A scenario analysis of CO2 emission trends from car travel:

Great Britain 2000–2030

Tae-Hyeong Kwon*

The Korea Transport Institute, 2311, Daehwa-dong, Ilsan-gu, Goyang-si, Gyeonggi-do 411-701, South Korea

Received 24 March 2004; revised 4 January 2005; accepted 19 January 2005

Available online 24 February 2005

Abstract

This study projects CO2 emissions from car travel in Great Britain over the period of 2000–2030, by building various scenarios based on

the ‘IZPAT’ identity. The results reveal the difficulty of achieving a modest CO2 target set in this study by changing either affluence (A)

factor or technology (T) factor alone. In addition, even in the most optimistic scenario of changes in Affluence factors and Technology

factors, it is very difficult to achieve the CO2 target as early as in year 2010.

q 2005 Elsevier Ltd. All rights reserved.

Keywords: Scenario; IZPAT identity; Car driving distance; CO2 efficiency

1. Introduction

Carbon dioxide (CO2) accounts for more than 60% of the

additional greenhouse effect accumulated since industrialis-

ation (UNEP, 2002). Although CO2 emissions in the UK fell

by 20% between 1970 and 2000, the emissions from road

transport rose by 93% during the same period. The proportion

of road transport in total carbon dioxide emissions has

increased from 9 to 21%.1 Thus, road transport is one of the

key sectors in which policy change is needed in order to meet

the reduction target for CO2 emissions. This paper concerns

the growing emissions of CO2 from road transport, in

particular CO2 emissions from car travel, which has the

largest share of emissions in road transport. More specifi-

cally, this paper projects CO2 emissions from car travel in

Great Britain over the period of 2000–2030 based on past

trends. Since there is huge uncertainty about the future

changes in fuel technology as well as travel behaviour, this

study relies on a scenario analysis under various assumptions

of future changes in travel patterns and fuel technologies.

The amount of CO2 emissions from car travel can be

represented by its compositional factors, which are

0967-070X/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.tranpol.2005.01.004

* Tel.: C82 31 9103135; fax: C82 31 9103226.

E-mail address: tkwon@koti.re.kr.1 Data source: DEFRA (2002).

determined by different causal forces. That is, there were

various different forces determining the amount of CO2

emissions from car travel. A useful starting formula to

investigate driving forces of changes in an environmental

impact is the so-called ‘IZPAT’ identity.2

Impact Z Population!Affluence!Technology

According to the IPAT identity, the impact of human

activity on the environment can be viewed as the product of

three different factors; Population, per capita consumption,

which is determined by Affluence and the environmental

impact per quantity of consumption, which is determined by

Technology.

Although the IPAT identity summarises the general

relationship between human behaviour and its environmen-

tal impact, it can also be applied to a specific environmental

issue. For example, in the case of CO2 emissions from car

travel, the amount of CO2 emissions from car travel

(environmental impact) can be represented by the product

of population, per capita car driving distance (affluence) and

CO2 emission per car driving distance (technology). Then

per capita car driving distance (affluence) can be rep-

resented by the product of per capita car trip distance and

(the reciprocal of) occupancy rate. In addition, CO2

Transport Policy 12 (2005) 175–184

www.elsevier.com/locate/tranpol

2 According to Ekins (2000), the IPAT formula was first suggested by

Ehrlich and Holdren (1971), Commoner (1971) and Holdren and Ehrlich

(1974) in slightly different forms.

T.-H. Kwon / Transport Policy 12 (2005) 175–184176

intensity of car driving (technology) can be represented by

the products of three factors; fuel structure, fuel efficiency

and CO2 intensity of fuel. Eq. (1) summarises these

decomposition relationships.

C Z P!Tc

P!

D

Tc

!X

i

Di

D!

Fi

Di

!Ci

Fi

� �(1)

4 http://www.tempro.org.uk.5 A detailed explanation of the forecasting method of Tempro can be

where C is CO2 emission, P is population, Tc is car trip

distance, D is car driving distance, F is fuel consumption

and i stands for different types of fuel.

Kwon (forthcoming) provides the estimations of trends

for each compositional factor in Eq. (1) for Great Britain

over the period of 1970–2000. Further to Kwon (forth-

coming), this study projects CO2 emissions from car travel

in Great Britain over the period of 2000–2030. The

outcomes from the past trend analysis play a key role in

projecting future CO2 emissions from car travel in this

study.

The UK government has a domestic target of CO2

emissions, which set a 20% reduction from the 1990

emission level by 2010 (DETR, 2000a). It also has a long-

term target of cutting CO2 emissions by 60% from the

current level by 2050 (DTI, 2003). It seems to be extremely

difficult to achieve the CO2 target set by 2010 in the car

travel sector even in the most optimistic scenario, as will be

shown in this paper. Therefore, this study sets a more

modest CO2 target; a reduction to the 1990 emission level

by 2010 and then a 20% reduction from the 1990 emission

level by 2030.3 This modest target would be acceptable on

the condition that the other sectors can achieve a more

radical reduction of CO2 emission level. This study will

investigate what assumptions of future changes in the

affluence (A) and technology (T) factors are required to

achieve this target.

