INTEGRATED TRAVEL EMISSIONS PROFILES

INTEGRATED TRAVEL EMISSIONS PROFILES

CASE STUDY REPORT

Christian Brand

Transport Studies Unit &

Environmental Change Institute University of Oxford

Working paper N° 1017

March 2006

Transport Studies Unit Oxford University Centre for the Environment

http://www.tsu.ox.ac.uk/

ESRC Integrated Travel Emissions Profiles Final – March 2006 ESRC Ref. No. RES-000-22-0564 Case Study Report

© Transport Studies Unit and Environmental Change Institute, University of Oxford ii

Integrated Travel Emissions Profiles

Case Study Report

Christian Brand

Transport Studies Unit & Environmental Change Institute

University of Oxford

Final March 2006

ESRC Award Reference RES-000-22-0564

(Also TSU Working Paper Ref. 1017)

This research was supported by the ECONOMIC & SOCIAL RESEARCH COUNCIL


© Transport Studies Unit and Environmental Change Institute, University of Oxford iii

ACKNOWLEDGEMENTS

I gratefully acknowledge the many contributions made by organisations and individuals consulted or surveyed as part of the research, and by the authors of previous studies and literature reviews which I have cited. I particularly thank Jillian Anable and Brenda Boardman for their advice and guidance early on in the project; and Fiona Rajé and Takeshi Takama for their moral and hands-on support during the survey work. I have made extensive use of the work experience gained by colleagues at the UKERC (Jillian Anable, Brenda Boardman); and of my own extensive experience on integrated transport, energy and emissions modelling, which was mainly funded by the European Commission.


© Transport Studies Unit and Environmental Change Institute, University of Oxford iv

CONTENTS

ACKNOWLEDGEMENTS .......................................................................................................... 3

1 INTRODUCTION .................................................................................................................1

2 THE CASE STUDIES: APPROACH.................................................................................... 2

2.1 Overall Approach................................................................................................................................... 2

2.2 The Surveys............................................................................................................................................. 3

2.3 Emissions Evaluation............................................................................................................................ 4

3 ANALYSIS, VALIDATION AND PRESENTATION OF RESULTS................................ 8

3.1 Cars and Motorcycles ............................................................................................................................ 8

3.2 Air Travel ................................................................................................................................................ 9

3.3 Public Transport and Ferry ................................................................................................................ 10

3.4 Synthesis and Aggregation to Higher Levels ................................................................................... 11

3.5 Error and Uncertainty ......................................................................................................................... 11

4 CASE STUDY RESULTS .....................................................................................................13

4.1 The Survey Sample .............................................................................................................................. 13

4.2 Area-wide travel emissions profiles................................................................................................... 16

4.3 Household and individual travel emission profiles ......................................................................... 23

4.4 Aggregation to higher levels ............................................................................................................... 36

5 DISCUSSION OF THE RESULTS .....................................................................................38

6 CONCLUSIONS...................................................................................................................45

REFERENCES............................................................................................................................46

ANNEX A ASSUMPTIONS AND CONVERSION FACTORS USED...................................49

A.1 Annualisation factors........................................................................................................................... 49

A.2 Fuel Characteristics and Lifecycle Emissions Factors .................................................................... 49

A.3 Climate Impact Factors ....................................................................................................................... 49

A.4 Assumptions for analysis of errors and uncertainty........................................................................ 50

ESRC Integrated Travel Emissions Profiles Final - March 2006 ESRC Ref. No. RES-000-22-0564 Case Study Report

© Transport Studies Unit and Environmental Change Institute, University of Oxford 1

1 INTRODUCTION

The Integrated Travel Emissions Profiles (iTEP) project is investigating greenhouse gas (GHG) pollutant emissions and related climate change impacts from transport at the personal, household and local levels. It provides an improved understanding of the extent to which individual and household travel activity patterns, choice of transport mode, geographical location and socio-economic factors impact on climate change related pollutant emissions. As the UK is facing tough choices as how to respond to climate change, it is crucial to know who is contributing to the problem and to what extent will different groups of the population be affected by those choices. The transport sector is a major contributor to total GHG emissions and accounts for 26% of UK emissions (DTI, 2005). Passenger cars account for an estimated half of carbon emitted by transport activities, with other road vehicles accounting for a further 35% (NAEI, 2003; DfT, 2005). Transport is the only sector with a predicted rise in emissions of 15% in the period to 2020, while total emissions are forecast to fall by around 11% (IPPR, 2003).1 Despite the reduction in local air pollution with the advent of catalytic converters and cleaner fuels, energy consumption and associated pollutant emissions by transport (all modes) have increased continuously since 1981, rising by 62%. This has happened despite improvements in fuel content and engine efficiency for private road transport because people are driving more and heavier vehicles further (Kwon and Preston, 2005).

The overall aim of this work is to develop and test an innovative methodology for profiling climate change emissions from personal travel at the local, household and individual levels and to use this information to develop measurement and information tools for use in the identification and monitoring of local transport strategies.

To achieve this aim, this research has the following working objectives:

• Objective 1: To extend previous work on Travel Emissions Profiles to include public transport, air travel, motorcycles, walking and cycling.

• Objective 2: To collect detailed disaggregate data as a basis for future travel and energy auditing at the household and individual levels via interviews and web-based and conventional paper-and-pen based surveys in urban and rural areas.

• Objective 3: To implement this methodology in a detailed emissions model and to apply the model using the data collected from targeted households to make aggregate forecasts.

• Objective 4: To highlight policy implications for local and national governments, with the vision of personal energy/carbon auditing as a potential basis for pricing, benchmarking, trading, etc.

The primary purpose of this Report is to present the findings of the case studies, hence focussing primarily on objectives 2 and 3. Chapter 2 summarises the case study methodology including survey and evaluation techniques used, whilst Chapter 3 provides details about the analysis, validation and presentation of results. Chapter 4 then presents the main results of this work, including area-wide, household and individual travel emissions profiles and aggregation to higher levels. Chapter 5 finally provides a discussion of these results in terms of potential applications of this research, policy implications and contentious issues before concluding with an outlook on further research priorities (Chapter 6).

1 This forecast excludes international air travel.



2 THE CASE STUDIES: APPROACH

The methods developed and tested in this work are described in detail in Working Paper 2 (Brand, 2005). This Chapter gives a brief overview of the overall approach and research techniques used. 2.1 Overall Approach

The methodology is based around a substantive case study involving geographical areas with urban, peri-urban and rural elements2:

• Large urban areas (population 100-250k): Oxford City.

• Medium urban areas (population 25-100k): e.g. Abingdon, Banbury, Bicester, Witney.

• Small urban areas (population 10-25k): e.g. Carterton, Didcot.

• Peri-urban and rural areas around the urban centres: Oxfordshire villages and rural areas. The methodology employs three stages of data collection, preparation and analysis as shown in Figure 1.

Figure 1: Methodology outline

ANALYSIS

SURVEY

Local/national

travel and

emissions data

Pen-and-paper

questionnaire

Web-based

questionnaire

- Interpretation

- Policy implications

EVALUATION

Direct energy and

emissions from

personal travel

Indirect energy and

emissions from

personal travel

Emissions factors

Load factors

Travel distances

Fuel lifecycle

emissions factors

Integrated travel

emissions profiles of

the sample

Aggregation to higher

levels

Interviews

2 As described in Brand (2005), it was originally intended to develop a second case study in a large town in collaboration with Sustrans and Socialdata. However, the collaboration proved unsuccessful due to data protection issues and differences of opinion in terms of methodology and data requirements. Nevertheless, the richness and quantity of the data collected in the main case study proved adequate to test the methodology and derive meaningful conclusions from this work.



1. First, an innovative household survey technique based on weekly and annual estimates of day-to-

day and casual travel activity was used to collect the data required to measure the key determinants of climate change emissions from travel (Section 3.2). This was augmented by secondary data from national and local sources.

2. Secondly, the results were entered into a newly developed evaluation tool designed to incorporate this data with emission rates for each climate change pollutant for a range of transport modes, vehicle characteristics and operating conditions (Section 3.3). Secondary data were collected for this stage on local emissions factors, fuel lifecycle emissions, public transport vehicle fleet and passenger loadings.

3. Thirdly, the travel activity data were translated into emissions profiles, analysed at the individual and household levels and aggregated to higher levels. This included validation in terms of comparison and triangulation with more aggregate datasets (e.g. NTS, IPS).

2.2 The Surveys

Survey Design

The survey built on previous work (particularly Anable et al, 1997; Broeg et al, 2004) and was designed to collect information from the following study groups in each of the geographic case study areas:

• The household;

• The individual, i.e. all household members including children of 6 years or older3. Within each of the study groups, examination was made of how household location, structure, income, occupation, age, gender, vehicle ownership, choice of transport mode and access to public transport and services may affect travel emissions. Two versions of the survey questionnaire were developed, piloted and then employed on a larger scale: a mail out paper-and-pen version and web-based version. The mailed out paper-and-pen questionnaire included a Household Form, a maximum of five Person Forms and five Vehicle Forms. The Household Form mainly requested details of the socio-economic background of the household members. The Person Forms requested details on regular daily/weekly travel and irregular travel by non-private transport modes over the past year. The Vehicle Forms collected vehicle-specific data (such as fuel type and recorded annual mileage) as well as vehicle use data for each driver in the household. The paper version of the survey questionnaire is provided as Annex A to the Final Report (Brand et al, 2006). The web-based questionnaire was identical in terms of type and number of questions asked. The web site was developed in html and php scripting using the PHP EditTM software package and was linked to a MySQL database on the Departments’ web-server. This enabled interactive navigation through the questionnaire. For the researcher it was also a convenient and consistent way of coding data from the pen-and-paper survey. The questionnaire can be accessed at www.tsu.ox.ac.uk/oxontravel (guest login: oxontravel, password: onthemove). In particular, respondents were asked to give details on all private travel activity (mileage, duration, trips) over a period of 12 months. This differed between private motorised (car, motorcycle), public/air transport modes and non-motorised modes. The main variables that were collected were:

3 Children were not included in the original study by Anable et al (1997) but are included in this study for ages of 6 years or older. Although not of driving age, children of this age group travel by public transport and air travel, with at times significant travel emissions from those activities.



• Car/motorcycle: annual mileage (total, plus shares by road type and trip length), average fuel purchase, shared use of vehicle by household members, average occupancy, vehicle fuel type, age, engine size.

• Local bus, express coach, intercity rail, regional rail, taxi, ferry, walking and cycling: number of trips, mileage. A distinction was made between regular (on a daily/weekly basis) and irregular travel activity (the main casual trips) and peak/off-peak travel for bus/rail.

• Air travel: number of trips, origin/destination and stop-overs, duration and plane occupancy of all privately paid flights were recorded

For private travel by car, the questionnaire included additional questions about the shared nature of the means of transport within a household and individual responsibilities that result. It was decided to allocate all emissions to the driver of a vehicle (not owner) (see Brand, 2005). For a household-owned vehicle, the driver is “responsible” for the trip in most cases in some way or another. Survey Administration and Responses

The pen-and-paper questionnaires were sent out to 900 private addresses in Oxfordshire. These were selected by systematic random sampling, taking every 250th non-commercial, private address from the randomly sorted 2002 Postal Address File (PAF4). To stimulate higher response rates, it was administered with pre-paid return envelopes and a prize draw. Reminder procedures were used to chase non-respondents after two weeks of the initial mail-out. Overall, the survey was administered and the responses chased and collected over a 12-week period in winter/spring 2005. The web-based version was emailed to about 500 individuals selected from within the University of Oxford (including a University Department and a Graduate College). Secondary data

In addition, interviews with local transport planners (Oxfordshire County Council, Oxford City Council), public transport operators (Oxford Bus Company, Stagecoach Oxfordshire, The Go-Ahead Group) and air travel authorities (Civil Aviation Authority) provided local travel and emissions data for public transport and air travel. The discussions also highlighted important policy implications of a strategy development or awareness raising tool for local and national governments. 2.3 Emissions Evaluation

Tool development

The evaluation tool built on:

• Previous work undertaken at the University of Oxford (in particular Anable et al, 1997);

• The European COPERT II (EEA, 1998), COPERT III (EEA, 2000), TRENDS (EC, 2002), STEEDS (Moon and Brand, 1999) and TREMOVE II (De Ceuster et al, 2004) models;

• other databases (e.g. Barlow et al, 2001) and;

• complementary methods on air travel (e.g. CfIT, 2001; EC, 2002; Jardine, 2005; FAR and Germanwatch, 2005).

