data gathering and analysis to assess the impact of mileage on … · 2016. 11. 23. · data...

Data gathering and analysis to assess the impact of mileage on the cost effectiveness of the LDV CO2 Regulations

Framework Ref: CLIMA.C.2/FRA/2012/0006

Final report for European Commission – DG Climate Action

Ricardo-AEA/R/ED59296 Issue Number 1 Date 26/09/2014


i Ricardo-AEA/R/ED59296/Issue Number 1

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DG CLIMA Craig Dun

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This report is the Copyright of the European Commission and has been prepared by Ricardo-AEA Ltd under contract to DG Climate Action dated 06/12/2013. The contents of this report may not be reproduced in whole or in part, nor passed to any organisation or person without the specific prior written permission of the European Commission. Ricardo-AEA Ltd accepts no liability whatsoever to any third party for any loss or damage arising from any interpretation or use of the information contained in this report, or reliance on any views expressed therein.

Author:

Craig Dun, Alison Pridmore, Gena Gibson, Sujith Kollamthodi (Ricardo-AEA), Ian Skinner (TEPR)

Approved By:

Alison Pridmore

Date:

26 September 2014

Ricardo-AEA reference:

Ref: ED59296- Issue Number 1


ii Ricardo-AEA/R/ED59296/Issue Number 1

Executive summary

The purpose of this study was to gather and analyse data in order to better understand the impact of mileage on the cost effectiveness of the Light Duty Vehicle (LDV) CO2 Regulations.

The key aims of this study were to:

(i) Collect and analyse information from a wide range of sources, in order to establish whether there are relationships between the lifetime distance travelled (mileage) and the mass and/or footprint for cars and light commercial vehicles (LCVs); and

(ii) Provide an assessment of the likely cost impacts that would arise from using these relationships to adjust the application of CO2 reduction targets in potential future regulations.

A review of the availability of data for use in this study was conducted. Sufficiently detailed data at the Member State level was available for the UK, Belgium and France. Statistical analysis of these datasets provided key findings as follows:

The data suggested that there is a substantial difference between the average lifetime mileage for petrol and diesel cars. On average, diesel cars in the datasets were found to have a lifetime mileage of 219,000 km, compared to 150,000 km for petrol cars.

In the case of petrol cars, there appears to be a positive correlation between mass/footprint and lifetime mileage. That is, larger/heavier cars tended to be driven further over their lifetimes.

For diesel cars, the lifetime mileage appeared to be relatively constant across all mass/footprint categories (217,000km for small diesel cars, rising to only 223,000km for large diesel cars on average). A possible reason for this trend is that consumers who drive longer distances tend to prefer diesel vehicles due to their greater fuel efficiency and the (on average) lower cost of diesel fuel.

As with diesel passenger cars, for light commercial vehicles (LCVs), there was no apparent relationship between the lifetime mileage and different mass/footprints. Overall, the data suggested that lifetime mileage was relatively constant across all LCV class sizes (205,000 km for Class I, 206,000 km for Class II and 202,000 km for Class III diesel LCVs).

The current passenger car and LCV CO2 Regulations (European Commission, 2009, 2011) calculate CO2 reduction targets on a sales-weighted basis only (under the assumption that all vehicles, regardless of size are driven the same distance over their lifetimes). An analysis of the potential effects of adopting a mileage and sales weighted system suggested that the cost-effectiveness of the Regulations could be improved. The mileage-weighting in this case is realised by imposing a proportionately more stringent CO2 reduction target on larger/heavier vehicles in recognition of the relationships between vehicle mass and lifetime mileage discussed above1,2. This analysis suggested that the introduction of a mileage-weighted system of this form into a post-2020 Regulation would have the following impacts:

The overall fleet-wide marginal costs to manufacturers of achieving the same level of CO2 reductions would decrease by 1.75% per vehicle where mass is retained as a utility parameter.

1 The correlation between lifetime mileage and footprint were not deemed sufficiently robust for use in an equivalent analysis based on footprint.

2 It should be noted here that whilst there was a strong correlation between mass and lifetime mileage for petrol passenger cars, little correlation

was found with diesel vehicles.


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The overall fleet-wide marginal costs to manufacturers of achieving the same level of CO2 reductions would decrease by 1.62% per vehicle based on footprint as a utility parameter.


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Table of contents

1 Introduction ................................................................................................................ 6 1.1 Overview ............................................................................................................ 6 1.2 Current EU car and light commercial vehicle CO2 Regulations ........................... 6 1.3 Potential benefits of a applying mileage-weighting to the CO2 targets ................ 7 1.4 Aims of the study................................................................................................ 8

2 Data collection: Approach and results ..................................................................... 9 2.1 Data collection: approach and datasets identified .............................................. 9 2.2 Evidence on the link between mileage and vehicle size ....................................10

3 Review of data sources analysed ............................................................................14 3.1 Primary data sources identified .........................................................................14 3.2 Data cleaning and limitations ............................................................................14 3.3 Assumptions .....................................................................................................15

4 Relationship between lifetime mileage versus mass and footprint .......................16 4.1 Correlation approach .........................................................................................16 4.2 Results ..............................................................................................................17

4.2.1 Passenger cars .........................................................................................17 4.2.2 Limitations of the correlation functions generated .....................................23 4.2.3 Variation in mileage travelled with vehicle age ..........................................25 4.2.4 Light Commercial vehicles ........................................................................26

5 Cost impacts of applying mileage weighting factors to car CO2 targets ...............28 5.1 Introduction .......................................................................................................28

5.1.1 Defining a mileage weighted system .........................................................28 5.1.2 Cost implications of a mileage weighted system .......................................29 5.1.3 Cost curve analysis for passenger cars .....................................................30

5.2 End user impact analysis ..................................................................................35 5.2.1 Lifetime societal analysis...........................................................................35 5.2.2 Five-year analysis .....................................................................................37

6 Conclusions ...............................................................................................................40 6.1.1 Availability and suitability of existing and future datasets ..........................40 6.1.2 Development of relationships linking (i) mass and (ii) footprint to lifetime mileage 40 6.1.3 Impact of possible mileage-weighted CO2 reduction targets on costs of compliance for manufacturers ...................................................................................41 6.1.4 Impact on the end user .............................................................................41

6.2 Further work ......................................................................................................42 6.2.1 Improvements in the definition of lifetime mileage .....................................42 6.2.2 Improvements in the understanding of the age profile for vehicle mileage .42 6.2.3 Impact of the inter Member State second-hand vehicle market .................42 6.2.4 Analysis of additional datasets ..................................................................42

7 Appendix ....................................................................................................................44 7.1 Appendix 1: Overview of literature that suggests that heavier vehicles tend to travel farther than lighter vehicles ................................................................................44 7.2 Appendix 2: Country specific mileage data analysis ..........................................50 7.3 Appendix 3: Additional analysis of the full dataset .............................................60

7.3.1 Detailed tables for analysis by mass bins ..................................................60 7.3.2 Detailed tables for analysis by footprint bins .............................................61


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7.3.3 Statistical analysis of full dataset ...............................................................62 7.4 Appendix 4: Frequency distribution plots ...........................................................67

7.4.1 Petrol passenger cars ...............................................................................67 7.4.2 Diesel passenger cars ...............................................................................70

7.5 Appendix 5: Additional statistical analysis on LCV dataset ................................73 7.5.1 LCV frequency distribution plots ................................................................75

7.6 Appendix 6: Additional cost analysis results for passenger car segments .........76


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1 Introduction

1.1 Overview

Ricardo-AEA, supported by TEPR, was contracted by the European Commission to gather and analyse data in order to improve the understanding of the impact of lifetime vehicle mileage on the cost-effectiveness of the Light-Duty Vehicle (LDV) CO2 Regulations. These Regulations set fleet-wide targets for the CO2 emissions performance of new passenger cars and light commercial vehicles (LCVs) (European Commission, 2009, 2011). The targets3 for both of these Regulations are based on measurements of CO2 emissions per km (gCO2/km) for vehicles driven over the New European Drive Cycle (NEDC) on a chassis dynamometer.

Whilst these measurements provide an indication of comparative emissions performance on a unit distance basis, they do not reflect the total emissions released by vehicles over their full lifetimes. That is, vehicles that are used more intensively (i.e. those that have higher lifetime mileage) may contribute more to total CO2 emissions from road transport compared to vehicles that are used less intensively.

If differences in lifetime mileage were consistently different for different types of vehicles, mileage-weighting of the CO2 reduction targets could potentially improve the cost-effectiveness of the Regulations. This would involve applying more stringent CO2 reduction targets to vehicles that contribute more to overall CO2 emissions (i.e. those with higher lifetime mileages).

In order to investigate the potential for applying lifetime mileage-weighted targets, robust data on the relationship between vehicle utility parameters (whether measured by mass or footprint) and lifetime mileage is required. The overarching purpose of this study was to identify and analyse such data, and carry out a scoping analysis to quantify the impacts of applying mileage-based CO2 reduction targets on the costs of compliance for vehicle manufacturers and end users.

1.2 Current EU car and light commercial vehicle CO2 Regulations

EU action on passenger car CO2 can be traced back to 1995 with the publication by the European Commission of strategy to reduce the CO2 emissions from passenger cars (European Commission, 1995). As a result of this strategy, voluntary agreements were concluded in 1998 with the European car manufacturers’ association (ACEA), and with its Japanese and Korean counterparts (JAMA and KAMA, respectively), in which the associations agreed that their members would reduce the CO2 emissions from their new car fleets.

By 2006, it became clear that the CO2 emission reductions were not on track to meet the agreed targets (i.e. fleet-weighted average of 140 gCO2/km by 2008/2009); therefore the Commission published a new LDV CO2 strategy. This proposed the introduction of a regulatory framework for reducing the average CO2 emissions of the new car fleet and also proposed a similar regulatory framework for LCVs (European Commission, 2007). The commitment to a regulatory framework culminated in the passenger car CO2 Regulation (European Commission, 2009) and the LCV CO2 Regulation (European Commission, 2011).

Together, these Regulations are referred to as the LDV CO2 Regulations and set, respectively, a fleet-wide target of 130 gCO2/km for new cars to be met by 2015, and a similar target for new LCVs of 175 gCO2/km to be met by 2017. The Regulations both set indicative targets for 2020, of 95 gCO2/km for cars and 147 gCO2/km for LCVs, which have

3 The targets are discussed in more detail in Chapter 1.2.



now been confirmed in amending Regulations, although the target for cars is to be met one year later than planned, i.e. in 2021 (European Commission, 2014b, 2014c)4.

The annual fleet-average CO2 targets for each manufacturer are directly linked to the sales-weighted average mass of its vehicles that were registered as new in the EU market. The respective targets are calculated according to a formula in Annex I of the respective car or LCV CO2 Regulations, which determines a manufacturer’s target as a function of the mass of its new vehicles that are registered that year. Any change in the average mass of a manufacturer’s new car (or van) fleet will change the manufacturer’s target that year. The process for setting targets does not take into account any differences in the total lifetime CO2 impacts of different types of vehicles.

1.3 Potential benefits of a applying mileage-weighting to the CO2 targets

The current approach adopted in the LDV CO2 Regulations treats each vehicle on a sales-weighted basis, with CO2 emissions measured on a per kilometre basis using the New European Drive Cycle (NEDC).

The total emissions impacts of any particular vehicle may therefore not be accurately reflected by the current gCO2/km metric. However, it is possible that different types of vehicles have different usage profiles over their lifetime. Vehicles that are driven further over their full lifetime are likely to have greater lifetime emissions impacts than those that are used less intensively. With this in mind, it may be possible to improve the cost effectiveness of the Regulations by applying more stringent (tougher) targets to those vehicles responsible for higher lifetime CO2 emissions and less stringent targets for vehicles with lower lifetime CO2

emissions. Improving the cost-effectiveness of the Regulations will provide greater benefits to manufacturers, consumers and society in general.

