Choice of activity data

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Activity data may be provided either by fuel consumption or by vehicle kilometres travelled VKT. Use of adequate VKT data can be used to check top-down inventories.


Emissions from road vehicles should be attributed to the country where the fuel is sold; therefore fuel consumption data should reflect fuel that is sold within the country's territories. Such energy data are typically available from the national statistical agency. In addition to fuel sold data collected nationally, inventory compilers should collect activity data on other fuels used in that country with minor distributions that are not part of the national statistics (i.e., fuels that are not widely consumed, including those in niche markets such as compressed natural gas or biofuels). These data are often also available from the national statistical agency or they may be accounted for under separate tax collection processes. For Tier 3 methods, the MOBILE or COPERT models may help develop activity data.

It is good practice to check the following factors (as a minimum) before using the fuel sold data:

• Does the fuel data relate to on-road only or include off-road vehicles as well? National statistics may report total transportation fuel without specifying fuel consumed by on-road and off-road activities. It is important to ensure that fuel use data for road vehicles excludes that used for off-road vehicles or machinery (see Off-Road Transportation Section 3.3). Fuels may be taxed differently based on their intended use. A Road-Taxed fuel survey may provide an indication of the quantity of fuel sold for on-road use. Typically, the on-road vehicle fleet and associated fuel sales are better documented than the off-road vehicle population and activity. This fact should be considered when developing emission estimates.

• Is agricultural fuel use included? Some of this may be stationary use while some will be for mobile sources. However, much of this will not be on-road use and should not be included here.

• Is fuel sold for transportation uses used for other purposes (e.g., as fuel for a stationary boiler), or vice versa? For example, in countries where kerosene is subsidized to lower its price for residential heating and cooking, the national statistics may allocate the associated kerosene consumption to the residential sector even though substantial amounts of kerosene may have been blended into and consumed with transportation fuels.

• How are biofuels accounted for?

• How are blended fuels reported and accounted for? Accounting for official blends (e.g. addition of 25 percent of ethanol in gasoline) in activity data is straightforward, but if fuel adulteration or tampering (e.g. spent solvents in gasoline, kerosene in diesel fuel) is prevalent in a country, appropriate adjustments should be applied to fuel data, taking care to avoid double counting.

• Are the statistics affected by fuel tourism?

• Is there significant fuel smuggling?

• How is the use of lubricants as an additive in 2-stroke fuels reported? It may be included in the road transport fuel use or may be reported separately as a lubricant (see Box 3.2.4.).

Two alternative approaches are suggested to separate non-road and on-road fuel use:

(1) For each major fuel type, estimate the fuel used by each road vehicle type from vehicle kilometres travelled data. The difference between this road vehicle total and the apparent consumption is attributed to the off-road sector; or

(2) The same fuel-specific estimate in (1) is supplemented by a similarly structured bottom-up estimate of off-road fuel use from a knowledge of the off-road equipment types and their usage. The apparent consumption in the transportation sector is then disaggregated according to each vehicle type and the off-road sector in proportion to the bottom-up estimates.

Depending on national circumstances, inventory compilers may need to adjust national statistics on road transportation fuel use to prevent under- or over-reporting emissions from road vehicles. It is good practice to adjust national fuel sales statistics to ensure that the data used just reflects on-road use. Where this adjustment is necessary it is good practice to cross-check with the other appropriate sectors to ensure that any fuel removed from on-road statistics is added to the appropriate sector, or vice versa.

As validation, and if distance travelled data are available (see below vehicle kilometres travelled), it is good practice to estimate fuel use from the distance travelled data. The first step (Equation 3.2.6) is to estimate fuel consumed by vehicle type i and fuel type j.