The main data source incorporating emissions data from these studies is the National Atmospheric Emissions Inventory (NAEI, 2003), which formed the basis for the road transport emissions data of

4 For this study, only private addresses lying within the boundaries of Oxfordshire County were used, i.e. mainly addresses with postcodes beginning with OX, but also RG, SN and HP.



this project. Road traffic data and speeds from the DfT (2005) were used to assign average speeds to road types5, and average trip lengths to survey trip distance bands6. Fuel life-cycle emissions from oil and gas production, refinery production and transport to the fuel station were included based on previous work by EUCAR (2003), with additional data from Lewis (1997) and Hickman et al (1999). The factors used are based on upstream pollutant emissions per amount of energy/fuel (e.g. electric trains, petrol cars, kerosene aircraft) consumed during vehicle operation. Overview of the emissions calculation techniques used

The tool employs a number of techniques to derive emissions totals, largely depending on the mode of transport, data input from the survey and emissions data availability. The main techniques are summarised below, with further details provided in Brand (2005). � Cars and motorcycles

Travel activity data for travel by car and motorcycle were collected to a high level of detail, allowing the calculation of pollutant emissions according to up to four different methods (Table 1).

Table 1: The four alternative methods for calculating emissions from car and motorcycle travel

ref# Method Description Relationship A Transport activity

and disaggregate average emissions factors

Based on travel activity data collected in the surveys and emissions factors disaggregate by fuel type, engine size, EURO band and average speed/road type.

emissions = emissions factor x transport activity (miles, hours). Cars: including cold start excess emissions

B Transport activity and official vehicle specific emissions factors

Based on travel activity data collected in the surveys matched with vehicle specific fuel use and emissions data published by the UK Vehicle Certification Agency

emissions = f(car make, model, fuel, engine size, age), taking official figures from the VCA database

C Fuel consumption Direct conversion of fuel use to emissions via carbon balance

emissions = f(fuel consumption and type) (carbon balance)

D Fuel expenditure Direct conversion of fuel purchase and fuel type to fuel use and emissions via carbon balance

emissions = f(fuel expenditure, price and type) (carbon balance)

The main primary data required for Methods A and B were the annual mileages per driver, discounted by the share of business mileage and accounting for any other members of the household using the vehicle. To that extent, the travel activity data collected in the surveys included annual mileages from the most recent MoT certificates, registration documents and personal estimates (incl. a qualitative statement of the degree of accuracy). MoT records can be considered the most reliable source of information, followed by Registration details and personal estimates by stated accuracy7. To derive total annual mileage, a simple decision tree was applied:

• If MoT data were available, successive odometer readings were taken to give annual mileage.

• If MoT data were not, but Registration document data in combination with current mileage was available, an estimate of the mileage done in 2004 was derived.

5 Default average speeds: ‘urban’ 40kph (25mph), ‘rural/single carriageway’ 77kph (48mph), ‘motorway/dual carriageway’ 113kph (70mph). 6 Distance classes: ‘short’ <3.2km (2miles), ‘middle’ 3.2-32km (2-20miles), ‘long’ >32km (20miles). 7 Note the time period of the survey (12 months in 2004/05) and successive MoT certificates do not necessarily overlap. As there is no way of checking this with the data collected, the underlying assumption here is that travel activity patterns do not change in the periods that do not overlap.



• If none of these were provided, the driver’s own estimate was used as the annual mileage. The main improvements over the original TEP study were:

• Method A: updated emissions factors and speed-emission curves; data used by local authorities for air quality monitoring and greenhouse gas reporting (hence A is well placed as reference method for local strategy development); an assessment of error and uncertainty; expanded range of emissions types (CH4, N2O).

• Method B: improved Vehicle Certification Agency (VCA) data based on EC test driving cycles for all new cars from 2000 onwards (48% of all cars in the survey); disaggregation by urban (now includes cold starts) and extra-urban; for older cars data only limited to CO2 (SMMT database); an assessment of error and uncertainty; expanded range of emissions types (CH4, N2O).

• Method C and D: respondents were asked to provide degree of accuracy of their fuel use estimates Further to recommendations by the authors of the original TEP study and internal discussion during the course of this project, a number of decisions were made as how and when to use various methods:

• As in the original TEP work, Method A was chosen as the reference method, against which the results of the other methods were compared with and validated against (see Section 3.4). Method B comes close in terms of accuracy and applicability; however, since the improved VCA database is only available for new cars from 2000 onwards, Method B is limited to about half of the car population in this sample. In future, this method may be a good option for supporting the development of strategies at the national level. However, as the average age of cars in the UK is about 12 years, it may be 5-10 years before the underlying data can be considered reliable and consistent.

• Emissions from car and motorcycle travel were allocated to the driver, taking care not to double-count mileages and emissions from household members sharing a car or motorcycle. Business miles were subtracted from the total to give private miles only.

� Air travel

For air travel, total emissions were derived by multiplying travel activity indicators (passenger-km, time spent in the air) by average emissions factors.8 In reference method E, passenger-km were derived by taking the origin-destination information from the surveys and obtain Great Circle Distance (GCD) mileages from public sources and calculate emissions for each leg of the flight, taking into account the number of take off and landings, average passenger loadings, cargo factors, detours from the GCD, holding patterns and taxiing at the airports. The sum of these gives the total emissions per passenger per year. In the alternative methods F and G, CO2 emissions were derived from the time spent in the air (F: provided by respondents, G: from ‘official’ sources for each origin-destination pair) multiplied by average cruising speeds and emissions factors, plus average fuel consumption figures for take-off and landings. Both out and (if any) return journeys count for all methods. This part of the analysis work was somewhat time-consuming as distances and duration had to be coded and looked up online for each of the 1,468 take-off and landings. Note this could be automated in future work by employing a now common GCD distance calculator (see e.g. FAR and Germanwatch, 2005). In addition to CO2 emissions, the CO2 equivalent of all climate change emissions was derived by converting the climatic impacts of the different aircraft pollutants to those of CO2. Emissions for flights with cruising altitudes of less than 30,000 feet (i.e. all flights of less than 450 miles) were calculated without a Radiative Forcing Index (RFI) but the emissions above 30,000 feet altitude were

8 This involved deriving distance-emissions curves based on average-weighted fuel consumption of the most common aircraft types, which were further split into two distance classes (short-haul, <3500km and long-haul, >3500km). As the total distance includes all legs of a journey (single or return-out and return-back), so do the emissions totals include all legs, independent of where the aircraft was refuelled.



calculated with a RFI factor of 3. A RFI of 3 was applied here rather than the IPCC’s best estimate of 2.7, as 2.7 is an average for all flights, i.e. including short-haul flights which do not reach the necessary altitude for the increased climate impact. The common range for RF is 2-4, providing an uncertainty range of about ±30% around the central estimate. In effect this meant a RFI of 2.9 for the sample as a whole. � “The rest”: bus, rail, taxi, ferry, walking and cycling

Emissions from bus, rail, taxi and ferry travel are less detailed and less accurate due to the lack of reliable emissions testing data, higher uncertainties in driving conditions, load factors, etc. The main method for these modes is based on transport activity (as recorded in the surveys), vehicle emissions factors and passenger loadings per vehicle. In terms of emissions and load factors, two alternative sources of data were used:

• National and regional averages (i.e. England outside London);

• Local (i.e. Oxford and Oxfordshire) emissions factors and occupancy rates (for bus, rail). For rail, the range of emissions factors included two types of rail (Intercity, regional) and two types of fuel (diesel, electric). Emissions rates for electric rail were derived by conversion of electricity consumption to upstream emissions of energy production and distribution, based on the current electricity generation mix as reported in DTI (2005).



3 ANALYSIS, VALIDATION AND PRESENTATION OF RESULTS

The main travel emissions results – mileage and emissions totals – were corrected for response bias by adjusting figures according to the demographic composition of the sample compared to the population at the County level as a whole, including sex, age and economic activity. In addition, uncertainty was dealt with by conducting an error analysis of key result variables, taking into account any uncertainties in sample bias, data collection and differences in methods. Before emissions profiles derived from the data can be synthesised and presented, the quality and integrity of the data must be assessed. Various areas of validation were used as outlined below. 3.1 Cars and Motorcycles

The four methods for deriving emissions totals allowed some assessment of the degree to which individuals were consistent in their estimations. They also gave a general indication of the integrity of the data even before it was entered into the emissions evaluation tool. Overall, methods A and B produced remarkably similar results, with respective sample total climate change impacts of 604 and 596 tCO2

tot and means of 2.27 and 2.24 tCO2tot per driver per year (base: 266 drivers). The other two

methods, however, gave higher means (Figure 2). Here it is important to note that the driver bases differed considerably from methods A and B (method C: 205 drivers, method D: 141 drivers).

Figure 2: Comparison of alternative methods of calculating CO2tot emissions from car travel compared to the reference method (A)

-1.3%

15.9%

23.5%

7.1%

-5% 0% 5% 10% 15% 20% 25%

B: Official vehicle specific

emissions factors

C: Fuel expenditure

D: Fuel consumption

All methods

percentage difference from the reference mean (Method A)

Emissions varied considerably on a case by case basis. When comparing the car CO2

tot total impact of method B with the reference method A, 90% of the respondents’ totals were within 20% of each other, but only 27% were within 5% of each other. Figure 3 illustrates this point by showing CO2

tot climate change impacts per car driver, ranked by miles driven. The Figure also shows the effects the two methods have on individual CO2

tot impacts. While the curve for method A shows the effect of the different vehicle technologies, fuel types, driving conditions, trip lengths and vehicle emissions performance characteristics, the curve for Method B shows the variations in emissions for different vehicle makes, models and fuel types. 30% of car impacts in terms of CO2

tot emissions were within 20% of method A for both methods C and D, and only 8% and 5% were within 5% of the reference method. In particular, a significant proportion of respondents appeared to have hugely over- or under-estimated (or possibly incorrectly



entered) average fuel costs or inputs, as only a respective 63% and 64% of car CO2tot emissions were

within 50% of method A. This confirmed the recommendation from previous TEP work to conduct the main emissions analysis using methods A and B.

Figure 3: Comparison between Methods A and B of calculating total climate change impacts in terms of CO2tot emissions from car travel (ranked by car miles per driver)

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

car drivers ranked by miles driven

kg

CO

2to

t per

year

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

miles p

er

year

A: disaggregate average emissions factors

B: official vehicle specific emissions factors

Total car miles per driver

3.2 Air Travel

The three methods for calculating the climate change impact from air travel gave a general indication of the integrity of the data. Method F (6.3 tCO2

tot ± 2.6%) as well as Method G (6.4 tCO2tot ± 2.5%) were

within 0.3-3.1% of reference method E (6.2 tCO2tot ± 2.4%). Figure 4 illustrates this by comparing the

three methods with the mean of all methods. Figure 5 shows that climate change impacts in terms of CO2

tot varied more on a case by case basis. When comparing air travel CO2

tot totals of methods F and G with the reference method E, a respective 75% and 97% of the respondents’ totals were within 20% of each other, and 26% and 57% were within 5% of each other. These figures suggest that at the sample level, distance and duration estimates can be used interchangeably as the key parameters determining emissions from air travel. At the individual level, some caution with regards to the level of accuracy may have to be exercised – this uncertainty has been calculated at about ±40%, based on uncertainty in occupancy rates, cabin factors, the detour factor, the cargo factor and the RF index (see below).



Figure 4: Comparison of alternative methods of calculating climate change impacts of CO2tot from air travel compared to the mean of all methods

-1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5%

E: Distance-emissions

curves by distance class

F: Duration from

respondents, e-factor

per hour and ToL, by

distance class

G: Duration from 'official'

sources, e-factor per

hour and ToL, by

distance class

percentage difference from the mean of all methods

Base: 269 flyers

Figure 5: Comparison between Methods E and F of calculating total climate change impacts in terms of CO2tot emissions from air travel (ranked by air miles per flyer)

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

1 21 41 61 81 101 121 141 161 181 201 221 241 261

flyers ranked by air miles

kg

CO

2to

t p

er

year

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

miles p

er

year

E: distance based method

F: method based on respondents' flight duration

estimates

Air miles per respondent

Base: 269 flyers

3.3 Public Transport and Ferry

In addition to mileage figures, respondents were asked to give details of the number of trips (day-to-day and casual) for travel by bus, rail, taxi, ferry, walking and cycling. By comparing these with national and,



where available, regional figures from the NTS, an assessment of the degree to which individuals were consistent in their estimations could be performed. Table 2 gives summary results of travel activity data by comparing survey sample with national and regional average figures obtained from the NTS. Overall, the figures compare well, with the exception of rail, local bus and cycling where the sample averages are clearly higher than the NTS figures. This can be explained by differences in the samples and methodology, e.g. the survey sample included trips by international high speed rail (e.g. Eurostar) whereas the NTS figures exclude non-domestic trips. Also, Oxford(shire) is in commuting distance to London, with very good rail and coach connections. Some respondents were clearly commuting to London on a daily basis, thus raising the average distance travelled by coach or train. The modal share of cycling in Oxford City (11%) is also well above the national/regional average (2%) (OCC, 2000).