An initial analysis of the potential impact of weighting the 2020 CO2 targets for cars according to the mileage undertaken by different classes of vehicle in the course of their lifetime was conducted by TNO et al (2011). Their report estimated that using mileage-weighted targets could reduce the cost of meeting the 2020 CO2 target for cars by 2%. The data used for this estimate segmented the new car fleet into small, medium and large cars for each fuel type (i.e. petrol and diesel) depending on a vehicle’s engine capacity (see Section 1). However this approach had two main limitations:

It did not directly relate a car’s CO2 emissions to either its mass or its footprint.

It had only three discrete data points for each fuel type.

This meant that the analysis was not sufficiently detailed to determine a relationship to incorporate mileage-weightings in LDV CO2 legislation. TNO et al (2011) acknowledged the limitations of the dataset and recommended that additional work be undertaken to determine the relationships between mileage and utility parameter for different types of fuel types or propulsion systems. In this respect, the report suggested that the most appropriate approach to estimate these relationships would be to generate additional data points, in order to identify a function that describes the relationship between mileage and utility in a continuous manner. This project is being undertaken to address these limitations.

4 Regulation (EU) No 253/2014 amending Regulation (EU) No 510/2011 to define the modalities for reaching the 2020 target to reduce CO2

emissions from new light commercial vehicles; Regulation (EU) No 333/2014 amending Regulation (EC) No 443/2009 to define the modalities for reaching the 2020 target to reduce CO2 emissions from passenger cars



1.4 Aims of the study

The aims of the study were to:

(i) Investigate information from a wide range of sources in order to establish relationships between lifetime distance travelled (mileage) and mass and/or footprint for cars and LCVs;

(ii) Identify whether there are specific vehicle classes that are outliers from any general relationships found; and

(iii) Provide an assessment of the likely cost reduction that would result from the introduction of mileage-weightings based on these relationships into future (post-2020/2021) legislation on CO2 emissions from LDVs.

This remainder of this report is structured as follows:

Chapter 2 provides a review of existing data and literature relating to potential links between vehicle mass/footprint and mileage.

Chapter 3 provides a review of the robustness of the data sourced for the study, as well as its limitations. Assumptions made during the data cleaning process are also detailed.

Chapter 4 examines the relationship between lifetime mileage and the mass of cars and LCVs in several European Union Member States.

Chapter 5 undertakes a high-level cost analysis of the effects of adopting a mileage-weighted CO2 targets for cars and LCVs. Here, both mass and footprint (for cars only) were assessed as potential utility parameters, as well as the impacts on vehicle manufacturers and the societal costs.



2 Data collection: Approach and results

Data were collected from a number of different countries. Section 2.1 summarises the datasets that were considered and those that were subsequently used as a basis for further analysis within the project. A summary of additional evidence is presented in Section 2.2, including broader evidence on usage patterns relating to mileage degradation over time (i.e. that older vehicles tend to be driven less).

2.1 Data collection: approach and datasets identified

A summary of the data collection process and results is presented in Table 2-1.

Table 2-1: Summary of the data search process and results

Member State

Datasets identified Suitability for this project

France ‘Contrôle technique’ – data from the annual vehicle tests

Data recorded on mileage of vehicles, as well as their make/type, mass, year of registration and fuel type; more detailed analysis was undertaken (see Section 3)

Italy No datasets that contain data on vehicle mileage were identified for Italy

N/A

Germany Fahrleistungserhebung 2002 (National mileage assessment)

Data on mileage by vehicle segment (see Section 2.2); no more recent data available; a new mileage assessment for 2014 will be available by the end of 2015

Data on mass and footprint as part of registration data

Data would cost several thousand Euros. No information on mileage.

Mobilität in Deutschland 2008 (National travel survey)

Data on mileage by segment extracted from online data viewer (see Section 2.2); there is a charge for more detailed datasets (for non-commercial use)

Das Deutsche Mobilitätspanel (Panel data travel survey)

Contains data on annual mileage of cars (on the basis of an estimate by the survey participant), but nothing on mass or footprint

Kraftfahrzeugverkehr in Deutschland 2010 (National vehicle traffic survey)

Contains data on mass and mileage (but only for one day)

UK MOT data, i.e. data from the annual road-worthiness vehicle tests

Publically available data on mileage, but did not have corresponding information on mass and footprint; more detailed analysis was undertaken (see Section 3)

Belgium ‘Contrôle technique/ Autokeuring’ – data from the annual vehicle road-worthiness tests

Data recorded on mileage of vehicles, as well as their make/type, mass, year of registration and fuel type; more detailed analysis was undertaken (see Section 3)

Sweden Fordonsbeståndet 2012 Information collated by Statistics Sweden (see Section 2.2); underlying data cannot be shared, as a result of strict privacy policy



Member State

Datasets identified Suitability for this project

Netherlands Statistics Netherlands presents figures on its website linking distance travelled to the mass of cars and LCVs

Analysis was undertaken by bringing together two separate datasets (see Section 2.2): Data on distances was from a sample of a dataset of odometer readings purchased by Statistics Netherlands from a private company; data on vehicle characteristics was obtained from the Netherlands Road Authority (under licence and for a fee)

Ireland StatBank Ireland Contains data linking vehicle mode (i.e. car, van, etc.) with fuel type and distance travelled

National Vehicle and Driver File

Contains some information on cars and on their drivers, but not enough to be of use for this project

SEI’s Energy Statistics Databank

Contains data on average distance travelled for cars, but only by fuel type and engine capacity (cc)

Datasets for France, the UK and Belgium were analysed further within this project. Data for additional Member States could be purchased in order to generate a larger dataset, but this was not possible within the resources available for this current study.

2.2 Evidence on the link between mileage and vehicle size

The only EU-wide dataset identified was that used in the previous work on mileage-based targets for the Commission (see Section 1.3). In addition to the datasets that were analysed in more detail (see Section 3), the remaining national datasets consistently suggested that:

i. Cars in larger size categories (and in one case LCVs) tend to be driven further than those in smaller categories. However, only two of the datasets determined the size of a vehicle by its mass5, while the remaining datasets used broad size categories; and

ii. Diesel cars tend to be driven further than petrol cars of a similar size category. However, none of the datasets provided information on the relationship between vehicle mileage and its footprint.

The findings are summarised in Table 2-2

Table 2-2: Summary of additional evidence that suggests that there is a link between vehicle mileage and size category

Country Source and detail Categorisation of the vehicles

What the evidence suggests (on average)

EU TNO et al (2011) “Support for the revision of Regulation (EC) No 443/2009 on CO2 emissions from cars”

Splits the EU car fleet according to three size categories – small, medium and large – for petrol and diesel cars on the basis of their respective engine capacities (measured in cc)

1) As the engine capacity of a car increases, it tends to be driven further

2) For all sizes of car, diesel cars are driven further than the same size of petrol car.

5 Size categories were defined according to different criteria in these datasets, ranging from engine capacity to market segment type. See

Appendix 1 for further information.





Germany “Entwicklung eines Szenariomodells zur Simulation der zukünftigen Marktanteile und CO2-Emissionen von Kraftfahrzeugen” (Mock, 2010)

Survey data from Mercedes-Benz garages from 1999 to 2006 splits the cars into small, medium-sized and larger cars (basis unclear)

Smaller cars tend to have lower annual mileage compared to medium sized cars, which, in turn, have lower annual mileage compared to larger cars

Germany “Fahrleistungserhebung 2002 - Inländerfahrleistung” (Bundesanstalt für Straßenwesen, 2002)

Mileage by car segment

1) Cars in the segments that tend to contain smaller vehicles (e.g. mini, supermini, lower medium) are driven less annually than those cars in other segments (e.g. upper medium, executive and luxury)

2) For all segments, diesel cars are driven further annually than petrol cars

“Mobilität in Deutschland 2008”, report for Bundesministerium für Verkehr, Bau und Stadtenwicklung (DLR and Infas, 2009)

Mileage by car segment

1) Cars in the segments that tend to contain smaller vehicles (e.g. mini, supermini, lower medium) have lower annual mileage compared to cars in other segments (e.g. upper medium, executive and luxury), although diesel cars in the mini segment are driven more (annually) on average than diesel cars in the supermini segment

2) For all segments, diesel cars are driven further annually than petrol cars

Netherlands Website of Statistics Netherlands (Centraal Bureau voor de Statistiek, 2013)

Splits the car fleet according to its mass into four mass categories (i.e. those cars weighing less than 850 kg, 851 kg to 1,150 kg, 1,151 kg to 1,500 kg and over 1,500 kg) by fuel type (i.e. petrol, diesel and other)

1) For both petrol and diesel cars, the annual distance travelled increases as mass increases

2) In each mass category, diesel cars are driven further annually than petrol cars (with cars using other fuels driven less than diesel cars but more than petrol cars)

Sweden Website of Statistics Sweden (Statistics Sweden, 2014)

Splits the car fleet according to its kerb weight into 13 different mass categories, but does not distinguish by fuel type

The annual distance travelled increases as mass increases until around 1,700 kg at which point the distance travelled declines slightly until 3,000 kg after which it declines significantly.





Splits the light commercial vehicle fleet (i.e. that GVW of less than 3 tonnes) into 4 mass categories according to its maximum permissible weight, but does not distinguish by fuel type

The annual distance travelled increases as mass increases, although the distance travelled by the heaviest mass category given in the source (maximum permissible weight of 2,501 kg to 3,000 kg) is only slightly longer than the next lightest category (2,001 kg to 2,500 kg).

Source: Ricardo-AEA/TEPR research undertaken for this project

Notes: Further details are provided in Appendix 1

It should be noted that this evidence comes from a number of countries, three of which (Germany, Sweden and the Netherlands) are not analysed in further detail in Section 3. These examples demonstrate that there is a range of evidence that suggests heavier cars and LCVs are driven further on an annual basis than lighter vehicles. This in turn suggests there could be a potential benefit from identifying (or constructing) a more comprehensive dataset to analyse whether or not relationships between lifetime mileage and mass/footprint of cars and LCVs could be determined.

A further question of relevance is the stability of relationships between lifetime mileage and vehicle mass/footprint over time, which is clearly an important aspect for any long-term policy framework. In this respect, the work undertaken in the TRACCS project (EMISIA et al, 2013) is relevant. Information on the mileage of vehicles linked to their respective ages was provided for Denmark, which showed that newer vehicles are generally used more than older vehicles. On the basis of this data, a methodology was developed within the TRACCS project to enable the annual distance travelled for all vehicles from all of the countries covered to be modified according to the age of a vehicle. An example of such a modification is represented in Figure 2-1. According to this relationship, a ten-year-old vehicle travels approximately half of the annual distance compared to a new vehicle. The fact that vehicles are typically used more in the early years of their lives will have implications for the estimation of the cost-effectiveness of the car and LCV CO2 Regulations, particularly when this is not evaluated in relation to emissions reductions delivered over the lifetime of the vehicle. This issue is discussed further in Section 4.2.3.



Figure 2-1: Example of a function indicating how mileage changes with the age of a vehicle as a proportion of the distance travelled in a vehicle’s first year

Source: EMISIA et al (2013)



3 Review of data sources analysed

3.1 Primary data sources identified

Following the literature review, three key sources of data were identified that contained sufficient detail, as summarised in Table 3-1.

Table 3-1- Data sources used for detailed analysis

Country Data source Coverage

United Kingdom

Most recent (2013) anonymous MOT road-worthiness data (publically available) (UK Department for Transport, 2013)

All roadworthy vehicles present in 2013, covering all vehicle types required by law to be tested. This includes current and historic vehicles.

No details on vehicle mass or footprint, but was used due to its public availability, and comprehensive coverage of the UK vehicle fleet.

France 2010-2013 ‘Contrôle technique’ data from UTAC. (UTAC CERAM Group, 2014)

Limitations were placed on the amount of accessible data. A sample dataset that contained one day per month of tested vehicles taken at random over the last four years.

