up to address the key challenges to achieve more sustainable transport patterns and a closer integration of environmental and health concerns into transport policies.

EU policy context

The EU has set itself the objective to reduce the link between economic growth and passenger transport demand ('decoupling') in order to achieve more sustainable transport.

Reducing the link between transport growth and GDP is a central theme in EU transport policy for reducing the negative impacts from transport: 

  •  The objective of decoupling passenger transport demand from GDP was first mentioned in the Transport & Environment (T&E) integration strategy that was adopted by the Council of ministers in Helsinki. Here, the expected growth in transport demand was named as an area where urgent action was needed. In the sustainable development strategy that was adopted by the European Council in Gothenburg, the objective of decoupling is set in order to reduce congestion and other negative side-effects of transport.
  • In the review of the T&E integration strategy in 2001 and 2002, the Council reaffirmed the objective of reducing the link between the growth of transport and GDP.
  • In the Sixth Community Environmental Action Programme, decoupling of economic growth and transport demand is named as one of the key objectives in order to deal with climate change and to alleviate health impacts from transport in urban areas.

Shifting transport from road to rail is an important strategic element in the EU transport policy. The objective was first formulated in the Sustainable Development Strategy (SDS). In the review of the T&E integration strategy in 2001 and 2002, the Council states that the modal split should remain stable for at least the next ten years, even with further traffic growth.

In the White Paper on the Common Transport Policy (CTP) "European Transport Policy for 2010: Time to Decide", the modal shift is central and the Commission proposes measures aimed at the modal shift.

The White Paper on the Common Transport Policy also says that common transport policy alone will not provide all the answers. It must be part of an overall strategy integrating sustainable development, to include: a) economic policy and changes in the production process that influence demand for transport; b) land-use planning policy and in particular town planning; c) social and education policy;  d) urban transport policy; e) budgetary and fiscal policy to, to link the internalisation of external, and especial environmental, costs with competition of trans-European network; f) competition policy, to ensure, in line with the objectives of high-quality public services, and in particularly in rail sector, that the opening-up of market is not harmed by the dominant  companies already present on market; g) research policy for transport in Europe.

The European Neighbourhood Policy stressed that generating more trade and tourism between the Union and its neighbours, requires efficient, multimodal and sustainable transport systems. EU should develop an Actions plan for cooperation with its neighbors to improve the physical transport networks connecting the Union with neighboring countries, to step up aviation relations with partner countries with the aim to open up markets and to co-operate on safety and security issues.  The Action Plans will also contain specific provisions to address the vulnerability of transport networks and services vis-A-vis terrorist attacks. The highest attention will be paid to enhance the security of air and maritime transport.

EECCA policy context

EECCA Environmental Strategy recognizes the need to incorporate environmental concerns into transport policies and sets this action as one of the Strategy objectives.

One of the actions selected by THE PEP is 'demand side management and modal shift and with special attention to the needs of the countries of Eastern Europe, Caucasus and Central Asia (EECCA) and of South-Eastern Europe, as well as issues related to ecologically particularly sensitive areas'.


Structural goals and targets

  • Implement transport strategies for sustainable development (WSSD

Pan-European level


  • A switch to more efficient and cleaner forms of transport including better organisation and logistics" ( '6EAP')
  • Motorways of the Sea: operating by 2010  (TEN)
  • To increase railway passenger share from 6 to 10% by 2020  (COM 2001/ 370)
  • "...a shift in transport use from road to rail, water and public passenger transport ... [so]  the share of road transport in 2010 is no greater than in 1998" (EU Sust. Dev. Strategy, 2001)
  • introducation of incentives for sustainable transport, including public transport  ( EECCA Strategy )
  • modernization of transportation facilities, including use of less energy intensive transport modes ( EECCA Strategy)

Efficiency targets


  • Decouple transport growth significantly from GDP   ( 6th EAP)


Link to other policy goals and targets


  • '...develop transport infrastructure further through ... networks, better trafic management ... An intermodal approach' (ECMT, Council of Ministers, 1997)


  • Noise from transport no longer presents a health concern (reference policy document?)
  • 140g CO2 average passenger car fleet emissions by 2008
  • 120g CO2 by 2012 (EC/industry agreement)
  • '..give priority to infrustructure investments for public transport and railways...' (EU Sust. Dev. Strategy, 2001)
  • Open up rail markets and support new rail infrastructure
  • '...Link sea, inland water and rail transport...' (COM 2001/ 370)
  • Extention of pan-European transport corridors to neighbouring areas (2004 Santiago de Compostela Conference)


  • Develop and implement national transport strategies for sustainable development to: improve affordability, efficiency, convenience, GHG emissions, urban air quality, health. ( EECCA Strategy)

Related policy documents



Methodology for indicator calculation

To obtain outlook of decoupling of passenger demand from economic growth, the trends from 2000 to 2050 of volume of passenger transport in passenger-km and GDP in billion USD are compared and shown separately on a graph. Relative decoupling occurs when passenger transport demand grows at a rate below that of GDP. Absolute decoupling occurs when passenger transport demand falls while GDP rises or remains constant.