Table 2: Comparison of key travel data between survey sample vs. South East and national data

Trips per person per year Average miles per trip Average miles per person per

year

Mode of travel1 Sample S.E.E.2 99-01

UK 2004

Sample S.E.E.2 99-01

UK 2004

Sample S.E.E.2 99-01

UK 2004

Walk 133 252 246 1.8 0.8 0.7 239 193 196

Bicycle 102 19 15 3.6 2.4 2.4 372 45 36

Local bus3 32 25 45 7.1 4.7 4.6 228 117 206

Non-local bus 7 4 1 94.2 100.5 96.6 659 402 75

Surface rail 16 19 14 81.6 29.9 30.8 1,319 568 384

Taxi/minicab 7 9 10 6.3 6.0 4.2 46 54 49

Notes: 1 Car travel is excluded from this Table as no trip data was collected. For further comparison of mileages by mode see Table 6. 2 S.E.E. = South East England. 3 Outside London.

Sources: DfT, 2005; own calculations

3.4 Synthesis and Aggregation to Higher Levels

The synthesis of the survey and emissions evaluation data was developed in a series of inter-linked spreadsheets, providing a transparent and convenient way of analysing the results at the individual and household levels. Emissions totals from all modes were aggregated to give an indication of total climate change impact according to global warming potentials and radiative forcing of all direct and indirect GHG. Further analysis of the socio-economic background of the sample triangulated with more aggregate datasets (Census, NTS) provides estimates of emissions levels at the local/regional (County) and national levels (GB, UK). 3.5 Error and Uncertainty

The survey and evaluation tool involves many calculation steps, with sources of uncertainty and error at each step in the calculations. An attempt was made to assess the errors and uncertainty in the final results, including:

• Uncertainty in survey input variables (e.g. annual car mileage, flight duration, day-to-day public transport mileages);

• Uncertainty in published and assumed input variables (e.g. Radiative Forcing index, emissions factors and occupancy rates);

• Statistical errors (overall sample error based on Gaussian error propagation for downstream variables, taking care of distinguishing between independent and dependent variables).



At the individual level, uncertainty in the final results was considerable, as uncertainty increases at each step in the calculations, depending on the uncertainty factors for each parameter used.9 For instance, the individual uncertainty of CO2

tot emissions from car travel was derived as between ±14% and ±30%. Similarly, an uncertainty of ±42% was derived for the individuals’ climate change impact of CO2

tot emissions from air travel (mainly due to uncertainty in actual occupancy rates and the RF factor used). At the sample level, however, this uncertainty often averages out, as respondents can be considered independent of each other. Taking the same examples from above, the sample uncertainty of CO2

tot emissions from car travel was derived as ±1.3%, while an uncertainty of ±2.4% was derived for the sample’s climate change impact of CO2

tot emissions from air travel. Sample uncertainty for public transport was slightly higher at between ±4% (rail, bus) and ±9% (ferry).

9 The following uncertainty factors were used as default: cars and m/bikes: MoT = 2.5%, R/Doc = 5%, "personal records" = 5%, "about right" = 10%, "pure guesswork" = 25%, work share = 10%, road type/speed share = 10%, distance class share = 10%, e-factor (method A) = 10%, e-factor (method B) = 5%, fuel purchase (method C) = 33%, fuel use (method D) = 33%. Buses, coaches, taxi and rail: annualisation factors = 5%, weekly miles (local) = 33%, weekly miles (regional) = 19%, irregular miles = 34%, load factor (bus & coach) = 25%, load factor (rail) = 25%, load factor (taxi) = 0%, load factor (ferry) = 25%. Air: detour factor = 100%, stated time = 10%, aircraft fuel consumption per km = 10%, aircraft capacity = 25%, cabin factor = 10%, average speed = 10%, aircraft fuel consumption per duration = 30%, RF factor = 30%.



4 CASE STUDY RESULTS

This Chapter presents the results of this study, focussing on providing emissions profiles for greenhouse gases at different levels of analysis. 4.1 The Survey Sample

The paper-and-pen survey achieved a response of 171 out of 900 completed returns (19%), providing 339 individual travel profiles.10 In addition, the web-based survey achieved a response of 117 out of 500 individual online submissions (23%). Those individuals lived in 107 households, 32 of which can be considered complete household submissions, and 75 incomplete. This provided a total sample size of 456 individuals living at 278 addresses. Of these, 72% of individuals held a driving licence, 59% drove cars and/or motorcycles, 59% made at least one flight over the year, 69% had used buses or coaches, 46% taxis, 52% rail and 15% ferry. Some 20% of respondents (91 out of 456) drove but didn’t fly, while incidentally the same number (but not necessarily the same sub-sample) of respondents didn’t drive but flew at least once. 39% (178) did both, 21% (96) did neither. Table 3 shows the response summary by separating out complete and incomplete households. The analysis in this work uses the entire response base at the individual level. At the household level, only complete households are used to ensure better comparison with national statistics.

Table 3: Survey response summary

Households Individuals Driving licence Car users Flyers

Responses P W ∑ P W ∑ P W ∑ P W ∑ P W ∑

- Complete 171 32 203 339 117 456 246 26 272 215 15 233 186 28 214

- Incomplete 0 75 75 0 0 0 0 56 56 0 36 36 0 55 55

Total 171 107 278 339 117 456 246 82 328 215 51 266 186 83 269

Notes: P = paper & pen survey, W = web-based survey, ∑ = total response

In terms of demographic characteristics, Table 4 shows how the sample compares with the Census data. Among the respondents, there is some overrepresentation of individuals of pensionable age, while 36-50 year olds are slightly underrepresented. Similarly, non-working students are slightly overrepresented. The share of economically active individuals is remarkably close to County figures. Car ownership levels compare very well in areas outside Oxford City, whereas households owning 2 cars or more and living in Oxford are under-represented in our sample11. Overall, however, the respondents are representative of the County population in terms of age structure, gender and most aspects of economic activity. The Table also shows the figures for Oxford City as compared to the surrounding Districts (non-Oxford: Cherwell, South Oxfordshire, Vale of White Horse, West Oxfordshire). The comparison with Census data is broadly similar here, with slight overrepresentation of female and economically active respondents in Oxford City.12

10 This excludes 9 returns with ‘address unknown’ and 10 that were returned ‘untouched’. 11 Car ownership levels have slightly increased since 2001. The NTS for 2004 shows that the share of non-car owning households in medium urban areas (such as Oxford City) is down from 29% in 1998/2000 to 26% in 2004; in small urban/rural areas it is down from 18% to 17%. 12 There is a slight discrepancy between area coverage as the sample was drawn from postcode addresses that start with ‘OX’, while the census areas cover Oxfordshire. For example, Henley-on-Thames and some villages in the Vale of White Horse lie outside the ‘OX’ areas but are part of Oxfordshire.



Table 4: Demographic comparison of the sample compared to the 2001 Census

Responses 2004/05 Census 2001

OXON Oxford City non-Oxford OXON Oxford City non-Oxford

Population (6yrs+) 456 194 262 562417 126476 435941

Gender

Male 47% 42% 51% 49% 49% 49%

Female 53% 58% 49% 51% 51% 51%

Age >18yrs

19 to 25 11% 17% 6% 11% 24% 8%

26 to 35 19% 26% 15% 19% 22% 19%

36 to 50 23% 24% 23% 28% 23% 30%

51 to 65 21% 19% 23% 22% 16% 24%

66 to 75 16% 9% 21% 10% 8% 10%

76 to 90 8% 4% 12% 8% 7% 9%

90 and older 1% 1% 1% 1% 1% 1%

Economic activity

Employed, full-time 1 39% 38% 40% 38% 32% 40%

Employed, part-time 1 11% 9% 11% 11% 9% 11%

Full time student 1 3% 5% 2% 2% 4% 2%

Unemployed 1% 1% 1% 1% 2% 1%

Student (not working) 6% 11% 2% 5% 16% 2%

Home keeper carer 4% 4% 5% 4% 3% 4%

Retired / pensioner 9% 8% 11% 10% 9% 11%

Other 1% 2% 1% 2% 2% 2%

Not aged 16 to 74 27% 23% 28% 27% 23% 28%

Economic active 53% 53% 53% 52% 47% 54%

HH car ownership

no car 32% 50% 14% 18% 33% 14%

1 car 43% 38% 47% 43% 46% 42%

2 cars 22% 10% 34% 31% 17% 34%

3 cars 2% 1% 2% 6% 3% 7%

4 cars or more 1% 0% 2% 2% 1% 2%

All cars 268 87 181 320152 48595 271557

All households 278 139 139 241218 51732 189486

Cars per household 0.96 0.63 1.30 1.33 0.94 1.43

Note: 1 For sample and Census, part-time is defined as working 30 hours or less a week. Full-time is defined as working 31 or more hours a week. 2 Assuming 30% of all students at university and in further education are economically active or working.

The relatively high degree of accuracy of representation allows discussion of the emissions and travel profiles to be discussed without major adjustment for response bias. All the same, the results presented in this report were corrected for response bias by sex, age and occupation. Some caution with regard to the lower car ownership levels of the sample as compared to the County as a whole may have to be exercised. Also, income levels were available from the survey but not from the Census, adding another potential source of misrepresentation. Figure 6 illustrates the geography of where the respondents live, overlaid with population density statistics obtained from Census data for 2001. This suggests a generally good representation of the sample for the study area as a whole.



Figure 6: Response geography and population density of study area

Source: Census 2001 and own mapping data

The following Sections present the results according to different geographical resolutions and units of analysis: first at the area-level, followed by individual and household levels and finally at aggregate higher levels (County and Country).



4.2 Area-wide travel emissions profiles

The relatively high number of individual responses and good geographical spread allows analysis at a range of geographical levels. The District level was considered to be too aggregated, as there are medium and small urban centres as well as deep rural areas within each District. Similarly, the Census area types were seen as too coarse in terms of geo-demographic resolution. Therefore, four geographical area types based on built-up/non-built area and population density characteristics were defined for the analysis, as shown in Table 5.

Table 5: Definition of geographical levels for purpose of this work

Definition Description Locations Responses

Large urban Built up areas, 50-250k population Oxford City (mainly within Ring Road) 194 Medium urban Built up areas, 25-50k population Bicester, Abingdon, Banbury, Witney 87 Small urban Built up areas, 10-25k population Kidlington, Thame, etc. 64 Rural Non built up areas Villages & deep rural 111

County level Mixed urban/rural All areas 456

Distance travelled

Total and average distance travelled disaggregated by mode and geographical area are shown in Table 6. As expected, the dominant modes are car and air, accounting for 77% of total miles. Respondents living in medium and large urban areas travelled less by car and air (73%) than respondents living in small and rural areas (87%). For respondents living in large and medium urban areas, travel by public transport (rail, bus, coach, taxi) was significantly higher than the national average, reflecting the location of Oxford City and its surrounding towns in London’s commuter belt and, for UK standards, good provision of public transport services.

Table 6: Total and average distance travelled by mode of transport and geographical area

Distance (miles) Mode Car 1 Moto Bus 2 Cycle Walk Rail Taxi Ferry Air Survey total (‘000) 1,704 31 414 170 109 602 21 17 2,915

Person average 4 3,736 69 908 372 239 1,319 46 37 6,392 - Large urban 2,718 6 1,427 651 319 1,802 53 39 8,409 - Medium urban 3,668 164 550 108 202 1,968 62 45 5,492 - Small urban 4,025 36 507 161 147 420 14 4 4,862 - Rural 5,400 123 514 212 183 486 40 46 4,454 National average 3,469 34 331 36 196 434 49 32 4,126 3

Household average 5 7,357 74 1,337 486 362 2,123 79 51 10,888 - Large urban 5,142 - 1,953 888 423 2,648 73 55 12,718 - Medium urban 6,911 256 1,041 201 382 3,722 117 84 10,343 - Small urban 9,790 89 1,101 270 345 960 33 10 11,652 - Rural 9,810 17 804 248 262 535 78 36 8,293 National average6 8,326 82 794 86 470 1,042 118 77 9,902 3

Notes: 1 Car as driver only. 2 Includes local bus, express coach and community/minibus. 3 Mileages obtained from IPS data for 2004 (National Statistics, 2005a). These were corrected for business miles, then doubled for return trips for better comparison with survey data. 4 Individual averages from all households. 5 Household averages from complete households only. 6 Based on the national average 2.4 persons per household.

