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You are here: Home / Data and maps / Indicators / APE_F04: Emissions of ozone precursors - outlook from WBCSD

APE_F04: Emissions of ozone precursors - outlook from WBCSD

This content has been archived on 11 Nov 2013, reason: Content not regularly updated
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Contents
 

Assessment versions

Published (reviewed and quality assured)
  • No published assessments

Justification for indicator selection

Emissions of non-methane volatile organic compounds (NMVOCs), nitrogen oxides, carbon monoxide and methane contribute to the formation of ground-level (tropospheric) ozone. Their relative contributions can be assessed on the basis of their tropospheric ozone-forming potential (TOFP) (de Leeuw 2002).

Ozone is a powerful oxidant and tropospheric ozone can have adverse effects on human health and ecosystems. It is a problem mainly during the summer months. High concentrations of ground-level ozone adversely affects the human respiratory system and there is evidence that long-term exposure accelerates the decline in lung function with age and may impair the development of lung function. Some people are more vulnerable to high concentrations than others, with the worst effects generally being seen in children, asthmatics and the elderly. High concentrations in the environment are harmful to crops and forests, decreasing yields, causing leaf damage and reducing disease resistance.

Why to assess emissions of primary particulates from transport sector?

Transport is one of the major contributors to the pollution of ozone precursors..

Scientific references:

Indicator definition

Definition: Generally the indicator 'emissions of ozone precursors'  tracks projected trends in anthropogenic emissions of ozone precursors: nitrogen oxides, carbon monoxide, methane and non methane volatile organic compounds, each weighted by their tropospheric ozone-forming potential.
The outlook form IEA/SMP model provides information only for nitrogen oxides and carbon monoxide in transport sector.

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

Units

The indicator is measured in thousand tonnes per year.

Policy context and targets

Context description

The reduction of  pollutant emissions is one of the strategic priorities stated in the White Paper on the Common Transport Policy (CTP) "European Transport Policy for 2010: Time to decide". Moreover, all of these declared as priority research themes with a contribution to make to the implementation of the transport policy recommended in the White Paper.

Emission ceiling targets for NOx and NMVOCs are specified in both the EU National Emission Ceilings Directive (NECD) and the Gothenburg protocol under the United Nations Convention on Long-Range Transboundary Air Pollution (LRTAP Convention) (UNECE 1999).

There are no specific EU emission targets set for either carbon monoxide (CO) or methane (CH4). However, there are several Directives and Protocols that affect the emissions of CO and CH4. 


EECCA policy context

Mostly of all EECCA countries ratified the 1979 Convention on Long-Range Transboundary air pollution. Hower, only two of them signed in the Protocol to abate acidification, eutrophication and ground-level ozone, Gothenburg, 1999.

A list of countries ratified the 1979 Convention: Armenia (1997), Azerbajan (2002), Belarus (1980), Georgia (1999), Kazakhstan (2001), Kyrgyzstan (2000), Republic of Moldova (1995), Russian Federation (1980), the Ukraine (1980).

A list of countries that signed in the Protocol but still in process of its ratification: Armenia (1999), Republic of Moldova (2000).

Targets

There are no specific targets for CO emissions. Hovewer, emissions of NOx are covered by the EU National Emission Ceilings Directive (NECD) (2001/81/EC) and the Gothenburg protocol under the United Nations Convention on Long-Range Transboundary Air Pollution (LRTAP Convention) (UNECE 1999). See targets here.

  • European  NOx emissions sould be cut at least 41%.
  • To set tight limit values for specific emission sources (e.g. cars and lorries) and requires best available techniques to be used to keep emissions down.

Related policy documents

Key policy question

What are prospects in reducing emissions of ozone precursors across Europe?

Specific policy question

How do different sectors and processes contribute to emissions of ozone precursors?

Methodology

Methodology for indicator calculation

NOx and CO emissions were calculated within the IEA/SMP model for transport sector. NMVOCs and methane are excluded from calculation procedures.

At a world regional average level of aggregation, there is no information in the model about where vehicles are traveling (e.g. urban v. rural) or how various emissions translate into atmospheric concentrations. The emissions trends are included to provide a general directional sense of whether total emissions from road vehicles increasing or decreasing over time. Five types of pollutants are tracked: nitrogen oxides (NOx), particulate matter (PM-10), carbon monoxide (CO), hydrocarbons (HC or VOC) and lead (Pb). A pollutant emission tracking has been developed only for road vehicles - no tracking for rail, air or shipping.

The approach used for light-duty vehicles has been to rely primarily on existing tailpipe emissions standards for new vehicles around the world, and the announced plans for phase-in of future, generally tighter, standards. For the developing world, in cases where information on existing or planned future standards was unavailable, simple assumptions were made regarding adoption of standards similar to the EU system (EURO 1 through EURO 5) in the future, at a certain time-lag after these have been implemented in Europe.

For other road vehicles (2/3 wheelers, trucks and buses), since the model does not track new vehicles or stock turnover, but only the existing stock of vehicles, estimates are based on assumed average emissions across the vehicle stock, and evolution of this average.

Several types of data are thus needed to generate estimates: average emissions for new and existing vehicles in the base year (2000), and estimated emissions of new vehicles, or in the case of non-LDVs estimated improvements in the stock average, in the future. While the new LDV emissions estimates are based primarily on current and future emissions standards, other sources were needed for the estimates related to existing vehicles. Few such sources exist that cover non-OECD regions.