The projections for the volume of passenger transport and GDP are taken from the IEA/WBCSD Sustainable Mobility Project (SMP) model. To cover pan-European region these data were extracted from the publicly available IEA/SMP model spreadsheet (version 1.6)  for the following geographical areas: OECD Europe, Eastern Europe, and Former Soviet Union.
Outlook for the modal split share for passenger transport in total inland transport was extracted from the same model.

Overview of the SMP Spreadsheet Model

(The flowchart on the page 4 of the IEA/SMP model spreadsheet privides an example of the logic behind the model on the basis of light-duty vehicles(e.g. automobiles)).

The IEA/SMP Transport Spreadsheet Model is designed to handle all transport modes and most vehicle types. It produces projections of vehicle stocks, travel, energy use and other indicators through 2050 for a reference case and for various policy cases and scenarios. It is designed to have some technology-oriented detail and to allow fairly detailed bottom-up modeling. The SMP spreadsheet model 1.60 is the most recent version and is available for a more detailed inspection (and use, though no user guide has been prepared and there are no plans, at this time, of providing on-going usersupport for the model. A very basic outline of how to use the model is provided in the first sheet of the model spreadsheet).

The model does not include any representation of economic relationships (e.g., elasticities) nor does it track costs. Rather, it is an "accounting" model, anchored by the "ASIF" identity:

  • Activity (passenger and freight travel)
  • Structure (travel shares by mode and vehicle type)
  • Intensity (fuel efficiency)
  • Fuel type = fuel use by fuel type (and CO2 emissions per unit fuel use).
Various indicators are tracked and characterized by coefficients per unit travel, per vehicle or per unit fuel use as appropriate.

The modes, technologies, fuels, regions and basic variables are included in the spreadsheet model. Not all technologies or variables are covered for all modes. Apart from energy use, the model tracks emissions of CO2, and CO2-equivalent GHG emissions (from vehicles as well as upstream), PM, NOx, HC, CO and Pb. Projections of safety (fatalities and injuries) are also incorporated.

The most detailed segment of the model covers light-duty vehicles. The flow chart  on the page 4 of the Model Documentation provides an overview of the key linkages in the light-duty vehicle section of the model. For other passenger modes (such as buses, 2-wheelers), the approach is similar, however there is no stock model. Stocks are projected directly; vehicle sales needed to achieve these stocks is not currently tracked.

Overview of the projections, regions and viraibales used by the IEA/SMP transport spreadsheet model is peresented in the table below:

Sectors / Modes Vehicle
Regions Variables
vehicles (cars, minivans,
* Medium trucks
* Heavy-duty (long-haul)
* Mini-buses ("paratransit")
* Large buses
* 2-3 wheelers
* Aviation (Domestic +
* Rail freight
* Rail passenger
* National waterborne
(Inland plus coastal)
* Int'l shipping
* Internal combustion engine:
* Gasoline
* Diesel
* Ethanol
* Biodiesel
* Hybrid-
Electric ICE (same fuels)
* Fuel-cell vehicle
* Hydrogen
(With feedstock
differentiation for biofuels
and hydrogen)
* OECD Europe
* OECD North
* OECD Pacific
(Japan, Korea,
Australia, NZ)
* Former Soviet
Union (FSU)
* Eastern Europe
* Middle East
* China
* India
* Other Asia
* Latin America
* Africa
Passenger kilometres
of travel
* Vehicle sales (LDVs
* Vehicle stocks
* Average vehicle fuelefficiency
* Vehicle travel
* Fuel use
* CO2 emissions
* Pollutant emissions
(PM, NOx, HC, CO,
* Safety (road fatalities
and injuries)

Key model assumptions for the reference case

The reference case projects one possible set of future conditions, based on recent trends in various important indicators and other variables. Adjustments are made for expected deviations from recent trends due to factors such as existing policies, population projections, income projections and expected availability of new technologies. Expectations for other future changes in trends, such as saturations in vehicle ownership, are also incorporated.