This equated to a sample of 48 days’ worth of testing data. A dataset of around 3 million vehicles covering vehicles types such as private vehicles, LDVs, taxis and emergency vehicles.

Belgium 2013 ‘Contrôle technique/Autokeuring’ data from GOCA (GOCA, 2014).

Almost 4.5 million vehicles worth of data for passenger cars and LCVs. Most of the entries do not have mass data (88%) and there was no footprint data available. Given the timescales, only data with mass was useful and so 500,000 vehicles were left after cleaning.

3.2 Data cleaning and limitations

The data were cleaned before detailed correlation analysis was performed. The data cleaning process included involved:

Removing unnecessary vehicle categories (leaving passenger cars and LDVs)

Removing all null mass/footprint entries (as well as corrupted data).

Removing vehicles that are clearly outliers (i.e. vehicles that have driven less than 100 miles in five years or over a million miles over 15 years).

A schematic of the process is shown in Figure 3-1.



Figure 3-1- Data cleaning process

The publically available MOT6 dataset for the UK does not include data for mass and footprint. Additional work was carried out to add mass and footprint data to vehicles within the database. The make, model and year variations of the MOT database were matched to the average mass and footprint of each vehicle model. This approach included instances (for a low number of cases) where slight variations in vehicle model yielded large variations in vehicle mass and footprint. However, the level of accuracy was deemed sufficient given the scope of the work.

3.3 Assumptions

This study focused on vehicle mileage recorded at three points:

Five years after first registration;

10 years after first registration; and

15 years after first registration.

For the purposes of this study, a vehicle age of 15 years was used as a proxy for vehicles at the end of their lifetimes, and this was agreed with the Commission during this study. While the Impact Assessments supporting the current LDV CO2 Regulations assumed that both passenger cars and LCVs have average lifetimes of 13 years, evidence suggests that the average age of vehicles is increasing and overall the vehicle fleet in Europe is getting older (International Fleetworld, 2013).

Precisely determining when a vehicle is at its end of life is very complex and given the availability of datasets there would be no straightforward and accurate way of knowing whether these vehicles are at the end of life. It is noted that the above approach may result in “missing” vehicles in the analysis that have lifetimes of, less than 15 years and above 10 years (for example, 12 years). As a sensitivity check, Appendix 3 provides a further investigation of the data for 10 year old vehicles, and it was found that this did not change the key conclusions drawn. That is, whilst the mileage values differed, the trend in results is consistent for vehicles at 10 and 15 years after first registration.

6 MOT refers to the “Ministry of Transport” test, the UK’s periodic roadworthiness test.

Obtain data

Inspect data and clean dataset (removing “null

values” data etc.)

Match with CO2

datasets

to obtain mass/footprint data

Are both mass and footprint

data included?

Analyse results



4 Relationship between lifetime mileage versus mass and footprint

The aim of this chapter is to identify and select a suitable approach for determining a correlation between lifetime mileage and mass/footprint, and to use this approach to obtain utility-specific correlation functions.

The study focussed on the areas identified in Table 4-1. With respect to LCVs, it is known that a significant proportion – 96% according to TNO et al (2012) – use diesel. Therefore only diesel LCVs were analysed. In addition, analysis of footprint as a utility parameter was omitted for LCVs as evidence also from TNO et al (2012) suggested that footprint was not considered to be a suitable alternative to mass in the LCV CO2 Regulations (unlike passenger cars).

Table 4-1: Scope of the study

Mileage against Cars LCVs

Diesel Petrol Diesel Petrol

Mass

Footprint

Due to the limited information on the distances that alternative powertrain vehicles are driven, these types of vehicles were also excluded from the study. In future, increased knowledge on usage patterns and mileage of electric vehicles and other alternative powertrain vehicles may show that they substantially different from those of conventional cars, which would require the identification of separate relationships for mileage as function of utility parameter for these vehicles.

4.1 Correlation approach

Frequency distribution analysis was used to visualise the compiled mileage data from all three countries. Frequency distribution analysis enables mileage trends for vehicles of different discrete sizes to be observed more clearly. Further regression analysis on the dataset can be found in Appendix 3.

Segmenting the data into mass and footprint categories shows how far a vehicle of certain fuel type and mass/footprint travels during its lifetime, as well as enabling outliers to be identified.

Figure 4-1 shows the frequency distribution of lifetime mileages for all petrol and diesel cars in the database. This suggested that two separate correlation functions are needed for petrol and diesel fuel types in order ensure a statistically valid and robust result. As with any dataset with a lower constraint (i.e. mileage must be non-negative), a level of positive skew is expected (i.e. the distribution is not a perfect normal distribution). A long tail is visible for both fuel types, but is more evident for the petrol passenger cars.

The frequency distribution plots for all mass categories (both for petrol and diesel vehicles) are shown in Appendix 4.



Figure 4-1 –Assumed lifetime mileage (km) frequency distribution plots of petrol and diesel cars7

Using a frequency weighted average approach, a lifetime mileage value was assigned to each mass and footprint category and a profile of lifetime mileage against utility parameter was built. This profile was used to determine potential correlation functions using regression analysis.

In terms of mass and footprint categories, 100 kg mass ‘bins’ and 0.1 m2 footprint ‘bins’ were considered to be appropriate and was discussed and agreed with DG CLIMA. In using this approach, it was also necessary to ensure that a suitable number of vehicles were included in each bin. For example, petrol cars with a mass in running order of less than 800 kg are far more common than diesel cars of equivalent mass, so in categorising diesel cars, the smallest weight category chosen was <900kg in order to capture more vehicles within the analysis.

4.2 Results

The following section looks at results from the combined dataset of all three countries, first for passenger cars and then for LCVs. Individual analysis of the respective national datasets can be found in Appendix 2.

4.2.1 Passenger cars

4.2.1.1 Mass bins versus mileage

Figure 4-2 and Figure 4-3 show the frequency weighted averages respectively for petrol and diesel cars. The “trim mean” function has been plotted to remove outliers. This function uses the middle 90% of the data, which helps to increase the robustness for mixed and/or heavily-skewed distributions (as is the case for mileage data; see Figure 4-1). The data points plotted above and below the ‘trim mean’ show the upper and lower quartiles of the data. Detailed tables of the results shown in the graphs are provided in Appendix 3.

7 Based on a total of 405,051 petrol vehicles and 184,827 diesel vehicles.



Figure 4-2 - Petrol car assumed lifetime mileage by mass bin

Figure 4-3 - Diesel car assumed lifetime mileage by mass bin

The above results show that whilst there appears to be a link between increasing mass and increasing mileage for petrol cars, there is not a similar relationship for diesel vehicles. For petrol cars there is an upward trend for the lifetime (15 year) mileage starting at around 109,000 km for the smallest vehicle bin (<800 kg) and rising to around 212,000 km for the largest vehicles (>2000 kg). For petrol cars, the average lifetime distance travelled by



vehicles in the mass category with highest average lifetime mileage is more than double (101%) compared to that for vehicles in the mass category with the lowest average lifetime mileage.

For diesel vehicles the variation in lifetime mileage across the mass categories is much smaller, at only 22%. Vehicles across the middle categories (i.e. vehicles with mass values between 1100 kg and 1200 kg) appearing to be driven the furthest.

For diesel cars, vehicles less than 900 kg appear to be driven significantly less than all other diesel vehicle categories, with a lifetime average of less than 195,000km. However while this may be a true indication of vehicle mileage for this mass category, it should also be acknowledged that the sample size is relatively small (owing to there being relatively few 15-year old diesel cars of this size in the UK vehicle parc).

Overall, diesel vehicles appear to be driven significantly further over their lifetimes compared to petrol vehicles, with the largest differences seen in smaller size segments. For larger vehicles this difference gradually grows smaller.

While it is beyond the scope of this project to investigate causation rather than correlation, the differences between petrol and diesel cars could potentially be linked to the generally higher fuel efficiency of diesel cars (and hence fuel cost savings), which may attract high mileage drivers.

In order to investigate the potential for developing correlation functions from these data, a least squares regression line was fitted to the midpoint of each mass category data. Additional analysis of the full dataset can be found in Appendix 3, which concludes that it there are unlikely to be non-linear relationships.

The results for petrol cars (Figure 4-4) show there is a fairly strong positive linear correlation between mass and lifetime mileage through the ten data points (R2=0.86). Appendix 3 analyses the regression output of the combined8 petrol passenger car dataset. From this, appears that lower mass vehicles have a higher variance in terms lifetime mileage (see Figure 7-19). In Appendix 3, it is also shown that there is a slight positive slope for the combined petrol dataset despite a low correlation coefficient between mass and mileage (meaning that mass increases with mileage). It should be noted that the strong R2 value above is an artefact of the mass binning approach taken, and so care must be taken in interpreting the strength of the link between mass and mileage.

8 Combined UK, French and Belgian datasets



Figure 4-4 – Regression analysis; correlation of lifetime distance travelled with mass for petrol cars

The same analysis was conducted for diesel cars (Figure 4-5), where a lower correlation between mass and mileage was found (again this is analysed further in Appendix 3). It should also be noted that with the removal of the lowest mass data point9 the R2 value diminishes significantly to a level that indicates there is not a relationship between the mass of diesel vehicles and their lifetime mileages. Therefore, care must be taken when interpreting such data.

Figure 4-5 – Regression analysis; correlation of lifetime distance travelled with mass for diesel cars

Overall, the analysis suggests that:

9 The sub-900 kg weight bin for diesel cars is an outlier as diesel engines are not popular in small passenger cars and there are relatively few

diesel cars in this weight bin included in the analysis.



For petrol passenger cars, there is a positive correlation between mass and mileage

For diesel passenger cars, lifetime mileage is largely independent of vehicle mass.

Reflecting that this is a scoping study and bearing in mind the remainder of this study requires an assumption on the expected lifetime mileage of different vehicles, the linear functions in Figure 4-4 and Figure 4-5 allow the analysis in later sections to be undertaken.

Comparing the results of these correlations to the mileage assumptions used in the LDV CO2 Regulations, it is clear there are significant differences (see Table 4-2).This is more closely analysed in Chapter 5.

Table 4-2 - Sales weighted lifetime mileages (15 years) by fuel type versus previous mileage assumption

Petrol sales weighted average Diesel sales weighted

average

Estimated observed lifetime mileages (this study)

150,578 km 219,934 km

Previously assumed lifetime mileages (EC Impact assessment. 2012)

182,000 km 208,000 km

% Difference -17% 6%

Grouping the mileage data further in the six key segments gives the values in Table 4-3.

Table 4-3 - Sales weighted lifetime mileages (15 years) by segment

Petrol Diesel

Small Medium Large Small Medium Large


141,725 km 163,436 km 181,338 km 217,260 km 220,224 km 223,037 km

4.2.1.2 Footprint versus mileage

As with mass, the results presented in the charts below show a trend between increasing footprint and increasing lifetime mileage in petrol cars. However, this is not as strong as the link between lifetime mileage and vehicle mass. The situation for diesel cars is a lot more uneven. There is much lower variation in lifetime mileage values for diesel cars with different footprints than for petrol cars. Detailed results are presented in Appendix 3. It is also worth noting that, as with the analysis relating mileage to mass, similar results can be seen from the analysis undertaken on the three national datasets (see Appendix 2).

There is an apparent discontinuity in the 3.60-3.70 m2 footprint category. For both petrol and diesel vehicles in this footprint category, the assumed lifetime mileage is much lower than the values observed for the adjacent footprint categories. However, further analysis has indicated that this may be an artefact of the sample of data being used. This discontinuity may be attributed to the high proportion of vehicles present in the database that had a footprint of 3.60-3.70m2 which were generally vehicles of low mass (<1100kg) and therefore care should be taken when drawing conclusions about the expected lifetime mileage of such vehicles.

Due to the limitations in the footprint data mentioned above and with the remainder of the study in mind it was decided to perform the cost analysis using the data generated from the analysis of mass versus mileage (Figure 4-4 and Figure 4-5. A key aim of the study was to investigate the possible cost savings from a mileage based system and performing these using results from a link between footprint and mileage was deemed to be too uncertain.