This paper first builds a business as usual (BAU)

scenario, which is based on the past trends analysis, for

the affluence (A) factors (Section 2) and technology (T)

factors (Section 3), respectively. In Section 4, the future

trends of CO2 emissions from car travel are projected from

the BAU scenario and other alternative scenarios, and then

they are compared with the CO2 target. Finally, the key

implications of this analysis are outlined in the conclusion.

This paper is based on the DPhil thesis of the author (Kwon,

2004).

3 Earlier (in 1994) the Royal Commission on Environmental Pollution

(RCEP) set a target of limiting CO2 emissions from surface transport in

2000 to the 1990 level and reducing CO2 emissions from surface transport

in 2020 to no more than 80% of the 1990s level (RCEP, 1994). Therefore,

the CO2 target suggested in this study is 10 year delayed from the target of

RCEP (1994). Meanwhile, Banister et al. (2000) suggests CO2 reduction of

25% between 1995 and 2020 as a policy target for sustainable mobility

(including all passenger and freight transport in the EU level).

2. The BAU scenario of future changes in per capita car

driving distance

2.1. Changes in the ratio of population groups by household

car ownership levels

This study emphasises the significance of changes in car

ownership level as a key driving force behind the growth in

car trip distance. The ‘car ownership effect’ explains about

the half of the changes in car trip distance over time (46%

between 1975/1976 and 1989/1990 and 57% between

1989/1990 and 1999/2001) (Kwon and Preston, forth-

coming). Similarly, this study projects per capita car driving

distance by breaking it down into car ownership effect and

‘car use effect’. The car ownership effect represents the

impact of changes in the ratio of population with different

car ownership levels in the household and the car use effect

represents changes in car use in the same car ownership

level.

Households with multiple cars have increased continu-

ously whereas households with no car have decreased over

time. This trend is expected to continue over the period of

2000–2030. To forecast future household car ownership

levels, this study relies on a forecast by Tempro version

4.2,4 which is a National Trip Forecasting program of the

Department for Transport (DfT). Car ownership model of

Tempro is similar to DETR (1998, Working Paper No. 1). It

uses information on household income, household-type

(defined by the number and age structure of residents) and

area-type (loosely defined by population density) to derive a

probability that a given household will own 0, 1, or 2Cvehicles. The logistic form is used for the probability

function of car ownership and explanatory variables are

combined in the ‘linear predictor’.5 Fig. 1 shows the forecast

of the ratio of people with different household car ownership

levels based on the forecast of Tempro.6

The change in the ratio of population with different car

ownership levels in the household will lead to changes in per

capita car trip distance because there is a huge difference in

car trip distance according to household car ownership

levels—car ownership effect. For example, according to

Kwon and Preston (forthcoming), which is based on the

analysis of the National Travel Survey (NTS) data of Great

Britain, people with one car in their households travel about

referred to in New Tempro Version 4.2 Guidance Note from http://www.

tempro.org.uk.6 Tempro provides the number of households with different car

ownership levels over the whole forecasting period of this study. However,

to calculate the ratio of population by car ownership level in a household,

which is required in this study, the ratio of households with different car

ownership should be weighted because the average number of persons per

household differs according to car ownership levels. This study assumes the

same weighting over the whole forecasting period as the data in the year

2000.

0

0.1

0.2

0.3

0.4

0.5

0.6

2000

2003

2006

2009

2012

2015

2018

2021

2024

2027

2030

rati

o o

f p

op

ula

tio

n

no car

one car

two and more

Fig. 1. Forecast of the ratio of population by household car ownership

levels.

T.-H. Kwon / Transport Policy 12 (2005) 175–184 177

five times further per year by car than people in households

with no car. In addition, people in households with two or

more cars travel by car about eight times further than people

in households with no car. However, there have also been

changes in per capita car trip distance in the same group

over different time periods—car use effect. The next section

focuses on changes in car trip numbers among the ‘car use

effects’ and the subsequent section projects changes in

average distance per car trip. The car use effect excludes

changes in car trip numbers or average distance per car trip

caused by the growth of car ownership.

2.2. Changes in number of car trips

During the last 10 years (1989/1991–1999/2001), the

growth rate of car trip numbers became much slower than in

previous periods. In fact, without the car ownership effect

car trip numbers would decrease as found in Kwon and

Preston (forthcoming). In particular, the decrease in per

capita car trip number in the population group with multiple

cars was the most significant. It seems that as the proportion

of households with multiple cars rises over time, even

households that do not require frequent use of second cars,

tend to own second cars. The BAU scenario assumes a

continuous decline in car use in the households with

multiple cars. The detailed figures of the assumptions of the

BAU scenario are provided later in the paper.

Kwon and Preston (forthcoming) also found a slight

decline in car trips for commuting and business purposes

and suggested the increased use of the telecommuting

technology as one of its possible reasons. The BAU scenario

assumes that the increased use of telecommuting technology

will further contribute to the decline of car trips for

commuting and business purposes in the future although its

impact is relatively small compared to the A2 scenario to be

proposed later in this paper.

According to the NTS data, the number of car trips for

educational purposes experienced the highest growth rate in

the 1990s but it seems to have stabilised in the latest NTS

data set (1999/2001). The BAU scenario assumes a further

slight increase in the future.

The car trip number of personal business/shopping trips

also increased rapidly over the last 10 years. The rapid

increase of personal business/shopping trips could be due to

the increase of the number of shopping places out of town.