Additional sources: National Statistics, 2004; National Statistics, 2005a; National Statistics, 2005b; DfT, 2005.

Figure 7 shows average mileages per household, further highlighting the geographical differences in travel distances and mode choice. It also shows how the (corrected) sample averages compare to national averages from the NTS and IPS.



Figure 7: Comparison of average distance travelled per household by mode of transport and geographical area

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5,000

10,000

15,000

20,000

25,000

Household

average

- Large urban - Medium urban - Small urban - Rural National

average per

household

miles p

.a.

Air

Ferry

Rail

Walk

Cycle

Taxi

Bus & coach

Moto

Car (as driver)

Base: complete house holds only (203 in total, 78 large urban, 46 medium urban, 26 small urban, 53 rural)

The respondents’ estimates of the share of total miles by road type/speed and trip distance were compared with regional figures from the NTS. For instance, respondents travelled less on urban roads (29%) than the national average (39%). This makes sense as Oxfordshire is a lot less urban than large urban areas (London in particular) that tend to dominate national averages. Motorway share was higher (35% compared with 18%), however respondents may have taken double-carriageways as motorways. Rural (37% compared with 41%) about the same than national figures (DfT, 2005). Emissions and climate change impact

For the sample as a whole, average climate change impacts were 5.25 tCO2tot per person per year,

accounting for all modes, emission sources and impact categories. Figure 8a provides the modal breakdown of this average total. Private travel by car and air dominate the climate change impact of personal travel activity. This largely confirms results of other work, e.g. Hillman and Fawcett (2005), National Statistics (2004) and DfT (2005), even though methodologies differ and neither of these studies include fuel lifecycle emissions. Emissions from air travel are the exception, where averages per person were significantly higher than e.g. in the National Statistics report. The explanations for the differences were concluded to be a combination of differences in accounting methods (e.g. this work accounts for detours from the “as-the-crow-flies” route, taxiing, cargo factor), emissions data sources and methods, local vs. regional variations (higher share in the sample of younger residents living in Oxford, e.g. a higher share of 20-35 old overseas students who tend to fly significantly more than the average person, see below), and “easier than regional” access by car and public transport to major international airports (Heathrow, Gatwick, Luton, Birmingham, Stansted). Note that in order to compare the results of this study with results of other work which exclude RF, the figures for air travel should be divided by 2.9 (the average RF index for the sample). Figure 8b provides summary results without applying a RF index. The direct comparison with Figure 5 shows the importance of RF.



Figure 8a: Average climate change impacts in terms of CO2tot for all responses by mode of transport (with RF)

Car (method A)

25.3%

Motorcycle (method A)

0.3%

Bus & coach (national)

1.2%

Taxi (national)

0.3%

Rail (national)

2.0% Ferry (national)

0.5%

Air (method E, with RF)

70.4%

Other

4.3%

Figure 8b: Average climate change impacts in terms of CO2tot for all responses by mode of transport (without RF)

Other

7.6%

Air (method E, no RF)

47.7%

Ferry (national)

0.9%Rail (national)

3.5%

Taxi (national)

0.5%

Bus & coach (national)

2.2%

Motorcycle (method A)

0.5%

Car (method A)

44.7%

Base: all 456 individual responses. Note: ‘National’ means national average emissions and load factors.

Table 7 and Figure 9 show how total GHG emissions vary by geographical area, providing some important findings:

• Average emissions from residents living in large urban areas (Oxford City) are higher than the average total, whereas emissions are lower for other areas. In particular, emissions from air, bus &



coach travel are proportionally higher for Oxford City residents. For all other areas, emissions from car travel are higher than the average total. It was concluded that higher car ownership levels in small/medium and rural areas and higher share of respondents with a higher propensity to travel by air in large urban areas were the main reasons for this variation.

• Public transport emissions were higher than the regional average figures reported in National Statistics (2005b). As with higher mileages by public transport, this can be explained by better access and service provision in Oxfordshire when compared to the regional average. The 3.5% share of the total for bus, coach, minibus, rail and taxi suggests that emissions from all land public transport can be considered insignificant in the grand scheme of things.

• Although boasting the highest emissions rates per passenger-km, travel by ferry has low impact on GHG emissions overall (0.5% share).

Table 7: Total and average climate change impacts in terms of CO2tot emissions by mode of transport and geographical area

CO2tot emissions (kg p.a.)

Mode Car1 Moto Bus2 Rail Taxi Ferry Air 3 Survey total (‘000) 586 6 31 47 6 12 1,706

Individual average4 1,285 14 68 104 14 27 3,741 - Large urban 919 1 106 151 16 29 5,107 - Medium urban 1,324 28 37 128 19 32 3,100 - Small urban 1,374 6 35 33 4 3 2,556 - Rural 1,842 29 44 43 12 31 2,538 South East average6 1,248 12 48 72 24 N/A 1,800

Household average5 2,604 15 95 163 25 36 6,251 - Large urban 1,856 - 131 219 23 38 7,331 - Medium urban 2,512 53 74 248 37 59 5,841 - Small urban 3,452 18 77 74 10 7 6,643 - Rural 3,368 3 69 52 25 25 4,826 South East average6 3,120 30 120 180 60 N/A 4,500

Notes: 1 Using reference Method A. Car emissions allocated to drivers. 2 Includes local bus, express coach and community/minibus. 3 Using reference Method E (distance based), including detours, taxiing, cargo factor, etc. Employs RFI of 3 above 30,000ft altitude. 4 Individual averages from all households. 5 Household averages from complete households only. 6 Based on 2.5 persons per household for England’s South East region. For comparison with this work, this excludes lifecycle emissions and, for air emissions, an RFI of 3 over the published figures was used.

Additional sources: National Statistics, 2004; DfT, 2005; IPCC, 2001; RCEP, 2002.



Figure 9: Average climate change impacts of CO2tot per person by mode of transport and geographical area

- 1,000 2,000 3,000 4,000 5,000 6,000 7,000

Individual average (all

households)

- Large urban

- Medium urban

- Small urban

- Rural

Car (as driver) Moto Bus & coach Rail Taxi Ferry Air

Bases: Individual average 456 cases, large urban 194, medium urban 87, small urban 64, rural 111

Given the scale of car and air travel emissions it is worth looking at the patterns underlying these totals. Figure 10 and Figure 11 highlight the differences in emissions levels from car travel between the four area types, emphasising the unequal distribution between individuals, households and area types. However, the Figures also illustrate the similarities in emission profiles amongst the area types. A large proportion of the population produce similar travel and carbon dioxide emissions profiles, whilst a few are responsible for a disproportionately large share of the total.

Figure 10: Personal climate change impacts of CO2tot from car travel for each area type

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81

Observations ranked by emissions level

kg

CO

2to

t p.a

.

Large Urban - Individuals

Medium Urban - Individuals

Small Urban - Individuals

Rural - Individuals

Base: 266 car drivers



Figure 11: Household CO2tot emissions from car travel for each area type, ranked by emissions level

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18,000

20,000

1 6 11 16 21 26 31 36 41 46 51 56 61


kg

CO

2to

t p.a

.

Large Urban - Households

Medium Urban - Households

Small Urban - Households

Rural - Households

Base: 178 car driving households

Emissions for air travel ranked by emissions level follow a similar profile taking the shape of an exponential curve (Figure 12 and Figure 13). Note the “outlier” (off chart) with individual and household emissions totals of 89 and 93 tCO2

tot – i.e. more than 10 times the UK household average for air travel alone!

Figure 12: Personal CO2tot emissions from air travel for each area type including radiative forcing, ranked by emissions level

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116


kg

CO

2to

t p.a

.

Large Urban - Individuals

Medium Urban - Individuals

Small Urban - Individuals

Rural - Individuals

One "outlier" at 89 tCO2eq

Base: 269 flyers



Figure 13: Household CO2eq emissions from air travel for each area type including radiative forcing, ranked by emissions level

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91


kg

CO

2to

t p.a

.

Large Urban - Households

Medium Urban - Households

Small Urban - Households

Rural - Households

One "outlier" at 93 tCO2eq

Base: 176 households with at least one member who flew in 2004

Composition of emissions totals

The total emissions presented above include all relevant sources and impact categories, i.e. tailpipe CO2, upstream fuel lifecycle CO2 and GHG-equivalent non-CO2 emissions. Figure 14 provides a decomposition of average emissions of CO2 per person by emission source and impact category, suggesting that:

• Tailpipe and fuel lifecycle emissions of CO2 from car and air travel are quite similar.

• The radiative forcing of non-CO2 aircraft emissions dominates the overall impact of aircraft emissions.

• Only tailpipe emissions from public transport have any significance when compared to other modes.



Figure 14: Comparison of average CO2 emissions by emission source and impact categories

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Car (as driver) Air Other modes

kg

CO

2 p

.a.

Fuel lifecycle CO2 GHG equivalent

Tailpipe CO2 GHG equivalent or RF

Tailpipe CO2

Base: all 456 responses

Notes:

• The figures for car are from method A, for air from method E and for public transport from national average figures. ‘Other modes’ include bus, coach, minibus, taxi, InterCity rail, regional rail and ferry.

• ‘Tailpipe CO2 GHG equivalent or RF’ represents the GHG equivalent or radiative forcing impact of non-CO2 emissions.

• All rail emissions were allocated to tailpipe CO2, although emissions from electric rail are of course indirect lifecycle emissions.

4.3 Household and individual travel emission profiles

When the analysis of emission profiles moves from the area level to the individual, the scale of the disparities in travel and emissions becomes even clearer. In addition to the continuous distribution of emissions, the results were reduced so as to rank each individual or household according to where they lie in a scale of ‘high’ or ‘low’ polluters, represented by emissions quintiles each containing 20% of respondents that travelled by the respective mode. All modes

The analysis of climate change impacts by mode of travel shows that respondents in the highest emissions quintile accounted for between 46.8% (car) and 74.9% (taxi) of emissions. Overall, 60.6% of emissions were produced from respondents in the highest emissions quintile, but only 0.7% of emissions were generated from the lowest quintile, yielding a high-over-low factor of 90. The corresponding average climate change impacts were 16.6 tCO2

tot per person in the highest quintile, and 0.19 tCO2

tot per person in the highest quintile. Further analysis reveals that the top 10% of the sample release 42.6% and the bottom 10% of the respondents only 0.1% of the CO2

tot resulting from all travel in this study.



Figure 15: Proportions of climate change impacts in terms of CO2tot in each ranked category from ‘low’ to ‘high’, by mode of travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Car Motorcycle Bus &

coach

Taxi Rail Ferry Air All modes

Ind

ivid

ual sh

are

s o

f C

O2to

t

Highest quintile

4th quintile

3rd quintile

2nd quintile

Lowest quintile

Base: 266 (car), 12 (motorcycle), 313 (bus/coach), 210 (taxi), 239 (rail), 68 (ferry), 269 (air)

Notes: Car – method A, air – method E, public transport – national averages The rich dataset and high disaggregation made it possible to analyse the results according to a number of key determinants of emissions for each transport mode, including:

• The influence of socio-economic indicators on emissions, including gender, income, occupation and age

• The influence of journey lengths on car and motorcycle drivers’ emissions

• The influence of cold start emissions on car drivers’ emissions

• The influence of average speeds / road types on car drivers’ emissions

• Local vs. regional/national emissions and load factors for public transport

• The relative importance of day-to-day vs. casual travel emissions for public transport Socio-economic indicators

Further analysis of the ‘high’ vs. ‘low’ ends of the sample distribution provides a means of testing the strength of relationships between emission levels and various socio-economic factors. The findings are illustrative of the extent to which such surveys can be used to elicit predictors of emission levels from any given population or to target policies more effectively. For car travel, the average level of CO2

tot emissions in the high and low quintiles differs by a factor of 15, although this ignores the extreme ends of the distribution revealed above (Figure 10). The ratio for air travel is higher at 23. For public transport (bus/coach, taxi, rail, ferry), this ratio is even higher between 57 (taxi) and 71 (rail).