Figure 4-6 - Petrol car lifetime mileage (15 years) by footprint bin

Figure 4-7 – Diesel car lifetime mileage (15 years) by footprint bin

Following the same approach as was used for vehicle mass categories, correlation functions to relate lifetime mileage with footprint were considered for both petrol and diesel cars. Figure 4-8 shows the analysis for petrol cars an R2 value of 0.7, which suggests a positive linear relationship between footprint and lifetime mileage.



Figure 4-8 - Regression analysis; correlation of lifetime (15 years) distance travelled with footprint for petrol cars

Figure 4-9 presents the results of similar correlation analysis for diesel cars; as with the analysis of mass, the lifetime mileage values appear to be relatively independent of footprint.

Figure 4-9 – Regression analysis; correlation of lifetime distance (15 years) travelled with mass for diesel cars

4.2.2 Limitations of the correlation functions generated

At this stage it is useful to acknowledge the limitations of developing such correlations between mileage and mass/footprint:



Uncertainty at end points – It should be noted that due to limitations in the data available there is some uncertainty at the end points of the functions developed. Further work is needed in order to gain a greater insight into the lifetime mileages of these vehicles. However, since the vehicle types represented at the extremities of these functions comprise a small proportion of the overall fleet, the effect on the subsequent analysis of cost impacts in Chapter 5 is expected to be minimal.

Completeness of mileage data – The above conclusions have been drawn using large datasets from only three Member States and therefore may not be representative of the EU as a whole. There may be variations in other countries due to geographical or social reasons (amongst others). However, despite this limitation, the UK, France and Belgium represent large car markets. These countries represent around 45% of total new EU vehicle registrations each year, as well as 27.5% of the total EU registered passenger car fleet (ACEA, 2013; ICCT, 2013). Appendix 2 shows that very similar conclusions are drawn from when the data for each of the three individual Member States are analysed, suggesting that mileage trends are similar across these markets.

Possibility of analysing outdated data - Analysing data on vehicles aged 15 years as a proxy for lifetime mileage introduces a time lag, and changes in driving behaviour over time could reduce the value of this analysis if the findings are not likely to be representative of current and future driving behaviours. The EU ODYSEE project has figures suggesting that average car distance driven has decreased by some 750 km per year since 2000 (European Commission, 2012). This suggests that overall average mileage has not changed fundamentally, although changes in mileage for vehicles of different mass and/or fuel types (i.e. the primary aspect of interest in this study) are not represented in these aggregate figures. Further analysis of the stability of the trends found over time would help to inform the basis of any policy-making, in order to ensure that legislative assumptions are based on robust assumptions. Finally, it is noted that any legislative change to the regulations regarding the impact of mileage would have to be based on historic data as there is no suitable alternative to approaching the analysis in this way.

Odometer fraud – There is the possibility that certain mileage readings in the database may be inaccurate due to historic odometer fraud. In future, a potential key data resource would relate to Directive 2014/45/EU on periodic roadworthiness tests which requires the accurate and legal recording of mileage in the roadworthiness certificate with results provided electronically. Ongoing work by the European Commission (European Commission, 2014a) reports "5% of consumers citing that they had experienced" odometer fraud when buying second hand cars. Furthermore, it is acknowledged that research is currently ongoing to determine the scale of odometer fraud in Europe in order to address the issue and thus, the situation is expected to improve over time.

Impact on exported vehicles and the second hand market – The analysis performed to date focuses on the use of the vehicles within each Member State and does not take into account the impacts of cross-border vehicle trading in the second-hand market.

However, set against these concerns, the investigations based on the available data (including more detailed analysis shown in Appendix 1) indicate that this initial evidence supports of the findings of the work.



4.2.3 Variation in mileage travelled with vehicle age

This section of the study aims to develop a detailed profile of mileage driven compared to vehicle age. This will allow a review of the cost-effectiveness analysis on the current targets detailed in the existing impact assessment (European Commission, 2012) to be performed.

Figure 4-10 presents the current profile used in regulatory cost effectiveness studies underpinning the Regulations (European Commission, 2012). There are two areas here that are of importance.

All petrol vehicles were assumed to be driven 14,000 km per year and all diesel vehicles are assumed to be driven 16,000 km per year irrespective of mass/footprint.

The above assumptions were assumed to hold true over each year of the full life of every vehicle – i.e. irrespective of vehicle age.

The analysis presented in the previous sections suggests that the first of these assumptions should be reviewed (especially for petrol cars).

The second assumption of constant mileage irrespective of vehicle age may also require further investigation – this is assessed in further detail in this section. The same methodology as described in Section 4.1 was applied to vehicles that were both five and ten years old, allowing for an approximate sales-weighted mileage profile over time to be generated (See Figure 4-11).

Figure 4-10 - Previous assumption of mileage profile over time used in the LDV CO2 Regulation Impact Assessment for cars



Figure 4-11 - Observed mileage profile over time

Detailed results from this analysis are shown below in Table 4-4. The findings show that diesel cars appear to be driven more intensively in the earlier stages of their lifetime than previously assumed, while the opposite is true for petrol vehicles (Table 4-4). Finally, it is also apparent that the intensity of vehicle usage declines with vehicle age, rather than being constant over the vehicle’s lifetime.

This finding will affect any cost effectiveness analysis that focuses on the earlier years of a vehicle’s life as it would appear that, on average, vehicles are driven further over this period than previously assumed.

Table 4-4 - Comparison of accumulative mileage profile over time

Sales weighted averages (km)

Petrol Diesel


5 year observed

50,720 km 58,868 km 65,588 km 83,935 km 90,357 km 96,452 km

5 year previously assumed

70,000 km 70,000 km 70,000 km 80,000 km 80,000 km 80,000 km

10 year observed

109,849 km 123,436 km 134,640 km 158,645 km 164,529 km 170,114 km

10 year previously assumed

140,000 km 140,000 km 140,000 km 160,000 km 160,000 km 160,000 km

4.2.4 Light Commercial vehicles

The existing mass categorisation for LCVs is shown below in Table 4-5.

Table 4-5 – LCV mass categorisation

LCV Class Mass range

Class I Up to 1,305 kg



Class II 1,305 kg to 1,740 kg

Class III Over 1,740 kg

The following table is based on over 52,000 diesel LCVs, analysed at their assumed lifetime age (15 year old vehicles). Using the same approach as explained in Section 4.2.1, the expected lifetime mileages for these vehicles have been determined and appear to show a slight variation in mileage across the three classes (Table 4-6).

Table 4-6 - Diesel LCV lifetime (15 years) mileages by class

LCV Class Sample size Frequency weighted mean (km)

Trim mean (km)

Lower quartile (km)

Upper quartile (km)

Class I 30,037 208,241 205,199 154,876 255,194

Class II 15,135 210,859 206,179 147,810 263,749

Class III 7,795 207,959 202,697 147,894 256,836

Total 52,967

As can be seen, lifetime mileage for the different LCV classes is broadly similar, which suggests that there would be no benefits in applying mileage weighting factors to the CO2 targets for LCVs..

The data shows that vehicles in the highest mass category (Class III) have the lowest lifetime mileage (albeit marginally). There are a number of reasons why this may be the case. Since these vehicles are largely being driven for delivery or trade work, it may be that mass/size plays no part in driving behaviours. For example, LCVs used for postal deliveries will be driven extremely high distances and these are generally relatively small vehicles. LCVs such as trade vehicles (plumbers, electricians etc.) will largely be driven more locally and therefore the overall mileage these do may be lower.

Finally it should be noted that current cost effectiveness calculations in the regulations consider LCVs to drive 235,000 km in their lifetime, on the basis of the above analysis this may be an overestimation of around 10%.

Further statistical analysis of the full LCV dataset is provided in Appendix 5 along with frequency distribution plots of the three mass classes.



5 Cost impacts of applying mileage weighting factors to car CO2 targets

5.1 Introduction

This chapter provides an illustrative analysis of the cost impacts of applying mileage-based weighting factors to passenger car CO2 targets as a way to improve the cost effectiveness of the car CO2 Regulation. The weighting factors used are based on the lifetime mileage relationships identified in Chapter 4. The following approach is used to:

Identify a lifetime mileage value for the relevant cars and LCVs in our database;

Use this mileage value to calculate “new” mileage-weighted CO2 targets; and

Use cost curves to identify the costs of achieving these targets to identify whether a mileage based system could be more cost effective in achieving CO2 targets.

The hypothesis is that CO2 targets could be achieved at lower costs. This is underpinned by the evidence that:

1) On average, diesel vehicles of all sizes appear to be driven significantly further than petrol vehicles over their lifetime; and

2) On average, petrol vehicles with a greater mass and footprint appear to be driven further than those with a smaller mass and footprint.

Therefore it could be argued that placing more stringent CO2 reduction targets on vehicles with higher lifetime mileages would deliver greater CO2 reductions over the lifetimes of the vehicles concerned. This could potentially improve the cost-effectiveness of the car CO2 Regulation.

5.1.1 Defining a mileage weighted system

When defining a mileage based system, the first stage consisted of associating the lifetime mileage with each vehicle that is included in our complete dataset of vehicle mileages. The database used in this analysis is the EEA CO2 monitoring database from 2012 (European Environment Agency, 2012), which also contains new car and van sales.

Using this data, mileage based targets were calculated for each manufacturer. The “new” manufacturer targets were calculated by taking the specific CO2 emissions target for each vehicle, as defined in the Regulations under a hypothetical post 2020/2021 (WLTP10) system11, and weighting this by both sales (which is used to define the current manufacturers targets) and mileage, as derived in Chapter 4. Hence, a manufacturer’s “new” target was calculated as the sum product of each vehicle model’s specific emissions target, its sales and specific mileage value, divided by the sum product of sales and mileages over all models. In other words;

∑(𝑆𝑎𝑙𝑒𝑠)𝑉 ∗ (𝐶𝑂2 𝑡𝑎𝑟𝑔𝑒𝑡)𝑉 ∗ (𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑚𝑖𝑙𝑒𝑎𝑔𝑒)𝑉

(𝑆𝑎𝑙𝑒𝑠)𝑉 ∗ (𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑚𝑖𝑙𝑒𝑎𝑔𝑒)𝑉

𝑛

𝑉=1

where V signifies each individual vehicle in a manufacturers fleet.

New sales and mileage-based targets for each manufacturer were estimated using mass as the utility parameter. The targets for cars take account of each manufacturer’s petrol and

10

Wold harmonised light duty vehicles test procedure 11

A hypothetical target was used in the analysis here. The target slope was defined using the existing ‘equal measures’ reduction approach as used in setting the 2015 and 2020/2021 target slope.



diesel fleets as given by the EEA monitoring database (European Environment Agency, 2012).

Note that a more complex method that would involve redefining the target slope (using mileage weighted regression analysis) could be used as an alternative approach. This method would also involve a reset of the current (2020/2021) target slope as this itself is defined using an “equal effort” reduction of the 2015 target. However with a change in test cycle (from NEDC to WLTP) occurring soon, this method might be considered as the target slope will need to be revised.

5.1.2 Cost implications of a mileage weighted system

The current regulatory system treats all vehicles (of differing sizes and fuel types) as equal when setting overall fleet wide CO2 targets, and there are many assumptions underlying the agreed target. One of these is that the target implicitly assumes goals for total CO2 emissions reductions from LDVs.

