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Getting to know your vehicle stock: a step-by-step guide to baseline setting

1. Introduction

In order to track trends in stock-wide light-duty vehicle (LDV) emissions intensity and progress in improving fuel economy toward national and international targets (i.e. the 50:50 targets), policymakers must establish a starting point from which to measure and monitor emissions. Some countries worldwide have already undergone this important inventory, and the Global Fuel Economy Initiative (GFEI) has distilled the basic steps and considerations for your ease of use in this guide. A good understanding of the starting point, or baseline, will allow policymakers to choose the right combination of technology and policy instruments needed to meet national emission, energy security, and efficiency goals. In this guide we deal with fuel economy from newly registered LDVs. With the fuel economy information, it is also possible to estimate average CO2 intensity.

The GFEI methodology uses 2005 as the baseline year, for ease of comparison of data from numerous countries. Once this first 2005 baseline year is established, we recommend that the same calculations be done for 2008 and then, ideally, for every 2 years after that in order to establish a trend and to have a solid base from which to monitor the impact of auto fuel economy policy.

The base and subsequent year measurements are taken from vehicles entering a country’s vehicle stock for the first time, including new vehicles manufactured in the country, new vehicles imported and second hand vehicles imported into the country – in other words, all vehicles newly registered in that year. However, it is useful to keep separate track of these three categories of vehicles, as well as creating a combined average set of information.

In summary, the baseline-setting exercise consists of the following steps:

  1. Establish the baseline year (e.g. the GFEI uses 2005)
  2. Establish the data points you will need to collect in order to calculate a robust baseline
  3. Find and evaluate available new LDV vehicle registration data sources and their quality
  4. Calculate your baseline year average fuel economy and other characteristics for newly registered vehicles
  5. Repeat the same exercise using uniform methodology at regular intervals. In section 5 below, we provide practical examples from countries that have undergone baseline-setting exercises

 

2. Data needs for vehicle database development

The baseline, as mentioned above, should only consider vehicles that are new to the country’s vehicle stock for that year, i.e. newly manufactured or newly imported (including second hand imports) and thus newly registered in that year; a car that is already in-country, but is re-registered because it is re-sold should not be counted. (It may also be useful to track total vehicle stock characteristics, but this would take a different approach, such as street surveys, and is not covered in this document.)

Before you start gathering information for calculating the fuel intensity for the new vehicle registrations for a given baseline year, there are certain key data items that are required to fully develop a clearer picture of the vehicles in operation. These basic data items (or characteristics) should form the backbone of a database of the newly registered vehicle characteristics for that year (e.g. 2005); from this database, the country’s average fuel economy for that year will be calculated.

While in this guide we are primarily focused on LDV’s, some countries may be interested in including additional categories of vehicles. Vehicle segmentation (or categorization of types by weight, interior volume, cc's) has no clear definition or assigned values, and differs from one region to another, although it is often based on body shape and interior volume. Vehicle fuel economy standards are typically differentiated based on vehicle category. While there is no agreed international classification scheme in place, we recommend using the following categories for guidance:

Vehicle Segment Examples
A: Mini / Micro / Small town car

Smallest cars, with a length between 2.50m to 3.60m.

Citroën C1
Fiat Panda
Smart Fortwo

B: Small compact

Slightly more powerful than the Minis; still primarily for urban use; length between 3.60m and 4.05m

Mitsubishi Colt
Opel Corsa
Suzuki Swift

C: Compact

Length between 4.05m – 4.50m

Mazda 3
Subaru Impreza
Volvo S40

D: Family cars

Designed for longer distance; fits 5- 6 people; length is 4.50m to 4.80m

BMW 3 series
Chrysler Sebring
Lexus IS

Light vans

Size is similar to D, but interior volume is maximised to accommodate larger families

Chevrolet Uplander
Ford Galaxy
Volkswagen Sharan

Big / Full size cars

Have generous leg room; can comfortably transport 5 - 6 people; generally have
V8 engines and are 5m or longer in length
Cadillac DTS
Jaguar XJ
Mercedes-Benz E Class

SUV / All terrain

The original cars were utility cross-country vehicles with integral transmissions like the Jeep

