APE_F02: Emissions of acidifying substances - outlook from WBCSD (Outlook 018) - Assessment published Jun 2007

Indicator definition

Definition: In general, the indicator 'emissions of acidifying pollutants' tracks trends in anthropogenic emissions of acidifying substances such as nitrogen oxides, ammonia, and sulphur dioxide, each weighted by their acidifying potential.
Outlook form IEA/SMP model provides information only for emissions of nitrogen oxides from transport sector.

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}}

Units

The indicator is measured in thousand tonnes of NOx per year.

Policy context and targets

Context description

Pan-European policy context

At the Pan-European level this indicators is related to the implementation of the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. The Protocol sets emission ceilings for 2010 for four pollutants: sulphur, NOx, VOCs and ammonia. These ceilings were negotiated on the basis of scientific assessments of pollution effects and abatement options. Parties whose emissions have a more severe environmental or health impact and whose emissions are relatively cheap to reduce will have to make the biggest cuts.

EU policy context

[1] Directive 2001/81/EC, on national emissions ceilings (NECD) for certain atmospheric pollutants. (consolidated version)

Methodology

Methodology for indicator calculation

Outlook of NOx emissions from transport sector was calculated based on the IEA/SMP model developed by the International Energy Agency and World business council fo Sustainable Development in the framework of the Sustainable Mobility project. They are calculated as a function of projection of vehicle sales, stocks and travel.

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

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

• The IEA/SMP Transport Spreadsheet Model

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    

 Buss
 
 

 Mini-
 
 bus  

 2-3
 wheel
 

 Water
 
       

 OECD regions
 

 Non-OECD regions
 

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

No uncertainty has been specified