With this in mind, this analysis assumes that the product of total sales, lifetime mileages (as calculated in Chapter 4), and required CO2 reduction (in gCO2/km) achieved under a mileage weighted target system, is set equal to the product of total sales and required CO2 reduction (in gCO2/km) achieved under a non-mileage weighted target system. This ensures that the total reduction of CO2 (in grams) required to meet both a non-mileage and a mileage-weighted target are equal and therefore comparable. In other words;

[∑(𝑆𝑎𝑙𝑒𝑠)𝑉 ∗ (𝑀𝑖𝑙𝑒𝑎𝑔𝑒 𝐶𝑂2 𝑡𝑎𝑟𝑔𝑒𝑡)𝑉 ∗ (𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑚𝑖𝑙𝑒𝑎𝑔𝑒)𝑉

𝑛

𝑉

]

− [∑(𝑆𝑎𝑙𝑒𝑠)𝑉 ∗ (𝑁𝑜𝑛 𝑚𝑖𝑙𝑒𝑎𝑔𝑒 𝐶𝑂2 𝑡𝑎𝑟𝑔𝑒𝑡)𝑉 ∗ (𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑚𝑖𝑙𝑒𝑎𝑔𝑒)𝑉

𝑛

𝑉

] = 0

where V signifies each individual vehicle in a manufacturers fleet.

The optimal solution was calculated using the Excel “solver” function, which is a numerical optimisation algorithm that iteratively finds the optimal solution within the constraints given. That is, it calculates the minimum marginal costs to manufacturers to achieve the overall fleet reduction to the target gCO2/km for each segment. This calculation was performed for:

1) A non-mileage system using sales weighted targets.

2) Sales and mileage weighted targets with the added constraint of maintaining the same constant reduction in total CO2 (grams) as in the non-mileage case (as in the equation above).

In order to estimate the marginal costs to manufacturers of meeting the “new” mileage-weighted targets, a set of updated cost curves were used. These have been developed for a post 2020/2021 target system under the WLTP12.

The methodology used to calculate the total costs for achieving possible post 2020/2021 targets was based on TNO et al (2011), although it included a modified list of technical options for reducing CO2 emissions from cars that have been adjusted to take into account effects of switching from the old NEDC to the new WLTP. Ricardo-AEA’s in-house vehicle technology cost curve tool was used to produce new cost curves using these updated lists of technological options13.

A summary of the cost curves for passenger cars is presented in Figure 5-1 and Figure 5-2, with information on the maximum abatement potential presented in Table 5-1.

12

Assuming the uptake of technologies analysed in the context of the 2020 targets. 13

Note it is the CO2 reduction potential that has been modified (due to the switch in test cycle procedures) and not the list of technologies themselves.



Figure 5-1 - Summary of cost curves for petrol passenger cars

Figure 5-2 - Summary of cost curves for diesel passenger cars

Table 5-1 - Maximum CO2 abatement and associated cost for each car segment

Petrol Diesel

Vehicle segment Small Medium Large Small Medium Large

Max CO2 reduction against baseline values (%)

75.8% 76.1% 76.4% 71.8% 72.2% 72.8%

Cost for achieving maximum CO2 reduction

(EUR) € 11,100 € 12,450 € 13,890 € 9,910 € 11,150 € 12,400

As these cost curves have been developed for small, medium and large, petrol and diesel cars, the results in relation to costs will be generated according to this segmentation (see Table 4-3). It should be noted however, that the difference between these results and those in the previous work (TNO et al, 2011) will be that the mileages associated with the vehicles will have been generated using substantially more detailed analysis and an improvement in data quality for cars and LCVs.

5.1.3 Cost curve analysis for passenger cars

5.1.3.1 Mass

The results for the overall fleet are shown below (Figure 5-3) in terms of CO2 emissions per segment required to achieve an overall target and associated costs for achieving these emissions targets (Figure 5-4). It is important to note here that all costs shown are marginal costs to manufacturers only and do not take into account fuel saving over the course of the



vehicle’s life. The results show that under a mileage-weighted system, the CO2 targets for cars will be achieved with lower overall marginal costs to manufacturers. This is due to the focus of effort on vehicle segments which contribute more to total lifetime emissions of CO2.

Figure 5-3 - Comparison of target CO2 emissions (g/km) per car segment in order to attain a hypothetical post 2020/2021 CO2 reduction target under mileage-weighted and non-mileage-weighted systems with mass as a utility parameter*

* Here (and in similar charts below), the y-axis is an indicator of target stringency.

Figure 5-3 shows the optimum (least cost) way in which the vehicle fleet can achieve the overall target across the six key segments. The results show that under a mileage-based system, the cost associated with achieving a hypothetical post-2020/2021 CO2 reduction target would be achieved more cost effectively by virtue of focusing efforts on vehicle segments that are known to be responsible for a greater proportion of total lifetime emissions, due to their higher lifetime mileage values. Figure 5-3 shows that more emissions abatement effort would need to be applied to large petrol cars as well, and medium and large diesel cars as these types of vehicles are responsible for greater overall lifetime emissions. This would therefore relax the effort required from smaller vehicles and as a result of this redistribution of effort across the six segments, the overall fleet wide manufacturing cost of achieving the same CO2 reduction would decrease by 1.75% per vehicle as shown in Figure 5-4.



Figure 5-4 - Percentage difference in average marginal costs to manufacturers of achieving a hypothetical post 2020/2021 CO2 reduction target of a mileage-weighted system compared to a non-mileage-weighted system for cars with mass as a utility parameter

In short, under a mileage-weighted system, vehicle segments with high lifetime mileages will contribute more to meeting the average emissions target, in line with their greater contribution to total vehicle lifetime CO2 emissions. This approach is calculated to be more cost efficient than the current regulatory approach.

Looking also at the cost per tonne of CO2 avoided to reach the overall target, a similar story can be seen. Figure 5-5 shows that adopting a mileage based system would be cheaper overall in terms of manufacturing costs per unit CO2 reduction. To reiterate, all costs shown are marginal costs to manufacturers and do not take into account fuel saving over the course of the vehicle’s life.



Figure 5-5 – Manufacturing cost per tonne of CO2 avoided to reach target with mass as utility parameter under mileage-weighted and non-mileage-weighted systems

5.1.3.2 Footprint

Under a system where footprint is the utility parameter, the overarching results follow same trend as for parameter mass-based system (see Figure 5-6 and

Figure 5-7). However, it is interesting to note that over all of the segments the achieved cost savings are not as great as in a mass based system. An overall manufacturing cost saving with a mileage system implemented is however still seen – in this case, 1.62% per vehicle.

Figure 5-6 - Comparison of target CO2 emissions (g/km) per car segment in order to attain a hypothetical post 2020 CO2 reduction target under mileage-weighted and non-mileage-weighted systems with footprint as a utility parameter



Figure 5-7 - Percentage difference in average marginal costs to manufacturers of achieving a hypothetical post 2020 CO2 reduction target of a mileage-weighted system compared to a non-mileage-weighted system for cars with footprint as a utility parameter

Again looking at the cost per tonne of CO2 avoided to reach the overall target, Figure 5-8 shows again, that adopting a mileage based system would be cheaper overall in terms of unit CO2 reduction.

Figure 5-8 – Manufacturing cost per tonne of CO2 avoided to reach target with footprint as utility parameter



5.2 End user impact analysis

Additional costs incurred by vehicle manufacturers in order to achieve CO2 targets, may translate into additional costs to the end user if manufacturers choose to pass on these costs. However, there will also be fuel cost savings for the user from the increased efficiency of these new vehicles. This section assesses the optimum level of target ambition beyond 2020 that would be most cost effective for the user.

Under the current LDV CO2 Regulations, the impact of the implementation of the 2020/2021 CO2 targets on fuel savings for private consumers and business owners was previously calculated in a study for the European Commission. Moving to 95 gCO2/km and 147 gCO2/km in the new car and LCV fleets respectively implies reductions in annual fuel consumption of about 27% and 16% respectively, compared to the 2015/2017 targets (European Commission, 2012).

However, empirical mileage data found in this study means that the cost effectiveness of the Regulations to society must also be reviewed. Table 5-2 shows the previous assumptions used in the Regulatory impact assessment (EC Impact Assessment, (2012) – which were based on modelled data) differ substantially from the estimated lifetime mileages found in this study (based on empirical data).

Table 5-2 - Observed lifetime mileages (km) calculated in Section 4 versus mileages previously assumed by EC in impact assessment

Petrol Diesel



141,725 163,436 181,338 217,260 220,224 223,037


182,000 182,000 182,000 208,000 208,000 208,000

Difference 40,275 18,564 662 -9,260 -12,224 -15,037

5.2.1 Lifetime societal analysis

The total cost of ownership (TCO) against differing levels of CO2 performance was calculated for the original and revised mileage data. This TCO calculation takes into account the fuel costs over the lifetime of a vehicle (using previous mileage assumptions and updated ones) as well as the additional cost of purchasing the new vehicle itself (using current cost curves). In the figures that follow, the minima of the cost curves denotes the optimal point for setting the CO2 target for each specific segment considered, i.e. where the TCO for the end user is the lowest.



Figure 5-9 compares the TCO curves for the for small petrol cars, which suggests that the optimum CO2 reduction target under a mileage based system would be higher (less stringent – increasing by around 5 g/km), reflecting the lower usage intensity of these vehicles when using the updated assumptions. The overall TCO for end users would also be lower (by around €1400 at the optimal point), meaning that consumers would benefit due to lower vehicle purchase prices because manufacturers have to meet less stringent targets. Similar trends also apply to the other segments where the previous mileage assumptions used in the EC analysis were higher than those found in this study – including medium petrol cars (i.e. the optimal target CO2 level would be less stringent, and TCO would also be lower). Additional charts are shown in Appendix 6.



Figure 5-9 - Small petrol passenger car TCO versus level of target ambition

Note: PS = petrol-engined small car

Figure 5-10 shows the results of the same calculations for large diesel cars, where the reverse conclusions can be drawn – that is, the optimum target CO2 reduction level would be slightly lower (more stringent by 2 g/km) for these vehicles. However, the TCO would be higher (due to higher purchase costs), increasing by around €740 at the optimal point.

However, the change in TCO is less between the mileage - and non-mileage systems because the greater use intensity translates into higher fuel cost savings, which offset the higher vehicle purchase prices to a greater degree. Similar conclusions also apply to other segments for which the mileages were previously underestimated, i.e. large petrol, small diesel and medium diesel cars.



Figure 5-10 – Large diesel passenger car TCO versus level of target ambition

Note: DL = diesel-engined large car

Additional charts on the remaining car segments can be found in Appendix 6.

5.2.2 Five-year analysis

Similar analysis is also considered over a shorter five year period to reflect consumer myopia. Here the same discount rate has been applied as in Section 5.2.1 (to align with previous work done in the previous impact analysis); however other analyses have used a higher discount rate in view of the shorter time period14.

Table 5-3 - Observed five year mileages calculated in Section 4 versus mileages previously assumed by EC in impact assessment

Petrol Diesel



50,720 58,868 65,588 83,935 90,357 96,452


70,000 70,000 70,000 80,000 80,000 80,000

Difference 19,280 11,132 4,412 -3,935 -10,357 -16,452

The graphs below present the five-year cost of ownership against differing levels of CO2 performance for small petrol cars and large diesel cars respectively. As before, this takes into account the fuel costs over the lifetime of a vehicle (using previous mileage assumptions and

14

Based on UK Committee for Climate Change rate



updated ones) as well as the additional cost of purchasing the new vehicle itself (using current cost curves). It should be noted that this analysis assumes that the first user pays all the additional cost (i.e. not taking into account ‘resale value). Research shows that new cars are estimated to lose a significant proportion of their value in the first three years – for example, 60% in the UK (AA.com, 2012); however it was not possible to take this into account within the scope of the current study.

The analysis suggests that for all sizes of petrol cars, the optimum CO2 reduction target under a mileage based system would be higher (less stringent), reflecting the lower usage intensity of these vehicles when using the updated assumptions. The overall TCO for end users would also be lower for these vehicles.

For diesel cars, the reverse conclusions can be drawn – that is, the optimum target CO2 reduction level would be slightly lower (more stringent) and the TCO would be higher for these vehicles.

Like the lifetime analysis, the figures show that whilst the TCO for petrol cars significantly reduces, for diesel cars it increases, but to a lesser extent.

Again, additional charts on the remaining car segments can be found in Appendix 6.