Dodge Durango
Jeep Grand Cherokee
Nissan Patrol
Toyota Land Cruiser


Regardless of the number of categories you choose to include in your baseline database in addition to LDV’s, the absolute minimum information required for each vehicle includes:

  • Vehicle make and model, and if possible “configuration” (this typically is labelled by the manufacturer using a sub-model number or other designation; it can indicate transmission type, trim level, optional accessories, etc.)
  • Model production year
  • Year of first registration, if different from model year
  • Fuel type
  • Engine size
  • Domestically produced or imported
  • New or second hand import
  • Rated Fuel Economy per model and test cycle basis. This can be done either by getting data from country of origin or manufacturer (see Resources section below for links), or by testing of a select sample of vehicles. For more information on vehicle emission test cycles, see the test cycle summary, here)
  • Number of sales by model

Additional information that would be useful for advanced analysis and should be collected, if possible, includes:

  • Vehicle Information / Identification Number
  • Fuel type (petrol or diesel)
  • Injection system type
  • Body type
  • Transmission type and other vehicle configuration details, as available
  • Vehicle foot print
  • Vehicle curb weight
  • Emissions certification level
  • Use of vehicle (private, public, for hire, etc.)
  • Vehicle price

Second hand vehicles imported into the country will be registered for the first time within the country and thus are “new registrations”. Although they are clearly not ‘new’ vehicles, they are new to that market and thus should still be counted, particularly as they constitute a significant proportion of new registrations in some countries; for example, around 80% of ‘new’ vehicles in Kenya are second hand ex-Japan imports. These should be clearly defined as second hand, so that analysis can be done on all new registrations as well as on new versus second hand vehicles.

The database should contain the initial registration of each vehicle, with the date of initial registration and the model year of the vehicle. If vehicles are re-registered each year or after a re-sale, and these are included in the government database, these re-registrations should be taken out of a baseline database so as to avoid double-counting.

The image below shows an example of part of a database that 1) differentiates between vehicle types by make and model and 2) gathers information for those vehicles along the basic vehicle characteristics needed to form a picture or baseline of the average fuel economy for any given year.


Make

Model

Condition

Body Type

Engine CC

Fuel Type

Model Year

Registration Date

L/100km

CO2

BMW

316I

Used

S.WAGON

1596

Petrol

1989

2005

7.5

176

CHEVROLET

OPTRA

Used

SALOON

1799

Petrol

2005

2005

6.2

145

CHEVROLET

NULL

Used

S.WAGON

1799

Petrol

2005

2005

6.2

145

NISSAN

SUNNY

Not Specified

SALOON

1970

Diesel

1998

2005

6.6

177

MITSUBISHI

LANCER

Used

SALOON

1600

Diesel

1998

2005

6.9

185

SKODA

OCTAVIA

Used

SALOON

1800

Diesel

2004

2005

7.0

188

SKODA

OCTAVIA

Used

SALOON

1800

Diesel

2005

2005

7.0

188

TOYOTA

COROLLA

New

S.WAGON

1970

Diesel

1998

2005

7.0

188

TOYOTA

COROLLA

New

SALOON

2000

Diesel

1998

2005

7.0

188

FORD

RANGER

New

VAN

2500

Petrol

2005

2005

8.1

170

HONDA

CR-V

NULL

S.WAGON

1970

Petrol

1998

2005

9.3

217

 

3. Sources of Information

The majority of OECD countries and a few other countries currently track all vehicle sales and vehicle characteristics (via registration records) and have well-established systems for measuring average new LDV fuel economy on a yearly basis. All countries should be able to do this, but data collection and analysis systems must first be established.

When vehicle registration data is available: The best approach for gathering baseline fuel economy data and developing estimates involves obtaining official records of new vehicle sales or registrations. Registration data is usually collected by national governments as part of the system of regulation and taxation of vehicle ownership within the country. For example, in Kenya, this is the Kenya Revenue Authority, and in Chile, the Ministry of Transport keeps records of new registrations.

Many countries store registration records in digital databases or are starting to develop them from paper-based records. If data is available in paper form, it will have to be inputted by hand into a specialized auto fuel economy spreadsheet for use in analysis. This time-consuming exercise should be taken into consideration when planning for the required resources to set up a database and collect information.