Also the legalisation of Sunday shopping could have

contributed to the increase of shopping trips. However,

although there may be a similar trend in the future, there is

also a counteracting effect that could lead to the reduction of

car trips for personal business and shopping in the future.

That is, the increase of on-line shopping could lead to the

decrease of car trips for shopping. For example, Dodgson et

al. (2000) suggest that car shopping travel will be reduced

by 5% by 2005 and by 10% by 2010 due to teleshopping,

compared to what it would otherwise be. Which force will

be more significant in the future is highly uncertain. The

BAU scenario assumes a slight increase in car trips for

personal business/shopping purposes.

Meanwhile, the number of car trips for leisure purposes

has declined slightly over the last 10 years. However, this

may be a temporal phenomenon, one of the reasons for

which could be the substitution of some leisure trips by

shopping trips on Sunday after the legalisation of Sunday

shopping. Also, there is a possibility of further increases of

leisure trips if a growth of teleworking leads to an increase

in leisure time. Thus, the BAU scenario assumes a slight

increase in the number of car trips for leisure purposes in the

future.

The BAU scenario assumed no change in the number of

holiday car trips, apart from the decline of car use in

multiple car ownership households, which is assumed for all

trip purposes. Since holiday car trips include the car trips to

airports for holiday purposes in the NTS, it will not be

affected significantly by the increase in holidays abroad.

2.3. Changes in average distance per car trip

The growth of average distance per car trip was a key

driving force for the increase in car trip distance during the

last decade. The average distance per car trip increased for

all purposes in the 1990s (Kwon and Preston, forthcoming).

It will probably increase continuously in the future as urban

sprawl continues. It is expected that about 40% of land for

new residential uses will be provided from not previously

developed land area according to the government policy

target (DETR, 2000b). The BAU scenario assumes a

continuous increase of average distance per car trip for all

trip purposes with a slight reduced rate from the 1990s,

except holiday trips, which are assumed unchanged over the

period.

2.4. Changes in occupancy rate

The occupancy rate of car trips is decreasing for most trip

purposes. The decline of occupancy rate could be explained

by the decline of the average number of persons in a

household as well as by the increase of households with

T.-H. Kwon / Transport Policy 12 (2005) 175–184178

multiple cars and the growth of driving license holders in a

household (Kwon and Preston, forthcoming). In particular,

the occupancy rate of leisure and holiday trips dropped

quickly in the 1990s. It seems that the occupancy rates for

leisure and holidays declined faster than other car trips

because the proportion of trips by all household members is

higher in these two trips than other trip purposes. That is, the

occupancy rate of leisure trips and holiday trips is strongly

affected by the average size of the household. The

occupancy rate of car trips for business and education did

not experience a uni-directional change in the 1990s

although it decreased overall over the period. These trips

seem to be less affected by changes in the average size of

household (Kwon and Preston, forthcoming).

It is expected that the decrease of occupancy rate will

continue in the future because the average size of house-

holds will decline continually while the proportion of

households with multiple cars will increase in the future.

Thus, the BAU scenario assumes a decrease of occupancy

rate in the future. The decrease rate is higher in the

following order in the BAU scenario; holiday, leisure,

personal business (shopping), commuting, and business.

The occupancy rate of educational trips is assumed to be

unchanged over the forecasting period in the BAU scenario.

3. The BAU scenario of future changes in CO2 efficiencyof car driving

7 There is a possibility of faster improvement of fuel efficiency due to the

ACEA (European Automobile Manufacturers Association) agreement, in

which major car manufacturing companies committed to achieve an

average CO2 emission figure of 140 g/km by 2008. JAMA (Japan

Automobile Manufacturers Association) and KAMA (Korean Automobile

Manufacturers Association) also agreed a similar commitment to the

European Union. However, some literature raises doubts about the success

of this voluntary agreement. For example, Kageson (2000) claims that

without additional financial incentives/disincentives manufacturers will not

meet its commitments.

3.1. Changes in fuel efficiency

Kwon (2004) produced a regression model of the

fuel consumption rate. According to this regression model,

the elasticity of engine size on fuel consumption rate was

0.585 for petrol cars. That is, a 1% increase of engine

capacity is estimated to incur a 0.585% increase in the fuel

consumption rate. Then changes in the new car fuel

consumption rate can be decomposed into the effect of

change in average engine capacity and pure technical

change by using a regression model like Eq. (2).

ln f ðtÞ Z aðtÞC0:585 ln cðtÞ (2)

where f(t) is fuel consumption rate at time t, c(t) is average

engine size at time t and a(t) is a variable representing

engine fuel efficiency at time t.

With regard to future changes in the fuel consumption

rate of a new car, this study suggests a BAU scenario based

on past trend as follows. First, the variable a(t) in Eq. (2),

which represents pure technological improvement, declines

annually over the whole forecasting period by an average

change rate of the last 10 years (1990–2000), equivalent to

0.54% per year. Secondly, the average engine size of newly

registered cars remains constant from the year 2001 level.

The change of a(t), which represents pure technical

improvement of the fuel consumption rate of new cars, was

much faster in the 1970s and 1980s than in the 1990s. Its

average annual improvement rate over the period of 1978–

2000 was 1.0%. The reason behind the slow improvement of

fuel efficiency in the 1990s seems to be due to the increase

of car weight caused by safety equipment, additional utility

equipment such as air-conditioning and CD players, and

pollution abating equipment such as catalytic converters.