Figure 5-11 – 5 year small petrol passenger car cost of ownership versus level of target ambition



Figure 5-12 - 5 year large diesel passenger car cost of ownership versus level of target ambition


41

Ricardo-AEA in Confidence

Ref: Ricardo-AEA /ED59296/Issue Number 1

6 Conclusions In order to provide a framework for this section and inform discussion, the project conclusions are set out against the aims of the project.

The key aims of this study were to:

(i) Collect and analyse information from a wide range of sources to establish whether there are relationships between the lifetime distance travelled (mileage) with the mass and/or footprint for cars and Light Commercial Vehicles (LCVs); and

(ii) Provide an assessment of the likely cost reduction that would occur from using these relationships to adjust the application of CO2 reduction targets in potential future regulations.

6.1.1 Availability and suitability of existing and future datasets

The first part of the study reviewed and analysed available datasets to establish relationships between lifetime mileage and vehicle mass/footprint. The study provided an overview of the availability of existing and potential future datasets. The emphasis was not on the plausibility of a new mileage based regulation per se but rather to provide an assessment of whether the data is (or could be) available to inform the analysis that would be required to underpin such a Regulation. It is apparent that whilst several studies and datasets do exist that could help inform a potential mileage-based system, the level of detail that would be required to undertake the required analysis was not present in most of these sources.

Appropriately detailed data at the Member State level was available for the UK, Belgium and France and was used in the subsequent analysis for this project. Substantial work was required to clean the datasets and to match make and model information with mass and footprint data. Statistical analysis on these refined datasets allowed conclusions in relationships between mileage and mass and footprint to be drawn.

In the future a potential key future data resource would relate to Directive 2014/45/EU on periodic roadworthiness tests which requires the recording of mileage in the roadworthiness certificate with results provided electronically. Comparisons with other sources of data as shown in Appendix 1 also provide a cross-check of the validity of the datasets.

6.1.2 Development of relationships linking (i) mass and (ii) footprint to lifetime mileage

Chapter 3 critically reviewed the data and Chapter 4 examined the relationships between mileage and the mass of cars and LCVs using data from the UK, Belgium and France. Key findings from this analysis were as follows:

The data suggested a substantial difference between average lifetime mileages of petrol and diesel cars. On average, diesel cars were found to have a lifetime mileages of 219,000 km compared to 150,000 km for petrol cars. This difference in mileage between fuel types is particularly pronounced for vehicles of lower mass and footprint15.

15

This is due to the positive relationship between mass/footprint and mileage for petrol vehicles, whereas mileage for diesel vehicles was relatively independent of mass/footprint.


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In the case of petrol cars, there appears to be a positive correlation between mass/footprint and lifetime mileage. That is, larger/heavier cars tended to be driven further over their lifetimes.

For diesel cars, the lifetime mileage appeared to be relatively constant across all mass/footprint categories (217,000km for small diesel cars rising to only 223,000km for large diesel cars on average). A possible reason for this trend is that consumers who drive longer distances tend to prefer diesel vehicles due to their greater fuel efficiency and the (on average) lower cost of diesel fuel.

As with diesel passenger cars, for LCVs there was no apparent relationship between the lifetime mileage and different mass/footprints. Overall the data suggested that lifetime mileage was relatively constant across all LCV class sizes (205,000km for Class I, 206,000km for Class II and 202,000km for Class III diesel vans).

6.1.3 Impact of possible mileage-weighted CO2 reduction targets on costs of compliance for manufacturers

Chapter 5 undertook a detailed cost analysis of the effects of adopting a mileage weighted system for determining CO2 reduction targets for passenger cars. Caution is required here with regard to the use of the correlation function for diesel cars given the limitations identified in Chapter 4. With these caveats in mind, the analysis suggested that the introduction of a mileage-weighted system of this form into a post-2020 Regulation would have the following impacts:

The overall fleet wide manufacturing cost of achieving the same CO2 reduction would decrease by 1.75% per vehicle where mass is retained as a utility parameter.

The overall fleet wide manufacturing cost of achieving the same CO2 reduction would decrease by 1.62% per vehicle based on footprint as a utility parameter

6.1.4 Impact on the end user

Current analysis (such as that undertaken in the EC Impact Assessment (European Commission, 2012) underpinning the Regulations) used an assumption on lifetime mileages associated with petrol and diesel cars. The assumption was that all petrol and diesel cars (in all segments) travel the same distance over their lifetime (with diesel cars assumed to travel further). In this study, whilst diesel cars were indeed found to travel further than petrol cars, assuming that all petrol cars travel the same distances over their lifetimes irrespective of their mass may not be appropriate. Analysis undertaken in Section 4 allowed a more detailed evaluation of lifetime mileages for the six key segments of passenger cars to be performed. The results suggested that the optimum CO2 targets for small and medium petrol cars would be less stringent whereas the optimum CO2 targets for large petrol and diesel cars would be more stringent.


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6.2 Further work

Research areas which could help improve existing assumptions were identified through this project. These are detailed in turn below.

6.2.1 Improvements in the definition of lifetime mileage

Further data and analysis are required to improve understanding and definitions of lifetime mileage. This research could involve an initial, scoping, data gathering exercise, with potential data sources including: the use of scrappage rates; use of information provided in vehicle databases to track vehicles over time to identify scrappage year; certificates of destruction; reclaiming of road tax. This scoping study could be Pan-European in nature and would benefit from a consistent approach in line with those countries targeted and accessed through this study. The availability and suitability of these different data sources would be determined before a more analytical stage which would decide the most appropriate approach to inferring lifetime mileage. This approach would then be applied. At this stage one key potential source and approach would be the use of the vehicle identification numbers in the UK MOT data set (UK Department for Transport, 2013) which would allow the tracking of vehicles over time and would allow when they left the dataset and were no longer in use to be established.

6.2.2 Improvements in the understanding of the age profile for vehicle mileage

Further analyses to improve understanding on the age profile could also be performed on the existing datasets. For example by analysing the total mileages recorded for vehicles of different ages, it would be possible to establish a better model of how annual mileage might be expected to vary as vehicles get older. This was performed in this study for 5, 10 and 15 years however by analysing data from all years of life, an much more detailed understanding of how these age profiles vary over time can be generated.

6.2.3 Impact of the inter Member State second-hand vehicle market

The analysis performed to date focuses on the use of vehicles within each Member State and does not take into account the cross-border second-hand market. A study by the European Commission (European Commission - DG Climate Action, 2010), provides some indications of the scale of cross-border movement of second hand vehicles. This shows that while the scale of second-hand vehicle movements is rather limited for the UK, it is more significant for France and highly significant in the case of Belgium.

An initial scoping and literature review of existing data sources will be required here to understand the type and availability of different data sources and the extent to which further analysis could be undertaken. Once this review had been undertaken the potential for further analysis would be assessed. This would be informed, in part, by the level of imports and exports determined. This potential analysis could include the assessment of the mileage undertaken in the ‘second’ country. However, detailed assessment would depend on mileage data being available in the ‘second’ country and the consistent, cross country, use of vehicle identification markers - this assessment could therefore be challenging. It may therefore be more appropriate to estimate the scale of impact based on broader data on second-hand vehicle use.

6.2.4 Analysis of additional datasets

In addition to the data accessed and used for this project, priced, survey mileage data for Germany from DLR (DLR and Infas, 2009) could be used. The survey contains data for some


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35,000 cars from 25,000 households. The dataset includes individual mileage records for survey participants and their vehicles which would enable the calculation of average values for each vehicle model. Footprint and mass for the survey participants’ vehicles could be calculated using the approach used in this project.

For the Netherlands and Swedish datasets (Centraal Bureau voor de Statistiek, 2013; Statistics Sweden, 2014), the research suggested that appropriate datasets were available, or had been constructed, but these could not be accessed for this study for reasons of cost and privacy, respectively. This suggests that with more resources, a larger dataset might be purchased and constructed for the Netherlands dataset. The cost associated with this is currently unknown however. The privacy issues surrounding the Swedish dataset could be looked into further however it is also unknown whether this could be bypassed.


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7 Appendix

7.1 Appendix 1: Overview of literature that suggests that heavier vehicles tend to travel farther than lighter vehicles

The dataset used by TNO et al (2011) for the preliminary analysis of the potential to use mileage-weighted targets to improve the cost-effectiveness of the passenger car CO2 Regulation (see Section 2.2) did suggest a relationship between vehicle size and mileage. This analysis was based on data collated by the FLEETS project, which is a pan-European dataset created through engagement with Member States and which has subsequently been integrated into the TREMOVE model. The dataset has six main segments for cars: small, medium and large, with diesel and petrol variants for each of these size categories. The determination of whether a car is small, medium or large is based on its engine capacity, with a car being classified as small if its engine capacity is below 1400cc, while a large car has an engine capacity of more than 2000cc. The results, in relation to the annual mileage of new cars, are presented in Table 7-1.

Table 7-1- Average annual mileage for new cars by fuel type and engine capacity based on FLEETS data

Size*

Fuel

Small Medium Large

Petrol 14,438 16,772 16,839

Diesel 23,041 24,574 26,318

* Based on engine capacity; Source: TNO et al (2011)

Table 7-1, above, suggests that 1) as the engine capacity of a car increases, it tends to be driven further, and 2) For all sizes diesel cars are driven further than petrol cars. This initial indication that larger cars, albeit measured by engine capacity, are used more than smaller cars suggests that it is important to look for evidence relating mileage to mass and footprint, in order to determine whether overall vehicle size is correlated with lifetime mileage. It is worth emphasising that the initial analysis in TNO et al (2011) which provided the motivation for the current study was based on modelled data (taken from annual mileage statistics) and not empirical evidence.

There is some empirical evidence that ‘larger’ cars are driven farther than ‘smaller’ cars, although full European datasets were not available. For example, in Germany, an analysis of survey data from Mercedes-Benz garages from 1999 to 2006 shows the distribution of annual distances travelled. This showed that smaller cars (i.e. those labelled “klein” in Table 7-1) tend to be driven less than medium sized (i.e. “mittel”) cars, which, in turn, are used less than larger (i.e. “gross”) cars, as the respective peaks in the distributions occur at progressively higher average annual mileages.

Other datasets for Germany show that cars in the segments where cars tend to be smaller (e.g. mini, supermini, lower medium) are driven less than those cars in segments where the cars tend to be larger (e.g. upper medium, executive and luxury; see Figure 7-2 and Figure 7-3). While the segments are not defined according to either the mass or footprint of vehicles (and therefore are not directly comparable for the purposes of this project), these figures do


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suggest that similar conclusions might be drawn for both ‘mass’ and ‘footprint’, as cars in the smaller segments will generally be lighter and have smaller footprints than cars from other segments.

An analysis of French annual mileage data by vehicle segment between 1995 and 2005 is shown in Figure 7-4. The data are from the analysis of 822 different passenger cars and show the cumulative distance driven over the lifetime of the vehicles. They show that on average, diesel cars – no matter which segment they are in – are used more. By the tenth year of their lifetime, diesel cars from all segments have been driven, on average, at least 170,000 km, 10,000 km more than those even in the most highly-used petrol segment. Finally, the figures show that the smaller segments, such as ‘Economic’, ‘Small’ and ‘Lower Medium’, are used less on average than the larger segments. This applies to both petrol and diesel cars although the difference between the distances travelled by the different segments is greater for petrol cars than for diesel cars. As with the data on distance by segment for Germany above, Figure 7-4 also suggests that similar conclusions might be drawn for both ‘mass’ and ‘footprint’, as cars in the smaller segments will generally be lighter and have smaller footprints than cars from other segments.