The best source found for average in-use emissions in 2000 was a very recent (and as of May 2004, still unpublished) report from an OECD Environment Directorate study, part of their MOVE II project. The IEA has not been involved in that study, but obtained relevant estimates through internal communication. The study is highly relevant since it generates average emissions for all types of road vehicles by region (with a similar but not identical regional classification system as used here), vehicle type, vehicle vintage, and emissions control category (with seven categories, from uncontrolled up through the equivalent of EURO IV control levels). However, the authors warn that estimates are not final and could change. The IEA also relied on input from SMP Workstreams 2 and 3, including both data and review of the estimates and projections contained herein.

An important difference between the projections here and the OECD projections is the assumption here that all world regions will eventually adopt the same emissions standards being implemented in OECD regions. The OECD report restricts improvements to those emissions standards already announced or nearly finalized. This leads to a large difference in the projection - if developing regions do not continue to follow the OECD country lead (with some regions such as Africa and the Middle East assumed not to adopt any standards at all), then total emissions for each of the four pollutants in the developing world rises over time, rather than dropping in the projections are used here, with the assumption of a 10-15 year lag time in adopting OECD emissions standards in the developing world (described in more detail here).

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
Technologies/
Fuels
Regions


Variables


Light-duty
vehicles (cars, minivans,
SUVs)
- Medium trucks
- Heavy-duty (long-haul)
trucks
- Mini-buses ("paratransit")
- Large buses
- 2-3 wheelers
- Aviation (Domestic +
Int'l)
- Rail freight
- Rail passenger
- National waterborne
(Inland plus coastal)
- Int'l shipping

- Internal combustion engine:
- Gasoline
- Diesel
- LPG-CNG
- Ethanol
- Biodiesel
- Hybrid-
Electric ICE (same fuels)
- Fuel-cell vehicle
- Hydrogen
(With feedstock
differentiation for biofuels
and hydrogen)

- OECD Europe
- OECD North
America
- 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
only)
- Vehicle stocks
- Average vehicle fuelefficiency
- Vehicle travel
- Fuel use
- CO2 emissions
- Pollutant emissions
(PM, NOx, HC, CO,
Pb)
- 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.

Calculation of emissions

The indicator's calculations are based on the data from the indicators from the IEA/SMP models such as averagein-use emissions and total energy use across sectors, fuel and regions; and have to take into account current and future emissions standards.

Pollutant emissions tracking was implemented in the model to allow the Sustainable Mobility Project to better understand the vehicle emissions trends that result from the projection of vehicle sales, stocks and travel. At a world regional average level of aggregation, there is no information in the model about where vehicles are traveling (e.g. urban v. rural) or how various emissions translate into atmospheric concentrations. The emissions trends are included to provide a general directional sense of whether total emissions from road vehicles increasing or decreasing over time. Five types of pollutants are tracked: nitrogen oxides (NOx), particulate matter (PM-10), carbon monoxide (CO), hydrocarbons (HC or VOC) and lead (Pb). Note that for lead, a different approach is used which is discussed after the other pollutants. Pollutant emissions tracking have been developed only for road vehicles - no tracking for rail, air or shipping.

The approach used for light-duty vehicles has been to rely primarily on existing tailpipe emissions standards for new vehicles around the world, and the announced plans for phase-in of future, generally tighter, standards. For the developing world, in cases where information on existing or planned future standards was unavailable, simple assumptions were made regarding adoption of standards similar to the EU system (EURO 1 through EURO 5) in the future, at a certain time-lag after these have been implemented in Europe.

For other road vehicles (2/3 wheelers, trucks and buses), since the model does not track new vehicles or stock turnover, but only the existing stock of vehicles, estimates are based on assumed average emissions across the vehicle stock, and evolution of this average.

Methodology for gap filling

No methodology for gap filling has been specified. Probably this info has been added together with indicator calculation.

Methodology references

Uncertainties

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.  

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



 Truck



 Frt Rail



 Pass Rail



 Bus



 Mini- bus



 2-3 wheel



 Water



 OECD regions









Activity (passenger
or tonne km)

-

-

-

-

-

-

i

i

 

New vehicle
characteristics
(sales, fuel
consumption)

-

 

 

 

 

 

 

 

 

Stock-average
energy intensity

-

-

-

-

-

-

i

i

 

Calculation of
energy use and
vehicle CO2
emissions

-

-

-

-

-

i

i

i

 

 Non-OECD regions









Activity (passenger
or tonne km)

i

-

i

-

-

i

i

i

 

New vehicle
characteristics
(sales, fuel
consumption)

i

 

 

 

 

 

 

 

 

Stock-average
energy intensity

i

i

i

i

i

i

i

i

 

Calculation of
energy use and
vehicle CO2
emissions

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 uncertainties related to methodology and data sets are  important for the results assement. Any uncertainties involved in the calculation and in the data sets need to be accurately communicated in the assessment, to prevent erroneous messages influencing policy actions or processes.

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

Ownership

No owners.

Identification

Indicator code
Outlook 006
Specification
Version id: 1

Permalinks

Permalink to this version
9bf674544178b128712c4aa27f31e968
Permalink to latest version
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Classification

DPSIR: Driving force
Typology: Descriptive indicator (Type A - What is happening to the environment and to humans?)

Geographical coverage

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