Since it is judged that the strong demands for safety or

pollution related equipment for new vehicles will persist for

the time being, this study relies on the average annual

improvement rate of the last 10 years rather than the higher

improvement rate of the 1970s and 1980s for the BAU

scenario.7 Meanwhile, the average engine capacity of newly

registered cars showed a slight decline over the last few

years, contrary to the long-term trends. Since it is not clear

whether there will be a further shift of demand to smaller

cars, this is assumed to be constant in the BAU scenario.

The changes in new car fuel consumption rates will lead

to changes in fleet fuel consumption rates. To calculate the

fleet fuel consumption rate from the new car fuel

consumption rate, this study takes into account the

following three factors; first, the scrappage rate of cars

according to car age, secondly, mileage distribution among

cars with different types and ages, and, lastly, the difference

between actual road fuel consumption rates and the official

new car fuel consumption rates.

First, to estimate the scrappage rate of vehicles according

to vehicle age, this study applied a modified two parameter

Weibull function, which was suggested by the FOREMOVE

project of EU (Zachariadis et al., 1995, 2001). The final

form of survival rate function suggested by Zachariadis

et al. (1995, 2001) is as follows. The more detailed

discussion and the parameter estimation was provided in

Kwon (2004).

4iðkÞ Z exp Kk Cbi

Ti

� �bi� �

and 4ið0Þh1 (3)

where kZthe age (expressed in years), fi(k)Zthe presence

probability of vehicles of type i having age k, biZthe

failure steepness for vehicle type i and TiZthe characteristic

service life for vehicles type i.

The distribution of mileage between different vintages of

vehicles is based on data from the NTS. There is a

significant mileage reduction with age. For example, new

cars less than 1 year old have nearly twice the annual

T.-H. Kwon / Transport Policy 12 (2005) 175–184 179

mileage of 10-year-old cars. Another related issue is that

diesel cars tend to be more heavily used than petrol cars. In

2000, it was estimated that diesel cars travelled 1.45 times

longer distance on average than petrol cars according to the

data set used in previous chapters. This study assumes the

same proportion over the whole period. For the alternative

fuel vehicles (AFVs), this study assumes the same mileage

as the average of total car stock.

It is a well known fact that there is some difference

between the official new car fuel consumption rate and the

actual fleet fuel consumption rate because official tests do

not take into account various real-world driving conditions,

which adversely affect fuel efficiency. Previous research

such as Schipper and Tax (1994) and Kwon (2004) suggests

an about 10% gap for the Great Britain. The same gap is

assumed in calculating the fleet fuel consumption rate over

the whole forecasting period.

3.2. Changes in fuel structure

The demand for diesel cars rose rapidly in Great Britain

during the 1980s and early 1990s. The new car market share

of diesel increased from 1% in 1980 to 22% in 1994.8 Since

then, its popularity dropped slightly and it had an 18% share

of the new car market in 2001. The drop of the diesel market

share seems to be related to the decline of the gap between

diesel and petrol prices in the UK. Acutt and Dodgson (1998)

projected the future proportion of new cars which are diesel

as 20% as a base case. They claimed that the UK government

would not encourage further diesel/petrol substitution

because increasing questions are being asked about the

environmental advantages of diesel fuel. However, accord-

ing to the estimation of DTLR (2001), the proportion of the

car fleet using diesel will increase continuously, reaching

29.6% in 2031. By compromising these two estimations, this

study assumes a slight increase of diesel car share in the

conventional car market, reaching 25% in 2030.

A more important issue regarding the future prospects of

fuel structure is the market prospect of AFVs. There have

been much research on the market potential of AFVs in

recent years, such as Difiglio and Fulton (2000), EIA

(2002), IEA (2001), Lave and MacLean (2002), Owen and

Gordon (2002), Sakaguchi (2000) and Halsnaes et al.

(2001). Although these cannot be conclusive, the following

points can be drawn from the review of this previous

research. First, in the short-term, hybrid cars, which use both

petrol/diesel engine and electric motors, seem to be the most

likely competitors to conventional vehicles with internal

combustion engines. Hybrid cars are already on mass sale in

Japan and the United States. They have an advantage in that

they do not require radical change in terms of fuel supply

and vehicle use when compared to conventional vehicles.

Secondly, in the long-term, hydrogen fuel cell vehicles are

8 Source: Data provided by DfT.

generally regarded as the most promising alternative to

conventional vehicles in the previous literature. Thirdly,

although huge efforts are being made for the development of

alternative vehicles and some of the models are already on

the market, it is also true that there are various transitional

barriers to the introduction of AFVs such as limited fuel

availability and lack of scale economics. That is, any

attempt to estimate the impact of AFVs on the vehicle

market in the future would be enormously uncertain.

Considering the huge uncertainty of future changes in

technology and people’s preferences, it would be more

reasonable to suggest various scenarios about the future

market prospects of AFVs. First, this study assumes in

the BAU scenario that AFVs have only a marginal market

share over the whole forecasting period (2000–2030). Then

alternative scenarios based on different assumptions of the

market prospect of AFVs will also be investigated later in

this paper. The estimation of CO2 intensity of alternative

fuel and vehicles is based on Owen and Gordon (2002) and

IEA (2001). A full list of the key assumptions can be

referred to in Kwon (2004).