When data is not available: It may be the case that vehicle registration data is not available (or not available to groups interested in establishing the baseline estimate). If it is determined that a full database of vehicle registrations is either not available or not easily utilized, there are other approaches to obtaining at least an indicative estimate of baseline fuel economy levels, e.g. via a representative sample of new vehicle sales. Statistical methods may be applied to determine a representative sample.

One option in this case is to work with importers and manufacturers to obtain their data on vehicles imported and / or sold within the country. Such reporting may already be a requirement in many countries. If such an approach is taken, it is important to cover enough importers and manufacturers to be confident that a representative sample of different vehicle types has been collected. 

While including all manufacturers is clearly best, at least all major manufacturers / sellers should be included in the sample and those not covered should be checked to see if their exclusion is likely to skew the sample in some manner (e.g. if those specializing in particularly large or sporty vehicles are excluded, etc.).

It may also be possible to collect vehicle information via a vehicle count sampling system – essentially, a visual inspection of vehicles in use around the country, for example in parking areas. However, this approach requires careful efforts to obtain a representative sample, along with a detailed-enough inspection of each vehicle to obtain key attributes of the vehicle and to ascertain that the vehicle is new to the vehicle stock (which can be very time-consuming). Inspection of the VIN (vehicle identification number) can help in this regard since this number usually indicates the year of vehicle manufacture and key attributes of the vehicle. The registration number or licence plate also often indicates when the vehicle was first registered in the country, indicating if it is new to the stock. Additional guidance from the GFEI on this approach is forthcoming. If you would like additional advice on this survey method, please contact clean.transport [at]unep.org.

 

4. Estimating baseline average fuel economy, fuel consumption and CO2 intensity

Once the database has been compiled with ‘new’ vehicles registered for the base year, it should contain records (probably several hundreds or thousands) that show all the different vehicle makes / models / configurations that have been sold in the country for a given model year, the number of vehicles sold and their fuel economies.

If the tested fuel economy number for the vehicle is not included in the registration data, they can be obtained elsewhere and mapped into the database. The fuel economy figures for a given make, model and year can usually be retrieved from the vehicle manufacturers (for example, via websites), and from various international organizations. The International Energy Agency, UNEP and partner organizations are compiling a list of fuel economies into a common database for use by countries undertaking baseline-setting exercise. Please contact clean.transport[at]unep.org for more information.

While vehicle fuel economy and may be reported according to various test drive cycles, we recommend that for the sake of comparison, all drive cycles, data obtained be converted to the NEDC cycle. Conversion factors are conveniently available in a work sheet from the International Council on Clean Transportation: downloadable from www.theicct.org/info/data/GlobalStdReview_Conversionfactor.xlsx.

Once your database is as complete as possible, and checked for errors, it can be used to develop baseline estimates.

At the simplest level, taking a weighted average (by sales) of all new (including newly imported second hand) vehicles in the database will provide the average fuel economy of new vehicles sold in the country in the given year:

In a similar way, average CO2 intensity can be obtained through weighted average with the sales of each model:

If you have decided to include multiple categories of vehicles in your baseline database (as mentioned in Section 2 above, e.g. LDV, heavy duty etc), this will allow the generation of distributions of sales by vehicle class and the average fuel economy in each class. This can be very helpful in tracking how sales and fuel economy changes in the future, and for comparing to other countries.

Finally, fuel economy can be compared to variables such as vehicle size, weight, or engine power, to see how these variables relate. This can help in establishing different vehicle standards by weight or size class. For a global comparison of your country’s figures for base year 2005 and onwards, contact the GFEI for support on your baseline-setting exercise and for guidance on methodology issues at clean.transport[at]unep.org.

5. Country examples

Numerous countries have undergone baseline-setting exercises and you may find the following examples useful in your own approach:

 

6. Resources


The following sources of information for fuel economy baselines, conversion tools and downloads can assist in the development of your baseline database:

View baseline setting guide [French] [Spanish]

 

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The information contained on this website is intended as practical guidance coupled with examples of auto fuel economy policies and approaches in use around the world. It is not a complete collection of all national examples, nor does it track national and global progress on improving auto fuel economy. It is a work in progress and is updated regularly.This website does not support IE 5 and below.

 


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