� Gender

Table 8 shows how gender is disproportionately likely13 to lead to a respondent falling into either the highest or lowest category of CO2

tot emissions. Although there were slightly more women in the sample than men, men are more likely (52%) to fall into the highest impact quintile than women (48%). In particular, men are responsible for a higher than average share for emissions from travel by car (58%), motorcycle (100%) and rail (56%). The gender disproportionality for these modes is even more pronounced in the highest emissions quintile. In contrast, women are slightly more likely to fall into the lowest quintile for car, bus, taxi and air travel. Interestingly, air travel appears to be used equally by both women and men, yet motorbiking is 100% dominated by men.

Table 8: Gender composition of climate change impacts in terms of CO2tot emissions

Mode of travel (base)

Sex Lowest quintile (bottom 20% of cases)

Highest quintile (top 20% of cases)

All cases

Car Female 54% 33% 42% (266 cases) Male 46% 67% 58%

Motorcycle Female 0% 0% 0% (12 cases) Male 100% 100% 100%

Bus & coach Female 58% 50% 53% (313 cases) Male 42% 50% 47%

Taxi Female 53% 31% 38% (210 cases) Male 47% 69% 62%

Rail Female 47% 38% 44% (239 cases) Male 53% 62% 56%

Ferry Female 37% 57% 51% (68 cases) Male 63% 43% 49%

Air Female 53% 50% 52% (269 cases) Male 47% 50% 48%

All modes Female 55% 48% 49% Male 45% 52% 51%

� Income

There is strong evidence that respondents in high income groups (>£40k per year) are more likely to fall into the highest impact quintile than respondents from low income groups (<£10k per year), in particular for ‘all travel’, car, taxi and rail. In terms of emissions from all travel, 27% of the highest earners also rank top, while 74% of the lowest earners rank bottom. Even though the highest earners only comprise 12% of the respondents who supplied details of their income (417 respondents), they comprise 23% of total climate change impacts of CO2

tot at a staggering average of 11.3 CO2tot p.a., earning at least four times as much and producing

on average 3.6 times the annual emissions level of the lowest earners. The modal breakdown is shown in Table 9. To assess the significance of the results, chi-square tests were performed on the actual (frequency in each quintile) and expected (20% of total in each quintile) values. The tests gave a very low value of 1.03E-12 for ‘all travel’, 0.002 for car, 0.012 for bus/coach, 0.001 for rail and 0.11 for air. This suggests that different income levels are likely to have an effect on total (all modes), car and rail travel emissions, while air travel emissions are less likely to be influenced by income.

13 The likeliness of falling into one or the other quintile was derived by calculating, for each socio-economic attribute (e.g. ‘male’, ‘on low income <£10k’), the number of cases falling into a specific quintile. For example for car travel, 18 out of 266/5 (=53.2) cases per quintile, or 33%, were lowest earners falling into the lowest emissions quintile. Or for all travel, 61 out of 434/5 (=86.8) cases per quintile, or 70%, were lowest earners falling into the lowest emissions quintile.



Table 9: Income level composition of climate change impacts in terms of CO2tot emissions (as % share of low and high earning respondents falling into each quintile)

Mode of travel (base1)

Income band Lowest emission quintile

Highest emission quintile

All cases1 (avg. CO2)

Car Lowest earners (<£10k) 35% 15% 25% (257 cases) Highest earners (>40k) 12% 33% 17% Quintile average CO2 355 5,425 2,276

Motorcycle Lowest earners (<£10k) 0% 0% 0% (11 cases) Highest earners (>£40k) 33% 67% 64% Quintile average CO2 80 2,099 633

Bus & coach Lowest earners (<£10k) 33% 39% 40% (297 cases) Highest earners (>£40k) 15% 11% 11% Quintile average CO2 5 322 93

Taxi Lowest earners (<£10k) 36% 20% 37% (202 cases) Highest earners (>£40k) 7% 22% 16% Quintile average CO2 2 115 31

Rail Lowest earners (<£10k) 49% 22% 32% (232 cases) Highest earners (>£40k) 9% 28% 16% Quintile average CO2 10 684 187

Ferry Lowest earners (<£10k) 17% 15% 28% (64 cases) Highest earners (>£40k) 8% 23% 17% Quintile average CO2 9 634 178

Air Lowest earners (<£10k) 31% 23% 32% (260 cases) Highest earners (>£40k) 8% 21% 13% Quintile average CO2 799 18,116 6,257

All modes Lowest earners (<£10k) 74% 20% 39% (417 cases) Highest earners (>£40k) 4% 27% 12% Quintile average CO2 179 16,846 5,534

Notes: 1 Cases providing income information only. Figure 16 suggests that the share of high earners falling into the highest emissions quintile increases dramatically from the 4th to the 5th quintile. Based on the same data, Figure 17 further shows that the proportion of each climate change impact ranking made up of high earners steadily increases as the emissions level increases so that conversely the lowest polluters are also the lowest earners. The trend is mirrored for car (Figure 18) and air (Figure 19) travel of the high and middle earners. However, the relationships are not as clear for air travel of low earners, which may be explained by the relatively high proportion of international students living in Oxfordshire and the abundance of and accessibility to cheap air travel in 2004.



Figure 16: Overall climate change impacts for all travel modes by income levels (in terms of CO2tot emissions)

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

Lowest quintile 2nd quintile 3rd quintile 4th quintile Highest quintile

emissions ranking in quintiles

kg

CO

2to

t p

.a.

Highest earners (>£40k)

£30k - £40k

£20k - £30k

£10k - £20k

Lowest earners (<£10k)

Figure 17: Composition of climate change impacts for all travel modes by income levels (in terms of shares of the number of cases falling into a specific emissions quintile)

0%

20%

40%

60%

80%

100%

Lowest quintile 2nd quintile 3rd quintile 4th quintile Highest quintile


perc

en

tag

e o

f em

issio

ns in

each

in

co

me g

rou

p


£30k - £40k

£20k - £30k

£10k - £20k


Base: 266 (car), 12 (motorcycle), 313 (bus/coach), 210 (taxi), 239 (rail), 68 (ferry), 269 (air)



Figure 18: Composition of CO2tot (car travel only) by income levels

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Lowest

quintile

2nd quintile 3rd quintile 4th quintile Highest

quintile


perc

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ion

s in

ea

ch

in

co

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gro

up


£30k - £40k

£20k - £30k

£10k - £20k


Base: 266 drivers

Figure 19: Composition of CO2tot (air travel only) by income levels

0%

10%

20%

30%

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50%

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Lowest

quintile

2nd quintile 3rd quintile 4th quintile Highest

quintile


pe

rcen

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in

ea

ch

in

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£30k - £40k

£20k - £30k

£10k - £20k


Base: 269 flyers



� Economic activity

Similarly, the proportion of each climate change impact ranking made up of full- and part-time workers steadily increases as the emissions level increases so that conversely the lowest polluters are the non-workers (at school, retired/pensioner, keeping house/carer, at university, looking for work). 77% of those in the top emission quintile were in work, whilst 78% of those in the lowest quintile were not in work (Table 10). Even though the workers comprised 51% of the 433 respondents that supplied information about economic activity, they were responsible for 63% of the total climate change impacts in terms of CO2

tot at an average of 6.8 tCO2tot p.a. and producing on average 1.64 times the annual

emissions level of the non-workers.

Table 10: Composition of climate change impacts in terms of CO2tot emissions by economic activity (as % share of workers and non-workers falling into lowest and highest quintiles)

Mode of travel (base2)

Economic activity1 Lowest emission quintile



Car Workers 52% 85% 68% (266 cases) Non-workers 48% 15% 32% Average emissions 362 5,318 2,270

Motorcycle Workers 67% 100% 92% (12 cases) Non-workers 33% 0% 8% Average emissions 74 1,924 598

Bus & coach Workers 59% 56% 47% (312 cases) Non-workers 41% 44% 53% Average emissions 5 313 91

Taxi Workers 43% 62% 53% (209 cases) Non-workers 57% 38% 47% Average emissions 2 119 32

Rail Workers 46% 81% 58% (238 cases) Non-workers 54% 19% 42% Average emissions 10 708 193

Ferry Workers 57% 85% 55% (67 cases) Non-workers 43% 15% 45% Average emissions 10 601 170

Air Workers 57% 67% 57% (268 cases) Non-workers 43% 33% 43% Average emissions 796 18,287 6,245

All modes Workers 22% 77% 51% (433 cases) Non-workers 78% 23% 49% Average emissions 186 16,682 5,490

Notes: 1 Workers comprise respondents in full-time (5 days/week), part-time (1-4 days/week) and casual work (less than 1 day/week). Non-workers comprise respondents looking for work, in further education, at university, at school, in retirement/pensioner and keeping the house/carer. 2 All cases providing economic activity information.

For rail, in particular, Table 10 reveals that although workers comprised 58% of the total of 196 rail users, they fell more likely into the highest emissions quintile (81%) than non-workers (19%). They were responsible for 76% of the climate change impacts from rail travel at an average of 0.25 tCO2

tot p.a. and producing on average 2.32 times the annual emissions level of the non-workers. This can explained by the relatively high share of rail users (likely to be London commuters) amongst the sample. Similar to the results for income groups, the proportion of each climate change impact ranking made up of full- and part-time workers steadily increases as the emissions level increases so that conversely the lowest polluters are the non-workers (at school, retired/pensioner, keeping house/carer, at university, looking for work).



Here the chi-square tests gave a very low value of 1.27E-11 for ‘all travel’, 1.04E-03 for car, 5.83E-04 for rail, 0.06 for bus/coach and 0.35 for air travel. This suggests that working status and economic activity are likely to have an effect on total (all modes), car, rail and bus/coach travel emissions, while air travel emissions are less likely to be influenced by working status. � Age

Figure 20 provides the climate change impacts in terms of CO2tot by mode and age group, suggesting

that overall age and position in the family lifecycle have a big influence on emissions totals within a population.

Figure 20: CO2tot emissions by mode and age group

0

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Base: all 456 respondents

Further analysis of emissions quintiles reveals that 36-65 year old respondents (the ‘middle ground’ age group) are more likely to fall into the highest impact quintile than any other age group, while conversely respondents in retirement age (>65 years) are most likely to fall into the lowest quintile of emissions. Even though the 36-65 year olds comprised only 42% of the 430 respondents that supplied information about age, they were responsible for 56% of the total climate change impacts in terms of CO2

tot at an average of 7.1 tCO2tot p.a. and producing on average 3.6 times the annual emissions level of

the respondents in retirement age (>65 years). On the other hand, children comprised 7% of the respondents supplying information about age, but were responsible for only 3% of impacts at an average of 2.8 tCO2

tot p.a. (mainly from air travel). The chi-square tests gave a very low value of 6.15E-10 for ‘all travel’, low values of 0.002 for car, 0.001 for rail and 0.003 for air travel. This suggests that age and position in the family lifecycle are likely to have an effect on total (all modes), car, rail and air travel emissions. Other modes do not appear to have such a strong link.