In general, no major new policies are assumed to be implemented beyond those already implemented in 2003. An exception to this is where there is clear evidence of what might be called "policy trajectories" - future policy actions that are either explicit or implicit in other trends. For example, a clear trend is emerging in the developing world to adopt vehicle emissions standards of a form similar to those already implemented in OECD countries. It is assumed that this "policy trajectory" will continue in the future. In contrast, no such policy trajectory is evident for reduced light-duty vehicle (LDV) fuel consumption; we therefore only incorporate existing fuel consumption programmes through the year they currently end; we assume a return after that date to historical (non-policy-driven) trends in fuel consumption.

In general, the model tried to avoid introducing significant changes in trends after 2030. We run the trends assumed to exist in 2030 out to 2050 in order to see the net effects and directions in that latter year of actions and events that often occurred years earlier.

For more infomation click here

Methodology for gap filling


Methodology references



Methodology uncertainty

Uncertainties related to indicator calculation

All data should be based on movements on national territory, regardless of the nationality of the vehicle. It is unknown what the assumptions are regarding movement of the transport when the assigned regions.  

To answer the question of whether passenger demand is being decoupled from economic growth we need to look at the intensity of passenger transport relative to changes in real GDP. A reduction in intensity should signal relative decoupling. This has some implications on the interpretation one makes of the observed intensity values. GDP in constant prices simply takes away the effect of price increases from year X to year Y but it does not guarantee that GDP in year X for country A is comparable to GDP in country B (as year X is the result of price increases from previous years etc). Therefore, cross-country comparisons of transport intensities based on real GDP may be relevant for trends (i.e. growth/changes over time) but not for comparing intensity values in specific years. If we are interested in knowing whether passenger transport intensity is higher in one country than in another, GDP should ideally be measured in purchasing power parities. These are currency conversion rates that both convert to a common currency and equalise the purchasing power of different currencies (i.e. they eliminate the differences in price levels between countries).

It is arguable, however, whether purchasing power parities are the best currency unit for time-series analysis. One way to avoid such problems is to use population instead of GDP. This would in principle be appropriate for the comparison of intensities between countries as well as for looking at trends over time. It seems also more equitable. To respond to the question of whether or not we are decoupling transport demand from economic activity (i.e. looking at growth rates over time) we would still need to use GDP.

See more on the CSI 035 - Passenger transport demand.

Uncertainties related to IEA/SMP transport model

The model does not include any representation of economic relationships (e.g.,
elasticities) nor does it track costs. The IEA has a cost-optimization model capable of this, the ETP model, but this model was not employed in the SMP's work due to its lack of transparency and its complexity.

Uncertainties related to use of outlooks

to be filled

Data sets uncertainty

The table below provides a simplified picture of what types of variables and the level of
detail modelled for each major transport mode in the IEA/SMP transport spreadsheet model. As can be seen in the next table, there is a range of coverage by mode, as well as variations in the quality of the data available (indicated by x or i). In general, there is better data available for light-duty vehicles than for other modes, though for non-OECD regions most data is quite poor, except for aggregate estimates of transport energy consumption. New vehicle characteristics are only tracked for light-duty vehicles; existing stock is used as the basic vehicle indicator for all other modes.

The reference case includes the modes and variables identified in the table below:

Modes and Variables Covered in the Reference Case Projection

Auto Air

OECD regions

Activity (passenger
or tonne km)
* * * * * * i

New vehicle
(sales, fuel

energy intensity
* * * * * * i

Calculation of
energy use and
vehicle CO2
* * * * * i

Non-OECD regions

Activity (passenger
or tonne km)
* i
* * i

New vehicle
(sales, fuel

energy intensity
i i i i i i i i
Calculation of
energy use and
vehicle CO2
i * i * * i i i *
Note: * = have data of fair to good reliability; i = have data but incomplete or of poor reliability; blank = have nothing or have not attempted to project. Note that data of fair reliability is available for energy use across all road vehicles in non-OECD countries, but breaking this out into various road modes (cars, trucks, buses, 2- wheelers) is difficult and relatively unreliable.
For more information click here

Rationale uncertainty

The relevance of the modal split policy for environmental impact of passenger transport arises from differences in environmental performance (resource consumption, greenhouse gas emissions, pollutant and noise emissions, land consumption, accidents etc.) of transport modes. These differences are becoming smaller on a passenger-km basis, which makes it increasingly difficult to determine the direct and future overall environmental effects of modal shifting. The total environmental effect of modal shifting can in fact only be determined on a case-by-case basis, where local circumstances and specific local environmental effects can be taken into account (e.g. transport in urban areas or over long distances).

Data sources

Other info

DPSIR: Driving force
Typology: Descriptive indicator (Type A - What is happening to the environment and to humans?)
Indicator codes
  • Outlook 017
EEA Contact Info


Geographic coverage



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