4. Scenario outcomes of future changes in CO2 emissions

from car driving

As explained in the introduction, this study relies on

the ‘IPAT’ type decomposition analysis to investigate

the determining factors of CO2 emission trends from car

travel over the next 30 years. There could be various future

paths of each compositional factor of the affluence (A) and

technology (T), which will result in diverse outcomes of CO2

emission trends. By building scenarios, this study attempts to

bound the uncertainty on future changes in CO2 emissions

from car travel in Great Britain (Schoemaker, 1991).

4.1. The BAU scenario

The assumptions of the BAU scenario were suggested in

the previous sections for each compositional factor of

affluence (A) and technology (T). The population (P) trend

is based on a projection (2001-based projection) by the

Government Actuary. The BAU scenario is a trend-based

scenario, especially with more weight on trends over the last

10 years. However, domain knowledge including research

outcomes of previous literature on the underlying causes of

changes in each compositional factor also played an

important role in building the BAU scenario. Table 1

summarises the key assumptions of the BAU scenario,

whereas Table 2 summarises the past trends of the key

variables. More detailed explanation on the assumptions of

the BAU scenario and the past trend of each factor can be

referred to in Kwon (2004) and Kwon and Preston

(forthcoming).

Fig. 2 shows the CO2 emission trend from car travel in

the BAU scenario. CO2 emissions increase from 20.5

Table 1

Summary of the BAU scenario

Factors Assumptions of future changes of each factor

Number of car

trips

Annual change rate

Commuting: K0.25%

Business: K0.25%

Education: 0.5%

Persona business: 0.25%

Leisure: 0.25%

Holiday: 0%

Population group of multiple car ownership:

additional K0.25% for all trip purposes

Average distance

per car trip

Annual change rate

Commuting: 0.5%

Business: 0.5%

Education: 0.5%

Personal business: 0.5%

Leisure: 0.25%

Holiday: 0%

Occupancy rate Linear changes from 2000 to 2030

Commuting: 1.14/1.1

Business: 1.13/1.1

Education: 1.86/1.86

Personal business: 1.76/1.6

Leisure: 1.92/1.7

Holiday: 2.45/2.2

Fuel efficiency Annual change rate of a(t) in Eq. (2): K0.54%

Average new car engine capacity: no change

Market structure

of new cars

Diesel: growth up to 25%

Alternative fuel vehicle (AFV): less than 1%

Other factors Population growth: 2001-based projection of the UK

Government Actuary; 61,787 (thousand) in 2030

Car ownership growth: forecast by Tempro version 4.

2 of DfT; 36,610 (thousand) in 2030

Table 2

Summary of the past trends of key variables

Factors Past trend of each factor

Number of car trips Annual change rate (1975/1976–1989/1991,

1989/1991–1999/2001)

Commuting: 1.4%, K0.5%

Business: 4.0%, K1.1%

Education: 2.7%, 3.1%

Persona business: 3.8%, 1.3%

Leisure: 2.6%, K0.2%

Holiday: K1.1%, 0.3%

Average distance

per car trip

Annual change rate (1975/1976–1989/1991,

1989/1991–1999/2001)

Commuting: 1.2%, 1.6%

Business: 1.4% (1989/1991–1999/2001)

Education: 0.7%, 1.6%

Personal business: 0.1%, 1.0%

Leisure: 0.5%, 0.5%

Holiday: 0.7%, 0.3%

Occupancy rate (1989/1991/1999/2001)

Commuting: 1.19/1.14

Business: 1.13/1.13

Education: 2.01/1.86

Personal business: 1.81/1.76

Leisure: 2.08/1.92

Holiday: 2.71/2.45

Fuel efficiency Average annual change rate of a(t) in Eq. (2):

K0.54% (1990–2000)

Average new car engine capacity: 4.6%

increase (1979–2000)

Market structure

of new cars

New car market share of diesel in 2001: 18%

Data source: National Travel Survey, Kwon (2004) and Kwon and Preston

(forthcoming).

15.0

20.0

25.0

30.0

ioxi

de e

mis

sion

nn

es

of c

arb

on

)

T.-H. Kwon / Transport Policy 12 (2005) 175–184180

million tonnes (in terms of carbon weight) in 2000 to 25.2

million tonnes in 2030—up by 23% over 30 years. The

future trend in CO2 emissions from car travel from 2000 to

2030 is flatter than the trend between 1970 and 2000.

Although the household car ownership level is still

increasing, a shift from one car ownership to multiple car

ownership does not seem to cause as a big impact on the

growth of car trip distance and CO2 emissions as a shift from

no car to one car ownership.

Fig. 3 decomposes the emission trend of the BAU

scenario into each compositional factor in the IPAT

formula. As shown in the figure, CO2 emission (impact)

rises gradually along with the trend of vehicles km per

person (affluence). That is, the growth effect of the affluence

(A) factor is more significant than the counteracting effect of

the technology (T) factor. The continual growth

of population (P) also contributes significantly to the rise

of emissions over the forecasting period.

0.0

5.0

10.0

1970

1975

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

Car

bon

D( m

illio

n to

Fig. 2. CO2 emission trend from the BAU scenario.