Table 11: Composition of climate change impacts in terms of CO2tot emissions by age group (as % share of respondents in each age group falling into lowest and highest quintiles)

Mode of travel (base)

Age groups Lowest emission quintile



Car Children (<19y) 0% 0% 0% (266 cases) Young folk (19-35y) 15% 21% 22% Middle ground (36-65y) 43% 74% 56% Wise guys (66+y) 43% 6% 22% Average emissions 362 5,318 2,270

Motorcycle Children (<19y) 0% 0% 0% (12 cases) Young folk (19-35y) 0% 33% 8% Middle ground (36-65y) 67% 67% 83% Wise guys (66+y) 33% 0% 8% Average emissions 74 1,924 598

Bus & coach Children (<19y) 5% 5% 7% (309 cases) Young folk (19-35y) 19% 34% 30% Middle ground (36-65y) 56% 47% 41% Wise guys (66+y) 21% 15% 21% Average emissions 5 316 91

Taxi Children (<19y) 2% 0% 2% (206 cases) Young folk (19-35y) 43% 36% 37% Middle ground (36-65y) 41% 56% 45% Wise guys (66+y) 14% 8% 16% Average emissions 2 116 31

Rail Children (<19y) 19% 0% 6% (235 cases) Young folk (19-35y) 31% 54% 40% Middle ground (36-65y) 35% 43% 42% Wise guys (66+y) 15% 2% 12% Average emissions 10 706 193

Ferry Children (<19y) 0% 0% 9% (67 cases) Young folk (19-35y) 36% 38% 27% Middle ground (36-65y) 43% 46% 43% Wise guys (66+y) 21% 15% 21% Average emissions 10 601 170

Air Children (<19y) 7% 4% 7% (265 cases) Young folk (19-35y) 31% 35% 34% Middle ground (36-65y) 39% 62% 43% Wise guys (66+y) 22% 0% 16% Average emissions 805 17,284 6,064

All modes Children (<19y) 11% 2% 7% (430 cases) Young folk (19-35y) 28% 29% 30% Middle ground (36-65y) 22% 67% 42% Wise guys (66+y) 39% 1% 22% Average emissions 187 16,047 5,370

Notes: 1 All cases providing information about age

Journey lengths

National travel trends continue to show an increase in average trip lengths rather than the generation of additional journeys (DfT, 2005). In addition to the direct implications of this ‘trip length surge’ on the increased distances travelled by car, changes in average journey lengths are significant in terms of emissions. Journey lengths determine the fraction of total distance undertaken ‘cold’ as well as having a less direct influence on the road types used and average speeds. In this study there were no specific



questions about trip length. Instead distances were apportioned to categories of journey length: ‘short’ (<2miles, or <3.2km), ‘middle’ (2 to 20miles, or 3.2 to 32km) and ‘long’ (>20miles, >32km) distances14. A significant proportion of total distance (12%) was generated by short trips and these are the most susceptible to mode switching away from motorised private vehicles. Data from the NTS confirm this trend. In 2003, 23% of car trips (as driver) were less than 2 miles (3.2km) in length, corresponding to about 4% of short car miles (national average)15. Cold starts emissions per driver

Cold starts have a detrimental effect on fuel consumption and emissions, particularly when combined with the ineffectiveness of a catalytic converter during this period. For method A, the ‘cold miles’ were estimated by assuming average trip lengths in each of the above distance classes (from NTS data) and applying the European average figure of 2.1 miles (3.4 km) as the initial portion of each trip undertaken without a fully warmed engine.16 This effectively meant that 100% of the short distance mileages, 30% of the middle distance mileages and 4% of the long distance mileages were ‘cold’. The result was that 28% of total car miles were attributable to cold miles, or in emissions terms, 8% of tailpipe emissions were excess emissions due to cold starts. The cold miles share is slightly less than the European-wide aggregated population average of 34% estimated by Eggleston et al (1991), but an understandable result for a non-metropolitan location where average journeys are longer. On an individual basis in this survey, as miles increased, the proportion of cold miles fell, confirming that any increase in mileage is generated by increased journey lengths and not more trips. This is illustrated in Figure 21, providing a case-by-case comparison of tailpipe CO2 emissions from driving under hot exhaust and cold start conditions.

14 Respondents were asked to estimate shares of their personal driving for each of these categories. Although this method is potentially uncertain, the validation results in Chapter 3 showed a relatively good match with published sources, in particular at the area level. 15 Please note that the figures used in this study are expressed as a proportion of total miles travelled and not number of trips. It must be borne in mind that some respondents may have given their answers in terms of trips and not miles. 16 In contrast, the official emissions data used in method B already include cold start emissions as part of the urban driving test cycle.



Figure 21: Direct hot and cold excess exhaust CO2 emissions from cars

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observations ranked in order of emissions level

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CO2 hot exhaust

Base: 266 drivers

Average speed / road type

Three road types were used in methods A and B of the car model as synonymous for certain driving patterns and speeds. The respondents’ estimates of the share of total miles by road type/speed and trip distance were compared with regional figures from the NTS. For instance, respondents travelled less on urban roads (29%) than the national average (39%). This makes sense as Oxfordshire is a lot less urban than large urban areas (London in particular) that tend to dominate national averages. Motorway share was higher (35% compared with 18%), however respondents may have taken dual-carriageways as motorways (e.g. A34). Rural (37% compared with 41%) about the same than national figures (DfT, 2005). For method A, both distance travelled and emissions levels were broken down into their respective average speed components. As shown in Figure 22, this reflects the U-shaped speed-emissions curves for CO2 emissions, with the minimum of CO2 emissions per vehicle-km at around 70kph (or close to average rural speeds of 77kph). In terms of individual emissions profiles, particular respondents can once again be identified as having detrimental effects due to the share of their total distance spent travelling at high speeds. As would be expected, these tend to be those people travelling longer distances and with longer trips spent on faster roads. Figure 23 highlights these individuals whilst showing that ‘cold miles’, when contrasted to the higher speed categories, remain a relatively constant proportion of total carbon dioxide emissions generation.



Figure 22: Composition of car travel distance and direct CO2 emissions by average speed / road categories

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Figure 23: Direct CO2 emissions from car travel by speed category per driver

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Base: 266 drivers

The emission factors from which the above analysis of speed has been generated are based on certain assumptions about average speeds on given road types (see Footnote 5). In order to test the tool for



sensitivity to changes in assumptions about average speeds, the emissions factors used for motorway driving were varied for speeds between 100kph (62mph, low estimate) and 130kph (81mph, high estimate). The resulting direct emission levels from cars, that is tailpipe emissions across all distance travelled, were 7% higher (high estimate) and 4% lower (low estimate) than the default case (central estimate based on 113kph average speed). This simple calculation suggests that enforcing motorway speeds to the already existing legal limit could save up to 4% of CO2 from all car travel. Note that the speed-emissions curves used by the NAEI are currently under review as part of on-going DfT and EU work and may change in the near future. Local vs. national emissions and load factors for public transport

Overall, total and average emissions from public transport were low in comparison with other modes (car and air in particular). When looking exclusively at public transport, average emissions per household and individual were slightly higher when using local emissions and load factors rather than national average figures (Figure 24 and Figure 25). This was mainly due to different emissions rates and load factors for local buses and regional rail services. In particular, the local bus companies based in Oxford operate a relatively modern bus fleet, many of which are low floor with space for pushchairs and wheelchairs (lower capacity), some with air conditioning (higher emissions factors per km) and larger engines than the national average (Go-Ahead, 2002). Hence their average energy use and CO2 emissions per passenger-km was estimated at about 30% higher than the national average. Similarly, rail travel around Oxford is 100% diesel, with 24% higher average CO2 emissions than electric rail (based on 2004 generation mix).

Figure 24: Individual and household average climate change impacts of CO2tot from public transport use – comparison of nationally vs. locally derived emissions totals

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Bus & coach (national)Bus & coach (local)Taxi (national)Taxi (local)Rail (national)Rail (local)

Base: ‘Individual (all)’ = 456; ‘Individual (users only)’ = 313 for bus & coach, 210 for taxi, 239 for rail. ‘Household (all)’ = 278; ‘Household (users only)’ = 212 for bus & coach, 151 for taxi, 176 for rail.



The relative importance of day-to-day vs. casual travel emissions for public transport

Average direct emissions per individual (base: 456 respondents) were higher for casual travel than for day-to-day travel (Figure 25), notably for travel by express coach and ferry. Note that for day-to-day travel, respondents were asked to indicate whether they normally travelled at peak or off-peak times. This had an effect on occupancy rates and hence average emissions per passenger-km (not shown).

Figure 25: Individual average emissions of tailpipe CO2ult from public transport use – comparison of day-to-day vs. casual and nationally vs. locally derived emissions

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4.4 Aggregation to higher levels

Further to the local level analysis of the results, an attempt was made to aggregate the sample-based figures to higher levels such as County and Country. This aggregation involved an extrapolation of the sample figures with population figures for residents of 6 years of age or older, each corrected by socio-economic variations (sex, age, economic activity) in the make-up of the respective populations.17 The results are shown in Table 12, suggesting a good match with published sources (National Statistics, 2005b) for some modes, in particular for the main modes car and air, but an overestimation over published sources for travel by bus, rail and bicycle. Air miles were overestimated by about 20%; it was concluded that this was due to the respondents benefiting from higher than average accessibility to major international airports, comprising a higher share of foreign students and slightly different methodological approaches (e.g. detour miles per flight leg).

17 The main caveat of this approach is the omission of correcting for income levels, as income plays an important role in determining overall travel activity and its climate change impacts (see previous Section). Unfortunately, this information is not available from 2001 Census data.



Table 12: Distance and tailpipe CO2ult emissions of non-business travel – aggregation to higher levels

Miles (billion)

Car 1 Moto Bus 2 Taxi Rail Ferry Air Cycle Walk

Oxfordshire 2.1 0.0 0.5 0.0 0.7 0.0 3.6 0.2 0.1

England 159.8 2.7 35.8 1.9 51.4 1.5 252.9 14.6 9.8

Wales 8.9 0.1 2.0 0.1 2.6 0.1 13.6 0.8 0.5

Scotland 16.6 0.3 3.9 0.2 5.4 0.2 27.1 1.6 1.0

NI 4.8 0.1 1.1 0.1 1.6 0.0 7.9 0.5 0.3

UK 190.1 3.2 42.8 2.2 61.0 1.8 301.5 17.4 11.6

GB 185.2 3.1 41.6 2.2 59.4 1.8 293.6 17.0 11.3

GB (Nat. Stat.)3 182.6 2 to 6 4 19.4 2.3 20.2 1.9 241.4 2.1 11.5

CO2ult + CO2eq (Million tonnes)

Oxfordshire 0.6 0.0 0.0 0.0 0.1 0.0 1.9 - -

England 48.5 0.5 2.2 0.5 3.6 0.9 136.5 - -

Wales 2.7 0.0 0.1 0.0 0.2 0.1 7.3 - -

Scotland 5.0 0.1 0.2 0.1 0.4 0.1 14.7 - -

NI 1.5 0.0 0.1 0.0 0.1 0.0 4.3 - -

UK 58 0.6 2.6 0.6 4.3 1.1 163 - -

GB 56 0.6 2.5 0.6 4.1 1.1 158 - -

GB (Nat. Stat.) 61 5 0.4 5 3.2 5 1.5 5 2.3 5 1.3 5 160 6 - -

Sources: Census 2001, National Statistics (2005b), National Statistics (2004), DfT (2005) and own calculations

Notes: 1 Car as driver only. 2 Includes bus, express coach and minibus. 3 Calculated by multiplying non-business passenger-miles for each mode taken from the NTS (National Statistics, 2005a) with GB population for 2004. 4 Depending on source: ‘2’ from National Statistics (2005b), ‘6’ from DfT (2005). 5 Taken from National Statistics (2004). 6 Steps involved: 31.8 Mt CO2 obtained from DfT (2005); multiply with (2*2.9) to account for return trip mileage and RF; discounted business mileage (13%); this finally gives 160 Mt CO2

tot.



5 DISCUSSION OF THE RESULTS

Integrated travel emission profiles based on two versions of self-completion questionnaires have been successfully developed and tested in a case study application. They have produced a rich dataset, in particular for car and air travel, with coherent results at a variety of levels. More generally, elements of travel behaviour specific to geographical location have been confirmed. These include the higher than national average annual distance travelled by car drivers in small urban and rural areas and the higher car ownership levels. Equally, people living in medium and large urban areas tend to travel relatively more by air than residents living in rural and small urban areas. 5.1 Key Results

With respect to the climate change impact of CO2tot, the top 10% of the sample are responsible for

42.6% and the bottom 10% of the respondents only 0.1% of the CO2tot resulting from all travel in this

study, with the top quintile of polluters 90 times more polluting than the bottom quintile. This ratio was 15 for car travel alone, and 23 for air. Whilst this revelation is important for the realistic setting of targets and the realisation that intervention must be particularly aimed at a minority of the population in order to be effective, it is the composition of both the specific travel related causes of these highest emissions levels and the people producing them that are the most interesting and useful pieces of evidence to be disclosed from this work. As would be expected, the amount of travel, choice of mode and, to some extent, the choice of technology within private modes are the greatest causal factors in emissions generation. Carbon dioxide in particular is most closely related to distance travelled and almost all those respondents falling in the highest distance quintiles were also the worst offenders with respect to CO2

tot. In terms of variation by socio-economic factors, the most conclusive evidence from this study has been the relationship between income and emissions. About a quarter of the highest quintile of earners in this survey also ranked highest in terms of emissions of CO2

tot, while more than two thirds of the bottom quintile of earners ranked lowest. The integrated emissions profiles and auditing approach could be used in mainly two ways: 1. For developing policy strategies and assessing impacts of policies and measures to reduce negative

environmental impacts of travel activity, and; 2. For awareness raising, informing, advising and giving feedback on individual travel behaviour and

environmental impact. The distinguishing features of the integrated travel emissions profile tool lie in the principles adopted for data extraction and how the use of disaggregated data at the household, individual and higher levels could aid, and in some cases transform, the processes of policy formation and implementation. The two roles are explored below. 1. A tool for strategy development and impact appraisal

The results of this study concur with the view of the scientific and policy communities that mitigation of climate change in the transport sector will require a reduction in demand for travel, in particular for the most carbon-intensive modes, i.e. car and air (see e.g. Banister and Hickman, 2005; Skinner et al, 2004). This changing policy culture has created a need for new sources of data and new tools and procedures for retrieving this information, the more so at the private and household levels. Though existing sources such as the NTS and Census data can be broken down and applied at the local level, there is a need for new data sources to capture as much information as possible at the disaggregate level.