Figure 7-1- Distribution of annual distance travelled* (in km) of cars in Germany by size

* Development of a scenario model for the simulation of future market shares and CO2 emissions from motor vehicles

Source: Mock (2010)


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Figure 7-2 - Distribution of annual distance travelled (in km) of cars in Germany by segment (2002)

Source: (Bundesanstalt für Straßenwesen, 2002)

Figure 7-3 - Distribution of annual distance travelled (in km) of cars in Germany by segment (2008)

Source: (DLR and Infas, 2009)


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Figure 7-4 - Cumulative annual average mileage of petrol and diesel vehicles as a function of the segment, France, analysis covers 1995 to 2005

Note: ‘Eco+SC’ represents ‘Economic’ and ‘Small’ cars; ‘LM’ and ‘UM’, lower and upper medium cars, respectively; ‘Sup+Pre’ covers ‘Superior’ and ‘Prestige’ cars, while the final category represents multi-purpose vehicles (MPVs, denoted as ‘Comp’), as well as all 4x4s and SUVs.

Source: (Christina Bampatsou, 2011)

However, the above studies directly relate distance travelled only to the vehicle segment, which cannot be matched to mass/footprint in sufficient detail. Data from Netherlands and Sweden provides a better level of disaggregation. Figure 7-5 provides a breakdown of the average annual distance travelled by cars in the Netherlands in 2010 by weight range. This shows that the average distance travelled by petrol and diesel cars in the Netherlands typically increase with weight and, as a group, diesel cars tend to have the highest annual mileage within each weight class. There are however some areas of uncertainty here as there is very little difference between the mileages driven between the 1151-1500kg and >1500kg diesel categories.

Figure 7-6 suggests that for vehicles up to a mass of 1700 kg, heavier cars are driven further on average in Sweden, although these figures do not distinguish between petrol and diesel variants. Based on this data, it is important to note that above 1700 kg, there is a reduction in annual distance travelled as vehicle mass increases. Figure 7-6 shows that for light commercial vehicles in Sweden, annual distance travelled increases with vehicle mass.


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Figure 7-5 - Annual distance (km) travelled by cars in the Netherlands by weight class and fuel type, 2010

Source: (Centraal Bureau voor de Statistiek, 2013)

Figure 7-6- Average annual distances (km) driven by cars in Sweden by kerb weight, 2013

Source: (Statistics Sweden, 2014)


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Figure 7-7- Average distances (km) driven by commercial vehicles in Sweden by maximum permissible weight, 2013 (for vehicles weighing less than 3 tonnes)

Source: (Statistics Sweden, 2014)


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7.2 Appendix 2: Country specific mileage data analysis

Results for the data for France

Mass versus mileage

Table 7-2 – France: Petrol car lifetime (15 years) mileage by mass

Mass Bin Sample size Frequency weighted mean (km)

Trim mean (km) Lower quartile (km)

Upper quartile (km)

<800 kg 18,289 109,545 115,781 76,134 152,195

801-900 kg 54,565 142,609 137,205 95,072 177,465

901-1000 kg 36,797 139,340 130,718 86,253 174,132

1001-1100 kg 24,817 153,197 152,254 109,719 193,716

1101-1200 kg 12,789 155,906 155,013 109,895 201,237

1201-1400 kg 16,776 170,472 169,775 127,945 212,206

1401-1600 kg 6,402 186,748 184,888 140,794 228,783

1601-1800 kg 1,353 193,975 198,876 147,012 247,947

1801-2000 kg 670 158,011 158,728 123,338 196,179

>2001 kg 156 163,642 179,421 145,365 221,624

Total 172,614

Figure 7-8 French Petrol car lifetime (15 years) mileage by mass


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Table 7-3 – France: Diesel car lifetime (15 years) mileage by mass


Trim mean (km)

Lower quartile (km)

Upper quartile (km)

<800 kg 345 206,345 206,047 166,326 244,910

801-900 kg 2,915 203,405 202,708 154,258 250,670

901-1000 kg 25,800 227,897 227,065 177,165 280,180

1001-1100 kg 11,076 222,086 219,877 166,210 279,303

1101-1200 kg 38,477 235,110 235,887 186,552 290,149

1201-1400 kg 22,313 249,351 249,005 192,708 308,207

1401-1600 kg 19,834 233,212 231,966 183,613 280,155

1601-1800 kg 5,461 257,145 255,296 183,519 324,729

1801-2000 kg 3,915 222,329 221,525 178,10 266,491

>2000 kg 1,395 249,252 247,756 198,219 296,605

Total 131,531

Figure 7-9 – France: Diesel car lifetime (15 years) mileage by mass


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Footprint versus mileage

Table 7-4 – France: Petrol car lifetime (15 years) mileage by footprint

Footprint Bin Sample size Frequency weighted mean (km)

Trim mean (km)

Lower quartile (km)

Upper quartile (km)

3.00-3.10 m2

19,328 107,359 109,323 66,703 150,854

3.10-3.15 m2 4,598 132,837 124,548 82,141 161,348

3.15-3.20 m2 13,355 145,238 142,129 101,175 181,253

3.20-3.30 m2 36,794 149,80 142,778 104,576 179,893

3.30-3.40 m2 31,893 136,894 130,654 85,695 173,974

3.40-3.50 m2 6,775 143,715 148,200 102,608 191,617

3.50-3.60 m2 15,460 162,162 158,983 118,534 199,024

3.60-3.70 m2 6,595 141,970 126,108 72,351 180,248

3.70-3.80 m2 11,746 161,923 161,950 120,239 201,952

3.80-3.90 m2 7,535 182,253 179,316 133,798 224,288

3.90-4.10 m2 4,506 185,989 180,099 140,304 220,274

4.00-4.20 m2 7,173 177,492 174,507 131,948 216,865

4.20-4.50 m2 1,974 193,361 192,717 152,477 236,345

>4.50 m2 406 167,703 172,799 131,856 203,916

Total 168,138

Figure 7-10 – France: Petrol car lifetime (15 years) mileage by footprint


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Table 7-5 – France: Diesel car lifetime (15 years) mileage by footprint


Trim mean (km)

Lower quartile (km)

Upper quartile (km)

3.00-3.10 m2

3,067 164,680 163,308 90,253 226,612

3.10-3.15 m2 519 203,813 200,299 142,757 252,072

3.15-3.20 m2 3,186 212,540 211,979 170,917 254,153

3.20-3.30 m2 12,652 246,592 243,909 192,512 294,656

3.30-3.40 m2 14,426 207,815 208,188 160,852 263,192

3.40-3.50 m2 6,054 240,413 237,767 181,360 297,168

3.50-3.60 m2 18,299 225,554 226,837 179,070 278,912

3.60-3.70 m2 9,522 204,688 203,449 121,848 279,707

3.70-3.80 m2 18,656 251,566 250,383 201,81 299,263

3.80-3.90 m2 16,916 262,718 261,310 208,746 312,896

3.90-4.10 m2 5,267 234,822 233,648 186,381 278,384

4.00-4.20 m2 14,843 249,754 247,315 193,544 299,490

4.20-4.50 m2 4,373 267,887 266,886 213,319 320,567

>4.50 m2 3,699 70,485 173,188 106,988 236,980

Total 131,479

Figure 7-11 – France: Diesel car lifetime (15 years) mileage by footprint


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Results for the UK data

Mass versus mileage

Table 7-6 – UK Petrol car lifetime (15 years) mileage by mass



Upper quartile (km)

<800 kg 1,221 107,166 105,953 77,663 133,642

801-900 kg 14,950 136,116 134,121 98,874 168,147

901-1000 kg 30,548 131,766 130,058 95,708 162,965

1001-1100 kg 41,526 119,569 117,448 81,607 152,066

1101-1200 kg 32,184 137,532 135,218 95,288 174,073

1201-1400 kg 39,347 184,916 182,217 136,870 226,361

1401-1600 kg 29,951 205,119 202,741 155,429 249,022

1601-1800 kg 8,537 199,345 196,708 150,432 241,747

1801-2000 kg 3,484 190,450 188,159 144,399 230,429

>2001 kg 4,012 228,611 228,859 188,301 268,349

Total 205,760

Figure 7-12 – UK Petrol car lifetime (15 years) mileage by mass


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Table 7-7 – UK Diesel car lifetime (15 years) mileage by mass


Trim mean (km)

Lower quartile (km)

Upper quartile (km)

801-900 kg 2,882 180,460 178,953 138,446 219,961

901-1000 kg 143 176,352 174,964 132,255 217,152

1001-1100 kg 80 163,003 163,133 117,476 209,014

1101-1200 kg 24 204,902 210,283 170,652 241,823

1201-1400 kg 17,832 171,737 168,117 122,748 210,058

1401-1600 kg 2,189 177,157 174,365 134,39 211,253

1601-1800 kg 6,668 191,592 188,046 140,663 232,169

1801-2000 kg 2,792 201,877 199,974 154,679 244,347

>2000 kg 8,164 230,087 229,164 189,325 268,840

Total 40,774

Figure 7-13 – UK Diesel car (15 years) assumed mileage by mass


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Footprint versus mileage

Table 7-8 – UK Petrol car lifetime (15 years) mileage by footprint


Trim mean (km)

Lower quartile (km)

Upper quartile (km)

3.00-3.10 m2

4920 123,240 88,821 153,444 121,535

3.10-3.15 m2 24990 134,369 98,521 165,127 132,424

3.15-3.20 m2 39615 114,526 78,477 147,429 112,823

3.20-3.30 m2 984 126,206 90,242 156,035 123,129

3.30-3.40 m2 1790 125,242 92,047 154,606 123,805

3.40-3.50 m2 5520 186,673 147,337 224,878 186,023

3.50-3.60 m2 29510 163,063 120,912 197,672 160,303

3.60-3.70 m2 12261 105,448 61,235 140,497 101,602

3.70-3.80 m2 749 237,055 182,218 277,432 232,850

3.80-3.90 m2 6953 187,139 143,766 226,397 185,352

3.90-4.10 m2 11414 202,953 151,379 249,078 200,550

4.00-4.20 m2 30452 193,492 146,272 235,849 191,563

4.20-4.50 m2 15036 228,919 179,085 272,221 226,538

>4.50 m2 2422 210,639 148,167 235,448 192,475

Total 186,616

Figure 7-14 – UK Petrol car lifetime (15 years) mileage by footprint

0

50.000

100.000

150.000

200.000

250.000

300.000

Life

tim

e m

ileag

e (

km)

Footprint (m2)

Lower quartile

Trim mean

Upper quartile


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Table 7-9 – UK Diesel car lifetime (15 years) mileage by footprint16


Trim mean (km)

Lower quartile (km)

Upper quartile (km)

3.10-3.15 m2 146 63,338 176,865 176,996 133,558

3.15-3.20 m2 74 56,907 163,003 163,708 117,449

3.20-3.30 m2 2,008 58,650 149,495 149,504 117,024

3.40-3.50 m2 1,814 69,222 237,723 237,743 192,812

3.80-3.90 m2 731 74,535 231,444 231,448 190,214

3.90-4.10 m2 1,201 76,598 276,269 276,107 226,906

4.00-4.20 m2 8,060 70,841 229,429 229,426 188,228

4.20-4.50 m2 529 84,732 271,828 271,878 218,585

Total 14,563

Figure 7-15 – UK Diesel car lifetime (15 years) mileage by footprint

16

Reduced results due to data availability


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Results for the data for Belgium17

Mass versus mileage

Table 7-10 – Belgium: Petrol car lifetime (15 years) mileage by mass



Upper quartile (km)

801-900 kg 19 45,330 51,219 36,911 66,499

901-1000 kg 228 52,339 51,192 23,190 81,737

1001-1100 kg 188 75,858 73,360 43,692 97,769

1101-1200 kg 1,999 110,837 109,625 68,219 150,126

1201-1400 kg 8,366 120,015 118,393 76,168 160,447

1401-1600 kg 9,259 123,927 122,282 78,721 164,315

1601-1800 kg 4,177 141,280 139,795 97,093 180,668

1801-2000 kg 1,503 153,997 151,987 106,835 196,341

>2001 kg 937 151,882 149,627 99,987 196,478

Total 26,676

Figure 7-16 - Belgium: Petrol car lifetime (15 years) mileage by mass

17

No footprint data was provided for Belgium


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Table 7-11 - Belgium: Diesel car lifetime (15 years) mileage by mass


Trim mean (km)

Lower quartile (km)

Upper quartile (km)

1201-1300 kg 79 170,975 170,974 109,987 235,658

1301-1400 kg 277 182,543 183,672 138,288 223,450

1401-1500 kg 830 202,677 202,178 156,123 245,153

1501-1600 kg 1,806 175,691 176,957 114,604 239,768

1601-1700 kg 2,255 194,690 196,033 145,918 251,799

1701-1800 kg 2,021 216,118 216,268 171,392 262,680

1801-1900 kg 2,341 220,002 218,700 171,512 266,728

1901-2000 kg 1,247 231,574 230,118 177,769 284,283

2001-2100 kg 413 231,171 230,498 178,325 280,119

2101-2200 kg 110 244,285 242,279 183,454 288,295

>2000 kg 1,138 216,596 215,098 153,077 278,405

Total 12,517

Figure 7-17 - Belgium: Diesel car lifetime (15 years) mileage by mass


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7.3 Appendix 3: Additional analysis of the full dataset

7.3.1 Detailed tables for analysis by mass bins

Lifetime mileage results for petrol and diesel cars against mass in running order (kg) are shown in Table 7-12 and Table 7-13 respectively for petrol and diesel cars.

