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Car ownership - outlook from WBCSD

Indicator Specificationexpired Created 12 Apr 2007 Published 08 Jun 2009 Last modified 04 Sep 2015, 06:59 PM
This content has been archived on 12 Nov 2013, reason: Content not regularly updated
Indicator codes: Outlook 056
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Assessment versions

Published (reviewed and quality assured)
  • No published assessments


Justification for indicator selection

Private car transport is one of the main sources of greenhouse gases and also gives rise to significant air pollution, which can seriously damage human health and ecosystems. The indicator helps to understand developments in the private car transport sector (transport's 'magnitude' and transport patterns), which in turn explains observed trends in transport's impact on the environment.

Scientific references

Indicator definition

Definition: Car ownership is a number of cars per 1 000 inhabitants; passenger cars refer to motor vehicles other than two-wheelers, intended for the carriage of passenger and designed to seat no more than nine people (including the driver).

Model used: IEA/SMP

Ownership:  World Business Council for Sustainable Development 

Temporal coverage: 1990 - 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


Unit is number of cars per 1000 capita.

Policy context and targets

Context description

There are no policies at the paneuropean level related to car ownership rates. However, a number of policies concerning  passenger transportation can influence indirectly on the indicator data.  

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

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'.


The level of car ownership is closely related to car use (so to the volume of mobility) and - especially in urban areas - also to traffic congestion.

There are no pan-european,EU or Member State, and EECCA objectives or targets relating to vehicle fleet size.

Related policy documents

Key policy question

What are the trends in using private and public transport?


Methodology for indicator calculation

The projections for the car ownership 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.

The main driver for the projection of vehicle sales and stock was car ownership rates. Several projections of car ownership rates in the future are available in the literature. It is chosen to adopt the approach taken by Dargay and Gately, 1996, which fits a logistic function to car ownership by region on the basis of income and projected income growth. Their curve was adapted to the present context, including adjusting for the somewhat different OECD income projections used here. In some cases growth rates were slowed somewhat to reflect the possible difficulties in keeping up with demand growth implied by the initial projections, and to take into account growth in 2-wheelers that, in some regions, could slow the growth in 4-wheelers (the total of 2 and 4 wheelers per capita were also tracked, to ensure that the combined total stayed within a reasonable range,well below 1 per person). As a rough guide, the following table indicates the approximate rate of change in car ownership with change in income (as an elasticity). Until the regional average income reaches $5k per year, ownership growth is very slow, but takes off at this point. It then has a very high elasticity of 1.3% change in car ownership for each 1% increase in average income until the regions reaches 300 cars per capita. It then slows, reflecting an inflection in the logistic curve and the beginning ofasymptotic (saturation) behaviour.

Overview of the SMP Spreadsheet Model

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).

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

Data specifications

EEA data references

  • No datasets have been specified here.

External data references

Data sources in latest figures


Methodology uncertainty

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.

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 private cars arises from differences in environmental performance (resource consumption, greenhouse gas emissions, pollutant and noise emissions, land consumption, accidents etc.) of transport modes. 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).

Further work

Short term work

Work specified here requires to be completed within 1 year from now.

Long term work

Work specified here will require more than 1 year (from now) to be completed.

General metadata

Responsibility and ownership

EEA Contact Info

Anita Pirc Velkavrh


No owners.


Indicator code
Outlook 056
Version id: 1


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DPSIR: Driving force
Typology: Performance indicator (Type B - Does it matter?)
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