4.2. Alternative scenarios of changes in affluence factors

This section considers three alternative scenarios, which

assume further controls of affluence (A) factors by

government policy or other socio-economic forces. It must

be noted that to quantify the effect of a particular

government policy on car travel is not a purpose of this

paper.

First, the ‘Car trip reduction initiative’ (A1) scenario

supposes that various government efforts to reduce car trips

succeed in reducing the annual change rate of car trip

numbers for all purposes in each population group by

0.25%, compared to the BAU scenario. In addition,

considering the fact that currently the UK government

seems to have a priority to reduce car commuting trips and

school trips by Workplace Travel Plans and School Travel

Plans (DfT, 2002), a further reduction (by 0.25%) of

0.0

5.0

10.0

15.0

20.0

25.0

30.0

2000 2005 2010 2015 2020 2025 2030

Car

bo

n D

ioxi

de

emis

sio

ns

(mil

lio

n t

on

nes

of

carb

on

)

BAU

A1

A2

A3

Fig. 4. CO2 emission trends of alternative scenarios of affluence (A).

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.01970

1976

1982

1988

1994

2000

2006

2012

2018

2024

2030

Ind

ex (

year

200

0 =

100

)

Carbon dioxideemission

Car driving distanceper person per year

Carbon dioxideemission per cardriving distance

Population

Fig. 3. Index of IPAT in the BAU scenario.

T.-H. Kwon / Transport Policy 12 (2005) 175–184 181

the annual change rate of car commuting trip numbers and a

slight rise of occupancy rate of commuting and education

trips is added into the A1 scenario.

Secondly, the ‘Telecommuting technology big impact’

(A2) scenario supposes that in addition to the assumptions

of the A1 scenario, telecommuting technology will have a

more significant impact in substituting car travel than

assumed in the BAU scenarios. In particular, teleworking,

online shopping and other telecommuting technology has a

big impact in reducing car trips for commuting, business and

personal business/shopping in the A2 scenario. It assumes a

further reduction of the annual change rate of car trip

numbers for these purposes by 0.5%, compared to the A1

scenario. The A2 scenario also assumes that the annual

change rate of car trip numbers for leisure purposes

increases slightly (by 0.25%), compared to the A1 scenario,

because telecommuting technology could lead to an

increase of leisure time.

Lastly, the ‘Sustainable planning’ (A3) scenario sup-

poses that in addition to the assumptions of the A2 scenario,

an effective planning policy and other factors succeed in

curbing the growth of the average distance per car trip for all

purposes in each population group. Table 3 summarises the

Table 3

Summary of alternative scenarios of changes in affluence (A)

Scenarios Different assumptions from the BAU scenario

‘Car trip reduction

initiative’ (A1)

Reduction of the annual change rate of car

trip numbers by 0.5% for commuting trip and

by 0.25% for other purposes

The gradual increase of occupancy rate of

commuting trips and education trips (up to

5%)

‘Telecommuting

technology big impact’

(A2)

In addition to A1 scenario

Reduction of the annual change rate of car

trip numbers by 0.5% for commuting,

business and personal business (shopping)

trips

Increase of the annual change rate of car trip

number for leisure trips by 0.25%

‘Sustainable planning’

(A3)

In addition to A2 scenario

No change in average distance per car trip

from 2000

key assumption of the three alternative scenarios of changes

in affluence (A).

Fig. 4 compares the outcomes of the three alternative

scenarios of change in affluence (A) factors. First, the A1

scenario fails to curb the growth of CO2 emissions from car

travel although the growth rate is much smaller than the

outcome of the BAU scenario. CO2 emissions increase from

20.5 million tonnes (of carbon) in 2000 to 22.1 million

tonnes in 2030—up by 8%. Secondly, in the A2 scenario,

CO2 emissions from car travel increase until the middle of

the 2020s and then show a slight decline. Overall, the

emissions increase to 20.8 million tonnes in 2030—up by

2% over 30 years. Thirdly, in the A3 scenario, after a slight

rise in the early period, CO2 emissions keep decreasing to

18.7 million tonnes in 2030—down by 9% over 30 years.

However, even the A3 scenario fails to meet either of the

CO2 targets suggested in this study: a reduction to the 1990

emission level by 2010 and a 20% reduction from the 1990

emission level by 2030. This illustrates that it is extremely

difficult to achieve the CO2 emissions targets by controlling

the affluence (A) factors alone.

Fig. 5 illustrates the trends of each compositional factor

in the IPAT formula under the A3 scenario. The CO2

emission trend is falling because the affluence (A) effect as

well as the technology (T) effect is working as a decay force

for the trend of CO2 emissions. Only the population (P)

trend in the IPAT formula is working as a growth force of

environmental impact.

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

1970

1976

1982

1988

1994

2000

2006

2012

2018

2024

2030

Inde

x (y

ear

2000

= 1

00)

Carbon dioxideemission Car driving distanceper person per year

Carbon dioxideemission per cardriving distance

Popluation

Fig. 5. Index of IPAT in the A3 scenario.