Table 7-12 - Petrol car lifetime (15 years) mileage by mass bins



Upper quartile (km)

<800 kg 19,511 109,397 105,911 65,477 146,557

801-900 kg 69,534 141,186 138,819 97,610 178,418

901-1000 kg 67,573 135,622 133,546 93,321 172,595

1001-1100 kg 66,531 131,990 129,576 88,465 168,503

1101-1200 kg 46,972 141,393 139,379 96,128 181,955

1201-1400 kg 64,489 172,739 170,654 123,484 216,639

1401-1600 kg 45,612 186,060 183,948 132,878 234,563

1601-1800 kg 14,067 181,576 179,143 130,218 226,505

1801-2000 kg 5,657 176,924 174,808 127,794 218,892

>2000 kg 5,105 212,635 213,143 166,550 260,569

Total 405,051

Table 7-13 - Diesel car lifetime (15 years) mileage by mass bins



Upper quartile (km)

<900 kg 6,123 194,356 km 193,063 km 147,604 km 238,726 km

901-1000 kg 25,630 228,452 km 227,630 km 177,891 km 280,528 km

1001-1100 kg 11,102 222,040 km 219,827 km 166,160 km 278,813 km

1101-1200 kg 38,396 235,362 km 236,125 km 186,794 km 290,216 km

1201-1400 kg 37,978 215,781 km 213,050 km 148,384 km 275,903 km

1401-1600 kg 20,553 223,683 km 222,857 km 173,920 km 272,163 km

1601-1800 kg 15,514 224,513 km 221,878 km 161,798 km 280,737 km

1801-2000 kg 9,673 219,855 km 218,416 km 172,477 km 263,974 km

2001-2200 kg 9,617 235,005 km 234,006 km 92,692 km 275,669 km

>2200 kg 1,380 233,061 km 231,235 km 170,754 km 289,820 km

Total 175,966


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7.3.2 Detailed tables for analysis by footprint bins

Table 7-14 - Petrol car lifetime (15 years) mileage by footprint bins



Upper quartile (km)

3.00-3.05 m2

9,778 101,496 98,471 58,869 139,344

3.05-3.10 m2 39,460 127,898 125,687 87,879 162,146

3.15-3.20 m2 57,568 123,113 121,354 84,416 156,527

3.20-3.30 m2 37,778 149,181 146,677 106,873 185,314

3.30-3.40 m2 33,683 136,276 134,053 88,952 177,598

3.40-3.50 m2 12,295 162,995 160,952 110,594 209,805

3.50-3.60 m2 44,970 162,758 160,560 119,940 199,787

3.60-3.70 m2 18,856 118,228 114,422 68,264 154,139

3.70-3.80 m2 12,495 166,445 164,164 120,065 206,829

3.80-3.90 m2 14,488 184,589 183,037 139,196 227,090

3.90-4.00 m2 15,920 198,148 196,051 148,495 242,282

4.00-4.20 m2 37,625 190,444 188,508 142,994 233,063

4.20-4.50 m2 17,010 224,789 222,643 174,856 268,871

>4.50 m2 2,828 210,639 188,556 140,726 234,401

Total 354,754

Table 7-15 – Diesel car lifetime (15 years) mileage by footprint bins



Upper quartile (km)

3.00-3.10 m2 3,213 165,176 163,964 91,889 225,998

3.10-3.20 m2 3,779 210,430 209,521 167,051 253,126

3.20-3.30 m2 14,660 233,300 230,241 172,931 285,329

3.30-3.40 m2 14,426 207,815 208,188 160,852 263,192

3.40-3.50 m2 7,868 239,789 238,104 185,273 294,426

3.50-3.60 m2 18,299 225,554 226,837 179,070 278,912

3.60-3.70 m2 9,522 204,688 203,449 121,848 279,707

3.70-3.80 m2 18,656 251,566 250,383 201,813 299,263

3.80-3.90 m2 17,647 261,432 259,905 207,702 311,046

3.90-4.00 m2 6,468 242,489 241,482 192,483 287,836

4.00-4.20 m2 22,903 242,595 240,199 191,392 288,028

4.20-4.50 m2 4,902 268,337 267,100 214,526 319,928

>4.50 m2

3,699 70,485 173,188 106,988 236,980

Total 146,042


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7.3.3 Statistical analysis of full dataset

Whilst the analysis in Section 4 was appropriately undertaken to look at mileage patterns at discrete mass and footprint categories, this analysis is based on plotting the full dataset for each vehicle against its mileage.

Petrol cars

Figure 7-18- Petrol passenger car regression analysis

Regression Statistics Multiple R 0.263036723 R Square 0.069188317 Adjusted R Square 0.069186019 Standard Error 72513.81238 Observations 405051

df SS MS F Significance F

Regression 1 1.58314E+14 1.58E+14 30107.76 0

Residual 405049 2.12985E+15 5.26E+09 Total 405050 2.28816E+15

Coefficients

Intercept 72826.73439

Slope 68.23459919

As shown in the data above, the slope and intercept coefficients indicate a positive slope for petrol cars (i.e. mileage increases with mass).

The ‘Significance F’ indicates the probability that the regression output could have been obtained by chance. For a confidence level of 95%, if the ‘Significance F’ is <0.05, then the null hypothesis is rejected (there is a statistically significant association between mass and mileage). Conversely, if the ‘Significance F’ is >0.05, then the null hypothesis is accepted (there is no statistically significant association between mass and mileage). In this case, the


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‘Significance F’ = 0, so the null hypothesis is rejected and so there is a statistically significant association between mass and mileage.

The residuals show how far away the actual data points are from the predicted data points (using the linear equation). This plot shows as suspected that for lower masses there seems to be much more variation in how far cars are being driven over their lifetime. There is some skew as would be expected due to the lower bound but overall it would appear as if a non-linear relationship is not being overlooked.

Figure 7-19 – Petrol passenger car residual plot


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Diesel cars

Figure 7-20 - Diesel passenger car regression analysis

Regression Statistics Multiple R 0.026188955 R Square 0.000685861 Adjusted R Square 0.000680455 Standard Error 91410.68988 Observations 184827


Regression 1 1.05996E+12 1.06E+12 126.8513 2.04699E-29

Residual 184825 1.54438E+15 8.36E+09 Total 184826 1.54544E+15

Coefficients


Slope -6.896037227

Here the results of the regression analysis show a very slight negative slope. This implies that mileage does in fact slightly decrease with increasing mass.

The residual plots for diesel shown in Figure 7-21 again show how far away the actual data points are from the predicted data points (using the linear regression coefficients). As with the petrol passengers cars there is more variation for lighter weight vehicles.


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Figure 7-21 – Diesel passenger car residual plot

Detailed 10 year analysis

In order to be satisfied that results of the “lifetime mileage” were consistent with vehicles of earlier years, regression analysis was also studied for a sample of data of vehicles at 10 years of age. The results in Figure 7-22 and Figure 7-23 coupled with the coefficient data signify that consistent results have been found at the 10 year lifespan (positive upward trend for petrol vehicles and flatter, negative trend for diesel vehicles). T

Figure 7-22: Petrol passenger car regression analysis for 10 year old vehicles

Coefficients


Slope 32.42588168


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Figure 7-23: Diesel passenger car regression analysis for 10 year old vehicles

Coefficients


Slope -9.963450633


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7.4 Appendix 4: Frequency distribution plots

7.4.1 Petrol passenger cars


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70




71



7.4.2 Diesel passenger cars


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73




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7.5 Appendix 5: Additional statistical analysis on LCV dataset

Regression Statistics Multiple R 0.003366006 R Square 1.133E-05 Adjusted R Square -7.55018E-06 Standard Error 95855.58894 Observations 52967


Regression 1 5513896840 5513896840 0.60010018 0.0438543264

Residual 52965 4.86658E+14 9188293930 Total 52966 4.86664E+14

Coefficients Standard Error

Intercept 210119.7183 1571.025355

Slope -0.891113113 1.150326053

Figure 7-24- LCVs regression analysis

As with diesel passenger cars, the extremely low R2 confirms the earlier findings adding further evidence that there is no link between mass and mileage for LCVs. Results of the regression analysis show a very slight negative slope. The ‘Significance F’ result suggests a 4% chance that the regression output was merely a chance occurrence, thus confirming the validity of the analysis.


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Figure 7-25 - LCVs residual plot


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7.5.1 LCV frequency distribution plots


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7.6 Appendix 6: Additional cost analysis results for passenger car segments

Figure 7-26 - 5 year medium petrol passenger car cost of ownership versus level of target ambition


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Figure 7-27 - 5 year large petrol passenger car cost of ownership versus level of target ambition

Figure 7-28 - 5 year small diesel passenger car cost of ownership versus level of target ambition


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Figure 7-29 - 5 year medium diesel passenger car cost of ownership versus level of target ambition

Figure 7-30 - Medium petrol passenger car TCO versus level of target ambition


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Figure 7-31 - Large petrol passenger car TCO versus level of target ambition

Figure 7-32 – Small diesel passenger car TCO versus level of target ambition


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Figure 7-33 – Medium diesel passenger car TCO versus level of target ambition


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In the below charts, an 8% discount rate has additionally been applied and the same calculations been undertaken as in Section 5.2. This additional work has been undertaken to mirror the analysis performed in the current impact assessment (EC Impact Assessment, 2012)

Figure 7-34 – Small petrol passenger car TCO versus level of target ambition (with 8% discount rate applied)


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Figure 7-35 – Medium petrol passenger car TCO versus level of target ambition (with 8% discount rate applied)

Figure 7-36– Large petrol passenger car TCO versus level of target ambition (with 8% discount rate applied)


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Figure 7-37– Small diesel passenger car TCO versus level of target ambition (with 8% discount rate applied)

Figure 7-38– Medium diesel passenger car TCO versus level of target ambition (with 8% discount rate applied)


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Figure 7-39 – Large diesel passenger car TCO versus level of target ambition (with 8% discount rate applied)


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Figure 7-40 - 5 year small petrol passenger car cost of ownership versus level of target ambition (with 8% discount rate applied)

Figure 7-41 - 5 year medium petrol passenger car cost of ownership versus level of target ambition (with 8% discount rate applied)


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Figure 7-42 - 5 year large petrol passenger car cost of ownership versus level of target ambition (with 8% discount rate applied)

Figure 7-43 - 5 year small diesel passenger car cost of ownership versus level of target ambition (with 8% discount rate applied)


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Figure 7-44 - 5 year medium diesel passenger car cost of ownership versus level of target ambition (with 8% discount rate applied)

Figure 7-45 - 5 year large diesel passenger car cost of ownership versus level of target ambition (with 8% discount rate applied)

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