� Policy implications of the results

Socio-economic, location-specific and cross-modal group data for individuals and households rarely exist together. The travel emission profiles piloted here are an example of the type of flexible, multi-output yet potentially policy-specific methods which need to be developed and employed nationally if the interpretation and influencing of behaviour is to become a serious and effective component of policy. The results of the emissions calculation exercise were presented in terms of comparisons between the geographical locations, the travelling individuals and their corresponding households. This has highlighted the following:

• Travel by air and car dominate overall emissions levels and climate change impacts, hence climate change mitigation policy should focus primarily on these modes.

• Particular attention should be paid to air which can account for 70% of passenger transport climate change impacts at the individual level.

• Climate change impacts are closely related to distance travelled. For each mode of travel, it is a minority of users, travelling comparatively long distances, who account for the differences between high and low quintiles. To reduce impacts it is therefore vital to address long-distance travel by car and air.

• Lifecycle emissions of GHG are moderately significant in climate change terms. As they are directly linked with hydrocarbon fuel use, any policy to switch away from hydrocarbon fuels will reduce GHG impacts of lifecycle emissions.

• Excess emissions from cold starts of cars are not significant in climate change terms. However, they are significant in local air pollution terms; hence any policy to address GHG emissions from cars should be designed not to sacrifice local air quality. (The often quoted case is lean burning in internal combustion and aircraft jet engines, which reduces fuel consumption but increases NOX emissions – in itself a GHG pre-cursor and local/regional air pollutant.)

• In location terms, car travel and emissions are highest from residents living in rural areas while air mileages and emissions are highest for residents living in large urban areas (Oxford City). Disproportionally high climate change burdens from all travel come from residents living in large urban areas, in particular from air travel.18

• There is clear evidence that price decreases affect the demand for flying, in particular for non-business trips (Brons et al, 2002). Empirical research by Dargay and Hanly (2001) suggests that about 40% of the increase in leisure air travel by UK residents between 1990 and 1998 was due to real reductions in the cost of flying. However, it is not clear at present whether the same behaviour can be observed if the reverse happened, i.e. prices would go up considerably. Hence it is inconclusive whether economic measures – which make flying more expensive – could provide an effective means of demand restraint. Further analysis of the data collected in this work could improve our knowledge of the socio-economic fabric underlying the fliers and non-fliers. As demonstrated so far, the link between income and flying is comparatively weak, indicating that prices would have to go up considerably to have a restraining effect on demand.

• The results of this work suggest that even substantial increases in fuel tax or distance-based charges are likely to be equitable. Respondents in the highest impact decile were primarily residents in their 30s and 40s, in full-time work or at university and earning £30k or more. 45 out of 46 of these respondents flew regularly, clocking up an average of 33,566 air-miles per year (646 miles per week). 33 out of 46 drove a car an average of 8,388 driver-miles per year – almost 2.5 times the national average (DfT, 2005).

18 Of the highest 10% emitters, 63% come from large urban areas (43% of the sample). In contrast, of the lowest 10% emitters, 33% come from large urban areas. Interestingly, 33% of the lowest emitters come from medium urban areas (19% of the sample). The 36-50 year olds make up the highest share (46%) of the highest 10% of emitters (compared to 21% for all respondents), whereas children (28% compared to 7%) and people older than 75 years (20% compared to 8%) fell more likely in the lowest decile.



• In contrast, respondents in the lowest impact decile were primarily children at school age and retired residents older than 75 years, on low income (<£10k) and with lower than average access to a car. Only one respondent in this latter category made (little) use of a car, and none flew. Again, these residents would not necessarily be affected even by substantial increases in fuel tax or distance-based charges.

• The results further suggest that enforcing motorway speeds to the already existing legal limit could save up to 4% of CO2 from all car travel, well in line with figures estimated in other work (e.g. RCEP, 1994; Anable et al, 2006).

• The climate change burden from flying alone was 19.2 tCO2tot per year, or 1.6 tCO2

tot per month. This has huge implications for any future carbon allowance scheme, where allowances will be reduced year on year to a “safe” level in terms of climate change (see e.g. Hillman and Fawcett, 2005 for explanation). The above burden implies that the top 10% of the population may use up most of their future carbon allowance in a month.

• It has been widely recognised that the scale of climate change impacts of international air travel forces it on the climate policy agenda ever more. It will be a failure of international community to ignore the scale of the problem and continue to exclude international air travel from post-Kyoto agreements and treaties.

A key result of this work is that the shape of the curve ranking respondents by their emissions level is surprisingly similar when compared between modes, location and unit of analysis (individual, household). This suggests that the majority of the residents in the survey areas exhibit similar travel patterns. It is a minority of residents travelling comparatively long distances who cause the sharp rise in the curves at high emissions levels. Further analysis then can begin to associate these drivers with particular income, speed and vehicle characteristics as attempted above. This leads to an important measurement issue – these gross polluters must be accurately sampled through targeted measurement/surveys. � Impact studies, longitudinal studies and forecasting potential

Travel activity surveys have previously been used to assess the modal shift and other key transport indicators from interventions aimed at changing travel behaviour (Brand et al, 2004). However, most of this work is limited to the transport level, not taking the additional steps required to assess emissions impacts of these interventions (DfT, 2004a; Cairns et al, 2004). The iTEP approach has the potential to provide such an assessment, in particular for area-wide studies but also at the individual level (see below). However, this would require careful design of the survey in terms of sample size and geographical areas (impact group vs. control group, etc.). An important application would be to assess the climate change and emissions impacts of interventions aimed at changing people’s travel behaviour – often called ‘soft measures’ or ‘smarter choices’ (Anable et al, 2005). Many of the interventions described in the soft measures report by Cairns et al (2004) are already candidates for such an assessment, in particular Individualised Travel Marketing© projects such as the DfT Sustainable Travel Demonstration Towns (TravelSmart: Sustrans, 2005) as well as Travel Blending© projects (Rose and Ampt, 2001). However, the latter approaches only provide day-to-day travel activity data obtained from travel diary surveys, therefore additional data would need to be collected on less regular, casual travel such as annual holidays, visiting family and friends and air travel. Other elements of this work would need to be incorporated, e.g. survey questions on any private vehicle, annual mileages and household income. If repeated year on year with the same households, the auditing approach has the potential for projecting and monitor impacts on particular sub-groups, for instance by age, economic activity or income. Once again, there is a need for data sources such as this to measure the effectiveness of policies, identify changing needs and identify specific trends.



Furthermore, travel and emissions forecasts for car travel are projecting increases in car ownership and distance travelled, particularly those created through longer car journeys (DfT, 2004; CfIT, 2003). Nevertheless, travel growth will not take effect equally amongst all sectors of the population, nor across all geographic regions, nor for all journey types (leisure, commuting, shopping). Detailed ‘end-use’ models based on survey data such as that performed here could measure travel at the source of consumption and may therefore be better able to explain, predict, and modify travel behaviour. An end-use based travel and emissions model may make lower and arguably more realistic projections about travel growth than a purely econometric model employing changes in overall income levels and price as the main forecasting indicators. The more detailed auditing of end use method could be used to investigate the marginal changes in price and income among socio-demographic groups more specifically. There is an opportunity to integrate end-use energy and transport modelling into a domestic end-use model, covering all personal activities consuming energy and building on DECADE (see ECU, 1995, 1997) and ERAD (Larsen and Nesbakken, 2004). There is an opportunity to integrate end-use energy and transport modelling into a domestic end-use model, covering all personal activities including household energy use and personal travel. This would allow to assess in detail the household and area-wide impacts of interventions covering both areas of energy consumption such as tele-working and tele-shopping, which may or may not have positive impacts on overall emissions from domestic energy use and travel. 2. A tool for awareness raising and providing advice to households and individuals

At the level of the individual and household, the iTEP approach can be used to raise awareness about climate change impacts and deliver advice and feedback on impact levels and any savings on a year-by-year basis. Feedback from the data is (in theory) the key to changing knowledge, attitudes and behaviour. Increasing knowledge of emissions and awareness of the effects of emissions, is different from, and preliminary to, delivering advice on how to reduce impacts. The context in which information is delivered and received is important. People are more predisposed to receive information at some moments rather than others, and some links can be exploited through careful targeting. Travel and emissions audits could be delivered in several ways:

• Individualised Travel Marketing© and Travel Belding© projects covering different geographical areas;

• As part of national and local travel awareness campaigns such as ‘Travelwise’;

• At the same time as an MoT test and general maintenance programmes for newer vehicles when people are in receipt of data specifically about their car, and when much of the information necessary for the type of analysis used in this study is easily available;

• Through local Energy Efficiency Advice Centres;

• Online via Home Energy Surveys. Feedback on personal and household fuel consumption, emissions, costs and any savings based on the results of the questionnaire could be presented to people in comparative form. This would:

• Inform each individual or household as to how they compare with average emissions levels nationally, in their area, by household size/composition, age group and occupation (benchmarking). One way to do this is by highlighting individuals and households on the emissions ranking curves presented above, and presenting averages as horizontal lines;

• Demonstrate how much better or worse their travel emissions profile is compared to others nationally and/or in the area;

• Notify them if they fall into the highest or lowest emissions quintiles;

• Provide non-contradictory advice on how to reduce impacts, emissions and, for private cars and motorcycles, running costs. For instance, individuals could be informed on how much CO2

tot they would have saved if they had not flown twice to Spain in the previous year, and compare this



information other emissions levels (e.g. the average CO2tot of driving a car for one year, the average

number of washing machine cycles).

• To make this as clear and simple as possible, this could be done through an emissions level labelling scheme, which effectively gives the range of emissions achieved by similar households (A = lowest quintile, …, E = highest quintile).

To be most effective, awareness campaigns should be targeted. Knowing where to channel scarce resources for such campaigns requires initial research to exploit peoples’ concerns and to identify those most ‘susceptible’ to change (Curtis and Headicar 1997). This could mean targeting the most polluting users (highest quintile), certain socio-economic groups known to “care about the environment”, or certain trip purposes (e.g. non-work car, leisure air trips). 5.2 The main issues arising from this work

In light of these potential and important uses of the integrated travel emissions methodology and tool, the methods used in this study are reviewed below, including a discussion of how they could be improved or targeted for each of the perceived uses, in terms of aspects of data collection, interpretation, aggregation, analysis and forecasting. The main issues were:

• Which method is appropriate for each mode? The disaggregate emissions factor method (A) is recommended for car and motorcycle travel, in particular for Local Authority strategy development, as it uses the same database of emissions factors (NAEI) than for the UK Air Quality Strategy process. For air travel, the distance based method (E) is likely to produce the most accurate results, in particular at the individual level. For awareness raising campaigns, the duration-based Method F can provide a cost-effective alternative. Some questions on the respective car and air travel forms are redundant if one or the other method is chosen, thus reducing completion time.

• Should public transport be included? Public transport is a key alternative to medium and long distance car trips, whilst 21% of respondents in this survey did not travel by car or plane, but are potential users of public transport. On the other hand, for the purpose of household or personal carbon audits, public transport can arguably be excluded given the relatively low overall impact of public transport modes (4% of CO2

tot, see Figure 8), the relatively high uncertainty in the actual emissions levels and occupancy rates, and the potentially high number of trips/transactions per year. To reduce uncertainty, substantial investment into IT and infrastructure (Smart cards) would be required. This argument is supported by a more detailed discussion in a recent paper by the UK Energy Research Centre (Bottrill, 2006).