Table 4

Summary of alternative scenarios of changes in technology (T)

Scenarios Different assumptions from BAU scenarios

‘Smaller car choice’ (T1) Decrease of average engine size of new

cars by 0.5% per annum

‘Gradual shift to AFVs’

(T2)

In addition to T1 scenario

Growth of AFV market share up to 50%

‘Rapid shift to AFVs’ (T3) In addition to T1 scenario

Growth of AFV market share up to 99%0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

1970

1976

1982

1988

1994

2000

2006

2012

2018

2024

2030

Ind

ex (

year

200

0 =

100)

Carbon dioxideemission

Car driving distance

Carbon dioxideemission percardriving distance

Popluation

per person per year

Fig. 7. Index of IPAT in the T3 scenario.

T.-H. Kwon / Transport Policy 12 (2005) 175–184182

4.3. Alternative scenarios of changes in technology factors

Technology scenarios are constructed by the different

prospects of the market share of AFVs and average engine

size of new cars. Although there are other technology

variables with huge uncertainty as to their future changes,

such as the CO2 intensity of AFVs and fuel efficiency of

conventional vehicles, it was judged that these variables are

relatively more predictable than the market share of AFVs.

Depending on future developments of vehicle technology

and fuel infrastructure as well as changes in consumer

preferences, the car market structure could have a totally

different shape in the future. In the BAU scenario, AFVs

only had a marginal market share of less than 1%.

First, the ‘Smaller car choice’ (T1) scenario assumes that

the average engine size of new cars will decrease by 0.5%

annually. The BAU scenario assumed that it remains

unchanged over the whole forecasting period. Secondly,

the ‘Gradual shift to AFVs’ (T2) scenario, in addition to the

assumption of T1, assumes a gradual (linear) growth of the

market share of AFVs, starting from 2005 up to 50% in

2030. It should be noted that this assumption is based on the

market share of new cars, not of all the car fleet. Thirdly, the

‘Rapid shift to AFVs’ (T3) scenario, assumes a rapid growth

in the market share of AFVs, in addition to the assumption

of T1. It assumes that the market share of AFVs will linearly

increase up to 99% in 2030, starting from 2005. Table 4

summarises the key assumptions of the three alternative

scenarios of changes in technology (T).

Fig. 6 compares the outcomes of the three alternative

scenarios of changes in technology (T) factors. First, in the

T1 scenario, CO2 emissions from car travel increase

gradually until the late 2010s and then start to fall slowly.

0.0

5.0

10.0

15.0

20.0

25.0

30.0

2000 2005 2010 2015 2020 2025 2030

BAU

T1

T2

T3

Car

bo

n D

ioxi

de

emis

sio

ns

(mill

ion

to

nn

es o

f C

arb

on

)

Fig. 6. CO2 emission trends of technology scenarios.

Overall, the emissions increase from 20.5 tonnes (of carbon)

in 2000 to 21.8 million tonnes in 2030—up by 7%. Secondly,

in the T2 scenario, after a slight rise in the early period, CO2

emissions fall gradually to l9.3 million tonnes in 2030—

down by 6%. Thirdly, a similar pattern is also confirmed in

the T3 scenario. An early rise of CO2 emissions is followed

by a continuous decline in emissions with a more substantial

rate. Overall, CO2 emissions decrease to 15.2 million tonnes

in 2030—down by 26% over 30 years.

All scenarios fail to meet either of the CO2 targets

suggested in this paper; the reduction to 1990 emission

levels by 2010 and a 20% reduction from the 1990 emission

level by 2030, although the emission level in the T3 scenario

is very close to the second CO2 target. It seems that although

the reduction potential of CO2 emissions is greater for the

technology (T) scenarios than the affluence (A) scenarios,9 it

would still be extremely difficult to achieve the CO2 targets

by controlling the technology (T) factors alone.

Fig. 7 illustrates the trends of each compositional factor

in the IPAT formula under the T3 scenario. CO2 emissions

(impact) start to decline fast after a slight rise in the early

period owing to the rapid decline in CO2 intensity of car

driving (technology), which is caused by the fast increase of

the market share of AFVs.

4.4. Combined scenarios of changes in affluence and

technology factors

Table 5 shows the outcome of the combined scenarios

of change in the affluence (A) and technology (T)

factors. ‘F’ in the table indicates that the scenario

failed to meet the modified CO2 targets suggested in this

paper, whereas ‘S’ represents its success in achieving the

target. As illustrated in the table, all scenarios failed to

meet the first CO2 target (a reduction to the 1990

9 In other words, borrowing the concept suggested by York et al. (2002),

the plasticity of technology (T) is greater than that of affluence (A) in this

case. ‘Plasticity’ means ‘the potential for population, affluence, or

technology to move in different directions, either in response to historical

processes or in response to policy, and to thereby influence specific

impacts’ (York et al., 2002, p. 21).

Table 5

The outcomes of the combined scenarios of affluence and technology

Affluence

technology

BAU A1 A2 A3

BAU F(2010), F(2030) F, F F, F F, F

T1 F, F F, F F, F F, F

T2 F, F F, F F, F F, S

T3 F, F F, S F, S F, S

T.-H. Kwon / Transport Policy 12 (2005) 175–184 183

emission level by 2010), although the A3/T3 scenario

approaches very closely to the target by 2010. The

amount of CO2 emissions from car travel in 1990 was

19.0 million tonnes of carbon, which is lower than the

emission level of 2000 by about 7%. To reduce CO2

emissions from car travel by such an amount within 10

years seems to be extremely difficult even under the most

optimistic scenario regarding the control of affluence of

car travel (A) and changes in fuel technology (T).