Equation 3.2.6 Validating fuel consumption

Estimated Fuel = 2 [Vehiclesj t •Distancei t • Consumptioni ,■ t ] i, j,t


Estimated Fuel =total estimated fuel use estimated from distance travelled (VKT) data (l)

Vehicles^ = number of vehicles of type i and using fuel j on road type t

Distance^ = annual kilometres travelled per vehicle of type i and using fuel j on road type t (km)

Consumption^ = average fuel consumption (l/km) by vehicles of type i and using fuel j on road type t i = vehicle type (e.g., car, bus)

j = fuel type (e.g. motor gasoline, diesel, natural gas, LPG)

If data are not available on the distance travelled on different road types, this equation should be simplified by removing the "t" the type of road. More detailed estimates are also possible including the additional fuel used during the cold start phase.

It is good practice to compare the fuel sold statistics used in the Tier 1 approach with the result of equation 3.2.6. It is good practice to consider any differences and determine which data is of higher quality. Except in rare cases (e.g. large quantities of fuel sold for off-road uses, extensive fuel smuggling), fuel sold statistics are likely to be more reliable. This provides an important quality check. Significant differences between the results of two approaches may indicate that one or both sets of statistics may have errors, and that there is need for further analysis. Areas of investigation to pursue when reconciling fuel sold statistics and vehicle kilometre travelled data are listed in Section 3 2.3, Inventory quality assurance/quality control (QA/QC).

Distance travelled data for vehicles by type and fuel are important underpinnings for the higher tier calculations of CH4 and N2O emissions from road transport. So it may be necessary to adjust the distance travelled data to be consistent with the fuel sold data before proceeding to estimating emissions of CH4 and N2O. This is especially important in cases where the discrepancy between the estimated fuel use (Eq 3.2.6) and the statistical fuel sold is significant compared to the uncertainties in fuel sold statistics. Inventory compilers will have to use their judgement on the best way of adjusting distance travelled data. This could be done pro rata with the same adjustment factor applied to all vehicle type and road type classes or, where some data are judged to be more accurate, different adjustments could be applied to different vehicle types and road types. An example of the latter could be where the data on vehicle travelled on major highways is believed to be reasonably well known and on the other hand rural traffic is poorly measured. In any case, the adjustments made for reasons of the choice of adjustment factor and background data as well as any other checks should be well documented and reviewed.


While fuel data can be used at Tier 1 for CH4 and N2O, higher tiers also need vehicle kilometres travelled (VKT) by vehicle type, fuel type and possibly road type as well.

Many countries collect, measure, or otherwise estimate VKT. Often this is done by sample surveys counting vehicle numbers passing fixed points. These surveys can be automatic or manual and count vehicle numbers by type of vehicle. There may be differences between the vehicle classification used in the counts and other data (e.g. tax classes) that also give data on vehicle numbers. In addition they are unlikely to differentiate between similar vehicle using different fuels (e.g. motor gasoline and diesel cars). Sometimes more detailed information is also collected (e.g. vehicle speeds as well as numbers) especially where more detailed traffic planning has been performed. This may only be available for a municipality rather than the whole country. From these traffic counts, transport authorities can make estimates of the total VKT travelled in a country. Alternatives ways to determine the mileage are direct surveys of vehicle owners (private and commercial) and use of administrative records for commercial vehicles, taking care to account for outdated registration records for scrapped vehicles (Box 3.2.3 provides an approach to estimate the remaining fleets).

Where VKT is estimated in a country it is good practice to use this data, especially to validate the fuel sold data (see section


If CH4 or N2O emissions from road transportation are a key category, it is good practice to obtain more information on parameters that influence emission factors to ensure the activity data is compatible with the applicable Tier 2 or Tier 3 emission factor. This will require more dissagregated activity data in order to implement Equation 3.2.3 or 3.2.5:

• the amount of fuel consumed (in terajoules) by fuel type (all tiers);

• for each fuel type, the amount of fuel (or VKT driven) that is consumed by each representative vehicle type (e.g., passenger, light-duty or heavy-duty for road vehicles) preferably with age categories (Tiers 2 and 3); and

• the emission control technology (e.g., three-way catalysts) (Tiers 2 and 3).

• It may also be possible to collect VKT data by type of road (e.g. urban, rural, highway)

If the distribution of fuel use by vehicle and fuel type is unknown, it may be estimated from the number of vehicles by type. If the number of vehicles by vehicle and fuel type is not known, it may be estimated from national statistics (see below).