• Cost-effectiveness. It took respondents between 5 and 45 minutes to complete the survey19. At the lower end (5-15 minutes) this can be considered ‘acceptable’ to residents and ‘typical’ for a travel survey. For some respondents, however, completion of the questionnaire clearly took too much time, as they had to fill in up to 30 questions, some of which requiring respondents to dig out the two most recent MoT certificates and recall every non-business flight (and flight leg) over a 12-month period (in Annex A of the Final Report: Brand et al, 2006). The other downside of a longer questionnaire is a relatively low response rate of about 20%. The benefit of this approach, however, is the wealth and detail of data that would not otherwise have been possible. If a travel emissions audit became a government backed scheme, or even compulsory like a MOT for car owners of cars older than 3 years, response rates would probably increase.

• Resources used, and room for streamlining. Apart from minor production costs20, a major resource factor was the time taken to code and prepare the data. All paper-and-pen responses had to be coded manually. Also, intermediate data for car (method B) and air travel such as the official

19 Survey user login and submission data provided these completion duration figures; no duration figures were collected in the paper-and-pen survey. The lower end was generally for respondents who didn’t drive or fly, while the upper end was for respondents with multiple car use and more than 2 return flights. 20 The “household survey pack” cost about £2 per to produce, distribute and return by SAE.



emissions factors (for each vehicle make and model) and flight distance (for each flight leg – 1,468 in total, or more than three per individual response) had to be coded manually, taking up more time than originally anticipated. By automating some data coding and streamlining the methodology (i.e. only include the methods and questions that have been recommended) the time taken for coding and preparation could be reduced by about half. In this sense, the web-based version of the survey is the way forward as the respondent is navigated through the questionnaire, avoiding redundant questions, thus reducing the time to complete the survey. All data are automatically coded into a database on the web-server, with easy access for researchers to process and analyse the data. It remains to be seen if the scale of a comprehensive survey such as this developed here is appropriate for online deployment.

• The appropriate unit of analysis. Two options for the unit of analysis have emerged from this and previous work by Anable et al (1997): the household and the individual. The household is an important decision-making group. Its members share the same location, have the same socio-economic status, common experiences and history, and some shared values. The household will clearly be the appropriate unit for household-based audits of energy use, for example for any Domestic Tradable Quota scheme (Anderson and Starkey, 2004) or a future “Household Energy and Transport Audit”. The individual, on the other hand, should be the focus for awareness campaigns as travel choices are largely personal choices and not so much household based. The individual emissions audit could feed into any future Personal Carbon Allowance scheme, as proposed by Hillman and Fawcett (2005). However, there will be difficulties in allocating emissions between household members, in particular for car travel. Anable et al (1997) provided a detailed discussion on this contentious issue; hence it is not taken up further here.

• Include children? If yes, who is responsible for their climate change impacts? In contrast to the previous TEP work, this study included children aged 6 to 18 years. The lower limit was chosen so as to include children who potentially travel on their own and make, to some extent at least, their own travel choices. Some children travel independently from their parents on public transport and by air. So they should be included in an audit. Should they be responsible for their emissions? This could be argued both ways, and the answer depends on objectives of the audit and any policy needs. In this work, the emissions generated by children were presented separately to show their relative travel and emissions profiles.

• Air travel and its climate change impacts. The use of radiative forcing as a measure of climate change impact in this study is seen as appropriate since models have shown that the change in globally averaged surface temperatures is usually approximately proportional to radiative forcing (RCEP, 2002). However, when comparing the climate change impacts of aviation (using the ‘instantaneous’ RF approach) with non-aviation modes (using the GWP approach implying a certain timeframe – here 100 years) the following limitation should be borne in mind. There is no direct way of assigning a value to the radiative forcing effect of introducing a tonne of CO2 into the atmosphere, as radiative forcing is the change in energy balance between two different points in time, and is affected by the changing profile of emissions and sink strength in that period. Between two timeframes, greenhouse gases have been cumulatively emitted to the atmosphere, and also removed by sinks. However, it is robust to think of average values, and to attribute the change in radiative forcing over a year to the emissions in that year. For example, in 2004, a certain amount of greenhouse gas emissions were introduced into the atmosphere (and some historical emissions removed), which, overall, resulted in a specific increase in radiative forcing. If the total sum of those emissions was known, together with the overall value for the radiative forcing that occurred in that year, it would be possible to work out the average amount of radiative forcing that each unit of emission was responsible for. This data is not readily available, so, in order to examine aviation’s relative contribution to radiative forcing in 2004, I assigned a value of 3 for travel above 30,000ft over and above the effect of CO2 alone (IPCC, 2001).21

21 Note the value of 30,000ft is an average value, below which it is less likely that aircraft emissions form contrails. As well as the seasonal variation in atmospheric conditions, which would require a general ceiling on flight altitudes (summer: 31,000



• Return trip or outbound only? The results on air travel emissions presented in this work include the whole trip (single or outbound and return). In contrast, environmental impact studies at the national level either only account for domestic air travel (e.g. DfT, 2005) or the outbound leg of the journey and allocate emissions of the return trip to the country of return trip origin (e.g. National Statistics, 2004). For individual and household based travel emissions profiling, however, both directions should be accounted for as only this truly reflects the household’s or individual’s impacts on climate change.

• Is this not covered already by other travel surveys? No, not quite. The main and most prominent alternative travel survey that could form the basis for an assessment of individual and household impacts on climate change is the National Travel Survey. It employs one week travel diaries to capture day-to-day travel, with additional questions on long-distance, casual travel. However, it does not cover annual mileage of a car (which may differ greatly from weekly mileage times an annualisation factor), and long-distance trips are recorded only for an extra three week period preceding the travel diary week. Also, only trips within Great Britain are included in the NTS, thus excluding all international air travel. From this it is clear that the NTS cannot provide the data needed for travel emissions profiling.

feet, winter: 24,000 feet), there are also significant day-to-day variations, so any contrail reduction strategy would work better if it were reactive on a daily basis.



6 CONCLUSIONS

The rich dataset enables a great deal more analysis work to be done, even without further data collection. Further funding will be sought as an extension to the ESRC contract or from alternative funding sources. In conclusion, the following areas of further research have been identified: 1. Consolidation of methods. The tool employed a number of alternative methods and calculation

techniques, some of which have been found to be either to inaccurate or too resource consuming. Further work could streamline the methods and focus on the one or two methods that have proved to be most promising and accurate in providing iTEP at all levels of analysis.

2. Tool streamlining and automation. As some elements of the process of data collection, coding and analysis were relatively time-consuming, the tool could be improved by, for example, developing and/or purchasing an air distance/duration database that can be linked to the spreadsheet tool (rather than manual coding and calculation). Similarly, it was thought that the car emissions calculation method (method B, the ‘official’ method) should be developed further. If so, a major improvement in terms of cost-effectiveness would be to integrate the VCA car emissions database into the iTEP tool, saving days of data coding and collation.

3. Collaborative work with Local Authorities and government agencies. The travel emissions tool developed here took 3 pages per head of household, plus 4 to 6 pages per household member (depending on the travel modes used). This could be modified: (i) by a local authority to develop a local transport strategy. A simpler version without the most detailed address-specific level of data collection; and (ii) by the Vehicle Certification Agency, linking the car results to an MOT certificate emission test results. This could take emissions data electronically from test equipment fitted on the vehicle, as at the moment, together with the VCA/SMMT database of vehicle test figures from on car efficiency and mileage data from the MOT certificates or odometer. This detail would be needed to give an official rating or certificate.

4. Incorporating accessibility into travel emissions profiling. Settlement size together with the availability of local facilities, services and employment, are the key determinants of travel and energy consumption (Banister, 1993). This conclusion by Banister could be tested and scrutinised by linking household location data and the Accession accessibility planning tools already used by some LAs.

5. Extension to all personal carbon emissions. From a climate change and personal energy use perspective, the natural next step would be to develop a sufficiently accurate and detailed tool to assess emissions from all household activities at the personal, household and higher levels, thus integrating travel and domestic energy use (see Section 4.5). It is envisaged that a proposal will be made along these lines in due course. One interesting application of such a tool would be to accurately assess the ‘before’ and ‘after’ effects of home working amongst large scale employers on overall carbon emissions. In addition, staff of the UKERC Demand Reduction Theme at Oxford University are currently developing a detailed web-based household and individual carbon calculator, which will build on the experience gained from this work.

6. Developing a predictive model for household based transport and domestic energy use. The basis of the current work has been retrospective. However, if a series were to be collated over the years and trends extrapolated in the same way as end-use models mentioned above, the results could be prospective and used as a time series for monitoring purposes. Trends include patterns of ownership and use for selected socio-economic groups in the population. By monitoring the same households over time, it would be possible to establish which incentives and policy instruments have the most effect in exploiting the susceptibilities which exist amongst a significant minority in most localities towards reducing the demand for travel.



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ANNEX A ASSUMPTIONS AND CONVERSION FACTORS USED

A.1 Annualisation factors

The following annualisation factors were used to derive emissions from day-to-day travel activity and fuel use/purchase data:

No Unit Comment

300 days per year (less than full year to account for holidays, days off sick, etc.)

43 weeks per year (less than full year to account for holidays, days off sick, etc.)

12 months per year

A.2 Fuel Characteristics and Lifecycle Emissions Factors

Fuel type petrol diesel kerosene heavy fuel oil

Sources

avg price in 2004 (£/litre) 1 0.816 0.844 DfT (2005)

density (kgFuel/litreFuel) 0.745 0.832 0.800 0.970 JRC (2003)

energy content (MJ/kg) 43.2 43.1 42.1 40.5 JRC (2003)

carbon content (kgC/kgFuel) 0.869 0.856 0.860 0.890

ultimate CO2 content (kgCO2/kgFuel) 3.183 3.138 3.150 3.260

carbon content (kgC/litreFuel) 0.647 0.712 0.688 0.863

carbon content (kgCO2/litreFuel) 2.372 2.610 2.520 3.162

GHG lifecycle emissions (A) (gCO2eq/km) 27.0 26.0 JRC (2003)

GHG lifecycle emissions (gCO2eq/MJf) 12.5 14.3 14.5 6.7 JRC (2003)

GHG lifecycle emissions (B) (kgCO2eq/litre of fuel)

0.402 0.513 0.488 0.261

GHG lifecycle emissions (B) (kgCO2eq/kg of fuel)

0.540 0.616 0.610 0.269

Notes: 1 average of April 2004 and April 2005 prices A.3 Climate Impact Factors

RFI 3 e.g. IPCC (2001), DTI (2002), RCEP (2002)

GWP CH4 23 IPCC (2001)

GWP N2O 296 IPCC (2001)



A.4 Assumptions for analysis of errors and uncertainty

Cars & motorcycles Uncertainty factor

Comments

MOT uncertainty 2.5% Assumed non-zero value due to date mismatches and false meter-readings

REG uncertainty 5%

"personal records" uncertainty 5%

"about right" uncertainty 10%

"pure guesswork" uncertainty 25%

Work share uncertainty 10%

Road type/speed share uncertainty 10%

Distance class share uncertainty 10%

e-factor uncertainty, method A 10% Average uncertainty in emissions band e.g. within small petrol car

e-factor uncertainty, method B 5% Assumed non-zero uncertainty due to limitations of the official testing method

Fuel purchase uncertainty, method C 33% This is mostly guesswork, therefore higher uncertainty

Fuel use uncertainty, method D 33% This is mostly guesswork, therefore higher uncertainty

Bus, taxi and rail

Annualisation uncertainty 5% +/- 2 weeks based on 43 weeks a year

Weekly miles uncertainty, local 33% = half the bandwidth of the miles ranges

Weekly miles uncertainty, regional 19% = half the bandwidth of the miles ranges

Irregular miles uncertainty 34% = half the bandwidth of the miles ranges

Load factor uncertainty, Bus 25%

Load factor uncertainty, Rail 25%

Load factor uncertainty, Taxi 0%

Load factor uncertainty, Ferry 25%

Air

Detour factor uncertainty (method E and G) 31.1 miles (absolute, not %)

Stated flight duration uncertainty 10% e.g. 1 hour on a 10 hour flight

CO2 emissions factor uncertainty (method E) 10%

Aircraft capacity uncertainty 25%

Aircraft cabin factor uncertainty 10%

Aircraft average speed uncertainty 10%

RF index uncertainty 30% ‘Bandwidth’ of the 2 to 4 range

Take-off/landing fuel consumption uncertainty, methods F/G

29% (derived from other uncertainty factors)

Cruising fuel consumption uncertainty, methods F/G 30% (derived from other uncertainty factors)

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