Meanwhile, the second CO2 target (a 20% reduction

from the 1990 emission level by 2030) is achieved in four

scenarios; A1/T3, A2/T3, A3/T2 and A3/T3 (shaded in the

table). The conditions to achieve the second CO2 target can

be summarised as follows. First, a rapid transition to AFVs

in the new car market should be accompanied with various

efforts to reduce car trip rates. Secondly, if the growth of

average distance per car trip is restrained possibly due to

sustainable planning policy and car trip rates are reduced

more substantially, for example, due to the impact of

telecommuting technology, the CO2 target could be

achieved by a gradual market transfer to AFVs. Fig. 8

illustrates the CO2 emission trends of affluence (A)–

technology (T) combined scenarios.

5. Conclusion

This paper projected the future trend of CO2 emissions

from car travel in Great Britain over the next 30 years. Its

main purpose is not to forecast precisely the amount of CO2

emissions in the long-term future. Considering the huge

uncertainty of changes in future travel behaviour and

technology, a precise forecast of the emission trend would

be impossible. Rather, this study attempted to build various

0

5000

10000

15000

20000

25000

30000

2000

2003

2006

2009

2012

2015

2018

2021

2024

2027

2030

Car

bo

n D

ioxi

de

emis

sio

ns

(th

ou

san

dto

nn

es o

f C

arb

on

)

BAU

A3/T2

A3/T3

A2/T2

A1/T2

Fig. 8. CO2 emission trends of affluence (A)–technology (T) combined

scenarios.

scenarios of emission trends based on different assumptions

of changes in the affluence (A) factors (car driving distance

per person) and technology (T) factors (CO2 efficiency of

car driving). We can summarise the main points of the

scenario analysis in this study as follows.

First, CO2 emissions from car travel in Great Britain will

increase continuously over the next 30 years under the

assumptions of the BAU scenario. The growth effect of the

affluence (A) factor is more significant than the counter-

acting effect of the technology (T) factor. The continual

growth in population (P) also contributes significantly to the

rise in emissions (I).

Secondly, this paper compared three alternative scen-

arios on changes in affluence of car travel (A). The car trip

reduction initiative (A1) scenario, which assumes a

reduction of car trip rates by various policies, does not

succeed in curbing the growth of CO2 emissions from car

travel over the next 30 years. The telecommuting technol-

ogy big impact (A2) scenario, which assumes a more

substantial impact of telecommuting technology on car

trips, in addition to the assumptions of the A1 scenario,

manages to downturn the CO2 emission trend in the middle

of the 2020s. In the sustainable planning (A3) scenario,

which assumes no increase of average distance per car trip

due to land use planning and other policies further to

the assumptions of the A2 scenario, CO2 emissions from car

travel start to fall after a slight increase in the early period.

However, even the A3 scenario fails to meet either of the

modified CO2 targets suggested in this paper: a reduction to

the 1990 emissions level by 2010 and a 20% reduction from

the 1990 emissions level by 2030. This illustrates that it is

extremely difficult to achieve the CO2 emissions targets by

controlling only the affluence (A) factors.

Thirdly, this paper compared three alternative scen-

arios of changes in technology (T) factors. In the smaller

car choice (T1) scenario, which assumes the shift to

smaller cars in the future car market, CO2 emissions

from car travel increase gradually until the late 2010s

and then start to fall slowly over the simulation period.

In the gradual shift to AFVs (T2) scenario, which

supposes a gradual transfer to AFVs in the new car

market, in addition to the assumption of the T1 scenario,

CO2 emissions decrease gradually after a slight rise in

the early period. Similar to the T2 scenario, in the rapid

shift to AFVs (T3) scenario, which supposes a rapid

transfer to AFVs in the new car market, in addition to

the assumption of the T1 scenario, an early rise of CO2

emissions is followed by a continuous decline of the

emissions with a more substantial rate. All three

scenarios fail to meet either of the CO2 targets suggested

in this paper although the emission level in the T3

scenario is very close to the second CO2 target (a 20%

reduction from the 1990 emission level by 2030). It

seems that although the scope for potential reductions of

CO2 emissions from the technology (T) scenarios is

bigger than from the affluence (A) scenarios, it is still

T.-H. Kwon / Transport Policy 12 (2005) 175–184184

very difficult to achieve the CO2 targets by controlling

the technology (T) factors alone.

Fourthly, to substantially reduce CO2 emissions from car

travel as early as by 2010 would be very difficult even under

the most optimistic scenario of changes in the affluence (A)

and technology (T) factors. However, meeting the CO2

target by 2030 is possible under the following conditions.

First, a rapid transition to AFVs in the new car market

should be accompanied with various efforts to reduce car

trip rates. Secondly, if the growth of average distance per

car trip is restrained, possibly due to sustainable planning

policy, and car trip rates are reduced more substantially, for

example, due to the impact of telecommuting technology,

the CO2 target can be achieved by a gradual market transfer

to AFVs.

Lastly, this study emphasises the necessity of continuous

monitoring of each compositional factor determining CO2

emission trends from car travel. By tracing the path of CO2

emission trends as well as those of the compositional factors

and key underlying causes, it is possible to establish the

main factors in explaining the difference between the actual

trend and the scenario projection. This will enable us to

renew the scenario model and search for suitable policy

options to achieve the CO2 targets.

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