Vehicle technology, which is usually directly linked to the model and year of vehicle, affects CH4 and N2O emissions. Therefore, for Tier 2 and Tier 3 methods, activity data should be grouped based on Original Equipment Manufacturer (OEM) emission control technologies fitted to vehicle types in the fleet. The fleet age distribution helps stratify the fleet into age and subsequently technology classes. If the distribution is not available, vehicle deterioration curves may be used to estimate vehicle lifespan and therefore the number of vehicles remaining in service based on the number introduced annually (see Box 3.2.3).

In addition, if possible, determine (through estimates or from national statistics) the total distance travelled (i.e., VKT) by each vehicle technology type (Tier 3). If VKT data are not available, they can be estimated based on fuel consumption and assumed national fuel economy values. To estimate VKT using road transport fuel use data, convert fuel data to volume units (litres) and then multiply the fuel-type total by an assumed fuel economy value representative of the national vehicle population for that fuel type (km/l).

If using the Tier 3 method and national VKT statistics are available, the fuel consumption associated with these distance-travelled figures should be calculated and aggregated by fuel for comparison with national energy balance figures. Like the Tier 2 method, for Tier 3 it is suggested to further subdivide each vehicle type into uncontrolled and key classes of emission control technology. It should be taken into consideration that typically, emissions and distance travelled each year vary according to the age of the vehicle; the older vehicles tend to travel less but may emit more CH4 and N2O per unit of activity. Some vehicles, especially in developing countries, may have been converted to operate on a different type of fuel than their original design.

To implement the Tier 2 or 3 method, activity data may be derived from a number of possible sources. Vehicle inspection and maintenance (I/M) programmes, where operating, may provide insight into annual mileage accumulation rates. National vehicle licensing records may provide fleet information (counts of vehicles per model-year per region) and may even record mileage between license renewals. Other sources for developing activity data include vehicle sales, import, and export records.

Alternatively, vehicle stocks may be estimated from the number of new vehicle imports and sales by type, fuel and model year. The populations of vehicles remaining in service may be estimated by applying scrappage or attrition curves.

Higher tier methods involving an estimate of cold start emissions require knowledge of the number of starts. This can be derived from the total distance travelled and the average trip length. Typically, this can be obtained from traffic surveys. This data is often collected for local or traffic studies for transport planning.

Vehicle deterioration (scrappage) curves

Deterioration (scrappage) curves can be used to adjust data obtained from fleet statistics based on vehicle licensing plates, where older vehicles are out of service but still registered in official records, leading to overestimation of emissions. They are approximated by Gompertz functions limiting maximum vehicle age.

In the case of Brazil, the maximum vehicle age of 40 years was used for the National Communication of Greenhouse Gases (MCT,2002 and old/veicul03.htm )

utilizing the S-shaped Gompertz scrapping curve illustrated in this box, Vehicle Scrappage Function. This curve was provided by Petrobras, and is currently utilized by environmental agencies for emission inventories. The share of scrapped vehicles aged t is defined by the equation S(t) = exp [ - exp (a + b(t)) ]; where (t) is the age of the vehicle (in years) and S(t) is the fraction of scrapped vehicles aged t. In the year 1994, national values were provided for automobiles (a = 1.798 and b= -0.137) and light commercial vehicles (a= 1.618 and b= -0.141).

(Ministerio da Ciencia e Tecnologia (2002), Primeiro Inventario Brasileiro De Emissoes

Antropicas De Gases De Efeito Estufa Relatorios De Refencia Emissoes De Gasses De Efeito Por Fontes Moveis, No Setor Eergetico. Brasilia, Bazil 2002)


Scrapping curves in Brazil.

Antropicas De Gases De Efeito Estufa Relatorios De Refencia Emissoes De Gasses De Efeito Por Fontes Moveis, No Setor Eergetico. Brasilia, Bazil 2002)

Scrapping curves in Brazil.


18 20 22 vehicle age

12 14

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18 20 22 vehicle age

12 14

24 26

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